Apr 27, 2009 - My committee members: Pam, like Serdar, you have been part of every level of my ... teacher of nurses, and now you've taught me again. I thank ...... The racial divide in U.S. electoral politics has historically trumped all others,.
HEALTH, ACCESS TO CARE, AND POLICY PRIORITIES: DETERMINANTS OF POLITICAL PARTY AFFILIATION
BY MELISSA J. SCHMIDT B.S., Decker School of Nursing, Binghamton University, 1993 M.S., Decker School of Nursing, Binghamton University, 1999
DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Rural Nursing in the Graduate School of Binghamton University State University of New York 2009
UMI Number: 3371619 Copyright 2009 by Schmidt, Melissa J. All rights reserved
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iii
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Rural Nursing in the Graduate School of Binghamton University State University of New York 2009
April 27, 2009 A. Serdar Atav, Decker School of Nursing, Binghamton University Pamela Stewart Fahs, Decker School of Nursing, Binghamton University David Brown, Decker School of Nursing, Binghamton University David Cingranelli, Harpur College, Binghamton University
iv ABSTRACT Introduction: In an effort to explore the intersection of health, health care, policy priorities, and political party affiliation, evaluate the applicability of various theories of voting behavior in explaining this intersection, as well as to add to the bodies of knowledge on rural health and nursing, an investigation was undertaken into these areas. Review of the Literature: The literature on voting behavior, rural health, self-reported health, health disparities, access to health insurance, health care as a policy priority, universal health care, U.S. political history, and nursing involvement in policy and politics was explored. As a result of this review, a conceptual model of determinants of voting behavior was synthesized. Methods: The conceptual model was operationalized with variables of interest to rural nursing, and the relationships proposed in the model formed the basis for twenty-eight research questions. A secondary data set of 2006 polling data was analyzed using binary and multiple logistic regression in this non-experimental correlational study. Results: The sample was described, and then the research questions were answered. The constructs in the conceptual model were supported as determinants of political party affiliation, and a number of proposed relationships were confirmed statistically. Most significantly, income had the strongest and most pervasive influence on all of the other study variables and concepts. Additional findings related to the rural respondents in the study were also described. Discussion: The implications of the findings for understanding voting behavior, rural health, nursing education and practice, and health care policy, were all explored, as were limitations and recommendations for future research. This research was a unique inquiry
v combining concepts from nursing, health, health care, policy, politics, and voting, and suggests avenues for future endeavors in nursing research.
vi DEDICATION
This work is dedicated to all the women in my family who came before me. To my great-grandmothers, who toiled in anonymity on farms and in textile mills, and who came across oceans to start new lives for their children. To my grandmothers, who typed, and filed, and cleaned factories at night, who ran bake sales and the school board with equal skill. To my mother, who was the first woman in her family to go to college. Because of you, my daughters can say their mother was the first woman in her family to get a PhD. Because of you, my daughters can do anything. Thank you.
vii ACKNOWLEDGEMENTS The author would like to thank: My children Brontë, Mikhail, Annelise, Bridget, and Molly, who ate frozen dinners and learned to do laundry, and only sometimes made me feel guilty about it. My mother Joan and step-father Ned, who logged countless miles transporting children; not to mention feeding, entertaining, and generally putting up with them; not to mention feeding, entertaining and generally putting up with me. My father Jim, who also logged countless miles and hours helping with the kids (and the house, groceries, dogs, cats, garden……); made me fried eggs and coffee whenever I asked, and also has a blessedly inexhaustible capacity for talking about politics and policy. My chair, Serdar. There are no words which can suffice, so when I say thank you, I hope you understand that those words carry with them all the gratitude that my heart can hold. You have been my teacher, mentor, role model, and friend. You have given me faith, hope, confidence, inspiration, and perseverance. More than anything, though, you have lent me, in equal share and when I needed them most, your ears, your shoulder, your humor, and your kindness. My committee members: Pam, like Serdar, you have been part of every level of my nursing education. You taught me how to be a nurse, and then you taught me how to be a teacher of nurses, and now you’ve taught me again. I thank you from the bottom of my heart for all of those things. You are an inextricable part of my professional development, and for that I am both lucky and eternally grateful. David B.: You broadened my horizons, piqued my interest, made me think, and forced me to learn how to write concisely. My education and my life are both richer for having known you, and your perspective, experience, and insights have been invaluable in this endeavor. Thank you so very much. David C.: Although we only met once, you were preceded by your wonderful reputation. I am both honored and privileged to have you on my committee. Thank you so much for your time, involvement, and input. My colleagues at Tompkins Cortland Community College: you have been inordinately patient and flexible with me over the last three years. Your support has buoyed me along, and your excellence inspires me daily. Thank you.
viii TABLE OF CONTENTS LIST OF TABLES ............................................................................................................ xii LIST OF FIGURES ......................................................................................................... xiv CHAPTER ONE: INTRODUCTION ..................................................................................1 Introduction ..............................................................................................................1 Background ..............................................................................................................3 Rationale ..................................................................................................................6 Goal and Purpose ...................................................................................................11 Research Questions ................................................................................................12 Scope and Limitations............................................................................................19 Significance and Implications ................................................................................20 Conclusion .............................................................................................................22 CHAPTER TWO: REVIEW OF THE LITERATURE .....................................................24 Introduction ............................................................................................................24 Voting Theories .....................................................................................................24 Individual Identity..................................................................................................27 Self-reported Health Status ....................................................................................28 Demographic Influences on Self-reported Health .....................................30 Disparities in Health Status ........................................................................31 Health Status and Voting ...........................................................................34 Access to Health Insurance ........................................................................35 Rural Residence and Health .......................................................................39 Issue Ownership .....................................................................................................42 Health Care as a Political Issue ..................................................................44 Party Preference for Health Care Issues ....................................................47 Universal health Care .................................................................................48 Combination Theories ............................................................................................52 Additional Influences on U.S. Voting Behavior ....................................................52 Entertainment .............................................................................................53 Media .........................................................................................................53 Advertising .................................................................................................53 The Role of Nursing in Public Policy ....................................................................55 The American Nurses Association.............................................................55 Nursing Literature ......................................................................................56 Conceptual Model ..................................................................................................58 Conclusion .............................................................................................................59 CHAPTER THREE: METHODS ......................................................................................61 Introduction ............................................................................................................61 Conceptual Model ..................................................................................................61 Design ....................................................................................................................63 Data Set ..................................................................................................................64
ix Sample....................................................................................................................66 Instrument ..............................................................................................................69 Definitions..............................................................................................................73 Demographic Variables .............................................................................74 Health Care Variables ................................................................................77 Issue Ownership Variables ........................................................................79 Voting Behavior .........................................................................................80 Research Questions and Hypotheses .....................................................................80 Relationships among Identity Variables ................................................................81 Relationships among Issue Ownership Variables ..................................................84 Relationships between Identity and Issue Variables ..............................................85 Identity, Issue Ownership, and Voting Behavior ...................................................90 Data Analysis .........................................................................................................91 Conclusion .............................................................................................................92 CHAPTER FOUR: RESULTS ..........................................................................................94 Introduction ............................................................................................................94 Conceptual Model ..................................................................................................94 Descriptive Analysis of the Sample .......................................................................95 Individual Identity Variables .....................................................................95 Geography ......................................................................................96 Gender and Family.........................................................................97 Age .................................................................................................98 Race................................................................................................99 Education .....................................................................................100 Employment .................................................................................101 Income..........................................................................................102 Self-reported Health .....................................................................104 Access to Health Insurance ..........................................................104 Satisfaction with Health Care ......................................................105 Issue Ownership Variables ......................................................................105 Political Importance of Health Care ............................................105 Support for Universal Health Care ..............................................105 Party Preference for Managing Health Care ................................105 Voting Behavior .......................................................................................106 Political Party Affiliation .............................................................106 Ideology .......................................................................................108 Answers to Research Questions ...........................................................................109 Relationships among Individual Identity Variables .................................112 Question 1 ....................................................................................112 Question 2 ....................................................................................115 Question 3 ....................................................................................117 Question 4 ....................................................................................119 Question 5 ....................................................................................123 Question 6 ....................................................................................124 Summary of Relationships between Individual Identity Variables .........126
x Relationships among Issue Ownership Variables ....................................127 Question 7 ....................................................................................127 Question 8 ....................................................................................128 Question 9 ....................................................................................129 Question 10 ..................................................................................129 Interpretation: Issue Ownership Variables...............................................130 Relationships between Individual Identity and Issue Ownership ............131 Question 11 ..................................................................................132 Question 12 ..................................................................................135 Question 13 ..................................................................................135 Question 14 ..................................................................................136 Interpretation: Political Importance of Health Care.................................136 Question 15 ..................................................................................137 Question 16 ..................................................................................140 Question 17 ..................................................................................140 Question 18 ..................................................................................141 Interpretation: Party Preference for Managing Health Care ....................142 Question 19 ..................................................................................142 Question 20 ..................................................................................144 Question 21 ..................................................................................145 Question 22 ..................................................................................145 Interpretation: Support for Universal Health Care...................................146 Summary of Relationships between Identity and Issue ...........................147 Political Party Affiliation .........................................................................148 Question 23 ..................................................................................148 Question 24 ..................................................................................150 Question 25 ..................................................................................151 Question 26 ..................................................................................151 Question 27 ..................................................................................152 Question 28 ..................................................................................152 Interpretation and Summary of Variables Influencing Party ...................153 Summary Analysis of Determinants of Political Party Affiliation ......................155 Additional Findings .............................................................................................156 Description of the Sample by the polling Organization.......................................158 Summary of Unexpected Findings.......................................................................163 Conclusion ...........................................................................................................165 CHAPTER FIVE: DISCUSSION ....................................................................................166 Introduction ..........................................................................................................166 Voting Behavior ...................................................................................................169 Rural Health .........................................................................................................175 Nursing Advocacy ...............................................................................................177 Nursing Education ...................................................................................170 Nursing Practice .......................................................................................180 Health Care Policy ...............................................................................................182 Limitations ...........................................................................................................185
xi Conclusions ..........................................................................................................186 APPENDIX A ..................................................................................................................188 APPENDIX B ..................................................................................................................190 APPENDIX C ..................................................................................................................192 REFERENCES ................................................................................................................203
xii LIST OF TABLES Table 1: Voter Turnout, Poverty Rates, and Income Disparity by Country ........................4 Table 2: Research Questions Grouped by Variables ...........................................19, 81, 110 Table 3: Geographic Distribution of the Sample ...............................................................97 Table 4: Gender and Family Distribution of the Sample ...................................................98 Table 5: Racial Distribution of the Sample......................................................................100 Table 6: Educational Distribution of the Sample.............................................................101 Table 7: Employment Distribution of the Sample ...........................................................102 Table 8: Income Distribution of the Sample ....................................................................104 Table 9: Comparison of Party-Affiliated and Non-Party-Affiliated Respondents ..........107 Table 10: Ideological and Party Distribution of the Sample............................................108 Table 11: Factors Influencing Likelihood of Not Having Health Insurance ...................113 Table 12: Demographic Influences on Likelihood of Having Poor Self-Reported Health ...............................................................................................................116 Table 13: Access Influence on Likelihood of Having Poor Self-Reported Health..........118 Table 14: Demographic Influences on Likelihood of Dissatisfaction with Quality ........120 Table 15: Demographic Influences on Likelihood of Dissatisfaction with Cost .............121 Table 16: Access Influence on Likelihood of Dissatisfaction with Quality ....................123 Table 17: Access Influence on Likelihood of Dissatisfaction with Cost .........................124 Table 18: Health Influence on Likelihood of Dissatisfaction with Quality .....................124 Table 19: Health Influence on Likelihood of Dissatisfaction with Cost .........................125 Table 20: Summary of Significant Relationships among Individual Identity Variables…………. .........................................................................................126 Table 21: Issue Influences Opinion of Health Care as a Political Issue ..........................127
xiii Table 22: Issue Influences on Support for Universal Health Care ..................................128 Table 23: Influence of Support for Universal Care on Democratic Party Preference for Managing Health Care .............................................................129 Table 24: Influence of Policy Priorities on Democratic Party Preference for Managing Health Care .....................................................................................130 Table 25: Summary of Relationships among Issue Ownership Variables .......................131 Table 26: Identity Influences on Likelihood of Ranking Health Care Politically Important ........................................................................................133 Table 27: Identity Influences on Likelihood of Democratic Party Preference for Managing Health Care .....................................................................................138 Table 28: Identity Influences on the Likelihood of Supporting Universal Health Care ......................................................................................................143 Table 29: Summary of Relationships between Identity and Issue Ownership Variables ..........................................................................................................147 Table 30: Identity Factors Influencing Likelihood of Democratic Party Affiliation .......149 Table 31: Issue Factors Influencing Likelihood of Democratic Party Affiliation ...........152 Table 32: Summary of Factors Influencing Democratic Party Affiliation ......................154 Table 33: Summary Analysis of Determinants of Democratic Party Affiliation.............155 Table 34: Type of Insurance by Place of residence .........................................................156 Table 35: Marital Status, Age, Race, and Employment by Place of residence................157 Table 36: Ideology by Place of residence ........................................................................157
xiv LIST OF FIGURES Figure 1: Theorized Conceptual Model .....................................................................58, 167 Figure 2: Operationalized Conceptual Model ....................................................63, 109, 168 Figure 3: Conceptual Model of Significant Results .........................................................171
CHAPTER ONE 1
CHAPTER ONE: INTRODUCTION OF THE PROBLEM Introduction American civic life has long been plagued by declining voter participation in elections. The fact that approximately fifty percent of eligible voters abstain from expressing their electoral preference almost certainly has an impact on the political process and public policy (Bowler, Brockington, & Donovan, 2001), but efforts to understand voter participation over the last few decades have generally failed to reach consensus (Coate & Conklin, 2004; McDonald & Popkin, 2001). Voter turnout in presidential elections, generally the highest of any election cycle, has decreased dramatically over the last century, from approximately eighty percent in 1900 to under fifty percent in some elections during the last few decades (Newsweek, 2009). While there is some variation across elections, most notably a significant increase in voter turnout in the 2008 presidential election, in which sixty-one percent of those eligible did in fact vote (Newsweek, 2009), the overall trend has been one of decline. Although theories abound, from apathy to disenchantment to disgust, there is no concrete explanation for diminishing electoral participation. In non-presidential elections, turnout is generally even lower. Paradoxically, this downward trend in turnout has occurred alongside a simultaneous explosion in the availability and ease of access to information about politics and politicians via twenty-four news channels, satellite radio, and the internet.
CHAPTER ONE 2 At any given time, upwards of 45 million Americans, or between fifteen and sixteen percent of the population, lack health insurance (U.S. Census Bureau, 2008). Over nine million of those without health insurance are children, despite effort to improve access with programs (Holahan, Dubay, & Kenney, 2003) like State Child Health Insurance Programs (SCHIP), and a disproportionate number of both children and adults without insurance live in rural areas. In spite of the large number of individuals left without insurance coverage by the current U.S. health care delivery system, as a nation we spend more both per capita and as a percentage of gross domestic product on health care than any other industrialized nation. Inflation of health care costs has outpaced inflation in other areas of the economy, and most industries now spend more on health care than on raw materials. Over the last decade, health insurance premiums have increased four times faster than wages (Monegain, 2009). Despite spending so much on health care, as a nation we are less healthy than our peer nations, which consistently outperform the U.S. in all measures of health status. For example, in a ranking of rates of infant mortality per 100,000 live births, Sweden ranks second at 2.75, France ranks seventh at 3.36, Australia ranks twenty-seventh at 4.82, the United Kingdom ranks thirtieth at 4.93, and the U.S. ranks forty-second, at 6.30 (Central Intelligence Agency, 2009). In a ranking of life expectancy in years the U.S. ranks even lower. Comparing the same countries, Australia is seventh at 81.53, France is ninth at 80.87, Sweden is tenth at 80.74, The United Kingdom is thirty-sixth at 78.85, and the U.S. is forty-sixth at 78.14 (Central Intelligence Agency, 2009). These facts, combined with concerns about quality,
CHAPTER ONE 3 a chronic nursing shortage, and significant health disparities among socioeconomic and racial groups, ensure that health care reform remains a perennial issue on the political landscape. Background Certain characteristics of individuals, such as education, income, and strength of political party identification; and of elections, such as closeness of the race, tend to influence voter turnout (Fedderson, 2004; Green & Shachar, 2000). Other potential influences include perceived costs and benefits of voting, as well as perceived likelihood that one’s vote will influence the outcome (Fedderson, 2004; Kanazawa, 2000). Being disabled has also been found to negatively impact likelihood to vote, particularly for the elderly (Schur, Shields, Kruse, & Schriner, 2002; Schur, Shields, & Schriner, 2005). Research also suggests that voter turnout and the existence and power of liberal political institutions, including liberal political parties, policy traditions, and interest groups, and the resulting programs, have a direct negative influence on poverty rates in developed countries (Brady, 2003). Ironically, however, increasing income inequality tends to decrease, rather than increase, voter support for redistributive welfare programs (Moene & Wallerstein, 2001). A comparison of the U.S with a few of its peer nations provides some perspective on these issues. The table (Table 1) below shows voter turnout in a nation’s most recent election in percentage of eligible voters; the percentage of the population living below the nation’s poverty level; and the GINI, an international measure of income disparity, in which lower numbers indicate more equal income distribution and higher numbers indicate less equal income distribution.
CHAPTER ONE 4 Table 1: Voter Turnout, Poverty Rates, and Income Disparity by Country Voter Turnout Poverty Rate Income Disparity ___________________________________________________ Percent Percent GINI Country Australia 82.4 Sweden 80.6 France 76.8 U.S. 58.3 (Central Intelligence Agency, 2009)
12.1 6.3 11.2 15.4
35.2 23 28 45
In the U.S, then, with relatively lower voter turnout, higher poverty rates, and higher income inequality, compared to a selection of peer nations; as well as historically moderate, rather than liberal, political institutions, the implications for health care reform tend to suggest that lack of access to health care of a large number of citizens will motivate neither voter turnout nor support for a redistributive system of health care provision. Understanding and capturing the votes of those people who do participate in the electoral process is a complex and expensive business. Political strategists, employed not just by candidates but by political parties and interest groups, have sought increasingly sophisticated ways of identifying and influencing likely voters. Much about voting behavior and political party affiliation, however, remains unclear, otherwise political parties would recruit and prepare the “ideal” candidate, like a commercial product, and victory at the polls would be the result of simple calculations. A variety of factors influence voting behavior and political party affiliation, some of which are manipulable and some of which are not. Effective public education campaigns have been correlated with changing public opinion about key issues, such as national health policy (Koch, 1998; Lupia, 1994). Democrats, women, the young, non-
CHAPTER ONE 5 whites, those with lower educational levels, and urban residents are more likely to favor universal health care (Oliver, 2004; Steiber & Ferber, 1981). Differences in voting trends and political party prevalence between various regions of the country, as well as between rural and urban voters, have been noted by many researchers going back decades (Bullock, 1996; Bullock, Hoffman, & Gaddie, 2005; Knickrehm & Bent, 1988; Murauskas, Archer, & Shelley, 1988; Swauger, 1980; Webster, 1992). In addition, strength of conviction about a particular issue has been linked to voting behavior and party affiliation (Carmines & Layman, 1997; Cutler, 2002; Gill, Crosby, & Taylor, 1986; Nadeau & Lewis-Beck, 2001; Petrocik, 1996). Numerous theories seek to explain these electoral choices (Catt, 1996). Some emphasize political party identification, e.g. always voting for the Democratic candidate. Others explore the allure of the specific individuals running for office. This has become increasingly important in American elections, as few voters in fact cross party lines to vote, and so the battle is really for the relatively small percentage of voters who identify themselves as independent and are true swing voters. Since they don’t have party allegiance, presumably they will be swayed by the individual appeal of a particular candidate. Alternatively, voters may have key issues, or perhaps a single issue, about which they feel very strongly, and will vote for the candidate who most closely mirrors their own position on that issue, regardless of party or their position on other issues. Some theories speculate that these issues are more likely to be those of immediate personal concern, whereas others explore the motivation of a larger social consciousness, since
CHAPTER ONE 6 some voters consistently cast votes that would seem to be in conflict with their individual self-interest. How, therefore, and why people choose a political party or particular candidate are the key questions in the issue of voting behavior. Clearly, voting is a complex issue. How and why voters choose a particular party or candidate to support is the topic of endless discussion by pundits, but the particular alchemy of winning an election resists easy dissection and classification. Rationale There is broad support in the literature for the role of nurses and nursing in advocating for health-related policies which benefit our patient populations (Deschaine & Schaffer, 2003; Rubotzky, 2000), and advocacy has long been considered one of the roles and ethical responsibilities of nursing practice. In fact, the Code of Ethics, Scope and Standards, and Social Policy Statements of the American Nurses Association (ANA) all delineate political advocacy as a professional responsibility for nurses (ANA, 2001, 2003, 2004). In addition, professional nursing organizations such as the ANA, state nurses associations, and others, have a long history of involvement in politics and policy. These professional organizations routinely endorse candidates for political office, and donate money to campaigns via political action committees. They also issue position papers related to policy issues and lobby for policy change. These policies are not strictly limited to those directly impacting professional nursing practice, but encompass a wide variety of health and social issues which affect the population as a whole. For example, the ANA has long endorsed the concept of universal health care as the best solution for the American health care system (ANA, 2003).
