An Exploration of Urban and Rural Differences in Lung Cancer ...

 RESEARCH AND PRACTICE 

An Exploration of Urban and Rural Differences in Lung Cancer Survival Among Medicare Beneficiaries | Lisa R. Shugarman, PhD, Melony E. S. Sorbero, PhD, Haijun Tian, PhD, Arvind K. Jain, MS, and J. Scott Ashwood, MA

Rural areas are characterized by low population density, isolation of and long distances between individuals and communities, and long distances from urban centers. Rural communities face difficulties protecting their supply of health care providers; such difficulties include recruitment and retention of physicians and maintenance of the viability of rural hospitals.1,2 Elderly people in rural areas also travel longer and wait longer for outpatient care and use fewer preventive services than do their urban counterparts.3,4 In a recent study by Pathman et al.,5 older adults who lived in rural areas with low physician density were significantly more likely to travel 30 or more minutes to a provider. These factors predict negative effects on the health of the rural population, which suggests that urban and rural differences in health outcomes may exist because of differential access to services. Lung cancer is the second most common cancer and the leading cause of cancer death for both men and women.6,7 The American Cancer Society estimates that approximately 174 470 Americans will be diagnosed with lung cancer and approximately 162 460 individuals will die from it this year.6 Early diagnosis of the disease is challenging and often achieved by chance rather than by intention. Generally, by the time lung cancer is diagnosed, it has spread to regional lymph nodes or other sites in the body.8 The 5-year survival rate for those presenting with localized disease is approximately 50%; however, only about 16% of patients present with localized disease.6 The average survival across all stages is approximately 15%.6 The probability of developing lung cancer is higher among those 70 years and older relative to other age groups, equivalent to 1 in 16 men and 1 in 24 women in this age group.6 The US population is aging such that by the year 2030, the number of those 65 years and older is projected to increase to 70 million, or 20% of the total population.9 The

Objectives. We tested the relationship between urban or rural residence as defined by rural–urban commuting area codes and risk of mortality in a sample of Medicare beneficiaries with lung cancer. Methods. We used Surveillance, Epidemiology, and End Results data linked with Medicare claims to build proportional hazards models. The models tested hypothesized relationships between individual and community characteristics and overall survival for a cohort of Medicare beneficiaries 65 years and older who were diagnosed with lung cancer between 1995 and 1999 (N = 26 073). Results. We found no evidence that lung cancer patients in rural areas have poorer survival than those in urban areas. Rather, individual (Medicaid coverage) and regional (lower census tract–level median income) socioeconomic factors and a smaller supply of subspecialists per 10 000 individuals 65 years and older were positively associated with a higher risk of mortality. Conclusions. Although urban versus rural residence did not directly influence survival, rural residents were more likely to live in poorer areas with a smaller supply of health care providers. Therefore, we still need to be aware of rural beneficiaries’ potential disadvantage when it comes to receiving needed care in a timely fashion. (Am J Public Health. 2007;97:1280–1287. doi:10.2105/ AJPH.2006.099416) impact of this demographic boom will be felt most strongly in rural areas, where older adults are already 20% of the population and their presence continues to grow at a faster rate than in urban areas.10 Given that there is a high incidence of lung cancer among older populations, the increase in the elderly population may result in substantial increases in diagnosed cases of lung cancer. The limited existing literature does not report a consistent story of geographic variation in the United States for the incidence, treatment, or survival associated with lung cancer. In an early study of lung cancer incidence rates, Blot and Fraumeni11 found that incidence was higher in the South, in both urban and rural areas. Iezzoni et al.12 examined geographic variation in the purpose of hospital admissions for cancer treatment in 1985 and found that rural hospitals were more likely to admit patients for palliative care, whereas urban hospitals were more likely to admit patients for active intervention. In more recently published studies that explored these issues in non–US populations,13–15 rural residents were found to be at a

