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Local use of geographic information systems to improve data utilisation and health services: mapping caesarean section coverage in rural Rwanda
Author(s) -
Sudhof Leanna,
Amoroso Cheryl,
Barebwanuwe Peter,
Munyaneza Fabien,
Karamaga Adolphe,
Zambotti Giovanni,
Drobac Peter,
Hirschhorn Lisa R.
Publication year - 2013
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/tmi.12016
Subject(s) - caesarean section , medicine , health facility , population , geographic information system , geography , catchment area , environmental health , pregnancy , demography , obstetrics , socioeconomics , health services , cartography , drainage basin , genetics , sociology , biology
Objectives To show the utility of combining routinely collected data with geographic location using a G eographic I nformation S ystem ( GIS ) in order to facilitate a data‐driven approach to identifying potential gaps in access to emergency obstetric care within a rural R wandan health district. Methods Total expected births in 2009 at sub‐district levels were estimated using community health worker collected population data. Clinical data were extracted from birth registries at eight health centres (HCs) and the district hospital (DH). C ‐section rates as a proportion of total expected births were mapped by cell. Peri‐partum foetal mortality rates per facility‐based births, as well as the rate of uterine rupture as an indication for C ‐section, were compared between areas of low and high C ‐section rates. Results The lowest C ‐section rates were found in the more remote part of the hospital catchment area. The sector with significantly lower C ‐section rates had significantly higher facility‐based peri‐partum foetal mortality and incidence of uterine rupture than the sector with the highest C ‐section rates ( P < 0.034). Conclusions This simple approach for geographic monitoring and evaluation leveraging existing health service and GIS data facilitated evidence‐based decision making and represents a feasible approach to further strengthen local data‐driven decisions for resource allocation and quality improvement.