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Volume 29, Number 2

2012

Student Monograph

Eta Sigma Gamma

Published by Eta Sigma Gamma National Professional Professional Health Education Honorary

Volume 29, Number 2 2012

The Health Education Monograph Series

Guest Editor Michele Johnson Moore, Ph.D. Associate Professor Department of Public Health Brooks College of Health University of North Florida Jacksonville, FL 32224 Editor Monograph Series Mohammad R. Torabi, PhD, MPH, CHES Interim Dean, School of Health, Physical Education and Recreation Indiana University 1025 E. 7th Street Bloomington, IN 47405 Eta Sigma Gamma wishes to gratefully acknowledge the John P. McGovern Foundation for its generosity in helping make this monograph series possible

The Health Education Monograph Series is published by Eta Sigma Gamma, the National Profession Health Education Honorary, 2000 University Avenue, Muncie, Indiana 47306. A non-profit organization, Eta Sigma Gamma is dedicated to the elevation of standards, ideals, competence, and ethics of professionally trained men and women in the Health Science discipline. Third class, bulk-rate postage paid at Muncie, Indiana.

Student Monograph Volume 29 Number 2 2012

Published by Eta Sigma Gamma National Professional Health Education Honorary

The Health Education Monograph Ser es Journal of Eta Sigma Gamma Eta Sigma Gamma is dedicated to the elevation of standards, ideals, competence and ethics of professionally trained men and women in the Health Education discipline.

Eta Sigma Gamma National Board of Directors President

Ranjita Misra, PhD, CHES Texas A & M University Department of Health & Kinesiology 4243 TAMU RM #158V Read Building College Station, TX 77843 [email protected]

Vice President

Amy Thompson, PhD, CHES The University of Toledo Dept. of Health & Rehab Services 2801 West Bancroft Toledo, OH 43606 [email protected]

Immediate Past President

Marianne Frauenknecht, PhD Western Michigan University 4024-6SRC, Department of HPER 1903 W. Michigan Avenue Kalamazoo, MI 49008 m [email protected]

Editor, The Health Educator

Roberta J. Ogletree, HSD, CHES Southern Illinois University Dept. of Health Education & Recreation Carbondale, IL 62901-4632 [email protected]

Editor, The Monograph Series

Mohammad Torabi, PhD, MPH Indiana University Department of Applied Health Science 116 HPER Bloomington, IN 47405 [email protected]

Secretary & Treasurer

Jeffrey K. Clark, HSD Ball State University Dept. of Physiology & Health Science 2000 University Avenue Muncie, IN 47306 [email protected]

Executive Director Director of Chapter Development Irene O’Boyle, PhD, CHES Central Michigan University School of Health Sciences 2209 Health Professions Building Mt. Pleasant, MI 48859 [email protected]

Susan Koper, BA Eta Sigma Gamma 2000 University Avenue Muncie, IN 47306 765-285-2258 (Office) 800-715-2559 (Office) 765-285-3210 (FAX)

Member-At-Large

Megan L. Rickard, PhD, CHES Eastern Michigan University 319G Porter Building Ypsilanti, MI 48197 [email protected]

Member-At-Large

Amos O. Aduroja, PhD Western Michigan University Department of HPER 1903 West Michigan Ave. Kalamzaoo, MI 49008-5426 [email protected]

Historian

Delores C.S. James, PhD Room 5 Florida Gym, PO Box 118210 Department of Health Education and Behavior College of Health and Human Performance University of Florida Gainesville, FL 32611-8210 [email protected]

Student Representative

Brittany Rosen, BA Texas A&M University Department of Health & Kinesiology 4243 TAMU College Station, TX 77843 [email protected]

The Health Education Monograph, Journal of Eta Sigma Gamma (ISSN 8756-5943) is published twice yearly, Spring and Fall, by Eta Sigma Gamma, the national Professional Health Education Honorary, 2000 University Avenue, Muncie, Indiana 47306. A non-profit organization, Eta Sigma Gamma is dedicated to the elevation of standards, ideals, competence and ethics of professionally trained men and women in the Health Education discipline. Third class, bulk-rate postage paid at Muncie, Indiana. This journal is indexed in ERIC. Membership and subscription requests should be sent to: Executive Director, at the address above. Institutional subscriptions are $20.00 a year for The Health Educator or The Monograph Series and $25.00 a year for both. Individual issues are $6.00. Advertising that appears in The Health Educator is not necessarily endorsed by Eta Sigma Gamma.

THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012

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Foreword It was an honor and privilege to serve as Guest Editor for the 2012 Eta Sigma Gamma Student Monograph edition. This experience provided an incredible opportunity to work with the group of dedicated, passionate and talented student authors who submitted manuscripts this year. The students ranged from undergraduates to Master and Doctoral students. It was inspiring to see these students, at varying levels of their academic careers, work diligently to improve their manuscripts in response to the reviewers’ suggestions. I offer my sincere congratulations to all of the student authors who are published in this edition of the journal.

fessionals who will continue to propel our field forward. Being a first time editor, this role gave me profound insight and appreciation for the editorial process.

I would like to extend my sincere appreciation to Dr. Mohammed Torabi for having the faith in me to successfully carry out the responsibilities of Guest Editor. I would also like to thank Susan Koper, Executive Director of ESG, and Dr. Amy Thompson, National ESG President and a former Guest Reviewer, for all of their assistance and advice during the review process. Finally, I would like to thank Alexandra Howard, UNF MPH student and Research Assistant, who served as the The student authors wrote about noteworthy Editorial Assistant this year and whose assistance trends and issues in the field of health education. was invaluable. The manuscripts spanned topics of importance from health disparities, health risk factors and be- Sincerely, haviors, and environmental and policy influences on health to interventions the students imple- Michele Johnson Moore, PhD mented. It will be exciting to see these students Professor & MPH Director move into their professional careers and the posi- University of North Florida tive impact they will have on the health of their respective communities as they follow their passions. It was also a pleasure to work with the dedicated professionals in our field. The Faculty Mentors for each manuscript were responsible for encouraging and helping their students to submit their work. The reviewers took the time to carefully read and provide guidance to the student authors to help them improve not only their manuscripts, but their writing skills as well. It is wonderful to have colleagues throughout the nation who are committed to helping our collective students become proficient health education pro-

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Preface On behalf of your National Executive Committee of Eta Sigma Gamma (ESG), I would like to offer my sincere congratulations to all students who submitted papers for publication consideration in this student issue of The Health Education Monograph Series. This is a strong indication of our students’ commitment to research. I would like to extend my genuine appreciation to Dr. Michele Johnson Moore for the excellent job she has done as our Guest Editor for this issue. Further, I wish to thank all faculty advisors who encouraged and worked with the students in the manuscript preparation. My sincere appreciation and gratitude are extended to Mary Wampler for her assistance in preparing the final publication and Joyce Arthur for her technical assistance. Certainly, I must thank the Department of Applied Health Science of Indiana University for the in-kind support provided for the publication of the Monograph Series. I invite all faculty to encourage students to submit research papers for the next student issue of The Health Education Monograph Series. The deadline for submission is

January 20, 2013. Our guest editor for the next student issue will be Ranjita Misra, PhD, Department of Health and Kinesiology, Texas A&M University, College Station, TX 77843. Finally, thank you for sharing your comments with me regarding the past Monograph Series. As always, I am eager to hear your criticism, comments, and suggestions relative to this publication. I do hope that you, as loyal members of this National Honorary, check your college/university libraries and make sure that they receive The Health Education Monograph Series. If not, please request that they subscribe to these important publications. It is a privilege for me to serve ESG members and our profession. I look forward to hearing from you. Mohammad R. Torabi, PhD, MPH Editor, The Health Education Monograph Series Indiana University


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Table of Contents Volume 29, Number 2

Distracted Driving Perceptions, Behaviors, Casey Bailey and Knowledge among Young Adults Kayla O’Connell Rachel Otto Heather Gentile Alyssa Arey Erin Payne Incidence of Male Alcohol and Other Drug Abuse and Physical Violence against Pregnant Women in Ciudad Juárez, Mexico

2012

1

Thilina Bandara 6 John Moraros Yelena Bird

Implementation and Evaluation of the Matthew R. Bice C.A.T.C.H. Health Education Classroom Alex T. Ramsey Curriculum James W. Ball

13

Contributing Factors to Health Disparities Experienced in the Development, Diagnosis, and Treatment of Acute Coronary Syndrome in African American Men

Chaundra M. Bishop

19

The Association between Sensation Seeking, Sexual Risk Behavior and HIV Knowledge Among Undergraduate Students

Roberta E. Emetu

25

Healthy Food Access, the Built Environment and Youth Perceptions: A Review of Literature

Sharlene A. Gozalians

32

Current Trends in the Use of Social Media for Health Education and Health Promotion

Bethany A. Kies Amber T. Burtis

38

Association of Obesity and C-Reactive Protein Heather Colfax Parth 45 Levels in American Youth Ages 12-19 Years Jennifer Marie Richards Michael Eric McCown Circle of Friends: A Community Eric Payton Empowerment Approach to Reducing Timothy R. Jordan Racial/Ethnic Health Disparities Amy Thompson

51

Armed Campuses: The Current Status Karen Teeple 57 of Concealed Guns On College Campuses Amy Thompson James H. Price List of Contributors 65 List of Reviewers 67 Page iv


Distracted Driving Perceptions, Behaviors, and Knowledge Among Young Adults Casey Bailey, Kayla O’Connell, Rachel Otto, Heather Gentile, Alyssa Arey, and Erin Payne

Abstract The Sigma Chapter of Eta Sigma Gamma (ESG) at James Madison University (JMU) established Distracted Driving Awareness Day in 2009. The purpose of this biannual event is to raise awareness about distracted driving behaviors as well as to collect survey data regarding individuals’ perceptions and behaviors surrounding distracted driving. Surveys were distributed to male and female volunteers aged 17-38 years using a convenience sampling method. The survey data was analyzed using SPSS in order to determine if reported perceptions and behaviors regarding distracted driving align. The majority of the sample reported engaging in behaviors such as texting and talking on the phone while driving even though they perceived such behaviors as risky. Introduction According to recent studies, drivers do not realize the extent of their distraction when using hand-held or even hands-free mobile devices while driving (Horrey, Lesch, & Garabet, 2008). These studies show that when individuals participate in a driving simulator course while simultaneously using a cellular phone, simple faults occur such as swerving, inconsistent speeds, and slower reaction times (Horrey et al., 2008). These behaviors demonstrate that the participants were not paying full attention to driving and were therefore at higher risk for causing or being involved in an accident. When asked to rate their driving performances after completing the simulator course while on the phone, the participants were not able to recall these consequential driving errors (Horrey et al., 2008). The issue of distracted driving and cell phones, one of its major contributors, has recently encouraged the National Transportation Safety Board (NTSB, 2011) to attempt to make a federal law banning cell phone use while driving. Talking on the phone, text messaging, and playing music are all features of cell phones that are distracting to drivers and could cause very serious or even fatal accidents. Driving distracted places drivers and others on the road in serious danger. According to a study done by Michael Hanley, director of Ball State’s Institute for Mobile Media Research, the cell phone is a large part of the typical college student’s daily life with 99.8% of college students owning a cell

phone and 97% of the students surveyed reporting that they use text messaging as their main form of communication (as cited in Ziegler, 2010). Cell phones are a source of major distraction facing drivers today. In fact, talking on a cell phone delays reaction time the same amount as driving while legally drunk (Occupational Safety and Health Administration [OSHA], 2011). Texting is another major distraction on a cell phone. Studies show that “drivers who are texting take their eyes off the road 400% more than when they are not texting” (OSHA, 2011). This ultimately leads to more crashes and claims more lives. Within the United States, deaths from distracted driving rose 75% with every additional one million text messages sent (OSHA, 2011). Some of the other distractions that place people at higher risks for crashes are eating/drinking, grooming, smoking, sneezing, and adjusting mirrors or radio buttons (McEvoy, Stevenson, & Woodward, 2007). Drivers engage in many physically distracting tasks while driving. Another type of distraction is often overlooked. A study of University of West Florida students shows that distracted driving goes beyond being physically distracted: Mental distractions are also causing problems (Beede & Kass, 2006). Many of the drivers’ minds were not focused on the main task of driving, and this is why daydreaming and other mentally distracting behaviors are also an issue affecting drivers. The three major categories of distracted driving—visual (taking your eyes off the road), manual (taking your hands off the wheel), and cognitive (taking your mind off what you are doing)—have all contributed to the increasing number of driving fatalities (Centers for Disease Control and Prevention [CDC], 2011). The U.S. Department of Transportation (DOT, 2010, p. 1) reported that “in 2009, 5,474 people were killed on U.S. roadways and an estimated additional 448,000 were injured in motor vehicle crashes that were reported to have involved distracted driving.” Of these alarmingly high numbers, 24,000 of the injured reported the use of a cell phone as a distraction, while 995 of the fatal accidents were reported to involve a cell phone as a distraction (DOT, 2010). Although many people consider cell phone use as the major source of distracted driving, these statistics show that it is more than just cell phones that are distracting drivers.


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Distracted Driving Awareness Day was created at James Madison University (JMU) by Eta Sigma Gamma (ESG) members. The objectives of this project were to identify risky driving behaviors among JMU students, to evaluate students’ perceived risk of distracted driving, to identify discrepancies between perceptions and behaviors related to distracted driving, and to increase knowledge of distracted driving behaviors and consequences related to these behaviors. Findings on the first three objectives are reported in this manuscript. The project was approved by the JMU Institutional Review Board (IRB). Methods A Distracted Driving Awareness Day information table was established and managed by ESG members on the Commons and in the Festival Dining Center at JMU in April and November of 2009, 2010, and 2011. A written pilot survey was administered to a non-randomized convenience sample of volunteers that stopped by the information table. ESG members asked individuals to fill out the survey to the best of their ability (Appendix). Students were also given the opportunity to sign a pledge to not perform specific behaviors distracting them from their focus on driving. The information table, survey, and pledge were an attempt by faculty of the JMU Health Sciences Department and ESG to show that distracted driving is a risky health behavior and to increase awareness of the associated negative consequences. A Distracted Driving Awareness Day will continue to be held once a semester, each April and November, to raise awareness of distracted driving behaviors and consequences directly before students leave the University for their scheduled spring and Thanksgiving breaks. The data collected from the surveys will continue to be inputted into SPSS by ESG members and interpreted using the descriptive statistics function to compare and contrast individuals’ perceptions and behaviors. Population Overall, 749 students completed the survey. The sample consisted of 686 undergraduate students, 11 graduate students, and 52 respondents who did not indicate their status at JMU. The respondents ranged from 17 to 38 years of age and consisted of 204 males, 511 females, and 34 respondents who did not indicate a gender. Data Analysis The data collected through the pilot survey was entered manually by ESG members into SPSS, version 19. The data was then analyzed using the descriptive statistics function. Descriptive statistics, such as frequencies and cross-tabulations, were used to describe the sample, student behaviors, perceptions, and relationships, if any, between risk behaviors and perceptions.

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Results Respondents reported frequency of distracted driving behaviors and their attitudes about the risk of performing these behaviors while driving. These behaviors included texting or talking on the cell phone, being tired or fatigued, daydreaming, eating, putting on or taking off clothing, grooming (fixing makeup, shaving, brushing hair, etc.), and changing the controls on the radio, CD player, navigation device, or iPod. Texting while driving is a very common distraction among drivers with 17.6% of those polled reporting that they text “often” while driving. Out of students who reported “sometimes” texting while driving, 21% classified the behavior as “somewhat risky” and 78.2% classified the behavior as “very risky.” Out of students who reported “often” texting while driving, 25% classified the behavior as “somewhat risky” and 68.2% classified the behavior as “very risky.” Talking on the phone while driving is also a common distraction among drivers with 25.9% of those polled reporting that they talk on the phone “often” while driving. Of those polled, 14.4% of those who “often” talk on the phone and 16.5% of those who “sometimes” talk on the phone while driving feel that it is a “very risky” behavior. Of those who classify talking on the phone as “somewhat risky,” 43.8% “often” talk on the phone and 50% “sometimes” talk on the phone while driving. Regarding driving while tired or fatigued, 57.4% of those who “often” and 44.7% of those who “sometimes” drive while tired or fatigued feel that it is a “very risky” behavior. Of those polled, 25.9% of those who “often” and 42.8% of those who “sometimes” drive while tired or fatigued feel that it is a “somewhat risky” behavior. A majority of those who “rarely” drive while tired or fatigued feel that the behavior is either “somewhat risky” or “very risky.” Of those polled, 60.9% of those who “never” drive while tired or fatigued feel that the behavior is “very risky.” When asked about daydreaming while driving, the majority of the sample (56.8%) reported that they “rarely” or “never” daydream while driving, while 29.1% of the sample reported “sometimes” driving while daydreaming. About a quarter (26.7%) of the sample feel this behavior is “very risky;” 35.6% feel that this behavior is “somewhat risky;” and 25.8% had a neutral attitude toward this behavior. Regarding the behavior of eating while driving, only 10% of the sample reported that eating while driving was a “very risky” behavior. Of those polled, 35.9% of the sample reported that eating while driving was “somewhat risky” of a behavior and 48.9% of the sample reported they “rarely” eat while driving.


Putting on or taking off clothing while driving is also considered a distracted driving behavior. Most of the sample (91.7%) reported they “rarely” or “never” put on or take off clothing while driving. Of those polled, 57.7% reported that putting on or taking off clothing while driving was “very risky” and 29.6% of the sample reported that this behavior was “somewhat risky.” When analyzing behaviors related to grooming (fixing makeup, shaving, brushing hair, etc.) while driving, most of the sample (88.4%) reported they “rarely” or “never” engage in this behavior while driving. Of the sample, 37.9% reported this behavior was “very risky” and 38.6% reported this behavior was “somewhat risky.” Changing the controls on the radio, CD player, navigation device, iPod, and so forth is also a distraction while driving. Of those polled, 67.7% reported they “often” change controls on such devices while driving. Of that number, 34.1% feel that the behavior is “somewhat risky.” Out of the total sample, 31.6% feel that this behavior is “somewhat risky.” Discussion Overall, our objectives of identifying risky driving behaviors among JMU students, evaluating students’ perceived risk of distracted driving, and identifying discrepancies between perceptions and behaviors related to distracted driving were accomplished. On the positive side, the sample reported behavior that was consistent with their attitudes:

• When asked about frequency of driving while tired or fatigued, the category with the most respondents was “rarely” and the majority of the sample considered this behavior to be “very risky.”

• The majority of the sample reported “never” putting on/taking off clothing while driving and considered this behavior to be “very risky.” • The majority of the sample reported “never” grooming while driving and considered this behavior to be either “somewhat risky” or “very risky.”

• The majority of the sample reported “often” changing controls on devices while driving and either considered this behavior “not really risky” or had a neutral attitude.

The results of the data analyses also indicated an inconsistency between some of the behaviors and attitudes related to those behaviors. When asked about the frequency of texting while driving, the category with the most respondents was “sometimes” and the majority of the sample classified this behavior as “very risky.” When asked about the frequency of talking on the phone while

driving, the category with the most respondents was “sometimes,” and “somewhat risky” was the classification by the largest number of respondents who engaged in this behavior. The inconsistencies between distracted driving beliefs and behaviors are a major public health concern. Drivers who are texting take their eyes off the road 400% more than when they are not texting (OSHA, 2011). According to our research, the majority of people believe texting while driving is dangerous, but they continue to engage in this behavior. This means that the majority of people are risking their own lives along with the lives of others because they are under the impression that a text message is more important than their safety. The continuing behavior of using a cell phone while driving suggests that people believe texting or talking on a cell phone is not as dangerous as other behaviors, but “reaction time is delayed for a driver talking on a cell phone as much as it is for a driver who is legally drunk” (OSHA, 2011). Limitations and Delimitations Limitations to this study include possible reporting bias within the data, the use of a convenience sample, and possible biases in the demographic of our sample. A delimitation of this study is that we chose college students because they are a vulnerable population to the health issue of distracted driving. We chose to ask about texting and talking on their cell phones because these are common behaviors among this age group as well as common causes of driving accidents. Future studies could find a reason for the discrepancies between perceived danger of distracted driving behaviors and actual behaviors. They could also expand the population outside of college students in order to be able to compare the study to a larger population. Conclusions Inconsistencies in distracted driving perceptions and behaviors are what drive public health officials to research distracted driving. With the growing reliance on technology, the level of concern for this behavior is only increasing (Ziegler, 2010). Many people wrongly believe that using a hands-free phone to talk while driving is a safe alternative. There are just as many distractions when talking on a hand-held phone as using a hands-free device (Harrison, 2011). Distracted driving is continuing to grow as a public safety hazard. In 2010, a national summit brought together safety experts, industry leaders, and several U.S. senators to address the hazards of distracted driving and to examine possible regulatory solutions (Wilson & Stimpson, 2010). The Alliance of Automobile Manufacturers and the American Automobile Association have both joined this debate by announcing their support of bans


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on the use of hand-held devices while driving (Wilson & Stimpson, 2010). With continued research, heightened awareness, and behavior change, hopefully distracted driving behaviors will begin to decrease. Acknowledgements The Sigma Chapter of Eta Sigma Gamma at James Madison University would like to thank its members for raising awareness of distracted driving among the James Madison University community and gathering the data required for this study. We would also like to thank Dr. Theresa M. Enyeart Smith and Dr. Maria Theresa Wessel for helping with this research and providing our chapter with the resources necessary to complete our study. References

U.S. Department of Transportation, National Highway Traffic Safety Administration. (2010). Traffic safety facts: Distracted driving 2009 (DOT HS 811 379). Retrieved from http://www.distraction.gov/research/ PDF-Files/Distracted-Driving-2009.pdf Wilson, F. A., & Stimpson, J. P. (2010). Trends in fata - lities from distracted driving in the United States, 1999 to 2008. American Journal of Public Health, 100(11), 2213-2219. Ziegler, A. (2010, June 28). Smart phones beating out computers. Daily Nebraskan. Retrieved from http:// www.dailynebraskan.com/smart-phones-beating- out-computers-1.2277275 Appendix Distracted Driving Awareness Survey

Beede, K. E., & Kass, S. J. (2006). Engrossed in con- versation: The impact of cell phones on simulated Age: ____ driving performance. Accident Analysis and Preven- Gender (Circle one): Male/ Female tion, 38, 415-421. Year (Circle one): Freshman/ Sophomore/ Junior/ Senior/ Grad Centers for Disease Control and Prevention. (2011). In- Do you have a car on campus? (Circle one): Yes/ No jury prevention & control: Motor vehicle safety - Distracted driving. Retrieved from http://www.cdc. 1. How often do you drive a car or other motor gov/Motorvehiclesafety /Distracted_Driving/index. vehicle? (Circle one) html a. Never b. Once a week Harrison, M. A. (2011). College students. Accident c. Several days a week Analysis & Prevention, 43(4), 1516-1520. d. Everyday doi:10.1016/j.aap.2011.03.003 2. Do you have any of the following devices Horrey, W. J., Lesch, M. F., & Garabet, A. (2008). As- (that are accessible to you while driving)? sess ing the awareness of performance decrements (Circle all that apply) in distracted drivers. Accident Analysis & Preven - a. Cell phone tion, 40, 675-682. b. Beeper/pager c. PDA or Palm Pilot McEvoy, S. P., Stevenson, M. R., & Woodward, M. d. In-car navigation device (2007). The contribution of passengers versus e. Internet and e-mail access mobile phone use to motor vehicle crashes result- f. Music devices (radio, CD player, IPod, etc.) ing in hospital attendance by the driver. Accident Analysis & Prevention, 39, 1170-1176. 3. Using the scale below, indicate how often you find yourself doing ANY of the activities while driving. National Transportation Safety Board, Office of Pub- (4 = often, 3 = sometimes, 2 = rarely, 1 = never) lic Affairs. (2011). No call, no text, no update be- __ Texting on the cell phone hind the wheel: NTSB calls for nationwide ban on __ Talking on the cell phone PEDs while driving. Retrieved from http://www. __ Being tired or fatigued ntsb.gov/news/2011/111213 html __ Daydreaming __ Eating Occupational Safety and Health Administration. (2011). __ Putting on or taking off clothing Distracted driving: No texting [Brochure]. Retrieved __ Grooming (fixing makeup, shaving, brushing hair, etc) from http://www.osha.gov/Publications/3416distracted- __ Changing the controls on the radio, CD player, navi- driving-flyer.pdf gation device, IPod

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7. If there was new technology that was accessible for 4. Using the scale below, how RISKY is performing the following activities while driving. your cell phone that allowed people to understand (5 = very risky, 4 = somewhat risky, 3 = neutral, that you were driving and could not talk or text at the 2 = not really risky, 1 = not risky at all) moment (unless an emergency situation was taking ___ Texting on the cell phone place or short calls were allowed), would you buy or install it? (Circle one) ___ Talking on the cell phone ___ Being tired or fatigued Yes/ No ___ Daydreaming If you answered “no,” why not? ___________________ ___ Eating __________________________ ___ Putting on or taking off clothing ___ Grooming (fixing makeup, shaving, brushing hair, etc.) 8. What do you consider the MOST distracting be___ Changing the controls on the radio, CD player, havior in a car? _______________ navigation device, IPod 5. What behaviors do you consider SAFE to perform while driving? (Circle all that apply) Texting/ Eating/ Talking on a cell phone/ Daydreaming/ Being tired/ Taking off or putting on clothes/ Grooming yourself/ Changing the controls on the radio, CD player, IPod, navigation device/ None of the listed activities

9. Should distracted driving be a focus for a health education program? (Circle one) Yes/ No

6. If you perform any of the above mentioned tasks while driving, under what conditions are they performed? (Circle all that apply) Inclement weather (rain, snow, high winds, slippery conditions, etc.)/ At night/ Bumper to bumper traffic/ Fast moving traffic/ Unfamiliar areas/ Any time/ I do not perform any of the above mentioned tasks


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Incidence of Male Alcohol and Other Drug Abuse and Physical Violence Against Pregnant Women in Ciudad Juárez, Mexico Thilina Bandara, John Moraros, and Yelena Bird

Abstract The purpose of this study was to identify the nature and extent of physical violence against pregnant women by male partners under the influence of alcohol and other drugs (AOD) using a sample of women who presented for prenatal care at a major hospital in Ciudad Juárez, Mexico. Of the 215 women who completed the survey, 17.7% (n = 38) self-reported being victims of physical abuse, with 89.5% (n = 34) of them reporting their male intimate partner being under the influence of AOD at the time of the act (p < 0.01). The majority of these women were poor, young housewives with less than a high school education. Substance abuse treatment centers in Juárez, Mexico may wish to incorporate physical violence screening into their history-taking protocols and offer suitable violence prevention services. Introduction Intimate partner violence (IPV) is a significant public health problem that detrimentally affects the health status of millions of women globally (World Health Organization [WHO], 2002). IPV most often takes the form of a male battering his female partner, although femaleto-male and same-sex abuse also occurs. Alcohol and other drug (AOD) abuse is one of the most ubiquitous risk factors cited for male-to-female physical IPV (Field & Caetano, 2004; Schumacher, Feldbau-Kohn, Slep, & Heyman, 2001). In the US, many women, including those who are pregnant, have been victims of IPV (Bureau of Justice Statistics [BJS], 2012). Annually, approximately 1 in 4 women will experience physical violence from an intimate partner (Black et al., 2011). This victimization generally occurs at home and when these women are of reproductive age (BJS, 2012; Tjaden & Thoennes, 2000a). The occurrence of IPV varies across ethnic groups in the US. For instance, Field and Caetano (2004) reported that Hispanics’ annual rate of male-to-female partner violence was 3 times higher than the rate for Caucasians. This ethnic difference in IPV rates has been explained by the combined effects of socioeconomic factors and AOD abuse (Cunradi, Caetano, & Schafer, 2002; Field & Caetano, 2004).

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In Mexico, few studies have been conducted on IPV against women. In 1999, the National Institute for Statistics, Geography, and Information Technology (NISGIT) reported that at least “a third of the homes (in the Mexico City metropolitan area) experienced violence in various forms including emotional, physical and sexual abuse” (as cited in Torres, 2001, p. 1). Furthermore, the only study that was identified in which the relationship between violence and pregnancy in the general Mexican population was examined reported a prevalence rate of 32% (Castro & Ruiz, 2004). AOD abuse is an important dimension in IPV both in the US and Mexico. In many instances the male perpetrators have been reported to be drinking excessively or using illicit substances before committing an assault on their female intimate partners (BJS, 2012; Valdez-Santiago & Sanin-Aguirre, 1996). Male AOD abuse represents one of the major predisposing factors for IPV against pregnant women. Despite the fact that IPV perpetrated by males under the influence of AOD adversely affects the well-being of both the pregnant woman and her unborn fetus, limited research in the US and almost none in Mexico has simultaneously examined the relationship between male AOD abuse and IPV in the lives of pregnant women (Amaro, Fried, Cabral, & Zuckerman, 1990; Castra, Peek-Asa, Garcia, Ruiz, & Kraus, 2003; Cuevas, Blanco, Juárez, Palma, & Valdez-Santiago, 2006; Kyriacou et al., 1999). The majority of research in AOD abuse and IPV has focused on one form of violence, most typically physical assault. Data from the U.S. National Violence Against Women Survey indicated that both risk of injury from physical assault and incidence of rape increased when the perpetrator was under the influence of alcohol at the time of the event (Tjaden & Thoennes, 2000b). Additionally, Testa and colleagues (2003) concluded that among women who reported IPV, physical aggression was more severe in drinking episodes (58%) than in nondrinking episodes (37%). The present study extends our knowledge in this important arena by examining associations between the pregnant women’s experiences of physical violence and their male partners’ AOD abuse.