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disadvantage when it comes to diagnosis and treatment of lung cancer and to experience poorer survival. Much of the difference is attributed to rural residents having more-advanced disease at the time of diagnosis. Wilkinson and Cameron reported better survival rates among urban residents relative to rural residents for 10 cancer types.16 Mikeljevic et al.17 found that survival was influenced by longer travel times to required treatment centers in a cohort of breast cancer patients. In another study,18 the supply of important medical resources (e.g., computed tomography scanners) was positively associated with cancer survival. Given our understanding of urban and rural differences in health care access, supply, and utilization, we might expect to find similar patterns of care in the United States. Patients who present with symptoms suggestive of lung cancer may first visit their primary care doctor, who may, in turn, refer them to a specialist to receive a definitive diagnosis. In the process of being diagnosed and beginning treatment, there may be delays in getting care, which can significantly influence survival. Both physician- and patient-related

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factors may influence the likelihood of delays in diagnoses or obtaining treatment. Delays in the referral process may occur between primary care and specialty care or, once a referral has been made, with the oncologist beginning treatment. Although there is a body of literature that describes delays in beginning cancer treatment,19–21 these studies concentrated on provider delays in care rather than on patient delays, and none compared the length of delays across urban and rural regions. However, physician supply and provider payments are lower in rural areas,22 which might contribute to greater delays in care for rural than for urban residents. Although rural residents may be more likely to have a regular source of care, they report fewer visits to the doctor.23 Rural residents may delay seeing a doctor because of problems with transportation or the cost of care.22,24 A recent study suggested that fewer primary care providers located in close proximity is associated with longer travel distances to obtain outpatient care.5 For specialty care, such as cancer treatment, bypassing local providers may also be preferred or necessary to receive appropriate care.25,26 However, elderly adults, especially those in rural areas, may lack the means to travel necessary distances and generally prefer to stay with local providers,24,27 the implication being that older adults in areas with fewer providers may not be receiving timely, appropriate care for their condition. Rural residents perceive fewer access problems,28 but objective measures of access suggest a different story. As a result, rural residents may be more likely to experience delays in care once symptomatic, which may affect survival. For our study, we explored the relationship between rural residence and survival for a cohort of Medicare beneficiaries diagnosed with lung cancer.

data from 14 population-based cancer registries. The persons living in the catchment area of the registries represent 26% of the US population.29 In addition to demographic and clinical information about cancer cases, the data file has been linked to selected census tract–level variables to describe the communities in which cancer patients live. To better understand processes of care for cancer, the SEER data have been linked with the Medicare claims data including the Medicare denominator file, Medicare provider analysis and review files (inpatient and skilled nursing facility), carrier files (Part B, including physician, independent lab, and other provider claims), and institutional outpatient, home health agency, hospice, and durable medical equipment standard analytic files. The ARF is a computerized information system maintained by the Bureau of Health Professions. The data set includes countylevel information on the availability of health facilities, health professions, economic activity, and socioeconomic characteristics.30 These data were linked to SEER–Medicare data by state and county of residence to provide detail on the county-level supply of health care providers.

Cohort Description

METHODS

Our sample included all Medicare beneficiaries 65 years and older who were diagnosed with lung cancer (International Classification of Diseases, Ninth Revision, Clinical Modification: 162.x)31 between 1995 and 1999, whose first diagnosed cancer was lung cancer, and who were continuously enrolled in both Medicare Parts A and B for the year prior to diagnosis and during the diagnostic workup and initial treatment. Excluded from the sample were Medicare beneficiaries enrolled in managed care, those with end-stage renal disease, and those eligible for Medicare because of disability.

Data

Conceptual Framework

Data for this study came from 2 sources: the Surveillance Epidemiology and End Result (SEER) program data linked to Medicare claims and the Area Resource File (ARF). The SEER program is the most authoritative source of cancer incidence and survival information in the country; it collects

We adopted Andersen’s32,33 behavioral model of health services use, which suggests that use of health services can be characterized by the following: (1) the need for health care, (2) predisposing factors that affect use of medical care, and (3) factors that enable or impede the use of services (Figure 1).

