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

Diana Marín, Yoav Keynan, Shrikant I. Bangdiwala, Lucelly López, Zulma Vanessa Rueda

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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.
Idioma originalInglés
Número de artículo5423
PublicaciónInternational Journal of Environmental Research and Public Health
EstadoPublicada - 6 abr. 2023

Nota bibliográfica

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© 2023 by the authors.

Palabras clave

  • Clustered-data
  • Cross-Sectional Studies
  • Log-binomial regression
  • Modified Poisson regression
  • GEE
  • Multilevel analysis
  • Tuberculosis

Tipos de Productos Minciencias

  • Artículos de investigación con calidad A2 / Q2


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