Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Apr 21;21(1):372.
doi: 10.1186/s12913-021-06389-1.

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

Ahmed Nabil Shaaban  1   2 , Bárbara Peleteiro  3   4 , Maria Rosario O Martins  5
Affiliations

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

Ahmed Nabil Shaaban et al. BMC Health Serv Res. .

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.

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

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Flowchart for selecting a count regression model in analyzing length of stay among HIV patients in Portugal
Fig. 2
Fig. 2
Length of stay distribution
Fig. 3
Fig. 3
Comparisons among observed versus predicted probabilities among count models (N = 26,505). PRM – Poisson Regression Model; NBRM – Negative Binomial Regression Model, ZIP – Zero-inflated Poisson; ZINB – Zero-inflated Negative Binomial
Fig. 4
Fig. 4
Caterpillar plot of the hospital effects
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Parsons T., III Length of stay: managed care agenda or a measure of clinical efficiency? Psychiatry. 2006;3(6):46. - PMC - PubMed
    1. Brasel KJ, Lim HJ, Nirula R, Weigelt JA. Length of stay: an appropriate quality measure? Arch Surg. 2007;142(5):461–466. doi: 10.1001/archsurg.142.5.461. - DOI - PubMed
    1. Shaaban AN, Martins O, Rosario M. The importance of improving the quality of care among HIV/AIDS hospitalizations in Portugal. Front Public Health. 2019;7:266. doi: 10.3389/fpubh.2019.00266. - DOI - PMC - PubMed
    1. Carter EM, Potts HW. Predicting length of stay from an electronic patient record system: a primary total knee replacement example. BMC Med Inform Decision Making. 2014;14(1):26. doi: 10.1186/1472-6947-14-26. - DOI - PMC - PubMed
    1. Feng CX, Li L. Modeling zero inflation and overdispersion in the length of hospital stay for patients with ischaemic heart disease. Adv Stat Methods Data Sci. 2016:35–53. 10.1007/978-981-10-2594-5_3 Springer.