Modeling US County Premature Mortality James L. Wilson Department of Geography, Northern Illinois University & Christopher J. Mansfield Center for Health Systems Research and Development, Brody School of Medicine, East Carolina University

Modeling US County Premature Mortality Based on Previous Work: Mansfield, C. J., Wilson, J., Kobrinski, E. J., Mitchell, J. (1999). Premature Mortality in the United States: The Roles of Place, Socioeconomic Status, Household Type, and Availability of Medical Care. American Journal of Public Health, 89(6), Objectives: Describe recent US county premature mortality (Years of Potential Life Lost before Age 75) Show results from modeling YPLL-75 Examine Global and Local Methods of Modeling and Spatial Statistics Data Sources: Independent variables-- Area Health Resources Files (AHRF) US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Workforce, Rockville, MD. YPLL-75 RWF Version– University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation County Health Rankings YPLL-75 CMF Version—NCHS Compressed Mortality Files Series 20 CD-ROM Methods: Esri ArcGIS 10.1/.2 Ordinary Least Squares Regression Geographically Weighted Regression Global Moran’s I Anselin’s Local Moran’s I

Modeling US County Premature Mortality Fig 1. YPLL RWJ Version US Rate YLL per 10,000 people Categorized into 5 classes with approximately equal numbers of counties (quintiles) using a diverging color scheme with blue signifying low YPLL rates and red high rates (Fig. 1A). No rates provided for 147 counties. Standard deviation map shows where more extreme county rates are found (Fig. 1B). Globally, there is significant positive spatial autocorrelation (clustering) of counties with similar rates (Fig. 1C). Locally, there are significant county hotspots in the Southeast and Mountain West—significant coldspots are found in the northern Plains, Midwest, West including Texas, and the Northeast. Fig 2. YPLL CMF Version US Rate YLL per 10,000 people Categorized into 5 classes with approximately equal numbers of counties (quintiles) using a diverging color scheme with blue signifying low YPLL rates and red high rates (Fig. 2A). Nearly all counties have rates calculated. Standard deviation map shows where more extreme county rates are found (Fig. 2B). Globally, there is significant positive spatial autocorrelation (clustering) of counties with similar rates (Fig. 2C). Locally, there are significant county hotspots in the Southeast and Mountain West—significant coldspots are found in the northern Plains, Midwest, West including Texas, and the Northeast. Larger numbers of counties are found for both hot- and coldspots than for the RWJ version.

Modeling US County Premature Mortality Fig 3. OLS Regression Modeling of YPLL-75 County Rates 10 variables from previous stepwise modeling (in order of coefficients). % Families with Female Headed Households 2010 % Persons 25+ with LT HS diploma % Hispanic Population 2010 % Am Ind/Alaska Native Population 2010 Per Capita Income 2010 % Black/African Am Population 2010 Specialist MDs per 10K Pop 2010 Hospital Beds per 10K 2010 Primary Care MDs per 10K 2010 Unemployment Rate, Fig. 3B is the predicted US County YPLL- 75 Map based on these 10 variables. Fig. 3C depicts the residuals of the OLS model run. The Global Moran’s I test is used to determine if there is spatial autocorrelation among the residuals. A significant positive index value indicates that there are regions on the map where the model is not performing well. There is a significant degree of positive spatial autocorrelation occurring in the map. Fig. 3D shows Anselin’s Local Moran’s I to show where there is significant sa occurring locally in terms of “hotspots” or clusters of high rates and “coldspots” clusters of low rates. Used in GWR

Modeling US County Premature Mortality Fig. 4 Top 4 explanatory variables are entered into GWR. County R 2 values are categorized into quintiles. As each variable is added to the model the number of counties shifts upward, generally, into higher R 2 categories and thereby changing the appearance of the map. Geographically, independent variables will contribute different levels of explanation Tobler’s “First Law of Geography” is the fundamental reasoning behind this method. Independence is rare in geography. Discussion and Implications: OLS methods are based on the independence of observations. Homoscedasticity is an issue— testing for local spatial autocorrelation can point to areas where the model does not perform well and suggest other explanatory variables. The GWR method makes use of the idea of dependence and similarities among neighbors. It can illustrate the contributions and behaviors, spatially, the model variables have locally. It can also suggest other possible explanatory variables.