Abstract

There 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. We 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. The 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. Accurate 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.

Highlights

  • When it comes to population-based estimates of disease frequency, individual point estimates with confidence intervals are regularly used to inform research and policy

  • The results of the Bayesian analysis suggest that the combined prevalence mean was 8.6% with a credible interval of 6.8–10.6%

  • We estimate that the combined prevalence of mood and anxiety disorders in Ontario, Canada, using both survey and health administrative data sources, was 8.6%, which sits between estimates from administrative data-derived diagnoses and the survey-derived diagnoses

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Summary

Introduction

When it comes to population-based estimates of disease frequency, individual point estimates with confidence intervals are regularly used to inform research and policy. The accuracy of these individual estimates is a product of the strengths and limitations of both the measures and samples used. A more informative population estimate would incorporate prior information on measurement properties and would leverage the strengths of multiple measures to increase accuracy and precision. This integration of multiple sources of data could be useful in improving estimates for population surveillance and research.

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