Statistical models for analyzing count data
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2021 |
| Outros Autores: | , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/133478 |
Resumo: | 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). |
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Statistical models for analyzing count datapredictors of length of stay among HIV patients in Portugal using a multilevel modelCount data analysisHIVHospital performanceLength of stay (LOS)Multilevel model#Quality indicatorRandom - effects modelHealth PolicyImmunology and Microbiology (miscellaneous)Health Information ManagementEpidemiologyInfectious DiseasesInternal MedicineVirologyCare PlanningModelling and SimulationStatistics and ProbabilitySDG 3 - Good Health and Well-beingSDG 9 - Industry, Innovation, and InfrastructureSDG 10 - Reduced InequalitiesSDG 11 - Sustainable Cities and CommunitiesSDG 12 - Responsible Consumption and ProductionSDG 16 - Peace, Justice and Strong InstitutionsSDG 17 - Partnerships for the GoalsSDG 5 - Gender EqualitySaúde PúblicaFunding 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).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.Population health, policies and services (PPS)Global Health and Tropical Medicine (GHTM)Instituto de Higiene e Medicina Tropical (IHMT)RUNShaaban, Ahmed NabilPeleteiro, BárbaraMartins, Maria Rosario O.2022-02-23T23:16:47Z2021-04-212021-04-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/133478eng1472-6963PURE: 33049869https://doi.org/10.1186/s12913-021-06389-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:12:04Zoai:run.unl.pt:10362/133478Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:48.430427Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Statistical models for analyzing count data predictors of length of stay among HIV patients in Portugal using a multilevel model |
| title |
Statistical models for analyzing count data |
| spellingShingle |
Statistical models for analyzing count data Shaaban, Ahmed Nabil Count data analysis HIV Hospital performance Length of stay (LOS) Multilevel model# Quality indicator Random - effects model Health Policy Immunology and Microbiology (miscellaneous) Health Information Management Epidemiology Infectious Diseases Internal Medicine Virology Care Planning Modelling and Simulation Statistics and Probability SDG 3 - Good Health and Well-being SDG 9 - Industry, Innovation, and Infrastructure SDG 10 - Reduced Inequalities SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production SDG 16 - Peace, Justice and Strong Institutions SDG 17 - Partnerships for the Goals SDG 5 - Gender Equality Saúde Pública |
| title_short |
Statistical models for analyzing count data |
| title_full |
Statistical models for analyzing count data |
| title_fullStr |
Statistical models for analyzing count data |
| title_full_unstemmed |
Statistical models for analyzing count data |
| title_sort |
Statistical models for analyzing count data |
| author |
Shaaban, Ahmed Nabil |
| author_facet |
Shaaban, Ahmed Nabil Peleteiro, Bárbara Martins, Maria Rosario O. |
| author_role |
author |
| author2 |
Peleteiro, Bárbara Martins, Maria Rosario O. |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Population health, policies and services (PPS) Global Health and Tropical Medicine (GHTM) Instituto de Higiene e Medicina Tropical (IHMT) RUN |
| dc.contributor.author.fl_str_mv |
Shaaban, Ahmed Nabil Peleteiro, Bárbara Martins, Maria Rosario O. |
| dc.subject.por.fl_str_mv |
Count data analysis HIV Hospital performance Length of stay (LOS) Multilevel model# Quality indicator Random - effects model Health Policy Immunology and Microbiology (miscellaneous) Health Information Management Epidemiology Infectious Diseases Internal Medicine Virology Care Planning Modelling and Simulation Statistics and Probability SDG 3 - Good Health and Well-being SDG 9 - Industry, Innovation, and Infrastructure SDG 10 - Reduced Inequalities SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production SDG 16 - Peace, Justice and Strong Institutions SDG 17 - Partnerships for the Goals SDG 5 - Gender Equality Saúde Pública |
| topic |
Count data analysis HIV Hospital performance Length of stay (LOS) Multilevel model# Quality indicator Random - effects model Health Policy Immunology and Microbiology (miscellaneous) Health Information Management Epidemiology Infectious Diseases Internal Medicine Virology Care Planning Modelling and Simulation Statistics and Probability SDG 3 - Good Health and Well-being SDG 9 - Industry, Innovation, and Infrastructure SDG 10 - Reduced Inequalities SDG 11 - Sustainable Cities and Communities SDG 12 - Responsible Consumption and Production SDG 16 - Peace, Justice and Strong Institutions SDG 17 - Partnerships for the Goals SDG 5 - Gender Equality Saúde Pública |
| description |
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). |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-04-21 2021-04-21T00:00:00Z 2022-02-23T23:16:47Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/133478 |
| url |
http://hdl.handle.net/10362/133478 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
1472-6963 PURE: 33049869 https://doi.org/10.1186/s12913-021-06389-1 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
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17 application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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