Emergency Care Sensitive Conditions in Brazil: A Geographic Information System Approach to Timely Hospital Access.
Access to care
Brazil
Emergency Care
Emergency Care Sensitive Conditions
Emergency Medicine
Geographic Information System
Heart Attack
STEMI
Stroke
Trauma
Journal
Lancet regional health. Americas
ISSN: 2667-193X
Titre abrégé: Lancet Reg Health Am
Pays: England
ID NLM: 9918232503006676
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
11
06
2021
revised:
26
07
2021
accepted:
20
08
2021
entrez:
13
2
2023
pubmed:
10
9
2021
medline:
10
9
2021
Statut:
epublish
Résumé
The benefits of treatment for many conditions are time dependent. The burden of these emergency care sensitive conditions (ECSCs) is especially high in low- and middle-income countries. Our objective was to analyze geospatial trends in ECSCs and characterize regional disparities in access to emergency care in Brazil. From publicly available datasets, we extracted data on patients assigned an ECSC-related ICD-10 code and on the country's emergency facilities from 2015-2019. Using ArcGIS, OpenStreetMap, and WorldPop, we created catchment areas corresponding to 180 minutes of driving distance from each hospital. We then used ArcGIS to characterize space-time trends in ECSC admissions and to complete an Origin-Destination analysis to determine the path from household to closest hospital. There were 1362 municipalities flagged as "hot spots," areas with a high volume of ECSCs. Of those, 69.7% were more than 180 minutes (171 km) from the closest emergency facility. These municipalities were primarily located in the states of Minas Gerais, Bahia, Espiríto Santo, Tocantins, and Amapá. In the North region, only 69.1% of the population resided within 180 minutes of an emergency hospital. Significant geographical barriers to accessing emergency care exist in certain areas of Brazil, especially in peri-urban areas and the North region. One limitation of this approach is that geolocation was not possible in some areas and thus we are likely underestimating the burden of inadequate access. Subsequent work should evaluate ECSC mortality data. This study was funded by the Duke Global Health Institute Artificial Intelligence Pilot Project.
Sections du résumé
Background
UNASSIGNED
The benefits of treatment for many conditions are time dependent. The burden of these emergency care sensitive conditions (ECSCs) is especially high in low- and middle-income countries. Our objective was to analyze geospatial trends in ECSCs and characterize regional disparities in access to emergency care in Brazil.
Methods
UNASSIGNED
From publicly available datasets, we extracted data on patients assigned an ECSC-related ICD-10 code and on the country's emergency facilities from 2015-2019. Using ArcGIS, OpenStreetMap, and WorldPop, we created catchment areas corresponding to 180 minutes of driving distance from each hospital. We then used ArcGIS to characterize space-time trends in ECSC admissions and to complete an Origin-Destination analysis to determine the path from household to closest hospital.
Findings
UNASSIGNED
There were 1362 municipalities flagged as "hot spots," areas with a high volume of ECSCs. Of those, 69.7% were more than 180 minutes (171 km) from the closest emergency facility. These municipalities were primarily located in the states of Minas Gerais, Bahia, Espiríto Santo, Tocantins, and Amapá. In the North region, only 69.1% of the population resided within 180 minutes of an emergency hospital.
Interpretations
UNASSIGNED
Significant geographical barriers to accessing emergency care exist in certain areas of Brazil, especially in peri-urban areas and the North region. One limitation of this approach is that geolocation was not possible in some areas and thus we are likely underestimating the burden of inadequate access. Subsequent work should evaluate ECSC mortality data.
Funding
UNASSIGNED
This study was funded by the Duke Global Health Institute Artificial Intelligence Pilot Project.
Identifiants
pubmed: 36776707
doi: 10.1016/j.lana.2021.100063
pii: S2667-193X(21)00059-4
pmc: PMC9903578
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100063Informations de copyright
© 2021 The Author(s). Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
This study was funded by the Duke Global Health Institute Artificial Intelligence Pilot Project.
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