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

ABSTRACT 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,...

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Main Authors: Daniel Okita Uehara, Pedro Lucas Rosa, Matheus Cardoso Moraes, Renato Cesar Sato
Format: Article
Language:English
Published: Instituto Israelita de Ensino e Pesquisa Albert Einstein 2021-08-01
Series:Einstein (São Paulo)
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082021000100303&tlng=en
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author Daniel Okita Uehara
Pedro Lucas Rosa
Matheus Cardoso Moraes
Renato Cesar Sato
author_facet Daniel Okita Uehara
Pedro Lucas Rosa
Matheus Cardoso Moraes
Renato Cesar Sato
author_sort Daniel Okita Uehara
collection DOAJ
description ABSTRACT 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 doaj.art-b30f211862ea4531a692bb0e0cfe260f2022-12-21T17:34:39ZengInstituto Israelita de Ensino e Pesquisa Albert EinsteinEinstein (São Paulo)2317-63852021-08-011910.31744/einstein_journal/2021gs5914Impacts of decentralization in health systems in the state of São Paulo, BrazilDaniel Okita Ueharahttps://orcid.org/0000-0002-9824-370XPedro Lucas Rosahttps://orcid.org/0000-0002-0375-9472Matheus Cardoso Moraeshttps://orcid.org/0000-0002-6019-6653Renato Cesar Satohttps://orcid.org/0000-0002-9902-9086ABSTRACT 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082021000100303&tlng=enHealth services accessibilityHealth equityGeographic locationsHealth facilityHealth care rationing
spellingShingle Daniel Okita Uehara
Pedro Lucas Rosa
Matheus Cardoso Moraes
Renato Cesar Sato
Impacts of decentralization in health systems in the state of São Paulo, Brazil
Einstein (São Paulo)
Health services accessibility
Health equity
Geographic locations
Health facility
Health care rationing
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 sao paulo brazil
topic Health services accessibility
Health equity
Geographic locations
Health facility
Health care rationing
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082021000100303&tlng=en
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