CHAPTER ONE 7 Health status, as well as peoples’ perception of their own health, is related to a variety of factors besides simple physical illness or lack thereof. The context in which people live is an essential contributor to their health, both as they perceive it and in objectively measurable ways. Lower actual and self-reported health status has been linked to lack of health insurance (Bharmal & Thomas, 2005), living in a poor community (Molinari, Ahern, & Hendryx, 1998), lower educational level (Mirowsky & Ross, 2000; Regidor et al., 1999; Ross & Woo, 1996; Weinrich, Weinrich, Priest, Fodi, & Talley, 2001), rural residence, poverty, and unemployment (Zimmer, Natividad, Lin, & Chayovan, 2000), being non-white (Blake & Darling, 2000; Clemente & Sauer, 1976; Mutchler & Burr, 1991; Weinrich et al., 2001), low socio-economic status (Johnston & Ware, 1976; Mutchler & Burr, 1991; Weinrich et al., 2001), and being foreign-born (Huang, Yu, & Ledsky, 2006). The U.S. consistently spends more on health care, by any measure, than any other country, and yet remains the only industrialized nation with no nationalized or universal system of health insurance provision (Blank & Burau, 2004). As a result, upwards of 45 million Americans, representing over fifteen percent of the population, lack health insurance over the course of a typical year. The group of Americans lacking health insurance, however, is not a static number, as many Americans move in and out of being uninsured in any given year. Recent data suggests that when this issue is examined longitudinally, 86.7 million Americans, approximately a third of those under age 65, have gone without health insurance at some point in the last two years (Dunham, 2009).
CHAPTER ONE 8 There are 262 million Americans under the age of 65, and thirty-three percent of them report that for some period in the last two years they did not have health insurance, including 60.1 million adults and 26.6 million children. Seventy-five percent reported going without insurance for at least six months, and sixty percent for at least nine months (Dunham, 2009). Income was a significant factor in the likelihood of going without insurance for some period of time. Fifty-two percent of individuals and families with incomes between the federal poverty line, 21,200 dollars per year for a family of four, and twice the federal poverty line, went without health insurance for some period of time during 2007 and 2008 (Dunham). Because of the large number of Americans who lack access to routine health care, compared to other developed nations the U.S. fares relatively badly in all measures of the health of nations (Blank & Burau, 2004). Universal health care, on the other hand, is associated with longer life expectancy, more years of healthy life, and lower infant and maternal mortality, among other measures of the health of a nation (Blank & Burau). Rural residents, and those with lower income and educational levels, are more likely to be uninsured and in poor health (Johnston & Ware, 1976; Ross & Mirowsky, 2000; Ross & Wu, 1996; Ziller, Coburn, & Yousefian, 2006). The uninsured are more likely to face economic hardship, and to suffer poor health and disability, than those with health insurance (Huang et al., 2006; Quesnel-Vallee, 2004; Ross & Mirowsky, 2000). Health insurance has been found to be a strong predictor of peoples’ perception of their own health status (Bharmal & Thomas, 2005), and in turn peoples’ perception of their own health status is significantly related to both actual health status (Fielding & Li, 1997; Finkelstein, 2000; Idler & Benyamini, 1997; Lee, 2000) and reported quality of life
CHAPTER ONE 9 (Clemente & Sauer, 1976; Cox, Spiro, & Sullivan, 1988; Hampton & Marshall, 2000). The uninsured also receive inferior management of both acute and chronic illnesses than those with insurance, resulting in poorer outcomes and increased rates of preventable hospitalizations, and therefore incurring social and financial costs (Hicks et al., 2006; Saha, Solotaroff, Oster, & Bindman, 2007). Rural residents are more likely to be poor, lack health insurance, have low educational attainment, and be under-employed than their non-rural peers (Brown & Swanson, 2003; Jensen, Findeis, Hsu, & Schachter, 1999). In addition, the majority of persistent poverty counties are rural (Brown & Swanson, 2003). Further, although rural communities are generally less racially and ethnically diverse than non-rural communities, there are persistent pockets of African-American rural poverty, and the population of foreign-born immigrants is increasing in rural areas as well (Brown & Swanson). Residents of rural areas are also more likely than non-rural residents to lack access to health care. This includes physical access, limited by isolation, distance, lack of transportation, and unavailability of services related to economies of scale (Lee & Winters, 2006; Wakefield, 2005). It also includes financial access, due to higher rates of poverty, under-employment, low educational attainment, and lack of health insurance (Bushy, 1990; Larson & Hill, 2005). Given the relative disadvantages that rural residents face, and the fundamental intertwining of health and policies, voting behavior, such as political party affiliation, emerges as an important problem in analyzing and addressing the health needs of that population. Private solutions are unlikely to ameliorate these problems, since there is a
CHAPTER ONE 10 smaller base to support charitable organizations, and the lack of economies of scale makes provision of needed services unlikely to be profitable. Rural residents therefore stand to benefit disproportionately from policy solutions which address universal health care. Spiraling costs, quality concerns, an ongoing nursing shortage, the growth of managed care, and concern about financing Medicare as the Baby Boom generation nears retirement are among other health care issues of grave concern, keeping health care on the list of issues on which a significant portion of the electorate say they are focused, and granting it a seemingly perennial spot on the national political landscape. Although at times it attains prominence, such as during debate over the 1994 Clinton health care initiative, and at others seems to recede into the distance, it never completely disappears, most likely because it has never been resolved. Universal health care has been a plank in the Democratic Party platform since 1948, every attempt to pass some form of universal coverage has been initiated by a Democratic president or member of Congress, and the Republican Party has consistently opposed universal health care (Kronenfeld, 1997). In 1935 the Republican Party successfully had universal health care removed from President Franklin Roosevelt’s proposal for the Social Security Act. At the time, however, many Democrats believed that universal health care would follow within a few years. This didn’t come to pass, and in 1948 the Democratic Party added the goal of health care for every American to its platform of positions. At the same convention President Harry Truman endorsed racial integration, resulting in a walk-out of many Southern white Democrats, led by Strom Thurmond. This episode ended a century of
CHAPTER ONE 11 Democratic Party dominance in the South, called the “Solid South,” a legacy of residual Southern bitterness from the Civil War and Reconstruction. This fracturing of the Democratic Party continues to have echoes in national electoral politics. Opposition to the idea of universal health care was at the core of the Republican Party campaign against Truman, who won by only a very small margin. In 1965, after vigorous Republican opposition, the Medicare and Medicaid programs passed as amendments to the Social Security Act with only 70 Republican votes in the House and 13 in the Senate (Social Security Online, 2008). In 1994, Congressional Republicans successfully defeated Democrat Bill Clinton’s health care plan, and the threat of “socialized medicine” was used as a talking point in Republican John McCain’s 2008 presidential campaign against Democrat Barack Obama. For all of these reasons, then, an exploration of health-related factors associated with political party affiliation is a legitimate nursing inquiry, and represents an important addition to the body of nursing knowledge. This knowledge has the potential to significantly benefit rural residents, and to improve the quality of public education efforts by nurses aimed at voter turnout and policy advocacy. Goal and Purpose The goal of this study is to investigate the relationship between self-reported health status, health care, and political party affiliation, in an effort to increase the body of nursing knowledge related to political advocacy and health policy-making, as well as add to the overall understanding of voting behavior in the U.S. More specifically, the purpose of this research is to examine the relationships between certain demographic factors, including age, gender, race, income, educational
CHAPTER ONE 12 level, and place of residence; factors related to health, such as access to health insurance, satisfaction with the health care system, and self-reported level of health; and opinions related to voting behavior, such as the importance of health care as a political issue, support for universal health care, party preference for managing health care issues; and individual political party affiliation. A secondary purpose is to evaluate the efficacy of three different theories of voting behavior at explaining the political opinions and party preferences of U.S. voters. Concepts from the fields of both political science and nursing are integrated in this investigation. Research Questions In order to address the goal and purposes of this study, a number of research questions were asked. First, questions were asked regarding the relationships among individual identity variables, including demographics, self-reported health, having health insurance, and satisfaction with the quality and cost of the health care system. Next, questions were asked investigating the relationships among issue ownership variables, including importance of health care as a political issue, political party preference for managing health care, and support for universal health care. Third, questions were asked regarding the relationships between the individual identity variables and the issue ownership variables. Finally, questions were asked investigating the relationships between individual identity variables, issue ownership variables, and political party affiliation. Each research question is listed below, followed by its accompanying research hypothesis. Question 1: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and having
CHAPTER ONE 13 health insurance? Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are less likely to have health insurance than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 2: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and selfreported health? Those respondents who are over age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to have poor self-reported health than those respondents who are under age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 3: What is the relationship between having health insurance and selfreported health? Those respondents who do not have health insurance are more likely to have poor self-reported health than those respondents who do have health insurance. Question 4: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and satisfaction with the quality and cost of the health care system? Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are less likely to be satisfied with the quality and/or cost of the health care system than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 5: What is the relationship between access to health insurance and satisfaction with the quality and cost of the health care system? Those respondents who
CHAPTER ONE 14 have health insurance are more likely to be satisfied with the quality and/or cost of the health care system than those respondents who do not have health insurance. Question 6: What is the relationship between self-reported health and satisfaction with the quality and cost of the health care system? Those respondents with good selfreported health are more likely to be satisfied with the quality and/or cost of the health care system than those respondents with poor self-reported health. Question 7: What is the relationship between support for universal health care and opinion of the importance of health care as a political issue? Those respondents who support universal health care are more likely to consider health care an important political issue than those respondents who do not support universal health care. Question 8: What is the relationship between opinion of the importance of health care as a political issue and support for universal health care? Those respondents who consider health care an important political issue are more likely to support universal health care than those respondents who do not consider health care important as a political issue. Question 9: What is the relationship between support for universal health care and party preference for managing health care? Those respondents who support universal health care are more likely to prefer Democratic Party management of health care than those respondents who do not support universal health care. Question 10: What is the relationship between opinion of the importance of health care as a political issue and party preference for managing health care? Those respondents who consider health care important as a political issue are more likely to
CHAPTER ONE 15 prefer Democratic Party management of health care than those respondents who consider health care not important as a political issue. Question 11: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and opinion of the importance of health care as a political issue? Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to consider health care an important political issue than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 12: What is the relationship between having health insurance and opinion of the importance of health care as a political issue? Those respondents who have health insurance are less likely to consider health care an important political issue than those respondents who do not have health insurance. Question 13: What is the relationship between self-reported health and opinion of the importance of health care as a political issue? Those respondents with good selfreported health are less likely to consider health care an important political issue than those respondents with poor level of self-reported health. Question 14: What is the relationship between satisfaction with the quality and cost of the health care system and opinion of the importance of health care as a political issue? Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to consider health care an important political issue than those respondents who are dissatisfied with the quality and/or cost of the health care system.
CHAPTER ONE 16 Question 15: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and party preference for managing health care? Those respondents who are under age 61, women, single, unemployed, non-white, live in non-rural areas, have higher educational levels, or lower income levels are more likely to prefer Democratic Party management of health care than those respondents who are over age 61, men, married, employed, white, live in rural areas, have lower educational levels, or higher income levels. Question 16: What is the relationship between having health insurance and party preference for managing health care? Those respondents who do not have health insurance are more likely to prefer Democratic Party management of health care than those respondents who do have health insurance. Question 17: What is the relationship between self-reported health status and party preference for managing health care? Those respondents with poor self-reported health are more likely to prefer Democratic Party management of health care than those respondents with good self-reported health. Question 18: What is the relationship between satisfaction with the quality and cost of the health care system and party preference for managing health care? Those respondents who are dissatisfied with the quality and/or cost of the health care system are more likely to prefer Democratic Party management of health care than those respondents who are satisfied with the quality and/or cost of the health care system. Question 19: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and support for universal health care? Those respondents who are under age 61, women, single,
CHAPTER ONE 17 unemployed, non-white, live in non-rural areas, have higher educational levels, or lower income levels are more likely to support universal health care than those respondents who are over age 61, men, married, employed, white, live in rural areas, have lower educational levels, or higher income levels. Question 20: What is the relationship between having health insurance and support for universal health care? Those respondents who have health insurance are less likely to support universal health care than those respondents who do not have health insurance. Question 21: What is the relationship between self-reported health and support for universal health care? Those respondents with good self-reported health will be less likely to support universal health care than those respondents with poor self-reported health. Question 22: What is the relationship between satisfaction with the quality and cost of the health care system and support for universal health care? Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to support universal health care than those respondents who are dissatisfied with the quality and/or cost of the health care system. Question 23: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and political party affiliation? Those respondents who are under age 61, women, single, unemployed, non-white, live in non-rural areas, or have higher educational or lower income levels are more likely to identify themselves as Democrats than those respondents who are over age
CHAPTER ONE 18 61, men, married, employed, white, live in rural areas, have lower educational or higher income levels. Question 24: What is the relationship between access to health insurance and political party affiliation? Those respondents who have health insurance are less likely to be Democrats than those respondents who do not have health insurance. Question 25: What is the relationship between self-rated health and political party affiliation? Those respondents with poor self-rated health will be more likely to be Democrats than those respondents with good self-reported health. Question 26: What is the relationship between satisfaction with the quality and cost of the health care system and political party affiliation? Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to be Democrats than those respondents who are dissatisfied with the quality and/or cost of the health care system. Question 27: What is the relationship between opinion of the importance of health care as a political issue and political party affiliation? Those respondents who consider health care to be an important political issue are more likely to identify themselves as Democrats than those respondents who consider health care not to be an important political issue. Question 28: What is the relationship between support for universal health care and political party affiliation? Those respondents who support universal health care are more likely to identify themselves as Democrats than those respondents who do not support universal health care.
CHAPTER ONE 19 These questions can be further organized and summarized by groups of variables (see Table 2). Table 2: Research Questions Grouped by Variables Concept group Question Independent variable Individual Identity 1 Demographics 2 Demographics 3 Health insurance 4 Demographics 5 Health insurance 6 Self-reported health Issue Ownership 7 Support UHC** 8 Political importance 9 Support UHC 10 Political importance Identity + Issue 11 Demographics 12 Health insurance 13 Self-reported health 14 Satisfaction with HC 15 Demographics 16 Health insurance 17 Self-reported health 18 Satisfaction with HC 19 Demographics 20 Health insurance 21 Self-reported health 22 Satisfaction with HC Voting behavior 23 Demographics 24 Health insurance 25 Self-reported health 26 Satisfaction with HC 27 Political importance 28 Support for UHC *HC = health care; **UHC = universal health care
Dependent variable Health insurance Self-reported health Self-reported health Satisfaction with HC* Satisfaction with HC Satisfaction with HC Political importance Support UHC Party preference HC Party preference HC Political importance Political importance Political importance Political importance Party preference HC Party preference HC Party preference HC Party preference HC Support for UHC Support for UHC Support for UHC Support for UHC Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation
Scope and Limitations The scope of this research is to address the goal and purposes described above through analysis of a secondary data set, an ABC News/USA Today/Kaiser Family Foundation poll, September 2006, in which 1201 U.S. adults were surveyed by phone using random digit dialing, and asked a series of questions regarding their own
CHAPTER ONE 20 experiences with health care, as well as their political opinions and affiliations. The sample includes a representative distribution of people from all 48 continental states: both genders; a variety of racial groups; variable residential status, i.e. rural, suburban, and urban; and a variety of ages; and socioeconomic groups. There are some inherent limitations in this research. Due to use of a secondary data set, there was no opportunity to choose or have influence over the questions asked, such as past participation in the electoral process and intention to vote in the future. An additional limitation is that the data was collected before the 2008 presidential campaign, as well as the current economic decline, both of which may have had a significant impact on peoples’ perceptions and opinions regarding these issues. Significance and Implications Since most elections are decided by margins of a few percentage points, and that difference is itself a portion of only a fraction of eligible voters, then it holds that a very small percentage of votes determines the outcomes of elections, the results of which have profound implications for health-related policy decisions. Policy-makers, aware that their election and re-election relies on only a small number of so-called swing voters, may have little motivation to be responsive to the needs of vulnerable members of society. Policies that would potentially improve the health of rural residents are less likely to be implemented, since this population is a minority of American citizens and therefore less important in a political calculus in which only a small margin is necessary. This margin can be easily culled from voters living in non-rural areas, who may have less complex and less costly policy needs due to greater population density, shorter distances,
CHAPTER ONE 21 economies of scale, ease of access to transportation, and, generally speaking, more personal and financial resources. While these factors have less impact on the outcomes of individual local elections, or elections which are decided by districts, such U.S. Congressional Representative, the fact remains that in any system in which representation is apportioned by population, urban areas will be allotted more representatives than rural areas. This is evident in a state like New York, in which New York City and its surrounding exurbs, followed by the other urban centers in the state, are far more heavily represented in state government and the House of Representatives, than are the rural areas of the state. The effect on elections which are decided by statewide vote tallies is also significant. In elections for state governor and U.S. Senate, population centers will have far more influence on the outcome than rural areas. In particular, presidential elections, which are ultimately decided by electoral, rather than popular, votes, are even more heavily influenced by large population centers. In states with a mix of large cities and rural areas, the effect will be similar to that in statewide elections. However, states which are predominantly rural, and therefore have low total populations, are apportioned a small number of electoral votes and are therefore far less significant in the outcome of a national election than states with large populations. The votes of rural residents having been effectively de-valued by this process, their problems and needs, already shrouded by distance and isolation, may become even less visible.
CHAPTER ONE 22 Conclusion Voting behavior is a complex issue, influenced by many factors in American society and by the lives of individual voters. Historical declines in voter turnout are unfortunate, and potentially detrimental, especially to vulnerable populations whose needs are likely to be ignored by representatives who are, in reality, accountable to very few voters. Public policy has a great deal of importance in influencing health and wellbeing, and solutions for redressing disparity in health status will likely be policy-driven. Nurses concerned with the health of rural residents have a role, then, in advocating for favorable policies, educating communities about policy issues, and encouraging participation in the electoral process. By increasing our understanding of voting behavior, nurses and nursing organizations can design more effective public education campaigns related to health policy issues and get out-the-vote initiatives. In addition, if nursing wants an active role in shaping policies which have the potential to profoundly and fundamentally affect nursing practice and patient well-being, then inquiries which explore the intersection of health and politics are necessary. In the following four chapters, the details of this investigation will be explained. In the second chapter, the literature relating to theories of voting behavior, perceived health status, health insurance, universal health care, rural health, and the role of nurses in the political process is described, and a conceptual model which synthesizes the major theories of voting behavior emerges. In the third chapter this conceptual model is operationalized, the data set utilized in the investigation is describes, as well as sampling, instrumentation, design, and data analysis. In the fourth chapter the results of the
CHAPTER ONE 23 statistical analyses are described and utilized to answer the research questions, the results of which are then related back to the relevant literature. In the fifth chapter the results are discussed in relation to their implications for nursing, education, and policy.