July 2008, Vol 98, No. 7 | American Journal of Public Health

Patient clinical characteristics related to lung cancer are categorized as “need factors” (e.g., stage of disease), whereas demographic and clinical characteristics not related to lung cancer fall under “predisposing factors.” We hypothesized that individuals with preexisting chronic conditions may be more likely to have an existing physician relationship and to have clinical needs that predispose them to use medical care. Living in an urban or rural area has been categorized as an “enabling factor” for health services use,34 in part because it determines the availability of physicians, hospitals, specialty care, and travel time to sources of care. In addition, rural elderly adults are generally of lower socioeconomic status, another enabling factor, than are urban elderly adults.10

Measures Overall survival. Survival time in months was determined from the date of lung cancer diagnosis in the SEER data to the date of death; survivors were censored at the end of the study period. Because SEER captures only the month and year of cancer diagnosis, we assigned the date of diagnosis as the midpoint of the month reported. The Medicare enrollment files capture the actual date of death and were used to calculate survival time. Predisposing characteristics. The SEER and Medicare data capture a variety of patient sociodemographic characteristics, including gender, race/ethnicity (White, Black, other), age at diagnosis, and comorbidities. We created a count of comorbidities based on the same conditions used in the Charlson Index35,36 to control for the predisposing burden of comorbid disease in our analyses by using physician and hospital administrative claims for the 12 months prior to diagnosis with lung cancer. Enabling characteristics. The method commonly used to define rural locations is based on whether a county is part of a Metropolitan Service Area as defined by the Office of Management and Budget. All counties outside a Metropolitan Service Area are considered to be rural. However, county boundaries obscure a wide range of local characteristics, because each county contains a mix of urbanized and rural locations.37 Instead, we used the rural–urban commuting area (RUCA) codes, based on the US Census Bureau’s

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most extensive surgery performed were categorized as not having had surgery (to distinguish surgery for treatment from that for diagnostic purposes).

Analytic Approach We conducted descriptive analyses overall and by urban or rural designation for the predisposing characteristics, enabling characteristics, need characteristics, and treatment variables for the beneficiaries in our lung cancer sample. We conducted the χ2 test for categorical variables, and we conducted analyses of variance (repeated-measures analysis of variance) for continuous variables. We used Cox proportional hazard models to predict the time from incident disease to death as a function of patient characteristics and applied parametric tests of the proportional hazards assumption. The Cox proportional hazards model was estimated as follows:

FIGURE 1—Conceptual model of health services use and outcomes for Medicare beneficiaries with lung cancer.

definitions of urbanized areas and urban places, which use information on population density and work-commuting patterns to identify urban and rural regions at the census-tract level.38 We collapsed the 30 RUCA codes into 4 categories: urban, large rural city, small rural town, and isolated small rural town. Socioeconomic status (SES) is associated with mortality from cancer, in part because of stage at diagnosis and differences in treatment.8,39–47 Proxy measures of SES include median household income and the proportion that do not speak English well in the patient’s census tract. We created tertiles for median income on the basis of their distribution in the sample (< $29 000, $29 000–$41 000, > $41 000). The distribution of the population that does not speak English well across census tracts was highly skewed; therefore, we created an indicator variable for those who resided in census tracts with more than 15% of the population unable to speak English well. To explore the relationship between the supply of health care providers and lung cancer survival, we used the ARF to link measures of physician subspecialist supply and the number of hospitals per 10 000 elderly adults located in the county of beneficiary residence to Medicare claims data. (The ARF does not list oncologists as a separate

provider category; however, oncologists [hematology/oncology and medical oncology] are grouped with other subspecialists. We used a subspecialists measure as a proxy for oncologists in the study.) We created tertiles for each of these measures to examine the relative effect of supply of providers on lung cancer survival. We also included an indicator variable that identified those counties designated by the Health Resources and Services Administration as Health Professional Shortages Areas (HPSAs). Need characteristics. We included indicator variables that reflected the stage at which the lung cancer was diagnosed. In situ lung cancer (i.e., cancer cells are located only in the innermost lining and have not invaded other lung tissues) was a small portion of the total sample size (n = 209), so we grouped these patients with those diagnosed with stage 1 cancer for analyses. Two additional indicator variables were included for unstaged cancer patients and for those whose staging information was missing. Treatment. The major treatments considered in these analyses were surgery, radiation, and chemotherapy. Using data from the SEER registries, supplemented by claims data, we constructed indicator variables that reflected any receipt of surgery, radiation, or chemotherapy. Those for whom biopsy was the


