Algorithms for resource allocation of substance abuse prevention funds based on composite risk-factor index score: a case study on state of Florida--Part 2.

The purpose of Part 2 is to develop a model for resource allocation of state prevention funds to be distributed to its substate jurisdictions based on the relative need for prevention services measured in terms of composite risk-factor index (COMRISK) scores computed for each county. The risk factors are extracted from an extensive review of risk and protective factors addressed in the prevention literature. Based on twenty-two risk and protective factors identified, we were able to explain 71.3 percent of the total variation in student drug using behavior observed at the individual level. By aggregating individual COMRISK scores to the county level, we were able to determine aggregated COMRISK index scores at the county level. By determining the proportion of each county's share of the total statewide COMRISK and by weighting the latter proportion by the population size of each county, we have devised Prevention Needs Index (PNI) score based on the risks for each county. Finally, the county's share of PNI score as a proportion of the total statewide PNI score is computed. The latter proportion is then multiplied by the total amount of prevention resources available at the state. In this way, we were able to develop an alternative resource allocation model solely based on risk and protective factors for determining prevention needs of each county, independent of composite index score of drug use (COMDRUG) presented in Part 1. A comparison of three models for resource allocation has shown a significant amount of similarity of the total funds computed for each county. Accordingly, no preference is made among the resource allocation models suggested, although it is emphasized that the final decision concerning the level of funding must be made on the selection of the resource allocation algorithms rather than the suggested amount of funding computed for each county.

Algorithms for resource allocation of substance abuse prevention funds based on the estimated need: a case study on state of Florida--Part 1.

The purposes of Parts 1-3 of this article is to develop a framework for county-based prevention resource allocation algorithms based on the aggregated need for substance abuse prevention services estimated at the county level. The development of these algorithms is founded upon two databases: statewide student drug survey and a set of social indicators routinely collected and published by various agencies of the state of Florida. The resource allocation models are devised by developing several indices of prevention needs that are conceptualized in terms of: 1) county-based composite drug use index (COMDRUG), 2) the definitions of prevention target populations as envisioned by the Institute of Medicine (IOM), 3) composite risk-factor index score, and 4) a set of social indicators that are empirically related to COMDRUG observed at the county level. The first three models are based on the prevention needs estimated from the statewide student survey on substance abuse. The social indicator model, however, is presented as an alternative resource allocation model which may be used in lieu of or in the absence of statewide survey. The resource allocation algorithms found on these four conceptualizations are thought to be more equitable and appropriate to the prevention needs of various communities than may be contrived otherwise. Due to a significant amount of information leading to the development of these models, Part 1 of this series is devoted to the following three topics: 1) sampling method used, 2) poststratification weighting methods used to estimate county-based COMDRUG, and 3) the development of resource allocation models based on COMDRUG and the IOM definitions of prevention target populations.

Algorithms for resource allocation of substance abuse prevention funds based on social indicators: a case study on state of Florida--Part 3.

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.