Health Services and Outcomes Research Methodology

, Volume 14, Issue 3, pp 109–155 | Cite as

Estimating local prevalence of mental health problems

  • Steven SternEmail author


I present an improved methodology for estimating local prevalence rates using classical econometric methods. I provide information on the variation within national mental health surveys associated with ICD versus DSM coding. Conditional on the validity of national survey responses, I estimate precise and statistically significant models associated with binary measures of mental health diagnoses. I also present estimates from polychotomous discrete choice allowing for covariance in errors. Focusing on binary discrete measures, empirical results from NCS-R and NSADMHP are qualitatively similar though very different from NHIS. I speculate that, to a significant degree, this occurs because both NCS-R and NSADMHP rely on popular screening tools to mechanically diagnosis sample participants, while NHIS relies on self-diagnosis. I also discuss the effects on local prevalence estimates caused by unobserved community-specific effects. Finally, I use the results to make policy statements about the provision of public mental health services in central Virginia; the results document a severe shortage of services for people who are unlikely to be able to afford services in the private market.


Mental health prevalence Small area estimation Mental health policy 



I would like to thank Dori Stern and Yiyi Zhou for excellent research assistance, Ivora Hinton, Caruso Brown, Ted Lutterman, and Summer Durrant for help with data, Deborah Stanford for help with manuscript preparation, the Bankard Fund at the University of Virginia for financial support, and Beth Merwin and seminar participants at UVA, NYU, the Region Ten Community Services Board, and AHRQ for valuable comments. All remaining errors are mine.


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Authors and Affiliations

  1. 1.University of VirginiaCharlottesvilleUSA

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