Developing new measures to monitor disease burden in rural communities: Potential Lessons for HiAP from Maiji China

A pressing obstacle in implementing effective public health policies in rural communities is the lack of comprehensive multi-faceted data on causes of morbidity and mortality. Although individual government agencies collect extensive information for sector-specific reporting, each agency’s system provides a limited perspective of disease burden. This piece-meal approach restricts policy-makers in creating measures that effectively target the scope and multiple factors necessary for improving population health. This problem is amplified in developing countries where rural residents are routinely exposed to socio-economic inequalities and elevated health risks. Here, we deploy a collaborative strategy to address the complex factors impacting morbidity and mortality in rural settings, developing an inclusive evaluation measure (Comprehensive Grades Index, GI) to monitor the health and well-being of rural residents. Using Maiji district, China, as a case study, we combined weighted data from three prevalent indexes: (1) Infectious Diseases Monitoring System, (2) Causes of Death Monitoring System, and (3) New-Rural Cooperative Medical System, then assessed our GI measure against each of the single evaluation indexes. Because these indexes identify different contributors to the disease burden, we applied the analytic hierarchy process in our comprehensive index of the weighted data. Results suggest that reducing traffic accidents, cerebral vascular disease, ischemic heart disease, hypertension, and stomach cancer should be the main targets for improving community well-being. Following the health in all policies (HiAP) approach, our study offers potential lessons on how rural communities could benefit from inter-agency data sharing to better maximize population health and effective policy development.