Methods Data were collected using a self-reported survey and a cross-sectional design. The procedures included (a) the adaptation of an existing instrument designed to measure the nature and extent of victimization among pregnant women, (b) a pilot test of the instrument, (c) obtaining authorization to distribute the instrument, (d) in-service training for hospital personnel who would be involved in the instrument distribution, and (e) data collection and analysis. Descriptive statistics were used to analyze the data, including frequency distributions, measures of central tendency, chi-square tests, and variance analysis. The instrument used in this study was a 43-item questionnaire related to the possible occurrence, frequency, and severity of physical abuse a woman may have experienced at the hands of a male intimate partner under the influence of AOD during the woman’s current and prior pregnancies. All the items were closed-format multiplechoice questions that were categorical. The reliability of the survey instrument was assessed by conducting a pilot study. The pilot group consisted of 28 pregnant women who were randomly selected from a different prenatal hospital in Juárez. Analysis of the pilot data revealed the stability-reliability of the instrument to be r = 0.84. The population of interest included 215 clinically documented pregnant women who received prenatal care services during a consecutive two-month period at Hospital de la Mujer in Ciudad Juárez, Mexico. The study protocol was reviewed and approved by the Institutional Review Boards of New Mexico State University, Universidad Autonoma de Ciudad Juárez, and Hospital de la Mujer in Ciudad Juárez, Mexico. Data analysis included descriptive statistics (i.e., frequency counts, means, medians, and modes) and chisquare tests, using the SPSS 12.0 Statistical Software package. Percentages and mean values were used to summarize all data. The significance level selected for the study was set at 0.05. Results Sociodemographic Characteristics Of the 215 pregnant women who presented at the hospital during the data collection period, 17.7% (n = 38) reported being the victim of physical violence, and 89.5% (n = 34) of that number reported being abused by a male perpetrator under the influence of AOD. The demographic characteristics of the study population are presented in Table 1. The majority of respondents who suffered physical violence by a male intimate partner under the influence of AOD during their pregnancies were from 12 to 20 years

of age (58.8%; p = 0.04), were married (61.7%; p = 0.04), were housewives (82.3%; p = 0.00), had more than one pregnancy (61.8%; p = 0.02), had less than a high school education (85.3%; p = 0.00), and reported a family annual income of less than 10,000 pesos (1 U.S. dollar = 11 Mexican pesos) (85.3%; p = 0.00). Frequency, Form, and Anatomical Location of Physical Abuse Of the 38 women who reported being physically abused, 34 (89.5%) suffered such abuse at the hands of a male partner reportedly under the influence of AOD and mainly (64.7%) during the third trimester of their pregnancy. Being punched was the most common form of physical abuse reported by these women (58.8%). This was followed closely by being slapped (35.3%). Regardless of the form of physical violence suffered by the pregnant women, the incidence of physical abuse was significantly higher in all cases when AOD had been abused by the male perpetrator (p = 0.05). Chi-square analysis was used to identify significant correlations between the pregnant women being physically abused at the hands of a male perpetrator under the influence of AOD and the victim being punched in the abdominal area (p < 0.00), suffering an abortion (p < 0.00), being punched in the face (p < 0.00), and suffering fractures (p < 0.02). Additionally, the women were asked to indicate the multiple anatomical sites where they had experienced physical abuse. Approximately 7 out of every 10 (70.6%) reported being physically abused in the abdominal area by an offender under the influence of AOD. By contrast, the buttocks (9.0%) and genital regions (8.8%) were the body areas least frequently reported being physically abused (Figure1). Discussion In the US, researchers have indicated that AOD abuse plays an important role in IPV. Results of the 2000 National Family Violence Survey indicated that 30% to 40% of the men who perpetrated violence against their female partners were drinking at the time of the event. That same study linked high-volume drinking by Latino men to increased rates of physical violence (National Resource Center to End Violence Against Women, 2000). Specifically, Latinas living in the US with binge-drinking husbands were 10 times more likely to be physically assaulted than those with low to moderately drinking husbands (Kaufman-Kantor, 1990). In Mexico, alcohol abuse is a social factor that has been traditionally identified as a strong contributor to IPV. Medina-Mora, Berenzon, and Natera (1999) stated that in Mexico, the relative risk of physical violence is 3.3 times higher for a woman whose partner drinks every day compared to one whose partner does not consume alcohol.


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Similarly, in the present study, drinking and AOD-related problems were significantly associated with physical violence, especially among adolescent pregnant victims. It was discovered that nearly half of the pregnant women physically abused by a male intimate partner under the influence of AOD were adolescents (47.1%). These findings are comparable to those reported by Parker and colleagues (1993), who concluded that adolescents experience higher rates of abuse during pregnancy than adult women. In examining the relationship between the marital status and possible abuse rates, married pregnant women represented the majority of the victims whose male intimate partner had used AOD during the abusive act (61.7 %). To a large extent this finding was expected. Torres and colleagues (2000) corroborated that in Mexico specifically and in the Latino culture generally, married men, especially while intoxicated, feel a sense of entitlement over their wives. Due to this disturbing cultural norm of endorsing male violence against women, Mexican women married to men who abuse AOD may experience higher rates of physical abuse than their single counterparts. Demographic characteristics of the study population were also examined in relation to educational level and abuse status. Results revealed that the incidence of physical abuse at the hands of a male perpetrator under the influence of AOD was most frequent (85.3%) among pregnant women who indicated that they had not received a high school diploma (i.e., low educational level). This finding is supported by evidence presented by Tjaden and Thoennes (2000b), who found low education level to be associated with a 1.4 times greater risk for IPV. Addition-

ally, Hotaling and Sugarman’s (1986) review of over 400 empirical reports on husband-to-wife IPV found that higher educational level was associated with less physical violence in the majority of the studies they examined. In the present study, in terms of occupation, the majority of the pregnant women abused by a male under the influence of AOD were housewives (82.3%). This problem is further compounded if one takes into account the fact that nearly two thirds of the abused pregnant women (61.8%) had at least one dependent child in addition to the unborn infant. Furthermore, nearly 85% (n = 32) of these women indicated that they had limited personal financial resources—property, cash, or bank accounts— solely of their own. By comparison, the incidence of physical abuse among pregnant students may be associated with the complexity of the students’ lives. There are many factors associated with this finding. Students have less time to complete household chores, constitute a high economic cost, and have less time to provide attention to their abusing partner/spouse. It is possible that all these factors may have contributed to making students the second highest abused group within the study population. Poverty is an important risk factor for IPV (Goodman, Smyth, Borges, & Singer, 2009; Straus & Smith, 1990; Vest, Caitlin, Chen, & Brownson, 2002). Van Hightower and colleagues (2000) found that low-income Latinas living in rural settings had a higher risk of domestic violence if their husbands or partners used AOD. The present study revealed that respondents who reported a low family income (< 10,000 pesos [1 U.S. dollar = 11 Mexi-


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can pesos]) also more frequently reported being abused at the hands of a male perpetrator under the influence of AOD (85.3%) than those with a high family income. The fact that the majority of these women were housewives (82.3%) is particularly significant because it speaks of the female victim’s possible economic dependence on the male abuser. Physical abuse was reported to have mainly occurred during the third trimester of the victims’ pregnancies (64.7%). A contributing factor to these findings may be the fact that as the delivery due date approached, the expectant mother may have turned her attention to preparing for the arrival of the new baby. Consequently, she may have had less physical energy and time to devote attention to the needs of her substance-abusing male partner. On the other hand, the male intimate partner under the influence of AOD may have perceived the imminent arrival of the new family member as an unwanted financial hardship, one that may severely curtail the funds needed to sustain his destructive habit, and be displeased and frustrated enough to commit acts of physical violence against the source of his perceived newfound problems. In analyzing the responses of the 38 women who reported being physically abused, 34 (89.5%) of them claimed to be physically abused by a male intimate partner under the influence of AOD. This finding supports the principle that there is a common relationship between partner physical violence and the abuse of AOD (Field & Caetano, 2004). Fals-Stewart (2003) showed that men who drink alcohol have 8 times greater propensity for physical violence toward their female partners on the days they drink than those who do not consume alcohol. In the present study, particularly disconcerting was the assertion by pregnant women that the most common form of physical abuse suffered at the hands of a male intimate partner under the influence of AOD was being punched (58.8%), especially in their abdominal regions (70.6%). The reasons for such physical violence during pregnancy may include the fact that AOD abuse is known to impair the perpetrator’s judgment, reduce inhibitions, and increase aggression (Abbey, Zawacki, Buck, Clinton, & McAuslan, 2001; Fossos, Neighbors, Kaysen, & Hove, 2007; Raey, Hamilton, Kennedy, Scholey, 2006; Stith, Smith, Penn, Ward, & Tritt, 2004). Consequently, a male partner under the influence of AOD may attempt to terminate the pregnancy, particularly if he perceives the fetus to be an unwelcome burden (Bohn, 1990). In two studies (Campbell, Oliver, & Bullock, 1993; Campbell, Pugh, Campbell, & Visscher, 1995), researchers found that jealousy and anger toward the unborn child were two prime motives for male IPV toward pregnant women. These motives were most commonly manifested in the location of the abuse (e.g., beatings aimed at the Page 10

woman’s abdomen). Results indicated that men might see pregnancy as interfering with the woman’s traditional role and duties as a caretaker of her male partner. Conclusion The findings from this study should be viewed in light of the limitations associated with the research design. First, results were based solely on self-reports of physical abuse, which are prone to various forms of recall and response bias, such as inaccurate recollection of traumatic events, social desirability, and possible fears regarding confidentiality and stigma from health care professionals. Such biases are especially likely given the sensitive nature of the topics under study (e.g., physical violence and AOD abuse). Another limitation of this research is that it is based on a relatively small sample of abused women (n = 38) and an even smaller number of women physically abused by a male perpetrator who was reported to be under the influence of AOD (n = 34). These small samples resulted in smaller “cell-sizes” in some analyses. Furthermore, the research was based on an examination of responses from a convenience sample of pregnant women from Juárez, Mexico who attended a specified hospital-based prenatal care clinic that serves mainly low-income women; thus, the findings from the present study may not be generalizable to other groups of women, but indicators may be present. Despite the limitations of this investigation, the study results should be helpful in informing physicians, public health administrators, and reproductive health advocates about the concomitant dangers of male AOD abuse and physical violence against pregnant women. The findings underscore the importance of providing routine screening for physical abuse as well as male AOD abuse within the context of women’s health care in general and pregnant women’s health in particular. Although many international health care organizations and agencies have endorsed such screenings, the findings from this preliminary study suggest that the majority of female patients in Juárez, Mexico are not screened for physical abuse even during the provision of prenatal care visits. Considering the possible link between physical violence suffered by pregnant women and AOD abuse among their intimate male perpetrators, substance abuse treatment centers in Juárez, Mexico may wish to incorporate physical violence screening into their history-taking protocols and offer identified perpetrators suitable violence prevention and reduction services. Similarly, professionals who provide services to abused women (such as staff of domestic violence centers) should assess the possibility of their clients’ intimate partners having an AOD addiction and ensure that appropriate services are


provided to those who need them. For such screening and treatment procedures to be successful, substance abuse treatment providers and domestic violence care providers need to be cross-trained concerning both substance abuse and violence issues. These would be important steps in assuring that pregnant women experiencing physical violence at the hands of a male partner who suffers from AOD abuse problems are receiving optimal health care. Acknowledgments: This study was conducted with the support of Dr. Eduardo Maldonado Avila, Medical Director, Hospital de la Mujer, Juárez, Mexico. Financial assistance was provided through a Hot Projects Grant by the Paso del Norte Foundation. References

Castro, R., & Ruiz A. (2004). Prevalence and severity of domestic violence among pregnant women, Mexico. Rev Saude Publica, 38(1), 62-70. Cuevas, S., Blanco, J., Juárez, C., Palma, O., & Valdez- Santiago, R. (2006). Violence and pregnancy in female users of ministry of health care services in high ly deprived states in Mexico. Salud pública Méxicana, 48(2), s239-s249. Cunradi, C. B., Caetano, R., & Schafer, J. (2002). Socio- economic predictors of intimate partner violence among White, Black and Hispanic couples in the United States. Journal of Family Violence, 17(4), 377-389.

Abbey, A., Zawacki, T., Buck, P. O., Clinton, A. M., & McAuslan, P. (2001). Alcohol and sexual assault. Alcohol Health & Research, 25(1), 43-51.

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Amaro, H., Fried, L. E., Cabral, H., & Zuckerman, B. (1990). Violence during pregnancy and substance use. American Journal of Public Health, 80, 575- 579.

Field, C. A., & Caetano, R. (2004). Ethnic differences in intimate partner violence in the U.S. general population: The role of alcohol use and socioeconomic status. Trauma, Violence & Abuse, 5, 303-317.

Bohn, D. K. (1990). Domestic violence and pregnancy: Implications for practice. Journal of Nurse-Mid- wifery, 35, 86-98.

Fossos, N., Neighbors, C., Kaysen, D., & Hove, M. C. (2007). Intimate partner violence perpetration and problem drinking among college students: The roles of expectancies and subjective evaluations of alcohol aggression. Journal of Studies on Alcohol and Drugs, 68(5), 706-713.

Black, M. C., Basile, K. C., Breiding, M. J., Smith, S. G., Walters, M. L., Merrick, M. T., . . . Stevens, M. R. (2011). The National Intimate Partner and Sexu- al Violence Survey (NISVS): 2010 summary report. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Pre- vention. Bureau of Justice Statistics. (2012). Intimate partner violence in the US. Retrieved January 21, 2012, from http://bjs.ojp.usdoj.gov/content/intimate/ victims.cfm

Goodman, L. A., Smyth, K. F., Borges, A. M., & Singer, R. (2009). When crises collide: How intimate partner violence and poverty intersect to shape women’s mental health and coping? Trauma, Violence & Abuse, 10, 306-329. Hotaling, G. T., & Sugarman, D. B. (1986). An analysis of risk markers in husband to wife violence: The current state of knowledge. Violence and Victims, 1, 101-124.

Campbell, J. C., Oliver, C., & Bullock, L. (1993). Why battering during pregnancy? AWHONNS Clinical Kaufman-Kantor, G. (1990). Ethnicity, alcohol, and fam- Issues in Perinatal and Women’s Health Nursing, 4, ily violence: A structural and cultural interpretat- 343-349. tion. Paper presented at the Forty-Second Annual Meeting of the American Society of Criminology, Campbell, J. C., Pugh, L. C., Campbell, D., & Visscher, Baltimore, MD. M. (1995). The influence of abuse on pregnancy in- tention. Women’s Health Issues, 5, 214-223. Kyriacou, N. D., Anglin, D., Taliaferro, E., Stone, S., Tubb, T., Linden, J.A., . . . Kraus, J. F. (1999). Castra, R., Peek-Asa, C., Garcia, L., Ruiz, A., & Kraus, Risk factors for injury to women from domestic vio- J. F. (2003). Risks for abuse against pregnant His- lence against women. The New England Journal of panic women: Morelos, Mexico and Los Ange- Medicine, 341(25), 1892-1898. les County, California. American Journal of Pre- ventative Medicine, 25(4), 325-332.


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Medina-Mora, M. E., Berenzon, S., & Natera, G. (1999). El papel del alcholismo en las violen- cias [The role of alcoholism in violence]. Gaceta Médica de México, 135, 282-287.

Tjaden, P., & Thoennes, N. (2000b). Full report of the prevalence, incidence, and consequences of intimate partner violence: Findings from the National Violence Against Women Survey (NCJ183781). Washington, DC: National Institute of Justice, National Criminal Justice Research Service.

National Resource Center to End Violence Against Women. (2000). Estimates of incidence & prevalence of intimate partner violence. Washington, DC: Torres, E.P. (2001). Gender violence, health and rights National Academy Press. in America. Pan American Health Organization, International Symposium on Intimate Partner Parker, B., McFarlane, J., Soeken, K., Torres, S., & Violence. Cancun, Mexico, June 4-7, 2001. Campbell, D. (1993). Physical and emotional abuse in pregnancy: A comparison of adult and teenage Torres, S., Campbell, J., Campbell, D. W., Ryan, J., women. Nursing Research, 42, 173-177. King, C., Price, P., . . . Laude, M. (2000). Abuse during and before pregnancy: Prevalence and Raey, J. L., Hamilton C., Kennedy, D. O., & Scholey, A. cultural correlates. Violence and Victims, 15(3), B. (2006). MDMA polydrug users show process- 303-321. specific central executive impairments coupled with impaired social and emotional judgment processes. Valdez-Santiago, R., & Sanin-Aguirre, LH. (1996). La Journal of Psychopharmacology, 20(3), 385-388. violencia domestica durante el embarazo y su relacion con el peso al nacer (Domestic violence Schumacher, J. A., Feldbau-Kohn, S., Slep, A. M., & during pregnancy and its relationship with birth Heyman, R. E. (2001). Risk factors for male- weight). Salud Publica Mexicana, 38, 352-362. to-female partner physical abuse. Aggression and [Article in Spanish]. Violent Behavior, 6, 281-352. Van Hightower, N. R., Gorton, J., & DeMoss, C. L. Stith, S. M., Smith, D. B., Penn, C. E., Ward, D. B., & (2000). Predictive models of domestic violence and Tritt D. (2004). Intimate partner physical abuse per- fear of intimate partner violence among migrant petration and victimization risk factors: A meta- and seasonal farm worker women. Journal of analysis review. Aggression and Violent Behaviour, Family Violence, 15(2), 137-154. 10, 65-98. Vest, J., Caitlin, T., Chen, J., & Brownson, R. (2002). Straus, M. A., & Smith, C. (1990). Violence in Hispanic Multistate analysis of factors associated with families in the United States: Incidence rates and intimate partner violence. American Journal of structural interpretations. In M. A. Straus & R. J. Preventative Medicine, 22, 156-164. Gelles (Eds.), Physical violence in American families: Risk factors and adaptations in violence World Health Organization. (2002). Intimate partner vio- in 8,145 families (pp. 341-363). New Brunswick, lence. Retrieved from http://ww.who.int/violence_in NJ: Transactions Books. jury_prevention/violence/world_report/factsheets/ en/ipvfacts.pdf Testa, M., Quigley, B. M., & Leonard, K. E. (2003). Does alcohol make a difference? : Within-participant comparison of incidents of partner violence. Journal of Interpersonal Violence, 18(7), 735-743. Tjaden, P., & Thoennes, N. (2000a). Extent, nature and consequences of intimate partner violence: Find- ings from the National Violence Against Women Survey (NCJ 181867). Washington, DC: National Institute of Justice, National Criminal Justice Research Service.

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Implementation and Evaluation of the C.A.T.C.H. Health Education Classroom Curriculum Matthew R. Bice, Alex T. Ramsey, and James W. Ball

Abstract This study compared the effectiveness of two models of Coordinated Approach to Child Health (CATCH) implementation—a more traditional one that included a classroom teacher only (comparison group) and a new one that added a health education professional (experimental group). Five rural schools were randomly assigned to the two implementation groups, and 93 fifth-grade students responded to health knowledge and behavior questions at pretest and an eight-week posttest. Results indicated that health knowledge increased in both groups over the eightweek period, but the new model improved knowledge significantly more than the traditional implementation. The addition of the health education professional also decreased students’ overeating behaviors. Health departments and school administrators can apply these findings in structuring the implementation of coordinated school health programs. Introduction Obesity has become a major health concern for people in the United States, contributing to an estimated 125,000 deaths or more each year due to cardiovascular disease and obesity-related cancers alone (Flegal, Graubard, Williamson, & Gail, 2007). Obese individuals are more susceptible to developing serious health problems including high blood pressure, high cholesterol, diabetes, stroke, osteoarthritis, respiratory problems, and cancer (National Center for Health Statistics, 2010). Research indicates that current trends of childhood obesity have drastically increased in the past decade and could continue to rise (Flegal, Carroll, Ogden, & Curtin, 2010). Since 1980, the prevalence of overweight or obese children aged 2-5 has risen from 5% to 10.4%; in children aged 6-11, it has increased from 6.5% to 19.6%; and in adolescents aged 1219, it has climbed from 5.0% to 18.1% (Ogden & Carroll, Division of Health and Nutrition Examination Surveys, 2010). As obesity rates continue to increase, so does the need for health professionals’ efforts to counteract these trends and prevent the associated negative health outcomes. As children spend a substantial amount of time at school, this setting plays an important role in fostering and facilitating their health. Improving health knowledge

and establishing healthy habits in children at school can help prevent many chronic health problems later in life that can be attributed to unhealthy eating, sedentary lifestyle, and obesity. As a result, many public health professionals are interested in working with school systems to better meet the health needs of children (Franks et al., 2007). Coordinated school health programs have been designed to help schools increase students’ health knowledge, physical activity, and healthy dietary behaviors. A heavily researched and comprehensive program known as the Coordinated Approach to Child Health (CATCH) is currently being implemented across the nation (Hoelscher et al., 2010). Since 1989, research has demonstrated the benefits of utilizing the CATCH curriculum within school districts (Hoelscher et al., 2010). The CATCH curriculum fulfills three recommendations by The Surgeon General’s Vision for a Healthy and Fit Nation (USDHHS, 2010). Since the first major study, CATCH programs have consistently shown reductions in child obesity and increases in children’s physical activity, healthy eating habits, healthy eating knowledge, and participation in healthy lifestyle habits. CATCH has also demonstrated the positive impacts of lower fat cafeteria choices for students and has helped meet nutritional guidelines set by the USDA (Coleman et al., 2005; Hoelscher et al., 2010; McCullum-Gomez, Barroso, Hoelscher, Ward, & Kelder, 2006; McKenzie et al., 2003; Osganian et al., 2003). Due to limited funding and time constraints, many schools across the nation struggle to devote adequate attention toward child health, specifically preventative measures related to obesity. Fortunately, CATCH training is freely accessible to all schools in the Illinois Delta Region through Southern Illinois University Carbondale’s Center for Rural Health and Social Service Development and the Illinois CATCH onto Health! Consortium. As a result, many schools in the Delta Region have adopted the CATCH program and are at least partially implementing the coordinated school health program. The CATCH program is evidence-based, and research indicates that implementation is economical and teacher-friendly (Hoelscher et al., 2010; Luepker et al., 1996).


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The Present Study The current study is an evaluation of the CATCH classroom curriculum in fifth-grade classes in the Illinois Delta Region. One objective of this research is to examine the overall effectiveness of the CATCH classroom curriculum across multiple implementation models. Specifically, the study seeks to assess improvement in students’ health knowledge and behaviors using two models of CATCH implementation. The first of these models, which is more traditional and thus will serve as the comparison group, describes implementation by a CATCH-trained classroom teacher only. The second of these models, which comprises the experimental group, adds a CATCH health education professional to the implementation process as a supplement to the CATCH-trained classroom teacher. The primary purpose of this study is to compare the effectiveness of the more traditional implementation of CATCH with the Illinois CATCH onto Health model, which adds a health education professional in teaching the CATCH classroom curriculum. Specifically, the current research investigates the incremental utility of supplementing CATCH-trained classroom teachers with a CATCH health education professional. Hypotheses It is predicted that the health education professional and the CATCH classroom curriculum will both have effects on students. This study has four hypotheses which include: 1) Health knowledge scores in the experimental group will increase significantly from pretest to posttest, 2) Health knowledge scores in the comparison group will increase significantly from pretest to posttest, 3) Health knowledge scores will increase significantly more in the experimental group than in the comparison group, and 4) Overeating behaviors will decrease significantly more in the experimental group than in the comparison group. This study utilizes the social cognitive theory (Bandura, 2001), which postulates that learning occurs best through observation under the constructs of outcome expectations and expectancies, behavioral capability, skill development, and most importantly, self-efficacy. By providing a knowledge-based foundation through the CATCH classroom curriculum, which emphasizes dietary behavior, we anticipate children will be better equipped and more efficacious in making healthier eating decisions. Furthermore, we believe that well-trained teachers can effectively implement a coordinated school health program. Prior to CATCH implementation, teachers receive education and appropriate resources through the CATCH training course. By adding a CATCH health education professional as supplemental expertise to this educational experience, students are expected to benefit. Perhaps more importantly, classroom teachers have the opportunity to model their lesson plans and teaching Page 14

strategies after the health educator, a process that essentially serves as a reinforcement of their CATCH training. Methods Participants Participants included a total of 93 (44 male, 49 female) fifth-grade students at five different schools in rural school districts located in the Illinois Delta Region. The ethnic composition of this sample was 71% Caucasian, 11% Hispanic, 3% Native-American, 1% African-American, 1% Asian-American, and 13% of an ethnicity not listed (i.e., Other). The mean age of the students was 10.81 years old. Eligible schools included those with fifth-grade teachers who had been trained to implement the CATCH curriculum in their classrooms. Of those eligible, five schools willing to participate were selected. Schools were randomly assigned to groups: Three were selected to comprise the experimental group, and two were selected to comprise the comparison group. Of the total 93, the experimental group consisted of 52 participants and the comparison group consisted of 41 participants. For schools in the experimental group, the intervention was offered to each fifth-grade class. To ensure consistency, however, data was collected only in the classes that received the CATCH implementation from the same health education professional. For one school, this consisted of one fifth-grade class, and for the other two schools, this consisted of two joined fifth-grade classes. In the comparison group, all fifth-grade classes at each school were joined for data collection. Access to schools was granted by administrators at the participating schools. Student participation was approved upon return of parental consent and child assent forms to the research team. Instrument Each fifth-grade class began the study by completing the School Physical Activity & Nutrition survey (SPAN). The SPAN survey is a widely accepted measure of students’ physical activity and nutrition at school (Hoelscher, Day, Kelder, & Ward, 2003; Hoelscher et al., 2005; Thiagarajah et al., 2008). Previous administrations of the SPAN indicate generally sound psychometrics, including adequate reliability and validity in a rural sample (Hoelscher et al., 2003). For the five knowledge questions, the current study yielded a Cronbach alpha of .61, a Spearman-Brown correlation of .68, a Guttman split-half coefficient of .68, and a test-retest correlation of .52. Given that overeating behavior data only consisted of two items (i.e., number of snacks and restaurant food intake), it was not appropriate to compute reliability statistics for them. The 56-item instrument included three sections addressing students’ knowledge, attitudes, and behaviors regarding physical activity and nutrition. Of particular interest to the current study were the five nutritional knowl-


edge items and two overeating behavior items. A sample knowledge question was “How many total servings of fruits and vegetables should you eat each day?” A sample behavior question was “Yesterday, how many times did you eat food from any type of restaurant?” In line with the SPAN protocol, all behavior questions measured students’ health practices from the previous day. Survey administration was conducted in a controlled environment, typically either a school classroom or cafeteria, depending on the size of the group. Pretest and posttest questionnaires for all schools were administered by the same two members of the research team. Survey administrations took place on school days early in the morning, and all data was collected between February and April of 2011. Survey completion required approximately 20 minutes on average. Procedures One week prior to the intervention, participating students completed a pretest measure of the SPAN survey. An eight-week intervention period then transpired, characterized by eight lessons of the CATCH classroom curriculum. On average, CATCH lessons lasted approximately 45 minutes per session. During this period, CATCH lessons in the experimental group were taught primarily by a CATCH health education professional, whereas lessons in the comparison group were taught primarily by a CATCH-trained classroom teacher. In the current study, a CATCH health education professional was defined as an individual with a Master’s degree in health education or a health-related field and at least two years of health education experience, particularly with CATCH trainings and interventions. To help rule out alternative explanations, teachers in both the experimental and comparison groups were trained to teach the CATCH curriculum. One week after the intervention, the same procedures were followed to obtain posttest data from all the participants. Data Analysis Paired-samples t-tests were used to measure the effect of the CATCH program on students’ healthy eating knowledge for the experimental group (Hypothesis 1) and the comparison group (Hypothesis 2) separately, after accounting for pretest scores. For the experimental group, repeated-measures analyses of variance (ANOVA) were used to examine the incremental benefit of the CATCH program on students’ healthy eating knowledge (Hypothesis 3) and healthy eating behavior (Hypothesis 4) after accounting for the comparison group and pretest scores.

