Province-level Prevalence of Psychiatric Disorders: Application of Small-Area Methodology to the Iranian Mental Health Survey (IranMHS)

Abstract

Objective: National surveys revealed a high prevalence of psychiatric disorders in Iran. Province-level estimates are needed to manage the resources and focus on preventive efforts more efficiently. The objective of this study was to provide province-level estimates of psychiatric disorders.

Method: In this study, Iranian Mental Health Survey (IranMHS) data (n = 7886) was used to produce province-level prevalence estimates of any psychiatric disorders among 15-64 year old males and females. Psychiatric disorders were diagnosed based on structured diagnostic interview of the Persian version of Composite International Diagnostic Interview (CIDI, version, 2.1). The Hierarchical Bayesian (HB) random effect model was used to calculate the estimates. The mental health status of half of the participants was also measured using a 28-item general health questionnaire (GHQ).

Results: A wide variation in the prevalence of psychiatric disorders was found among 31 provinces of Iran. The direct estimates ranged from 3.6% to 62.6%, while the HB estimates ranged from 12.6% to 36.5%. The provincial prevalence among men ranged from 11.9% to 34.5%, while it ranged from 18.4% to 38.8% among women. The Pearson correlation coefficient between HB estimates and GHQ scores was 0.73.

Conclusion: The Bayesian small area estimation provides estimation with improved precision at local levels. Detecting high-priority communities with small-area approach could lead to a better distribution of limited facilities and more effective mental health interventions.