Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; ICES, Toronto, Canada; Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Centre on Drug Policy Evaluation, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, USA.
J Clin Epidemiol. 2024 Jun;170:111332. doi: 10.1016/j.jclinepi.2024.111332. Epub 2024 Mar 24.
Health administrative data can be used to improve the health of people who inject drugs by informing public health surveillance and program planning, monitoring, and evaluation. However, methodological gaps in the use of these data persist due to challenges in accurately identifying injection drug use (IDU) at the population level. In this study, we validated case-ascertainment algorithms for identifying people who inject drugs using health administrative data in Ontario, Canada.
Data from cohorts of people with recent (past 12 months) IDU, including those participating in community-based research studies or seeking drug treatment, were linked to health administrative data in Ontario from 1992 to 2020. We assessed the validity of algorithms to identify IDU over varying look-back periods (ie, all years of data [1992 onwards] or within the past 1-5 years), including inpatient and outpatient physician billing claims for drug use, emergency department (ED) visits or hospitalizations for drug use or injection-related infections, and opioid agonist treatment (OAT).
Algorithms were validated using data from 15,241 people with recent IDU (918 in community cohorts and 14,323 seeking drug treatment). An algorithm consisting of ≥1 physician visit, ED visit, or hospitalization for drug use, or OAT record could effectively identify IDU history (91.6% sensitivity and 94.2% specificity) and recent IDU (using 3-year look back: 80.4% sensitivity, 99% specificity) among community cohorts. Algorithms were generally more sensitive among people who inject drugs seeking drug treatment.
Validated algorithms using health administrative data performed well in identifying people who inject drugs. Despite their high sensitivity and specificity, the positive predictive value of these algorithms will vary depending on the underlying prevalence of IDU in the population in which they are applied.
通过提供公共卫生监测、规划、监测和评估信息，医疗行政数据可用于改善注射毒品者的健康。然而，由于在人群层面准确识别注射吸毒（IDU）方面存在方法学差距，这些数据的使用仍存在方法学差距。在这项研究中，我们验证了在加拿大安大略省使用医疗行政数据识别注射毒品者的病例确定算法。
包括参与基于社区的研究或寻求药物治疗的人群在内的近期（过去 12 个月内）IDU 队列的数据与安大略省从 1992 年至 2020 年的医疗行政数据相关联。我们评估了不同回溯期（即所有年份的数据[1992 年及以后]或过去 1-5 年）的 IDU 识别算法的有效性，包括用于药物使用的住院和门诊医师计费、药物使用或注射相关感染的急诊部（ED）就诊或住院、以及阿片类药物激动剂治疗（OAT）。
使用 15241 名近期 IDU 患者的数据（社区队列 918 名，寻求药物治疗 14323 名）验证了算法。由≥1 次医生就诊、ED 就诊或药物使用住院或 OAT 记录组成的算法可有效识别 IDU 史（敏感性 91.6%，特异性 94.2%）和近期 IDU（使用 3 年回溯期：敏感性 80.4%，特异性 99%）在社区队列中。在寻求药物治疗的注射毒品者中，算法的敏感性通常更高。
使用医疗行政数据验证的算法在识别注射毒品者方面表现良好。尽管这些算法具有较高的敏感性和特异性，但它们的阳性预测值将取决于应用它们的人群中 IDU 的基础流行率。