Results It was hypothesized that students in the experimental group would gain healthy eating knowledge over the eight-week intervention period due to the presumed effectiveness of the health education professional as a supplement to a CATCH-trained teacher (Hypothesis 1). As predicted, students in the experimental group showed significant increases in their healthy eating knowledge scores from pretest (M = 1.98) to posttest (M = 3.17), t(51) = 7.23, p < .001. Thus, students taught by a CATCH health education professional in addition to their CATCHtrained teacher gained healthy eating knowledge over the eight-week period. It was also anticipated that students in the comparison group would gain healthy eating knowledge as well, due to normal maturation and learning processes as well as the general effectiveness of CATCH training (Hypothesis 2). Indeed, students in the comparison group demonstrated significant increases in healthy eating knowledge from pretest (M = 2.37) to posttest (M = 3.02), t(40) = 3.97, p < .001. As such, students taught only by their CATCHtrained classroom teacher showed gains in knowledge over the eight-week period. Although both groups demonstrated improvement, it was expected that students in the experimental group would show greater knowledge increases than students in the comparison group (Hypothesis 3). In line with this, there was significantly more improvement in healthy eating knowledge for students in the experimental group from pretest to posttest (Mean Difference = 1.19) than for students in the comparison group (Mean Difference = 0.65), F(1, 91) = 5.067, p = .027. Therefore, students taught by a CATCH health education professional (in addition to their CATCH-trained classroom teacher) showed significantly greater gains in healthy eating knowledge as compared to students taught only by their CATCHtrained classroom teacher. In other words, after accounting for the comparison group and baseline scores, the addition of a CATCH-trained health education professional significantly improved healthy eating knowledge over the eight-week program. Figure 1 illustrates the substantial knowledge increase for both groups, with the interaction effect indicating a larger increase for the experimental group than the comparison group. In support of Hypothesis 4, a significantly greater decrease in overeating behaviors was found in the experimental group from pretest to posttest as compared to the comparison group, F(1, 88) = 7.01, p = .010. Therefore, after accounting for the comparison group and baseline scores, students in the experimental group showed significant decreases in overeating (i.e., eating restaurant food and snacking) from pretest to posttest. In other


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words, over the eight-week program, the addition of a CATCH-trained health education professional to the experimental group significantly improved healthy eating behaviors related to portion control above and beyond what occurred in the comparison group. Discussion The current study sought to investigate the effectiveness of the CATCH classroom curriculum using two models of program implementation. The primary purpose was to compare the effectiveness of these models by examining the incremental utility of having a health education professional aid in CATCH classroom implementation. This study provides further evidence that coordinated school health programs, specifically CATCH, can be an effective health tool for children in school settings. Our findings suggest that the benefits of the CATCH classroom curriculum can be enhanced by including the expertise of a health educator during implementation. Children in the experimental group demonstrated a significantly greater increase in knowledge concerning nutrition and general health over the eight-week intervention. Specifically, healthy eating knowledge improved an average of 1.19 points (on a 5-point scale) in the experimental group. This improvement can be at least partially attributed to the inclusion of the health education professional. The addition of a health education professional is a key component of the Illinois CATCH onto Health model. The health educator serves as an additional resource for students, teachers, and administration. This Page 16

specialist also aids in the implementation process by providing additional expertise, technical assistance, and credibility to classroom implementation. Furthermore, the health education professional serves as a model for teachers to observe teaching techniques from an expert in the field. This modeling process may have served as a training reinforcement and an opportunity to increase self-efficacy in teachers during the implementation process. Of note, students in the comparison group, taught only by a trained CATCH classroom teacher, also demonstrated significant increases in health knowledge over the intervention period. In fact, knowledge scores improved an average of 0.65 points (on a 5-point scale) in the comparison group. This finding highlights the effectiveness of the Illinois CATCH onto Health training seminar. As influential as a health professional can be, it is important to recognize the potential of well-trained teachers to promote substantial change in children. In addition, this study suggests that students’ health can be positively impacted by a non-health professional, which is encouraging for schools lacking the opportunity and resources to include a health educator in the classroom. Although both groups showed significant increases in student health knowledge, students in the experimental group improved significantly more than students in the comparison group. Specifically, on average health knowledge increased 0.54 points more in the experimental group than in the comparison group. This is substantial for a 5-point knowledge scale and considering that


a significant increase was also found in the comparison group. This finding suggests that classes taught by the health professional and teacher in accordance with the Illinois CATCH onto Health model led to significantly greater increases in health knowledge compared to classes that only utilized classroom teachers. Our findings indicate that while a CATCH-trained classroom teacher can cultivate positive health changes in children, the inclusion of a health education professional can strengthen the impact of the program over and above that which can be attained by a CATCH-trained teacher only. Again, it is important to note that benefits from the CATCH program can still be achieved without the aid of a health education professional. This study also demonstrates similar findings with regard to overeating behaviors in children, which lends some degree of support for the application of nutritional knowledge gains. Significant decreases in overeating, as represented by snacking behavior and eating restaurant food, were found in the experimental group in which the health education professional was included. This improvement supplements the nutritional knowledge findings by providing additional support for the inclusion of a health educator in teaching health lessons within schools. While this study does not conclude that an eight-lesson implementation of the CATCH curriculum yields longterm changes in child eating habits, it does suggest that knowledge acquired through the curriculum can potentially be applied to shape children’s dietary choices. The findings are encouraging enough to warrant longitudinal studies that investigate the long-term impact of stable implementations of the CATCH curriculum, particularly when in conjunction with health education professionals, with regard to students’ eating behaviors and overall health. Limitations There are limitations to this study that must be acknowledged. First, as students were consolidated across fifth-grade classes, some schools had limited space for teaching. As a result, classes for one of the experimental groups were taught in a gym rather than a classroom. To alleviate the problem, a minimum of three teachers were used to accommodate larger classes. Holding classroom instruction in varied environments may have limited the consistency of lessons between groups. Additionally, as all teachers had been previously trained to implement CATCH, it was assumed that lessons between schools and teachers were relatively consistent when in reality they may not have been, particularly in the comparison group. Future research would benefit from assigning an evaluator to periodically assess the consistency among lessons at each school to ensure the reliability of application.

Conclusion This study provides evidence that the CATCH program is effective across multiple methods of implementation. Additionally, this study demonstrates that the inclusion of a health professional along with the classroom teacher is perhaps more effective than more traditional methods of health education. This team teaching approach is a unique and innovative aspect of the Illinois CATCH onto Health model. This does not imply that the lack of a health educator will render a classroom health component ineffective; instead, we wish to highlight the influence of trained teachers on students and the positive results that can be acquired. While allocating a health educator to the classroom may not always be feasible, the present study demonstrates the substantial impact a CATCH health education professional can have on students’ health. References Bandura, A. (2001). Social cognitive theory: An agen- tic perspective. Annual Review of Psychology, 52, 1-26. Coleman, K. J., Tiller, C. L., Sanchez, J., Heath, E. M., Oumar, S., Milliken, G., & Dzewaltowski, D. A. (2005). Prevention of the epidemic increase in child risk of overweight in low income schools. Archives of Pediatric and Adolescent Medicine, 159(3), 217-224. Flegal, K., Carroll, M., Ogden, C., & Curtin, L. (2010). Prevalence and trends in obesity among U.S. adults.The Journal of the American Medical Association, 3, 235-241. Flegal, K., Graubard, B., Williamson, D., & Gail, M. (2007). Cause-specific excess deaths associated with underweight, overweight, and obesity. The Journal of the American Medical Association, 298(17), 2028-2037. Franks, A. L., Kelder, S. H., Dino, G. A., Horn, K. A., Gortmaker, S. L., Wiecha, J. L., & Simoes, E. J. (2007). School-based programs: Lessons learned from CATCH, Planet Health, and Not-On-Tobacco. Preventings Chronic Disease, Public Health Research Policy and Practice, 4(2), 1-9. Retrieved from http://www.cdc.gov/pcd/ issues/2007/apr/06_0105.htm Hoelscher, D. M., Day, R. S., Kelder, S. H., & Ward, J. L. (2003). Reproducibility and validity of the secondary level School-Based Nutrition Monitoring student questionnaire. Journal of the American Dietetic Association, 103, 186-194.


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Hoelscher, D. M., Perez, A., Lee, E. S., Sanders, J., National Center for Health Statistics. (2011). Health, Kelder, S. H., Day, R. S., & Ward, J. (2005). School United States, 2010: With special feature on death Physical Activity and Nutrition (SPAN) III Survey, and dying. Hyattsville, MD: Author 2004-2005. UT School of Public Health, Houston. Ogden, C., & Carroll, M., Division of Health and Nutri- Hoelscher, D. M., Springer, A. E., Ranjit, N., Perry, C. tion Examination Surveys. (2010). Prevalence of L., Evans, A. E., Stigler, M., & Kelder, S. H. (2010). obesity among children and adolescents: United Education in child obesity among disadvantaged States, trends 1963-1965 through 2007-2008. school children with community involvement: Hyattsville, MD: Centers for Disease Control and The Travis county CATCH trial. Obesity, 18(1), Prevention, National Center for Health Statistics. 202-205. Osganian, S. K., Hoelscher, D. M., Zive, M., Mitchell, Luepker, R. V., Perry, C. L., McKinlay, S. L., Nadar, P. D., Snyder, P., & Webber, L. S. (2003). Mainte- P. R., Parcel, G. S., Stone, E. J., . . . Wu, M. (1996). nance of effects of the Eat Smart school food Outcomes of a field trial to improve children’s service program: Results from the CATCH-ON dietary patterns and physical activity: The Child study. Health Education & Behavior, 30(4), 418- and Adolescent Trial for Cardiovascular Health 433. (CATCH). The Journal of the American Medical Thiagarajah, K., Fly, A. D., Hoelscher, D. M., Bai, Y., Association, 275(10), 768-776. Lo, K., Leone, A., & Shertzer, J. A. (2008). Validat- McCullum-Gomez, C., Barroso, C. S., Hoelscher, D. M., ing the food behavior questions from the elementary Ward, J. L., & Kelder, S. H. (2006). Factors in school SPAN questionnaire. Journal of Nutrition fluencing implementation of the Coordinated Education and Behavior, 40(5), 305-310. Approach to Child Health (CATCH) Eat Smart School Nutrition Program in Texas. Journal of the American Dietician Association, 106(12), 2039- 2044. McKenzie, T. L., Li, D., Derby, C. A., Webber, L. S., Luepker, R. V., & Cribb, P. (2003). Maintenance of effects of the CATCH physical education program: Results from the CATCH-ON study. Health Education & Behavior, 30(4), 447-462.

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Contributing Factors to Health Disparities Experienced in the Development, Diagnosis, and Treatment of Acute Coronary Syndrome in African American Men Chaundra M. Bishop

Abstract

Methods

Currently millions of Americans are afflicted with acute coronary syndrome (ACS), with a significantly higher prevalence occurring in African American men. This review explored possible reasons a higher prevalence of ACS exists among African American men compared to their Caucasian counterparts. As health care costs rise with increasing age, it is important to identify contributors to morbidity, mortality, and health disparities. Findings of this review suggest that access and availability of medical resources, socioeconomic status, and current increasing health disparities are gross contributors to the increased burden of ACS among African American men. These findings can be used by public health professionals in future prevention and intervention programs.

Data collection involved a myriad of public sources, including articles, reports, and governmental information. A literature review limited to peer-reviewed journals, books, and other public information was performed to gather information on coronary heart disease, ACS, African American men, and Caucasian men. When searching for information the following phrases were used: acute coronary syndrome, African American men, Caucasian men, and coronary artery disease. The databases used were EBSCO host, Medline Plus, and Google Scholar. While reviewing results of the online database search, if the title of an article appeared relevant, the link was followed. To be deemed relevant, the article had to address either CAD, CAD in African American men and Caucasian men, ACS, ACS in African American men, and ACS in African Americans compared to Caucasians. Fifty-four articles were collected and 31 were found to be relevant. The remaining 23 articles did not provide information in alliance with the common themes established. Once an article was determined applicable, the reference list of that article was appraised with the possibility of gaining more useful sources. Through this method, 6 additional articles were found and deemed pertinent for a total of 37 fitting articles. Governmental websites, such as the Centers for Disease Control and Prevention, National Library of Medicine, and National Institutes of Health, were also searched for important information.

Introduction Coronary artery disease (CAD) restricts blood flow to coronary arteries and can ultimately lead to acute coronary syndrome (ACS; Overbaugh, 2009; Panjrath & Herzog, 2009). ACS begins with a disturbance or rupture at the weakest and thinnest point of an atherosclerotic deposit, releasing enzymes which disrupt the structure of the deposit (Hansson, 2005). This disruption may result in thrombus formation, consequently obstructing the flow of blood through the arteries (Gorlin, Fuster, & Ambrose, 1986; Marshall, 2011; Rosen & Gelfand, 2009). Ruptured plaques, or thrombi, are the main stimuli for inflammation and coagulation, the two underlying causes for ACS. ACS consists of three subtypes: unstable angina, non-STsegment elevation myocardial infarction (NSTEMI), and ST-segment elevation myocardial infarction (STEMI; Marshall, 2011). Currently 1.7 million individuals are diagnosed and hospitalized with ACS annually. The number of African American men diagnosed or hospitalized with ACS is significantly higher than Caucasian men (Roger et al., 2011). Identifying and understanding why ACS has a higher prevalence in African American men than Caucasian men will be important because as the populations age, the economic burden increases. Once common themes are discovered, in the future African American males can be appropriately targeted with primary and secondary preventions instead of the costly tertiary preventions.

Results African American men are more prone to developing CAD and subsequently ACS than Caucasian men (Flack et al., 2009; Whittle, Conigliaro, Good, Hanusa, & Macpherson, 2002). Possible contributors to the development of these cardiovascular diseases include higher prevalence of hypertension (Centers for Disease Control and Prevention [CDC], 2010), sub-optimal diets (Flack, et al., 2009), increased stress (Burg, 2002), and a lower socioeconomic status (CDC, 2010; Flack et al., 2009) than their Caucasian counterparts. Given the larger burden of heart disease risk factors among African American men (Flack et al., 2009; Whittle et al., 2002), it is crucial that further research be done to evaluate the disconnect between them and the health care system.


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The existence of disparity within the healthcare system was consistently reported by both the US Department of Health and Human Services and the American Heart Association, specifically in regards to primary prevention (Bonow, Grant, & Jacobs, 2005). This should be addressed in efforts to reduce cardiac events in African American men and subsequently reduce health-carerelated expenditures. Researchers recommend that studies utilize only African American men to explore their susceptibility in developing ACS (Spertus, Safley, Garg, Jones, & Peterson, 2005). Further research will help health professionals as well as African American men with accurately identifying the signs and symptoms of ACS, which may ultimately result in more immediate initiation of life-saving procedures (Clark & Lingegowda, 2005; Flack et al., 2009), decreases in pre-hospital delays (Banks & Dracup, 2006; Clark & Lingegowda, 2005), and decreases in mortality rates due to ACS (Clark & Lingegowda, 2005). It may also provide valuable information for creating interventions highlighting primary and secondary prevention of ACS. Discussion Cardiovascular disease is the number one cause of premature death in America, and there are a number of different types, one of them being CAD. CAD is an umbrella term for the conditions of an ischemic heart, ranging from slight narrowing to sudden blockage of the coronary arteries (CDC, 2009; National Heart, Lung, and Blood Institute, 2011). CAD is the underlying disorder that may result in a diagnosis of ACS. Many factors contribute to the development of CAD, with hypertension (CDC, 2010), obesity (CDC, 2010; Flack et al., 2009), and lack of physical activity (Flack et al., 2009) being significantly more likely among African American men. Hypertension Hypertension, in particular, has been repeatedly negatively linked to CAD and later ACS (Flack et al., 2009). Hypertension occurs in about 25% of Caucasian men and 34% of African American men (Day, 2006). It is commonly caused by an imbalanced diet, lack of exercise, stress, and the lack of access to proper health care (Flack et al., 2009). The rate of hypertension can be linked to socioeconomic status (SES), the social standing or class of an individual or group, which is often measured as a combination of education, income, and occupation and has an effect on lifestyle behaviors (American Psychological Association [APA], 2011; Flack et al., 2009). SES is a strong predictor of health behavior since being in good health tends to be associated with having more income, higher education, a prestigious job, as well as living in neighborhoods where residents have similar statuses (APA, 2011; Flack et al., 2009).

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Compared to Caucasian men, hypertension in African American men appears earlier in life, is often more severe, and has the tendency to result in higher rates of mortality (Kotchen & Morley Kotchen, 1997). African American men are likely to carry a heavier burden of ACS due to high blood pressure because of the environmental dynamics that may exacerbate the condition. SES influences environmental and lifestyle choices through an inverse association of low SES and high rates for CAD and ACS. The mortality is the highest among African American men with the lowest health resources and fewest economic resources (Flack et al, 2009). Hypertension and hypertensive heart disease often elicit ischemic chest pain in the absence of a coronary occlusion (Clark & Lingegowda, 2005; Whittle et al., 2002). The presentation and pathophysiology of the clinical spectrum of ACS in African American men have shown to be very similar with Caucasian men (Clark & Lingegowda, 2005). Symptom presentation, however, is different. African American men usually have “silent” episodes and present with atypical symptoms because they are less likely to have obstructive atherosclerotic lesions (Clark & Lingegowda, 2005; Venkat et al., 2008). African American men generally present with chest pains that they don’t believe are attributed to any form of cardiovascular disease. This may lead the medical provider to attribute the pains to something else after the patient has informed them they have hypertension. Diet Diet is a crucial contributor to both raising and lowering blood pressure. Diet can be influenced by a number of things, but it is important to take SES into consideration when analyzing dietary intake. Numerous dietary differences exist between African American and Caucasian men, including a lack of potassium, fruits and vegetables, protein, and fiber among African American men (Flack et al., 2009; Jen, Brogan, Washington, Flack, & Artinian, 2007). Additionally, men of lower SES tend to have diets higher in fat, calories, and sodium due to the lack of available healthy food choices and the means to purchase these healthier options (Flack et al., 2009; Jen et al., 2007). Also contributing to poor diet in this population is the lack of grocery stores in lower SES areas, leaving only small convenience stores that provide limited dietary choices and are usually deficient in fruits and vegetables (Jen et al., 2007). Moreover, the cost(s) of fresh foods have increased over the past decade, including the prices of the much needed fruits and vegetables, while foods packed with sodium and fat remain affordable. Not only do these foods cause high blood pressure but research has also proven that diets riddled with fatty foods consumed over half a person’s lifetime contribute greatly to the risk factors associated with atherosclerotic CAD that can lead to ACS (Day, 2006).


Physical Inactivity Physical activity tends to decline as populations age (Flack et al., 2009). It is also inextricably linked to SES levels. Populations with low SES tend to get less physical activity than what is necessary. In fact, 11% of African American men are less active than Caucasian men (Flack et al., 2009). Barriers to physical activity in African American men include lack of access to gyms and exercise equipment and apprehension of walking around one’s neighborhood (Day, 2006; Flack et al., 2009). Because African American men of low SES tend to live in neighborhoods more susceptible to violence and crime, the ability and desire to engage in physical activity is hindered. One study found that 41% of non-Caucasians are fearful of walking around their neighborhoods (Day, 2006) while another described the lowered rates of hypertension in prosperous neighborhoods that promote healthier lifestyles (Flack et al., 2009). It has been proven that a sedentary lifestyle is a major risk factor for CAD. When barriers to physical activity exist, they need to be recognized and addressed. Stress African American men seem to be more vulnerable to stress for a myriad of reasons, including low SES. Stress is a highly subjective term, so identified daily stressors vary. Stress induces high blood pressure and heart rate, increasing turbulence in the blood stream that can damage the lining within the coronary arteries (Burg, 2002). The probability of existing atherosclerosis in those arteries is exorbitant. Atherosclerosis compounded by the physiological responses to stress make plaque disturbance more feasible, potentially causing one of the clinical manifestations of ACS (Burg, 2002). African American men may not have emotional outlets for their stress, so they may perhaps indulge in drinking alcohol or smoking cigarettes, both of which are risk factors of ACS. Only about 11% of the country’s smokers are African American men, but they tend to be heavier consumers of alcohol than Caucasian men (Flack et al., 2009; Fuchs et al., 2004; Ostfeld, 2002). Alcohol consumption and cigarette smoking both increase blood pressure, causing the heart to pump faster and work harder, hence creating the pathway for ACS (Zahler & Piselli, 1992). Health Disparity Health disparity, as defined by the Centers for Disease Control and Prevention (2011) and the U.S. National Library of Medicine (2011), refers to differences in access to services between groups of people that are differentially burdened by a health issue. The burden can be categorized as violence, injury, or disease. The groups usually consist of minority populations because they tend to have lower SES levels, thus lacking social, political,

and/or economic resources to dispute the disparity (The Greenlining Institute, 2012). Disparities result from individual and behavioral factors, environmental threats, or inadequate access to health care (CDC, 2011). Combating and eliminating health disparities has been and still is one of the overarching goals of the Department of Health and Human Services. Previous research suggests that increasing the education of health care providers may be the first step in reducing the health disparity of ACS. For example, less than one third of cardiologists are even aware of the existence of health disparities; moreover, physicians may have preformulated opinions about what will be expected when treating African American men, which is why disparity can be greatly combated via educating health care providers (Lurie et al., 2005). Studies have provided evidence revealing doctors’ seemingly unconscious bias that assumes African American men are less cooperative when it comes to invasive testing, less rational, and less receptive to advice meant to benefit their health (Bonow et al., 2005). Treatment for ACS in African American men should not differ from that of their Caucasian counterparts, but unfortunately it does. African American men tend to receive less aggressive treatments, like cardiac catheterization, with 42% less likely to be given the option to undergo percutaneous coronary intervention (PCI), 54% less likely to undergo bypass surgery, and 32% less likely to have an angiogram (Spertus et al., 2005). Although African American men who actually go to the hospital with chest pain tend to receive the same treatment as Caucasian men in terms of receiving beta blockers, aspirin, and ACE inhibitors, newer or resource-intense therapies tend to be reserved for Caucasian men (Sonel et al., 2005). African American men usually have access to hospitals where advanced cardiac technology services are limited or not available, affecting the type of interventions offered and received (Sirak, Simbo, Daka, & Simon, 2008). Therapies provided to African American men have primarily focused on improving their presenting symptoms, not so much the underlying causes or survival and quality of life afterwards (Spertus et al., 2005). A study exploring the outcome of African Americans one year after an ACS episode shows that a similar percentage of both African American and Caucasians died (7%). However, of the surviving African Americans, 43% of them experienced reoccurring angina, contrasting with 27.1% of Caucasian patients. These results show a worsened quality of life and physical function for surviving African Americans when compared to their Caucasian counterparts (Spertus et al., 2005). It would stand to reason that if African American men have higher risks and mediocre outcomes, they should be treated more aggressively.


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African American men are significantly less likely to see a board certified physician or receive specialty cardiac care and this could ultimately be the reason why African American men fair worse than Caucasian men in ACS outcomes (Clark & Ligegowda, 2005). There are certainly a lot of barriers to obtaining care that add to the cardiac disparity for minority patients. African American men, for instance, have reported negative experiences surrounding physician-patient interaction (Banks & Dracup, 2006). Communication between physician and patient is crucial when it comes to influencing adherence to certain health guidelines and treatments. If patients are unable to understand the explanation of the diagnosis and treatment options given by the treating physician, then they are less likely to comply with the orders. Among African American men, medical mistrust has been cited as one of the biggest barriers in seeking treatment. They often see racism within societal institutions, including the health care system. The mistrust comes from historical discriminatory practices, a prime example being the Tuskegee Study of Untreated Syphilis in the Negro Male conducted by the U.S. Public Health Service (Cheatham, Barksdale, & Rogers, 2008). The suspicion carried by African American men prevents them from seeking medical attention in a timely manner. Banks and Dracup (2006) confirmed that African American men arrive at the hospital, on average, about 3.5 hours after the onset of ACS symptoms. The pre-hospital delay times have been notably higher than Caucasian men and can be attributed to either previous negative experiences or the lack of knowledge about cardiac symptomology. The delay can be categorized into three types: patient, transportation, and hospital delays. Patient delay begins at the start of ACS symptoms and continues until the patient realizes that medical attention is required. Accessibility of transportation is another reason why African American men are delayed in getting to the hospital. They may not own their own vehicles or have access to one within their household and depend on an emergency vehicle as means to get to the hospital (Banks & Dracup, 2006). It was previously noted that many African American men live in areas where hospitals lack the extensive cardiac services necessary to adequately treat them. There could be longer wait times for these hospitals, prompting a further delay in beginning treatment.

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Conclusion Based on a review of literature, African American men are more prone to developing CAD and subsequently ACS than Caucasian men. African American men have a higher prevalence of hypertension, sub-optimal diets, increased stress, and a lower socioeconomic status. Given the larger burden of heart disease risk factors among African American men, it is crucial that further research be done to evaluate the disconnect between them and the health care system. The existence of disparity was consistently reported within the health care system in regards to primary prevention and treatment, and it should be addressed in efforts to reduce cardiac events in African American men. Recommendations for Future Research Interventions and studies targeting population-specific contributors, such as proper nutrition, physical activity, or high blood pressure, need to be implemented using African American men as participants in order to shed more light on their susceptibility to developing ACS. Effective interventions will not only intervene biologically but will also seek to improve the quality of life in places where many African American men spend most of their time: their homes, workplaces, and neighborhoods. It has become progressively more important that health professionals create interventions aimed at primary and secondary risk factors since health care costs have been steadily increasing. As the population of African American men ages, their dependence on governmental aids to cover the cost of medical treatments will begin to increase. Designing and implementing interventions to address risk factors will help ease the economic burden and provide both the primary and secondary prevention techniques necessary to prevent the increased economic burden of CAD and ACS for African American men specifically. Previous research indicates that access to care greatly contributes to health disparity; thus, it is important that it continues to be addressed. The first step in decreasing health disparities is to acknowledge the fact that they exist. Increasing health care providers’ knowledge and awareness of not just the existence of inequities but also of the varying manifestations and presentation of ACS in different populations will allow for proper diagnosis and treatment of the condition. Doctor-patient interactions seem to be the most logical place to begin when looking to address disparity gaps. Increasing diversity among health care providers may aid in decreasing disparity gaps because minority physicians tend to treat minority and underserved groups. Many minority groups are more comfortable seeking treatment from providers that are like them. Increasing cultural competency among providers may ease some discomfort as well.


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Fuchs, F. D., Chambless, L. E., Folsom, A. R., Eigen- brodt, M. L., Duncan, B. B., Gilbert, A., & Szklo, M. (2004). Association between alcoholic beverage consumption and incidence of coronary heart disease in Whites and Blacks: The Atherosclerosis Risk in Communities Study. American Journal of Epidemiology, 160(5), 466-474.

Gorlin, R. A., Fuster, V., & Ambrose, J. A. (1986). Anatomic-physiologic links between acute coronary syndromes. Circulation: Journal of the Bonow, R. O., Grant, A. O., & Jacobs, A. K. (2005). The American Heart Association, 74, 6-9. cardiovascular state of the union. Journal of the American Heart Association, 111, 1205-1207. The Greenlining Institute. (2012). Health outcomes. Retrieved from http://stage.greenlining.org/initia Burg, M. M. (2002). Stress, behavior, and heart disease. tives/bridges-to-health-new/health-outcomes#_ftnref In Yale University School of Medicine Heart Book (pp. 94-104). New York, NY: William Morrow & Hansson, G. K. (2005). Inflammation, atherosclerosis, Co. and coronary artery disease. New England Journal of Medicine, 352(16), 1685-1695. Centers for Disease Control and Prevention. (2009). Heart disease: Coronary artery disease (CAD). Jen, K.-L. C., Brogan, K., Washington, O. G. M., Flack, Retrieved October 6, 2011, from http://www.cdc. J. M., & Artinian, N. T. (2007). Poor nutrient intake gov/heartdisease/coronary_ad.htm and high obese rate in an urban African American population with hypertension. Journal of the Ameri- Centers for Disease Control and Prevention. (2010). A can College of Nutrition, 26(1), 57-65. closer look at African American men and high blood pressure control: A review of psychosocial Kotchen, T. A., & Morley Kotchen, J. (1997). Regional factors and systems-level interventions. Atlanta, variations of blood pressure environment or genes? GA: U.S. Department of Health and Human Circulation, 96, 1071-1073. Retrieved from http:// Services. circ.ahajournals.org/content/96/4/1071 full Centers for Disease Control and Prevention. (2011). Adolescent and school health: Health disparities. Retrieved from http://www.cdc.gov/healthyyouth/ disparities/index htm Cheatham, C. T., Barksdale, D. J., & Rogers, S. G. (2008). Barriers to health care and health-seeking behaviors faced by black men. Journal of the American Academy of Nurse Practitioners, 20, 555-562. Clark, L. T., & Lingegowda, U. (2005). Acute coronary syndrome in Black Americans: Is treatment different? Should it be? Current Cardiology Reports, 7, 249-254 Day, K. (2006). Active living and social justice. Journal of the American Planning Association, 72(1), 88-99. Flack, J. M., Nasser, S. A., Goel, A., Flowers, M., O’Connor, S., & Faucett, E. (2009). Unmasking racial/ethnic disparities in cardiovascular disease: Nutritional, socioeconomic, cultural, and health-care-related contributions. In K. C. Ferdinand & A. Armani (Eds.), Cardiovascular disease in racial and ethnic minorities (pp. 51-79). New York, NY: Humana Press.

