V. Empirical validity of indirect mental health needs-assessment models in Colorado

This article presents the results of empirical validation studies of six statistical models currently available for indirectly estimating the need for alcohol, drug abuse, and mental health (ADM) services across a large geographic area such as a state. Initial analyses show that the basic assumptions regarding geographic variations in need which are shared by all such models can be substantiated by data from the Colorado Social Health Survey (CSHS) of diagnosable disorders, dysfunction in everyday living, and demoralization. Next, the performance of the original versions of the six models on three statistical tests of the match between model predictions and surveyed ADM service needs is presented. The tests show how well each model's predictions of need prevalence in 48 small subareas of Colorado compared with surveyed needs in those same areas. Results indicate that in their original form, none of the models were accurate predictors of surveyed need for ADM services in Colorado, but that they did show predictive potential in terms of their epidemiologic relationships with surveyed need. Following "optimization" of the model equations by adjusting their parameters to best fit the CSHS data, repeated analyses indicated that all models except one were considerably more accurate predictors than a flat-rate model based on the assumption of equal need across all subareas. A modified two-variable linear regression model developed by Stem was the best all-around performer on the evaluations of prediction accuracy, followed by the Synthetic Estimation model. Two "experimental" regression models containing the better predictors found among the original models (poverty and divorce rates) also performed very well. The demonstrated performance of these optimized and experimental models provides additional evidence of the utility of selected models as services planning and budgeting tool for states. These models are further evaluated with respect to frequently designated high-priority "target" populations in the next article.