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

BMC Health Services Research

Maria Rosario O. Martins¹  Ahmed Nabil Shaaban²  Bárbara Peleteiro³ 

[1] Department of Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Rua da Junqueira N°100, 1349-008, Lisbon, Portugal;Department of Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Rua da Junqueira N°100, 1349-008, Lisbon, Portugal;EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal;EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal;Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal;

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

DOI  :  10.1186/s12913-021-06389-1

来源: Springer

【 摘 要 】

BackgroundThis study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal.ObjectiveTo provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal.MethodRegistered 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.ResultsThe 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.ConclusionsThis study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

【 授权许可】

CC BY