Local use of geographic information systems to improve data utilisation and health services: mapping caesarean section coverage in rural Rwanda
Authors: Sudhof, Leanna; Amoroso, Cheryl; Barebwanuwe, Peter; Munyaneza, Fabien; Karamaga, Adolphe; Zambotti, Giovanni; Drobac, Peter; Hirschhorn, Lisa R.
Source: Tropical Medicine & International Health, Volume 18, Number 1, 1 January 2013 , pp. 18-26(9)
Abstract:<title type="main">Abstract</title> <section xml:id="tmi12016-sec-0001"> <title type="main">Objectives</title> To show the utility of combining routinely collected data with geographic location using a Geographic Information System (GIS) in order to facilitate a data-driven approach to identifying potential gaps in access to emergency obstetric care within a rural Rwandan health district. </section> <section xml:id="tmi12016-sec-0002"> <title type="main">Methods</title> 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. </section> <section xml:id="tmi12016-sec-0003"> <title type="main">Results</title> 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). </section> <section xml:id="tmi12016-sec-0004"> <title type="main">Conclusions</title> 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. </section>
Document Type: Research article
Publication date: 2013-01-01