Evidence map›Paper›PMID 41851890›Full record

ArticleInternational journal of health geographics2026

Impact analysis of flood-induced changes in geographical accessibility and coverage to healthcare in both public and private sector, 2024, Kenya.

Bibian N Robert, Samuel K Muchiri, Emma W Kahoro, Boneya H Hindada, Helen Kiarie, Emelda A Okiro, Peter M Macharia

Abstract read
In one paragraph

Article in International journal of health geographics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Bibian N Robert *Population & Health Impact Surveillance Group, Kenya Medical Research Institute- Wellcome Trust Research Programme, Nairobi, Kenya. bibianrobert@gmail.com.
Samuel K Muchiri *Population & Health Impact Surveillance Group, Kenya Medical Research Institute- Wellcome Trust Research Programme, Nairobi, Kenya.
Emma W KahoroPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute- Wellcome Trust Research Programme, Nairobi, Kenya.
Boneya H HindadaInternational Center for Humanitarian Affairs, Kenya Red Cross Society, Nairobi, Kenya.
Helen KiarieDivision of Monitoring and Evaluation, Ministry of Health, Nairobi, Kenya.
Emelda A OkiroPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute- Wellcome Trust Research Programme, Nairobi, Kenya.
Peter M MachariaPopulation & Health Impact Surveillance Group, Kenya Medical Research Institute- Wellcome Trust Research Programme, Nairobi, Kenya.

Funding

Wellcome Trust Principal Fellowship #212176Wellcome Trust Senior Fellowship #224272
6 · The paper itself

Abstract

backgroundClimate change has caused more frequent and severe extreme weather events, threatening health system resilience worldwide. In April and May 2024, Kenya experienced unprecedented extensive floods with devastating outcomes. However, the quantitative impact of flooding on geographical access to healthcare remains unclear. This study, therefore, evaluates post-disaster accessibility to health facilities and quantifies geographical coverage losses resulting from flooding compounded by doctors' strike in Kenya.

methodsGeospatial datasets were assembled including health facility locations (public, private not-for-profit (PNfP), and private for-profit (PfP)), road network, land use/land cover, topography, population density, and flooding extents). A pre-flood baseline and three post-flood scenarios were defined using satellite-derived flooding extents (Sentinel 1 synthetic aperture radar (SAR) and National Oceanic and Atmospheric Administration - Visible Infrared Imaging Radiometer Suite (NOAA-VIIRS) satellites) and their combined maximal extents. Travel time (TT) to the nearest health facility by type was estimated using a least-cost path algorithm, accounting for ± 20% variations in travel speed and flood extent for sensitivity analysis. Population coverage was extracted within five 30-minute TT bands for each scenario, nationally and by subnational units (county).

resultsA total of 10,995 health facilities were assembled (public = 5,586; PNfP = 855; PfP = 4,554). Pre-floods, average TT to the nearest facility was 19.6 min, with public facilities at 20.7 min, PfP at 37.8 min, and PNfP at 49.2 min. Post-floods average TT increased across all sectors, longest across PNfP at 113.5 min and shortest for public facilities at 48.5 min. Pre-floods, 94.0% (52.5 million) of the population had access within 30-min and 20 out of 47 counties with an average TT of < 2 h. Under the maximal flood extents, coverage dropped to 73% (40.9 million) and only 5 counties retained < 2 h TT. County-level 30-min coverage losses ranged from 1.0% (Nairobi) to 51.0% (Narok). In several arid counties, populations facing 2 + hours TT rose to 15-31%, up from 4 to 12% pre-floods.

conclusionKenya's health system is highly vulnerable to floods, causing unequal disruptions in geographical access across subnational region. Incorporating disaster preparedness into county health care planning to strengthen health system resilience nationwide is needed.

Indexed as

FloodsHealth Services AccessibilityPrivate SectorPublic SectorGeographic Information SystemsHumansKenyaFloodingGeographical accessHealthcare systemPopulation affectedTravel time

Identifiers

PMID41851890
PMCPMC13122988

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LicenceCC BY
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Registered trials

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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.