All rural places are not created equal: revisiting the rural mortality penalty in the United States.

OBJECTIVES: I investigated mortality disparities between urban and rural areas by measuring disparities in urban US areas compared with 6 rural classifications, ranging from suburban to remote locales. METHODS: Data from the Compressed Mortality File, National Center for Health Statistics, from 1968 to 2007, was used to calculate age-adjusted mortality rates for all rural and urban regions by year. Criteria measuring disparity between regions included excess deaths, annual rate of change in mortality, and proportion of excess deaths by population size. I used multivariable analysis to test for differences in determinants across regions. RESULTS: The rural mortality penalty existed in all rural classifications, but the degree of disparity varied considerably. Rural-urban continuum code 6 was highly disadvantaged, and rural-urban continuum code 9 displayed a favorable mortality profile. Population, socioeconomic, and health care determinants of mortality varied across regions. CONCLUSIONS: A 2-decade long trend in mortality disparities existed in all rural classifications, but the penalty was not distributed evenly. This constitutes an important public health problem. Research should target the slow rates of improvement in mortality in the rural United States as an area of concern.

Conceptualizing rurality: The impact of definitions on the rural mortality penalty.

Background: In the U.S., inequality is widespread and still growing at nearly every level conceivable. This is vividly illustrated in the long-standing, well-documented inequalities in outcomes between rural and urban places in the U.S.; namely, the rural mortality penalty of disproportionately higher mortality rates in these areas. But what does the concept of "rural" capture and conjure? How we explain these geographic differences has spanned modes of place measurement and definitions. We employ three county-level rural-urban definitions to (1) analyze how spatially specific and robust rural disparities in mortality are and (2) identify whether mortality outcomes are dependent on different definitions. Methods: We compare place-based all-cause mortality rates using three typologies of "rural" from the literature to assess robustness of mortality rates across these rural and urban distinctions. Results show longitudinal all-cause mortality rate trends from 1968 to 2020 for various categories of urban and rural areas. We then apply this data to rural and urban geography to analyze the similarity in the distribution of spatial clusters and outliers in mortality using spatial autocorrelation methodologies. Results: The rural disadvantage in mortality is remarkably consistent regardless of which rural-urban classification scheme is utilized, suggesting the overall pattern of rural disadvantage is robust to any definition. Further, the spatial association between rurality and high rates of mortality is statistically significant. Conclusion: Different definitions yielding strongly similar results suggests robustness of rurality and consequential insights for actionable policy development and implementation.

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.

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.

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.