Impact of traffic congestion on spatial access to healthcare services in Nairobi.

catchment model healthcare accessibility sub-Saharan Africa traffic congestion universal healthcare access

Journal

Frontiers in health services
ISSN: 2813-0146
Titre abrégé: Front Health Serv
Pays: Switzerland
ID NLM: 9918334887706676

Informations de publication

Date de publication:
2022
Historique:
received: 01 10 2021
accepted: 25 10 2022
entrez: 17 3 2023
pubmed: 18 3 2023
medline: 18 3 2023
Statut: epublish

Résumé

Geographic accessibility is an important determinant of healthcare utilization and is critical for achievement of universal health coverage. Despite the high disease burden and severe traffic congestion in many African cities, few studies have assessed how traffic congestion impacts geographical access to healthcare facilities and to health professionals in these settings. In this study, we assessed the impact of traffic congestion on access to healthcare facilities, and to the healthcare professionals across the healthcare facilities. Using data on health facilities obtained from the Ministry of Health in Kenya, we mapped 944 primary, 94 secondary and four tertiary healthcare facilities in Nairobi County. We then used traffic probe data to identify areas within a 15-, 30- and 45-min drive from each health facility during peak and off-peak hours and calculated the proportion of the population with access to healthcare in the County. We employed a 2-step floating catchment area model to calculate the ratio of healthcare and healthcare professionals to population during these times. During peak hours, <70% of Nairobi's 4.1 million population was within a 30-min drive from a health facility. This increased to >75% during off-peak hours. In 45 min, the majority of the population had an accessibility index of one health facility accessible to more than 100 people (<0.01) for primary health care facilities, one to 10,000 people for secondary facilities, and two health facilities per 100,000 people for tertiary health facilities. Of people with access to health facilities, a sub-optimal ratio of <4.45 healthcare professionals per 1,000 people was observed in facilities offering primary and secondary healthcare during peak and off-peak hours. Our study shows access to healthcare being negatively impacted by traffic congestion, highlighting the need for multisectoral collaborations between urban planners, health sector and policymakers to optimize health access for the city residents. Additionally, growing availability of traffic probe data in African cities should enable similar analysis and understanding of healthcare access for city residents in other countries on the continent.

Sections du résumé

Background UNASSIGNED
Geographic accessibility is an important determinant of healthcare utilization and is critical for achievement of universal health coverage. Despite the high disease burden and severe traffic congestion in many African cities, few studies have assessed how traffic congestion impacts geographical access to healthcare facilities and to health professionals in these settings. In this study, we assessed the impact of traffic congestion on access to healthcare facilities, and to the healthcare professionals across the healthcare facilities.
Methods UNASSIGNED
Using data on health facilities obtained from the Ministry of Health in Kenya, we mapped 944 primary, 94 secondary and four tertiary healthcare facilities in Nairobi County. We then used traffic probe data to identify areas within a 15-, 30- and 45-min drive from each health facility during peak and off-peak hours and calculated the proportion of the population with access to healthcare in the County. We employed a 2-step floating catchment area model to calculate the ratio of healthcare and healthcare professionals to population during these times.
Results UNASSIGNED
During peak hours, <70% of Nairobi's 4.1 million population was within a 30-min drive from a health facility. This increased to >75% during off-peak hours. In 45 min, the majority of the population had an accessibility index of one health facility accessible to more than 100 people (<0.01) for primary health care facilities, one to 10,000 people for secondary facilities, and two health facilities per 100,000 people for tertiary health facilities. Of people with access to health facilities, a sub-optimal ratio of <4.45 healthcare professionals per 1,000 people was observed in facilities offering primary and secondary healthcare during peak and off-peak hours.
Conclusion UNASSIGNED
Our study shows access to healthcare being negatively impacted by traffic congestion, highlighting the need for multisectoral collaborations between urban planners, health sector and policymakers to optimize health access for the city residents. Additionally, growing availability of traffic probe data in African cities should enable similar analysis and understanding of healthcare access for city residents in other countries on the continent.

Identifiants

pubmed: 36925766
doi: 10.3389/frhs.2022.788173
pmc: PMC10012710
doi:

Types de publication

Journal Article

Langues

eng

Pagination

788173

Informations de copyright

Copyright © 2022 Mutono, Wright, Mutunga, Mutembei and Thumbi.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Nyamai Mutono (N)

Wangari Maathai Institute for Peace and Environmental Studies, University of Nairobi, Nairobi, Kenya.
Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya.
Paul G. Allen School for Global Health, Washington State University, Pullman, WA, United States.

Jim A Wright (JA)

School of Geography and Environment Science, University of Southampton, Southampton, United Kingdom.

Mumbua Mutunga (M)

Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya.
Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi, Kenya.

Henry Mutembei (H)

Wangari Maathai Institute for Peace and Environmental Studies, University of Nairobi, Nairobi, Kenya.
Department of Clinical Studies, University of Nairobi, Nairobi, Kenya.

S M Thumbi (SM)

Center for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya.
Paul G. Allen School for Global Health, Washington State University, Pullman, WA, United States.
Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi, Kenya.
Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.

Classifications MeSH