Tuberculosis in Prisons: Importance of Considering the Clustering in the Analysis of Cross-Sectional Studies

Diana Marín (), Yoav Keynan, Shrikant I. Bangdiwala, Lucelly López and Zulma Vanessa Rueda
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Diana Marín: Facultad de Medicina, Universidad Pontificia Bolivariana, Medellín 050034, Colombia
Yoav Keynan: Department of Medical Microbiology and Infectious Disease, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
Shrikant I. Bangdiwala: Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON L8S 4K1, Canada
Lucelly López: Facultad de Medicina, Universidad Pontificia Bolivariana, Medellín 050034, Colombia
Zulma Vanessa Rueda: Facultad de Medicina, Universidad Pontificia Bolivariana, Medellín 050034, Colombia

IJERPH, 2023, vol. 20, issue 7, 1-16

Abstract: The level of clustering and the adjustment by cluster-robust standard errors have yet to be widely considered and reported in cross-sectional studies of tuberculosis (TB) in prisons. In two cross-sectional studies of people deprived of liberty (PDL) in Medellin, we evaluated the impact of adjustment versus failure to adjust by clustering on prevalence ratio (PR) and 95% confidence interval (CI). We used log-binomial regression, Poisson regression, generalized estimating equations (GEE), and mixed-effects regression models. We used cluster-robust standard errors and bias-corrected standard errors. The odds ratio (OR) was 20% higher than the PR when the TB prevalence was >10% in at least one of the exposure factors. When there are three levels of clusters (city, prison, and courtyard), the cluster that had the strongest effect was the courtyard, and the 95% CI estimated with GEE and mixed-effect models were narrower than those estimated with Poisson and binomial models. Exposure factors lost their significance when we used bias-corrected standard errors due to the smaller number of clusters. Tuberculosis transmission dynamics in prisons dictate a strong cluster effect that needs to be considered and adjusted for. The omission of cluster structure and bias-corrected by the small number of clusters can lead to wrong inferences.

Keywords: clustered-data; cross-sectional studies; log-binomial regression; modified Poisson regression; GEE; multilevel analysis; tuberculosis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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