(1)

Hi (t) = λ0(t)exp{β1xi 1+ . . . +βk xik}

where the hazard of death for individual (i ) at time (t) is the product of a baseline hazard function (the γ function) and an exponentiated linear function of a set of k covariates where xik is the vector of predisposing, enabling, and need covariates previously described.48 The resulting regression coefficients represented the effect of baseline characteristics on the timing of each event.

RESULTS The results of our descriptive analyses are reported in Table 1. There were 26 073 people in our study sample; 84.2% lived in urban areas, 6.3% lived in large rural cities, 4.9% lived in small rural towns, and 4.6% lived in isolated small rural towns. The mean age of the sample was 75.2 years, and slightly more than half of the sample were women and married. The majority of the sample was White (85%). On average, Medicare beneficiaries had 3.9 preexisting comorbidities and slightly less than 15% were also enrolled in Medicaid. There were on average 9.1 subspecialists and 1.3 hospitals per 10 000 population 65 years and older. Mean survival from lung cancer was just less than 30 months but, as evidenced by the



standard deviation, there was wide variation in survival times. The majority of the sample (82%) died by the end of the study period. Almost one third of all lung cancer patients were diagnosed with stage 1 lung cancer and approximately 50% were diagnosed with stage 3 or 4 lung cancer. We found a nonlinear but significant association between urban or rural residence and age. Lung cancer patients in the rural areas were more likely to be male, White, and married but less likely to be enrolled in Medicaid. Urban lung cancer patients had a slightly higher preexisting comorbid disease burden than did similar patients in more rural areas. There were no significant differences across regions in cancer stage at diagnosis. Approximately 47% of the sample received radiation therapy, but fewer received this treatment in rural areas than in urban areas. We found no differences in receipt of chemotherapy or surgery to treat lung cancer across regions. The greatest differences across regions were among the community and provider supply characteristics. Patients in urban areas were significantly more likely to reside in areas where a large proportion of the population did not speak English well. Urban patients were also significantly more likely to live in areas with higher median incomes than were rural patients. Approximately 84% of beneficiaries in isolated rural towns lived in areas with a median income less than $29 000 compared with approximately 25% in urban areas with the same median income. There were 10.6 subspecialists per 10 000 population 65 years and older in urban areas compared with just 1.2 per 10 000 in isolated rural areas. There were, however, more hospitals per 10 000 population 65 years and older in rural areas relative to urban areas. Table 2 presents the results of the Cox proportional hazards regression analysis. After we controlled for predisposing, enabling, and need characteristics and treatment variables, location of residence defined by the RUCA categories was not significantly associated with mortality in this sample at conventional levels of significance (e.g., P $41 000

–0.049

0.95

–0.018

0.98

0.030

1.03

Does not speak English well HPSA county

< .05

Health care provider supply characteristics Subspecialistsa Low tertile (Ref) Middle tertile

–0.027

0.97

High tertile

–0.049

0.95

Middle tertile

–0.019

0.98

High tertile

–0.005

1.00

< .05

Number of hospitalsb Low tertile (Ref)

Notes. HPSA = health professional shortage area. a Number of subspecialists per 10 000 population 65 years and older by county of residence. b Number of hospitals per 10 000 population 65 years and older by county of residence.

