ArticleBMC health services research2021
Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 19 citations in OpenAlex.
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- Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology.NPJ digital medicine · 2024Article
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- Impact of COVID-19 epidemic on temporal pattern of new HIV diagnoses in Italy, 2021 database.European journal of public health · 2023Article
- Racial and Ethnic Differences in Length of Stay for US Children Hospitalized for Acute Osteomyelitis.The Journal of pediatrics · 2023Article
- Evaluating the performance of different Bayesian count models in modelling childhood vaccine uptake among children aged 12-23 months in Nigeria.BMC public health · 2023Article
- A comparison of statistical methods for modeling count data with an application to hospital length of stay.BMC medical research methodology · 2022Article
- Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy.International journal of environmental research and public health · 2022Article
- Deep phenotyping of oxidative stress in emergency room patients reveals homoarginine as a novel predictor of sepsis severity, length of hospital stay, and length of intensive care unit stay.Frontiers in medicine · 2022Article
- Postwar Decline in Healthcare Services Utilization With Rising Disparities: A Repeated Cross-Sectional Study Using Count Data Models.Inquiry : a journal of medical care organization, provision and financingArticle
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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