Disability and health outcomes in geospatial analyses of Southeastern U.S. county health data

David W. Hollar Jr., Ph.D

Disability and Health Journal, Vol. 10Issue 4p518–524
DOI: http://dx.doi.org/10.1016/j.dhjo.2017.01.003



People with disabilities tend to be at risk for secondary conditions. There is a need for comprehensive disability and health databases, including geographic information systems to evaluate trends in health, functioning, and employment.


We evaluated county levels in morbidity and mortality across the Southeastern United States using spatial regression, examining 2015 trends in accordance with Healthy People 2020 objectives.


We merged 2015 National County Health Rankings and the 2015 Social Security Administration’s Report on SSDI Beneficiaries, all for n = 1387 Southeastern U.S. county units. We used GeoDa to regress health and disability multivariable models for the dependent variable, age-adjusted Years of Potential Life Lost (YPLL) per 100,000 population.


The principal Health/Demographic multivariable model of factors impacting YPLL yielded an adjusted R2 = 0.743 (F = 188.3, p < 0.001) with percentage physically inactive, preventable hospital stays, percentage diabetics, and low college attendance figuring prominently. A Socioeconomic/Demographic multivariable model impacting YPLL yielded R2 = 0.631 (F = 156.0, p < 0.001), with disability and percentage unemployment being major associated variables.


For the Southeastern U.S., counties with higher prevalence of SSDI disability workers correlated with significantly higher YPLL and poorer health outcomes. The research augments CDC Disability and Health GIS systems to measure Healthy People 2020 outcomes for persons with disabilities nationwide. Spatial regression represents a robust approach for improved analysis of geographic data for population health measures.