Past-year mental health issues: discrepant survey & administrative data

Abstract Introduction Accurately measuring mental health disorder prevalence is crucial for public health planning. Administrative and survey data are common methods, but often yield differing results. This study compares case identification of mental health issues across these data sources in Slovenia. Methods We linked the 2019 European Health Interview Survey (EHIS) with three Slovenian health administrative databases: the National Hospital Health Care Statistics Database, Outpatient Prescription Drugs Database, and Absence From Work Database. Case identification utilized self-report of any mental health issue in the past 12 months and healthcare utilization records in 12 months preceding participation in EHIS. Multinomial logistic regression was used to examine the association between socio-demographic factors and case identification across data sources. Results Significant differences were found in 12 month prevalence of any mental health issue estimates between data sources. Only 45.9% of self-reported cases were identified in administrative data, and 36.6% of administrative cases self-reported mental health issues (Kappa = 0.302). Socio-demographics, including age, gender, education, and employment status, were significantly associated with the likelihood of identification in specific data sources. Discussion Our findings underscore the importance of data source and assessment tool selection on mental health prevalence estimates. Variations in identification across population subgroups could reflect differences in healthcare access and self-disclosure biases. Linking data sources and considering their inherent limitations is crucial for accurate burden estimation. Key messages • Data source and assessment methods significantly impact how we identify individuals with mental health issues in the past year. • Combining data sources and understanding their limitations is essential for improving mental health burden estimation.