Department of Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada.
Lawson Health Research Institute, London, Ontario, Canada.
Epidemiol Psychiatr Sci. 2021 Jan 8;30:e4. doi: 10.1017/S2045796020001080.
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.
目前，尚无用于基于人群的情绪和焦虑障碍监测的通用衡量标准。因此，使用多种关联措施可以更准确地估计人群的患病率。我们的主要目标是应用贝叶斯方法对两种常用的人群情绪和焦虑障碍衡量标准进行分析，从而推断出综合衡量标准的人群患病率和衡量特性。
我们使用了来自 2012 年加拿大社区健康调查-心理健康部分的数据，这些数据与加拿大安大略省的健康管理数据库相链接。使用问卷调查获得结构化访谈诊断，使用标准化算法确定健康管理诊断。这些两个患病率估计值，以及这些衡量标准与之前衡量其心理测量特性的估计值之间的一致性数据，用于为我们的综合估计提供信息。所有参数的边缘后验密度均使用哈密顿蒙特卡罗（HMC）估计，这是一种马尔可夫链蒙特卡罗技术。后验分布的摘要，包括均值和 95%相等尾部后验可信区间，用于解释结果。
综合患病率平均值为 8.6%，可信区间为 6.8-10.6%。该综合估计值位于基于行政数据诊断的贝叶斯患病率估计值（均值=7.4%）和基于调查诊断的患病率估计值（均值=13.9%）之间。敏感性分析的结果表明，改变基于调查的衡量标准的特异性对综合后验患病率估计值有明显影响。当改变其他先验信息时，我们的综合后验患病率估计值保持稳定。我们未检测到任何有问题的 HMC 行为，并且我们的后验预测检查表明我们的模型可以可靠地再现我们的数据。
准确的基于人群的疾病估计是卫生服务规划和资源分配的基石。随着越来越多的关联人群数据源可用，研究人员也有机会充分利用这些数据。情绪和焦虑障碍的真实人群患病率可能介于从调查数据和健康管理数据中获得的估计值之间。我们已经展示了如何使用贝叶斯方法更准确地估计人群中的情绪和焦虑障碍。这项工作为使用链接健康数据进行未来的基于人群的疾病估计提供了蓝图。
