Increasing access to thrombolytic treatment for ST-elevation myocardial infarction using the Location-Allocation Model in a low-income state in Brazil

Background Around 400,000 people die annually from cardiovascular diseases in Brazil. The state of Maranhao, which has an area of ​​similar size to Italy, has one of the highest mortality from ST-elevation myocardial infarction (STEMI). Only the state capital offers percutaneous coronary intervention, and no other city has a thrombolysis protocol, leaving 7 million people without assistance. We propose a strategic model to allocate STEMI thrombolysis services in high-demand areas of Maranhao. Purpose We used the Location-Allocation Model (LAM) as an automated tool to decide the best allocation of resources and optimize the distribution of thrombolysis services for STEMI in a low-resource region with the highest mortality rate from cardiovascular diseases in Brazil. This project can support future evidence that aims to reduce the time to access treatment for cardiovascular diseases in low- and middle-income countries, where choosing the best health facilities is based on population demand using open-source data to perform the analyses. Methods Ecological study using the LAM and the following parameters: population density and distribution (demand), location of health facilities (supply), and transportation network (mobility). The demand was estimated by dasymetric gridding from WorldPop, corresponding to 5,072,506 people in Maranhao. The supply points corresponded to the coordinates of the emergency public hospitals that are candidates to implement thrombolysis services, totaling 362 services across the state. The mobility was set with the roads available on OpenStreetMap. We used ArcGIS Pro 2.9 with the Maximize Attendance algorithm to set a 30-minute cutoff time for demand to reach the facilities using the mobility network. The optimal number of chosen facilities was defined as the plateau point at which additional facilities yield no significant increase in population coverage. Results The plateau point was 45 health facilities allocated in the regions of Maranhao with the highest population demand. Together, these hospitals could cover 94.56% of the population (about 4.8 million people) to perform thrombolysis in 30 minutes in the case of STEMI. Conclusion This automated, low-cost approach indicates that more strategic hospitals in low-resource settings are needed to improve timely access to acute care for STEMI, decentralize acute treatment for STEMI, and reduce cardiovascular disease mortality.