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Impacts of decentralization in health systems in the state of São Paulo, Brazil

OBJECTIVE: To evaluate a p-median model for health care services accessibility based on decentralization and optimal allocation of Primary Health Care Units in the State of São Paulo, Brazil. METHODS: Using geographical data of Primary Health Care Units located in the State of São Paulo, potential s...

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Autores principales: Uehara, Daniel Okita, Rosa, Pedro Lucas, Moraes, Matheus Cardoso, Sato, Renato Cesar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Instituto Israelita de Ensino e Pesquisa Albert Einstein 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253590/
https://www.ncbi.nlm.nih.gov/pubmed/34468592
http://dx.doi.org/10.31744/einstein_journal/2021GS5914
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author Uehara, Daniel Okita
Rosa, Pedro Lucas
Moraes, Matheus Cardoso
Sato, Renato Cesar
author_facet Uehara, Daniel Okita
Rosa, Pedro Lucas
Moraes, Matheus Cardoso
Sato, Renato Cesar
author_sort Uehara, Daniel Okita
collection PubMed
description OBJECTIVE: To evaluate a p-median model for health care services accessibility based on decentralization and optimal allocation of Primary Health Care Units in the State of São Paulo, Brazil. METHODS: Using geographical data of Primary Health Care Units located in the State of São Paulo, potential support and supply facility allocations were simulated by means of a random approach. Several constraints were then imposed on the system to simulate different scenarios. Results were assessed according to geographic disposition. RESULTS: Using a fixed number of supply facilities, ten as a constraint, the p-median approach allocated three facilities near the state capital (the area with the highest concentration of Primary Health Care Units), while remaining facilities were spread throughout the west of the state. A second round of tests assessed the impact of fixed costs alone on optimization, ranging from 71 optimal locations with a fixed unit cost to six optimal locations at a cost 300-fold higher. This finding was relevant to decision-making, since it encompassed scenarios in which only the final number of facilities or only the budget was known. A third set of simulations contemplates an intermediate scenario. CONCLUSION: The p-median approach was capable of optimizing a wide range of scenarios with an average running time of less than 2 hours and 30 minutes while considering a large dataset of more than 4,000 locations. In spite of some shortcomings, such as estimation of Euclidean distances, the method is simple yet powerful enough to be considered a useful tool to assist decision makers in the distribution of resources, and facilities across large areas with high number of locations to be supplied.
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spelling pubmed-82535902021-07-09 Impacts of decentralization in health systems in the state of São Paulo, Brazil Uehara, Daniel Okita Rosa, Pedro Lucas Moraes, Matheus Cardoso Sato, Renato Cesar Einstein (Sao Paulo) Health Economics and Management OBJECTIVE: To evaluate a p-median model for health care services accessibility based on decentralization and optimal allocation of Primary Health Care Units in the State of São Paulo, Brazil. METHODS: Using geographical data of Primary Health Care Units located in the State of São Paulo, potential support and supply facility allocations were simulated by means of a random approach. Several constraints were then imposed on the system to simulate different scenarios. Results were assessed according to geographic disposition. RESULTS: Using a fixed number of supply facilities, ten as a constraint, the p-median approach allocated three facilities near the state capital (the area with the highest concentration of Primary Health Care Units), while remaining facilities were spread throughout the west of the state. A second round of tests assessed the impact of fixed costs alone on optimization, ranging from 71 optimal locations with a fixed unit cost to six optimal locations at a cost 300-fold higher. This finding was relevant to decision-making, since it encompassed scenarios in which only the final number of facilities or only the budget was known. A third set of simulations contemplates an intermediate scenario. CONCLUSION: The p-median approach was capable of optimizing a wide range of scenarios with an average running time of less than 2 hours and 30 minutes while considering a large dataset of more than 4,000 locations. In spite of some shortcomings, such as estimation of Euclidean distances, the method is simple yet powerful enough to be considered a useful tool to assist decision makers in the distribution of resources, and facilities across large areas with high number of locations to be supplied. Instituto Israelita de Ensino e Pesquisa Albert Einstein 2021-07-01 /pmc/articles/PMC8253590/ /pubmed/34468592 http://dx.doi.org/10.31744/einstein_journal/2021GS5914 Text en https://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Health Economics and Management
Uehara, Daniel Okita
Rosa, Pedro Lucas
Moraes, Matheus Cardoso
Sato, Renato Cesar
Impacts of decentralization in health systems in the state of São Paulo, Brazil
title Impacts of decentralization in health systems in the state of São Paulo, Brazil
title_full Impacts of decentralization in health systems in the state of São Paulo, Brazil
title_fullStr Impacts of decentralization in health systems in the state of São Paulo, Brazil
title_full_unstemmed Impacts of decentralization in health systems in the state of São Paulo, Brazil
title_short Impacts of decentralization in health systems in the state of São Paulo, Brazil
title_sort impacts of decentralization in health systems in the state of são paulo, brazil
topic Health Economics and Management
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253590/
https://www.ncbi.nlm.nih.gov/pubmed/34468592
http://dx.doi.org/10.31744/einstein_journal/2021GS5914
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