Identifying the main predictors of length of care in social care in Portugal
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/121554 |
Resumo: | In this paper, we aim to identify the main predictors at admission and estimate patients' length of care (LOC), within the framework of the Portuguese National Network for Long-Term Integrated Care, considering two care settings: (1) home and community-based services (HCBS) and (2) nursing home (NH) units comprising Short, Medium, or Long Stay Care. This study relied on a database of 20,984 Portuguese individuals who were admitted to the official long-term care (LTC) system and discharged during 2015. A generalised linear model (GLM) with gamma distribution was adjusted to HCBS and NH populations. Two sets of explanatory variables were used to model the random variable, LOC, namely, patient characteristics (age, gender, family/neighbour support, dependency levels at admission for locomotion, cognitive status, and activities of daily living [ADL]) and external factors (referral entity, number of beds/treatment places per 1,000 inhabitants ≥65 years of age), maturity and occupancy rate of the institution, and care setting. The features found to most influence the reduction of LOC are: male gender, having family/neighbour support, being referred by hospitals to NH (or by primary care to HCBS), and being admitted to units with a lower occupancy rate and with fewer months in operation. Regarding the dependency levels, as the number of ADL considered "dependent"increases, LOC also increases. As for the cognitive status, despite the opposite trend, it was only statistically significant for NH. Furthermore, two additional models were applied by including "death,"although this feature is not observable upon admission. By creating a model that allows for an estimate of the expected LOC for a new individual entering the Portuguese LTC system, policy-makers are able to estimate future costs and optimise resources. |
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Identifying the main predictors of length of care in social care in PortugalIdentificação dos principais fatores preditivos do tempo de internamento nos cuidados continuados em PortugalDependency levelsHome and community-based servicesLength of careLong-term careNursing homesPortugalHealth PolicyPublic Health, Environmental and Occupational HealthSDG 3 - Good Health and Well-beingIn this paper, we aim to identify the main predictors at admission and estimate patients' length of care (LOC), within the framework of the Portuguese National Network for Long-Term Integrated Care, considering two care settings: (1) home and community-based services (HCBS) and (2) nursing home (NH) units comprising Short, Medium, or Long Stay Care. This study relied on a database of 20,984 Portuguese individuals who were admitted to the official long-term care (LTC) system and discharged during 2015. A generalised linear model (GLM) with gamma distribution was adjusted to HCBS and NH populations. Two sets of explanatory variables were used to model the random variable, LOC, namely, patient characteristics (age, gender, family/neighbour support, dependency levels at admission for locomotion, cognitive status, and activities of daily living [ADL]) and external factors (referral entity, number of beds/treatment places per 1,000 inhabitants ≥65 years of age), maturity and occupancy rate of the institution, and care setting. The features found to most influence the reduction of LOC are: male gender, having family/neighbour support, being referred by hospitals to NH (or by primary care to HCBS), and being admitted to units with a lower occupancy rate and with fewer months in operation. Regarding the dependency levels, as the number of ADL considered "dependent"increases, LOC also increases. As for the cognitive status, despite the opposite trend, it was only statistically significant for NH. Furthermore, two additional models were applied by including "death,"although this feature is not observable upon admission. By creating a model that allows for an estimate of the expected LOC for a new individual entering the Portuguese LTC system, policy-makers are able to estimate future costs and optimise resources.Centro de Investigação em Saúde Pública (CISP/PHRC)Comprehensive Health Research Centre (CHRC) - Pólo ENSPCMA - Centro de Matemática e AplicaçõesRUNLopes, HugoGuerreiro, GracindaEsquível, ManuelMateus, Céu2021-07-23T22:20:27Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/1215542504-3137PURE: 32248680https://doi.org/10.1159/000516141info:eu-repo/semantics/openAccessporreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:03:49Zoai:run.unl.pt:10362/121554Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:44:40.850618Repositório 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 |
Identifying the main predictors of length of care in social care in Portugal Identificação dos principais fatores preditivos do tempo de internamento nos cuidados continuados em Portugal |
title |
Identifying the main predictors of length of care in social care in Portugal |
spellingShingle |
Identifying the main predictors of length of care in social care in Portugal Lopes, Hugo Dependency levels Home and community-based services Length of care Long-term care Nursing homes Portugal Health Policy Public Health, Environmental and Occupational Health SDG 3 - Good Health and Well-being |
title_short |
Identifying the main predictors of length of care in social care in Portugal |
title_full |
Identifying the main predictors of length of care in social care in Portugal |
title_fullStr |
Identifying the main predictors of length of care in social care in Portugal |
title_full_unstemmed |
Identifying the main predictors of length of care in social care in Portugal |
title_sort |
Identifying the main predictors of length of care in social care in Portugal |
author |
Lopes, Hugo |
author_facet |
Lopes, Hugo Guerreiro, Gracinda Esquível, Manuel Mateus, Céu |
author_role |
author |
author2 |
Guerreiro, Gracinda Esquível, Manuel Mateus, Céu |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Centro de Investigação em Saúde Pública (CISP/PHRC) Comprehensive Health Research Centre (CHRC) - Pólo ENSP CMA - Centro de Matemática e Aplicações RUN |
dc.contributor.author.fl_str_mv |
Lopes, Hugo Guerreiro, Gracinda Esquível, Manuel Mateus, Céu |
dc.subject.por.fl_str_mv |
Dependency levels Home and community-based services Length of care Long-term care Nursing homes Portugal Health Policy Public Health, Environmental and Occupational Health SDG 3 - Good Health and Well-being |
topic |
Dependency levels Home and community-based services Length of care Long-term care Nursing homes Portugal Health Policy Public Health, Environmental and Occupational Health SDG 3 - Good Health and Well-being |
description |
In this paper, we aim to identify the main predictors at admission and estimate patients' length of care (LOC), within the framework of the Portuguese National Network for Long-Term Integrated Care, considering two care settings: (1) home and community-based services (HCBS) and (2) nursing home (NH) units comprising Short, Medium, or Long Stay Care. This study relied on a database of 20,984 Portuguese individuals who were admitted to the official long-term care (LTC) system and discharged during 2015. A generalised linear model (GLM) with gamma distribution was adjusted to HCBS and NH populations. Two sets of explanatory variables were used to model the random variable, LOC, namely, patient characteristics (age, gender, family/neighbour support, dependency levels at admission for locomotion, cognitive status, and activities of daily living [ADL]) and external factors (referral entity, number of beds/treatment places per 1,000 inhabitants ≥65 years of age), maturity and occupancy rate of the institution, and care setting. The features found to most influence the reduction of LOC are: male gender, having family/neighbour support, being referred by hospitals to NH (or by primary care to HCBS), and being admitted to units with a lower occupancy rate and with fewer months in operation. Regarding the dependency levels, as the number of ADL considered "dependent"increases, LOC also increases. As for the cognitive status, despite the opposite trend, it was only statistically significant for NH. Furthermore, two additional models were applied by including "death,"although this feature is not observable upon admission. By creating a model that allows for an estimate of the expected LOC for a new individual entering the Portuguese LTC system, policy-makers are able to estimate future costs and optimise resources. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-23T22:20:27Z 2021 2021-01-01T00:00:00Z |
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/10362/121554 |
url |
http://hdl.handle.net/10362/121554 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
2504-3137 PURE: 32248680 https://doi.org/10.1159/000516141 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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15 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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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 |
repository.mail.fl_str_mv |
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1799138053997985792 |