CHAPTER TWO 24
CHAPTER TWO: REVIEW OF THE LITERATURE Introduction The extensive body of literature on the topics and issues of this research were systematically analyzed in order to fully understand their background, history, and context, as well as to identify the ways in which they intersect and influence one another. The major concepts in this research were examined individually, and then the linkages among and between them were explored. The result of this review was the creation of a conceptual model of the constructs involved and the ways in which they influence one another. Voting Theories Numerous theories have been developed as political scientists have sought to explain voting behavior. These have developed primarily over the last hundred and fifty years, as modern political structures and traditions developed, but the majority of the research in this field has taken place in the last half century. Political campaigns as they exist today, utilizing mass media and sophisticated polling, bear little resemblance to those of even a hundred years ago, when few voters ever saw anything but print images of candidates, the media kept a respectful distance, and for all practical purposes the only audience members of consequence were white adult males. The first theories of voting behavior posited that people vote based largely on a set of personal predispositions, which tend to be fairly stable over time and are most
CHAPTER TWO 25 heavily influenced by social location, in other words people tend to vote for the same candidate that those with whom they work and socialize vote for. The Michigan model, a variation on this first model, included the idea that socio-demographic characteristics tend to influence social interactions. This model posits that people are most likely to interact with those to whom they share racial, religious, and class characteristics (Catt, 1996). As social barriers began to break down, allowing people of differing races, religions, and social classes to interact more frequently, increased mobility meant that people were more likely to meet and socialize with people from different geographic regions, and mass media gave people greater access to information about the lives and experiences of others, simple social location seemed inadequate to explain voting behavior. Rational choice models were developed, which emphasize individuality over group membership, and issues over identity. In these models, voters examine issues to determine which they will prioritize, form opinions of these issues, and the select the political party and candidates that are most closely aligned with their own priorities and beliefs. Variations on these models described a consumerist approach to voting, in which voters look for the party whose platform and philosophy overall appeals to them the most, rather than just opinions on key issues (Catt, 1996). However, as logical as the rational choice approach seemed, other theorists had a difficult time accepting that social and identity group factors were completely irrelevant to voting behavior, and continued to seek ways to explore their influence. A greater variety of socioeconomic variables were examined, such as education, health care,
CHAPTER TWO 26 employment, transportation, type of housing, and union membership. Others studied the role of geography, looking for spatial patterns of voting. In addition, the inquiries described above were taking place during periods of remarkable social upheaval, including the civil rights movement, the sexual revolution, and the women’s movement; changing demographic trends, including the increase in immigrant and minority populations, urbanization, and increasing economic disparity; and decreasing faith in government, spurred by opposition to the Vietnam War, the Watergate scandal, and culminating in the Reagan Revolution, which was based on the premise that government was more often a force of harm than good. Others argue that, to the extent that all of the theories described above are predicated on assumptions such as voting indicating support for a party or candidate, as well as rational voters making informed choices, they ignore key segments of the population, motivations, and outside influences such as the nature of the political system and the media (Catt, 1996). For example, in opinion polls voters frequently describe casting their vote for the proverbial lesser of evils, voting for one candidate in order to vote against the other, or casting a vote for one party, often a smaller “third” party such as the Green party, as a protest against the major political parties. The Republican Party actually has its origins as a third party, championing the single issue of abolition. Voters also frequently vote against their apparent self-interest. Sometimes this occurs because voters choose a party or candidate based on cultural or social, rather than economic, issues. Other times voters base their choices on a sense of larger purpose, such as white voters choosing a candidate who supports affirmative action. Further, in any given election cycle there is almost
CHAPTER TWO 27 certainly some degree of variability in terms of which factors, identity or issue, exert the most influence on individual voters. Recent examples from U.S. electoral history include concerns about national security, the wars in Iraq and Afghanistan, and the dramatic economic downturn which began in 2008. Individual Identity Although, as described above, they differ in some aspects, many current theories of voting behavior generally fall into one of three categories (Catt, 1996), and these are the theories that were tested in this research. The first category revolves around the concept of social determinism, the idea that peoples’ votes and party affiliations are primary influenced by their group and individual identities (Catt, 1996; Cutler, 2002; Schuessler, 1999). Examples of these social identity groups include social class, race, religion, gender, and geographic region. In American electoral history, a number of voting trends have fit well with these theories. There are well documented differences in voting behavior and political party affiliation related to gender (Bratton & Haynie, 1999; Howell & Day, 2000; Kaufmann, 2002; Kaufmann & Petrocik, 1999; Manza & Brooks, 1998; Norrander, 1997,1999, 2003; Schlesinger & Heldman, 2001; Welch & Hibbing, 1992), race (Bratton & Haynie, 1999; Knickrehm & Bent, 1988; Robertson, 2005; Shaw, 1997; Yale Law Journal, 1978), religion (Kaufmann, 2002; Layman & Carmines, 1997; Yale Law Journal, 1978), and socioeconomic status (Jarosz & Lawson, 2002; Nadeau & Lewis-Beck, 2001; Romero & Stambough, 1996; Welch & Hibbing, 1992). Regionalism and rural/urban differences have also had a significant impact on voting behavior and party affiliation, as evidenced by references to the “Solid South,” Nixon’s “Southern
CHAPTER TWO 28 Strategy,” and states being classified as “Red” or “Blue” (Bullock, 1996; Bullock et al., 2005; Murauskas et al., 1988; Nardulli, 1995; Swauger, 1980; Webster, 1992). The theorized relationships between individual identity and party affiliation were evaluated by inclusion a number of demographic factors among the independent variables for this study, including race, gender, age, marital status, educational level, employment status, and income. The relationship between these factors and political party affiliation was examined with a research question. Additional individual characteristics were also included among the variables in this study, and the bodies of literature in these areas are described in more detail below. These factors are self-reported health status, access to health insurance, and rural residence. Self-Reported Health Status There is a large body of research which examines factors which influence peoples’ perceptions of their own health, as well as the relationship between that perceived health status and other perceptions of oneself, such as quality of life and life satisfaction. Quality of life and perceived, or self-reported, health status have repeatedly been noted to be distinct but related constructs, and whereas self-reported mental health has more influence on reported quality of life, self-reported physical health has more influence on overall self-reported health status (Smith, Avis, & Assmann, 1999). The majority of studies which have examined these relationships have found a predictive association between self-reported health and quality of life (Moore, Newsome, Payne, & Tiansawad, 1993); between self-reported health, objective measures of health, and mortality (Idler & Benyamini, 1997); between self-reported health status, physician visits, and mortality, across age, gender, and other demographic characteristics
CHAPTER TWO 29 (Miilunpalo, Vuori, Oja, Pasanen, & Urponen, 1997); between self-reported health status, self-efficacy, and life satisfaction (Hampton & Marshall, 2000); between older adults’ self-reported health, functional decline, and mortality (Lee, 2000); and between poor physical well-being, bad environment, low scores on moods and emotions and increased use of health care services (Rajmil et al, 2006). Some researchers have examined which factors, other than physical functioning, contribute to quality of life as it relates to health. Both genetics and environment play a role in self-reported health (Roysamb, Neale, Tambs, Reichborn-Kjennerud, & Harris, 2003). Exercise participation, abstinence from alcohol, blood pressure, perceived health status, gender, number of chronic illnesses, and ability to perform activities of daily living are all predictors of quality of life (Lee, Ko, & Lee, 2006). In a study of health care workers, factors influencing self-reported health were the ability to use one’s mental abilities, to see, to think clearly, to love and be loved, to make one’s own decisions, to live at home, to walk, to maintain contact with family and friends, and to talk (Berg, Hallauer, & Berk, 1976). Emotional distress has also been linked to more negative self-reported health, which in turn causes increased distress, suggesting a cyclical relationship. Physical activity level has been correlated with severity of physiological symptoms of chronic illness and quality of life scores, and quality of life scores have also been correlated with self-reported health status scores (Hyland, Sodergren, & Singh, 1999). Self-reported health status has been extensively identified in the literature as being significantly correlated with actual health status, a predictor of morbidity and mortality as well as future levels of functioning, and an important influence on a person’s
CHAPTER TWO 30 perception of the quality of their life. Self-reported health was therefore included as a variable in this study, and research questions addressing both factors which may influence self-reported health, as well as the influence self-reported health may have on other study variables, were explored. Demographic Influences on Self-Reported Health Social, economic, and demographic factors have strong relationships with selfreported health status and quality of life. Clemente and Sauer (1976) found older age, Caucasian race, better self-reported health, and higher educational and income levels to all correlate with higher life satisfaction scores, with race and self-reported health status being the strongest predictors. These finding were validated by Weinrich et al. (2001), who noted that “good” self-reported health status was correlated with Caucasian race, higher education and income, and being married, whereas Wan (1976) found sociomedical factors to be better predictors of self-reported health than either socioeconomic or psychological indicators. Among elders, higher income, higher educational attainment, and larger social network have all been positively correlated with higher self-reported health status (Cox et al., 1988). Education, in particular, seems to play a significant role in health status. Gaps in physical functioning, well-being, and self-reported health exist between those with high and low educational attainment, with those with high attainment doing better in all areas. In physical functioning and well-being, this gap increases with age, and after adjusting for income, the results do not change, suggesting that education has an independent effect on health over the life course (Ross & Wu, 1996). Physicians and other university graduates have better self-reported health status than respondents with lower educational
CHAPTER TWO 31 levels (Stavern, Hofoss, Aasland, & Loge, 2001). Regidor et al. (1999) also found selfreported health to improve with increased educational attainment, that this difference increases with age, and that it is greater for men than for women. Gender may also play a role in other factors, besides education, which affect perceptions of health, particularly among rural residents. In a study of married rural adults, men’s self-reported health was more influenced by job satisfaction, women’s selfreported health was more influenced by parental satisfaction, and the self-reported health of both men and women was influenced by marital satisfaction (Wickram, Conger, Lorenz, & Matthews, 1995). In a study of rural elders, significant correlations were found between social quality of the community and self-reported health among women, and between physical quality of the environment and self-reported health among men, again suggesting that while environment has an influence on self-reported health status among elders, the nature of the influence differs between men and women (Molinari, Ahern, & Hendryx, 1998). The interaction of demographic factors, such as age, gender, marital status, employment, income, educational attainment, race, and residence, with self-reported health, has been noted by many previous studies, and was explored in this study by inclusion of a research question examining this issue, as well as the influence of selfreported health on a number of political issues, including opinion of the importance of health care as a political issue, support for universal health care, political party preference for managing health care, and political party affiliation. Disparities in Health Status Many of the articles described above allude to a variety of factors which are
CHAPTER TWO 32 associated with disparities in level of health. There is also a significant body of literature that examines this relationship in more detail. Several decades of research have noted relationships between socioeconomic factors and both actual and perceived health disparities. Lower income has been correlated with lower physical and mental health, lower self-reported health, and lower quality of life (Johnston & Ware, 1976). As described previously, race plays a role in health disparity. African Americans generally score worse on health measures, and although by holding socioeconomic status constant some racial health disparity disappears, self-rated health of African Americans remains poorer (Mutchler & Burr, 1991). In fact, by controlling for income, employment, and education, health disparities not only persist by are the largest at the highest socioeconomic status levels, suggesting that for African Americans higher income, employment, and education do not confer the same health advantages that they do for whites (Farmer & Ferraro, 2005). The health disadvantage that African Americans experience persists throughout the life course, with African Americans manifesting more serious, disabling, and fatal illnesses at earlier ages than whites (Ferraro & Farmer, 1996). African Americans and Hispanics are almost twice as likely as whites to report difficulty paying medical bills (Szabo & Appleby, 2009). White children are significantly more likely to have regular access to health care than African American or Hispanic children; controlling for health insurance and socioeconomic status did not eliminate this difference, although controlling for language ability did equalize the results for white and Hispanic children (Weinick & Krauss, 2000).
CHAPTER TWO 33 Foreign-born non-citizen children were also four times more likely than U.S.-born children from native families to lack health insurance; 40% more likely not to have visited a doctor; 80% more likely not to have visited a dentist; twice as likely to lack a usual source of care; and overall had worse physical health status (Huang et al., 2006). After adjusting for income, gender, age, and access to health insurance, members of racial and ethnic minorities, those in poor health, and those with lower educational levels were found to be significantly less likely to have received advice on quitting smoking from a health care provider (Houston, Scarinci, Person, & Greene, 2005). Education resurfaces as a significant factor in health disparity in other studies as well. Mirowsky and Ross (2000) found that each additional year of education increases perceived life expectancy by .7 years, while being unable to work, as well as past and current economic hardship all decrease perceived life expectancy; the authors note that these findings mirror objective data which has correlated socioeconomic status with life expectancy. Cornelius, Smith, and Simpson (2002) concluded that regardless of race, ethnicity, or place of residence, having a high school education and access to a regular source of care were the strongest and most significant predictors of obtaining preventative health care. In addition, almost all of the gains in life expectancy over the last few decades have been among well-educated Americans, with the life expectancy of those at lower levels of education levels remaining stagnant (Meara, Richards, & Cutler, 2008).
CHAPTER TWO 34 Health Status and Voting There is a small body of literature that has examined the relationship between an individual’s health status and their voting behavior. In general, people with poor health have difficulty exercising their voting rights, and are at risk for disenfranchisement. This may result from hospitalization, being home bound, or lacking the physical capacity to reach a polling place. Although accommodations, such as absentee ballots, are available, voters must make arrangements to utilize this method, and a sudden change in health status may make such measures impossible. In particular, being disabled, elderly and in poor health, having difficulty going out alone, experiencing the new onset of a disabling condition, and having a diagnosed mental illness have been found to decrease voter participation by up to twenty percent (Nash, 2002; Schur et al., 2002; Schur et al., 2005). Lack of participation in the electoral process has also been associated with poor self-reported health, which is exacerbated by low socioeconomic status (Blakely, Kennedy, & Kawachi, 2001). People with difficulty accessing health care, including those who lack access to health insurance, have shown an increasing tendency to vote for Democratic Party candidates over the last several election cycles (Ziegenfuss, Davern, & Blewett, 2008). These issues were explored by the inclusion of questions exploring the relationships between self-reported health status, satisfaction with the health care system, support for universal health care, opinion of the political importance of health care as an issue, party preference for managing health care, and political party affiliation.
CHAPTER TWO 35 Access to Health Insurance The articles described in the previous section describe a number of factors which are related to self-reported health status, as well as contribute to health disparities between groups. Many researchers have focused in more specifically on the role of health insurance in these factors. Andrulis (1998) reviewed selected research literature on access to health care, health insurance socioeconomic factors, and poor health outcomes, and concluded that while the relationships involved are complex, the lack of health insurance is consistently associated with difficulty accessing health care and poor health outcomes, especially for those living in poverty. Uninsured Americans are more likely to report difficulty paying medical bills; they are also more likely to report having delayed needed physician visits or recommended tests and procedures, delayed or skipped filling prescriptions, and taking a lower dose of a prescription to make it last longer (Forbes, 2009; Szabo & Appleby, 2009). Efforts at reducing health disparity in the U.S. must, then, necessarily be aimed at increasing access to health insurance. These results are confirmed by Byck (2000), who performed an analysis of data collected on over 50,000 children in the 1993 and 1994 National Health Interview Surveys which showed that uninsured children were significantly more likely to be in poor health than children enrolled State Children’s Health Insurance Programs (SCHIP), Medicaid, or children enrolled in private insurance plans. However, an analysis of 1994, 1998, and 2000 census data shows that despite expansions in SCHIP funding in the 1990s, certain groups of children, particularly poor
CHAPTER TWO 36 children, Hispanics, rural children, and children with foreign-born parents, are still disproportionately likely to be uninsured (Holohan et al., 2003). Public funding for insurance programs has a positive influence on access to health care, decreasing emergency room visits, increasing primary care visits, and improving satisfaction with the health care system (Smith-Campbell, 2000). Expansion of public funding was results in a decrease in preventable hospitalizations in a retrospective time series analysis of data collected between 1990 and 2000 (Saha et al., 2007). Comparisons between the health of insured Americans, uninsured Americans, and Canadians have consistently shown that health disparities are significantly higher in the U.S., with the insured and wealthy having much better health than the uninsured and poor, while in Canada, with its universal health care system, these disparities do not exist (Kennedy & Morgan, 2006; Sanmartin et al., 2006). Kunitz and Pesis-Katz (2005) performed an examination of historical and contemporary data regarding the life expectancy of white Americans, African Americans, and Canadians. The authors note two distinct trends. The first is that white Americans have historically had, and continue to have, longer life expectancy that African Americans. Although the gap in life expectancy has narrowed, it has not closed. The second trend is that until the 1970s, white Americans had slightly higher life expectancy than Canadians. However, Canada instituted national health insurance during this period, and from that time on Canadians have enjoyed longer life expectancy that both white and African Americans. While lack of insurance has been identified as a major barrier to obtaining health care for low income families, even those with public or private insurance continue to express concerns over cost and access, pointing to the disjointed and uncoordinated
CHAPTER TWO 37 nature of the U.S. health care system (DeVoe et al., 2007). Family practitioners, including both physicians and nurse practitioners, have been found to provide the most reliable source of access for poor and uninsured Americans, and therefore the diminishing number of these providers, compared to specialists, presents another possible barrier to the ability of the uninsured to obtain care (Ferrer, 2007). Community health centers and clinics, which meet the health care needs of some uninsured and poor Americans, are overwhelmed with greater numbers of patients, including many who until the recent economic downturn were able to get care through private providers (Associated Press, 2009). Women who lack access to health care, or lack health insurance, are less likely to receive routine screening mammograms when other demographic factors are held constant (Qureshi, Thacker, Litaker, & Kippes, 2000). Women are also more likely to have difficulty paying medical bills, as are unmarried Americans, including those who are widowed or divorced (Szabo & Appleby, 2009). Those with chronic illnesses are particularly vulnerable to economic hardship associated with lacking health insurance, due to the high costs of managing their health. Chronic disease management among those who lack health insurance is significantly inferior to that received by those who have health insurance (Hicks et al., 2006). Primary prevention and health maintenance activities, however, are most often sacrificed during periods of economic hardship, as are services, like dental care, which are considered less crucial by patients, all of which leads to increased costs later on, as conditions become more advanced and expensive to treat (Forbes, 2009; Szabo & Appleby, 2009). In fact, in general, health care expenditures overall are consistently lower among individuals who
CHAPTER TWO 38 lack health insurance than those who are insured, signifying lack of access to routine care (Ward & Franks, 2007). Bharmal and Thomas (2005) explored the relationship between health insurance status and health-related quality of life. The authors adjusted for co-variables such as socio-demographic factors, attitudinal differences, and medical conditions. Logistic regression was utilized for analysis. People without health insurance had significantly lower physical and mental health scores, and health insurance was found to be a stronger predictor of HRQOL than any other factor. This confirms the findings of earlier studies, such as findings that lack of health insurance has a negative affect on adult health which is cumulative over the number of uninsured years (Quesnel-Vallee, 2004). The relationship between demographic influences on access to health insurance, and of access to health insurance on self-reported health, satisfaction with the health care system, opinion of the importance of health care as a political issue, support for universal health care, party preference for managing health care, and political party affiliation were all explored. While factors such as one’s demographic groupings are more clearly individual identity characteristics, health-related factors such as self-reported health, satisfaction with the health care system, and access to health insurance are less clearly so. An argument could be made that these variables relate more closely to issue ownership than to individual activity. However, as will be discussed later in this chapter, there is a deep historical ambivalence among Americans about the appropriate role of government in the provision of health insurance. In addition, while lack of health insurance has been a welldocumented problem in the U.S. for over seventy years, it has failed to generate a strong
CHAPTER TWO 39 consensus about the appropriate solution. Similarly, access to health insurance has never succeeded in gelling into a distinct political movement, a goal around which voters coalesced and mobilized, in the way, for example the civil rights movement did. For these reasons, variables relating to health and health care were considered individual identity variables for the purposes of this research. Rural Residence and Health Residents of rural areas face a number of inherent challenges in accessing health care, as well as other services. Certain concepts have been identified as impacting rural residents, as well as having an effect on delivery of services to these populations (Bushy, 1990; Lee & Winters, 2004; Lee & Winters, 2006; Morgan, Fahs, & Klesh, 2007). The first of these is distance. Rural residents must travel greater distances, often over inferior infrastructure and with less access to public or mass transit, in order to reach locations where services are offered. Another theme related to the health of rural residents is isolation, which is certainly related to distance, but also encompasses having smaller and less diverse social networks. Hardiness, self-reliance, and a preference for utilizing informal networks of help and support, rather than seeking help from an outside entity, have also been described in the literature. Closely related concepts are those of insider and outsider, describing the history of relatively low social turnover in rural areas, and the extended period of time one must be a resident of an area to attain insider status within the local informal networks. The combination of these characteristics has implications for the access of rural residents to health services, since there is a lack of resources, too small a population base
CHAPTER TWO 40 to create economies of scale, and difficulty recruiting health care providers. Effective strategies to address rural issues must be driven by policy that takes these characteristics into account (Fluharty, 2002). An example of such a policy initiative would be the exemption of so-called critical access hospitals (CAH) from Medicare’s prospective payment system. CAH are, by definition, located in remote rural areas, and have fewer than 25 beds. Exempting CAH from the prospective payment system was necessary in order to keep such small hospitals from closing (Younis & Cissell, 2006). Rural residents are disproportionately likely to live in poverty, and while there remain pockets of deep and intractable rural poverty, due to the isolation and distance mentioned above this issue is often less visible that urban poverty. In all geographic areas, children under age eighteen are the most likely demographic group to be living in poverty, and while rates of child poverty have grown in almost all areas in the last eight years, rates of increase in rural areas have been even higher (O’Hare & Savage, 2006). In fact, rural residents of all ages, in all family types, and of all races and ethnicities, are more likely to live in poverty than non-rural residents, and this has been consistently true for as long as this data has been collected and measured (U.S. Department of Agriculture Economic Research Service, 2007). Rural residents are at higher risk for being unemployed, or underemployed, and also at higher risk losing full-time work at any given time, and many rural workers fit unto the classification of the working poor (Jensen et al., 1999). Because of this, the rural poor are often ineligible for Medicaid (Lichtenstein, Sharma, & Wheat, 2005). Rural workers are less likely than their non-rural peers to have employeesponsored health insurance, which is attributed to lower wages, as well lower likelihood
CHAPTER TWO 41 of being employed full time, working for a large employer, or belonging to a union (Larson & Hill, 2005). If rural workers do have employee-based health insurance, they are more likely to be underinsured, with insurance that is insufficient to meet their, or their families’, needs (Ziller et al., 2006) In addition, there is significant social pressure in rural areas not to apply for means-tested public assistance, which eliminates a number of opportunities for help with housing, food, utility, and, in particular, health care costs (Sherman, 2006). Uninsured rural residents are more likely to face financial barriers to accessing health care than uninsured non-rural residents (Reschovsky & Staiti, 2005). These facts lend support to the idea that a non-means-tested system of providing health care, such as universal health care, is the most promising mechanism to increase the access of rural residents to health services. Rural populations face some unique health challenges, and numerous studies have found a relationship between rural residence and health disadvantages. In particular, poverty, gender, and race may interact with rural residence to magnify the health disparities of vulnerable populations (Patrick, Stein, Porta, Porter, & Ricketts, 1988; Probst, Moore, Glover, & Samuels, 2004). Rural residents may have additional difficulty accessing specialty care, such as mental health and obstetric care, and this problem has persisted over time (Nesbitt, Connell, Hart, & Rosenblatt, 1990; Nesbitt, Larson, Rosenblatt, & Hart, 1997; Reschovsky & Staiti, 2005). Rural children are more likely than their non-rural peers to be uninsured (Lichtenstein et al., 2005). Uninsured and publicly insured rural children are more likely to have unmet dental, vision, and auditory health needs than privately insured rural
CHAPTER TWO 42 children (Adams et al., 2006), indicating that policy solutions, like universal health care, which would eliminate the disparity not just between the insured and the uninsured, but also between the publicly and privately insured. Uninsured rural children, particularly those living in remote areas, are also more likely to have unmet health care needs than uninsured urban children (Gresenz, Rogowski, & Escarce, 2006) again pointing to the potential for uninsured rural residents to benefit significantly from universal health care. Nurses concerned with the health of rural populations need to be on the forefront of designing and advocating for policies that will most effectively benefit those populations (Wakefield, 2005). The potential influence of rural versus non-rural residence on self-reported health, access to health insurance, satisfaction with the health care system, importance of health care as a political issue, support for universal health care, party preference for managing health care, and political party affiliation were all evaluated by including place of residence among the demographic variables of the study. Issue Ownership The second broad category of theories of voting behavior posit that, rather than group affiliation, people vote based on their opinions and preferences on key issues (Carmines & Layman, 1997; Catt, 1996; Feddersen, 2004; Gill et al., 1986; Koch, 1998; Lanoue, 1994; Petrocik, 1996). These preferences may be based on personal values, a sense of individual self-interest, or a larger social conscience. People may consider themselves single-issue voters, choosing from among the candidates based solely on their position on one key area, such as abortion rights or gun control.
