Tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH)

Thomy Tonia, Diana Buitrago-Garcia, Natalie Luise Peter, Cristina Mesa-Vieira, Tianjing Li, Toshi A. Furukawa, Andrea Cipriani, Stefan Leucht, Nicola Low, Georgia Salanti

Research output: Contribution to journalArticlepeer-review


Objective There is no standard tool for assessing risk of bias (RoB) in prevalence studies. For the purposes of a living systematic review during the COVID-19 pandemic, we developed a tool to evaluate RoB in studies measuring the prevalence of mental health disorders (RoB-PrevMH) and tested inter-rater reliability. Methods We decided on items and signalling questions to include in RoB-PrevMH through iterative discussions. We tested the reliability of assessments by different users with two sets of prevalence studies. The first set included a random sample of 50 studies from our living systematic review. The second set included 33 studies from a systematic review of the prevalence of post-traumatic stress disorders, major depression and generalised anxiety disorder. We assessed the inter-rater agreement by calculating the proportion of agreement and Kappa statistic for each item. Results RoB-PrevMH consists of three items that address selection bias and information bias. Introductory and signalling questions guide the application of the tool to the review question. The inter-rater agreement for the three items was 83%, 90% and 93%. The weighted kappa scores were 0.63 (95% CI 0.54 to 0.73), 0.71 (95% CI 0.67 to 0.85) and 0.32 (95% CI -0.04 to 0.63), respectively. Conclusions RoB-PrevMH is a brief, user-friendly and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for inter-rater agreement were fair to substantial. The tool’s validity, reliability and applicability should be assessed in future projects.

Original languageEnglish
Article numbere300694
JournalBMJ Mental Health
Issue number1
StatePublished - 29 Oct 2023
Externally publishedYes


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