Department of Epidemiology, Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.
Department of Epidemiology, Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.
Vaccine. 2020 Jul 14;38(33):5187-5193. doi: 10.1016/j.vaccine.2020.06.019. Epub 2020 Jun 19.
Observational studies of influenza vaccination are criticized as flawed due to unmeasured confounding. The goal of this cohort study was to explore the value and role of secondary claims data to inform the effectiveness of influenza vaccination, while systematically trying to reduce potential bias.
We iteratively reviewed the components of the PICO approach to refine study design. We analyzed Swiss mandatory health insurance claims of adult patients with chronic diseases, for whom influenza vaccination was recommended in 2014. Analyzed outcomes were all-cause mortality, hospitalization with a respiratory infection or its potential complication, and all-cause mortality after such hospitalization, adjusting for clinical and health care use variables. Cox and multi-state models were applied for time-to-event analysis.
Of 343,505 included persons, 22.4% were vaccinated. Vaccinated patients were on average older, had more morbidities, higher health care expenditures, and had been more frequently hospitalized. In non-adjusted models, vaccination was associated with increased risk of events. Adding covariates decreased the hazard ratio (HR) both for mortality and hospitalizations. In the full model, the HR [95% confidence interval] for mortality during season was 0.82 [0.77-0.88], and closer to null effect after season. In contrast, HR for hospitalizations was increased during season to 1.28 [1.15-1.42], with estimates closer to null effect after season. HR in multi-state models were similar to those in the single-outcome models, with HR of mortality after hospitalization negative both during and after season.
In patients with chronic diseases, influenza vaccination was associated with more frequent specific hospitalizations, but decreased risk of mortality overall and after such hospitalization. Our approach of iteratively considering PICO elements helped to consider various sources of bias in the study sequentially. The selection of appropriate, specific outcomes makes the link between intervention and outcome more plausible and can reduce the impact of confounding.
由于无法衡量混杂因素，观察性流感疫苗接种研究受到批评。本队列研究的目的是探索二次索赔数据的价值和作用，以提供流感疫苗接种的有效性信息，同时系统地努力减少潜在的偏差。
我们迭代审查了 PICO 方法的各个组成部分，以完善研究设计。我们分析了瑞士强制性医疗保险索赔数据，这些数据来自患有慢性病的成年患者，这些患者在 2014 年被推荐接种流感疫苗。分析结果为全因死亡率、因呼吸道感染或其潜在并发症住院、以及此类住院后的全因死亡率，同时调整了临床和医疗保健使用变量。应用 Cox 和多状态模型进行生存时间分析。
在纳入的 343505 人中，22.4%接种了疫苗。接种疫苗的患者平均年龄较大，患有更多的合并症，医疗保健支出更高，并且住院频率更高。在未调整的模型中，疫苗接种与事件风险增加相关。添加协变量后，死亡率和住院率的风险比（HR）均降低。在全模型中，季节期间的死亡率 HR [95%置信区间]为 0.82 [0.77-0.88]，接近季节后接近零的效果。相比之下，季节期间住院的 HR 增加到 1.28 [1.15-1.42]，接近季节后接近零的效果。多状态模型中的 HR 与单结果模型中的 HR 相似，住院后死亡率 HR 在季节期间和季节后均为负。
在患有慢性病的患者中，流感疫苗接种与更频繁的特定住院相关，但总体死亡率和住院后死亡率风险降低。我们迭代考虑 PICO 元素的方法有助于依次考虑研究中的各种偏倚来源。选择适当的、具体的结果使干预措施和结果之间的联系更加合理，并能减少混杂的影响。
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