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Tuberculosis in Prisons

Importance of Considering the Clustering in the Analysis of Cross-Sectional Studies

Bibliographic Data

ID15466561
AuthorsDiana Marín (0000-0002-4715-8388, Universidad Pontificia Bolivariana, corresponding author), Yoav Keynan (0000-0003-4948-4707, University of Manitoba), Shrikant I Bangdiwala (0000-0002-3111-0689, Population Health Research Institute), Lucelly López (0000-0002-1534-520X, Universidad Pontificia Bolivariana), Zulma Vanessa Rueda (0000-0001-6342-1812, Universidad Pontificia Bolivariana)
Year2023
Volume20
Issue7
Pages5423-5423
Publication date2023-04-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph20075423
PMID37048037
OpenAlexW4362666077
LanguageEN
References cited59

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

Cluster (spacecraft · Cluster analysis · Confidence interval · Cross-sectional study · Econometrics · Environmental health · Gee · Generalized estimating equation · Negative binomial distribution · Odds ratio · Poisson distribution · Poisson regression · Population · Regression · Regression analysis · Standard error · Statistics · Computer Science · Mathematics · Medicine · Pneumonia and Respiratory Infections · Statistical Methods and Bayesian Inference · Tuberculosis Research and Epidemiology

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