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Invited Commentary
Public Health
January 10, 2020

Limitations of Meta-analyses of Studies With High Heterogeneity

Author Affiliations
  • 1Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
JAMA Netw Open. 2020;3(1):e1919325. doi:10.1001/jamanetworkopen.2019.19325

Sabitova et al1 have performed an important service in compiling and summarizing 2 decades of studies on job burnout and satisfaction among physicians and dentists in middle-income countries and a few low-income countries. The authors followed a standard approach to performing a systematic review and meta-analysis to analyze studies that assessed job-related morale among physicians and dentists working in these countries, using levels of job burnout, job satisfaction, and job motivation as indicators of job morale. Data were extracted independently by several investigators following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines. The authors assessed the quality of the included studies for bias and conducted random-effects meta-analyses, planned subgroup analyses, and metaregression analyses. The study included results from 79 studies with 45 714 participants. The authors reported that, in their analysis of data from 21 studies including 9092 physicians and dentists, 32% of participants, who worked mainly in middle-income countries, exceeded the high threshold for job burnout, and in their analysis of 20 studies including 14 113 participants, 60% were satisfied with their jobs overall. Available data were insufficient for the authors to conduct an analysis of job motivation. The authors’ meticulously documented structured literature review will be useful to health services researchers and policy makers. However, as the authors acknowledge, the meta-analytic portion of their study was limited by significant heterogeneity observed across studies that could not be explained by subgroup analyses or metaregressions. Their results illustrate why rigorously conducted meta-analyses of highly heterogeneous studies may be less interpretable and useful than initially anticipated.

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