CHAPTER TWO 43 Other voters are concerned with a cluster of issues, such as the economy, health care, or the environment, and may identify a particular candidate or political party as being best-equipped to handle those issues. Historically, the Democratic Party has had an advantage with voters concerned about social welfare and justice, equality, and civil rights issues, whereas the Republican Party has had an advantage with voters concerned about lower taxes, government spending, and the size of the federal government (Petrocik, Benoit, & Hansen, 2003-04). An example of this effect is the case of the “Reagan Democrats,” voters whose group affiliations, for example trade union membership, had traditionally aligned them with the Democratic Party, but who began voting for Republican candidates in 1980 based on dissatisfaction with the handling of key issues, including taxes, the economy, and foreign relations. Issue-based voting can extend beyond an individual candidate to an entire political party, such as the Democratic Party losing popularity in the south after embracing racial integration (Nardulli, 1995; Webster, 1992), or the conventional wisdom that the Republican Party lost its Congressional majority in 2006 due to dissatisfaction with the war in Iraq. The proposed relationships between issue ownership and political party affiliation were tested by inclusion of opinions about health care as a political issue, including importance of health care as an issue, party preference for managing health care, and support for universal health care, among the independent variables. The relationships among these variables, as well as their influence on political party affiliation, were all explored with research questions.
CHAPTER TWO 44 Health Care as a Political Issue The importance that voters place on health care as a political issue varies over time and among groups of voters. Willingness of politicians, ever-conscious of shifting alliances and changing public opinion, to tackle such a thorny issue can be even more fickle. The Democratic Party has endorsed universal health care as a general principle since 1948, but that has not consistently translated into policy initiatives. In fact, when Bill Clinton included health care as a central theme in his 1993 presidential campaign, many in his own party were skeptical. There was, however, at least a small groundswell of support for universal health care as an idea whose time had come. Harris Wofford, a little-known Pennsylvania Democrat, won a 1991 special election to fill the U.S. Senate seat of the unexpectedlydeceased John Heinz. Wofford defeated Richard Thornburgh, a popular and well-known Republican, who happened to be a former member of the Reagan and first President Bush administrations, much to the surprise of pundits and observers. Paul Begala and James Carville, who went on to head Clinton’s successful Presidential bid, ran Wofford’s campaign. Wofford emphasized themes calculated to have working-class appeal, such as the idea that if criminals had the right to an attorney, working people should have the right to see a doctor. Clinton, and key advisors like Begala and Carville, saw victories such as Wofford’s as a signal that Americans were ready to embrace health care reform (Kronenfeld, 1997). Unfortunately, as has often been observed, the political realities and constraints of governing are quite different from the heady idealism of campaigning. Clinton took office in January, 1993, and a year later, in his 1994 State of the Union address,
CHAPTER TWO 45 introduced a proposal for a health care plan which would cover every American primarily through a combination of expanding existing public and private mechanisms. In the end, however, Clinton’s plan was defeated in Congress. Public support plummeted after a huge and successful advertising campaign mounted by the insurance industry, which exploited fear of change and lack of policy expertise by voters (Kock, 1998). Nervous politicians, wary of re-election bids, backed away from the plan. This episode highlighted a fundamental American ambivalence about health care and the role of government. While in the abstract opinion polls indicated support for reform, and for the government’s role in guaranteeing coverage, in the specific people proved vulnerable to suggestions that such a plan would decrease their freedom and individual choices regarding their care (Zis, Jacobs, & Shapiro, 1996). Chastened by their loss of both houses of Congress in 1994, and the Presidency in 2000, Democratic politicians were leery of reintroducing the issue on a national level. Instead, smaller and more incremental reforms, such as the SCHIP legislation, were favored (Hacker & Skocpol, 1997; Kronenfeld, 1997). Such fears still resonate today, and their echoes could be heard in the 2008 Presidential campaign. As a result, significant health care legislation in the last decade has largely been limited to the states, a few of whom have dared to tackle what the federal government wouldn’t. These include Vermont, Massachusetts, and Hawaii, all of whom have implemented some variation of universal coverage, as well as a number of other states where grassroots organizations and other key stakeholders have lobbied for such plans (Benko, 2000, Paul-Sheehan, 1998). States with such plans have lower percentages of people reporting difficulty paying medical bills; Hawaii has the lowest
CHAPTER TWO 46 rate, at twelve percent, compared to Mississippi, which at twenty-nine percent has the highest rate of people reporting difficulty paying medical bills (Szabo & Appleby, 2009). Even at the state level, though, debates about health care reform aren’t simply limited to Democrats and Republicans, liberals and conservatives. The discussion highlights very deep-seated philosophical beliefs about the role of government, who is deserving of public help, how the interests of divergent populations and geographic areas are best served (Oliver, 2004). There are indications that willingness for the government to take a more active role in health care reform may be peaking again, as it did in the early 1990s, but it doesn’t necessarily follow that increased public focus will translate into political action (McInturff & Weigel, 2008; Roper, 2007). In fact, the issue seems to be in ascendancy again, after having only middling importance for many voters in the 2004 election (Blendon, Altman, Benson, & Brodie, 2004; Jacobs & Illuzzi, 2004). For many voters, however, important issues include not just access for the uninsured but improving the affordability and quality of the coverage available, as well as controlling costs for both employers and the insured (Blendon, Hunt, Benson, Fleischfresser, & Buhr, 2006; DoBias, 2006; Fong, 2005; Goldsmith, 2007; Monegain, 2009; Schaeffer, 2007). As described above, however, Americans continue to have deep ambivalence about the policy changes that would be necessary in order to enact broad reform. While on the one hand incremental reforms are ineffective from an economic standpoint and unsatisfying in terms of the public’s desire for change, more significant changes would require changes in tax structures to which Americans are historically resistant, particularly those which opponents can paint as redistributive (Ruger, 2007).
CHAPTER TWO 47 Americans are overwhelmingly dissatisfied with both the health care system and with their own health care, and have been for decades, but cannot seem to come to consensus about the best remedy, so there remains a gap between public agreement on the need for reform and the principle of universal coverage and support for specific policy remedies (Blendon, Brodie, Benson, Altman, & Buhr, 2006; Klein, 2003). This issue was explored by inclusion of the importance of health care as a political issue among the study variables, and its influence was evaluated by asking research questions investigating both factors which may influence this opinion, as well as the influence that this opinion may have on support for universal health care, political party preference for managing health care, and political party affiliation. Party Preference for Managing Health Care Classical liberalism advocated the freedom and rights of the individual, but evolving interpretations of that doctrine have recognized the need for government to protect individuals from the excesses of the markets, as well as to provide some measure of social justice (Bodenheimer, 2005). In 20th and 21st century America, the principles of social justice and social democracy, which hold that the markets cannot provide for all human necessities, and that the government has a responsibility to provide at least a minimal standard of living to the vulnerable, have primarily been carried forward by the Democratic Party. Access to health care as a basic human right, recognized more broadly in most of America’s industrially developed peer nations, has similarly been prioritized by the Democratic Party and disputed by the Republican Party (Birn, Brown, Fee, & Lear, 2003). This argument has both social justice and utilitarian facets, and fundamentally the
CHAPTER TWO 48 argument comes down to one’s belief in health care as a necessity and a human right, or as a commodity. While opinion polls over the years indicate that upwards of sixty percent of Americans agree with the premise that health care is a basic right, as well as with the idea that government should guarantee that right, that has not generally translated into electoral outcomes (Bodenheimer, 2005; Kirchheimer, 2008). Women are more likely than men to see health care as an important political issue, and in opinion polls voters consistently give the Democratic party higher ranking in terms of their ability to handle this issue effectively (Lake, Crittenden, & Mermin, 2008). In addition, Democrats tend to indicate that health care reform is a higher priority than Republicans, and also prefer candidates who prioritize health care as a political issue (Blendon, Altman, Deane, Benson, Brodie, & Buhr, 2008; DoBias, 2008; Steiber & Ferber, 1981). Political party preference for managing health care was included as a study variable, and factors which influence this preference, both individual identity and other issue ownership variables, were explored with research questions. Universal Health Care The debate over universal health care in the U.S. is complicated by a number of factors, not least among which are the divides between professionals within the health care delivery system, politicians, and voters. For health care professionals, who contend with the consequences of inadequate access every day among their patient populations, the issue is much more concrete than for politicians, who tend to debate the issue in the abstract, and distorted by the lens of ideology. For voters, health care reform presents a hugely complex maze of competing proposals and policies, many of which are poorly-understood by the average health care
CHAPTER TWO 49 consumer. In addition to the ANA, many professional organizations of physicians also support universal health care, including the American Medical Association (AMA), as well as specialty organizations such as those for pediatricians, family practitioners, and obstetrician-gynecologists (Collins, 2000). Perhaps most significant among those is the AMA, whose vigorous opposition ensured that universal health care was not part of Roosevelt’s New Deal proposal and who lobbied equally vigorously thirty years later in opposition of Medicare and Medicaid (Dowd, 2004). Although organizations differ in how they propose to address the problem, there is agreement among professionals within the health care delivery system on the principle of some form of universal care. The deep-seated ambivalence of Americans regarding universal health care most likely arises from a number of factors. One such factor is the historical reverence for rugged individualism, and the romanticism in American iconography of such figures. Americans are traditionally skeptical of government intervention into what are perceived as state and local issues, particularly as relate to social issues like education and health care. This skepticism was heightened by regional resentment over desegregation and busing battles, events like the Vietnam War and Watergate scandal, and the rise to prominence of conservative politicians who advocated deregulation and devolution. As a result, while Americans overwhelmingly agree that the health care system is in crisis, and even agree that everyone should be covered, a far smaller percentage supports a universal government-run system (Jacobs, 2008). In addition, there is an unmistakable element of self-interest in peoples’ opinions of universal care. Successful advertising campaigns in 1994 shifted public opinion from
CHAPTER TWO 50 favoring Clinton’s plan to cover all Americans to disapproving the plan due to concerns that their own coverage would be compromised (Dowd, 2004). However, even those who generally oppose government intervention into social issues were more likely to support universal care if they themselves had experienced a lack of access to health care (Goldsteen, Goldsteen, Kronenfeld, & Hann, 1997). A number of polls and studies show, however, that Americans are more likely to support a system that they view as sharing responsibility among employers, consumers, and the government, perhaps because this represents a balance between the ideals of collective and individual responsibility, and also because it presents a pragmatic solution to a complex problem (Gusmano, Schlesinger, & Thomas, 2002; Lubell, 2008; Schlesinger, 2004). A number of factors have tended to obstruct the passage of sweeping health care reform. The structure of the federal government itself favors incrementalism, as does the reserve clause of the Constitution which many point to as failing to justify federal involvement in health care policy. Historically, incremental changes, such as SCHIP, have fared better than attempts at sweeping reform, and pragmatists point out that the most rapid way to expand coverage may be to utilize a variety of such smaller programs rather than standing on principle and insisting on comprehensive reform (Tooker, 2003). Other analysts note that such incremental change has failed, over the last fortyplus years, to fill all the cracks in the system through which so many Americans fall, and that anything short of a single-payer plan will be a waste of time, resources, and political capital, and will inevitably fail as have the current remedies (Himmelstein & Woolhandler, 2003).
CHAPTER TWO 51 Lack of agreement among advocates of universal care on the best approach, for example single-payer, subsidies to expand employer-based insurance, or tax credits to encourage individuals to purchase insurance, provides fracture lines which opponents of universal care can exploit to divide public opinion and erode support for politicians advocating reform and any proposals they might make (Gorin, 1997). Just as the issue of health care reform seems to emerge cyclically as a top priority among voters, as it has in the current period, support for universal health care which is largely policy-driven, as opposed to market-driven, may also be increasing. The dissatisfaction that the majority of Americans report feeling with the health care system and their own health care may signal an increased willingness to set aside antigovernment skepticism and support more sweeping reform (Monegain, 2009). There are a number of facets to this dissatisfaction. Certainly, those who lack health insurance and have experienced access problems are less likely to express satisfaction with the health care system. However, even those who have insurance report growing concerns over costs and quality, increasing fear of losing their coverage, and frustration with insurance companies which are perceived as being profit-driven rather than patient-focused. Over two thirds of insured Americans express fear of losing their insurance, as well as being unable to afford needed care and medication even with insurance (Forbes, 2009). These fears and frustrations may in fact be sufficient to overcome the aforementioned mistrust of government as well as the concerns most often seized on by opponents of reform, such as the importance of consumer choice (Reeher, 2003; Schlesinger, 2002). Support for universal health care was explored both as a dependent
CHAPTER TWO 52 variable, to the extent that it may be influenced by both individual identity and issue ownership variables, as well as an independent variable, to the extent to which support for universal health care may influence other political opinions and political party affiliation Combination Theories The third cluster of theories espouses the idea that there is probably a complex interaction of social determinism and issue ownership, and that both sets of factors most likely influence political party affiliation (Catt, 1996). For example, evangelical Protestants are likely to oppose abortion, so that voting for an anti-abortion candidate is a function of both group affiliation and issue preference. These combined explanations have been employed by pundits who have created voter categories such as “Soccer Moms” and “NASCAR Dads.” These classifications serve to describe groups who share certain demographic characteristics and tend to prioritize the same cluster of issues. The propositions of this theory were tested by looking for interaction effects among the independent variables in their effects on voting behavior. Additional Influences on U.S. Voting Behavior In addition to the factors identified above, other factors have been found to influence the behavior of voters in the U.S. and elsewhere. Certainly it stands to reason that access to information is a factor in political decision-making. Despite the growth of the internet, many people still get information, and form impressions, based on what they see on television.
CHAPTER TWO 53 Entertainment In the case of health care, this is not only the news but also the entertainment sector, where shows with a medical focus or setting have been popular since the early days of series television. While there are certainly exceptions, the majority of these shows focus on individual providers and patients, and tend to ignore or avoid the larger health care delivery system, including problems of access or policy debates (Turow, 1996). Media In evaluating media coverage, researchers have found that stories which explore disparity, discrimination, and lack of access have increased over the last decade, and that the traditional reverence in which the media held physicians may have given way somewhat to reporting of problems within the system (Taylor-Clark, Mebane, SteelFisher, & Blendon, 2007). When approaching extremely complex issues like health policy, many voters lack a thorough knowledge or understanding of all the factors involved. They may, however, be able to utilize a fairly basic understanding of an issue, communicated throught the media, combined with certain cues such as the opinions of influential figures, to make decisions which are similar to aggregates of voters who have more extensive factual knowledge of an issue (Lupia, 1994). Advertising When examining influences on public opinion as it relates to health care policy, advertising cannot be ignored. Many of the key stake-holders in health care delivery are extremely powerful and well-funded interest groups, who can afford to mount large and
CHAPTER TWO 54 expensive advertising campaigns to convince the public of their point of view. A number of such advertisements, run by the AMA as well as a coalition of pro-reform health care groups and advocating policy changes to improve access, were aired during the 2008 presidential campaign, and a new round of advertisements by these same groups have been airing since. While it is too soon to effectively evaluate the long-term impact on public opinion relating to health care, the example of “Harry and Louise” provides information about how voters respond to such advertising campaigns. Harry and Louise were a fictional husband and wife featured in a series of advertisements run by the Health Insurance Association of America (HIAA) in 1993 and 1994, during the debate surrounding the Clinton health care proposal. Harry and Louise, a middle-aged, middle-class, and in all ways middle-American white couple sat at their kitchen table or living room sofa in their pleasant suburban home and voiced their concerns about Clinton’s plan. These concerns included rationing, the fear that they wouldn’t be able to choose their own physician any more, and generalized anxiety over the government taking over their health care. Although it is unknown exactly how much influence Harry and Louise exerted, since the ads were running in the midst of a period of intense media attention and public debate, but tracking polls do indicate that before the ads began airing, and through about the midpoint of the campaign, public opinion generally favored Clinton’s plan, which was considered a threat by the HIAA. However, around the midpoint opinion began to turn, and by the end of the ad campaign the public was solidly opposed to the plan (Brodie, 2001; Goldsteen, Goldsteen, Swan, & Clemena, 2001).
CHAPTER TWO 55 The influence of Harry and Louise may well have had more effect on Congressional representatives’ re-election fears than on public opinion, but they drew a significant response from key players, including the Clintons, as well as from the media. Whatever the reason, however, the ads have assumed a somewhat iconic place in the history of the debate over Clinton’s plan, and have also spawned sequels like “Flo,” a senior citizen concerned about Clinton’s prescription drug plan, who wanted the government to stay out of her medicine cabinet. Harry and Louise also had a brief resurrection during the 2008 Presidential campaign, but this time their brow-furrowed concern was voiced in favor of health care reform. Regardless of the affect of these ads in the past, future reformers should be aware of the potential for deep-pocketed opposition to mount such campaigns again. The Role of Nursing in Public Policy Advocacy for patients is perhaps one of the oldest and most universal themes in professional nursing. From Florence Nightingale’s efforts for British soldiers in the Crimean War until the present day, nurses have had a role in speaking for the weak, the sick, and the vulnerable. Advocacy is not limited, however, to individual patients or families, or even communities, but has been broadly interpreted to mean advocating for the health of the nation. The American Nurses Association The ANA has issued numerous statements endorsing this role for nursing, as well as endorsing candidates, delineating the process for candidate endorsement, and advocating universal health care as the best policy solution for ameliorating health disparities and repairing the U.S. health care system (ANA, 2001, 2003, 2004). The ANA
CHAPTER TWO 56 has an active political action committee (PAC), through which it routinely donates money to candidates for political office who are in favor of universal health care (ANA, 2008a). State nurses associations, such as the New York State Nurses Association (NYSNA) have similar PACs, endorsement processes, and advocacy strategies and tools for nurses to become involved in the political process, as well as a history of endorsing universal health care (NYSNA, 2008). In 2008, the ANA issued an updated Health System Reform Agenda, in which the four priorities of access, cost, quality, and workforce issues are addressed (ANA, 2008b). In this document, the ANA reaffirms its commitment to health care as a basic human right, and to health system reform which incorporates universal health care, ideally in a single-payer system. Extreme concern is expressed for the number of Americans who lack health insurance, and to the affect this has on health disparity, stating that “ANA remains committed to the principle that health care is a human right and that all persons are entitled to ready access to affordable, high quality health care services” (ANA, 2008b, p. 4). Toward that end, this document commits professional nursing to take action as advocates for the public’s health, working to reform the health care system and supporting candidates and policies with similar goals (ANA, 2008b). Nursing Literature The nursing and health literature routinely contains information about upcoming elections, candidates, and issues. A brief survey revealed a large number of such articles over the sixteen years spanning the last five presidential elections. A small, representative sample of these articles was reviewed, and all contained similar characteristics. These included strong encouragement for nurses to be involved in politics, not just by voting
CHAPTER TWO 57 but by giving money and working for candidates that support nursing and issues important to nursing; endorsement of Democratic candidates; and endorsement of proposals to expand health care access (Burda, 2007; Curran, 1992; DeMoro, 2006; Silberner, 2000; Swedish, 2008; Toofany, 2005; Wakefield, 2000). In addition, the Pew Health Professions Commission includes political advocacy as among the 21 essential competencies for all health care professionals (O’Grady, 2000). Literature on the topic of nursing and public policy also makes note of the fact that despite all the compelling reasons for nurses to be involved as political advocates, often they are reluctant to do so, or are not seen by the public as having a role in politics. Buerhaus (2000), for example, notes that nursing is not perceived as having an important role in politics or public policy, and therefore is generally ignored in public opinion polls about health care and health care policy. Significant factors inhibiting nurses’ involvement in politics and policy development include lack of academic preparation and skills training in politics, as well as an inadequate sense of political competence (Deschaine & Schaffer, 2003), and while nurses have been found to believe that the actions of politicians and changes in policy have a significant impact on the nation’s health, they also expressed limited knowledge of how to change public policy (Oden, Price, Alteneder, Boardley, & Ubokudom, 2000). Nurses have the potential to be very effective advocates for the health of the nation, but to do so must continue to gain confidence and competence in the political area, as well as increased knowledge of the political and policy-making process (Rubotzky, 2000).
