Samadoulougou Sékou, Idzerda Leanne, Dault Roxane, Lebel Alexandre, Cloutier Anne-Marie, Vanasse Alain
Centre for Research on Planning and Development (CRAD) Laval University Québec Canada.
Evaluation Platform on Obesity Prevention Quebec Heart and Lung Institute Research Center Québec Canada.
Obes Sci Pract. 2020 Sep 4;6(6):677-693. doi: 10.1002/osp4.450. eCollection 2020 Dec.
Health care administrative databases are increasingly used for health studies and public health surveillance. Cases of individuals with obesity are selected using case-identification methods. However, the validity of these methods is fragmentary and particularly challenging for obesity case identification.
The objectives of this systematic review are to (1) determine the case-identification methods used to identify individuals with obesity in health care administrative databases and (2) to summarize the validity of these case-identification methods when compared with a reference standard.
A systematic literature search was conducted in six bibliographic databases for the period January 1980 to June 2019 for all studies evaluating obesity case-identification methods compared with a reference standard.
Seventeen articles met the inclusion criteria. International Classification of Diseases (ICD) codes were the only case-identification method utilized in selected articles. The performance of obesity-identification methods varied widely across studies, with positive predictive value ranging from 19% to 100% while sensitivity ranged from 3% to 92%. The sensitivity of these methods was usually low while the specificity was higher.
When obesity is reported in health care administrative databases, it is usually correctly reported; however, obesity tends to be highly underreported in databases. Therefore, case-identification methods to monitor the prevalence and incidence of obesity within health care administrative databases are not reliable. In contrast, the use of these methods remains relevant for the selection of individuals with obesity for cohort studies, particularly when identifying cohorts of individuals with severe obesity or cohorts where obesity is associated with comorbidities.
医疗保健管理数据库越来越多地用于健康研究和公共卫生监测。肥胖个体病例通过病例识别方法来选取。然而,这些方法的有效性并不完整,对于肥胖病例识别尤其具有挑战性。
本系统评价的目的是:(1)确定在医疗保健管理数据库中用于识别肥胖个体的病例识别方法;(2)总结与参考标准相比时这些病例识别方法的有效性。
在六个文献数据库中进行了系统的文献检索,检索时间为1980年1月至2019年6月,检索所有将肥胖病例识别方法与参考标准进行比较的研究。
17篇文章符合纳入标准。所选文章中使用的唯一病例识别方法是国际疾病分类(ICD)编码。肥胖识别方法的表现因研究而异,阳性预测值范围为19%至100%,而敏感性范围为3%至92%。这些方法的敏感性通常较低,而特异性较高。
当医疗保健管理数据库中报告肥胖时,通常报告正确;然而,肥胖在数据库中往往报告严重不足。因此,用于监测医疗保健管理数据库中肥胖患病率和发病率的病例识别方法不可靠。相比之下,这些方法对于选择肥胖个体进行队列研究仍然具有相关性,特别是在识别严重肥胖个体队列或肥胖与合并症相关的队列时。