PHEER Research Brief: Leveraging Population Mobility Data to Assess Health System Capacity after Hurricane Beryl

To examine the changes in health system facility load caused by population movement in the Mexican state of Yucatan during Hurricane Beryl, we used two location-based datasets: Healthsites.io, a crowd-sourced repository of health facility locations and types, and Meta Data for Good’s user mobility data. We constructed baseline measures of population per health facility in each municipality of Yucatan, then examined how this metric changed with movement of populations during the hurricane event.

Summary of Findings
- Baseline health facility load was highest in regions just outside of the state capital, Merida
- Municipalities in central and southern regions of the state experienced the greatest increases in health facility load during the 5-day period evaluated, with some regions having a 30-40% increase on Day 1 of the hurricane landfall
- Health facility load increases were short-lived in this setting, with 76 municipalities seeing increases on Day 1 and only 17 regions by Day 5, compared to a pre-hurricane baseline

Policy and Practice Implications
- Combining location-based datasets can rapidly provide a simple and dynamic measure of health system facility overload to inform allocation of emergency medical infrastructure or hospital load-sharing programs in near real-time
- Many opportunities exist to build on this metric with the addition of different health facility datasets to optimize usefulness for disaster responders