Lurie, N., Fremont, A., Jain, A. K., Taylor, S. L., McLaughlin, R., Peterson, E., . . . Ferguson, T., Jr. (2005). Racial and ethnic disparities in care: The perspectives of cardiologists. Circulation: Journal of the American Heart Association, 111, 1264-1269. Retrieved from http://circ.ahajournals. org/content/111/10/1264 Marshall, K. (2011). Acute coronary syndrome: Diagno- sis, risk assessment, and management. Nursing Standard, 25(23), 47-57. National Heart, Lung, and Blood Institute. (2011). What is coronary heart disease? Retrieved from http:// www.nhlbi.nih.gov/health/health-topics/topics/cad/ Ostfeld, A. (2002). Racial and ethnic differences in heart disease. In Yale University School of Medicine Heart Book (pp. 273-280). New York, NY: William Morrow & Co. Overbaugh, K. J. (2009). Acute coronary syndrome. American Journal of Nursing, 109(5), 42-52. Retrieved from http://journals.lww.com/ajnonline/ fulltext/2009/05000/acute_coronary_syndrome.28. aspx


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Panjrath, G. S., & Herzog, E. (2009). Diagnosis of acute coronary syndrome. In E. Herzog & F. Chaudhry (Eds.), Echocardiography in acute coronary syndrome (pp. 219-227). Springer-Verlag London Roger, V. L., Go, A. S., Lloyd-Jones, D. M., Adams, R. J., Berry, J. D., Brown, T. M., . . . Wylie-Rosett, J. (2011). Heart disease and stroke statistics—2011 update. Circulation: Journal of the American Heart Association, 123, 18-209. doi:10.1161/CIR.0b013e3182009701 Rosen, A. B., & Gelfand, E. V. (2009). Pathophysiology of acute coronary syndromes. In E. V. Gelfand & C. P. Cannon (Eds.), Management of acute coronary syndromes (pp. 1-12). Chichester, UK: Wiley. Sirak, T., Simbo, C., Daka, M., & Simon, C. (2008). Acute coronary syndrome in African Americans and Hispanic Americans. In M. K. Hong & E. Herzog (Eds.), Acute coronary syndrome: Multi- disciplinary and pathway-based approach (pp. 229-245). Springer-Verlag London. Sonel, A. F., Good, C. B., Mulgund, J., Roe, M. T., Gibler, W., Smith, S. C., Jr., . . . Peterson, E. D. (2005). Racial variations in treatment and outcomes of Black and White patients with high-risk non-ST- elevation acute coronary syndromes: Insights from CRUSADE (can rapid risk stratifica- tion of unstable angina patients suppress adverse outcomes with early implementation of ACC/AHA Guidelines?). Circulation: Journal of the American Heart Association, 111, 1225-1232. Retrieved from http://circ.ahajournals. org/content/111/10/1225

Spertus, J., Safley, D., Garg, M., Jones, P., & Peterson, E. D. (2005). The influence of race on health status outcomes one year after an acute coronary syndrome. Journal of the American College of Cardiol- ogy, 46(10), 1838-1844. U.S. National Library of Medicine. (2011). Health disparities. Retrieved from http://www nlm nih.gov/ hsrinfo/disparities.html Venkat, A., Hoekstra, J., Lindsell, C., Prall, D., Hol- lander, J. E., Pollack, C. V., Jr., . . . Gibler, W. B. (2003). The impact of race on the acute manage ment of chest pain. Academic Emergency Medicine, 10(11), 199-1208. Whittle, J., Conigliaro, J., Good, C., Hanusa, B. H., & Macpherson, D. S. (2002). Black-White differences in severity of coronary artery disease among individuals with acute coronary syndrome. Journal of General Internal Medicine, 17, 876-882. Zahler, R., & Piselli, C. (1992). Smoking, alcohol, and drugs. In Yale University School of Medicine Heart Book (pp. 71-84). New York, NY: William Morrow & Co.

Page 24 THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012

The Association Between Sensation Seeking, Sexual Risk Behavior, and HIV Knowledge Among Undergraduate Students Roberta E. Emetu Abstract HIV/AIDS is a global and national epidemic that affects college-aged individuals, a significant group that can be reached through prevention programs. The goal of this study was to determine whether an association between sensation seeking and sexual risk behavior exists and, if so, to assess whether HIV knowledge and sexual risk behaviors are predictors of sensation seeking among undergraduate students. Students at a Midwestern university (N = 719) were surveyed. Results indicated that sensation seeking was associated with sexual risk behaviors; however, knowledge was not a predictor of these variables. Therefore, developing university programs that not only address knowledge but also better decision making is suggested. Introduction In the United States, human immunodeficiency virus (HIV) infection leading to acquired immune deficiency syndrome (AIDS) affected a reported 8,300 young people between the ages of 13-24 in 2009 despite efforts to control the spread of the disease (Centers for Disease Control and Prevention [CDC], 2011). HIV infection can be attributed to multiple and complex determinants including cultural, environmental, and behavioral factors (Belbrave, Chambers, & Van Oss Martin, 2000; Jemmott, Jemmott, & Fong, 1992; Reininger, Evans, & Griffin, 2003). If modified, some of these factors can aid in the reduction of HIV infection. HIV/AIDS education has been and continues to be implemented as a means of prevention; however, some existing programs might need to address additional dimensions in order to have an impact. In an effort to prevent the spread of HIV/AIDS, it is important to understand which factors put people at risk for HIV infection before developing an intervention. While HIV infection has affected most all populations of the United States, one subpopulation that is at increased risk is adolescents (DiClemente et al., 2008; Lewis, Malow, & Ireland, 1997). Traditional undergraduate college students between the ages of 18 to 24 are also known as emerging adults (Arnett, 2000; McLean & Pratt, 2006), and this period of their lives marks the transition from adolescence to adulthood. For many university students, being in college may be the first time away

from home where they must make decisions on their own that impact their academic and social lives. Research conducted by Reininger et al. (2003) has illustrated that the period of adolescence is a stage when risky behavior is most common. The CDC (2011) estimates that 50% of all new sexually transmitted infections (STIs) acquired each year are among 15-24 year olds. Based on the emphasis on health for young adults, a need for preventative education is significant and needs to be implemented in most high schools (Kirby, Laris, & Rolleri, 2007). One would expect college students to be knowledgeable about HIV/AIDS; however, according to Lewis et al. (1997), there are misconceptions of how the disease is transmitted as well as neutral to negative attitudes about condom usage among them. According to Lewis et al. (1997), it is likely that many adults contracted HIV when they attended college. More than half of the HIV cases are diagnosed before age 36 (Lewis et al., 1997). Emerging adults are at increased risk for contracting HIV and other STIs because they often do not have the exposure to information that would help them make informed decisions (Kwan, Arbour-Nicitopoulos, Lowe, Taman, & Faulkner, 2010). HIV health education programs are needed to help reduce the number of new cases among young people. In order to create successful public health HIV prevention programs, it is essential to first have an understanding of emerging adults’ knowledge and behaviors (Gute, Eshbaugh, & Wiersma, 2008). Sensation seeking is a personality trait associated with risk taking, substance abuse, and risky sexual behavior (Miller & Quick, 2010; Stephenson, Hoyle, Palmgreen, & Slater, 2003). Research indicates that sensation seeking peaks during adolescence and remains high into emerging adulthood (Arnett, 2007a, 2007b). Traits associated with sexual sensation seeking may involve attending parties, drinking excessively, using illegal drugs, engaging in unprotected sex, and having multiple sexual partners (Gullette & Lyons, 2005), all of which can put one at risk for contracting STIs and HIV/AIDS. A study conducted at nine colleges in the US examined sensation seeking as a predictor of health-compromising behavior (Ravert et al., 2009). Results suggested that sensation seeking is a wide-ranging risk factor associated with other risk fac-


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tors that are common within the behaviors of this subpopulation (Ravert et al., 2009). The CDC defines sexual risk behaviors as actions that result in unintended health outcomes, such as STIs, unintended pregnancy, and HIV/AIDS (CDC, 2011). These actions include inconsistent or non-condom usage and multiple sexual partners (CDC, 2011). A study conducted on sexual behavior among college-aged individuals found that college students differed based on their level of education (freshman-senior) in rates of intercourse, responsibility, HIV testing, and partner trust (Siegel, Klein, & Roghmann, 1999). The CDC (2011) estimated that more males contract HIV/AIDS than females mainly because many males are contracting the virus through same-sex behaviors. More females contract the virus through heterosexual contact than males (CDC, 2011). Heterosexual couples agree to practice safe sex, such as condom usage, but are more reluctant to communicate about it (Lewis et al., 1997). Condom usage is beneficial because it protects against most STIs, including HIV/AIDS. Having multiple partners and not using condoms is a marker for risky sexual behavior. Assessing risky sexual behavior and knowledge about STIs is important. Engaging in risky behavior may be due in part to the lack of knowledge about STIs and risk taking (Gute et al., 2008). These factors may contribute to poor decision making. As a result, the best preventative method is comprehensive-based HIV/AIDS and STI intervention programs (DiClemente et al., 2008). To help decrease the numbers of newly occurring HIV cases among college students, a well-designed health-promotion program based on research is important. Though prior studies have examined the association between sensation seeking and HIV/AIDS risk within the college-level population, very little information is available to determine whether these risk behavior variables are predictive of seeking experiences that gratify sensation, which could be helpful in developing multidimensional approaches for interventions. The purpose of this study was to determine if there was an association between sensation seeking and sexual risk behavioral factors and, if so, to determine whether HIV knowledge and these sexual risk factors are predictors of sensation seeking. Methods A cross-sectional descriptive design was used to obtain students’ HIV knowledge, sexual risk behaviors, and sensation seeking. A structured self-administered questionnaire consisting of closed-ended questions was distributed to a convenience sample of college students. The setting of this study was a large urban public university located in the mid-west region of the United States. It is a state-owned institution with 83% of students being undergraduates and over 24,000 undergraduate enrollments. Page 26

It provides an array of undergraduate degrees in specialty areas: architecture and urban planning, business, engineering and applied science, health sciences, information studies, letters and science, nursing, social welfare, arts, and other special programs. The research protocol for this study was reviewed and approved by the university’s Institutional Review Board. Participants (N = 719) were drawn from a convenience sample of consenting undergraduate students; however, since traditional college-aged students were the dominant focus of this study, 62 surveys of those over the age of 29 or those who did not specify age were excluded from the analysis. Participants were recruited from all undergraduate academic levels (freshmen to seniors). All undergraduate students who were available or willing to participate on the days and times of recruitment were eligible for participation in the study. Contact was made with various professors who allowed the advertisement of the project and recruitment of subjects from their class. Students chose to complete the survey during class breaks and after class. Recruitment at the campus resident halls and the student union occurred by approaching students and informing them about the study and the option to participate if they desired. Students had the opportunity to participate anonymously and voluntarily without pressure. All students completed an informed consent form before they began the survey. The questionnaire for this study was composed of a combination of questions selected from HIV/AIDS and sexual risk behaviors scales used in prior studies. A total of 28 questions was selected from the CDC Youth Risk Behavior Survey (YRBS; CDC, 2009b), the Global SchoolBased Student Health Survey (GSHS; CDC, 2009a), and the Brief Sensation Seeking Scale (BSSS; Hoyle, Stephenson, Palmgreen, Lorch, & Donohew, 2002). Sociodemographic information was also collected and included age, gender, academic level, major, and ethnicity/race. Using questions from the GSHS, the HIV/AIDS knowledge levels of participants were measured using one question with an option of yes or no and eight questions on a three-point scale of yes, no, and I don’t know. The six sexual behavior questions taken from the YRBS included: (a) “have you ever had sexual intercourse” with the option of yes or no; (b) “how many people have you had sexual intercourse with” with six options, from never had sex to 20 or more people; (c) “in the last three months, how many people have you had sexual intercourse with” with six options, from never had sex to 6 or more people; (d) “the last time you had sexual intercourse, did you use a condom” with the option of never had sex, yes, or no; (e) “did you drink or use drugs the last time you had sexual intercourse” with the option of never had sex, yes, or no; and (f) “the last time you had sex, what method did you use to prevent pregnancy” with seven options, such as condoms


and birth control. Based on the sample, a reliability test was conducted on the YRBS questions, and the Cronbach alpha was 0.70. Eight questions on risk taking were taken from the BSSS (Hoyle et al., 2002) and were assessed on a five-point Likert-type scale that ranged from strongly agree to strongly disagree (Cronbach alpha = 0.74). The data was analyzed with SSPS, version 19.0. The software was used for descriptive statistics where data is shown as percentages, means, and standard deviations to show demographic information, factors related to HIV/AIDS knowledge, and risky sexual behaviors. HIV-knowledge questions and sensation-seeking questions were combined to give an aggregate score for each, creating continuous variables. T-tests were conducted to compare differences in gender, major, and age regarding HIV knowledge and sensation seeking. ANOVA was utilized to compare differences between race and level of education in relation to HIV knowledge and sensation seeking. To test the relationship of the six sexual behavior items and sensation seeking with age, gender, class level, major, race, and each other, the point biserial correlation was used because of the combination of dichotomous and continuous variables. Multiple regression assessment was conducted to assess the relationship between sensation seeking and several predictors (e.g., HIV/AIDS knowledge) and sexual risk behavior items, such as “the number of sexual partners in the last three months,” “alcohol and drug usage during last sexual encounter,” and “condom use during last sexual encounter.” The dependent variable was sensation seeking and the independent variables were HIV knowledge and the sexual behavior items. Results Demographics The sample consisted of 719 participants: 33% men (n = 236), 67% women (n = 472), and 2% that did not indicate gender. Age was divided into two groups: 1824 (90%) and 25-29 (10%). The majority of respondents were European American (69%) followed by African Americans (13%). Twenty-three percent were freshmen, 32% sophomores, 22% juniors, and 20% seniors. Majors were reported in two groups: 44% health sciences and 56% non-health sciences. A full description of sociodemographic information is shown in Table 1. Knowledge Knowledge on HIV/AIDS differed based on age, gender, and level of education. Women (6.71 ± 1.07, p < .001) scored significantly higher than men (6.42 ± 0.72, p < .001). HIV/AIDS knowledge increased with age (p < .05). Health science students, which included nursing majors, scored significantly higher (6.75 ± 0.57, p < .001) than students in other majors (6.49 ± 1.02, p < .001).

HIV/AIDS knowledge was not a predictor for sexual risk behavior or sensation seeking. Sexual Behaviors Of the total sample (N = 719), over three quarters (82%: n = 592) of the students indicated that they were sexually active. Those who were sexually active were asked to report the number of sexual partners that they had had within the last three months. Since there is a variation in HIV/AIDS risk for those with one partner versus those with two or more, participants were put into three groups: (a) “I have not had sex in the last three months” (15%), (b) one partner (54%), and (c) two or more partners (31%). The number of sexual partners within the past three months was greater for men than women. Freshmen and sophomores had more sexual partners in the past three months than compared to the upperclassmen. A little over half (55%) of the students who were sexually active reported that they or their partners used condoms during their last sexual intercourse. There was a positive correlation between condom use and age (r = .129, p < .01), indicating the older the students were, the more likely they were to report condom use. Sophomores (34%) made up the greatest percentage of students who reported not using condoms during their last sexual intercourse. More women reported using condoms the last time of sexual intercourse than men. Sensation Seeking There was an association between sensation seeking and the number of sexual partners within the last three months (r = .182, p < .01). Participants who reported having sexual intercourse with two or more partners scored higher in sensation seeking. Participants who reported drinking alcohol during the last time of sexual intercourse also scored higher on sensation seeking (r = .232, p < .01). On all eight items pertaining to sensation seeking, men scored higher than women (p = .001). Other variables such as race, major, and college level were not significant to sensation seeking. Multiple regression results were significant (R = .264, p < .001), and two out of three variables of sexual risk behavior (“the number of sexual partners in the last three months” and “alcohol and drug usage during last sexual encounter”) were significant (p < .001). Therefore, with the exception of condom use, sexual risk behavior variables were a predictor of sensation seeking; however, knowledge was not. Factors associated with sensation seeking are shown in Table 2.


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Table 1. Demographic Characteristics of Samplea Male Female n = 253 n = 497 Age group 18-24 206 426 25-29 29 43 Ethnic Group European American 184 348 Latino American 11 16 African American 33 69 Asian/Pacific Islander 19 30 Am Indian/Alaskan Native 4 8 Multiple Race 4 26 Class Level Freshman 71 99 Sophomore 61 167 Junior 58 107 Senior 41 90 Colleges Health Sciences 44 268 Non-Health Sciences 192 204 a Analyses were restricted to participants without missing values.

Participants No. (N = 719) (%) 632 (89.8) 72 (10.2) 532 (69.5) 27 (3.5) 102 (13.3) 49 (6.4) 8 (1.0) 30 (3.9) 170 (24.5) 228 (32.9) 165 (23.8) 131(18.9) 312 (44.1) 396 (55.9)

Table 2. Factors Associated With Sensation Seeking Unstandardized Score N

B

Std. Error

Standardized Coefficients

95% CI

Beta

Lower to Upper

P

Sexual Risk Behavior Partners in last 3 mos. Drugs or Alcohol (LSI) Used a Condom (LSI)

624 596 589

-1.232 .484 2.827 .683 -.039 .588

-.118 .192 -.003

-2.183 to -.280 1.486 to 4.169 -1.194 to 1.116

.011 .000 .947

HIV Knowledge

695

.535 24.5

.070

-.139 to 1.116

.119

LSI = last sexual intercourse.

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Discussion This study was conducted to determine whether an association between sensation seeking and sexual risk behaviors exists and, if so, to assess whether HIV knowledge and sexual risk behaviors were predictors of sensation seeking among college-aged individuals. The major findings are summarized into the following statements: (a) Students in this sample were knowledgeable about HIV/AIDS transmission and prevention; (b) the majority of participants were sexually active, but were inconsistent in their condom use; and (c) almost three quarters of students acknowledged that they were sensation seekers. Sensation seeking was associated with sexual risk behavior; however, knowledge was not a predictor. The results of this study have important implications for college health educators in the United States and globally. In this study, participants showed high levels of knowledge and awareness about HIV/AIDS. This is based on the high proportion of respondents that answered items correctly on the survey. One reason for the relatively high knowledge found in this study could be attributed to the fact that more women completed the survey than men, and a large proportion of these participants were in health-related majors and disciplines. It is assumed that students in these majors may have been exposed to messages about HIV/AIDS prevention through their course work and thus would have fewer misconceptions. However, in this study, HIV/AIDS knowledge was not associated with risky sexual behavior or sensation seeking. An interesting finding in this study is that even though participants were knowledgeable about HIV/AIDS, they continued to engage in risky sexual behaviors such as unsafe sexual practices and multiple sexual partners. These findings were consistent with those reported by Opt, Loffredo, Knowles, and Fletcher (2007), which compared the knowledge and perceptions of HIV/AIDS of nontraditional college-aged students to traditional students. Results indicated that most college students were knowledgeable about HIV risk but engaged in activities that put them at increased risk for becoming HIV-infected (Opt et al., 2007). Women in the current study were more knowledgeable than men and practiced safer sexual practices than their counterparts. This was consistent with the differences in gender found in sensation-seeking behaviors. The inconsistent use of condoms is another important finding in this study. Goodrich, Wellings, and McVey (1998) indicated that due to the impact of public education about HIV/AIDS, there was an increase in condom use. In the current study, the percentage of participants who stated that they did not use a condom the last time they had sexual intercourse was high. Suggested factors associated with non-condom use include shame or dis-

comfort about condom purchases, difficulty discussing condom use with a partner, the belief that condoms interfere with sexual pleasure, and the use of oral contraceptives (MacDonald et al., 1990). All of these barriers to condom use could certainly be addressed in a comprehensive health education program that could target better decision making for college-aged individuals (DiClemente et al., 2008). In this study, sexual risk behavior was associated with sensation seeking, but HIV/AIDS knowledge was not predictive of risk taking. According to Stephenson et al. (2003), sensation seeking is a personality trait associated with various risky behaviors. Congruent with previous research on college-aged individuals (Gullette & Lyons, 2006; Ravert et al., 2009), participants in this study scored high in risk taking, suggesting that sensation seeking is common. With the exception of condom use, sexual risk behaviors were predictive of sensation seeking. Since sensation seeking is a personality trait, it might be useful to know that sexual risk factors could be indicative of sensation-seeking levels when developing interventional strategies. Participants who scored high on sensation seeking had higher numbers of sexual partners. Men scored higher on sensation seeking than women, which suggests that men could be engaging in more risk-taking behaviors than women. Even still, men scored high on HIV/AIDS knowledge, which suggests the need for university programs that would target men and women separately and address other factors beyond knowledge such as better decision making. As with any study there were limitations. Sampling was contingent on professors agreeing to have their class surveyed. Due to this circumstance, a substantial number of health science and nursing majors were recruited. Also, answers on sexual risk behaviors are based on selfreported information without confirmation, which poses a validity concern and should be reviewed with caution. Despite these limitations, results from this study add to the existing literature on college students. Conclusion This current study is important to several stakeholders, including university health centers. Since college students are engaging in risky behaviors by not using condoms consistently and having multiple sexual partners, university health centers could develop intervention programs that promote better decision making. Research found that decision making regarding sexual activity included desire for intimacy, perceived relationship safety, family and peer relationship, and concern regarding pregnancy and STIs (Fantasia, 2008). As discussed earlier, men scored lower in HIV/AIDS knowledge and condom use but higher on sensation seeking behaviors than women. Programs that are gender-specific might be a successful method to


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target men. HIV/AIDS prevention is extremely complex. Successful programs that utilize comprehensive multidimensional approaches (DiClemente et al., 2008) and address the complex factors attributed to the disease are clearly needed. With these programmatic suggestions, college students may potentially develop healthier sexual practices and better decision making. References Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469-480. Arnett, J. J. (2007a). Emerging adulthood: What is it, and what is it good for? Child Development Perspectives, 1(2), 68-73. Arnett, J. J. (2007b). Suffering, selfish, slackers? Myths and reality about emerging adults. Journal of Youth and Adolescence, 36(1), 23-29. Belbrave, F., Chambers, D., & Van Oss Martin, B. (2000). Cultural, contextual, and interpersonal predictors of risky sexual attitudes among urban African American girls in early adolescence. Culture Diversity and Ethnic Minority Psychology, 6(3), 309-322. Centers for Disease Control and Prevention. (2009b). YRBSS: Youth risk behavior surveillance system. National Center for Chronic Disease Prevention and Health Promotion, Retrieved from http://www.cdc. gov/HealthyYouth/yrbs/index.htm Centers for Disease Control and Prevention. (2009a). Global school-based student health survey (GSHS). National Center for Chronic Disease Prevention and Health Promotion, Division of Adolescent and School Health, Retrieved from http://www.cdc.gov/ gshs/questionnaire/index.htm Centers for Disease Control and Prevention. (2011). Adolescent and school health - Sexual risk behav - ior: HIV, STD, & teen pregnancy prevention. Retrieved from http://www.cdc.gov/healthyyouth/ sexualbehaviors/index.htm DiClemente, R. J., Crittenden, C. P., Rose, E., Sales, J. M., Wingood, G. M., Crosby, R. A., & Salazar, L. F. (2008). Psychosocial predictors of HIV-associated sexual behaviors and the efficacy of prevention interventions in adolescents at-risk for HIV infection: What works and what doesn’t work? Psychosomatic Medicine, 70(5), 598-605.

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Fantasia, H. C. (2008). Concept analysis: Sexual decision-making in adolescence. Nursing Forum, 43(2), 80-90. Goodrich, J., Wellings, K., & McVey, D. (1998). Using condom data to assess the impact of HIV/AIDS preventive interventions. Health Education Research, 13(2), 267-274. Gullette, D. L., & Lyons, M. A. (2005). Sexual sensation seeking, compulsivity, and HIV risk behaviors in college students. Journal of Community Health Nursing, 22(1), 47-60. Gullette, D. L., & Lyons, M. A. (2006). Sensation seeking, self-esteem, and unprotected sex in college students. Journal of the Association of Nurses in AIDS care, 17(5), 23-31. Gute, G., Eshbaugh, E. M., & Wiersma, J. (2008). Sex for you, but not for me: Discontinuity in under graduate emerging adults’ definitions of “Having Sex.” Journal of Sex Research, 45(4), 329-337. Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation seeking. Personality and Individual Differences, 32(3), 401-414. Jemmott, J., Jemmott, L., & Fong, G. (1992). Reduction in HIV risk-associated behaviors among Black male adolescents: Effects of an AIDS prevention initiative. American Journal of Public Health, 82(3), 372-377. Kirby, D. B., Laris, B. A., & Rolleri, L. A. (2007). Sex and HIV education programs: Their impact on sexual behaviors of young people throughout the world. Journal of Adolescent Health, 40, 206-217. Kwan, M. Y., Arbour-Nicitopoulos, K. P., Lowe, D., Taman, S., & Faulkner, G. E. (2010). Student reception,sources, and believability of health-re lated information. Journal of American College Health, 58(6), 555-562. Lewis, J. E., Malow, R. M., & Ireland, S. J. (1997). HIV/AIDS risks in heterosexual college students. A review of a decade of literature. Journal of Ameri - can College Health, 45(4), 147-158. MacDonald, N. E., Wells, G. A., Fisher, W. A., Warren, W. K., King, M. A., Doherty, J.-A. A., & Bowie, W. R. (1990). High-risk STD/HIV behavior among college students. JAMA: The Journal of the Ameri- can Medical Association, 263(23), 3155-3159.


McLean, K., & Pratt, M. (2006). Life’s little and big lessons: Identity statuses and meaning making in the turning point narratives of emerging adults. Devel- opmental Psychology, 42(4), 714-722.

Reininger, B., Evans, A., & Griffin, S. (2003). Develop- ment of youth survey to measure risk behaviors, attitudes and assets: Examining multiple influences. Health Education Research, 18(4), 461-476.

Miller, C. H., & Quick, B. L. (2010). Sensation seeking and psychological reactance as health risk predictors for an emerging adult population. Health Communication, 25(3), 266-275.

Siegel, D. M., Klein, D. I., & Roghmann, K. J. (1999). Sexual behavior, contraception, and risk among college students. Journal of Adolescent Health, 25(5), 336-343.

Opt, S., Loffredo, D., Knowles, L., & Fletcher, C. (2007). College students and HIV/AIDS: A comparison of nontraditional and traditional student perspectives. Journal of American College Health, 56(2), 165-174.

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Healthy Food Access, the Built Environment, and Youth Perceptions: A Review of Literature Sharlene A. Gozalians

Abstract The purpose of this review was to assess the existing literature on the relationship between built environment, healthy food access, and youth perception. In the literature, limited numbers of studies are available that focus on factors of healthy food access in the built environment and youth perceptions of food access. Researchers report that youth perceive healthy food access to be difficult and limited. Unhealthy food items are easily accessible, cheaper, and better advertised to the younger population. Low-income youth are more likely to have easier and cheaper access to unhealthy food items such as fast food. A better understanding is needed to improve programs targeting youth and health disparities in low-income neighborhoods that suffer disproportionately from the lack of healthy food access. Introduction The purpose of this review was to assess the existing literature on the relationship between built environment, healthy food access, and youth perception. This literature review addressed the following questions: (a) What is the built environment and how does it affect food access? (b) What are “food deserts” and how do they impact communities? (c) What is the relationship between food access and obesity? and (d) What are youth perceptions of food access? Articles were collected from EBSCO Host and Google Scholar databases. Articles were limited to those that studied human subjects, were written in English, and used locations within the United States. Keyword searches included: food desert, built environment, food access, healthy food access, youth perception, low access community, and grocery stores. A total of 38 articles were collected; eight articles were excluded based on location exclusion criteria. A total of 30 articles were used for this review. The Built Environment and Food Access

influences lifestyle choices of the residents (Keast et al., 2010). Researchers have found that the built environment is related to walking and exercise patterns, mental health, safety, air and water quality, crime rates, housing costs, children’s health, and eating habits (Booth, Pinkston, & Poston, 2005; Jackson, 2003; Srinivasan, O’Fallon, & Dearry, 2003). Healthy food access, commonly interchanged with food security in the literature and a significant aspect of the built environment, continues to be of growing interest to the public health field. Healthy food access is the physical access points, availability, freshness and quality, use and preparation knowledge, cost, and aesthetics of food items available for purchase (Azuma, Gilliland, Vallianatos, & Gottlieb, 2010). Cost, location, and freshness have been identified as key factors in influencing food choice, especially when it comes to fruits and vegetables (Anderson et al., 2007). Researchers have found associations between the lack of healthy food access and poor dietary habits which increase risk factors for many chronic diseases, such as obesity, hypertension, diabetes, and high cholesterol (Larson, Story, & Nelson, 2009). The United States Department of Agriculture (USDA, 2009) anticipated that 23.5 million people currently live in low-income communities, where more than 40% of the population is below the 200% federal poverty threshold. Furthermore, they believed that 93% of those who live in low-income areas have restricted access to grocery stores by a car or other transportation method. Lacking access to larger grocery stores, many low-income community members turn to fast-food restaurants (Booth et al., 2005). In turn, the consumption of unhealthy, cheap and processed fast-food items leads to obesity in more than 30% of Americans. The USDA concluded that better supermarket access reduces the risk of obesity and that better access to fast-food restaurants and convenience stores increases the risk of obesity. Although this may seem logical, numerous communities suffer from poor built environments.

