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

Background Climate change is causing 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 accessibility to healthcare remains unclear. This study evaluates post-disaster accessibility to health facilities and quantifies geographical coverage losses resulting from flooding compounded by a doctors’ strike in Kenya. Methods We assembled geospatial datasets including health facility locations (public, private not-for-profit (PNfP), and private for-profit (PfP)), road networks, land use/land cover, topography, population density, and flooding extents. We defined a pre-flood baseline and three post-flood scenarios using satellite-derived flooding extents (Sentinel 1 SAR and 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 county. Results We assembled 10,995 health facilities (public = 5,586; PNfP = 855; PfP = 4,554). Pre-floods, average TT to the nearest facility was 19.6 min (16.4–24.4), with public facilities at 20.7 min (17.3–25.7), PfP at 37.8 min (31.6–47.1), and PNfP at 49.2 min (41.1–61.4). Post-floods average TT increased across all sectors, longest across PNfP at 113.5 min (94.6–191.5 min) and shortest for public facilities at 48.5 min (40.5–74.5 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-hours. Under the maximal flood extents, coverage dropped to 73% (40.9 million) and only 5 counties retained < 2 hours 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 4–12% 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.