Utilization of Climate Data Analytics in Healthcare Decision-Making Processes in Migori County, Kenya

Climate variability is a primary driver of malaria surges in Kenya’s Lake Victoria Basin, where rainfall pulses and humid, warm conditions amplify transmission; Migori County is especially exposed due to recurrent MAM/OND rains and flood-prone zones. The problem is that routine health decisions in Migori often remain reactive, mobilizing after cases rise rather than before. This study therefore analyzed how climate data analytics (CDA) are currently integrated into operational workflows for malaria preparedness in Migori County. Using secondary weekly facility records (2015–2024) and CDA outputs, we fitted a Negative Binomial regression and mapped significant signals to Standard Operating Procedures (SOPs). Results from the regression model show that rainfall at lag-1 week significantly increases malaria incidence (β = 0.018, IRR = 1.018, p < 0.01), while temperatures >24 °C display a modest negative association (β = −0.058, p = 0.012); seasonal harmonic terms align with MAM/OND peaks. Descriptively, weekly rainfall averaged 54.6 mm with temperature 22.3 °C, median malaria cases were 12 per facility-week with peaks up to 147 in Nyatike. Operationally, CDA is embedded through Green/Amber/Red risk bands that trigger targeted stock checks, pre-positioning, surge rosters, and outreach, with higher-resilience facilities experiencing muted spikes relative to comparable exposure. Despite these gains, barriers persisted data latency, fragmented stock visibility across facilities/central stores, and uneven analytic capacity which slow signal-to-action translation. Overall, evidence from the 2015–2024 facility-week dataset and the fitted regression confirms that CDA has shifted malaria management in Migori from reactive to anticipatory planning by providing short-lead risk signals that inform routine SOPs