Wesley James

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.

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.

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.

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.

Rural–Urban Analyses of Health-Related Quality of Life Among People With Multiple Sclerosis

CONTEXT Health-related quality of life (HRQOL) is a multi-dimensional construct including aspects of life quality or function that are affected by physical health and symptoms, psychosocial factors, and psychiatric conditions. HRQOL gives a broader measure of the burden of disease than physical impairment or disability levels. PURPOSE To identify factors associated with HRQOL among people with multiple sclerosis (MS) utilizing the SF-8 Health Survey. METHODS Data presented in this study were collected in a survey of 1,518 people with MS living in all 50 states. The survey sample was randomly selected from the database of the National Multiple Sclerosis Society, using ZIP codes to recruit the survey sample. A multiple linear regression model was employed to analyze the survey data, with the Physical Component Summary and the Mental Component Summary of the SF-8 the dependent variables. Independent variables were demographic characteristics, MS-disease characteristics, and health services utilized. FINDINGS People with MS in rural areas tended to report lower physically related HRQOL. Worsening MS symptoms were associated with reduced physical and mental dimensions of HRQOL. In addition, people with MS who received a diagnosis of depression tended to have reduced physical and mental dimensions of HRQOL. Receiving MS care at an MS clinic was associated with better physically related HRQOL, while having a neurologist as principal care physician was associated with better mental-related HRQOL. CONCLUSION The challenge is to increase the access that people living with MS in rural areas have to MS-focused specialty care.

Uneven Magnitude of Disparities in Cancer Risks from Air Toxics

This study examines race- and income-based disparities in cancer risks from air toxics in Cancer Alley, LA, USA. Risk estimates were obtained from the 2005 National Air Toxics Assessment and socioeconomic and race data from the 2005 American Community Survey, both at the census tract level. Disparities were assessed using spatially weighted ordinary least squares (OLS) regression and quantile regression (QR) for five major air toxics, each with cancer risk greater than 10−6. Spatial OLS results showed that disparities in cancer risks were significant: People in low-income tracts bore a cumulative risk 12% more than those in high-income tracts (p < 0.05), and those in black-dominant areas 16% more than in white-dominant areas (p < 0.01). Formaldehyde and benzene were the two largest contributors to the disparities. Contributions from emission sources to disparities varied by compound. Spatial QR analyses showed that magnitude of disparity became larger at the high end of exposure range, indicating worsened disparity in the poorest and most highly concentrated black areas. Cancer risk of air toxics not only disproportionately affects socioeconomically disadvantaged and racial minority communities, but there is a gradient effect within these groups with poorer and higher minority concentrated segments being more affected than their counterparts. Risk reduction strategies should target emission sources, risk driver chemicals, and especially the disadvantaged neighborhoods.

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

BackgroundChronic 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.MethodsWe 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.ResultsFourteen 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.ConclusionsCounty 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.

Long-Term Trends in Black and White Mortality in the Rural United States: Evidence of a Race-Specific Rural Mortality Penalty.

PURPOSE The rural mortality penalty-growing disparities in rural-urban macro-level mortality rates-has persisted in the United States since the mid 1980s. Substantial intrarural differences exist: rural places of modest population size, close to urban areas, experience a greater mortality burden than the most rural locales. This research builds on recent findings by examining whether a race-specific rural mortality penalty exists; that is, are some rural areas more detrimental to black and/or white mortality than others? METHODS Using data from the Compressed Mortality File from 1968 to 2012, we calculate annual age-adjusted, race-specific mortality rates for all rural-urban regions designated by the Rural-Urban Continuum Codes. Indicators for population, socioeconomic status, and health infrastructure, as a proxy for access to care, are used as predictors of race-specific mortality in multivariable regression models. FINDINGS Three important results emerge from this analysis: (1) there is a substantial mortality disadvantage for both black and white rural Americans, (2) the most advantageous regions of mortality for blacks exhibit higher mortality than the most disadvantageous regions for whites, and (3) access to health care is a much stronger predictor of white mortality than black mortality. CONCLUSIONS The rural mortality penalty is evident in race-specific mortality trends over time, with an added disadvantage in black mortality. The rate of mortality improvement for rural blacks and whites lags behind their same-race, urban counterparts, creating a diverging gap in race-specific mortality trends in rural America.

Relationship of Racial Composition and Cancer Risks from Air Toxics Exposure in Memphis, Tennessee, U.S.A.

African Americans in the U.S. often live in poverty and segregated urban neighborhoods, many of which have dense industrial facilities resulting in high exposure to harmful air toxics. This study aims to explore the relationship between racial composition and cancer risks from air toxics exposure in Memphis/Shelby County, Tennessee, U.S.A. Air toxics data were obtained from 2005 National Air Toxics Assessment (NATA), and the demographic data, including racial composition, were extracted from the 2000 United States Census. The association was examined using multivariable geographically weighted regression (GWR) analysis. The risk difference between African American and White concentrated areas was defined as the absolute disparity, and the percent difference as the relative disparity. GWR analyses show that cancer risks increase with respect to increasing percent of African Americans at the census tract level. Individuals in African American concentrated tracts bear 6% more cancer risk burden than in White concentrated tracts. The distribution of major roads causes the largest absolute disparity and the distribution of industrial facilities causes the largest relative disparity. Effective strategies for reduction in environmental disparity should especially target sources of large absolute disparities.