Informing the use of formula-based funding allocations in public health practice: Initial findings

Presenter: Patrick Bernet, Florida Atlantic University

Abstract

Rationale:

Public health program managers often use formula-based calculations to allocate funds among constituent jurisdictions, including allocations among states for federal programs and allocations among localities for state programs. With the exception of HRSA's Ryan White HIV CARE program, there has been little formal assessment of the impacts of various formula design options in public health, of the relationship between these options and program objectives, and of the feasibility of incorporating performance incentives into formulas. As a result of this deficit in public health systems research, program managers have a limited evidence base for determining whether to use a formula-based allocation and, if so, for designing an allocation formula.

Objectives:

The objective of this project is to provide public health managers with a broader evidence base for determining how best to use and design funding formulas. We will provide information on the impacts of various options for: 1) selecting a primary data source, such as population attributes, measures of need, or historical funding precedents, 2) adjusting allocations for variations in the cost of providing services or resource availability, and 3) incorporating performance incentives into formulas.

Methodology:

We use federal public health programs to test the impact of formula design options. We examine the effect of using different primary indicators, including a) population attributes that may affect health risks such as indicators of socioeconomic status, b) measures of need such as disease prevalence or incidence, the prevalence of disease-specific risk factors, markers of access to preventive or therapeutic health services, and the use of static versus trend measures for each, and c) proxy measures of service need such as population size.

We also test the impact of secondary indicators to adjust allocations for differences among states in the cost of providing services and the availability of state resources, which may offset needs for federal resources. These will include the Medicare Hospital Wage Index, a measure of the cost of healthcare labor, and indicators of state financial resources including the Federal Medical Assistance (FMA) Percentage and the Enhanced FMA Percentage, which are based on the gap between a state's and the national per capita income.

Results:

Thus far, we have been able to reduce the number of adjustments that warrant consideration in funding formulas. For example, since food stamps and poverty are highly correlated, we find that there is very little difference in allocations based on the two.

We also use legislative indices to measure likely political voting patterns. We find that minimum per State allocations are politically difficult to undo once done.

Conclusions:

There are a number of evaluation tools that can help policy analysts examine the political, equity and financial implications of various funding formula alternatives. There are also a number of possible adjustors that are often used in funding formulas. This project reduces the number of evaluations and adjustors that warrant consideration, simplifying the complex task of analyzing funding formulas.