Built environment is the design and land use of a community, including its transportation, food venues, safety, and aesthetic systems (Keast, Carlson, Chapman, & Michael, 2010). It defines and describes the community and Page 32


Food Deserts and Their Impact According to the USDA (2009), a “food desert” is a community where consumers have difficulty accessing retailers that sell healthy, fresh food and consistently offer a variety of choices at an affordable price, such as those found in a full-service grocery store. Consumers must be able to access these retailers with ease. This would require, for example, private or public transportation, availability of sidewalks, enough lighting, and no safety issues. Nutritious foods are foods that are needed to maintain a healthy body. Affordability is based on the price of the foods found in the food pyramid relative to the budget of the consumer. Literature centered on defining a food desert based on miles away from a supermarket varies. A report by Morton and Blanchard (2007) for the Rural Sociological Society addressed food deserts in rural areas. Morton and Blanchard defined food deserts as neighborhoods that are 10 or more miles away from a full-service grocery store. Based on a thorough literature review, McEntee and Agyeman (2010) found that one third of a mile seemed to be the average estimate when defining food desert communities and how far residents had to travel to a grocery store. However, differences exist for rural and urban areas by factors such as income level, transportation, and cost issues. Residents of food-desert communities often do not have representation when dealing with food retail in their communities; hence, they have to rely on smaller convenience stores where prices are generally higher and the quality of food is poor (Barratt, 1997; Wrigley, 2002). Pearson, Russell, Campbell and Barker (2005) stated that consequences of food deserts are poor diets, and if public health professionals address this issue, communities can decrease health inequities. Pearson et al. hypothesized that living in a food desert, fruit and vegetable price, socioeconomic deprivation, and a lack of a local accessible full-service market all play a role in the decreased consumption of fruits and vegetables. Pearson et al. surveyed 426 people (mean age of 50.7 years, 65% females) on their family demographics, food and shopping habits, and fruit and vegetable intakes. They concluded through a shopping trip observation, 24-hour recall, and linear regression that male shoppers tend to eat less fruits than females (p = 0.04). Pearson et al. (2005) noted that barriers to seeking fruits and vegetables included distance to a supermarket that carried them, price, and that a respondent’s current store did not carry fruits and vegetables consistently and at a fresh standard. Their data suggests that decreased consumption can somewhat be related to the lack of fresh and affordable fruits and vegetables at local stores. Although their study’s limitations included recall bias and a small sample size, it is important to note the study’s

qualitative responses that suggest the barriers that exist in accessing fruits and vegetables. Furthermore, the study explores the differences in vegetable and fruit intake that exist in gender and age categories. Azuma et al. (2010) proposed that minority communities are at higher risk for chronic diseases related to obesity because they lack access to stores that have affordable fresh, nutritious foods that are necessary for a healthy diet. Azuma et al. analyzed and evaluated three similar low-income communities in urban areas of Los Angeles, California using geographic information system (GIS) software to map all retail food outlets in the city, and they surveyed the stores to determine what was being sold and its nutritional content. The most common type of food outlets were fast-food restaurants (30%) and liquor stores (20%) (Azuma et al., 2010). Supermarkets accounted for less than 2% of the 1,023 food outlets and were only found in two of the three cities observed (Azuma et al., 2010). Common food products sold in convenience stores and liquor stores included Hot Cheetos (85%) and Pepsi (89%); only 32% of convenience stores and liquor stores sold carrots while 17% of food outlets carried broccoli (Azuma et al., 2010). Supermarkets were found to offer products at a lower price and had a great number of healthy food items, such as oatmeal, apples, low-fat milk, 100% juice, whole wheat bread, and oranges, when compared to corner stores, convenience stores, liquor stores, and fast-food restaurants. Convenience stores and liquor stores offered the least amount of healthy food items, and they were at a higher price (Azuma et al., 2010). Data suggest that although supermarkets have the most affordable prices and have items consistently, communities are still not able to access them because communities with limited food access (food desert communities) are limited and within long distances. Minority communities resort to shopping at local corner stores due to barriers such as transportation, convenience, budget, and language in some cases. These stores do not carry fresh fruits and vegetables and do not offer choices to their customers: They are seen as the only choice because of their locations. Families that face budget constraints think about feeding their families for the least cost; therefore, for convenience and cost issues, fast-food restaurants seem appealing. (Azuma et al., 2010). In Black and Macinko’s (2008) literature review, nine studies in the United States showed that access to stores that sell healthy food options consistently and at an affordable price was worse for low-income communities. Researchers confirmed that in east Los Angeles, a low-income minority food desert, 63% of fast-food restaurants are within walking distances of schools, and only 18% of markets sell fresh fruits and vegetables (Kipke et al., 2007). Furthermore, one study (Austin et al., 2005) concluded that 80% of schools in Chicago are within 800 m


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of a fast-food restaurant. When looking at neighborhoods and dietary intake, four cross-sectional studies confirmed a positive association between low socioeconomic status and poor dietary choices (Turrell, Blakely, Patterson, & Oldenburg, 2004; Zenk, et al., 2005). Food Access and Obesity Block, Scribner, and DeSalvo (2004) hypothesized that environmental factors contribute to the increasing obesity numbers, especially in Black and low-income populations. They analyzed the distribution of fast-food restaurants using GIS software. Results showed that in Black, low-income communities, there were 2.4 fast-food restaurants for every square mile, compared to 1.5 fast-food restaurants for every square mile in predominantly White neighborhoods. Block et al. concluded that this association may explain the increasing number of obese people in low-income communities. Fast-food restaurants specialize in foods high in fat and calories. Due to their convenient prices, often low-income populations feel they are the only food options available, especially since these options are so convenient and close in location to them. The constant eating of unhealthy foods is a risk factor for obesity. According to Rahman, Cushing, and Jackson (2011), the two main contributors to the childhood obesity epidemic are improper nutrition and physical inactivity, both heavily affected by the built environment. In their literature review, they found an increase of articles published on this topic since 1990 that resembled the rising obesity trend. Communities that had more access to improper nutrition sources, such as high-caloric fast-food venues and convenience stores, had increased risk of overweight and obese residents. Rahman et al. suggested that public health professionals need to combine multiple disciplines and move away from high-density environments that have a large amount of high-calorie unhealthy food venues. After their extensive literature review, Ford and Dzewaltowski (2008) presented three testable hypotheses as to why the US has experienced an increase in obesity over the last 25 years. First, Ford and Dzewaltowski believed that due to geographic differences, there are inequalities and disparities in access and availability of foods and retail outlets. Disparities include having a disadvantage in achieving healthy diets because of the lack of access and availability to healthy food items. They concluded that of the studies identified for their review, all had consistent evidence that identified gaps in the quality of food when looking at different geographic neighborhoods (low-income and high-income; Block & Kouba, 2005; Chung & Myers, 1996; Horowitz, Colson, Hebert, & Lancaster, 2004; Morris, Neuhauser, & Campbell, 1992). Second, Ford and Dzewaltowski hypothesized that neighborPage 34

hoods with low socioeconomic status and high numbers of minority residents have a “poor-quality retail food environment.” Third, Ford and Dzewaltowski hypothesized that people who are exposed to poor-quality food environments to begin with are more likely to continue to have diets that include the same types of foods (low nutritional and high caloric value). Ford and Dzewaltowski provided a comprehensive foundation as to where the obesity and environment association comes from. They also offer insight on the association between geographic and racial differences and how this causes a gap in access and availability of simple foods to keep one healthy. However, they only put into perspective the food retailers and do not discuss the physical inactivity that also contributes to obesity. Cutts, Darby, Boone, and Brewis (2009) believed that the built environment “shapes both behavior and health outcomes; more walkable neighborhoods and access to parks correlate with higher levels of physical activity and lower body mass index” (p. 1317). Communities that become obesogenic (obesity promoting) are so because residents are unable to do physical activity (Cutts et al., 2009). Cutts et al. found that neighborhoods and communities that are within short distances of many landmarks, such as markets, parks, and schools, and are safe encourage people to exercise. Low-income residents generally have transportation barriers that may restrict them to their neighborhoods, leaving them to walk everywhere but still not have access to resources such as healthy food options. While they may be getting their exercise, their health is still compromised because of their food choices in their communities, leading to obesity. Youth Perception of the Built Environment and Healthy Food Access Wandersman and Florin (2000) suggested that an individual’s perception of his or her neighborhood and environment shapes that individual’s involvement in addressing issues in the community, which is often a source of volunteerism. Furthermore, how an organization treats its participants and conducts its business influences involvement. Maton and Salem (1995) found that organizations that have strong empowerment generally have a strong foundation of growth and community building, meaningful opportunities for participants, a peer-based support system, and inspiring leadership. This leads to individuals having a higher regard for their communities and taking more ownership in problem solving and betterment. Croll, Neumark-Sztainer, and Story (2001) suggested that unhealthy eating may not be attributed to just growing older but is affected by an environment that lacks a variety of healthy choices, such as fruits and vegetables, grains, and low saturated-fat and low cholesterol foods. This promotes youth to engage in unhealthy food prac-


tices. In their study of nearly 200 adolescents from junior high and high schools in St. Paul, Minnesota, Croll et al. conducted 25 one-hour-long focus groups. They found that when the study participants were asked to explain what healthy/unhealthy eating and foods are, most of them identified “the natural stuff,” such as home-grown fruits and vegetables as the healthiest food items (p. 195). The participants identified candy, chips, fast food, and soda as the top unhealthy food items, and they also identified homemade desserts and foods soaking with grease and oil as unhealthy. However, participants pointed out that these unhealthy items are what are most readily available to them for an affordable price. A large proportion of them described accessing these unhealthy foods as situational: if they were really hungry after school or sport practice, if a parent or family member was not available to cook, or if they craved something sweet or greasy. Some of them said that if it is available for them to buy, then it should be alright to eat (Croll et al., 2001). When the participants in the study by Croll et al. (2001) were asked about the benefits and barriers to eating healthy foods, they most commonly identified the abundant use of butter in and outside their homes as a barrier to staying healthy. They felt that they did not know enough about cooking; therefore, whatever their parents made, that is what they ate, regardless of what was in it. Cost of unhealthy food items, such as chips and soda, was seen as a benefit and barrier. These foods were available to them at a low price, and they did not know where they could access healthy food items for the same price. The study participants identified packaged healthy items as looking “un-cool,” “nasty looking old stuff,” “boring,” and “no taste” (Croll et al., 2001, p. 196). Unhealthy items looked “colorful” and “desirable” and were considered social foods: foods you eat when hanging out with friends (Croll et al., 2001, p. 196). Participants identified healthy food items as taking more time to prepare because they come uncooked and unprocessed. This was seen as a barrier to the participants because of time but a benefit to them because they would be doing the cooking and know what was going into the food. Budget was seen as a reoccurring theme for all the participants: To them, eating fast food or processed food is cheaper than eating healthy food items (Croll et al., 2001). When asked about the importance of eating healthy, very few study participants actually thought eating healthy was important because they did not know their options. They also identified not knowing the exact consequences of healthy eating and, therefore, were unlikely to change their eating habits (Croll et al., 2001). The participants identified their future health with that of their parents: They thought that since their parents were eating and living in the same community and were fine, they would be too (Croll et al., 2001). Despite the knowledge these youth had about healthy food items, it is apparent

that barriers to eating healthy foods, such as fruits, vegetables, and whole grains, are budget and accessibility. Conclusions Unhealthy food items are easily accessible, cheaper, and better advertised to the younger population. Youth, especially those coming from low-income communities, are more likely to have easier and cheaper access to unhealthy food items such as that at fast-food restaurants. Instead of grocery stores, youth report accessing foods from corner stores, convenience stores, and liquor markets. As presented in this review, researchers confirm that geographic differences create inequalities and disparities in access to and availability of foods and retail outlets. A better understanding of youth perceptions, specifically of healthy food access, is needed. This can help improve programs targeting youth and health disparities in low-income neighborhoods that suffer disproportionately from the lack of healthy food access. Given that obesity, diabetes, cardiovascular disease, and other health disparities are no longer limited to adults, children should continue to be a focus of research and interventions to increase knowledge, self-efficacy, and empowerment and to create positive behavior change. Repeatedly in the articles in this review, low-income communities were found to suffer disproportionately compared to higher income communities when it comes to healthy food access and chronic diseases. Because residents rely on cheap and accessible food venues, liquor stores, corner stores, and fast-food restaurants are an easy option. Since their environment has limited or no accessible grocery stores, residents are limited in their choices. Residents from similar food desert communities should be educated in ways to eat healthier, while policy makers and advocates should focus on integrating a healthful approach in city and business planning. An initial method of educating youth is through school and the food that is provided in schools. Food access in the built environment has become a growing subfield of health education because of the increasing numbers of consequential health outcomes (Booth et al., 2005; Wyatt, Winters, & Dubbert, 2006). Healthy food access has been analyzed based on nutrition, yet there is a lack of literature on what factors enable and prevent access to healthy foods. In order to better connect with youth, public health professionals must explore evolving interventions to better engage the young population.


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Barratt, J. (1997). The cost and availability of healthy Kipke, M. D., Iverson, E., Moore, D., Booker, C., Ru- food choices in southern Derbyshire. Journal of Hu- elas, V., Peters, A. L., & Kaufman, F. (2007). man Nutrition and Dietetics, 10(1), 63-69. Food and park environments: Neighborhood-level risks for childhood obesity in east Los Angeles. Black, J., L., & Macinko, J. (2008). Neighborhoods and Journal of Adolescent Health, 40, 325-333. obesity. Nutrition Reviews, 66(1), 2-20. Larson, N., Story, M. T., & Nelson, M. C. (2009). Neigh- Block, D., & Kouba, J. (2005). A comparison of the borhood environments: Disparities in access to availability and affordability of a market basket healthy foods in the U.S. American Journal of in two communities in the Chicago area. Public Preventive Medicine, 36, 74 – 81. Health Nutrition, 9, 837-845. Maton, K. I., & Salem, D. A. (1995). Organizational Block, J. P., Scribner, R. A., & DeSalvo, K. B. (2004). characteristics of empowering community settings: Fast food, race/ethnicity, and income: A geographic A multiple case study approach. American Journal analysis. American Journal of Preventive Medicine, of Community Psychology, 23, 631-656. 27(3), 211-217. McEntee, J., & Agyeman, J. (2010). Towards the de- Booth, K. M., Pinkston, M. M., & Poston, W. S. C. velopment of a GIS method for identifying (2005). Obesity and the built environment. Journal rural food deserts: Geographic access in Vermont, of the American Dietetic Association, 105(5 Suppl. USA. Applied Geography, 30(1), 165-176. 1), 110-117. Morris, P., Neuhauser, L., Campbell, C. C. (1992). Food Chung, C., & Myers, S. (1996). Do the poor pay more security in rural America: A study of the availabil - for food? An analysis of grocery store availability ity and costs of food. Journal of Nutrition Educa- and food price disparities. Journal of Consumer Af- tion, 24(Suppl.), S52-S58. fairs, 33(2), 276-296. Morton, L. W., & Blanchard, T. C. (2007). Starved Croll, J. K., Neumark-Sztainer, D., & Story, M. (2001). for access: Life in rural America’s food deserts. Healthy eating: What does it mean to adolescents? Rural Realities, 1(4), 1-10. Journal of Nutrition Education, 33(4), 193-198. Pearson, T., Russell, J., Campbell, M. J., & Barker, M. Cutts, B. B., Darby, K. J., Boone, C. G., & Brewis, E. (2005). Do ‘food deserts’ influence fruit and veg- A. (2009). City structure, obesity, and environ- etable consumption?—A cross-sectional study. Ap- men tal justice: An integrated analysis of physical petite, 45(2), 195-197. and social barriers to walkable streets and park access. Social Science & Medicine, 69, 1314-1322. Page 36


Rahman, T., Cushing, R. A., & Jackson, R. J. (2011). Contributions of built environment to childhood obesity. Mount Sinai Journal of Medicine, 78, 49-57. Srinivasan, S., O’Fallon, L. R., & Dearry, A. (2003). Creating healthy communities, healthy homes, healthy people: Initiating a research agenda on the built environment and public health. American Journal of Public Health, 93, 1446-1450. Turrell, G., Blakely, T., Patterson, C., & Oldenburg, B. (2004). A multilevel analysis of socioeconomic (small area) differences in household food purchas- ing behaviour. Journal of Epidemiology and Com- munity Health, 58(3), 208-215. U.S. Department of Agriculture. (2009). Your food environment atlas: Food environment atlas documentation. Retrieved from www.ers.usda.gov/foodatlas/ documentation.htm

Wandersman, A., & Florin, P. (2000). Citizen participation and community organizations. In J. Rappaport & E. Seidman (Eds.), Handbook of community psychology (pp. 247-272). New York, NY. Kluwer Academic/Plenum Press. Wrigley, N. (2002). Food deserts in British cities: Policy context and research priorities. Urban Studies, 39, 2029-2040. Wyatt, S. B., Winters, K. P, & Dubbert, P. M (2006). Overweight and obesity: Prevalence, consequences, and causes of a growing public health problem. The American Journal of the Medical Sciences, 331(4), 166-174. Zenk, S. N., Schultz, A. J., Israel, B. A., James, S. A., Bao, S., & Wilson, M. L. (2005). Neighborhood racial composition, neighborhood poverty and the spatial accessibility of supermarkets in metropolitan Detroit. American Journal of Public Health, 95, 660-667.


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Current Trends in the Use of Social Media for Health Education and Health Promotion Bethany A. Kies and Amber T. Burtis

Abstract Current literature describes the potential of Web 2.0 technologies, or social media, to enhance health education and health promotion efforts. However, the diffusion rates and usage of these inexpensive web-based technologies remain low among health education specialists. The purpose of this paper is to provide a background of social media, describe current trends in the use of social media for health promotion, and discuss implications for practice. This review of literature revealed four main trends in social media use for health education and health promotion: social marketing campaigns, risk and crisis communication, health education delivery, and professional preparation. These trends provide a framework by which health education specialists can incorporate the promising Web 2.0 applications into practice. Introduction “Web 2.0,” a term first coined conceptually in 1999, is synonymous with social media and refers to Internetbased, or web-based, applications that were first available in 2003 (O’Reilly, 2005). Due to the wide variety of such applications, researchers have classified social media into the following five categories: (1) Blogs or online journals (e.g., Blogger), (2) Social networking sites (e.g., Facebook or MySpace), (3) Content communities (e.g., YouTube, Flickr, and Wikipedia), (4) Forums/bulletin boards, and (5) Content aggregators (e.g., RSS feeds) (Constantinides & Fountain, 2008). Social media enhance the static web platform of Web 1.0 and traditional media with their two-way nature and multidirectional flow of communication that occurs as users not only review information but also share and develop it (O’Reilly, 2005; Thackeray & Neiger, 2009; World Health Organization [WHO], 2011). Consumer use of the Internet and these new social media applications is on the rise. Researchers analyzing data from the National Cancer Institute’s 2007 Health Information National Trends Survey (HINTS) reported that at least 69% of adults have access to the Internet, and 23% visited a social networking site in the past 12 months. They also reported that social media, in general, are penetrating the U.S. population independent of socioeconomic status and race/ethnicity (Chou, Hunt, BeckPage 38

jord, Moser, & Hesse, 2009; Kontos, Emmons, Puleo, & Viswanath, 2010). In 2011, Pew Research Center researchers confirmed that adults are not only using the Internet but doing so to seek health information and to share knowledge with others, stating that 74% of adults use the Internet and 46% use social network sites. Additionally, among the Internet users, the Pew researchers found the following: 80% look online for information about health topics; 34% have read someone else’s commentary or experience about their health or medical issue; and 25% have watched an online health video (Fox, 2011). Web 2.0 was first described through its own channels, such as blog posts, YouTube videos, and Wikipedia, yet due to a need for greater clarity, O’Reilly’s 2005 seminal article “What Is Web 2.0?” was published. Soon after, social media and Web 2.0 technology emerged in peer-reviewed literature, primarily in the area of library science and clinical health or nursing (Kamel-Boulos, Maramba, & Wheeler, 2006; Kamel-Boulos & Wheeler, 2007; Skiba, 2006). In 2008, the health educators Hanson, Thackeray, Barnes, Neiger, and McIntyre took up the cause, publishing “Integrating Web 2.0 in Health Education Preparation and Practice,” a paper describing trends in Web 2.0 Internet usage that summarized its applications as platforms for health promotion, discussed guidelines for using its applications, and identified its learning outcomes. Several other authors have since identified the potential uses for social media within health promotion while pointing out the need for a greater evidence base for the effectiveness of such technologies and to guide best practices (Bardus, 2011; Gibbons et al., 2011; Kontos et al., 2010; Korda & Itani, 2011). Despite the heralding of the potential uses of Web 2.0 technology, acceptance within public health and health education remains limited, and the evidence for effectiveness is only just emerging. Avery et al. (2010) sought to examine the extent to which social media are adopted within public health agencies. They found that “across all levels of practice at the public health departments social media adoption seems dismally low” (p. 353). Public sector health departments were behind practitioners in other fields with a social media adoption rate of just 17% compared to 76% in a survey of Fortune 500 companies (Avery et al., 2010). A second study of 503 Certified Health Education Specialists (CHESs) was conducted, and re-


searchers found the following results: Only 34.8% used social networking sites; 23.5% used podcasts; and just 18.5% used media sharing sites within their work setting (Hanson et al., 2011). There does seem to be a wider acceptance at the national and international level of health promotion through organizations like the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), but even within these settings, social media use is not uniform (WHO, 2011). Bardus (2011) conducted a scoping review of papers published from January 2005 to May 2010 to examine outcomes associated with social media use and health communication initiatives. He found just 23 papers addressing 9 topics and concluded more research isolating the effects of social media on health-related behaviors is needed. In order to conduct such evaluative research, more health education and health promotion professionals must first begin incorporating social media into practice. The purpose of this paper is to provide a background of social media, describe current trends in the use of social media for health promotion, and discuss implications for practice. CINAHL, MEDLINE, Communication and Mass Media Complete, PsycINFO, and Google Scholar were searched using various combinations of the terms social media, “Web 2.0,” health promotion, and public health. Retrieved articles were then reviewed for relevance to the health education and promotion field. The four trends that emerged were social marketing, risk communication, delivery of health education, and professional preparation. The following description of the trends and the subsequent implications for practice can serve as a framework for health education specialists working to develop an evidence base through the incorporation of social media into health promotion programming. Four Trends in Current Use of Social Media Social Marketing In marketing, professionals must consider the four P’s: product, price, place, and promotion, with health education specialists focusing specifically on promotion (Thackeray, Neiger, Hanson, & McKenzie, 2008). Traditionally, professionals relied on “old media” such as newspapers, radio, and television to promote, or market, their product. In the late 1990s “new media” became available in the form of Web 1.0, or static webpages, which allowed for websites, advertisements, public service announcements, and e-mail messages via the Internet (Hanson et al., 2008). Even greater enhancements to this new media came with the development of Web 2.0, which allowed for interactive webpages and included applications for aggregation, social networking, and content sharing. Activities such as website optimization, RSS feeds, blogs, webcasts, and online media products for promotion be-

came available for use (Hanson et al., 2008). Web 2.0 applications directly engage consumers in the process, allowing them to not only receive information but also to develop it and share it through things like collaborative writing, content sharing, and social networking (Taubenheim et al., 2008; Thackeray et al., 2008). This flow of communication from a vertical to a multidirectional communication model not only influences health promotion practice in general but also the social marketing process (Thackeray & Neiger, 2009). A clear trend that emerged from the literature is the use of multiple forms of social media to market health promotion programming. For example, in 2007 and 2008, professionals working with The Heart Truth, a national health awareness campaign for women about heart disease, used multiple social media marketing and Internet marketing strategies for promoting campaign activities (Stroever, Mackert, McAlister, & Hoelscher, 2011; See Table 1). Similarly, the staff at the Alliance for Consumer Education (ACE) used a multifaceted social media strategy promoting awareness and prevention of inhalant abuse (Creighton, 2010). For the campaign, ACE used seven different social media strategies built progressively from 2002 to 2009 (Table 1). Both campaigns noted effectiveness. The Heart Truth researchers collected online advertising impressions and usage reports and found a 28% increase in social networking sites from 2007 to 2008 (Stroever et al., 2011). ACE researchers were able to reach their designated target audiences of teens and parents of teens and found that incorporating the social marketing applications into the already existing program website proved to be effective program management for a small staff (Creighton, 2010). Crisis and Emergency Risk Communication As technology has advanced and social media tools have become more robust, public health officials have taken advantage of ubiquitous platforms like Twitter, Facebook, and mobile applications (apps) to communicate with the public (e.g., the CDC’s H1N1 Flu Campaign and the Red Cross – Text Message Campaign For Haiti; See Table 1). Although these platforms do allow public health officials to reach large numbers of people in a very short amount of time, they have not completely replaced more traditional media channels such as television, newspapers, and standard websites. Instead, the public is using these in tandem with social media during emergencies and disasters (Tucker, 2011). One of the strengths of social media is that it provides new routes of information flow and a means to assist those in need of information during a disaster (Palen, Vieweg, Liu, & Hughes, 2009). Social media are able to serve as a powerful conduit for communication between governments, individuals, and communities as well as for relaying information to first responders about where and what


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Table 1. Selected Examples of Social Media Use by Trend Trend

Program Title 1

The Heart Truth

 Targeted email  Social network outreach (Blogger, Facebook)  Widget/Gadgets  Social bookmarking (Del.icio.us.com)  Outreach to online news sites

 Wikipedia  Content sharing (Flickr, YouTube)  Online banner campaign (PSA and Paid)  Pay-per-click campaign

ACE Inhalant Abuse Awareness2

 YouTube PSA’s and monitoring  MySpace account  Twitter account

   

CDC’s H1N1 Flu Campaign3

 YouTube account  MySpace account  Twitter account

 Facebook account  Widgets  Mobile technologies

Red Cross Text Message Campaign For Haiti3 SMASHING project4

   

RunKeeper5

 Mobile app integration

 Internet-based selfmanagement software

ACE Inhalant Abuse Awareness2

 Program website  Informational brochure for parents (online)  Inhalant Abuse Prevention Kit (online)  Blogs

 Outreach to key partners  Social marketing (see above)

Social Marketing

Risk Communication

Delivery of Health Education

Graduate public health course6 Professional Preparation

Social Media Application

Text messaging YouTube account Twitter account Internet-based selfmanagement software

Undergraduate women’s health course7

 Blogs

Graduate public health course8

 Blogs

Message board Blog (Blogger) Wikipedia Facebook (profile, fan page, and group)

 Facebook account  Flickr account  Internet-based asthma action plan

 Wikis

1

Stroever, Mackert, McAlister, & Hoelscher, 2011; 2Creighton, 2010; 3Tinker, Dumlao, & McLaughlin, 2009; 4van der Meer et al., 2010; 5Haithcox-Dennis, 2011; 6Cobus, 2009; 7Oomen-Early & Burke, 2007; 8Goldman, Cohen, & Sheahan, 2008.

