Bokern Marleen, Rentsch Christopher T, Quint Jennifer K, Hunnicutt Jacob, Douglas Ian, Schultze Anna
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, UK.
Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70086. doi: 10.1002/pds.70086.
During the pandemic, there was concern that underascertainment of COVID-19 outcomes may impact treatment effect estimation in pharmacoepidemiologic studies. We assessed the impact of outcome misclassification on the association between inhaled corticosteroids (ICS) and COVID-19 hospitalisation and death in the United Kingdom during the first pandemic wave using probabilistic bias analysis (PBA).
Using data from the Clinical Practice Research Datalink Aurum, we defined a cohort with chronic obstructive pulmonary disease (COPD) on 1 March 2020. We compared the risk of COVID-19 hospitalisation and death among users of ICS/long-acting β-agonist (LABA) and users of LABA/LAMA using inverse probability of treatment weighted (IPTW) logistic regression. We used PBA to assess the impact of non-differential outcome misclassification. We assigned beta distributions to sensitivity and specificity and sampled from these 100 000 times for summary-level and 10 000 times for record-level PBA. Using these values, we simulated outcomes and applied IPTW logistic regression to adjust for confounding and misclassification. Sensitivity analyses excluded ICS + LABA + LAMA (triple therapy) users.
Among 161 411 patients with COPD, ICS users had increased odds of COVID-19 hospitalisations and death compared with LABA/LAMA users (OR for COVID-19 hospitalisation 1.59 (95% CI 1.31-1.92); OR for COVID-19 death 1.63 (95% CI 1.26-2.11)). After IPTW and exclusion of people using triple therapy, ORs moved towards the null. All implementations of QBA, both record- and summary-level PBA, modestly shifted the ORs away from the null and increased uncertainty.
We observed increased risks of COVID-19 hospitalisation and death among ICS users compared to LABA/LAMA users. Outcome misclassification was unlikely to change the conclusions of the study, but confounding by indication remains a concern.
在疫情期间，人们担心新冠病毒病（COVID-19）结局的漏诊可能会影响药物流行病学研究中的治疗效果估计。我们使用概率偏差分析（PBA）评估了在疫情第一波期间，结局错误分类对英国吸入性糖皮质激素（ICS）与COVID-19住院和死亡之间关联的影响。
利用临床实践研究数据链奥鲁姆（Clinical Practice Research Datalink Aurum）的数据，我们在2020年3月1日定义了一个慢性阻塞性肺疾病（COPD）队列。我们使用治疗加权逆概率（IPTW）逻辑回归比较了ICS/长效β受体激动剂（LABA）使用者和LABA/长效M受体拮抗剂（LAMA）使用者中COVID-19住院和死亡的风险。我们使用PBA评估非差异性结局错误分类的影响。我们为敏感度和特异度分配了贝塔分布，并针对汇总水平抽样100000次，针对记录水平PBA抽样10000次。利用这些值，我们模拟结局并应用IPTW逻辑回归来调整混杂因素和错误分类。敏感性分析排除了ICS + LABA + LAMA（三联疗法）使用者。
在161411例COPD患者中，与LABA/LAMA使用者相比，ICS使用者发生COVID-19住院和死亡的几率更高（COVID-19住院的比值比为1.59（95%置信区间1.31 - 1.92）；COVID-19死亡的比值比为1.63（95%置信区间1.26 - 2.11））。在IPTW并排除使用三联疗法的人群后，比值比趋向于无效值。QBA的所有实施方式，包括记录水平和汇总水平的PBA，都使比值比略有偏离无效值并增加了不确定性。
与LABA/LAMA使用者相比，我们观察到ICS使用者发生COVID-19住院和死亡的风险增加。结局错误分类不太可能改变研究结论，但指征性混杂因素仍然令人担忧。
