ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2013
Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model.BMC health services research · 2021Article
- Improving Quality of Service Among Antiretroviral Therapy Users Through Strategic Problem-Solving Approach at Bure Primary Hospital, Northwest Ethiopia.Patient preference and adherence · 2021Article
- Article
- HIV/AIDS length of stay in Portugal under financial constraints: a longitudinal study for public hospitals, 2009-2014.BMC health services research · 2019Article
- The Importance of Improving the Quality of Care Among HIV/AIDS Hospitalizations in Portugal.Frontiers in public health · 2019Article
- How do patients and providers react to different incentives in the Chinese multiple health security systems?Chinese medical journal · 2015Article
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3 authors.
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Abstract
Inpatient length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed, representing a challenge for statistical analysis. Taking this feature into account, we seek to identify factors that are associated with HIV/AIDS through a hierarchical finite mixture model. A mixture of normal components is applied to adult HIV/AIDS diagnosis-related group data (DRG) from 2008. The model accounts for the demographic and clinical characteristics of the patients, as well the inherent correlation of patients clustered within hospitals. In the present research, a normal mixture distribution was fitted to the logarithm of LOS and it was found that a model with two-components had the best fit, resulting in two subgroups of LOS: a short-stay subgroup and a long-stay subgroup. Associated risk factors for both groups were identified as well as some statistical differences in the hospitals. Our findings provide important information for policy makers in terms of discharge planning and the efficient management of LOS. The presence of "atypical" hospitals also suggests that hospitals should not be viewed or treated as homogenous bodies.
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