Groenwold R H H, Hoes A W, Nichol K L, Hak E
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
Int J Epidemiol. 2008 Dec;37(6):1422-9. doi: 10.1093/ije/dyn173. Epub 2008 Aug 25.
The validity of non-randomized studies using healthcare databases is often challenged because they lack information on potentially important confounders, such as functional health status and socioeconomic status. In a study quantifying the effects of influenza vaccination among community-dwelling elderly we assessed whether additional information on not routinely available covariates was indeed associated with exposure to influenza vaccination and could, therefore, have led to residual confounding in healthcare databases.
We randomly selected 500 persons aged 65 years and older from the computerized Utrecht General Practitioner database. Information on exposure status and on demographics, co-morbidity status, prior healthcare use and medication use was extracted from the database. A questionnaire was used to obtain additional information on not routinely available risk factors [e.g. functional health status (SF-20), smoking status and alcohol consumption]. Missing data from the questionnaire was imputed and multivariable logistic regression analysis was applied to quantify the influence of covariates on the prediction of exposure to influenza vaccination. Within an existing dataset the potential impact of functional health status on the relation between influenza vaccination and mortality was simulated.
We obtained questionnaire data from 365 of 500 (73%) subjects. The model including routinely available data from the database appeared accurate in predicting exposure to influenza vaccination (c-statistic 0.86, 95% CI: 0.82-0.89). Functional health status was the only additional characteristic measured with the questionnaire that was not similar in vaccinated and unvaccinated subjects. However, extending the multivariable regression model with functional health status did not significantly improve the prediction of exposure to influenza vaccination, nor did it affect the relation between influenza vaccination and mortality.
The potential for unmeasured confounding on the association between influenza vaccination and health outcomes as quantified in healthcare databases seems small for non-randomized intervention studies within extensive and reliable databases.
使用医疗保健数据库的非随机研究的有效性常常受到质疑,因为这些研究缺乏关于潜在重要混杂因素的信息,如功能健康状况和社会经济状况。在一项量化社区居住老年人流感疫苗接种效果的研究中,我们评估了关于非常规可用协变量的额外信息是否确实与流感疫苗接种暴露相关,因此是否可能导致医疗保健数据库中的残余混杂。
我们从乌得勒支计算机化全科医生数据库中随机选择了500名65岁及以上的人。从数据库中提取暴露状态以及人口统计学、合并症状态、既往医疗保健使用情况和用药情况的信息。使用问卷调查来获取关于非常规可用风险因素的额外信息[如功能健康状况(SF - 20)、吸烟状况和饮酒情况]。对问卷中的缺失数据进行插补,并应用多变量逻辑回归分析来量化协变量对流感疫苗接种暴露预测的影响。在现有数据集中模拟了功能健康状况对流感疫苗接种与死亡率之间关系的潜在影响。
我们从500名受试者中的365名(73%)获得了问卷数据。包含数据库中常规可用数据的模型在预测流感疫苗接种暴露方面似乎很准确(c统计量为0.86,95%置信区间:0.82 - 0.89)。功能健康状况是通过问卷测量的唯一一项在接种和未接种疫苗的受试者中不相似的额外特征。然而,将功能健康状况纳入多变量回归模型并没有显著改善对流感疫苗接种暴露的预测,也没有影响流感疫苗接种与死亡率之间的关系。
对于广泛且可靠数据库中的非随机干预研究,医疗保健数据库中量化的流感疫苗接种与健康结局之间关联的未测量混杂可能性似乎很小。