Evidence map›Paper›PMID 36925766›Full record

ArticleFrontiers in health services2022

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

Nyamai Mutono, Jim A Wright, Mumbua Mutunga, Henry Mutembei, S M Thumbi

Open access · diamondFull text read
In one paragraph

Article in Frontiers in health services, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.3field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
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  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 3 countries.

Nyamai MutonoWangari Maathai Institute for Peace and Environmental Studies, University of Nairobi, Nairobi, Kenya.
Jim A WrightSchool of Geography and Environment Science, University of Southampton, Southampton, United Kingdom.
Mumbua MutungaCenter for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya.
Henry MutembeiWangari Maathai Institute for Peace and Environmental Studies, University of Nairobi, Nairobi, Kenya.
S M ThumbiCenter for Epidemiological Modelling and Analysis, University of Nairobi, Nairobi, Kenya.
University of Nairobi · KEUniversity of Edinburgh · GBUniversity of Southampton · GBWashington State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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: 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: 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: 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.

Indexed as

catchment modelhealthcare accessibilitysub-Saharan Africatraffic congestionuniversal healthcare access

Identifiers

PMID36925766
PMCPMC10012710
OpenAlexW4309206315

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read70
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.