Use of GIS for Analysis of Community Health Worker Patient Registries from Chongwe District, a Rural Low-Resource Setting, in Lusaka Province, Zambia
Thesis Chair: Travis Longcore | Thesis Committee: Daniel Warshawsky, Darren Ruddell
The growing accessibility of mobile phones in developing countries has led to increased innovation and utilization of handheld technology in managing health outcomes. Mobile health (mHealth) technologies enabled significant gains in localized data collection methods and increased timeliness in disease surveillance and control programs. Mobile technology has become an important tool for point of care productivity and effective task shifting for Community Health Workers (CHW) in many developing countries. Concurrently, GIS technology has increasingly been utilized in public health research, planning, monitoring, and surveillance within many developing countries and low-resource settings. This has resulted in opportunities for better understanding of spatial variation of diseases and the correlations with environmental factors.
To better understand community needs and burden of illnesses managed by CHWs, a geospatial analysis at the sub-district level was performed on CHW catchment area registries. Risk assessments and cluster analyses were conducted to identify community areas of high incidence of fever and fever related illnesses, malaria, diarrhea, and pneumonia in the rural district area of Chongwe, Zambia. Seventy CHWs recorded 7,674 cases were recorded by 70 CHW over a time-period of ten months, of which 3,130 cases were geocoded for geospatial analyses. The 15 rural health center catchment areas contained 141 village areas within 15 rural health center catchment areas. Results were used to create thematic maps illustrating sub-district disease distribution and risks for malaria, pneumonia, and diarrheal illnesses for each village area manage by CHWs. The use of mobile technology integrated with GIS to manage community health data and the application of GIS to analyze community level data may provide further insight into local area disease distribution, variability, and community needs than systems lacking GIS integration.