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type of relief is needed (Huang, Chan, & Hyder, 2010). Social media also promote public engagement during times of crisis, allowing more participation in the public discourse on the part of those affected and by those who wish to provide assistance (i.e., monetary donations, time, information from the ground, etc.). In fact, some scholars have suggested that a cultural shift toward being “empowered contributors” during times of crisis is occurring (Tinker, Dumlao, & McLaughlin, 2009). Delivery of Health Education As shown in Table 1, social media are being used in a number of new and exciting ways to deliver health education interventions, from mobile apps that allow users to track their exercise activity and fitness goals (e.g., RunKeeper) to Internet-based applications that help users manage chronic conditions such as asthma (e.g., the SMASHING study). The potential to use technology to promote healthy behaviors has never been greater. Haithcox-Dennis (2011) called on health education specialists to view Healthy People 2020’s Health Communication and Health Information and Technology objectives as a call to action to capitalize on the potential of mobile and web-based technologies for health behavior change. The U.S. Department of Health and Human Services (HHS, 2010) specifically pointed to Objective 5.1, which aims “to increase the proportion of persons who use the Internet to keep track of personal health information,” and Objective 6.3, which aims “to increase the proportion of persons who use mobile devices,” as particularly important to health education. Much of the success in the use of social media to deliver health education occurs because interventions are usually based on the user’s self-regulation of behavior rather than behavioral directives (Lau et al., 2011). Buis (2011) proposed a framework that explains how to use a self-regulation framework when designing health education interventions using social media. First, according to Buis, health education interventions should give users access to self-monitoring tools (i.e., exercise tracking and calorie counters) and tools that support goal setting and achievement (i.e., computer-assisted goal-setting software and charts that display progress over time). Second, interventions should provide users with the ability to be “self-reactive” through reflection, social support, and social learning. However, social media technologies should not be used just for the sake of using the newest technology. Interventions must still be well planned and driven by theory. Webb, Joseph, Yardley, and Michie (2010) found that Internet-based interventions that made extensive use of theory had greater effects on behavior than did interventions which made less use of theory. Specifically, Webb et al. found that the theory of planned behavior had the greatest effect of any of the theories used by the stud-

ies in their systematic review. Interestingly, interventions that used the most behavior change techniques were more effective than interventions that used fewer techniques, and those that targeted a single behavior were slightly more effective than those that targeted multiple behaviors (Webb et al., 2010). The use of social media for the delivery of health education is not without its challenges and may not always be the right channel for the delivery of every intervention. The unknown efficacy of long-term behavior change interventions, privacy issues, cyberbullying, and social networking sites that promote unhealthy behaviors such as eating disorders or self-injury are all concerns that will need to be addressed as the use of Web 2.0 technology for the delivery of health education increases (Buis, 2011). Professional Preparation A number of articles have been published recently on the topic of using social media tools in college-level health education and public health classrooms. A selection of the articles that are most relevant to this discussion is highlighted in Table 1. Lobb and McDonnell (2009) called on public health educators to use social media in the college classroom to train a new generation of public health workers on how to address the public health challenges of the 21st century. Burke, Snyder, and Rager (2009) argued that the preparation of future health educators in “today’s technology-driven society requires faculty to adopt new teaching strategies which motivate and engage the new tech-savvy Web 2.0 generation” (p. 1). Oomen-Early and Burke (2007) argued that social media tools “should be integrated into entry-level courses which prepare students to perform the professional competencies of the field” (p. 2). Social media technologies should be used in the classroom in a structured way to achieve desired learning objectives. Hanson et al. (2008) put forth a set of useful Web 2.0 learning outcomes for college-level health education preparation, including one titled “Using Web 2.0 Applications for Health Communication.” Hanson et al. argued that social media for health education has the potential to create social change through the empowerment of large communities of users. Creative classroom techniques that incorporate technology have, in fact, been shown to promote a more productive and enriched learning environment (Burke et al., 2009). Oomen-Early and Burke (2007) found that student-created blogs fostered critical thinking and nurtured writing skills in an undergraduate health education course on women’s health and suggested that blogging could even empower students to take social or political action outside of the classroom. Goldman, Cohen, and Sheahan (2008) found that student-created blogs stimulated student engagement and collaborative learning in

THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012 Page 41

a graduate-level public health class, and Cobus (2009) reported that students in a graduate-level public health course who created blogs and wikis became more confident and comfortable with social media tools. Conclusion Web 2.0 technologies are changing the field of health promotion in a number of ways. Consumers are becoming more active in the management of their health through mobile apps and other social media. College-level public health educators have begun to integrate Web 2.0 technologies into learning objectives in the classroom. Health education specialists are able to reach targeted audiences through social media campaigns and can reach the public with crisis information quicker than ever before. The trends identified in this review point to a number of avenues in which health education specialists can incorporate social media into health promotion programming in order to continue to develop the evidence base for best practice.

creased attention to needs assessments in social media planning. Bull et al. (2011) presented an ethics case study that provided a useful set of questions to ask when conducting research or delivering interventions using social media (Table 2), and Thackeray et al. (2008) proposed a set of strategic questions to ask before using social media for social marketing programs (Table 2). Additionally, Thackeray, Neiger, and Keller (2012) offered a four-step process for integrating social media and social marketing. Although we recognize that its use might not be warranted for every program, a needs assessment for social media should be part of every health promotion planning process. Furthermore, if its use is warranted, then an evaluation plan should be put into place from the beginning of the planning process. In our “Vision for the Future,” health education specialists of all generations will be aware of the different Web 2.0 platforms available to them, will consider social media during the planning process for health promotion programs, and will integrate social media evaluation into program plans.

Despite the availability of numerous examples of social media applications and guidance from public health organizations on how to implement social media (American Public Health Association, 2012; CDC, 2011), the professional literature related to the evaluation and assessment of it in health education and health promotion is still developing. Neiger et al. (2012) recently proposed a set of key performance indicators and metrics that could be used for the evaluation of social media in health promotion. In addition, several scholars have argued for in-

Table 2. Key Considerations for the Use of Social Media Ethical Considerations1 1. Is the use of social media justified? 2. Will more participants be reached this way than through any other channel? 3. Do participants understand informed consent? 4. Do the criteria for enrollment in the intervention compromise the confidentiality of the participant? 5. Is participant data collected only through the social media site? 6. Is participant data stored on a secure server?

Social Marketing Considerations2 Priority population preferences  Is social media practical and useful for the priority population? Resources  What is the cost-benefit analysis?  How difficult will it be to implement? Goals and objectives  Does social media help reduce barriers for the population?  Can the program be evaluated?

Adapted from 1Bull et al., 2011; 2 Thackeray, Neiger, Hanson, and McKenzie, 2008.


References American Public Health Association. (2012). Social media. Retrieved January 6, 2012, from http://www. apha.org/about/rssinfo/ Avery, E., Lariscy, R., Amador, E., Ickowitz, T., Primm, C., & Taylor, A. (2010). Diffusion of social media among public relations practitioners in health departments across various community population sizes. Journal of Public Relations Research, 22(3), 336-358. Bardus, M. (2011). The Web 2.0 and social media technologies for pervasive health communication: Are they effective? Studies in Communication Sciences, 11(1), 119-136. Buis, L. (2011). The potential for web-based social network sites and self-regulation for health promotion. American Journal of Health Promotion, 26(2), 73-76. Bull, S. S., Breslin, L. T., Wright, E. E., Black, S. R., Levine, D., & Santelli, J. S. (2011). Case study: An ethics case study of HIV prevention research on Facebook: The Just/Us Study. Journal of Pediatric Psychology, 36(10), 1082-1092. Burke, S., Snyder, S., & Rager, R. (2009). An assessment of faculty usage of YouTube as a teaching resource. Internet Journal of Allied Health Sciences & Practice, 7(1), 1-8. Centers for Disease Control and Prevention. (2011). Social media at CDC. Retrieved January 6, 2012, from http://www.cdc.gov/socialmedia/ Chou, W. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009). Social media use in the United States: Implications for health communication. Journal of Medical Internet Research, 11(4), e48. Cobus, L. (2009). Using blogs and wikis in a graduate public health course. Medical Reference Services Quarterly, 28(1), 22-32. Constantinides, E., & Fountain, S. J. (2008). Web 2.0: Conceptual foundations and marketing issues. Jour- nal of Direct, Data and Digital Marketing Practice, 9(3), 231-244 Creighton, C. (2010). Using social media to increase awareness of inhalant abuse. Journal of Communi- cation in Healthcare, 3(3-4), 197-213.

Fox, S. (2011). The social life of health information, 2011. Retrieved January 6, 2012, from Pew Inter- net & American Life Project website: http:// pewinternet.org/Reports/2011/Social-Life-of- Health-Info.aspx Gibbons, M. C., Fleisher, L., Slamon, R. E., Bass, S., Kandadai, V., & Beck, J. R. (2011). Exploring the potential of Web 2.0 to address health disparities. Journal of Health Communication, 16, 77-89. Goldman, R. H., Cohen, A. P., & Sheahan, F. (2008). Using seminar blogs to enhance student participation and learning in public health school classes. American Journal of Public Health, 98(9), 1658-1663. Haithcox-Dennis, M. (2011). Foursquare: A health education specialist checks-in--A commentary. Ameri- can Journal of Health Education, 42(4), 194-196. Hanson, C., Thackeray, R., Barnes, M., Neiger, B., & McIntyre, E. (2008). Integrating Web 2.0 in health education preparation and practice. American Journal of Health Education, 39(3), 157-166. Hanson, C., West, J., Neiger, B., Thackeray, R., Barnes, M., & McIntyre, E. (2011). Use and acceptance of social media among health educators. American Journal of Health Education, 42(4), 197-204. Huang, C. M., Chan, E., & Hyder, A. A. (2010). Web 2.0 and Internet social networking: A new tool for disaster management? -- Lessons from Taiwan. BMC Medical Informatics & Decision Making, 10(1), 57-61 Kamel-Boulos, M. N., Maramba, I., & Wheeler, S. (2006). Wikis, blogs and podcasts: A new generation of Web-based tools for virtual collaborative clinical practice and education. BMC Medical Education, 6, 41. Retrieved from http://www. biomedcentral.com/1472-6920/6/41 Kamel-Boulos, M. N., & Wheeler, S. (2007). The emerging Web 2.0 and social software: An en- abling suite of social technologies in health and health care education. Health Information & Libraries Journal, 24(1), 2-23. Kontos, E. Z., Emmons, K. M., Puleo, E., & Viswanath, K. (2010). Communication inequalities and public health implications of adult social networking site use in the United States. Journal of Health Com- munication, 15(Suppl. 3), 216-235.

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Korda, H., & Itani, Z. (2011). Harnessing social media for health promotion and behavior change. Health Promotion Practice. Advance online publication. Lau, A. Y. S., Siek, K. A., Fernandez-Luque, L., Tange, H., Chhanabhai, P., Li, S. Y. W., . . . Eysenbach, G. (2011). The role of social media for patients and consumer health. Contribution of the IMIA Con- sumer Health Informatics Working Group. Year book of Medical Informatics, 6(1), 131-138

Thackeray, R., & Neiger, B. L. (2009). A multidirectional communication model: Implications for social marketing practice. Health Promotion Practice, 10(2), 171-175. Thackeray, R., Neiger, B. L., Hanson, C. L., & McK enzie, J. F. (2008). Enhancing promotional strate gies within social marketing programs: Use of Web 2.0 social media. Health Promotion Practice, 9(4), 338-343.

Lobb, A., & McDonnell, S. (2009). Technology can Thackeray, R., Neiger, B. L., & Keller, H. (2012). Inte improve public health education. American Journal grating social media and social marketing: A four- of Public Health, 99(3), 390-392. step process. Health Promotion Practice, 13(2), 165-168. Neiger, B. L., Thackeray, R., Van Wagenen, S. A., Hanson, C. L., West, J. H., Barnes, M. D., & Fagen, Tinker, T. L., Dumlao, M., & McLaughlin, G. (2009). M. C. (2012). Use of social media in health promo- Effective social media strategies during times tion: Purposes, key performance indicators, and of crisis: Learning from the CDC, HHS, FEMA, evaluation metrics. Health Promotion Practice, the American Red Cross and NPR. Public Relations 13(2), 159-164. Strategist, 15(3), 25-39. Oomen-Early, J., & Burke, S. (2007). Entering the blogosphere: Blogs as teaching and learning tools in health education. International Electronic Journal of Health Education, 10, 186-196. O’Reilly, T. (2005). What is Web 2.0: Design patterns and business models for the next generation of software. Retrieved January 9, 2012, from http:// oreilly.com/web2/archive/what-is-web-20.html Palen, L., Vieweg, S., Liu, S. B., & Hughes, A. L. (2009). Crisis in a networked world: Features of computer-mediated communication in the April 16, 2007 Virginia Tech event. Social Science Computer Review, 27(4), 467-480. Skiba, D. J. (2006). Web 2.0: Next great thing or just marketing hype? Nursing Education Perspectives 27, 212-214. Stroever, S. J., Mackert, M. S., McAlister, A. L., & Hoelscher, D. M. (2011). Using social media to communicate child health information to low-income parents. Preventing Chronic Disease, 8(6), A148. Taubenheim, A. M., Long, T., Smith, E. C., Jeffers, D., Wayman, J., & Temple, S. (2008). Using social media and internet marketing to reach women with The Heart Truth. Social Marketing Quarterly, 14(3), 58-67.

Tucker, C. (2011). Social media, texting play new role in response to disasters. The Nation's Health, 41(4), 1-18. U.S. Department of Health and Human Services. (2010). 2020 Topics & objectives: Health communication and health information technology. Re- trieved from http://healthypeople.gov/2020/ topicsobjectives2020/objectiveslist.aspx?topicId=18 van der Meer, V., van Stel, H. F., Bakker, M. J., Roldaan, A. C., Assendelft, W. J., Sterk, P. J., . . . Sont, J. K. (2010). Weekly self-monitoring and treatment adjustment benefit patients with partly controlled and uncontrolled asthma: An analysis of the SMASHING study. Respiratory Research, 74(11), 1-9. Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4. World Health Organization. (2011). Mixed uptake of social media among public health specialists. Bulletin of the World Health Organization, 89(11), 784-785.


Association of Obesity and C-Reactive Protein Levels in American Youth Ages 12-19 Years Heather Colfax Parth, Jennifer Marie Richards, and Michael Eric McCown

Abstract Childhood obesity is currently epidemic in the United States and has been linked to low-grade inflammation which is hypothesized to increase risk of infectious disease. This cross-sectional case-control study examined the relationship between obesity and the inflammation marker C-reactive protein (CRP) in youth ages 12-19 years that were profiled in the 2007-2008 NHANES survey. Obesity was defined as a body mass index-for-age at or above the 95th percentile, and CRP was used as a proxy for immune function. Chi-square testing and multivariate logistic regression were performed, yielding an adjusted OR = 4.32 (95% CI 2.16-8.62) for the association between obesity and elevated CRP. These data show that obesity is positively associated with elevated CRP in American youth, potentially translating into increased infectious disease risk. Introduction The worldwide escalating obesity rate has become a serious health issue in both adult and pediatric populations (Karlsson & Beck, 2010). According to the body mass index (BMI), children with a BMI-for-age from the 85th to the 95th percentile have been categorized as overweight; children with a BMI-for-age at or above the 95th percentile have been termed obese (Ogden & Flegal, 2010). The prevalence of obesity in youth is increasing (Flegal, Tabak, & Ogden, 2006; Karlsson & Beck, 2010). Although the effect of obesity on chronic diseases has been well studied, a recurrent theme in the literature is that little is known about how obesity interacts with immune response to infectious disease (Karlsson & Beck, 2010).

The mechanism behind the pathogenesis of obesityrelated diseases describes a close relationship between obesity, cellular/molecular immunity, and inflammationmediated derangement in the cellular and molecular mediators of the body’s inflammatory response (Karlsson & Beck, 2010). This has given rise to the concept of “metainflammation,” defined as a chronic low-grade inflammatory response to obesity (Ford et al, 2001; Lumeng & Saltiel, 2011). This state of low-grade inflammation can result in immunodeficiency and may lead to increased susceptibility to bacterial and viral infections (Desruisseaux, Nagajyothi, Trujillo, Tanowitz, & Scherer, 2007; Karlsson & Beck, 2010). One Spanish study found that pediatric patients with BMIs in the obesity range were at three times greater risk of becoming Neisseria meningitidis carriers compared to non-obese patients (Uberos, Molina-Carballo, Fernández-Puentes, Rodríquez-Belmonte, & Muñoz-Hoyos, 2010). Another study by Eliakim, Schwindt, Zaldivar, Casali, and Cooper (2006) found a reduced antibody response to tetanus immunization in obese children and speculated that it may be related to decreased immune response due to low-grade inflammation.

These cited studies also support the assertion that elevated inflammatory markers like CRP are related to adult obesity, which can result in both poor chronic and acute health outcomes. However, much less is known about the association of CRP levels and obesity within the pediatric population, particularly with regard to immune function. Calls for further research into this topic were found repeatedly in the literature (McDade et al., 2008; Nieman et al., 1999; Skinner et al., 2010). While international studies have examined the relationship between CRP and childhood obesity, a review of the literature failed to find Analysis of C-reactive protein (CRP) is currently an in- any studies conducted with a U.S. pediatric population novative area of health research. CRP is one of several using the National Health and Nutrition Examination inflammatory markers that has demonstrated a strong, Survey (NHANES) data. With childhood obesity rates in positive association with increasing weight status in chil- the US currently at epidemic proportions and increasing, dren as measured with BMI (Skinner, Steiner, Henderson, the role that inflammation plays in health outcomes for & Perrin, 2010; Timpson et al., 2011). In an inflamma- obese children is a pressing topic that warrants further tory environment, excessive adipose tissue causes unfa- research (Lumeng & Saltiel, 2011). More study is needed vorable changes in cytokine secretion which leads to an to determine the association of obesity-related chronic overall dampened immune response (Karlsson & Beck, inflammation, elevated CRP levels, and bacterial and/or 2010). In addition to obesity, infectious agents also act as viral infection susceptibility in the pediatric population. inflammatory stimuli, increasing levels of CRP (McDade, The purpose of this study was to examine the relationship Rutherford, Adair, & Kuzawa, 2008). between obesity and elevated CRP among youth ages 1219 years in the US. THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012 Page 45

Methods and Procedures Data from the public-use 2007-2008 NHANES were obtained from the Centers for Disease Control and Prevention (CDC) website (http://www.cdc.gov/) and used for this study. The publicly available secondary data set has been stripped of all personal/characteristic identifiers by the National Center for Health Statistics (NCHS) prior to release to preserve subject anonymity (U.S. Department of Health and Human Services, CDC, NCHS, 2011). NHANES is conducted according to a stratified multistage probability sampling of non-institutionalized U.S. civilians, and it is designed to assess the health and nutrition status of adults and children in the country by combining personal interviews, physical examinations, and laboratory analyses. Because the data used in this study have been secured in this fashion, this study was granted exemption from full University of North Florida IRB review. The body measurement data for NHANES were collected from clinical exams conducted by trained health technicians with the assistance of a recorder. The respondent’s age at the time of the screening interview determined the body measurement examination protocol for each survey participant. General guidelines used for the body measurement procedures were based on the Anthropometric Standardization Reference Manual (Lohman, Roche, & Martorell, 1988). CRP laboratory analysis was performed according to the following protocol: Blood specimens were processed, stored, and shipped to the University of Washington, Seattle, WA. Additional details of specimen collection and processing can be found in the NHANES Laboratory Procedure Manual (USDHHS, CDC, NCHS, 2007). A cross-sectional case-control study design was used to compare CRP levels in obese and non-obese youth ages 12-19 sampled from the 2007-2008 NHANES. For the purpose of this study, all subjects ages 12-19 years who had been measured for BMI through the 2007-2008 NHANES were sampled (n = 1,185). This sample was stratified by both age and sex in order to define obese (age- and gender-specific BMI > 95th percentile) and non-obese youth (age- and gender-specific BMI < 95th percentile) for the purpose of comparing CRP levels between these groups. Among this age group, a sample of those tested for CRP level was also taken (n = 1,060). This sample was further divided into the categories abnormal or normal for CRP level (explained in the “Key Variables” section that follows). Categorizing the data in this way resulted in a final total combined sample size of 992. A Chi-square test for association between obesity and CRP level was then conducted. Since the presence of asthma, diabetes, and/or hepatitis C are potential confounders for CRP levels, an analysis of their effect in Page 46

this study sample was also included. SAS version 9.2 was used for data management. SUDAAN version 10.0 was used for all descriptive and correlational analyses. Key Variables CRP, also known as high-sensitivity CRP (hs-CRP). CRP is a protein produced by the liver during systemic inflammation. It is used as a non-specific inflammation marker in humans (“C-reactive protein,” 2011). CRP is coded as CRP_E in the 2007-2008 NHANES data set. Normal concentrations of CRP fall within the range of 0.0 mg/dL to 0.3 mg/dL (American Association for Clinical Chemistry, 2011); therefore, for the purposes of this study, CRP levels greater than 0.3 mg/dL were designated elevated and were coded as abnormal and CRP levels less than or equal to 0.3 mg/dL were considered within normal limits and were coded as normal. Obesity (childhood). Childhood obesity is defined as a BMI falling at or above the 95th percentile in the CDC 2000 growth charts, which are adjusted for both age and sex (Ogden & Flegal, 2010). BMI is coded as BMXBMI in the 2007-2008 NHANES data set. BMI values falling at or above the 95th percentile for age and gender were coded as obese, and BMI values falling below the 95th percentile for age and gender were coded as non-obese. Other Variables Since the focus of this study was obesity-related inflammation and its potential effects on CRP level, controlling for other inflammation sources was necessary. Asthma, diabetes, and/or hepatitis C status were identified as potential confounders because these conditions cause inflammation. Analyses of their effects in this study sample were also included. Asthma. Asthma is a chronic respiratory disease that affects persons of all ages and is characterized by episodic and reversible attacks of wheezing, chest tightness, shortness of breath, and coughing (Kliegman, Behrman, Jenson, & Stanton, 2007). For this study, asthma status was determined by the NHANES 2007-2008 “Medical Conditions” survey question “Has a doctor or other health professional ever told you that you have asthma?” Diabetes. Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with longterm damage, dysfunction, and failure of various organs, especially the eyes, kidneys, nerves, heart, and blood vessels (American Diabetes Association, 2004). This study based diabetes status on the NHANES 2007-2008 “Diabetes – DIQ” survey question “Other than during pregnancy, have you ever been told by a doctor or other health professional that you have diabetes or sugar diabetes?”


Hepatitis C. Hepatitis C is an infection caused by a virus that attacks the liver and leads to inflammation (Mayo Clinic staff, 2011). For this study, hepatitis C status was determined by hepatitis C (HCV) genotyping performed under the NHANES 2007-2008 laboratory procedures. Participants ages six and older were eligible for hepatitis C assessments. Race. The original race/ethnicity categories included in the NHANES 2007-2008 demographics questionnaire were “Mexican-American,” “other Hispanic,” “non-Hispanic White,” “non-Hispanic Black,” and “other Race.” Due to sample size considerations, Mexican-American and other Hispanic were merged into a new “Hispanic” category, and other Race (n = 61) was excluded from analysis due to insufficient sample size. Therefore, the final race/ethnicity categories for this study sample were non-Hispanic White, non-Hispanic Black, and Hispanic. Results Table 1 shows the sociodemographic characteristics of the study population of youth, ages 12-19 years, meeting the study criteria. The unweighted sample for this age group was composed of 1,210 subjects, 52.6% male

and 47.4% female. This group consisted of 32.8% nonHispanic White, 28.2% non-Hispanic Black, and 39.0% Hispanic subjects. Broken down by age, 12.7% were 12 years old, 11.3% were 13 years old, 14.5% were 14 years old, 11.9% were 15 years old, 14.1% were 16 years old, 11.7% were 17 years old, 11.3% were 18 years old, and 12.5% were 19 years old. Nearly 21% of the respondents in the included age range who were measured for BMI were obese. Of those tested for CRP level, 13.3% had abnormal CRP levels. Weighted percentages for these characteristics are also shown in Table 1. Of the subjects in this age range whose data included diabetes status (n = 1,209), there was a 0.9% prevalence of diabetes. All participants tested for hepatitis C (n = 1,070) tested negative. Asthma prevalence was determined to be 20.5%. Evaluation of the variables race, hepatitis C, asthma, diabetes, gender, and obesity was conducted using multivariate logistic regression to control for all independent covariates and quantify their association with abnormal CRP levels. Results of these analyses showed race, hepatitis C, and asthma to be insignificant and were subsequently dropped from the model.

Table 1. Sociodemographic Characteristics of the Study Population Characteristics n Percentage (unweighted) (unweighted)

Percentage (95% CI) (weighted)

Gender Males Females

1210 636 574

52.6 47.4

50.9 (47.5–54.4) 49.1 (45.6–52.5)

Race White, non-Hispanic Black, non-Hispanic Hispanic

1156 379 326 451

32.8 28.2 39.0

64.6 (56.2–72.1) 16.1 (11.9–21.4) 19.3 (13.7–26.5)

Age 12-year-olds 13-year-olds 14-year-olds 15-year-olds 16-year-olds 17-year-olds 18-year-olds 19-year-olds

1210 154 137 175 144 171 141 137 151

12.7 11.3 14.5 11.9 14.1 11.7 11.3 12.5

11.7 (8.8–15.3) 10.5 (8.7–12.7) 15.0 (12.2–18.2) 13.1 (11.0–15.6) 14.4 (11.6–17.6) 11.5 (10.4–12.9) 13.4 (11.3–15.8) 10.5 (8.0–13.6)

BMI Obese

1185 247

20.8

18.3 (14.8–22.4)

CRP Abnormal Levels

1060 141

13.3

12.3 (10.1–14.8)


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Table 2. Results of Multivariate Logistic Regression Analyses – Final Model Variables Obesity

OR 4.32

95% CI 2.16–8.62

Diabetes 4.17

1.11–15.67

Male gender

0.63

Significant associations were found for obesity, diabetes, and gender as they relate to CRP level. Using a significance of p = 0.05, obesity was statistically significantly associated with elevated CRP level (OR = 4.32; 95% CI 2.16-8.62). In addition, both diabetes (OR = 4.17; 95% CI 1.11-15.67) and male gender (OR = 0.63; 95% CI 0.410.97) were significantly associated with abnormal CRP levels. Results from the logistic regression analyses are shown in Table 2. These results indicate that obese youth and youth with diabetes have increased odds of having abnormal (elevated) CRP levels. The results also suggest that being a male between the ages of 12-19 years is protective against having abnormal (elevated) CRP levels. Discussion Findings from this study support the hypothesis that CRP levels are different in obese versus non-obese youth ages 12-19 years old. Specifically, a positive association exists between elevated CRP level and obesity. This cohort, selected from the 2007-2008 NHANES, showed the odds of having elevated CRP levels were 4.32 times greater in obese versus non-obese youth. Since CRP levels were also elevated for subjects with diabetes, this variable was included and controlled for in the statistical model so as not to adversely affect the study outcome. Where CRP levels are elevated in obese children, underlying inflammatory processes are indicated. According to a study by Karlsson and Beck (2010), in this inflammatory environment, excessive adipose tissue causes unfavorable changes in cytokine secretion which leads to an overall dampened immune response. This creates an internal metabolic climate that is conducive to immunodeficiency and, therefore, increased risk of infectious disease. Although studies have been done on this topic for the adult population, studies for the pediatric population are greatly lacking. Further research in this area is warranted. This study has several limitations. One limitation is that a number of agents and conditions can elevate CRP level, including acute infection, smoking exposure, arthritis, and depression (Chiu et al., 2011; Plant et al., 2000; Uddin et al., 2011). Given the vulnerable age staPage 48

0.41–0.97

tus of this study population, restricted access to sensitive data prevented these variables from being determined and thus controlled for in this study. While the association between obesity and elevated CRP was demonstrated, the subsequent link to infectious disease risk could not be empirically determined with the given data set. Another consideration is that BMI does not take body composition (lean muscle mass vs. adipose tissue) into consideration and therefore serves as an acceptable, but not perfect, proxy for obesity status. Lower BMI cutpoints for individuals of Asian descent have also been suggested to provide a more accurate obesity assessment (McDade et al., 2008); however, since the “other Race” classification was excluded from this study due to small sample size, the likelihood of a large Asian population skewing the results is small. A final consideration is that diabetes was found to be significantly associated with elevated CRP, but the diagnosis of diabetes was strictly based on self-reported information which may be subject to reporting bias. To conclude, obesity has been linked to increased risk for inflammation, chronic disease, and infectious disease in the adult population (Falagas & Kompoti, 2006). In addition, elevated levels of CRP have been implicated as cellular inflammation markers. However, little is known about the link between childhood obesity and inflammation. This study adds to the body of knowledge by demonstrating a positive association between childhood obesity and elevated CRP levels in U.S. youth 12-19 years old. Given the inherent health risks that chronic inflammation poses to this population, these findings underscore the need for further research to elucidate the relationship between childhood obesity and elevated CRP levels. In light of the important implications for pediatric health that have been discussed here, this study provides health educators with yet more compelling information to assist them in their advocacy against childhood obesity. As experts in the field acknowledge, both individual and group-based interventions can help decrease rates of obesity and obesity-related diseases (Pedrosa et al., 2011; Wärnberg, Moreno, Mesana, & Marcos, 2004); thus, further research into this topic may have implications for future public health policy.


References American Association for Clinical Chemistry. (2011). Lab tests online: hs-CRP. Retrieved October 25, 2011, from http://labtestsonline.org/understanding/ analytes/hscrp/tab/test American Diabetes Association. (2004). Diagnosis and classification of diabetes mellitus. Diabetes Care, 27, s5-s10. Chiu, Y.-H. M., Spiegelman, D., Dockery, D. W., Garshick, E., Hammond, S. K., Smith, T. J., . . . Laden, F. (2011). Secondhand smoke exposure and inflammatory markers in nonsmokers in the trucking industry. Environmental Health Per- spectives, 119(9), 1294-1300. Retrieved from ProQuest Medical Library. (Document ID: 2481221981) C-reactive protein. (2011). In A.D.A.M. Medical En- cyclopedia. Retrieved November 30, 2011, from http://www nlm nih.gov/medlineplus/ency/ article/003356 htm Desruisseaux, M. S., Nagajyothi, Trujillo, M. E., Tanowitz, H. B., & Scherer, P. E. (2007). Adipo- cyte, adipose tissue, and infectious disease. Infec- tion and Immunity, 75(3), 1066-1078. doi:10.1128/IAI.01455-06 Eliakim, A., Schwindt, C., Zaldivar, F., Casali, P., & Cooper, D. M. (2006). Reduced tetanus antibody titers in overweight children. Autoimmunity, 39(2), 137-141. Falagas, M. E., & Kompoti, M. (2006). Obesity and infection. The Lancet Infectious Diseases, 6(7), 438-446. Retrieved from ProQuest Central. (Docu- ment ID: 1666605201)

Kliegman, R. M., Behrman, R. E., Jenson, H. B., & Stanton, B. F. (Eds.). (2007). Nelson textbook of pediatrics (18th ed.). Philadelphia, PA: Saunders Elsevier. Lohman T. G., Roche A. F., & Martorell, R. (Eds.). (1988). Anthropometric standardization reference manual (Abridged ed.). Champaign, IL: Human Kinetics Books. Lumeng, C. N., & Saltiel, A. R. (2011). Inflammatory links between obesity and metabolic disease. Jour- nal of Clinical Investigation, 121(6), 2111-2117. Retrieved from ProQuest Central. (Document ID: 2376876511) Mayo Clinic staff. (2011). Hepatitis C: Definition. Re- trieved October 25, 2011, from http://www.mayo clinic.com/health/hepatitis-c/DS00097 McDade, T. W., Rutherford, J. N., Adair, L., & Kuzawa, C. (2008). Adiposity and pathogen exposure predict C-reactive protein in Filipino women. The Journal of Nutrition, 138(12), 2442-2447. Retrieved from ProQuest Central. (Document ID: 1607548501) Nieman, D. C., Henson, D. A., Nehlsen-Cannarella, S. L., Ekkens, M., Utter, A. C., Butterworth, D. E., & Fagoaga, O. R. (1999). Influence of obesity on immune function. Journal of the American Dietetic As- sociation, 99(3), 294-299. Retrieved from ProQuest Central. (Document ID: 39634060) Ogden, C. L., & Flegal, K. M. (2010). Changes in ter- minology for childhood overweight and obesity. Na- tional Health Statistics Reports, 25, 1-8.

