Accessibility to First-Mile health services: A time-cost model for rural Uganda.

This study estimates the geographical disconnection in rural Low-Middle-Income Countries (LMIC) between First-Mile suppliers of healthcare services and end-users. This detachment is due to geographical barriers and to a shortage of technical, financial, and human resources that enable peripheral health facilities to perform effective and prompt diagnosis. End-users typically have easier access to cell-phones than hospitals, so mHealth can help to overcome such barriers, transforming inpatients/outpatients into home-patients, decongesting hospitals, especially during epidemics. This generates savings for patients and the healthcare system. The advantages of mHealth are well known, but there is a literature gap in the description of its economic returns. This study applies a geographical model to a typical LMIC, Uganda, quantifying the time-cost to reach an equipped medical center. Time-cost measures the disconnection between First-Mile hubs and end-users, the potential demand of mHealth by remote end-users, and the consequent savings. The results highlight an average time-cost of 75 min, well above the recommended thresholds, and estimate that mHealth leads to significant savings (1.5 monthly salaries and 21% of public health budget). Community health workers and private actors may re-engineer healthcare resources through Public-Private Partnerships (PPP), remunerated with results-based financing (RBF). These findings can contribute to improving healthcare resource allocation in LMIC.

Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda.

BACKGROUND: Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. METHODS: GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact. RESULTS: About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. CONCLUSIONS: The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.