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How Can Childbirth Care for the Rural Poor Be Improved? A Contribution from Spatial Modelling in Rural Tanzania.

Fogliati P, Straneo M, Brogi C, Fantozzi PL, Salim RM, Msengi HM, Azzimonti G, Putoto G - PLoS ONE (2015)

Bottom Line: The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours' walking time.Our findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%.

View Article: PubMed Central - PubMed

Affiliation: Doctors with Africa-CUAMM, Padua, Italy.

ABSTRACT

Introduction: Maternal and perinatal mortality remain a challenge in resource-limited countries, particularly among the rural poor. To save lives at birth health facility delivery is recommended. However, increasing coverage of institutional deliveries may not translate into mortality reduction if shortage of qualified staff and lack of enabling working conditions affect quality of services. In Tanzania childbirth care is available in all facilities; yet maternal and newborn mortality are high. The study aimed to assess in a high facility density rural context whether a health system organization with fewer delivery sites is feasible in terms of population access.

Methods: Data on health facilities' location, staffing and delivery caseload were examined in Ludewa and Iringa Districts, Southern Tanzania. Geospatial raster and network analysis were performed to estimate access to obstetric services in walking time. The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.

Results: About half of first-line health facilities had insufficient staff to offer full-time obstetric services (45.7% in Iringa and 78.8% in Ludewa District). Yearly delivery caseload at first-line health facilities was low, with less than 100 deliveries in 48/70 and 43/52 facilities in Iringa and Ludewa District respectively. Wide geographical overlaps of facility catchment areas were observed. In Iringa 54% of the population was within 1-hour walking distance from the nearest facility and 87.8% within 2 hours, in Ludewa, the percentages were 39.9% and 82.3%. With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours' walking time.

Conclusions: Our findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%. Focused efforts on fewer delivery sites might assist strengthening delivery services in resource-limited settings.

No MeSH data available.


Outputs from sample walk and network analysis.The route covered by the volunteer (white dashed) corresponds to the trajectory traced by the software on the virtual network (blue line). Other tests are relative to four villages set at few kilometres away from motorable roads. The multimodal output (yellow dashed) automatically estimates the faster route to the hospital and is based both on virtual mesh lines and on existing motorable roads (red lines).
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pone.0139460.g003: Outputs from sample walk and network analysis.The route covered by the volunteer (white dashed) corresponds to the trajectory traced by the software on the virtual network (blue line). Other tests are relative to four villages set at few kilometres away from motorable roads. The multimodal output (yellow dashed) automatically estimates the faster route to the hospital and is based both on virtual mesh lines and on existing motorable roads (red lines).

Mentions: Findings from sample walk and network analysis are displayed in Fig 3. The route covered by the volunteer (white dashed) is partially overlapping with the trajectory traced on the virtual network by the application Route Analysis of ArcGIS™ (continuous blue line). Although in some areas shorter distances are automatically selected by the software, overall time to reach the nearest health facility is similar to that recorded by the volunteer (5 hours and 30 minutes estimated by the software versus 5 hours and 38 minutes recorded in the field). This can be explained by the more conventional walking speeds applied to the network analysis (Naismith-Langmuir Rule) compared to the walking pace of the enrolled walker. There is also evidence of validity for the multimodal transportation model. The software estimates the shortest travel time needed to reach health facilities by combining vehicular transport on motorable roads to pedestrian speeds on virtual network (Fig 3 red and dashed yellow lines).


How Can Childbirth Care for the Rural Poor Be Improved? A Contribution from Spatial Modelling in Rural Tanzania.

Fogliati P, Straneo M, Brogi C, Fantozzi PL, Salim RM, Msengi HM, Azzimonti G, Putoto G - PLoS ONE (2015)

Outputs from sample walk and network analysis.The route covered by the volunteer (white dashed) corresponds to the trajectory traced by the software on the virtual network (blue line). Other tests are relative to four villages set at few kilometres away from motorable roads. The multimodal output (yellow dashed) automatically estimates the faster route to the hospital and is based both on virtual mesh lines and on existing motorable roads (red lines).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589408&req=5

pone.0139460.g003: Outputs from sample walk and network analysis.The route covered by the volunteer (white dashed) corresponds to the trajectory traced by the software on the virtual network (blue line). Other tests are relative to four villages set at few kilometres away from motorable roads. The multimodal output (yellow dashed) automatically estimates the faster route to the hospital and is based both on virtual mesh lines and on existing motorable roads (red lines).
Mentions: Findings from sample walk and network analysis are displayed in Fig 3. The route covered by the volunteer (white dashed) is partially overlapping with the trajectory traced on the virtual network by the application Route Analysis of ArcGIS™ (continuous blue line). Although in some areas shorter distances are automatically selected by the software, overall time to reach the nearest health facility is similar to that recorded by the volunteer (5 hours and 30 minutes estimated by the software versus 5 hours and 38 minutes recorded in the field). This can be explained by the more conventional walking speeds applied to the network analysis (Naismith-Langmuir Rule) compared to the walking pace of the enrolled walker. There is also evidence of validity for the multimodal transportation model. The software estimates the shortest travel time needed to reach health facilities by combining vehicular transport on motorable roads to pedestrian speeds on virtual network (Fig 3 red and dashed yellow lines).

Bottom Line: The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours' walking time.Our findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%.

View Article: PubMed Central - PubMed

Affiliation: Doctors with Africa-CUAMM, Padua, Italy.

ABSTRACT

Introduction: Maternal and perinatal mortality remain a challenge in resource-limited countries, particularly among the rural poor. To save lives at birth health facility delivery is recommended. However, increasing coverage of institutional deliveries may not translate into mortality reduction if shortage of qualified staff and lack of enabling working conditions affect quality of services. In Tanzania childbirth care is available in all facilities; yet maternal and newborn mortality are high. The study aimed to assess in a high facility density rural context whether a health system organization with fewer delivery sites is feasible in terms of population access.

Methods: Data on health facilities' location, staffing and delivery caseload were examined in Ludewa and Iringa Districts, Southern Tanzania. Geospatial raster and network analysis were performed to estimate access to obstetric services in walking time. The present geographical accessibility was compared to a theoretical scenario with a 40% reduction of delivery sites.

Results: About half of first-line health facilities had insufficient staff to offer full-time obstetric services (45.7% in Iringa and 78.8% in Ludewa District). Yearly delivery caseload at first-line health facilities was low, with less than 100 deliveries in 48/70 and 43/52 facilities in Iringa and Ludewa District respectively. Wide geographical overlaps of facility catchment areas were observed. In Iringa 54% of the population was within 1-hour walking distance from the nearest facility and 87.8% within 2 hours, in Ludewa, the percentages were 39.9% and 82.3%. With a 40% reduction of delivery sites, approximately 80% of population will still be within 2 hours' walking time.

Conclusions: Our findings from spatial modelling in a high facility density context indicate that reducing delivery sites by 40% will decrease population access within 2 hours by 7%. Focused efforts on fewer delivery sites might assist strengthening delivery services in resource-limited settings.

No MeSH data available.