title: 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.
abstract: 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.
authors.affiliation: DataBase ER, Inc., Tampa, Florida, USA. sehwan@sprynet.com
authors.forename: S
authors.initials: S
authors.lastname: Kim
authors.forename: L
authors.initials: L
authors.lastname: Wurster
authors.forename: C
authors.initials: C
authors.lastname: Williams
authors.forename: N
authors.initials: N
authors.lastname: Hepler
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D005380:Financing, Government / Q000191:economics*; D005431:Florida; D015276:Health Care Rationing / Q000458:organization & administration*; D006801:Humans; D017059:Models, Econometric; D020380:Needs Assessment / Q000458:organization & administration*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012307:Risk Factors; D013334:Students / Q000523:psychology; D019966:Substance-Related Disorders / Q000191:economics* / Q000209:etiology / Q000517:prevention & control*