Benchmarks for Needed Psychiatric Beds for the United States: A Test of a Predictive Analytics Model.

Christopher G Hudson

doi:10.3390/ijerph182212205

Abstract

The ideal balanced mental health service system presupposes that planners can determine the need for various required services. The history of deinstitutionalization has shown that one of the most difficult such determinations involves the number of needed psychiatric beds for various localities. Historically, such assessments have been made on the basis of waiting and vacancy lists, expert estimates, or social indicator approaches that do not take into account local conditions. Specifically, this study aims to generate benchmarks or estimated rates of needed psychiatric beds for the 50 U.S. states by employing a predictive analytics methodology that uses nonlinear regression. Data used were secured primarily from the U.S. Census’ American Community Survey and from the Substance Abuse and Mental Health Administration. Key predictors used were indicators of community mental health (CMH) service coverage, mental health disability in the adult population, longevity from birth, and the percentage of the 15+ who were married in 2018. The model was then used to calculate predicted bed rates based on the ‘what-if’ assumption of an optimal level of CMH service availability. The final model revealed an overall rate of needed beds of 34.9 per 100,000 population, or between 28.1 and 41.7. In total, 32% of the states provide inpatient psychiatric care at a level less than the estimated need; 28% at a level in excess of the need; with the remainder at a level within 95% confidence limits of the estimated need. These projections are in the low range of prior estimates, ranging from 33.8 to 64.1 since the 1980s. The study demonstrates the possibility of using predictive analytics to generate individualized estimates for a variety of service modalities for a range of localities.

Highlights

One of the most enduring debates in mental health has been the wisdom of psychiatric deinstitutionalization
While some still question whether the dramatic reductions in psychiatric beds to date have been sufficient, many have suggested that some nations, such as the United States, have overshot the mark, that reductions have been precipitous, and that insufficient community mental health services have been developed [1]
This project employs a secondary analysis of publicly available data sources, one that uses a predictive analytics strategy with non-linear regression modeling for the estimation of both actual and needed levels of psychiatric hospitalization for the

Summary

Introduction

One of the most enduring debates in mental health has been the wisdom of psychiatric deinstitutionalization. While some still question whether the dramatic reductions in psychiatric beds to date have been sufficient, many have suggested that some nations, such as the United States, have overshot the mark, that reductions have been precipitous, and that insufficient community mental health services have been developed [1]. Most commentators have increasingly argued that what is needed is a comprehensive and balanced system of mental health services that range from the most to the least restrictive [2,3]. Such arguments require that mental health planners accurately gauge the need for various service modalities, determining the optimal levels, whether for inpatient beds, day programs slots, supported housing units, or various outpatient treatment options. An important alternative has involved the use of predictive analytic models [4] for generating estimates of needed service units, such as psychiatric hospital

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