ArticleEpidemiology and psychiatric sciences2021
A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures.
Article in Epidemiology and psychiatric sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Optimizing Workplace Digital Mental Health Interventions: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Prevalence estimates of major depressive disorder in 27 European countries from the European Health Interview Survey: accounting for imperfect diagnostic accuracy of the PHQ-8.BMJ mental health · 2023Pooled it
- Global, regional and national burden of anxiety disorders from 1990 to 2019: results from the Global Burden of Disease Study 2019.Epidemiology and psychiatric sciences · 2021Pooled it
- A tutorial on the what, why, and how of Bayesian analysis: Estimating mood and anxiety disorder prevalence using a Canadian data linkage study.PLOS mental health · 2025Article
- Improving prevalence estimates of mental health and well-being indicators among sexual minority men: a propensity-weighting approach.American journal of epidemiology · 2024Article
- Prevalence and risk factors of obesity among undergraduate student population in Ghana: an evaluation study of body composition indices.BMC public health · 2024Article
- Surveillance of Child and Youth Mental Disorders and Associated Service Use in Canada.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Article
- Estimating prevalence of child and youth mental disorder and mental health-related service contacts: a comparison of survey data and linked administrative health data.Epidemiology and psychiatric sciences · 2022Article
- Reading Between the Lines: A Pursuit of Estimating the Population Prevalence of Mental Illness Using Multiple Data Sources.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2022Article
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Authors and funding
6 authors.
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Abstract
aimsThere is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure.
methodsWe used data from the 2012 Canadian Community Health Survey - Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results.
resultsThe combined prevalence mean was 8.6%, with a credible interval of 6.8-10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data.
conclusionsAccurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.
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