Flegal, K. M., Tabak, C. J., & Ogden, C. L. (2006). Overweight in children: Definitions and interpreta- tion. Health Education Research, 21(6), 755-760.

Pedrosa, C., Oliveira, B. M., Albuquerque, I., Simões- Pereira, C., Vaz-de-Almeida, M. D., & Correia, F. (2011). Markers of metabolic syndrome in obese children before and after 1-year lifestyle intervention program. European Journal of Nutrition, 50(6), 391-400. Retrieved from ProQuest Central. (Docu- ment ID: 2432284351)

Ford, E. S., Galuska, D. A., Gillespie, C., Will, J. C., Giles, W. H., & Dietz, W. H. (2001). C-reactive protein and body mass index in children: Findings from the Third National Health and Nutrition Examination Survey, 1988-1994. The Journal of Pediatrics, 138(4), 486-492.

Plant, M. J., Williams, A. L., O’Sullivan, M. M., Lewis, P. A., Coles, E. C., & Jessop, J. D. (2000). Rela- tionship between time-integrated C-reactive protein levels and radiologic progression in patients with rheumatoid arthritis. Arthritis & Rheumatism, 43(7), 1473-1477. doi:10.1002/1529-0131

Karlsson, E. A., & Beck, M. A. (2010). The burden of obesity on infectious disease. Experimental Biol- ogy and Medicine, 235(12), 1412-1424. doi:10.1258/ebm.2010.010227

Skinner, A. C., Steiner, M. J., Henderson, F. W., & Perrin, E. M. (2010). Multiple markers of in flammation and weight status: Cross-sectional analyses throughout childhood. Pediatrics, 125(4), e801-e809. Retrieved from ProQuest Central. (Document ID: 2009781541)


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Timpson, N. J., Nordestgaard, B. G., Harbord, R. M., Zacho, J., Frayling, T. M., Tybjærg-Hansen, A., & Davey Smith, G. (2011). C-reactive protein levels and body mass index: Elucidating direction of causation through reciprocal Mendelian random ization. International Journal of Obesity, 35(2), 300-308. Retrieved from ProQuest Central. (Docu- ment ID: 2266556591) Uberos, J., Molina-Carballo, A., Fernández-Puentes, V., Rodríguez-Belmonte, R., & Muñoz-Hoyos, A. (2010). Overweight and obesity as risk factors for the asymptomatic carrier state of Neisseria meningitidis among a paediatric population. European Journal of Clinical Microbiology and Infectious Diseases, 29(3), 333-334. Retrieved from ProQuest Central. (Document ID: 1970572861)

U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. (2007). Labortatory Procedure Manual. Retrieved Decem- ber 3, 2011, from http://www.cdc.gov/nchs/data/ nhanes/nhanes_0708/crp_e_met.pdf. Wärnberg, J., Moreno, L. A., Mesana, M. I., & Marcos, A. (2004). Inflammatory mediators in overweight and obese Spanish adolescents. The AVENA Study. International Journal of Obesity and Related Metabolic Disorders, 28(Suppl. 3), S59-S63. Retrieved from ProQuest Central. (Document ID: 984421361)

Uddin, M., Koenen, K. C., Aiello, A. E., Wildman, D. E., de los Santos, R., & Galea, S. (2011). Epi- genetic and inflammatory marker profiles associated with depression in a community-based epide- mio logic sample. Psychological Medicine, 41(5), 997-1007. Retrieved from ProQuest Medical Library. (Document ID: 2304009481)

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Circle of Friends: A Community Empowerment Approach to Reducing Racial/Ethnic Health Disparities Erica Payton, Timothy R. Jordan, and Amy Thompson

Abstract African American women are disproportionately affected by breast cancer. In an attempt to reduce racial/ethnic health disparities in northwest Ohio, a group of public health students and faculty at a university in northern Ohio came together in 2011 and formed a research team to investigate potential solutions. The search for solutions began with a comprehensive review of the scientific literature and culminated in a grant award and the design of a community empowerment intervention to reduce the racial/ethnic health disparities in breast cancer for African American women. Introduction Despite decades of sizeable investments of U.S. taxpayer dollars and grant-funded programs to reduce them, significant racial/ethnic health disparities still exist. There are numerous reasons why racial/ethnic health disparities persist, including racism, discrimination, and poverty; inequalities in education, political leverage, and the quality of health services; and the lack of grass-roots involvement, access to services, and cultural competence among health care providers (Begvand, Gazarin, Howland, Trimis, & Yong, 2011; Fowler, Rodney, Roberts, & Broadus, 2005; LaViest, 2005; Williams & Jackson, 2005).

can women over age 40 do not receive the recommended mammograms. Among Ohio women, the rate of newly diagnosed breast cancer cases in African Americans is the same as Whites; however, the mortality rate for the African American women is 33% higher than that for the White women (Ohio Department of Health, 2010). Even if diagnosed early, African Americans are less likely to survive than Whites (Virnig et al., 2009). The five-year survivorship rate for African American women is 78% compared to 90% for White women (American Cancer Society, 2011). To address racial/ethnic health disparities in breast cancer in northern Ohio, a group of public health students and faculty members designed a community empowerment intervention. The purpose of this paper is to describe the planning process and the lessons learned as the research team progressed through the various steps of program design, engaging the priority population, recruitment and advertising, training, and implementation. Program Design Phase: The Community Empowerment Approach

African Americans have the highest cancer mortality rate of any racial group (American Cancer Society, 2012). In Ohio, for the years 2003-2007, the mortality rate among African Americans for all cancer types combined was 26% higher than that for Whites (Ohio Department of Health, 2010). Similar disparities exist in northwest Ohio (Lucas County, 2007), the region targeted for the intervention described in this article.

Many past efforts to reduce racial/ethnic health disparities have been uncoordinated and fragmented, and they focused primarily on a problems-based or needs-based philosophy led by academicians and/or service providers using a “top-down” method (Minkler & Wallerstein, 2002). Therefore, the research team decided to use a strengths-based community empowerment approach led by members of the community, specifically African American breast cancer survivors. The research team designed a program that would equip and empower African American women to be lay health leaders and strong advocates for breast health.

According to the American Cancer Society (2012), breast cancer is the most commonly diagnosed cancer and second most common cause of death among African American women. Among Ohio women, breast cancer represents nearly one third of all cancer diagnoses (National Cancer Institute, 2011). African American women are more likely than white women to be diagnosed with cancer at later stages and are less likely to receive preventive screening tests (Virnig, Baxter, Habermann, Feldman, & Bradley, 2009). Nearly 40% of African Ameri-

Community empowerment approaches feature involvement of the priority population in the design, implementation, management, and evaluation of the intervention. Empowerment involves releasing leadership of the program to members of the priority population (Crishna, 2007). Empowerment includes equipping members of the priority population to take control of their own health (Scarinci, Johnson, Hardy, Marron, & Partridge, 2009). Using members of the priority population as lay health workers has been successful in addressing health dispari-


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ties in culturally diverse communities (Israel et al., 2010; Jackson, Chu, & Garcia, 2006; Kim-Godwin, Clarke, & Barton, 2001; Meade, Menard, Martinez, & Calvo, 2007; Nemcek & Sabatier, 2003; Scarinci et al., 2009). The basic premise of such grass-roots approaches is that the collaborative efforts and resources of many have a much better chance of reducing racial/ethnic health disparities than the efforts and resources of a few. Development of the Circle of Friends Program: A Community-Based Participatory Research Approach Using this grass-roots community-empowerment approach as the foundation for program design, the research team received a small grant from the local affiliate of the Susan G. Komen for the Cure Foundation. The funds are being used to design, implement, and evaluate this project entitled “Circle of Friends.” The Circle of Friends Program is a leadership development system designed to help African American female breast cancer survivors become agents of change in their community. Breast cancer survivors are recruited to serve as mentors and leaders of small neighborhood groups called “Care Groups.” Once a survivor/mentor completes the training program, she is “launched” into the community to recruit, lead, grow, and replicate her group.

& Vinswanath, 2008). A steering committee serves in an oversight and advisory role and helps to ensure the program methods and materials are culturally relevant and acceptable. At its core, the community empowerment approach used to create the Circle of Friends Program is designed to develop African American female lay health leaders. There are numerous potential benefits to using lay health leaders in community-level interventions, particularly when focusing on reaching African American women (Brownstein, Bone, Dennison, Hill, Kim, & Levine, 2005; Eng, Parker, & Harlan, 1997; Glanz et al., 2008; LaViest, 2005). The following is a partial list of these benefits:

1. African American breast cancer survivors have established relationships, understand their neighborhoods, understand the culture, can relate well to other African American women, and can address cultural beliefs and barriers related to obtaining appropriate screening, diagnostic, and treatment services.

2. African American breast cancer survivors are more attuned to knowing how to meet the needs of the community to which they already belong.

3. When African American women in the community see one of their own taking charge of her health and advocating for health, it inspires them to take control of their own health-related decisions.

4. Because of their prominent place in African Ameri- can society, African American women have great potential to function as positive “change agents” and can positively impact their family members and other families for generations.

Once a Care Group is assembled, the group leader is required to enlist an apprentice leader. Over the next 6-9 months, the group leader provides the apprentice with training, mentoring, and opportunities for guided practice. When the apprentice leader is ready to lead a group, she leaves the existing group with 2-3 group members and starts a new group. The cycle of leadership development then repeats itself and is self-perpetuating.

5. The group leaders and the members of the steering committee benefit from increased educational and training opportunities provided by the program. Their consistent and regular interaction with the research team and their exposure to training and education better equips them to pursue additional education, enter the workforce, act as role models for others, and serve in leadership roles within the community.

The Circle of Friends Program was designed using principles of community-based participatory research (CBPR). CBPR is a collaborative research approach designed to ensure that members of the priority population are actively involved in all aspects of the research process to improve health and well-being through taking action, including social change (Agency for Healthcare Research and Quality, 2009). Enlisting members of the priority population to serve on a steering committee can be an effective method of involving them throughout all phases of a community-level intervention (Glanz, Rimer,

The design of the Circle of Friends Program is unique n several ways. First, breast cancer survivors learn to be lay health leaders experientially—by leading while receiving support, encouragement, and guidance from the research team. Learning experientially is a better method of learning than a passive, didactic style of education (Nilson, 2010). Second, the program is designed to be self-sustaining and self-perpetuating. Each group leader must recruit an apprentice leader whom she will train and develop to be a second generation Care Group Leader. New leaders will continually be recruited, developed, and

A Care Group is a small group of 5 to 10 African American women who meet on a regular basis in homes, churches, and other community locations to educate racial/ethnic minority women about breast health topics. These group meetings feature food, fun, relationshipbuilding, discussion, mutual support, peer education, and peer mentoring. The mentors/survivors lead interactive discussions on a variety of health promotion/disease prevention topics related to breast health.

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launched into the community to start new groups. Third, each member of the steering committee must recruit an apprentice whom she will train and develop to be a second generation member of a new steering committee. In doing so, the program continually develops new administrative leaders who will oversee, manage, and evaluate the program. Recruitment Phase: Engaging and Involving Members of the Priority Population After the Circle of Friends Program was designed, the research team recruited African American women to serve as group leaders and members of the steering committee. The research team utilized the key concepts of collaboration, advertising and marketing, and community outreach. Collaboration One of the first steps in the recruitment process was to build bridges of collaboration with other groups and organizations in the community that shared our desire to reduce racial/ethnic health disparities. Building partnerships with like-minded organizations that serve the same priority population increases the odds of program impact. Prior to writing the grant application to seek funding for Circle of Friends, the research team developed a strong partnership with the coordinator of the local Commission on Minority Health. The research team’s partnership with the Commission yielded numerous benefits: (a) increased awareness of our program in the community, (b) increased support for our program in the community, (c) the identification of other community groups working with minorities in breast cancer, and (d) shared recruitment efforts. Members of the Commission on Minority Health helped the research team to identify a local African American breast cancer support group. This support group has a long standing history in the African American community and is well respected by African American women. Thus, it was important for the research team to build a bridge of collaboration to this existing group and gain approval from their leadership. This collaborative partnership helped the research team recruit breast cancer survivors to serve on the steering committee for the Circle of Friends Program. Gaining support from influential leaders (i.e., gate keepers) in the priority population increases the odds that a program will be accepted by the priority population. As mentioned previously, another way to improve collaboration with members of the priority population is to organize a steering committee (Bullock & McGraw,

2006; Coward, 2005). Therefore, the research team spent considerable time and effort recruiting and identifying members for one. Members of a steering committee serve as “community experts.” They help establish the validity and acceptability of program methods and materials by reviewing and editing them prior to implementation. They also monitor and manage the implementation of the program and can help evaluate the program—all principles of community based participatory research. Having members of the priority population serve in these leadership roles helps to increase their leadership capacity and program ownership. This increased program ownership results in better planned programs, greater community support for the program, and greater numbers of people who will be willing to promote the program to others (Strycker, Foster, Pettigrew, Donnelly-Perry, & Glasgow, 1997). Advertising & Marketing Advertising and marketing health promotion and disease prevention programs can be very challenging. The process of marketing health programs to a priority population is based on the concepts of exchange theory (Kotler & Clarke, 1987). Put simply, to successfully market the Circle of Friends Program, the research team had to persuade African American women that the “product” (i.e., the program) provided benefits that they valued. The research team also had to convince people that those benefits could be “purchased” at a reasonable “price” (i.e., the tangible or intangible costs of participating). The research team’s community partners assisted greatly with advertising and marketing. The Commission on Minority Health sent out recruitment letters to their email distribution list. The Commission also helped promote the project via media channels in the African American community. Two local radio stations with predominately African American listening audiences aired public service announcements about the program. Two newspapers with primarily African American readership published articles about the program. The research team also placed flyers and posters throughout the African American community. They were distributed at places such as hair salons, beauty supply stores, local stores and shops, churches, and daycare centers. Although the research team achieved some success with these recruitment methods, the most successful recruitment and marketing method was one-on-one, faceto-face communication. The research team worked with its community agency partners and their members to identify women who were interested in joining the program. The research team then offered personal invitations to join the program. These personal invitations were more effective than the broader, more public appeals.


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Recommendations

Community Outreach Another lesson that the research team learned from its review of the literature and from experience is that it is best to take the program to the people rather than waiting for the people to come to the program. In the African American community, the church is a good location for taking the program to the people. There are many examples in the literature that recommend incorporating cultural beliefs, such as spiritual values, into health interventions for African Americans (Belin, Washington, & Greene, 2006; Boyd & Wilmoth, 2006; Hall et al., 2005; Kidder, 2008; Mitchell, Lannin, Mathews, & Swanson, 2002). Studies indicate that African American women believe that both religiosity and medical treatment are important when addressing breast cancer treatment and interventions (Gallia & Pines, 2009; Mitchell et al., 2002). For these reasons, the research team partnered locally with a large influential church. The church helped promote the program and also provided space for two community meetings. The meetings were held to introduce the program to the community and to recruit program participants. During each meeting, a town hall session was held to elicit suggestions and perceptions by African American women regarding how best to proceed with the program. Next Steps

As the program continues to grow and expand, the research team would like to suggest 10 recommendations for those who desire to design and implement similar community-level health interventions. These recommendations apply to a majority of community health contexts and types of programs:

1. Before designing a community-level intervention, network with members of the priority population to identify key opinion leaders within the priority population (Eng et al., 1997).

2. Before designing a community-level intervention, take time to reach out to opinion leaders and gate keepers to seek their support and approval before pro- ceeding (Eng et al., 1997).

3. Early in the planning process, recruit members of the priority population to serve on a steering committee for your project. Use the steering committee to help you plan, implement, and evaluate the intervention (Gehlert & Coleman, 2010).

4. Rather than using academicians as the “face” of your program, identify an influential leader in the priority population to fill the role. This will increase acceptance of your program within the priority population.

Currently, the research team is in the midst of training African American women who want to become group leaders. Potential group leaders are learning things such 5. Early in the process, take time to identify existing grass-roots citizen groups that may be aligned with as your purposes. Interact with them before you design 1. How to recruit an apprentice leader. your project and seek their support. Network through 2. How to mentor an apprentice leader. their membership to seek leaders for your intervention. 3. How to recruit potential group members. 4. How to find a location for group meetings. 6. Before starting the design of an intervention, be 5. How to set up the physical environment for small sure to collaborate with agencies and organizations group meetings. in the community that serve your priority population. 6. How to moderate and facilitate small group discus- Such partnerships can expand the reach of your inter- sions. vention and help to identify additional partners and 7. How to handle common challenges in leading small participants (Plescia, Herrick, & Chavis, 2008). group discussion. 8. How to prepare an apprentice leader to start her own 7. In addition to mass media and large group methods, new group. recruit and market your program using personal net - In addition, members of the research team are working with the steering committee to develop the discussion guides and teaching materials for the group meetings. Once these methods and materials are developed, they will be pilot tested with a convenience sample of African American women to help establish their validity and acceptability. Once the methods and materials are finalized and the training workshops are concluded, the first generation of Care Groups will be launched and evaluated.

working methods that are most acceptable to your priority population (Coward, 2005).

8. Use members of the priority population to be the “implementers” of your program. Prior to implementa tion be sure that you provide com prehensive and skill- based training to members of your priority population that will serve as “implementers” (Kratzke, Garzon, Lombard, & Karlowicz, 2010).

9. Plan for long-term sustainability by incorporating a leadership development system that features continual recruiting and replication of leaders. Page 54


Conclusion The community empowerment approach holds great promise in all areas of chronic disease prevention and management. Equipping and empowering grass-roots citizens to become lay health leaders and health advocates is a low-cost, highly sustainable method of reducing racial/ ethnic health disparities. Helping racial/ethnic minority adults become champions for health will have a positive impact for generations and bridge the gap between academia and the community. Through partnerships such as the one described in this article, these adults can become catalysts for positive health change and thereby create healthier communities. References Agency for Healthcare Research and Quality. (2009). AHRQ activities using Community-based participatory research to address health care disparities. Retrieved from http://www.ahrq.gov/research/ cbprbrief.htm

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American Cancer Society. (2012). Cancer facts & figures for African Americans 2011-2012. Retrieved Gallia, K., & Pines, E. (2009). Narrative identity and spirituality of African American churchwomen sur- from http://www.cancer.org/acs/groups/content/@ viving breast cancer survivors. Journal of Cultural epidemiologysurveilance/documents/document/ac Diversity, 16(2), 50-55. spc-027765.pdf Begvand, N. A., Gazarin, M., Howland, J., Trimis, J. L., & Yong, F. (2011). The ethnicity of breast cancer: Cultural discrepancies in diagnosis and treatment decisions. Global Studies Journal, 3, 78-83. Belin, P. L., Washington, T. A., & Greene, Y. (2006). Saving grace: A breast cancer prevention program in the African American community. Journal of Health & Social Work, 31, 73-76. Boyd, A. S., & Wilmoth, M. C. (2006). An innovative community-based intervention for African Ameri- can Women with breast cancer: The Witness Project. Journal of Health & Social Work, 31, 77-80. Brownstein, J. N., Bone, L. R., Dennison, C., Hill, M. N., Kim, M., & Levine, D. M. (2005). Community health workers as interventionists in the prevention and control of heart disease and stroke. American Journal of Preventive Medicine, 29(5 Suppl. 1), 128-133.

Gehlert, S., & Coleman, R. (2010). Using community- based participatory research to ameliorate cancer disparities. Health & Social Work, 35(4), 302-309. Glanz, K., Rimer, B., & Vinswanath, K. (2008). Health behavior and health education: Theory, research, and practice. San Francisco, CA: Jossey-Bass. Hall, C. P., Wimberley, P. D., Hall, J. D., Pfriemer, J. T., Hubbard, E. M., Stacy, A. S., & Gilbert, J. D. (2005). Teaching breast cancer screening to Af- rican American women in the Arkansas Mis- sissippi River Delta. Oncology Nursing Forum, 32, 857-863. Israel, B. A., Coombe, C. M., Cheezum, R. R., Schulz, A. J., McGranaghan, R. J., Lichtenstein, R. J., Bur- ris, A. (2010). Community-based participatory research: A capacity-building approach for policy advocacy aimed at eliminating health disparities. American Journal of Public Health, 100, 2094-2102.

Kidder, B. (2008). P.O.W (Protect Our Women): Results of a breast cancer prevention project targeted to old- Bullock, K., & McGraw, S. A. (2006). A community ca- er African American women. Journal of Social Work pacity-enhancement approach to breast and cervical in Health Care, 47, 60-72. cancer screening among older women of color. Journal of Health & Social Work, 31, 16-25.


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Kim-Godwin, Y. S., Clarke, P. N., & Barton, L. (2001). A model for the delivery of culturally competent community care. Journal of Advanced Nursing, 35, 918-925. Kotler, P., & Clarke, R. N. (1987). Marketing for health care organizations. Englewood Cliffs, NJ: Prentice- Hall. Kratzke, C., Garzon, L., Lombard, J., & Karlowicz, K. (2010). Training community health workers: Fac- tors that influence mammography use. Journal of Community Health, 35, 683-688. LaViest, T. A. (2005). Minority populations & health: An introduction to health disparities in the United States. San Francisco, CA: Jossey-Bass. Lucas County Health Assessment. (2007). Retrieved from: http://www.co.lucas.oh.us/documents/Family Council/Lucas_Assessment_2007[2].PDF Meade, C. D., Menard, J., Martinez, D., & Calvo, A. (2007). Impacting health disparities through community outreach: Utilizing the CLEAN look (culture, literacy, education, assessment, and networking). Cancer Control, 14, 70-77. Minkler, M., & Wallerstein, N. (Eds.). (2002). Commu- nity-based participatory research for health (2nd ed.). San Francisco, CA: Jossey-Bass. Mitchell, J., Lannin, D. R., Mathews, H. F., & Swanson, M. E. (2002). Religious beliefs and breast cancer screenings. Journal of Women’s Health, 11, 907- 915. National Cancer Institute. (2011). Previous version: SEER Cancer Statistics Review, 1975-2008. Retrieved from http://seer.cancer.gov/csr/ 1975_2008/

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Nemcek, M. A., & Sabatier, R. (2003). State of evaluation: Community health workers. Public Health Nursing, 20, 260-270. Nilson, L. (2010). Teaching at its best: A research-based resource for college instructors. San Francisco, CA: Jossey-Bass. Ohio Department of Health. (2010). Cancer incidence surveillance system (OCISS). Retrieved from http:// www.odh.ohio.gov/healthstats/ocisshs/ci_surv1.aspx Plescia, M., Herrick, H., & Chavis, L. (2008). Improv- ing health behaviors in an African American community: The Charlotte Racial and Ethnic Approaches to Community Health project. American Journal of Public Health, 98(9), 1678-1684. Scarinci, I. C., Johnson, R., Hardy, C., Marron, J., & Partridge, E. (2009). Planning and implementation of a participatory evaluation strategy: A viable approach in the evaluation of community-based participatory programs addressing cancer disparities. Evaluation and Program Planning, 32, 221-228. Strycker, L. A., Foster, L. S., Pettigrew, L., Donnelly- Perry, J., & Glasgow, R. E. (1997). Steering committee enhancements on health promotion program delivery. American Journal of Health Promotion, 11(6), 437-440. Virnig, B. A., Baxter, N. N., Habermann, E. B., Feldman, R. D., & Bradley, C. J. (2009). A matter of race: Early-versus late-stage cancer diagnosis. Health Affairs, 28, 160-168. Williams, D., & Jackson, P. (2005). Social sources of racial health disparities. Health Affairs, 24, 325-334.


Armed Campuses: The Current Status of Concealed Guns on College Campuses Karen Teeple, Amy Thompson, and James H. Price

Abstract On April 16, 2007, one of the deadliest college campus shootings in American history occurred on the Virginia Tech campus wherein 32 students and faculty lost their lives to an armed gunman. At Northern Illinois University in 2008, 6 students were killed and 16 wounded in another campus shooting. The response of the pro-gun lobby was to push for more firearms on campuses. Subsequently, there are 9 states that allow individual colleges to decide their own firearm policies on campus. Current legislative activity concerning “guns-on-campus” bills is ongoing in 23 states. This article will discuss the current status of this legislative activity, the pros and cons of firearms on campuses, and the public health implications. Introduction Following the fateful shootings at Virginia Tech in 2007, where 32 students and faculty were killed and 15 were wounded, and then at Northern Illinois University in 2008, where 6 students were killed and 16 wounded, the pro-gun lobby responded by pushing for more guns on all college campuses in our country. In recent years, the pro-gun lobby has mounted a campaign to undo existing gun laws and policies by pushing legislation that would prohibit a college or university from adopting policies regulating firearms on campus (Campaign to Keep Guns Off Campus and Coalition to Stop Gun Violence, 2011). Currently, the majority of the 4,300 colleges and universities in the United States prohibit or restrict the carrying of firearms on their campuses. These gun-free policies have helped make postsecondary educational institutions some of the safest places in the country (Armed Campuses, 2011). The purpose of this article is to examine college campus homicide rates, current legislative efforts to pass concealed carry laws on college and university campuses, proponent and opponent arguments of carrying firearms on campus, and public health implications of firearms on campus. College campuses have traditionally been gun-free zones with an overall homicide rate of .07 per 100,000 persons (U.S. Department of Education, 2011). In comparison, the homicide rate in the US was 5.7 per 100,000 persons and 14.1 for persons ages 17-29 (U.S. Department of Justice, Bureau of Justice Statistics, 2008). In

a retrospective study conducted by the Secret Service’s National Threat Assessment Center, 272 attacks occurred at higher learning institutions after the 2007 Virginia Tech massacre (Drysdale, Modzelski, & Simons, 2010). Also, in this report it was noted that from 1900 to 2008, the number of premeditated incidents of violence involving victims affiliated with an institution of higher learning rose from 1 in the 1900s to 79 in the 1990s and 83 in the 2000s. These numbers are extremely low and the researchers of this study point out that the slight increase may be attributed to higher enrollment numbers and increased media coverage and reporting. Moreover, research suggests that concealed carry laws can negatively impact gun homicide rates (Rosengart et al., 2005). Price, Thompson, and Dake (2004) have conducted research that suggests that in states where there are more guns present, there are more gun deaths. Furthermore, college campus police chiefs do not support the carrying of concealed guns on campus (Thompson, Price, Mrdjenovich, & Khubchandani, 2009). Despite the fact that college campuses have historically provided gun-free environments with low homicide rates, the concerted efforts by pro-gun organizations, especially the National Rifle Association (NRA), to change existing polices to allow the carrying of concealed firearms on campuses remain intensive. Due to this effort, the pro-gun lobby that encourages the enactment of guns-on-campus policies has achieved some success in helping pass such legislation. As a result, 25 two- and four-year colleges across the nation now allow the carrying of firearms on their premises (campus grounds, classrooms, dormitories, etc.) (Armed Campuses, 2011). The four states in which these 25 colleges and universities are located are Colorado, Michigan, Utah, and Virginia (Campaign to Keep Guns Off Campus and Coalition to Stop Gun Violence, 2011). For a comprehensive list of these institutions see Table 1. Methods For this article, a systematic review of the literature was completed using the Google Scholar, Pub Med, and PsycINFO databases and the keywords guns, firearms, campus safety, college campuses, concealed carry, and firearm policy. Research for pros and cons on the presence of concealed carry on college campuses was obtained from


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the Internet by searching for the two leading national student organizations actively involved in this issue: Students for Concealed Carry, and the Students for Gun Free Schools. Additionally, opinions of all involved parties are represented in this article: for example, the Brady Campaign, a leading national gun-control organization; and the NRA, a leading national pro-gun organization.

