Use of epidemiologic and other population measures in funding allocation formulas for public health programs

Epidemiologists may be called to assist in designing funding allocation formulas. Major challenges include identifying available, timely, and stable data that reflect program objectives and funding needs across constituent jurisdictions. Options include measures of service needs or adjustors for differences in program costs or resource access. To identify and recommend measures representative of common funding considerations, we 1) extracted population, health, economic, and program data from multiple federal agencies; 2) developed metrics to compare alternative allocations, including measures of absolute and proportionate impacts and a cumulative distribution function (Lorenz curve); 3) considered actual or hypothetical federal public health programs that allocate funds among states, including allocations based on total population size, the number living in poverty, the prevalence of behavioral or medical risks, mortality, and historical funding precedents, with specified or de facto minimums; 4) examined correlations among various data sources and resulting allocations based on measures of poverty, income disparity, costs, and potential taxable revenues; and 5) summarized the effects of using a distilled set of need measures and cost/resource adjustors. Compared to per-capita based allocations, substituting total population size with the number of people living in poverty or the number of households receiving Food Stamps had the greatest impacts on cumulative distribution metrics, followed by adjustments for salary costs, per capita income, and various income disparity measures, such as the GINI index. As a next step, translation of analysis results will lead to guidance and tools that epidemiologists and program managers can use in considering formula design options.