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测量多种合并症系列-社区居住成年人中自我报告与行政数据之间被忽视的复杂性比较:第 2 篇论文。患病率估计取决于数据源。

Measuring multimorbidity series-an overlooked complexity comparison of self-report vs. administrative data in community-living adults: paper 2. Prevalence estimates depend on the data source.

机构信息

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.

Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada; ICES, Toronto, Ontario, Canada; Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada.

出版信息

J Clin Epidemiol. 2020 Aug;124:163-172. doi: 10.1016/j.jclinepi.2020.04.019. Epub 2020 Apr 27.

Abstract

OBJECTIVE

The objective of the study was to compare multimorbidity prevalence using self-reported and administrative data and identify factors associated with agreement between data sources.

STUDY DESIGN AND SETTING

Self-reported cross-sectional data from four Canadian Community Health Survey waves were linked to administrative data in Ontario, Canada. Multimorbidity prevalence was examined using two definitions, 2+ and 3+ chronic conditions (CCs). Agreement between data sources was assessed using Kappa and Phi statistics. Logistic regression was used to estimate associations between agreement and sociodemographic, health behavior, and health status variables for each multimorbidity definition.

RESULTS

Regardless of multimorbidity definition, prevalence was higher using administrative data (2+ CCs: 55.5% vs. 47.1%; 3+ CCs: 30.0% vs. 24.2%). Agreement between data sources was moderate (2+ CCs K = 0.482; 3+ CCs K = 0.442), and while associated with sociodemographic, health behavior, and health status factors, the magnitude and sometimes direction of association differed by multimorbidity definition.

CONCLUSION

A better understanding is needed of what factors influence individuals' reporting of CCs and how they align with what is in administrative data as policy makers need a solid evidence base on which to make decisions for health planning. Our results suggest that data sources may need to be triangulated to provide accurate estimates of multimorbidity for health services planning and policy.

摘要

目的

本研究旨在比较使用自我报告和行政数据的多病症患病率,并确定与数据源间一致性相关的因素。

研究设计和地点

本研究将来自加拿大四项加拿大社区健康调查(CCHS)的横断面自我报告数据与加拿大安大略省的行政数据相链接。采用两种定义(2+ 和 3+ 种慢性病)来检查多病症患病率。使用 Kappa 和 Phi 统计量评估两种数据源间的一致性。使用逻辑回归估计每种多病症定义的一致性与社会人口统计学、健康行为和健康状况变量之间的关联。

结果

无论采用哪种多病症定义,使用行政数据时的患病率均较高(2+ 种 CC:55.5% vs. 47.1%;3+ 种 CC:30.0% vs. 24.2%)。两种数据源间的一致性为中度(2+ 种 CCs K = 0.482;3+ 种 CCs K = 0.442),尽管与社会人口统计学、健康行为和健康状况因素相关,但关联的大小和方向有时因多病症定义而异。

结论

政策制定者需要一个坚实的证据基础来做出健康规划决策,因此需要更好地了解哪些因素会影响个体对慢性病的报告方式,以及它们与行政数据中的内容如何匹配。我们的研究结果表明,可能需要对数据源进行三角测量,以提供准确的多病症患病率估计值,从而为卫生服务规划和政策提供依据。

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