Investigation of Current Status of Concealed Carry Policies on College Campuses

ees are not allowed to carry concealed weapons, the new law conflicts with a section of Mississippi law that strictly prohibits possessions of firearms on “educational property.” This direct conflict in laws has yet to be resolved in the courts. In Wisconsin, SB 93 allows concealed carry licensees to carry anywhere in the state, including within colleges and universities, yet the law permits colleges and universities to prohibit firearms in “buildings” and “special events” by posting signs at all entrances, stating firearms are prohibited in the building (Campaign to Keep Guns Off Campus and Coalition to Stop Gun Violence, 2011).

According to the Campaign to Keep Guns Off Campus, during the 2011 legislative sessions, guns-on-campus bills had been introduced in at least 23 states. The purported guns-on-campus bills failed or were deferred in 21 states, including Arizona, Idaho, Texas, and Louisiana. However, two states—Mississippi and Wisconsin—have enacted legislation that would allow anyone who possesses a permit to carry loaded concealed handguns on college campuses, including in school buildings, classrooms, sports stadiums, and student dormitories. Although, in Mississippi, HB 506 removed colleges and universities from the list of locations where concealed carry licens-

To date, of the 4,314 colleges and universities in the US, only 25 (0.6%) currently allow concealed handgun permit holders to carry guns on campuses (Armed Campuses, 2011). Although 0.6% is a small percentage that allows concealed carry guns, the potential outcomes of the 2011-2012 legislative session for guns-on-campus bills warrant serious attention. Bills to expedite guns on campuses were introduced in nearly half of the states in our country: Arizona, Arkansas, Colorado, Florida, Georgia, Idaho, Illinois, Kansas, Louisiana, Michigan, Mississippi, Nebraska, Nevada, New Hampshire, New Mexico, North Carolina, Oklahoma, Ohio, Tennessee, Texas, Vir-

Literature Review Findings

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ginia, Wisconsin, and West Virginia. Bills that were introduced in 2011 varied for each state. For instance, in Arizona, where five bills were introduced, the passage of this legislation would have allowed faculty to carry firearms on community college or university property, prohibited colleges or universities from regulating firearms on school property, prohibited educational institutions from regulating firearms on school property, allowed adults to possess loaded firearms in vehicles on school property, and allowed concealed carry weapon licensees to carry firearms on public right of ways. In another example, Ohio’s HB 256 would have completely done away with the requirement for having concealed carry weapon licenses and would have legally allowed guns on public and private college campuses. In spite of the variances in the bills introduced for each of these states, the principal goal of these bills remained unequivocal: to authorize concealed handgun permit holders to carry concealed handguns on college campuses. The push for handguns on campus has resulted in the Campaign to Keep Guns Off Campus approaching college presidents and asking them to sign a petition against firearms on campus. To date, 190 college administrators stepped forward to sign the resolution. For a comprehensive list see Table 2. The Case For or Against Guns on Campus Approving legislation to allow guns on college campuses purposively for the safety of faculty and attending students remains controversial. As in every heated political or social movement, there are proponents and opponents on the constitutional issues: Guns on campuses is no different. Several highly publicized events have sparked contentious social debate on this very issue, including the shootings at Virginia Tech and Northern Illinois, an increase in the number of mass shooting incidents on college campuses, and the push of the gun lobbies to promote guns on campuses. While some gun advocates support the NRA’s position that gun owners can prevent crimes on campuses, the Brady Campaign to Prevent Gun Violence (2011) opines that the NRA’s attempt to force colleges to allow concealed carry handguns will only increase gun violence. Proponents for guns on campuses believe the presence of concealed carry increases campus safety. One particular nationwide group dedicated to legalizing concealed firearms on college campuses is the Students for Concealed Carry (SCC). This group stresses the belief that holders of state-issued concealed handgun licenses should be allowed the same measure of personal protection on college campuses that they enjoy virtually everywhere else, therefore supporting the right of self-defense on campus (SCC, 2012). This organization has compiled a top-five list of reasons to allow concealed carry on campuses: (1) Legally-armed citizens already have

training and experience with firearms and have demonstrated responsibility; (2) Gun-free zones do not work; (3) Allowing concealed carry does not increase risks to a campus population and may even help; (4) Everyone deserves protection; and (5) Colleges cannot protect students (SCC, 2012). According to David Burnett, director of public relations of the SCC, “Gun-free policies are an open invitation to psychopaths . . . signs on the doors are an unenforceable lie that only robs licensed citizens of their ability to defend themselves . . . until colleges can guarantee our safety, they can’t criminalize self-defense” (SCC, 2012). Additional supporters of the guns-on-campus bills are the Gun Owners of America (GOA) and the Citizens Committee for the Right to Keep and Bear Arms (CCRKBA), both leading national gun rights organizations dedicated to preserving the Second Amendment rights of gun owners. Predictably, the NRA, which also advocates for the protection of Second Amendment rights, supports legislation that allows concealed firearms to be carried on campuses nationwide. In an effort to educate the next generation of gun rights advocates on exercising and maintaining their Second Amendment rights, the NRA has developed a comprehensive program for college students named “NRA University.” This two-hour training seminar includes learning more about the NRA, the history of the Second Amendment, the use of guns for self-defense, gun safety, examining the legislative threats to gun rights, and updates on the gun control debate. Additionally, the NRA-ILA staff will travel to a college campus and provide seminar attendees with the tools they will need to become more effective activists in the fight to protect freedom both on and off campus (NRA-ILA, 2011). Opponents of guns on campuses believe the absence of concealed carry increases campus safety. The group known as the Students for Gun Free Schools (SGFS) is a leading national student organization whose motto is “Armed with knowledge – not guns!” This group firmly opposes efforts to force universities and colleges to allow students and faculty to carry concealed weapons. The SGFS believes that students and faculty would be endangered by the presence of concealed handguns for the following reasons: Concealed handguns would detract from a healthy learning environment; more guns on campus would create additional risk for students; shooters would not be deterred by concealed carry permit holders; concealed carry permit holders are not always “law abiding” citizens; and concealed carry permit holders are not required to have law enforcement training (SGFS, 2012). The Brady Campaign, a leading national gun control organization and research center, is also a group fervently opposed to legislation for guns on campuses. “Our schools should be sanctuaries, not armed camps. .


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Table 2. Colleges and Universities That Have Joined the Campaign to Keep Guns Off Campus (As of December 1, 2011) To date, the American Association of State Colleges and Universities (AASCU) and 190 individual colleges and universities in 34 states have signed the resolution. Florida Alabama Alabama A&M University, Interim President Florida A&M University, Associate VP & Dean of Students Alabama State University, President University of Central Florida, President Athens State University, President Georgia Auburn University, President Savannah State University, President The University of Alabama, President Hawaii The University of Alabama-Huntsville, President The University of Hawaii System, President The University of North Alabama, President --University of Hawaii-Manoa The University of West Alabama, President --University of Hawaii-Hilo Arizona --University of Hawaii-West Oahu Arizona Board of Regents, President Idaho --Arizona State University North Idaho College, Lt. Campus Security --Northern Arizona University Illinois --The University of Arizona Southern Illinois University, President California --Southern Illinois University-Carbondale The California State University System, Chancellor --Southern Illinois University-Edwardsville --California State University Bakersfield University of Illinois at Chicago, Chancellor --California State University Channel Islands Indiana --California State University Chico Indiana University-Northwest, Chancellor --California State University Dominguez Hills Indiana University-East, Chancellor --California State University East Bay Purdue University Calumet, Chancellor --California State University Fresno Louisiana --California State University Fullerton University of Louisiana System, President --Humboldt State University --Grambling State University, President --California State University Long Beach --Louisiana Tech University, President --California State University Los Angeles --McNeese State University, President --California Maritime Academy --Nicholls State University, President --California State University Monterey Bay --Northwestern State University --California State University Northridge --Southeastern Louisiana University --California State Polytechnic University, Pomona --University of Louisiana-Lafayette, President --California State University Sacramento --University of Louisiana-Monroe, President --California State University San Bernardino, President Southern University A&M College, Chancellor --San Diego State University, President Maine --San Francisco State University University of Maine at Augusta, President --San Jose State University University of Maine at Fort Kent, President --California Polytechnic State University, San Luis Obispo Maryland --California State University San Marcos Morgan State University, President --Sonoma State University Salisbury University, Chief of University Police --California State University Stanislaus St. Mary’s College of Maryland, President Connecticut University of Baltimore, President Connecticut State University System, Chancellor University of Maryland, President --Central Connecticut State University University of Maryland, Baltimore Co, President --Eastern Connecticut State University Massachusetts --Southern Connecticut State University Massachusetts College of Art & Design, President --Western Connecticut State University Massachusetts Maritime Academy, President Wesleyan University, President Salem State University, President Delaware University of Massachusetts-Amherst, VC Student Affairs Delaware State University, President University of Massachusetts-Lowell, Vice ChancellorUniversity of Delaware, President Worcester State College, President

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Michigan Ramapo College of New Jersey, President Northern Michigan University, President Rowan University, President Oakland University, President Rutgers University-Newark, Chancellor Wayne State University, Assoc. VP New Mexico Minnesota New Mexico Institute of Mining & Technology, President Metropolitan State University, President New Mexico State University-Alamogordo, President University of Minnesota, President New York --University of Minnesota, Twin Cities Alfred State College, President --University of Minnesota, Crookston The College at Brockport, President --University of Minnesota, Duluth The College at Old Westbury --University of Minnesota, Morris Farmingdale State College, President --University of Minnesota, Rochester SUNY Cobleskill, VP Student Life Mississippi SUNY Cortland, President Alcorn State University, Chief of Campus Security SUNY Delhi, President Delta State University, VP Student Affairs SUNY Fredonia, President Jackson State University, President SUNY Geneseo, President Mississippi State University, VP Student Affairs SUNY Institute of Technology, President Missouri SUNY College at Oneonta, President Missouri Southern State University, President North Carolina Northwest Missouri State University, Provost University of North Carolina System, President University of Central Missouri, President --Appalachian State University University of Missouri System, President --East Carolina University, Chancellor --University of Missouri-Columbia --Elizabeth City State University --University of Missouri-Kansas City --Fayetteville State University --University of Missouri-St Louis --NC A&T State University --Missouri University of Science and Technology --North Carolina Central University Montana --NC State University University of Montana, President --UNC Asheville University of Montana Western, Chancellor --UNC-Chapel Hill Nebraska --UNC Charlotte University of Nebraska-Lincoln, Chancellor --UNC Greensboro University of Nebraska-Omaha, Chancellor --UNC Pembroke New Jersey --UNC Wilmington, VC Student Affairs The College of New Jersey, President --UNC School of the Arts Ramapo College of New Jersey, President --Western Carolina University Rowan University, President --Winston-Salem State University Rutgers University-Newark, Chancellor --NC School of Science and Mathematics New Mexico North Dakota New Mexico Institute of Mining & Technology, President Minot State University, President New Mexico State University-Alamogordo, President Ohio Missouri Central State University, President Missouri Southern State University, President Ohio University Northwest Missouri State University, Provost The University of Toledo, VP Student Affairs University of Central Missouri, President Oklahoma University of Missouri System, President Cameron University, President --University of Missouri-Columbia Langston University, President --University of Missouri-Kansas City Northwestern Oklahoma State University, President --University of Missouri-St Louis Oklahoma State University, VP Student Affairs --Missouri University of Science and Technology Southwestern Oklahoma State University, President Montana University of Central Oklahoma, President University of Montana, President Pennsylvania University of Montana Western, Chancellor Cheyney University of Pennsylvania, President Nebraska Kutztown University, President University of Nebraska-Lincoln, Chancellor Lincoln University, Director of Public Safety University of Nebraska-Omaha, Chancellor Millersville University, President New Jersey Shippensburg University, President The College of New Jersey, President Slippery Rock University, President Temple University, President THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012

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South Carolina Clemson University, President Medical University of South Carolina, President South Carolina State University, President University of South Carolina, President University of South Carolina-Aiken, Chancellor University of South Carolina-Beaufort, Chancellor University of South Carolina-Upstate, Chancellor Winthrop University, President Tennessee East Tennessee State University, President The University of Tennessee-Chattanooga, Chancellor The University of Tennessee-Martin, Chancellor Texas LeTourneau University, President Midwestern State University, VP Student Affairs Paul Quinn College, President Southwestern University, Chief-University Police Texas College, President University of the Incarnate Word, Asst. to the President Virginia George Mason University, President

Longwood University, Chief of Police Lynchburg College, President Norfolk State University, President University of Mary Washington, President University of Virginia, President Virginia Polytechnic & State University, President Virginia State University, VP Student Affairs Wisconsin University of Wisconsin System, President --University of Wisconsin-Eau Claire --University of Wisconsin-Green Bay --University of Wisconsin-La Cross --University of Wisconsin-Madison --University of Wisconsin-Milwaukee --University of Wisconsin-Oshkosh --University of Wisconsin-Parkside --University of Wisconsin-Platteville --University of Wisconsin-River Falls --University of Wisconsin-Stevens Point --University of Wisconsin-Stout --University of Wisconsin-Superior --University of Wisconsin-Whitewater

Source: Campaign to Keep Guns Off Campus (www keepgunsoffcampus.org). . After the massacre at Virginia Tech, we should make it harder, not easier, for dangerous people to get guns, and harder to acquire the high-capacity firepower wielded by the Virginia Tech shooter,” states Paul Helmke, president of the Brady Center to Prevent Gun Violence (as cited in Siebel & Rostron, 2007). Additionally, Helmke raises concern about arming students who use drugs and alcohol or exhibit mental health issues or suicidal tendencies as well as the possibility of gun thefts. In “The Case Against Guns on Campus,” Brian Siebel (2008), a senior attorney of the Brady Center Legal Action Project, suggests that guns will only increase the risk of violence on campus, enhance the potential for accidental shootings, and violate the legal duty to provide a safe campus environment. Furthermore, Siebel (2008) discusses the strong connection between college student gun ownership and the increased likelihood of these students engaging in dangerous activities (e.g., engaging in binge drinking, using cocaine or crack, being arrested for a DUI, vandalizing property, and getting in trouble with police). Public Health Implications In the International Association of Campus Law Enforcement Administrators (IACLEA) Position Statement of August 12, 2008, several concerns were raised with respect to public health and the carrying of concealed firearms on college campuses. The IACLEA is concerned that concealed carry laws have the potential to dramatically increase violence on college and university campuses, and that the public’s safety is threatened by student Page 62

gun owners (IACLEA, 2008). According to the position statement, the IACLEA feels strongly that the potential is high for accidental discharge or misuse of firearms at on-campus or off-campus parties where large numbers of students are gathered or at student gatherings where alcohol or drugs are being consumed. The potential for guns to be used as a means to settle disputes might also be realized between or among students (IACLEA, 2008). The IACLEA states that that two thirds of gun-owning college students engage in binge drinking and that this population is more likely than unarmed college students to drink “frequently and excessively” and then engage in risky activities, such as driving while under the influence of alcohol, vandalizing property, and getting into trouble with police (IACLEA, 2008; Miller, Hemenway, & Wechsler, 2002). Moreover, the IACLEA suggests that to allow college students and any other persons to carry concealed weapons on campus may increase homicide and suicide rates in the student population and increase exposure of campus police to gun-related injuries. College students make up a very unique population given the fact that many of them are becoming independent for the first time and are exposed to a myriad of new environmental and social changes. This can result in students having anxiety, depression, and many other mental health issues. As a consequence, each year about 1,100 college students commit suicide and 24,000 attempt sui-


cide (Violence Policy Center, 2000). In addition, research shows that college counseling centers do not ask students that visit the centers about their access to firearms (Price, Mrdjenovich, Thompson, & Dake, 2009). Given the statistics, having guns available in dormitory rooms and on campuses may further increase the likelihood of collegeaged suicides. In the 2007 “No Gun Left Behind” report, regarding college students and suicides, Siebel and Rostron state that “according to a new study by the National Center on Addiction and Substance Abuse at Columbia University, ‘[n]early half of America’s 5.4 million fulltime college students abuse drugs or drink alcohol on binges at least once a month’” (Siebel & Rostron, 2007, p. 7). “A binge-drinking, drug-using student is dangerous enough; let’s not give him or her a gun,” states Siebel and Rostron (2007, p. 7). Discussion The NRA’s desire to sanction college students with some of the powers of professional law enforcement officers may set a dangerous precedent. In Ohio, for example, to receive a permit to carry a concealed weapon, the concealed carry law requires the applicant to take a written examination after 10 hours of instruction on gun safety, handling, and storage with only two hours of gun training on a firing range. The law establishes no criteria for passing the examination and only minimum requirements for the contents of the test (Redmond, 2005). There are also no requirements to regularly train in order to maintain firearm skills. Should a student accidently shoot another student they are not required to carry liability insurance. It should be noted that Ohio has one of the more stringent sets of requirements compared to states like Vermont, which does not require any permit or training to carry a concealed firearm. The 2011 legislative sessions for guns-on-campus bills will continue into 2012 to 2013. The bills that have already passed legislation and now permit concealed carry weapon licensees to carry guns on university or college campuses (e.g., Colorado, Michigan, Utah, and Virginia) may have a profound public health impact on gun-related death, injury, and suicide rates. Thus far, public health data for those campuses that allow concealed carry has yet to be a subject of statistical or sociological studies. Will the loosening of gun regulations on college campuses be deemed a risky epidemiological experiment? We will learn over the next few years whether the laws we had in place had a significant impact in mitigating death and injury from firearms (Drazen, Morrissey, & Curfman, 2008). Future studies on incidents of violence between campuses with concealed carry and those without will help to create clarity over the issue and provide support for legislators making decisions on whether to pass or defeat

bills relating to it (LaPoint, 2010). Moreover, prospective opportunities for data collection by public health specialists may be used to implement educational and safety programs for both concealed carry and non-concealed carry campuses to decrease gun violence there. The social repercussions of the concealed carry debate will certainly affect campus life and will undoubtedly shape the strategic plans of the parents of prospective college students: Will parents be choosing colleges where guns are allowed to be carried on campus grounds, in classrooms, in dormitories, or at sporting events, or will they choose gun-free campuses? Time will tell, but most importantly we must remain cognizant of the legal proceedings for guns-on-campus bills in the coming years as the outcomes may negatively impact the safety of the faculty/student populations at each college or university campus in our nation. References

Armed Campuses. (2011). Colleges and Universities that Allow Guns on Campus: A Guide for Students and Parents. Retrieved December 20, 2011, from http://www.armedcampuses.org Armed Campuses. (2011). Number of Colleges that Allow Guns on Campus. Retrieved December 19, 2011, from www.armedcampuses.org/content/ number-colleges-allow-guns-campus. Brady Campaign to Prevent Gun Violence. (2008). State gun laws - Guns on campus: State battles. Retrieved December 20, 2011, from http://www.bradycam paign.org/stategunlaws/statebattles Drazen, J. M., Morrissey, S., & Curfman, G. (2008). Guns and health. New England Journal of Medi- cine, 359(5), 517-518. Drysdale, D., Modzelski, W., & Simons, A. (2010). Campus attacks: Targeted violence affecting institutions of higher education. Retrieved January 6, 2011, from http://www.inpathways net/ipcnlibrary/ viewBiblio.aspx?aid=9675 International Association of Campus Law Enforcement Administrators. (2008). IACLEA position statement: Concealed carrying of firearms proposals on college campuses. Retrieved December 20, 2011, from http://www.iaclea.org/visitors/PDFs /Con cealedWeaponsStatement_Aug2008.pdf LaPoint, L. A. (2010). The up and down battle for concealed carry at public universities. Journal of Student Affairs, XIX, 16-21.


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Miller, M., Hemenway, D., & Wechsler, H. (2002). Guns Siebel, B. J., & Rostron, A. K. (2007). No gun left and gun threats at college. Journal of American behind: The gun lobby’s campaign to push guns into College Health, 51(2), 57-65. colleges and schools. Washington, DC: Brady Center to Prevent Gun Violence. National Rifle Association, Institute for Legislative Ac- tion. (2011). NRA University – Bring us to Students for Concealed Carry. (2011). Top 5 reasons to your campus this spring! Retrieved March 19, 2012, allow concealed carry on campus. Retrieved from http://www nraila.org/Legislation/Federal/ December 19, 2011, from http://concealedcampus. Read.aspx?id=7188 org/2011/01/top-5-reasons-to-allow-concealed- carry-on-campus/ NRA-ILA Institute for Legislative Action. (2011). NRA University – Bring Us To Your Campus This Students for Gun Free Schools. (2011). Why our cam- Spring! Retrieved December 21, 2011, from http:// puses are safer without concealed handguns. www nraila.org/legislation/federal-legislation/2010/ Retrieved December 20, 2011, from http://www.stu nra-university-bring-us-to-your-camp.aspx dentsforgunfreeschools.org /SGFSWhyOurCampus es-Electronic.pdf Price, J., Mrdjenovich, A. J., Thompson, A., & Dake, J. A. (2009). College counselors’ perceptions and Thompson, A., Price, J., Mrdjenovich, A., & Khub- practices regarding anticipatory guidance on chandani, J. (2009). Reducing firearm-related firearms. Journal of American College Health, violence on college campuses--Police chiefs’ 58(2), 133-139. perceptions and practices. Journal of American College Health, 58(3), 247-254. Price, J., Thompson, A., & Dake, J. A. (2004). Factors associated with state variations in homicide, suicide, U.S. Department of Education. (2011). Campus crime and unintentional firearm deaths. Journal of Com- statistics homicide rates. The Campus Safety munity Health, 29, 271-283. and Security Data Anaylsis Cutting Tool. Retrieved December 21, 2011, from http://ope.ed.gov/security Redmond, I. (2005). Ohio’s concealed carry law: Practi- cal applications and possible revisions. Retrieved U.S. Department of Justice, Bureau of Justice Statistics. January 6, 2012, from http://www.saf.org/jour Uniform Crime Report (2008). Homicide rates in nal/17/ohio.pdf the United States. Retrieved December 21, 2011, from http://bjs.ojp.usdoj.gov Rosengart, M., Cummings, P., Nathens, A., Heagerty P., Maier, R., & Rivara, F. (2005). An evaluation of Violence Policy Center. (2000). Unsafe in any hands: state firearm regulations and homicide and suicide Why America needs to ban handguns. Retrieved death rates. Injury Prevention, 11(2), 77-83. December 20, 2011, from http://www.vpc.org/stud ies/unsafe htm Siebel, B. J. (2008). The case against guns on campuses. George Mason University Civil Rights Law Journal, 18, 319.

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Contributors Alyssa Arey Amber T. Burtis, MLIS James Madison University Southern Illinois University Carbondale Sigma Chapter Alpha Alpha Chapter 3914 Red Leaf Court Carbondale, IL 62901 Point of Rocks, MD 21777 Phone: 419-283-7824 Phone: 301-471-2893 E-mail: [email protected] E-mail: [email protected] Roberta E. Emetu, MLS Casey Bailey Indiana University James Madison University Eta Sigma Gamma Nu Chapter Sigma Chapter 1025 E. 7th St. 182 Worthington Lane Bloomington, IN 47401 Bluemont, VA 20135 Phone: 414-678-8933 Phone: 540-771-0777 E-mail: [email protected] E-mail: [email protected] Heather Gentile James W. Ball, M.S. James Madison University Southern Illinois University Carbondale Sigma Chapter Alpha Alpha Chapter 175 Doretta Street 901 E. Walnut St. #10 River Vale, NJ 07675 Carbondale, IL 62901 Phone: 973-769-6459 Phone: 608-385-3011 E-mail: [email protected] E-mail: [email protected] Sharlene A. Gozalians, DrPH(c), MPH, CHES Thilina Bandara, BSc Loma Linda University University of Saskatchewan 1844 Sherer Lane Epsilon Delta Chapter Glendale, CA 91208 Phone: 306-374-0873;Fax: 306-966-7920 Phone: 818-521-1844 E-mail: [email protected] E-mail: [email protected] Matthew R. Bice, M.S. Southern Illinois University Carbondale Alpha Alpha Chapter P.O. Box 2744 Carbondale, IL 62902 Phone: 432-557-5657 E-mail: [email protected]

Timothy R. Jordan, Ph.D., M.Ed. Associate Professor The University of Toledo Iota Chapter 2801 West Bancroft St. Toledo, OH 43606 Phone: 419-530-4725; Fax: 419-530-4759 E-mail: [email protected] Yelena Bird, MD, PhD., MPH Assistant Professor Bethany A. Kies, MPH University of Saskatchewan Southern Illinois University Carbondale Epsilon Delta Chapter Alpha Alpha Chapter Phone: 306-966-8432; Fax: 306-966-7920 Carbondale, IL 62901 E-mail: [email protected] Phone: 608-304-1401 E-mail: [email protected] Chaundra M. Bishop Western Illinois University Michael Eric McCown, DVM, DACVPM Pi Chapter University of North Florida 8922 S Cregier Alpha Gamma Chapter Chicago, Illinois 60617 UNF Drive Phone: 773-332-1933 Jacksonville, FL 32224 E-mail:[email protected] Phone: 904-553-6511 E-mail: [email protected]


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Contributors John Moraros, MD, PhD., MPH, CHES Assistant Professor University of Saskatchewan Epsilon Delta Chapter Phone: 306-966-8578; Fax: 306-966-7920 Email: john [email protected]

James H. Price Ph.D., MPH Professor Emeritus The University of Toledo Iota Chapter [email protected]

Alex T. Ramsey, M.A. Kayla O’Connell Southern Illinois University Carbondale James Madison University Alpha Alpha Chapter Sigma Chapter 1125 Lincoln Drive, Mail Code 6502 23 Wilcox Road, Milford Carbondale, IL 62901 Connecticut 06460 Phone: 573-225-0046 Phone: 203-556-7995 Email: [email protected] Email: [email protected] Jennifer Marie Richards, PT Rachel Otto University of North Florida James Madison University Alpha Gamma Chapter Sigma Chapter 1 UNF Drive 2606 Greene Lane Jacksonville, FL 32224 Fallston, MD 21047 Phone: 904-448-1671 Phone: 410-692- 9539 Email: PTDiver@comcast net E-mail: [email protected] Theresa M. Enyeart Smith Heather Colfax Parth Assistant Professor University of North Florida James Madison University Alpha Gamma Chapter Sigma Chapter Faculty Sponsor 1 UNF Drive Phone: (540) 568-3951 Jacksonville, FL 32224 Karen Teeple Phone: 904-262-7608 The University of Toledo E-mail: parth [email protected] Iota Chapter Erin Payne 2801 West Bancroft St. James Madison University Toledo, OH 43606 Sigma Chapter Phone: 419-530-4171; Fax: 419-530-4759 2098 W. Courthouse Road [email protected] Crewe, VA 23930 Amy Thompson, Ph.D., M.S.Ed. Phone: 434-294-2487 Associate Professor Email: [email protected] The University of Toledo Iota Chapter Faculty Advisor Erica Payton, MPH, CHES 2801 West Bancroft St. The University of Toledo Toledo, OH 43606 Iota Chapter Phone: 419-530-4171; Fax: 419-530-4759 2801 West Bancroft St. Email: [email protected] Toledo, OH 43606 Phone: 419-530-4725; Fax: 419-530-8521 Email:[email protected]

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Reviewers Dr. Trent Applegate Indiana University

Dr. Fredanna M’Cormack Coastal Carolina University

Dr. Elissa Barr University of North Florida

Ms. Julie Merten University of North Florida

Dr. Rebecca Brey Ball State University

Dr. Sharon Morrison University of North Carolina at Greensboro

Dr. Lydia Burak Bridgewater State University

Ms. Sharon Murray RMC Health

Dr. Jeffrey Clark Ball State University

Ms. Deborah Owen University of North Florida

Dr. Ted Coleman California State University, San Bernardino

Dr. Lisa Pealer University of Florida

Dr. Carolyn Cox Truman State University

Dr. Judy Perkin University of North Florida

Dr. Lee Crandall Clemson University

Dr. Kathleen Phillips Eastern Illinois University

Dr. Sharon Desmond University of Maryland

Dr. Keisha Robinson Youngstown State University

Dr. Amanda Divin Western Illinois University

Dr. Seronda Robinson North Carolina Central University

Dr. Virginia Dodd University of Florida

Ms. Julie Schafer University of North Florida

Dr. Theresa Enyeart Smith Dr. Denise Seabert James Madison University Ball State University Dr. Barbara Hernandez Lamar University

Dr. Alan Sofalvi SUNY Cortland

Dr. Andrea Hope Monmouth University

Dr. Amy Thompson The University of Toledo

Dr. Edessa Jobli University of North Florida

Ms. Holly Turner Moses University of Florida

Dr. Tammie Johnson University of North Florida

Dr. Ashley Walker Georgia Southern University

Dr. Georgia Keeney Dr. Robert Weiler University of Minnesota-Duluth University of Florida Dr. Erin Largo-Wight University of North Florida

Dr. Deitra Wengert Towson State University

Dr. Jeffrey Lennon Dr. Kelly Wilson Liberty University University of Texas- San Marcos THE HEALTH EDUCATION MONOGRAPH SERIES, Volume 29, Number 2, 2012

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ETA SIGMA GAMMA CL325 2000 University Avenue Muncie, Indiana 47306 CHANGE SERVICE REQUESTED

ETA SIGMA GAMMA CL325 2000 University Avenue Muncie, Indiana 47306 CHANGE SERVICE REQUESTED

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