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

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

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE: Biases affect how certain we are about the available evidence, however no standard tool for assessing the risk of bias (RoB) in prevalence studies exists. For the purposes of a living systematic review on prevalence of mental health disorders during the COVID-19 pandemic, we developed a RoB tool to evaluate prevalence studies in mental health (RoB-PrevMH) and tested interrater reliability.

METHODS: We reviewed existing RoB tools for prevalence studies until September 2020, to develop a tool for prevalence studies in mental health. We tested the reliability of assessments by different users of RoB-PrevMH in 83 studies stemming from two systematic reviews of prevalence studies in mental health. We assessed the interrater 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 signaling questions guide the application of the tool to the review question. The interrater agreement for the three items was 83%, 90% and 93%. The weighted kappa was 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: We developed a brief, user friendly, and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for interrater agreement were fair to substantial. The tool's validity, reliability, and applicability should be assessed in future projects.

Original languageEnglish
JournalmedRxiv : the preprint server for health sciences
DOIs
StatePublished - 2 Feb 2023

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