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Comparison Of Small-Area Deprivation Measures As Predictors Of Chronic Disease Burden In A Low-Income Population, Ana Lopez-De Fede, John E. Stewart, James W. Hardin, Kathy Mayfield-Smith Jan 2016

Comparison Of Small-Area Deprivation Measures As Predictors Of Chronic Disease Burden In A Low-Income Population, Ana Lopez-De Fede, John E. Stewart, James W. Hardin, Kathy Mayfield-Smith

Publications

Background: Measures of small-area deprivation may be valuable in geographically targeting limited resources to prevent, diagnose, and effectively manage chronic conditions in vulnerable populations. We developed a census-based small-area socioeconomic deprivation index specifically to predict chronic disease burden among publically insured Medicaid recipients in South Carolina, a relatively poor state in the southern United States. We compared the predictive ability of the new index with that of four other small-area deprivation indicators. Methods: To derive the ZIP Code Tabulation Area-Level Palmetto Small-Area Deprivation Index (Palmetto SADI), we evaluated ten census variables across five socioeconomic deprivation domains, identifying the combination of …


Small-Area Variation In Hypertension Prevalence Among Black And White Medicaid Enrollees, Kellee White, John E. Stewart, Ana Lopez-De Fede, Rebecca C. Wilkerson Jan 2016

Small-Area Variation In Hypertension Prevalence Among Black And White Medicaid Enrollees, Kellee White, John E. Stewart, Ana Lopez-De Fede, Rebecca C. Wilkerson

Publications

Objectives: To examine within-state geographic heterogeneity in hypertension prevalence and evaluate associations between hypertension prevalence and small-area contextual characteristics for Black and White South Carolina Medicaid enrollees in urban vs rural areas.

Design: Ecological.

Setting: South Carolina, United States.

Main outcome measures: Hypertension prevalence.

Methods: Data representing adult South Carolina Medicaid recipients enrolled in fiscal year 2013 (N=409,907) and ZIP Code Tabulation Area (ZCTA)-level contextual measures (racial segregation, rurality, poverty, educational attainment, unemployment and primary care physician adequacy) were linked in a spatially referenced database. Optimized Getis-Ord hotspot mapping was used to visualize geographic clustering of hypertension prevalence. Spatial regression …