Robert BN
Muchiri SK
Kahoro EW
Hindada BH
Kiarie H
Okiro EA
Macharia PM
Int J Health Geogr. 2026;
BACKGROUND: Climate 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. METHODS: Geospatial 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 ┬▒ÔÇë20% 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). RESULTS: A total of 10,995 health facilities were assembled (publicÔÇë=ÔÇë5,586; PNfPÔÇë=ÔÇë855; PfPÔÇë=ÔÇë4,554). Pre-floods, average TT to the nearest facility was 19.6┬ámin, with public facilities at 20.7┬ámin, PfP at 37.8┬ámin, and PNfP at 49.2┬ámin. Post-floods average TT increased across all sectors, longest across PNfP at 113.5┬ámin and shortest for public facilities at 48.5┬ámin. Pre-floods, 94.0% (52.5┬ámillion) of the population had access within 30-min and 20 out of 47 counties with an average TT of <ÔÇë2┬áh. Under the maximal flood extents, coverage dropped to 73% (40.9┬ámillion) and only 5 counties retainedÔÇë<ÔÇë2┬áh TT. County-level 30-min coverage losses ranged from 1.0% (Nairobi) to 51.0% (Narok). In several arid counties, populations facing 2ÔÇë+ÔÇëhours TT rose to 15-31%, up from 4 to 12% pre-floods. CONCLUSION: Kenya'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.