CHAPTER TWO 58 Conceptual Model Examination of these three theories of voting behavior resulted in the generation of a conceptual model which represents the relationships and propositions in the theories. This conceptual model contains variables and relationships associated with both individual identity and issue ownership theories, as well as depicting the relationship between these groups of variables proposed by combined theories. Socioeconomics (alterable)
Demographics (unalterable)
Group affiliations Individual characteristics: life context, circumstances, & experiences
Issue priorities
Policy preferences
Individual identity
Issue ownership Voting behavior
Public opinion
Figure 1: Theorized Conceptual Model The model depicted above theorizes that there are several factors which contribute to individual identity. These include unalterable demographics, such as age, race, gender, and sexuality; alterable socioeconomic factors such as educational attainment, marital status, employment, income, and place of residence; group affiliations, such as religious groups, volunteer, and professional organizations; and additional individual characteristics, such as life context, circumstances, and experiences. There are also
CHAPTER TWO 59 several factors depicted in the model as contributing to issue ownership. These include issue priorities, policy preferences, and public opinion. The two constructs of individual identity and issue ownership are depicted as influencing both one another and voting behavior. Many of the elements in the model will be operationalized in the next chapter with variables of interest to rural nursing, and the theoretical propositions will form the bases for the research questions. However, the purpose of this model is to provide conceptual guidance only, and while many of the individual relationships within it will be tested, the model itself will not be tested. Conclusion The existing body of research on voting, voting behavior, health status, health insurance, rural health, health insurance as a political issue, party preference for managing health care, the role of nursing in public policy, and universal health care was examined. The same demographic variables influence voting behavior, self-reported health status, and access to health insurance. Poor health status is linked to voting behavior, and health status and access to health insurance influence one another in both directions of causality. Universal health care is related to improved health status in a nation, and is supported by professional nursing organizations. Strength of support for any issue, including universal health care, is related to voting behavior, and public education campaigns related to health issues influence voting behavior. In the U.S., the Democratic Party is alone among the two major parties in its more than half-century of support for universal health care. However, while a number of associations and relationships among these issues have been found in the existing
CHAPTER TWO 60 research, an examination of the particular relationship between self-reported health status, access to health insurance, support for universal health care, and political party affiliation described here represents a new and unique contribution to the literature. By increasing the body of nursing knowledge on these issues, nurses will have more information at their disposal in their efforts at public education, political advocacy, and policy formulation. Policies aimed at increasing access, availability, and affordability of health care and health insurance, have the potential to disproportionately benefit disadvantaged and vulnerable rural populations, improving their health status in meaningful ways. In addition, three theories of voting behavior are tested for their adequacy in explaining the relationships between health, health care, and voting. The conceptual model generated by the preceding review of the literature guided the selection of a data set, variables, research questions, and research hypotheses, which will be discussed in the next chapter.
CHAPTER THREE 61
CHAPTER THREE: METHODS Introduction The relationships between health, health care, and voting are potentially numerous and complex. The review of the related literature resulted in the generation of a conceptual model of theorized relationships among a number of variables. This conceptual model represents theoretical relationships between variables associated with the three theories of voting behavior under examination in this research. The theoretical model was then operationalized utilizing variables relevant to rural nursing, and a data set appropriate to testing this model was located. Conceptual Model The conceptual model derived from the literature and depicted in Chapter Two was operationlized to guide the research. The individual identity variables were selected, as discussed in the previous chapter, based on a review of the relevant literature. These included demographic variables, including age, race, gender, marital status, employment, income, education attainment, marital status, and rural versus non-rural residence. Both alterable and unalterable demographic and socioeconomic variables were grouped together in this study. Self-reported health status, access to health insurance, and satisfaction with the health care system were all included as individual characteristics. All of these individual identity variables, and the potential relationships between them which formed the basis for the research questions, are shown enclosed within a broken line.
CHAPTER THREE 62 The issue ownership variables were also selected following a review of the literature. Support for universal health care is utilized to operationalize policy preference, opinion of the importance of health care as a political issue is utilized to operationalize issue priority, and political party preference for managing health care is utilized to operationalize public opinion. These variables, and the relationships between them, are also enclosed within a broken line. Finally, political party affiliation is utilized to operationlize voting behavior. It is important to note that this operationalized conceptual model was used to guide the research, and the development of research questions, but is not being tested as a whole. The model guided statistical analysis, as well as variable selection and formulation of research questions. When testing the relationships in the model, depicted as arrows, between individual variables within a conceptual grouping, either individual identity or issue ownership, a different technique was used for analysis than when testing the relationships between groups of variables, for example between the individual identity variables and the issue ownership variables. Data analysis is discussed in detail later in this chapter.
CHAPTER THREE 63
Race, age, & gender Marital status Income Employment Education Place of residence (rural/nonrural)
Individual Identity
Health insurance
Health status
Satisfaction with the health care system
Party affiliation Issue Ownership Importance of health care as a political issue
Support for universal health care
Party preference for managing health care
Figure 2: Operationalized Conceptual Model Design This study utilized an ex post facto analysis of a secondary data set in a descriptive correlational design. This design is appropriate to answer the research questions for several reasons. Having explored the related literature, the particular relationships of interest in this study, as shown in the conceptual model, have not previously been examined in this way. Each of the individual concepts has been described quite well in the research, and some of the theorized relationships have been
CHAPTER THREE 64 explored, but this model represents a unique proposition of these issues and the complex potential relationships among and between them. Therefore, while additional descriptive research is not necessary, further exploration and testing of the theoretical prepositions, through a descriptive correlational design, is appropriate (Polit & Hungler, 1991). An additional factor lending support to this choice of design is the fact that many of the independent variables of interest are either inherently not manipulable, including demographic characteristics; not practically manipulable, such as whether respondents have health insurance; or beyond the scope of inquiry of this research, including respondents’ health status and level of satisfaction with the health care system. Such constraints indicate the appropriateness of a nonexperimental design (Polit & Hungler). Finally, while others of the study variables, such as opinion of the importance of health care as a political issue and support for universal health care, could potentially be the target of interventions in the future, they are also beyond the scope of this research, the primary goal of which is to test the conceptual model derived from the literature. Data Set The data set analyzed for this study was collected September 7 through 12, 2006, as part of a series of monthly opinion polls conducted by ABC News, USA Today, and the Kaiser Family Foundation to measure the public’s views on various social issues and their impact on the upcoming midterm Congressional elections (ABC News/USA Today/Kaiser Family Foundation, 2006). While the primary focus of the poll was health, respondents were asked generally which issues would be most important to them as they made their candidate decisions.
CHAPTER THREE 65 Respondents were then asked a series of questions about their own health care coverage, including whether they currently had coverage, if not how long they had been without, if so what type of insurance, and their satisfaction with their coverage. They were also asked questions about their health and the health of their families, whether they or a family member had had a serious illness or injury in the last year, and whether those costs were covered by insurance. Respondents were asked whether they had ever gone without treatment because of costs, and how they felt about a variety of health care issues, such as the cost and quality of health care and insurance, the number of uninsured Americans, and possible solutions. Opinions were sought regarding factors contributing to the rising costs of health care, as well as what types of treatments should be covered, such as life-extending treatments for the very old or terminally ill, whether employers should be required to provide insurance coverage to their employees, whether tax breaks should be provided to employers, and whether they would approve of all Americans being mandated to have insurance. Questions also probed political opinions, such as respondents’ political party affiliation and ideology, and which party they preferred to manage health care issues. Respondents were questioned about their support for universal health care, and whether they would approve of using tax revenues to pay for it. Last, respondents were asked a series of questions regarding demographic characteristics (ABC News/USA Today/Kaiser Family Foundation, 2006). This data set was located via a search of the Inter-University Consortium for Political and Social Research website, using search terms such as “public opinion,” “health status,” “access to health care,” and, “rural health.” After locating an appropriate
CHAPTER THREE 66 data set, it was accessed through Binghamton University’s affiliation as a member of the consortium. Approval was sought and received from Binghamton University’s Human Subjects review board (Appendix A) to utilize this data set for secondary analysis. The Terms of Use of the data set (Appendix B) allows for this type of analysis and publication, provided that the source of the data is appropriately cited. Sample The population of interest was all persons aged eighteen and older with telephones living in the contiguous 48 United States. The sample was obtained using a series of techniques designed to be as representative as possible of the target population. Taylor Nelson Sofres Intersearch, now known as TNS Global, conducted the sampling and data collection for this series of polls. This organization has been the primary polling organization used by ABC News for thirty years (ABC News/USA Today/Kaiser Family Foundation, 2006). TNS Global is an international market research organization, with divisions specializing in a variety of industries, media, and social and political research (TNS Global, 2009). TNS Global used a multi-step sampling process to select a sample of households within the continental U.S. The first step in selecting the sample was a random digit dialing procedure, designed to ensure that listed and unlisted phone numbers were included, and that all possible numbers had an equal probability of selection. The Genesys Sampling System was utilized, which is a computerized sampling system which allows users to generate random digit dialing samples with any desired specifications (Marketing Systems Group, 2009). All active area codes and exchanges within the contiguous 48 U.S. states were included in the sampling frame. The Genesys system
CHAPTER THREE 67 database is updated on a rolling basis, with twenty-five percent of the database updated every four to six weeks (Langer, 2008). The first step of the sampling technique involved sorting all working area codes into nine geographical regions, defined by the U.S. Census Bureau. These were New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, and Pacific. Within each region, each area code and telephone exchange was then assigned a county designation. When a telephone exchange was used in more than one county, it was assigned for categorization purposes to the county where the exchange was most prevalent (Langer, 2008). Each county was then classified as metropolitan or nonmetropolitan, utilizing the U.S. Office of Management and Budget definitions. A metropolitan statistical area (MSA) county is defined by the Office of Management and Budget as having a core urban area of 50,000 or more residents (U.S. Census Bureau, 2009a). Counties may also be flagged as being part of an MSA if they are adjacent to an MSA county, and are highly socially and economically integrated with that MSA county, usually measured in terms of the people commuting from an adjacent to an MSA county for work (U.S. Census Bureau, 2009a). At this point in the sampling, all U.S. telephone area codes and exchanges had been divided into eighteen groupings, comprised of MSA and non-MSA counties within each of the nine geographic regions described above (ABC News/USA Today/Kaiser Family Foundation, 2006). The next step in the sampling was for each of these eighteen groups to be sorted by state, and then further classified by median income using U.S. Census data. Within each of these groupings of telephone area codes and exchanges, defined by median household income, U.S. state, MSA or non-MSA county, and Census
CHAPTER THREE 68 region, a random sample of telephone exchanges was selected by choosing every nth exchange. Known business numbers, as well as those with multiple listings for the same address, also assumed to be businesses, were eliminated. Next, a sample of working residential telephone number suffixes within each exchange was randomly selected. These telephone numbers were pre-dialed using a non-ringing auto-dialer, to reduce the number of non-working numbers in the sample (ABC News/USA Today/Kaiser Family Foundation). The above procedures were utilized to ensure as random as possible a sample of telephone numbers to be dialed by computerized auto-dialer. Once a phone was answered, another set of procedures was utilized to select as random and representative as possible a sample of actual respondents. The call was taken over by a live interviewer at this point. Interviewers asked to speak with a resident of the household who was both over age eighteen and had the most recent birthday. Since women are easier to reach via home phone, interviewers initially requested to speak with a male, over age eighteen, with the most recent birthday, seventy-five percent of the time, and a female meeting the same criteria twenty-five percent of the time. If a household member of the selected gender was unavailable, then interviewers asked to speak with the household member, meeting the same criteria, of the other gender (ABC News/USA Today/Kaiser Family Foundation, 2006). Although cell phones were not included in the sampling frame, those households with a cell phone only, and no landline, do not differ from households with landlines, either demographically or in terms of attitudes, enough so that this has not been found to contribute significantly to sampling error (Langer, 2008). Sampling error was reported to be calculated, using a standardized formula of [SQRT (.25/sample size)] x
CHAPTER THREE 69 1.96, at the ninety-five percent confidence interval (ABC News/USA Today/Kaiser Family Foundation). Instrument This particular set of data was collected in an effort to gauge which issues would be most important in the 2006 Congressional elections, as well as which party respondents preferred to manage health care, how satisfied they were with their health care and the health care system, how important and pressing the issue of the uninsured was, whether not the respondents had insurance, their own self-rated health status, and then a series of questions regarding various universal health care proposals. Demographic data were also collected. In the file made available to the public, telephone numbers, zip codes, and county were removed from the data set in order to protect the confidentiality of respondents. However, zip codes and county information for the aggregate data were made available (ABC News/USA Today/Kaiser Family Foundation, 2006). The questionnaire used to conduct the interview contains a total of 62 questions (Appendix C), however not all respondents were asked all questions, since a number of the items are follow-up questions which are dependent on a respondent’s answer to a previous question. The first two questions address the respondent’s opinion of what will be the most important and second most important issues in the respondent’s vote for Congress that year. The choices include the campaign against terrorism, the war in Iraq, the economy, immigration, gas prices, and health care, and the interview script included instructions to scramble the order of the choices with each interview. The third question asked respondents their opinion of which political party they preferred to manage health care. The next two questions addressed the respondent’s satisfaction with the cost and
CHAPTER THREE 70 quality of the health care system, and the sixth question asked whether the respondent currently had some form of health insurance or coverage. At this point the series of questions was determined by the respondent’s answer to the sixth question, with those who answered “no” asked questions about the length of time they had been uninsured and those who answered “yes” asked some questions about the type of insurance they had, as well as if they had gone without insurance in the last twelve months. The next series of questions asked respondents to rate their satisfaction with a number of health care issues, including the quality of the care they receive, the costs of their health care, including insurance premiums and out-of-pocket expenses, ability to get a doctor’s appointment when they want one, ability to see top-quality specialists if needed, ability to get the latest, most sophisticated treatments if needed, quality of communication with their physician, ability to get emergency medical care, and ability to get non-emergency treatments without having to wait. The interview script included instructions to scramble the order of these items with each interview. The next group of questions asked respondents whether they or anyone in their family had had a serious illness or injury under their current insurance plan, whether they or anyone in their family had a chronic illness, and if yes to either of them how satisfied they were with their insurance coverage of the injury or illness. Respondents were then asked whether they believed that expensive doctors provide better medical care, and whether expensive drugs, treatments, and technologies produce better results. They were then asked a series of questions regarding their opinions about which issues were contributing most significantly to the rising cost of
CHAPTER THREE 71 health care, including people getting treatments they don’t need, drug and insurance companies making too much money, doctors and hospitals making too much money, too many malpractice lawsuits, more people getting better medical care than in the past, the aging population, the use of expensive new drugs, treatments, and technologies, administrative costs of handling insurance claims, people needing more care because of unhealthy lifestyles, and fraud and waste. The interview script includes instructions to scramble the items for each interview. The next set of questions centered around medical bills, and asked respondents whether they or a member of their family had had a problem paying a medical bill in the last year, and if so what kind of impact that had had on them; whether they or a member of their family had delayed treatment because of cost in the last year, and if so how serious the condition was for which treatment was delayed; whether their costs for insurance premiums had been going up or down; and whether their co-pays and deductibles had been going up or down. Respondents were then asked about their concerns for the future, including whether they were worried about losing insurance due to loss of a job, being unable to afford insurance in the years to come, and being unable to afford health care in the years to come. Respondents were asked how much difficulty they had had getting their insurance to pay for care in the past, whether they had ever had all or part of a claim refused by their insurance company, and whether insurance companies should pay for expensive new treatments or medications if they hadn’t yet been proven more effective than existing treatments. The next group of questions probed opinions surrounding universal health care. Respondents were asked which was more important, reducing costs or covering more
CHAPTER THREE 72 Americans; holding down taxes or covering all Americans; and whether they would prefer a universal health care system run by the government or the current system. Respondents were asked which types of limitations they would find acceptable under a universal health care system, including limiting their choice of doctors, waiting lists for non-emergency treatments, paying more taxes or higher premiums, and that some treatments currently covered by insurance would no longer be covered. Script instructions included scrambling these items for each interview. The next three questions asked whether respondents felt that a universal health care system would make certain issues better, worse, or have no effect, including the quality of their health care, the availability of treatments to them and their family, and their choice of doctors or hospitals. The script instructions included scrambling these items for each interview. The next set of questions examined respondents’ opinions about the responsibility of the government, including requiring businesses to offer insurance to all full-time employees, requiring businesses to offer insurance to all part-time employees, offering text credits to low-income people to help them buy insurance, expanding Medicare to cover people age 55-64 years without insurance, expanding state programs for lowincome people, including Medicaid and SCHIP, requiring all Americans to have health insurance, and offering tax or other incentives to businesses to help them insure their employees. Script instructions included scrambling these items for each interview. The last set of opinion questions asked respondents whether they agreed with the Massachusetts plan to require all residents to have insurance coverage, whether they are currently in a plan with a deductible over $1000 for an individual or over $2000 for a
CHAPTER THREE 73 family, whether they would support a plan which covered only major medical expenses and individuals paid for smaller expenses out of a health savings account, and how effective they think the current employer-based system is. They were then asked whether they thought two different measures would be effective in controlling costs; these were letting individuals shop around for the best prices and having the government regulate health care costs. Respondents were asked if terminally ill people should be kept alive as long as possible regardless of costs or a judgment should be made about whether or not the cost is justified, and whether insurance companies should be allowed to charge higher premiums to overweight people and smokers. Respondents were then asked to rate their own level of health. The last cluster of questions evaluated political affiliation, asking whether they considered themselves Democrats, Republicans, or Independents, and whether they considered their views generally liberals, conservatives, or moderates. Finally, data on respondents’ demographic characteristics were collected. These characteristics included education, age, marital status, whether they had children under age eighteen living at home, employment status, race, household income, gender, and whether they would be willing to have a reporter from ABC News call them to talk about these issues more. The authors do not provide any specific reliability or validity data for this instrument, but state that all polling data must meet certain reliability and validity standards, and report that the results have a three percent margin of error (Langer & Sussman, 2006). Definitions Each of the individual propositions depicted in the conceptual model formed the basis for a research question, and the associated hypothesis was drawn from the related
CHAPTER THREE 74 literature. Variables of interest to rural nursing were utilized to operationalize the conceptual model. The first variables defined are those pertaining to individual identity, including demographics, satisfaction with the health care system, access to health insurance, and health status. Demographic Variables Demographic variables for this study include age, race, gender, marital status, educational level, employment status, income, and residence. Age is defined as chronological age in years, and was operationalized at the categorical level as 18 to 30 years, 31 to 44 years, 45 to 60 years, or 61 years and older. For some analyses, where only a distinction between respondents likely to be retired and covered by Medicare was appropriate, a recoded age variable with two categories, under age 61 and over age 61, were used. Race is defined as a person’s self-identification of their racial group, and was operationalized at the categorical level as white, black, white Hispanic, black Hispanic, Hispanic with no race given, Asian, or other race. Race was recoded into two categories for analysis, with white coded as white, and all other categories coded as non-white. The rationale for this recoding is that in the literature on health status and voting, and in terms of access to health insurance, the largest difference is seen between respondents who are white and those who are non-white. Gender is defined as a person’s self-identification of their gender group, and was operationalized at the categorical level as male or female. Marital status is defined as a person’s self-identification of the existence and status of a primary intimate relationship, and was operationalized at the categorical level as married and living with one’s spouse,
CHAPTER THREE 75 separated, divorced, widowed, or never married. Marital status was recoded into two categories for analysis, with married and separated coded as married, and divorced, widowed, and never married coded as unmarried. The rationale for this recoding is that for economic purposes, and in terms of access to health insurance, the primary distinction is between respondents who are married and those who are not married. Educational level is defined as the amount of formal education completed, and was operationalized at the categorical level as eighth grade or less, some high school, graduated high school, some college, graduated college, or post-graduate. Educational level was recoded into two categories for analysis, with eighth grade or less, some high school, or graduated high school coded as high school or less, and some college, graduated college, or post-graduate coded as at least some college. The rationale for this recoding is that in the literature on the topics of both health and voting, the largest distinctions are seen between respondents who have no college education and those who have at least some college education. Employment status is defined as a person’s self-identification of their work status, and was operationalized at the categorical level as employed full-time, employed parttime, unemployed, laid off, retired, full-time homemaker, or student. Employment was recoded for analysis into four categories, with employed full-time, employed part-time, and retired left as is and unemployed, laid off, full-time homemaker, and student coded as unemployed. The rationale for this recoding is that in relation to the other variables of interest, particularly access to health insurance, the major distinctions are likely to be between people employed full-time, part-time, and unemployed respondents, in relation
CHAPTER THREE 76 to access to private insurance or some forms of public insurance; retired respondents are likely to be covered by Medicare. Income is defined at the amount of money in dollars earned by a household from all sources over the course of a year, and was operationalized at the categorical level as under 20 thousand, 20 to under 35 thousand, 35 to under 50 thousand, 50 to under 75 thousand, 75 to under 100 thousand, and 100 thousand or more. Place of residence is defined as the location of a person’s primary domicile, and was operationalized by the data collectors in two ways. The first was whether or not the respondent was a resident of a metropolitan statistical area (MSA), defined as a county with an urban center of 50,000 or more people, and includes counties adjacent to and economically and socially integrated with a county with an urban center of 50,000 or more people; or not a resident of an MSA county. In addition, respondents were coded as urban, suburban, or rural, which was determined by zip code. However, the polling organization which collected the data did not provide information on the criteria used to make the distinctions between urban, rural, or suburban zip codes. In addition, within the data set this urban, rural, suburban variable is coded as a string variable. SPSS will not allow most statistical analyses to be performed on string variables. Initially the rural urban suburban variable was recoded for analysis into two categories, with rural coded as rural, and urban and suburban coded as non-rural. The rationale for recoding this variable is that the focus of this study is investigating differences between rural and non-rural voters, rather than differences among non-rural, i.e. urban and suburban, voters.
