Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

Ahmed Nabil Shaaban, Bárbara Peleteiro, Maria Rosario O. Martins

Research output: Contribution to journalArticlepeer-review

Abstract

Background: This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective: To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method: Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results: The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions: This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Original languageEnglish
Article number372
Pages (from-to)1-17
Number of pages17
JournalBMC Health Services Research
Volume21
Issue number1
DOIs
Publication statusPublished - 21 Apr 2021

Keywords

  • Count data analysis
  • HIV
  • Hospital performance
  • Length of stay (LOS)
  • Multilevel model#
  • Quality indicator
  • Random - effects model

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