Refining a traditional urban-rural classification approach to better assess heterogeneity of treatment effects in patient-centered outcomes research.

This article describes a rationale and approach for modifying the traditional rural-urban commuting area (RUCA) coding scheme used to classify U.S. ZIP codes to enable suburban/rural vs. urban core comparisons in health outcomes research that better reflect current geographic differences in access to care in U.S. populations at risk for health disparities. The proposed method customization is being employed in the Patient-Centered Outcomes Research Institute-funded Management Of Diabetes in Everyday Life (MODEL) study to assess heterogeneity of treatment effect for patient-centered diabetes self-care interventions across the rural-urban spectrum. The proposed suburban/rural vs. urban core classification scheme modification is based on research showing that increasing suburban poverty and rapid conversion of many rural areas into suburban areas in the U.S. has resulted in similar health care access problems in areas designated as rural or suburban.•The RUCA coding scheme was developed when a much higher percentage of U.S. individuals resided in areas with very low population density.•Using the MODEL study example, this study demonstrates that the RUCA classification scheme using ZIP codes does not reflect real differences in health care access experienced by medically underserved study participants.•Both internal and external validation data suggest that the proposed suburban/rural vs. urban core customization of the RUCA geographic coding scheme better reflects real differences in healthcare access and is better able to assess the differential impact of clinical interventions designed to address geographic differences in access among vulnerable populations.

Access to primary care physicians differs by health insurance coverage in Mississippi.

OBJECTIVES: The objective of the study was to measure how access to primary health care in Mississippi varies by type of health insurance. METHODS: We called primary care physician (general practitioner, family practice, internal medicine, obstetrics/gynecology, and pediatric) offices in Mississippi three times, citing different types of health insurance coverage in each call, and asked for a new patient appointment with a physician. RESULTS: Of all of the offices contacted, 7% of offices were not currently accepting new patients who had private insurance, 15% of offices were not currently accepting new Medicare patients, 38% were not currently accepting new Medicaid patients, and 9% to 21% of office calls were unresolved in one telephone call to the office. CONCLUSIONS: Access to health insurance does not ensure access to primary health care; access varies by type of health insurance coverage.

Familiarity with and advocacy of Healthy People 2010 goals by Mississippi Chiropractic Association members.

OBJECTIVE: The purpose of this study was to determine the familiarity with and stated advocacy of Healthy People 2010 objectives by member doctors of the Mississippi Chiropractic Association. METHODS: Peer experts established face validity of a questionnaire regarding the Leading Health Indicators. This survey was distributed to 157 Mississippi Chiropractic Association members in 2009 during a conference and a follow-up by postal mail. RESULTS: Most doctors of chiropractic in the sample (n = 68, or 43% response) consider themselves wellness-oriented health care providers. Forty-two percent had read, 29% had not read, and another 29% were unsure whether they had read the Healthy People 2010 national objectives. Almost half (44%) strongly or somewhat agreed that their office practice reflects support for the Healthy People 2010 objective. In contrast, 27% disagree and 29% were unsure if their practice reflects the Healthy People 2010 objectives. There were differences between support and practice behaviors for some of the objectives. Chiropractors who have read the objectives tend to be more supportive of the national goals. Doctors of chiropractic in this sample are supportive of most Leading Health Indicators, and the majority reports that they incorporate these public health goals into their practices. CONCLUSION: Familiarity with reading the Health People objectives seems to be related to reported practice behaviors. There is a need to improve the percentage of practicing doctors of chiropractic who are familiar with Healthy People objectives. Future health education initiatives may assist doctors of chiropractic in further incorporating public health objectives into their practice behaviors and improving quality health care.

Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health.

BACKGROUND: Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need. METHODS: We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns. RESULTS: Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses. CONCLUSIONS: County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.

Underlying causes of the emerging nonmetropolitan mortality penalty.

The nonmetropolitan mortality penalty results in an estimated 40 201 excessive US deaths per year, deaths that would not occur if nonmetropolitan and metropolitan residents died at the same rate. We explored the underlying causes of the nonmetropolitan mortality penalty by examining variation in cause of death. Declines in heart disease and cancer death rates in metropolitan areas drive the nonmetropolitan mortality penalty. Future work should explore why the top causes of death are higher in nonmetropolitan areas than they are in metropolitan areas.

Preliminary evidence for an emerging nonmetropolitan mortality penalty in the United States.

We discovered an emerging non-metropolitan mortality penalty by contrasting 37 years of age-adjusted mortality rates for metropolitan versus nonmetropolitan US counties. During the 1980s, annual metropolitan-nonmetropolitan differences averaged 6.2 excess deaths per 100,000 nonmetropolitan population, or approximately 3600 excess deaths; however, by 2000 to 2004, the difference had increased more than 10 times to average 71.7 excess deaths, or approximately 35,000 excess deaths. We recommend that research be undertaken to evaluate and utilize our preliminary findings of an emerging US nonmetropolitan mortality penalty.

Evaluating heart disease presciptions-filled as a proxy for heart disease prevalence rates.

Heart disease is the leading cause of death in the U.S. Yet, prevalence rates are not reported at the county level. Not knowing how many have the disease, and where they are, may be a knowledge barrier to effective health care interventions. We use heart disease drug prescriptions-filled as a proxy measure for prevalence of heart disease. We test the correlation to the Behavioral Risk Factor Surveillance System (BRFSS) and find positive, statistically significant correlations. Next we illustrate the geographic patterns revealed using the county-level prevalence estimate maps. This information can be used to provide a better understanding of sub-state variations in disease patterns and subsequently target the delivery of health resources to small areas in need.

Persistent clusters of mortality in the United States.

We explored how place shapes mortality by examining 35 consecutive years of US mortality data. Mapping age-adjusted county mortality rates showed both persistent temporal and spatial clustering of high and low mortality rates. Counties with high mortality rates and counties with low mortality rates both experienced younger population out-migration, had economic decline, and were predominantly rural. These mortality patterns have important implications for proper research model specification and for health resource allocation policies.

A brief visual primer for the mapping of mortality trend data.

Maps are increasingly used to visualize and analyze data, yet the spatial ramifications of data structure are rarely considered. Data are subject to transformations made throughout the research process and then used to map, visualize and conduct spatial analysis. We used mortality data to answer three research questions: Are there spatial patterns to mortality, are these patterns statistically significant, and are they persistent across time? This paper provides differential spatial patterns by implementing six data transformations: standardization, cut-points, class size, color scheme, spatial significance and temporal mapping. We use numerous maps and graphics to illustrate the iterative nature of mortality mapping, and exploit the visual nature of the International Journal of Health Geographics journal on the World Wide Web to present researchers with a series of maps.

Mapping high or low mortality places across time in the United States: a research note on a health visualization and analysis project.

This research note reports progress in visualizing and analyzing United States mortality data at the county level. The data visualization technique employed here may be applicable to other research situations. We dichotomized the range of mortality rates into high or low mortality counties, mapped them, and explored the clustering of high or low mortality rate counties across both space and time. We find visual evidence that high or low mortality counties spatially cluster together during individual periods of time (5 years). We find further visual evidence that there is a spatial persistence over time (30 years) of these counties with high or low mortality. This evidence leads us to conclude that relatively high or low mortality is anchored over time within a spatial region and population, suggesting that research efforts may be focused on these clusters to assess local causes of high or low mortality rates. Future research will examine the permanence of the resident population (i.e., population mixing), characteristics of the resident population, and characteristics of their place of residence over time.