CHAPTER THREE 77 However, following this recoding, descriptive statistics were run on both the rural non-rural variable and the MSA non-MSA variable. The results indicated that the categories of rural and non-MSA are virtually identical, as are the categories of non-rural and MSA. In a line by line comparison in the data set, there was only one respondent for whom categorization differed between these two variables. There was one respondent who was classified as MSA but also coded as rural. Since MSA non-MSA is a numerical variable in the data set, and therefore available for all appropriate statistical analyses, and in a data set over 1201 respondents a difference of one respondent will not influence statistical significance, this is the definition that was used to operationalize place of residence, and this was the variable used to measure this concept in the model. As depicted in the model, demographic characteristics are conceptualized as one variable with multiple components. For this reason, all of these characteristics are grouped together as the independent variables in a number of research questions, and an appropriate statistical technique was used. Health Care Variables Satisfaction with the health care system is defined as a person’s perception of the quality and cost of health care delivery in the U.S., and is operationalized at the categorical level as very satisfied, somewhat satisfied, somewhat dissatisfied, or very dissatisfied. This variable was measured with two separate items, one pertaining to satisfaction with the cost of health care and one pertaining to satisfaction with the quality of health care. These variables were recoded for analysis with very satisfied and somewhat satisfied coded as satisfied, and somewhat dissatisfied and very dissatisfied coded as dissatisfied. The rationale for this recoding is that the focus of the research is
CHAPTER THREE 78 investigating both factors which tend to make a person more or less likely to be satisfied, and the ways in which being satisfied or dissatisfied influence other variables, rather than with making distinctions between levels of satisfaction. When satisfaction with the quality and cost of health care were dependent variables in a research question, the relationship between the independent variable in question and satisfaction with the quality of health care was analyzed separately from the relationship between the independent variable in question and satisfaction with the cost of health care. However, when satisfaction with the quality and cost of health care was the independent variable in a research question, they were conceptualized as a single variable with two characteristics, and an appropriate statistical technique was used. Self-reported health status is defined as a person’s subjective rating of their own health, and was operationalized at the categorical level as excellent, good, not so good, and poor. Health status was recoded for analysis with excellent and good coded as good, and not so good and poor coded as poor. The rationale for this recoding is that the focus of the research is investigating both factors which tend to make a person more or less likely to be healthy, and the ways in which being health or unhealthy influence other variables, rather than with making distinctions between levels of health. Access to health insurance is defined as having some form of third party insurance or coverage which pays some or all of a person’s health-related expenses. Health insurance is operationalized at the categorical level as yes, the respondent has health insurance, or no, the respondent does not have health insurance.
CHAPTER THREE 79 Issue Ownership Variables The next set of variables defined are those pertaining to issue ownership, including importance of health care as a political issue, party preference for managing health care issue, and support for universal health care. Opinion of health care as a political issue is defined as a person’s perception of whether health care is an issue which will have a significant influence on their choice of candidates. This variable is operationalized at the categorical level as a respondent rating health care as either the first or second most important issue in their choice of candidate in the next election, or a respondent not rating health care among the top two most important issues to them in the next election. Party preference for managing health care is defined as a person’s perception of whether the Democratic Party or the Republican Party will do a better job and can best be trusted in managing this issue in a way which best aligns with the respondent’s own views. This variable is operationalized at the categorical level as the Democrats or the Republicans. Respondents who did not indicate a preference were coded as missing for this item. Support for universal health care is defined as a person’s belief that universal health care would be beneficial for the U.S. or not, and is operationalized at the categorical level as whether a respondent would prefer to maintain the current system of health insurance in the U.S. or whether they would prefer a universal health care system which covers everyone, is run by the government, and financed by taxes.
CHAPTER THREE 80 Voting Behavior The final variable of interest in this study is voting behavior, which is defined as a person’s alignment with one or the other of the two major political partied in the U.S., and is operationalized at the categorical level as whether a respondent generally considers them self a Democrat, a Republican, or neither. Those respondents who did not indicate affiliation with either party were coded as missing for this item. Research Questions and Hypotheses In order to address the goal and purposes of this study, a number of questions related to the intersection of perceived health, health care, and voting were investigated in this research. The goal and purposes directed the literature review, which in turn resulted in the construction of a conceptual model based on existing voting theories and the bodies of research related to health, health insurance, rural health, and voting. Each question represents a proposed relationship in the conceptual model, and is followed by its corresponding research hypothesis, with directions of hypothesized relationships drawn from the literature. For logic in terms of explanation and analysis, the questions are grouped according to variable (see Table 2).
CHAPTER THREE 81 Table 2: Research Questions Grouped by Variables Concept group Question Independent variable Individual Identity 1 Demographics 2 Demographics 3 Health insurance 4 Demographics 5 Health insurance 6 Self-reported health Issue Ownership 7 Support UHC** 8 Political importance 9 Support UHC 10 Political importance Identity + Issue 11 Demographics 12 Health insurance 13 Self-reported health 14 Satisfaction with HC 15 Demographics 16 Health insurance 17 Self-reported health 18 Satisfaction with HC 19 Demographics 20 Health insurance 21 Self-reported health 22 Satisfaction with HC Voting behavior 23 Demographics 24 Health insurance 25 Self-reported health 26 Satisfaction with HC 27 Political importance 28 Support for UHC *HC = health care; **UHC = universal health care
Dependent variable Health insurance Self-reported health Self-reported health Satisfaction with HC* Satisfaction with HC Satisfaction with HC Political importance Support UHC Party preference HC Party preference HC Political importance Political importance Political importance Political importance Party preference HC Party preference HC Party preference HC Party preference HC Support for UHC Support for UHC Support for UHC Support for UHC Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation
Relationships among Identity Variables The first set of questions was drawn from the variables pertaining to individual identity; demographics, health status, health insurance, and satisfaction with the quality and cost of health care system, and the potential relationships between them. As mentioned above, and depicted in the model, demographics are conceptualized as one variable with multiple characteristics, and therefore grouped together in research questions. Similarly, satisfaction with the quality and cost of health care is conceptualized
CHAPTER THREE 82 as a single variable with two characteristics, and therefore also grouped together in research questions. Identity Variables affecting Access to Health Insurance: Question 1 The first question examines what influence demographic characteristics have on access to health insurance. Question 1: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and having health insurance? Hypothesis 1: Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are less likely to have health insurance than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Identity Variables affecting Perceived Health Status: Questions 2 and 3 The next set of questions examines the influence of demographic characteristics and access to health insurance on self-reported health status. Question 2: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and selfreported health? Hypothesis 2: Those respondents who are over age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to have poor self-reported health than those respondents who are under
CHAPTER THREE 83 age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 3: What is the relationship between having health insurance and selfreported health? Hypothesis 3: Those respondents who do not have health insurance are more likely to have poor self-reported health than those respondents who do have health insurance. Identity Variables affecting Satisfaction with Health Care: Questions 4-6 The last set of questions drawn from the individual identity variables examines the influence of demographic characteristics, self-reported health status, and access to health insurance on satisfaction with the quality and cost of the health care system. Question 4: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and satisfaction with the quality and cost of the health care system? Hypothesis 4: Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are less likely to be satisfied with the quality and/or cost of the health care system than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 5: What is the relationship between access to health insurance and satisfaction with the quality and cost of the health care system?
CHAPTER THREE 84 Hypothesis 5: Those respondents who have health insurance are more likely to be satisfied with the quality and/or cost of the health care system than those respondents who do not have health insurance. Question 6: What is the relationship between self-reported health and satisfaction with the quality and cost of the health care system? Hypothesis 6: Those respondents with good self-reported health are more likely to be satisfied with the quality and/or cost of the health care system than those respondents with poor self-reported health. Relationships among Issue Ownership Variables The next group of questions examines the relationships among the issue ownership variables; including opinion of the importance of health care as a political issue, party preference for managing health care, and support for universal health care. Issue Variables affecting Political Importance of Health Care: Question 7 Question 7: What is the relationship between support for universal health care and opinion of the importance of health care as a political issue? Hypothesis 7: Those respondents who support universal health care are more likely to consider health care an important political issue than those respondents who do not support universal health care. This question was answered using logistic regression. Issue Variables affecting Support for Universal Heath Care: Question 8 The next question drawn from the issue ownership variables examines the influence of opinion of the importance of health care as a political issue on support for universal health care.
CHAPTER THREE 85 Question 8: What is the relationship between opinion of the importance of health care as a political issue and support for universal health care? Hypothesis 8: Those respondents who consider health care an important political issue are more likely to support universal health care than those respondents who do not consider health care important as a political issue. Issue Variables affecting Party Preference for Managing Health Care: Questions 9 and 10 The next two questions examine the influences of support for universal health care and importance of health care as a political issue on part preference for managing health care. Question 9: What is the relationship between support for universal health care and party preference for managing health care? Hypothesis 9: Those respondents who support universal health care are more likely to prefer Democratic Party management of health care than those respondents who do not support universal health care. Question 10: What is the relationship between opinion of the importance of health care as a political issue and party preference for managing health care? Hypothesis 10: Those respondents who consider health care important as a political issue are more likely to prefer Democratic Party management of health care than those respondents who consider health care not important as a political issue. Relationships between Identity and Issue Variables The next set of questions explores the relationships between the individual identity variables and the issue ownership variables. The first questions examine the
CHAPTER THREE 86 influence of identity variables on opinion of the importance of health care as a political issue. Identity Variables affecting Political Importance of Health Care: Questions 11-14 The next set of four questions examines the influence of individual identity variables, including demographics, access to health insurance, self-reported health, and satisfaction with the health care system, on opinion of the political importance of health care. The relationship between each of the individual identity variables and the issue identity variables are asked as separate questions. However, as depicted in the conceptual model, their influence was analyzed as a whole, using an appropriate statistical method. Question 11: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and opinion of the importance of health care as a political issue? Hypothesis 11: Those respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to consider health care an important political issue than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Question 12: What is the relationship between having health insurance and opinion of the importance of health care as a political issue? Hypothesis 12: Those respondents who have health insurance are less likely to consider health care an important political issue than those respondents who do not have health insurance.
CHAPTER THREE 87 Question 13: What is the relationship between self-reported health and opinion of the importance of health care as a political issue? Hypothesis 13: Those respondents with good self-reported health are less likely to consider health care an important political issue than those respondents with poor level of self-reported health. Question 14: What is the relationship between satisfaction with the quality and cost of the health care system and opinion of the importance of health care as a political issue? Hypothesis 14: Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to consider health care an important political issue than those respondents who are dissatisfied with the quality and/or cost of the health care system. Identity Variables affecting Party Preference for managing Health Care: Questions 1518 The next set of questions examines the influence of identity variables on party preference for managing health care. Question 15: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and party preference for managing health care? Hypothesis 15: Those respondents who are under age 61, women, single, unemployed, non-white, live in non-rural areas, have higher educational levels, or lower income levels are more likely to prefer Democratic Party management of health care
CHAPTER THREE 88 than those respondents who are over age 61, men, married, employed, white, live in rural areas, have lower educational levels, or higher income levels. Question 16: What is the relationship between having health insurance and party preference for managing health care? Hypothesis 16: Those respondents who do not have health insurance are more likely to prefer Democratic Party management of health care than those respondents who do have health insurance. Question 17: What is the relationship between self-reported health status and party preference for managing health care? Hypothesis 17: Those respondents with poor self-reported health will be more likely to prefer Democratic Party management of health care than those respondents with good self-reported health. Question 18: What is the relationship between satisfaction with the quality and cost of the health care system and party preference for managing health care? Hypothesis 18: Those respondents who are dissatisfied with the quality and/or cost of the health care system are more likely to prefer Democratic Party management of health care than those respondents who are satisfied with the quality and/or cost of the health care system. Identity Variables affecting Support for Universal Health Care: Questions 19-22 The next set of questions explores the influence of identity variables on support for universal health care.
CHAPTER THREE 89 Question 19: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and support for universal health care? Hypothesis 19: Those respondents who are under age 61, women, single, unemployed, non-white, live in non-rural areas, have higher educational levels, or lower income levels are more likely to support universal health care than those respondents who are over age 61, men, married, employed, white, live in rural areas, have lower educational levels, or higher income levels. Question 20: What is the relationship between having health insurance and support for universal health care? Hypothesis 20: Those respondents who have health insurance are less likely to support universal health care than those respondents who do not have health insurance. Question 21: What is the relationship between self-reported health and support for universal health care? Hypothesis 21: Those respondents with good self-reported health will be less likely to support universal health care than those respondents with poor self-reported health. Question 22: What is the relationship between satisfaction with the quality and cost of the health care system and support for universal health care? Hypothesis 22: Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to support universal health care than those respondents who are dissatisfied with the quality and/or cost of the health care system.
CHAPTER THREE 90 Identity, Issue Ownership, and Voting Behavior The final set of questions explores the influence of both individual identity variables and issue ownership variables on political party affiliation. While the influence of each of the individual identity and issue ownership variables forms its own research question, as depicted in the conceptual model, the variables within each of the groups were conceptualized as forming a single construct; one construct was individual identity, the other was issue ownership. An appropriate statistical method was used in analyzing the relationships between these two constructs and political party affiliation. Question 23: What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and political party affiliation? Hypothesis 23: Those respondents who are under age 61, women, single, unemployed, non-white, live in non-rural areas, or have higher educational or lower income levels are more likely to identify themselves as Democrats than those respondents who are over age 61, men, married, employed, white, live in rural areas, have lower educational levels, or have higher income levels. Question 24: What is the relationship between access to health insurance and political party affiliation? Hypothesis 24: Those respondents who have health insurance are less likely to be Democrats than those respondents who do not have health insurance. Question 25: What is the relationship between self-rated health and political party affiliation?
CHAPTER THREE 91 Hypothesis 25: Those respondents with poor self-rated health will be more likely to be Democrats than those respondents with good self-reported health. Question 26: What is the relationship between satisfaction with the quality and cost of the health care system and political party affiliation? Hypothesis 26: Those respondents who are satisfied with the quality and/or cost of the health care system are less likely to be Democrats than those respondents who are dissatisfied with quality and/or cost of the health care system. Question 27: What is the relationship between opinion of the importance of health care as a political issue and political party affiliation? Hypothesis 27: Those respondents who consider health care to be an important political issue are more likely to identify themselves as Democrats than those respondents who consider health care not to be an important political issue. Question 28: What is the relationship between support for universal health care and political party affiliation? Hypothesis 28: Those respondents who support universal health care are more likely to identify themselves as Democrats than those respondents who do not support universal health care. Data Analysis Two primary statistical techniques were utilized to analyze the data. First, descriptive statistics were used to assess the parameters and characteristics of the data set. The second statistical method, which was employed to answer the research questions, was logistic regression. This is a form of analysis based on the concept of maximum likelihood estimation, which generates the parameters which are most likely to have
CHAPTER THREE 92 resulted in the observed measurements (Polit & Hungler, 1991). The relationships between multiple independent variables and categorical dependent variables can be analyzed for the probability of a given dependent variable occurring in relation to the occurrence of any of the independent variables (Polit & Hungler). This is a statistical method appropriate for use with categorical variables, and yields an odds ratio which allows the researcher to interpret the data in the context of likelihood of association between various factors and potential outcomes (Munro, 2005). These odds ratios also lend themselves well to relatively straightforward and understandable interpretation and explanations, including display in chart form. Some of the research questions were answered using multiple logistic regressions, when the relationships proposed in the conceptual model were between several independent variables which together formed one theoretical construct, for example all of the characteristics contained under demographics, and one dependent variable, therefore making the inclusion of several variables in the same equation logical. Others of the research questions were answered using binary logistic regression, when the relationships proposed in the conceptual model were between a single independent and dependent variable. Conclusion A review of the literature relating to health and voting led to the generation of a conceptual model of theorized relationships between individual identity, issue ownership, and voting behavior. This conceptual model was operationalized using variables of interest and relevance to rural nursing. The theoretical propositions relating these variables to one another were turned into testable research questions, a data set was
CHAPTER THREE 93 selected and obtained, and a design and methods appropriate to testing these propositions were described. In the next chapter, the results of the statistical analyses will be reported. First the sample will be described, then the research questions will be answered and the answers discussed in terms of the related literature.
CHAPTER FOUR 94
CHAPTER FOUR: RESULTS Introduction As part of a public opinion poll performed in September, 2006, at the request of ABC News in partnership with USA Today and the Kaiser Family Foundation, a sample of 1201 adults was obtained using a variety of procedures designed to obtain as representative as possible sample of the target population of all adults over age eighteen in the contiguous 48 U.S. states with home telephones. The respondents were asked a series of questions designed to elicit their opinions regarding a variety of health-related issues, including their perception of their own health status, whether or not they had health insurance, satisfaction with the cost and quality of health care, opinions about health care as a political issue, support for universal health care, demographic variables, and political party affiliation. These answers were entered into a data set by the polling organization, and made available for academic and research purposes. This data set was obtained and analyzed to answer a series of research questions investigating the relationships between factors related to health and opinions related to voting. Conceptual Model A conceptual model derived from the literature was used to guide the selection of appropriate variables, as well as to construct research questions. Although the model as a whole was not tested, the individual relationships shown among the variables in the
CHAPTER FOUR 95 model were tested by answering the research questions. The variables in the model were examined individually, and some of them were also conceptualized and examined as contributing to larger theoretical constructs. When relationships between single variables were tested, binary logistic regression was used. Demographic characteristics, which included age, race, gender, educational level, marital status, employment status, income level, and place of residence, was one such construct. Multiple logistic regression was used whenever demographics were the independent variable in a research question, so that the influence of each of the traits within demographics were included in the same equation. Similarly, satisfaction with the quality and cost of health care was, when an independent variable, analyzed as a single construct using multiple logistic regression. Finally, when all of the variables within one of the conceptual grouping in the model, either individual identity or issue ownership, were examined as a single theoretical construct, multiple logistic regression was used for analysis. In the tables depicting the results of the statistical analyses, when multiple logistic regression was used the results are shown within a single table. Descriptive Analysis of the Sample Following examination of the results reported by the sponsors of the poll, independent analyses were performed. First, descriptive analyses were performed, both of the data as originally collected and then, where applicable, of the recoded categories. Individual Identity Variables The first statistical results examined were those describing the individual identity variables, both those selected for this study, including demographic characteristics, access to health insurance, self-reported health status, and satisfaction with the health
CHAPTER FOUR 96 care system, and some not specifically examined in this study but which nonetheless provide valuable information about the composition of the sample. Demographic Variables-Geography: The first demographic characteristics of the sample which were analyzed were geographic factors, staring with time zone. Forty-nine percent of the sample resides in the Eastern time zone, 28.1 percent in the Central time zone, 4.5 percent in the Mountain time zone, and 18.5 percent in the Pacific time zone. While the U.S. Census Bureau does not report population distribution by time zone, rough estimates calculated by tallying total state populations within the four time zones show that the percentages in the sample are highly congruent with the population as a whole. There may be a slight under-representation of the Mountain and Central time zones, by less than two percentage points each, and a slight over-representation of the Pacific and Eastern time zones, also by less that two percentage points each. The next demographic characteristics reported were census region and division. Nineteen percent of the sample resided in the Northeast region, comprised of the New England and Mid-Atlantic divisions; 22.3 percent resided in the Midwest region, comprised of the East North Central and West North central divisions; 36 percent resided in the South region, comprised of the South Atlantic, East South Central, and West South central divisions; and 22.7 percent resided in the West region, comprised of the Mountain and Pacific divisions. These percentages are all within a fraction of a percentage point of the percentages of actual U.S. population reported by the U.S. Census Bureau for these regions (U.S. Census Bureau, 2009b). The last of the geographic variables of interest is metropolitan non-metropolitan distribution of the sample; 79.6 percent of the sample resided within an MSA, and 20.4
CHAPTER FOUR 97 percent resided outside an MSA. These percentages are very similar to U.S. Census data, in which 80.3 percent are reported as metropolitan and 19.7 percent are reported as nonmetropolitan, and 79 percent are reported as residing in urban areas, 21 percent in rural areas (U.S. Census Bureau, 2009b). The description of the sample in terms of geographic distribution by time zone, region, and MSA validates that the sample is highly representative of the target population in these characteristics (see Table 3). Table 3: Geographic Distribution of the Sample Sample % MSA 79.6 Non-MSA 20.4 Rural 79.6 Non-rural 20.4
U.S. Population % 80.3 19.7 79 21
Demographic Variables-Gender and Family: The next demographic characteristics examined were gender and family structure. As described in the sampling techniques, men are more difficult to reach by phone than women, and despite the techniques used by the polling organization to compensate for this, the gender distribution of the sample does differ somewhat from that of the population as a whole; 42.9 percent of the sample was male and 57.1 percent was female, compared to the U.S. population in which 49.1 percent is male and 50.9 percent is female (U.S. Census, 2009b). The distribution of marital status in the sample also deviated somewhat from that of the overall population. Married people are somewhat over-represented in the sample; 64.3 percent compared to 54.4 percent of the population. Divorced people are also somewhat over-represented in the sample; 12.4 percent compared to 9.7 percent of the population. Conversely, never married people were significantly under-represented in the
CHAPTER FOUR 98 sample; 13.8 percent compared to 27.1 percent of the population. Possible explanations for this discrepancy are the tendency of never married people to be younger overall than other populations, perhaps meaning they are at home less and are more likely to have a cell phone as their primary phone instead of a land line. Although the sample does differ from the population in distribution of gender and marital status (see Table 4), it is unclear what impact this had on the results of the study. While women have been described in the literature as possessing certain individual identity and issue ownership characteristics, the over-representation of which may influence the results, both married and divorced people have been described as possessing different individual identity and issue ownership characteristics, the overrepresentation of which may or may not balance out the over-representation of women. Table 4: Gender and Family Distribution of the Sample Sample % U.S. Population % Male 42.9 49.1 Female 57.1 50.1 Married 64.3 54.4 Divorced 12.4 9.7 Never Married 13.8 27.1
Demographic Variables-Age: The congruence of the age distribution of the sample with that of the population as a whole is difficult to quantify, since the target population is adults over age eighteen, which is 74.3 percent of the entire U.S. population. In the sample age is reported in ten year blocks from age eighteen to age 49, an eleven-year block of those ages 50 to 61, and those over 61 grouped in one block; the U.S. Census Bureau reports age in five year blocks up to age 24, ten year blocks from age 25 to age 85, and those over 85 grouped in one block.
