TY - JOUR
T1 - Statistical models for analyzing count data
T2 - predictors of length of stay among HIV patients in Portugal using a multilevel model
AU - Shaaban, Ahmed Nabil
AU - Peleteiro, Bárbara
AU - Martins, Maria Rosario O.
N1 - Funding Information:
This research was co-financed by Saúde Global e Medicina Tropical, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa, Portugal, ref. UID/04413/2020 and Unidade de Investigação em Epidemiologia – Instituto de Saúde Pública da Universidade do Porto (EPIUnit), ref. UIDB/04750/2020; and the Foundation for Science and Technology – FCT (Portuguese Ministry of Science, Technology and Higher Education) [grant number PD/BD/128066/2016 (A. N. Shaaban)].
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/4/21
Y1 - 2021/4/21
N2 - 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.
AB - 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.
KW - Count data analysis
KW - HIV
KW - Hospital performance
KW - Length of stay (LOS)
KW - Multilevel model#
KW - Quality indicator
KW - Random - effects model
UR - http://www.scopus.com/inward/record.url?scp=85104610439&partnerID=8YFLogxK
U2 - 10.1186/s12913-021-06389-1
DO - 10.1186/s12913-021-06389-1
M3 - Article
C2 - 33882911
AN - SCOPUS:85104610439
SN - 1472-6963
VL - 21
SP - 1
EP - 17
JO - BMC Health Services Research
JF - BMC Health Services Research
IS - 1
M1 - 372
ER -