The supply of hospitals was not influential in these analyses. Nonsurgical cancer care often takes place on an outpatient basis, so hospital supply may not be as important as the number of physicians in the local area. Additionally, regions with large populations that do not speak English well were not associated with risk of mortality. This suggests that perhaps providers in areas with large populations that speak English as a second language may adapt to the needs of the population. It is possible that in areas where individuals for

whom English is a second language are a smaller proportion of the population, those populations may experience more communication and access problems than those living in areas with larger populations that speak English as a second language. We did not have individual-level data on language to test this hypothesis.

Study Limitations There were 2 main limitations to this study: (1) the constraints imposed by use of


secondary data, which limited us to the existing measures available within the data set and (2) the possible threat to external validity from using data for a cohort selected from a subset of all Medicare beneficiaries diagnosed with lung cancer. For example, the SEER data do not include Eastern Cooperative Oncology Group performance status, a measure of how a patient’s disease is progressing. Additionally, we lacked individual-level data on socioeconomic status (e.g., language spoken, income), although geocoded data from the ARF and census files provided reasonable proxies for SES measures. Still, the SEER–Medicare data set are not severely hampered by these limitations. The Institute of Medicine has identified the SEER–Medicare data set as one of the few population-based data sources available for the examination of cancer care with appropriate clinical variables,61 and it is the most comprehensive data set available to explore regional variation in cancer care. Because the SEER is population-based, it does not suffer from selection or reporting bias. Although the SEER population tends to be slightly more urban and have more foreign-born individuals than does the general US population, the population covered by the SEER is comparable to the general US population with regard to measures of poverty and education.29 All SEER registries maintain the North American Association of Central Cancer Registries’ highest level of data quality certification, have at least 98% case ascertainment, and perform data-quality and completeness studies annually.62 These data provide excellent coverage for the description of cancer care for Medicare beneficiaries. Of the US population 65 years or older, 97% is eligible for Medicare.63 Although we excluded those not continuously enrolled in both Medicare Parts A and B, our ability to generalize to the Medicare population is not substantially limited; nearly 100% of all Medicare beneficiaries have Part A coverage and the vast majority of elderly Medicare beneficiaries also enroll in Part B. Our findings cannot be generalized to beneficiaries enrolled in managed care, with end-stage renal disease or with a disability, or those younger than 65 years. However, these populations are not comparable to the majority of Medicare beneficiaries.



Although urban versus rural residence was not directly associated with the risk of mortality in this analysis, supply and poverty measures were significantly associated with this outcome. Given that rural residents reside in poorer areas with fewer providers relative to their urban counterparts, we still need to be aware of their potential disadvantage when it comes to receiving needed care in a timely fashion. Focusing on and expanding existing policies that encourage providers to locate in medically underserved or shortage areas may improve access to care in a timely fashion. Further work needs to be done to understand whether patient or provider treatment behavior that might influence survival differs across regions.

About the Authors Lisa R. Shugarman is with the RAND Corporation, Santa Monica, Calif. Melony E. S. Sorbero and J. Scott Ashwood are with the RAND Corporation, Pittsburgh, Pa. At the time of the study, Haijun Tian was a graduate student in the Pardee RAND Graduate School, Santa Monica. Arvind K. Jain is with the RAND Corporation, Arlington, Va. Requests for reprints should be sent to Dr Lisa R. Shugarman, RAND Corporation, 1776 Main St, PO Box 2138, Santa Monica, CA 90407-2138 (e-mail: lisas@ rand.org). This article was accepted January 14, 2007.

Contributors L. R. Shugarman and M. E. S. Sorbero originated the study. L. R. Shugarman supervised all aspects of the study’s implementation and led the writing. H. Tian and A. K. Jain assisted with data analysis. J. S. Ashwood constructed the data set for analysis.

Acknowledgments This study was funded in part by a grant from the Health Resources and Services Administration Office of Rural Health Policy (R04-RH03596-01-00). The Gerontological Health Section of the American Public Health Association gave the “Excellence in Research on Aging and Rural Health” award to the research team for this paper during its 2005 Annual Meeting in Philadelphia, Pa.

Human Participant Protection This study was approved by the RAND Corporation’s Human Subjects Protection Committee.

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