CHAPTER FOUR 99 In general, though, two conclusions about the relative similarity of the sample to the population can be drawn. The first is that the sample mirrors the population in the extent to which the percentages of the sample in the various age categories increase with age up to age 61, and then decreases {8.9 percent between ages eighteen and 29; 14.9 percent between ages 30 and 39; 23.1 percent between ages 40 and 49; 33.6 percent between ages 50 and 61; and 19.1 percent over age 61}. This reflects the size of the now middle-aged “baby boom” generation, with fewer people in the age groups both older and younger. The second is that people over age 61 are somewhat over-represented in the sample. While adults age 65 and over represents 12.4 percent of the total U.S. population, and 16.7 percent of the target population of people over age eighteen, adults over age 61 comprise 19.1 percent of the sample. Some of this may reflect the early cusp of the Baby Boom, who make part of the group between ages 61 and 65, which represents an area of discrepancy of categories between the sample and the U.S. Census. It also likely reflects the fact that people in that age group are more likely to be home to answer the phone, and more likely to have a land line (U.S. Census Bureau, 2009b). Demographic Variables-Race: The sample differs somewhat from the general population in racial distribution (see Table 5), with whites over-represented in the samples and racial minorities under-represented. According to the U.S. Census Bureau (2009b), whites represent 75.1 of the U.S. population, but 80.4 percent of the sample; blacks comprise 12.3 percent of the population, but only 6.3 percent of the sample; Hispanics make up 12.5 percent of the population but 6.0 percent of the sample; Asians represent 3.6 percent of the population but 1.2 percent of the sample; other races make up 6.3 percent of the population but 3.9 percent of the sample. Based on the literature related
CHAPTER FOUR 100 to voting and health, as well as on analysis of the data set, black, Hispanic, Asian, and other races were recoded into one variable representing racial and ethnic minorities, comprising 19.6 of the sample. Table 5: Racial Distribution of the Sample Sample % White 80.4 Black 6.3 Hispanic 6.0 Asian 3.6 Other races 3.9
U.S. Population % 75.1 12.3 12.5 1.2 6.3
Demographic Variables-Education: The educational distribution of the sample also deviates somewhat from that of the overall population (see Table 6), with people with a high school education or less somewhat under-represented. In the U.S. population over age twenty-five years, 7.5 percent has less than a ninth grade education, 12.1 percent has some high school education but did not graduate, and 28.6 percent has a high school diploma, for a total of 48.2 percent with a high school education or less (U.S. Census Bureau, 2009b). In the sample, one percent has an eighth grade education or less, 4.6 percent has some high school education but did not graduate, and 26.6 percent has a high school diploma, for a total of 32.2 percent with a high school education or less. Conversely, those with at least some college education are somewhat overrepresented in the sample (see Table 6). In the U.S. population over age twenty-five years 21 percent have some college education but did not get a degree, 21.8 percent have an associate’s or bachelor’s degree, and 8.9 percent have a graduate degree, for a total of 51.7 percent with at least some college education (U.S. Census Bureau, 2009b). In the sample, 27.5 percent have some college education but did not graduate, 23.9 have an
CHAPTER FOUR 101 associate’s or bachelor’s degree, and 15.9 percent have a graduate degree, for a total of 66.6 percent with at least some high school education. Table 6: Educational Distribution of the Sample Sample % th th < 8 /9 grade 1.0 Some HS 12.1 HS diploma 28.6 Some college 27.5 Undergraduate degree 23.9 Graduate degree 15.9
U.S. Population % 7.5 4.6 26.6 21.0 21.8 8.9
Demographic Variables-Employment: The way in which the polling organization reported the employment characteristics of the sample was inconsistent with the ways in which federal agencies such as the U.S. Census Bureau and Bureau of Labor Statistics report employment, but some comparisons could still be drawn (see Table 7). Among sample respondents, 9.6 percent were employed part time and 54 percent were employed full time, for a total of 63.9 percent of the sample indicating some form of employment. According to the U.S. Census Bureau, at the time of this poll, 63.4 percent of the population was employed in the civilian labor force, with no distinction between full-time and part-time employment (U.S Census Bureau, 2009b). Among the sample respondents, 6.5 percent were unemployed and 0.6 percent had been laid off, for a total of 7.1 percent of the sample indicating that they were not currently employed. In 2006, the time frame during which the data were collected, the U.S. unemployment rate was 4.6 percent, with no distinction between being unemployed and laid off (U.S. Census Bureau, 2009b). Unemployed respondents may have been slightly over-represented in the sample in due to being easier to reach by home phone.
CHAPTER FOUR 102 Among sample respondents, 23.6 percent reported being retired, 3.6 percent were full-time homemakers, and 0.8 were full-time students, for a total of 28 percent indicating that they were voluntarily out of the labor force in some way. During that time period, 36 percent of the U.S. population was reported as being out of the labor force, with no distinction between being retired and other causes of being out of the labor force (U.S. Census Bureau, 2009b). This discrepancy between the sample composition and that of the actual population is difficult to explain, given the over-representation of people over age 61 in the sample as well as the increased likelihood of being able to reach people who are out of the labor force on their home phone. Table 7: Employment Distribution of the Sample Sample % Employed 63.9 PT -9.6 FT -54.0 Out of the labor force 35.1 Unemployed -7.1 Retired -23.6 Homemakers -3.6 Students -0.8
U.S. Population % 63.4 36.0 -4.6
Demographic Variables-Income: Income distribution across the sample was also difficult to compare to the population as a whole due to the categories used for data collection (see Table 8). Of the 89.4 percent of respondents willing to provide their household income, 12.7 percent reported an annual household income below 20 thousand dollars, whereas the U.S. Census Bureau (2009b) reports that 15.8 percent of the population has an annual household income below 15 thousand dollars, and another 12.8 percent has an annual household income between 15 and 25 thousand dollars. Low income individuals and families were under-represented in the sample, based on this comparison.
CHAPTER FOUR 103 Among the sample respondents, 15.6 percent reported an annual household income between 20 and 35 thousand dollars, while the U.S. Census Bureau (2009b) reported that in addition to the 12.8 percent of the population with annual household incomes between 15 and 25 thousand dollars mentioned above, another 12.8 percent of the population has an annual household income between 25 and 35 thousand dollars. Since it is unclear from the numbers above how much of the U.S. population has an annual household income between 20 and 25 thousand dollars, a precise comparison cannot be drawn, but it appears that the sample is similar to the total population in this income category. Among the sample respondents, 17.5 percent reported an annual household income between 35 and 50 thousand, while the U.S. Census Bureau (2009b) reported that 16.5 percent of the population has annual household income in this range; and 19.8 percent of the sample respondents reported an annual household income of between 50 and 75 thousand dollars, while 19.5 percent of the U.S. population had an income in that range. Across those two income strata, the sample is also quite similar to the total population. Overall, then between the annual household incomes of 20 and 75 thousand dollars, the sample is very similar to the population as a whole. Of the sample respondents, 15.5 percent reported an annual household income between 75 and 100 thousand dollars, and 18.8 percent reported an annual household income of over 100 thousand dollars. The U.S. Census Bureau (2009b), however, reported 10.2 percent of the U.S. population had an annual household income between 75 and 100 thousand dollars, and 12.3 percent of the population had an annual household
CHAPTER FOUR 104 income of over 100 thousand dollars. High income individuals and families, therefore, are somewhat over-represented in the sample. Table 8: Income Distribution of the Sample Income Range Sample % < $20,000 12.7 < $15,000 $15-25,000 $25-35,000 $20-35,000 15.6 $35-50,000 17.5 $50-75,000 19.8 $75-100,000 15.5 > $100,000 18.8
U.S. Population % 15.8 12.8 12.8 16.5 19.5 10.2 12.3
Self-Reported Health: The majority of the sample rated their own health positively, with 31 percent reporting excellent health and 57.5 percent reporting good health, for a total of 88.5 percent with good self-reported health after recoding. Only 8.2 percent rated their health as not so good, and 3 percent rated their health as poor, for a total of 11.2 percent with poor self-rated health after recoding. Access to Health Insurance: At the time of the study, 90.8 percent of the sample said they currently had some form of health insurance, and 9.2 percent said they did not. An additional 5 percent reported going without health insurance at some point during the previous twelve months, for a total of 13.2 percent who lacked health insurance at some point in the year the data were collected. This percentage approaches the U.S. Census Bureau (2008) estimate that 15 percent of the population lacks insurance in any given year. The fact that certain groups, including married respondents, respondents over age 61, whites, those with at least some college education, and those in the upper income brackets, are over-represented in the sample may explain the fact that the sample fairs better overall in terms of access to
CHAPTER FOUR 105 health insurance that the population as a whole. Of those respondents who reported having some form of health insurance, 19.8 percent had Medicare, 3.4 percent had Medicaid, and 76.9 percent having private insurance. Satisfaction with Health Care: The sample was fairly evenly split between those who were satisfied with the overall quality of health care, 45.2 percent, and those who were not, 53.1 percent. The sample was much less satisfied overall with the cost of health care, however, with only 17.5 percent reporting being satisfied with cost and 80.4 reporting being dissatisfied. Issue Ownership Variables The next group of characteristics examined were those relating to issue ownership, and the sample was described across those parameters. Political Importance of Health Care: When asked what would be the most important issue in their decision about the next election, 13.1 percent chose health care. When asked what would be the second most important issue, 15.9 percent chose health care. Overall, then 29 percent considered health care a highly important political issue. Support for Universal Health Care: Forty-one percent of the sample expressed a preference for maintaining the current health care delivery system, whereas 52.9 percent reported that they would prefer a universal health care system. Party Preference for Managing Health Care: Of the 78 percent of respondents who expressed a preference of one party over another for managing health care, 60.6 percent said that they preferred the Democratic Party, and 39.4 said that they preferred the Republican Party. This is consistent with the literature, in which health care is among
CHAPTER FOUR 106 the issues more likely to be identified with the Democratic Party (Lake et al., 2008; Petrocik et al., 2003-04). Voting Behavior The final characteristics of the sample described were those relating to voting behavior, including political party affiliation and political ideology. Political Party Affiliation: Of the 63.9 percent of the sample respondents who expressed an affiliation with one of the two major political parties, 54.9 reporting being Democrats and 45.1 percent reported being Republicans. While percentages vary over time, Democrats have traditionally held an advantage in terms of the percentage of Americans registered with a party affiliation, despite the fact that that does not always translate into electoral victory. Since only 63.9 percent of respondents affiliated themselves with one of the two major political parties, the sample was examined to determine if non-party-affiliated respondents differed substantially in any way from party-affiliated respondents (see Table 9).
CHAPTER FOUR 107 Table 9: Comparison of Party-Affiliated and Non-Party-Affiliated Respondents Party-Affiliated (n = 768) Non-Party-Affiliated (n = 433) % % Democrats 55 n/a Republicans 45 n/a MSA 79 81 Non-MSA 21 19 Income $100,000 18 15 Male 39 50* White 82 78 Married 66 66 Employed PT 9 10 Employed FT 53 52 Unemployed 13 16 Retired 24 22 At least some college 68 64 Age > 61 20 17 Insured 91 90 Good self-reported health 89 87 Satisfied with quality 46 44 Satisfied with Cost 19 15 Prefer Democrats for health care 53 66* Support universal health care 52 54 Prioritize health care politically 30 27 Liberal 21 19 Moderate 39 46* Conservative 37 27* * = > 5% difference between party-affiliated and non-party affiliated respondents Overall, the groups were quite similar in demographic and other individual identity variables, with the exception of gender. Non-party-affiliated respondents were markedly more likely to be male than party-affiliated respondents. The groups were also quite similar in two of the three issue ownership variables, but non-party-affiliated
CHAPTER FOUR 108 respondents were notably more likely to prefer the Democrats’ management of health care issues. Non-party-affiliated respondents were also much more likely to identify themselves as ideologically moderate, and less likely to identify themselves as ideologically conservative, than party-affiliated respondents. While it is impossible to quantify the impact these differences may have on the data analyses, it is likely that they tend to balance one another out to some extent. Men and ideological moderates are both more likely to identify themselves as Republicans, while those who prefer the Democrats’ management of health care are more likely to identify themselves as Democrats. Ideology: When asked about ideology, as opposed to political party affiliation, far more respondents were willing and/or able to classify themselves (see Table 10). Fully 97.4 percent were able to describe their ideology as falling into one of three categories, with 21.3 percent describing themselves as liberals, 42.9 percent as moderates, 34.4 as conservatives, and only 1.5 percent reporting that they didn’t think of themselves in those terms. Interestingly, while ideology and party affiliation certainly have a relationship to one another, they are not synonymous. Ninety percent of liberals described themselves as Democrats, but so did 63 percent of moderates and 26 percent of conservatives. That left 10 percent of liberals identifying themselves as Republicans, as well as 37 percent of moderates, and 74 percent of conservatives. Table 10: Ideological and Party Distribution of the Sample Democrats Republicans Liberal 90 10 Moderate 63 37 Conservative 26 74
Non-affiliated 19 46 27
Total 21.3 42.9 34.4
CHAPTER FOUR 109 Answers to Research Questions In order to address the goal and purposes of this study, as well as test the proposed relationships depicted in the conceptual model, a number of research questions were asked. Individual Identity Race, age, & gender Marital status Income Employment Education Place of residence (rural/nonrural)
Health insurance
Health status
Satisfaction with the health care system
Party affiliation Issue Ownership Importance of health care as a political issue
Support for universal health care
Party preference for managing health care
Figure 2: Operationalized Conceptual Model First, questions were asked regarding the relationships among individual identity variables, including demographics, self-reported health, having health insurance, and satisfaction with the health care system. Next, questions were asked investigating the relationships among issue ownership variables, including importance of health care as a
CHAPTER FOUR 110 political issue, political party preference for managing health care, and support for universal health care. Third, questions were asked regarding the relationships between the individual identity variables and the issue ownership variables. Finally, questions were asked investigating the relationships between individual identity variables, issue ownership variables, and political party affiliation (see Table 2). Table 2: Research Questions Grouped by Variables Concept group Question Independent variable Individual Identity 1 Demographics 2 Demographics 3 Health insurance 4 Demographics 5 Health insurance 6 Self-reported health Issue Ownership 7 Support UHC** 8 Political importance 9 Support UHC 10 Political importance Identity + Issue 11 Demographics 12 Health insurance 13 Self-reported health 14 Satisfaction with HC 15 Demographics 16 Health insurance 17 Self-reported health 18 Satisfaction with HC 19 Demographics 20 Health insurance 21 Self-reported health 22 Satisfaction with HC Voting behavior 23 Demographics 24 Health insurance 25 Self-reported health 26 Satisfaction with HC 27 Political importance 28 Support for UHC *HC = health care; **UHC = universal health care
Dependent variable Health insurance Self-reported health Self-reported health Satisfaction with HC* Satisfaction with HC Satisfaction with HC Political importance Support UHC Party preference HC Party preference HC Political importance Political importance Political importance Political importance Party preference HC Party preference HC Party preference HC Party preference HC Support for UHC Support for UHC Support for UHC Support for UHC Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation Party affiliation
CHAPTER FOUR 111 Within each of these groups of questions the research question is restated, followed by its accompanying research hypothesis, and statistical results are presented in table form. In the conceptual model, there is a broken-lined box containing all of the individual identity variables. The relationships among these variables form the basis for questions one through six, and are listed in Table 2 under the appropriate concept group. As mentioned previously, demographics are considered one theoretical construct, and are therefore depicted that way in the conceptual model, Table 2, and the research questions. In terms of analyses, the influence of demographics is always evaluated using multiple logistic regression, whereas relationships between the other individual identity variables are evaluated using binary logistic regression, as at this point in the model they are considered individual constructs. There is also a broken-lined box in the model containing all of the issue ownership variables. The relationships between these variables form the basis for questions seven through ten, and are also listed in Table 2 under the appropriate concept group. At this point in the analyses these are all considered relationships between individual theoretical constructs, and are therefore evaluated using binary logistic regression. The influence of the individual identity construct in the model and the issue ownership construct forms the basis of questions eleven through twenty-two, and are listed in Table 2 under the appropriate concept group. For logic in terms of answering the research questions, the individual identity variables are all treated individually, but since in the conceptual model they all contribute to one theoretical construct, their influence was evaluated using multiple logistic regression. When variables appear together in a
CHAPTER FOUR 112 table, this signifies the fact that they were analyzed in this way, and were all part of the same statistical equation. Finally, the relationships in the conceptual model between the two constructs of individual identity and issue ownership and political party affiliation form the basis of questions twenty-three through twenty-eight, and are listed in Table 2 under the appropriate concept group. As above, each factor in the larger construct forms its own research question, but the influence of the construct as a whole was evaluated using multiple logistic regression. Relationships among Individual Identity Variables First, questions were asked regarding the relationships among individual identity variables; these variables were demographics, including age, gender, race, marital status, employment status, educational level, and income; and health-related variables, including self-reported health, having health insurance, and satisfaction with the health care system. Question 1 What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and having health insurance? Hypothesis 1 Those respondents who are under age 61, women, single, unemployed, nonwhite, live in rural areas, or have lower educational or income levels are less likely to have health insurance than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Data Analysis and Results This question was answered using multiple logistic regression (see Table 11).
CHAPTER FOUR 113 Table 11: Factors Influencing Likelihood of Not Having Health Insurance Characteristic (reference category) n p Odds ratio Age (> 61) Age < 61 977 .002* 5.067 Gender (Male) Female 686 .636 1.123 Education (> HS) HS or < 425 .329 1.279 Martial status (Married) Unmarried 436 .075 1.569 Employment (Retired) Employed PT 115 .091 2.235 Employed FT 613 .616 1.232 Unemployed 171 .004* 3.289 Race (white) Non-white 261 .018* 1.851 Income (>$100,000) < $20,000 136 .000* 31.955 $20-35,000 168 .000* 16.915 $35-50,000 188 .002* 10.484 $50-75,000 213 .117 3.524 $75-100,000 167 .619 1.582 Residence (MSA) Non-MSA 245 .838 .944 * = p < .05 Age: The research hypothesis was supported, as there was a statistically significant relationship between age and having health insurance. Respondents under age 61 (n = 977) were 5.067 times less likely to have health insurance than those respondents over age 61 (p = .002). Gender: The research hypothesis was not supported; there was no statistically significant relationship between gender and having health insurance. Educational Level: The research hypothesis was not supported; as there was no statistically significant relationship between education and having health insurance. Marital Status: The research hypothesis was not supported; there was no statistically significant relationship between marital status and having health insurance.
CHAPTER FOUR 114 Employment Status: The research hypothesis was supported, as there was a statistically significant relationship between employment status and having health insurance. Unemployed respondents (n = 171) were 3.289 times less likely to have health insurance than retired respondents (p = .004). Race: The research hypothesis was supported, as there was a statistically significant relationship between race and having health insurance. Respondents who were members of minorities (n = 261) were 1.851 times less likely to have health insurance than white respondents (p = .018). Income: The research hypothesis was supported, as there was a statistically significant relationship between income and having health insurance. Respondents with annual household incomes under 20 thousand dollars (n = 136) were 31.955 times (p = .000) less likely to have health insurance than respondents with annual household incomes over 100 thousand dollars. Respondents with annual household incomes between 20 and 35 thousand dollars (n = 168) were 16.915 times less likely (p = .000), and respondents with annual household incomes between 35 and 50 thousand dollars (n = 188) were 10.484 times less likely (p = .002), to have health insurance than respondents with annual household incomes over 100 thousand dollars. Place of Residence: The research hypothesis was not supported; there was no statistically significant relationship between MSA and non MSA residence and having health insurance. Interpretation: Access to Health Insurance The relationship between age and access to health insurance is a logical result, since everyone over age 65 years is eligible for Medicare. The relationships between
CHAPTER FOUR 115 marital status, educational level, income, and employment status and access to health insurance reflect those noted in the literature review (Johnston & Ware, 1976; Ross & Mirowsky, 2000; Ross & Wu, 1996; Weinick & Krauss, 2000; Ziller et al., 2006), in which women, members of minorities, unmarried adults, low income adults, and those with lower educational levels are all more likely to lack access to health insurance (Holohan et al., 2003; Szabo & Appleby, 2009). Question 2 What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and self-reported health? Hypothesis 2 Those respondents who are over age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to have poor self-reported health than those respondents who are under age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Data Analysis and Results This question was answered using multiple logistic regression (see Table 12).
