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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Geography, Humans, Medically Underserved Area, Primary Health Care, Rural Health Services, Rural Population, South Africa.
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Does new approach to spatially analyse primary health care usage patterns in rural South Africa? Context: To develop indices to quantitatively assess and understand the spatial usage patterns of health facilities in the Hlabisa district of South Africa. We mapped and interviewed more than 23 000 homesteads (approximately 200 000 people) in Hlabisa district, South Africa and spatially analysed their modal primary health usage patterns using a geographical information system. We generated contour maps of health service use and quantified the relationship between clinic catchments and distance-defined catchments using inclusion and exclusion error. We propose the distance usage index (DUI) as an overall spatial measure of clinic usage. This index is the sum of the distances from clinic to all client homesteads divided by the sum of the distances from clinic to all homesteads within its distance-defined catchment. The index encompasses inclusion, exclusion, and strength of patient attraction for each clinic. Eighty-seven per cent of homesteads use the nearest clinic. Residents of homesteads travel an average Euclidean distance of 4.72 km to attend clinics. There is a significant logarithmic relationship between distance from clinic and their use by homesteads (r(2)=0.774, P < 0.0001). The DUI values range between 31 and 198% (mean=110%, SD=43.7) for 12 clinics and highlight clinic usage patterns across the district.
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The DUI is a powerful and informative composite measure of clinic usage. The results of the study have important implications for health care provision in developing countries. Final Decision: yes