Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK.
Addiction. 2020 Dec;115(12):2393-2404. doi: 10.1111/add.15111. Epub 2020 Jun 9.
Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'.
Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status.
We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval = 0.74-0.94%) of 15-64-year-olds, which is similar to a published estimate based on capture-recapture analysis.
Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach.
对于仅观察到一部分个体的人群，如问题药物使用者（PDU），需要使用间接估计方法来估计其规模。捕获-再捕获和乘数方法被广泛应用，但它们受到了偏差的批评。我们提出了一种新的方法，通过使用链接数据库，从致命药物相关中毒（fDRP）数量估计 PDU 的流行率，解决了简单“死亡率乘数”的关键局限性。
我们的方法需要将一个大的已知 PDU 队列的数据与死亡率登记处和关于队列外观察到的其他 fDRP 的汇总信息进行链接。我们对队列中的 fDRP 率进行建模，并假设在未观察到的 PDU 中，治疗期间的比率与队列中的比率相等。流行率是在贝叶斯统计框架中估计的，在该框架中，我们同时拟合 fDRP 率和流行率的回归模型，允许两者都因人口统计学因素和前者也因治疗状态而变化。
我们报告了一项案例研究分析，估计了 2008/09 年英格兰阿片类药物依赖的流行率，按性别、年龄组和地理区域划分。15-64 岁人群的总体流行率估计为 0.82%（95%可信区间为 0.74-0.94%），与基于捕获-再捕获分析的已发表估计值相似。
我们的建模方法从药物相关死亡率数据中估计流行率，同时解决了简单乘数的主要局限性。这为常见情况提供了一种替代方法，即可用数据源不符合有效捕获-再捕获估计所需的严格假设。在案例研究分析中，我们方法的流行率估计值与现有的捕获-再捕获估计值惊人地相似，但我们认为，这些估计值是基于更客观和合理的建模方法。