CHAPTER FOUR 116 Table 12: Demographic Influences on Likelihood of Having Poor Self-Reported Health Characteristic (reference category) n p Odds ratio Age (> 61) Age < 61 977 .174 1.582 Gender (Male) Female 686 .659 .903 Education (> HS) HS or < 425 .762 1.076 Martial status (Married) Unmarried 436 .655 .895 Employment (Employed PT) Employed FT 613 .470 .726 Unemployed 171 .001* 3.994 Retired 264 .031* 2.718 Race (white) Non-white 261 .581 .859 Income (>$100,000) < $20,000 136 .000* 9.986 $20-35,000 168 .014* 3.275 $35-50,000 188 .182 1.916 $50-75,000 213 .428 1.482 $75-100,000 167 .799 1.168 Residence (MSA) Non-MSA 245 .911 .971 * = p < .05 Age: The research hypothesis was not supported; there was not a statistically significant relationship between age and health status. Gender: The research hypothesis was not supported, as there was no statistically significant relationship between gender and self-reported health. Educational Level: The research hypothesis was not supported; there was not a statistically significant relationship between educational level and self-reported health. Marital Status: The research hypothesis was not supported; there was not a statistically significant relationship between marital status and self-reported health. Employment Status: The research hypothesis was supported, as there was a statistically significant relationship between employment and health status. Unemployed respondents
CHAPTER FOUR 117 (n = 171) were 3.994 times more likely (p = .001), and retired respondents (n = 264) were 2.718 times more likely (p = .031) to rate their own health has poor than respondents who were employed part-time. Race: The research hypothesis was not supported, as there was no statistically significant relationship between race and self-reported health status. Income: The research hypothesis was supported, as there was a statistically significant relationship between income and self-reported health status. Respondents with annual household incomes below 20 thousand dollars (n = 136) were 9.986 times more likely (p = 000) and respondents with annual household incomes between 20 and 35 thousand dollars were 3.275 times more likely (p = .014) to rate their own health as poor than respondents with annual household incomes over 100 thousand dollars. Place of Residence: The research hypothesis was not supported; there was no statistically significant relationship between MSA and non MSA residence and self-reported health status. Question 3 What is the relationship between having health insurance and self-reported health? Hypothesis Three Those respondents who do not have health insurance are more likely to have poor self-reported health than those respondents who do have health insurance. Data Analysis and Results This question was answered using logistic regression (see Table 13).
CHAPTER FOUR 118 Table 13: Access Influence on Likelihood of Having Poor Self-Reported Health Characteristic (reference category) n p Odds ratio Insurance (Yes) No 111 .000* 3.908 * = p < .05 The research hypothesis was supported, as there was a statistically significant relationship between health insurance and health status. Those respondents without health insurance were 3.908 times more likely to rate their own health as poor than respondents without health insurance (p = .000). Interpretation: Self-Reported Health The relationships between various demographic factors and self-reported health status reflect the results of many previous studies, in which employment and income (Clemente & Sauer, 1976; Cox et al., 1988; Johnston & Ware, 1976; Weinrich et al., 2001) have both been found to influence a person’s perception of their own health. The relationship between employment status and self-reported health status was most likely largely a reflection of age, since retired respondents were more likely to be over age 61. The relationship between having health insurance and self-reported health has been well-documented in the literature, which has noted that lack of health insurance is consistently associated with difficulty accessing health care and poor health outcomes, especially for those living in poverty (Andrulis, 1998); that the insured and wealthy have much better health than the uninsured and poor (Kennedy & Morgan, 2006; Sanmartin et al., 2006); and that uninsured Americans are more likely to report difficulty paying medical bills; they are also more likely to report having delayed needed physician visits or recommended tests and procedures, delayed or skipped filling prescriptions, and taking
CHAPTER FOUR 119 a lower dose of a prescription to make it last longer (Forbes, 2009; Szabo & Appleby, 2009). These results are confirmed by Byck (2000), whose analysis showed that uninsured children were significantly more likely to be in poor health. Chronic disease management among those who lack health insurance is significantly inferior to that received by those who have health insurance (Hicks et al., 2006), as are primary prevention and health maintenance activities (Forbes, 2009; Szabo & Appleby, 2009). In fact, in general, health care expenditures overall are consistently lower among individuals who lack health insurance than those who are insured, signifying lack of access to routine care (Ward & Franks, 2007). Bharmal and Thomas (2005) noted that people without health insurance had significantly lower physical and mental health scores, and lack of health insurance has a negative affect on adult health which is cumulative over the number of uninsured years (Quesnel-Vallee, 2004). Question 4 What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and satisfaction with quality and cost of the health care system? Hypothesis 4 Respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are less likely to be satisfied with the quality and/or cost of the health care system than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels.
CHAPTER FOUR 120 Data Analysis and Results The first part of this question, examining the influence of demographic characteristics on satisfaction with the quality of health care, was answered using multiple logistic regression (see Table 14). Table 14: Demographic Influences on Likelihood of Dissatisfaction with Quality Characteristic (reference category) n p Odds ratio Age (> 61) Age < 61 977 .051 1.578 Gender (Male) Female 686 .012* 1.391 Education (> HS) HS or < 425 .298 .854 Martial status (Married) Unmarried 436 .611 1.080 Employment (Retired) Employed PT 115 .135 1.529 Employed FT 613 .926 .980 Unemployed 171 .319 1.304 Race (white) Non-white 261 .581 .859 Income (>$100,000) < $20,000 136 .031* 2.046 $20-35,000 168 .005* 1.959 $35-50,000 188 .000* 2.636 $50-75,000 213 .027* 1.580 $75-100,000 167 .164 1.349 Residence (MSA) Non-MSA 245 .078 1.328 * = p < .05 The second part of this question, examining the influence of demographic characteristics on satisfaction with the cost of health care, was also answered using multiple logistic regression (see Table 15).
CHAPTER FOUR 121 Table 15: Demographic Influences on Likelihood of Dissatisfaction with Cost Characteristic (reference category) n p Odds ratio Age (> 61) Age < 61 977 .266 1.317 Gender (Male) Female 686 .057 1.389 Education (> HS) HS or < 425 .856 1.037 Martial status (Married) Unmarried 436 .546 1.131 Employment (Retired) Employed PT 115 .198 1.597 Employed FT 613 .096 1.587 Unemployed 171 .293 1.424 Race (white) Non-white 261 .282 .798 Income (>$100,000) < $20,000 136 .584 1.214 $20-35,000 168 .058 1.795 $35-50,000 188 .001* 2.830 $50-75,000 213 .044* 1.702 $75-100,000 167 .113 1.540 Residence (MSA) Non-MSA 245 .589 .893 * = p < .05 Age: The research hypothesis was not supported; there was not a statistically significant relationship between age and satisfaction with either the quality or cost of health care. Gender: The research hypothesis was partially supported. There was a statistically significant relationship between gender and satisfaction with the quality of health care, with women (n = 686) 1.391 times more likely than men to be dissatisfied with the quality of health care than men (p = .012), but there was no statistically significant relationship between gender and satisfaction with the cost of health care. Educational Level: The research hypothesis was not supported, as there was no statistically significant relationship between educational level and satisfaction with the cost or quality of health care.
CHAPTER FOUR 122 Marital Status: The research hypothesis was not supported; there was not a statistically significant relationship between marital status and satisfaction with either the quality or cost of health care. Employment Status: The research hypothesis was not supported; there was not a statistically significant relationship between employment status and satisfaction with the quality or cost of health care. Race: The research hypothesis was not supported; there was no statistically significant relationship between race and satisfaction with the quality or cost of health care. Income: The research hypothesis was supported. There was a statistically significant relationship between income and satisfaction with the quality of health care. Lower income respondents were more likely to express dissatisfaction with the quality of health care; compared to the reference category of annual household income over 100 thousand dollars, respondents with annual household incomes below 20 thousand dollars (n = 136) were significantly more likely (odds ratio = 2.046, p = .031) to be dissatisfied with the quality of health care, as were respondents with incomes between 20 and 35 thousand dollars (n = 168, odds ratio = 1.959, p = .005), respondents with incomes between 35 and 50 thousand dollars (n = 188, odds ratio = 2.636, p = .000), and respondents with incomes between 50 and 75 thousand dollars (n = 213, odds ratio = 1.580, p = .027). There was also a statistically significant relationship between income and satisfaction with the cost of health care, although the only income brackets in which the results were significant were between 35 and 50 thousand dollars (n = 188), in which respondents were 2.830 times more likely to be dissatisfied with the cost of health care (p = .001), and between 50 and 75 thousand dollars, in which respondents were 1.702 times more likely to be
CHAPTER FOUR 123 dissatisfied with the cost of health care (p = .044), than respondents in the reference category of income over 100 thousand dollars. Place of Residence: The research hypothesis was not supported; there was not a statistically significant relationship between MSA and non MSA residence and satisfaction with the quality or cost of health care, Question 5 What is the relationship between access to health insurance and satisfaction with the quality and cost of the health care system? Hypothesis 5 Respondents who have health insurance are more likely to be satisfied with the quality and/or cost of the health care system than those respondents who do not have health insurance. Data Analysis and Results The two parts of this question were each answered using logistic regression. First, the relationship between having health insurance and satisfaction with quality of health care was analyzed (see Table 16). Table 16: Access Influence on Likelihood of Dissatisfaction with Quality Characteristic (reference category) n p Odds ratio Insurance (Yes) No 111 .000* 3.185 * = p < .05 Then, the relationship between having health insurance and satisfaction with the cost of health care was analyzed (see Table 17).
CHAPTER FOUR 124 Table 17: Access Influence on Likelihood of Dissatisfaction with Cost Characteristic (reference category) n p Odds ratio Insurance (Yes) No 111 .001* 4.014 * = p < .05 The research hypothesis was supported, with those respondents who did not have health insurance (n = 111) 3.185 times more likely to be dissatisfied with the quality of health insurance (p = .000) and 4.014 times more likely to be dissatisfied with the cost of health insurance (p = .001) than respondents who did have health insurance. Question 6 What is the relationship between self-reported health and satisfaction with quality and cost of the health care system? Hypothesis 6 Respondents with good self-reported health are more likely to be satisfied with the quality and/or cost of the health care system than those respondents with poor selfreported health. Data Analysis and Results The two parts of this question were each answered using logistic regression. First, the relationship between self-reported health and satisfaction with the quality of health care was analyzed (see Table 18). Table 18: Health Influence on Likelihood of Dissatisfaction with Quality Characteristic (reference category) n p Odds ratio Self-reported health (Good) Poor 138 .000* 2.002 * = p < .05 Next, the relationship between self-reported health status and satisfaction with the cost of health care was analyzed (see Table 19).
CHAPTER FOUR 125 Table 19: Health Influence on Likelihood of Dissatisfaction with Cost Characteristic (reference category) n p Odds ratio Self-reported health (Good) Poor 138 .175 1.434 * = p < .05 The research hypothesis was partially supported. Respondents with poor selfreported health (n = 138) were 2.002 times more likely to be dissatisfied with the quality of health care (p = .000) than respondents with good self-reported health, but there was no statistically significant relationship between self-reported health status and satisfaction with the cost of health care. Interpretation: Satisfaction with Health Care Quality and Cost The relationship between gender and satisfaction with the quality of health care is reflected in the literature. Women who lack access to health care, or lack health insurance, are less likely to receive routine screening mammograms when other demographic factors are held constant (Qureshi et al., 2000). Women and members of minorities have also been noted to have significantly more difficulty paying medical bills (Szabo & Appleby, 2009). Interestingly, those in middle income brackets were more likely to be dissatisfied with the cost of health care than those in the lower and higher income brackets; this could be a reflection of the fact that while the lowest income Americans, and those over age 65 who are often on a fixed income, can access public insurance programs, those in the middle income range do not qualify for such programs. In addition, while both middle and upper income Americans generally have to pay either all or some of the cost of their own health insurance, that expense represents a disproportionate percentage of the income of those in the middle brackets.
CHAPTER FOUR 126 Relationships between having health insurance, good self-reported health status, and satisfaction with the health care system are most likely related to having lower out of pocket expenses, less necessary contact with the health care system, and ability to access private sources of care. In addition, the uninsured and those with chronic illnesses face more economic hardship in relation to health care (Hicks et al., 2006). Summary of Relationships among Individual Identity Variables Although many of the hypothesized relationships in the conceptual model, suggested by the literature, were not in fact supported by this analysis, two individual identity variables consistently had a significant influence over the others; income, and access to health insurance (see Table 20). Higher incomes respondents were significantly more likely to have health insurance, and respondents with higher incomes and health insurance were significant more likely to describe their own health as good, as well as to express satisfaction with both the quality and cost of health care. This result supports one of the underlying premises of this study, which is that universal health care will improve the overall health of Americans. Table 20: Summary of Significant Relationships among Individual Identity Variables Independent Variable Dependent Variable Significance Level Age Health insurance ** Employment ** Race * Income *** Employment Self-reported health ** Income *** Health insurance *** Gender Quality satisfaction * Income *** Health insurance *** Self-reported health *** Income Cost satisfaction ** Health insurance ** * = p < .05 ** = p , .01 *** = p < .001
CHAPTER FOUR 127 Relationships among Issue Ownership Variables Next, questions were asked investigating the relationships among issue ownership variables, including importance of health care as a political issue, political party preference for managing health care, and support for universal health care. Question 7 What is the relationship between support for universal health care and opinion of the importance of health care as a political issue? Hypothesis 7 Respondents who support universal health care are more likely to consider health care an important political issue than those respondents who do not support universal health care. Data Analysis and Results This question was answered using logistic regression (see Table 21). Table 21: Issue Influences Opinion of Health Care as a Political Issue Characteristic (reference category) n p Support universal health care (No) Yes 635 .000* * = p < .05
Odds ratio 2.002
The research hypothesis was supported, as there was a statistically significant relationship between support for universal health care and opinion of the political importance of health care. Respondents who expressed support for universal health (n = 635) care were 2.002 times more likely to consider health care an important political issue (p = .000) than respondents who did not support universal health care.
CHAPTER FOUR 128 Question 8 What is the relationship between opinion of the importance of health care as a political issue and support for universal health care? Hypothesis 8 Respondents who consider health care an important political issue are more likely to support universal health care than those respondents who do not consider health care important as a political issue. Data Analysis and Results This question was answered using logistic regression (see Table 22). Table 22: Issue Influence on Support for Universal Health Care Characteristic (reference category) n p Health care politically important (No) Yes 348 .000* * = p < .05
Odds ratio 2.002
The research hypothesis was supported, as there is a statistically significant relationship between importance of health care as a political issue and support for universal health care. Respondents who consider health care a politically important issue (n = 348) are more likely to support universal health care (odds ratio = 2.002, p = .000). Both Questions Seven and Eight were included among the research questions because the concepts and proposed relationships were represented in the conceptual model. However, due to the dichotomous variable coding of both importance of health care as a political issue and support for universal health care, these two question and their statistical results were in fact identical.
CHAPTER FOUR 129 Question 9 What is the relationship between support for universal health care and party preference for managing health care? Hypothesis 9 Respondents who support universal health care are more likely to prefer Democratic Party management of health care than those respondents who do not support universal health care. Data Analysis and Results This question was answered using logistic regression (see Table 23). Table 23: Influence of Support for Universal Care on Democratic Party Preference for Managing Health Care Characteristic (reference category) n p Odds ratio Support universal health care (No) Yes 635 .000* 2.483 * = p < .05 The research hypothesis was supported; there was a statistically significant relationship between support for universal health care and political party preference for managing health care. Respondents who support universal health care (n = 635) were 2.483 times more likely to prefer Democratic Party management of health care (p = .000). Question 10 What is the relationship between opinion of the importance of health care as a political issue and party preference for managing health care? Hypothesis 10 Respondents who consider health care important as a political issue are more likely to prefer Democratic Party management of health care than those respondents who consider health care not important as a political issue.
CHAPTER FOUR 130 Data Analysis and Results This question was answered using logistic regression (see Table 24). Table 24: Influence of Policy Priorities on Democratic Party Preference for Managing Health Care Characteristic (reference category) n p Odds ratio Health care politically important (No) Yes 348 .000* 6.478 * = p < .05 The research hypothesis was supported, as there was a statistically significant relationship between opinion of the political importance of health care and party preference for managing health care. Respondents who consider health an important political issue (n = 348) were 6.478 times more likely to express a preference for the Democratic Party on the issue of health care (p = .000). Interpretation: Issue Ownership The findings of these analysis mirror many of those noted in the literature; historically, the Democratic Party has had an advantage with voters concerned about social welfare and justice, equality, and civil rights issues, whereas the Republican Party has had an advantage with voters concerned about lower taxes, government spending, and the size of the federal government (Petrocik et al., 2003-04). The significant relationships found in this study (see Table 25) among support for universal health care, prioritization of health care as a political issue, and party preference for health care are also reflected in the literature (Birn et al., 2003; Lake et al., 2008). Democrats tend to indicate that health care reform is a higher priority than Republicans, and also prefer candidates who prioritize health care as a political issue (Blendon et al., 2008; DoBias, 2008; Steiber & Ferber, 1981). Access to health care as a basic human right, recognized more broadly in most of America’s industrially developed peer nations,
CHAPTER FOUR 131 has similarly been prioritized by the Democratic Party and disputed by the Republican Party (Birn et al., 2003). These results further support additional premises of this study, that voters who tend to prioritize health care as a political issue will also be voters who have reached the conclusion that universal health care represents the best solution for reforming health care in the U.S., and that these same voters will have also concluded that the Democratic Party is, based on historical and current evidence, the more likely of the two major parties to enact this type of reform. Table 25: Summary of Relationships among Issue Ownership Variables Independent Variable Dependent Variable Significance level Political importance of health care Support universal health care *** Support universal health care Political importance of health care *** Political importance of health care Party preference for health care *** Support universal health care *** *** = p < .001
Relationships between Individual Identity and Issue Ownership Variables The next set of questions explored the relationships between individual identity variables, including demographics, self-reported health status, access to health insurance, and satisfaction with the cost and quality of health care, and issue ownership variables. At this point, as depicted in the conceptual model, all of the individual identity variables are considered to contribute to one theoretical construct. Therefore, while the influence of each of the identity variables with the issue ownership variables forms the basis for its own research question for clarity of analysis, the relationships were evaluated using multiple logistic regression, with all of the individual identity variables included in one equation. Questions Eleven through Fourteen were answered using a single regression equation, and the results are depicted in Table Twenty-six.
CHAPTER FOUR 132 Question 11 What is the relationship between age, gender, educational level, marital status, employment status, race, income, and place of residence, and opinion of the importance of health care as a political issue? Hypothesis 11 Respondents who are under age 61, women, single, unemployed, non-white, live in rural areas, or have lower educational or income levels are more likely to consider health care an important political issue than those respondents who are over age 61, men, married, employed, white, live in non-rural areas, or have higher educational or income levels. Data Analysis and Results This question was answered using multiple logistic regression (see Table 26).
CHAPTER FOUR 133 Table 26: Identity Influences on Likelihood of Ranking Health Care Politically Important Characteristic (reference category) n p Odds ratio Age (> 61) Age < 61 977 .372 1.246 Gender (Male) Female 686 .000* 1.912 Education (> HS) HS or < 425 .882 1.025 Martial status (Married) Unmarried 436 .558 1.103 Employment (Retired) Employed PT 115 .387 .769 Employed FT 613 .159 .710 Unemployed 171 .021* .509 Race (white) Non-white 261 .456 1.146 Income (>$100,000) 61) Age < 61 977 .020* 2.098 Gender (Male) Female 686 .137 1.2834 Education (< HS) > HS 776 .051 1.461 Martial status (Married) Unmarried 436 .167 1.307 Employment (Retired) Employed PT 115 .925 .964 Employed FT 613 .309 1.361 Unemployed 171 .034* 2.159 Race (white) Non-white 261 .000* 3.258 Income (>$100,000) 61) Age < 61 977 .336 1.268 Gender (Male) Female 686 .722 1.054 Education (< HS) > HS 776 .721 1.062 Martial status (Married) Unmarried 436 .639 1.082 Employment (Retired) Employed PT 115 .462 1.259 Employed FT 613 .428 1.220 Unemployed 171 .650 1.149 Race (white) Non-white 261 .015* 1.585 Income (>$100,000) 61) Age < 61 977 .856 1.065 Gender (Male) Female 686 .021* 1.538 Education (< HS) > HS 776 .318 1.242 Martial status (Married) Unmarried 436 .099 1.423 Employment (Retired) Employed PT 115 .852 1.081 Employed FT 613 .630 1.174 Unemployed 171 .417 .729 Race (white) Non-white 261 .000* 2.968 Income (>$100,000) $100,000)
