Algorithms for Resource Allocation of Substance Abuse Prevention Funds Based on Social Indicators: A Case Study on State of Florida—Part 3
Authors: Kim Sehwan , Wurster Leslie , Williams Charles , Hepler Nancy 
Published: May 2005

The purpose of Part 3 is to develop an algorithm for an equitable distribution of state prevention funds to its substate jurisdictions based on the need for prevention services. In this series, the need for prevention services is measured in terms of the existing social indicators observed at the county level. In order to establish a conceptual link as well as the empirical relevance of the selected social indicators as proxy measurements of the estimated need for prevention at the county level, we have employed both concurrent and construct validity tests using the following three constructs as the criterion variables in a multiple regressing setting: 1) county-based composite drug use index score (COMDRUG) measured via the statewide drug survey; 2) county-based proportions of prevention target populations using the conceptual definition advanced by the Institute of Medicine (IOM); and 3) the composite risk factor score (COMRISK) assembled from a list of twenty-two risk and protective factors observed for each county. These constructs were identified previously in Parts 1 and 2. While employing eight social indicators to estimate the overall prevention needs observed at the county level, the social indicators thus selected were able to explain 69 percent of the variations in COMDRUG, 68 percent of the variation in the proportions of students in need of prevention services using IOM definition, and 60 percent of the variation in COMRISK. Following successful validations of the social indicators as viable media with which to estimate county-based prevention needs, the ensuing multiple regression equation is, then, used to build a resource allocation model by determining the proportion of each county's share of the total statewide COMDRUG-predicted from the social indicators and, then, by weighting the latter proportion by the population size of each county under age eighteen. In this way, we have devised county-based Prevention Needs Index (PNI) scores based solely on social indicators. Finally, the county's share of PNI score is computed as a proportion of to the total statewide PNI score. Following this line of algorithm for resource allocation, we were able to develop yet another resource allocation model solely based on social indicators without the benefits of survey data. Comparing the funding results originating from four resource allocation models (i.e., COMDRUG, IOM Definition, COMRISK, and Social Indicators), it has been learned that there is a remarkable similarity from one funding level to another. Since all four schedules of county-based prevention funding levels have shown very high intercorrelations with a range from .9862 to .9993, it has been determined that these schedules are measuring essentially either the same domain or latent domains that are functionally equivalent to one another. Accordingly, no preference is made among the resource allocation models suggested, although it is suggested that the final decision on the level of funding must be based on the selection of the schedule for resource allocation rather than the suggested amount or level of funding computed for each county.