Publications
Dotse-Gborgbortsi, Winfred; Tatem, Andrew J.; Matthews, Zoë; Alegana, Victor; Ofosu, Anthony; Wright, Jim
Delineating natural catchment health districts with routinely collected health data from women’s travel to give birth in Ghana Journal Article
In: BMC Health Services Research, vol. 22, no. 772, 2022.
Abstract | Links | BibTeX | Tags: Ghana, Health, maternal health, Public health
@article{nokey,
title = {Delineating natural catchment health districts with routinely collected health data from women’s travel to give birth in Ghana},
author = {Winfred Dotse-Gborgbortsi and Andrew J. Tatem and Zoë Matthews and Victor Alegana and Anthony Ofosu and Jim Wright },
doi = {10.1186/s12913-022-08125-9},
year = {2022},
date = {2022-06-13},
urldate = {2022-06-13},
journal = {BMC Health Services Research},
volume = {22},
number = {772},
abstract = {Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators.
Methods
A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.
Results
Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.
Conclusion
Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.},
keywords = {Ghana, Health, maternal health, Public health},
pubstate = {published},
tppubtype = {article}
}
Methods
A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.
Results
Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.
Conclusion
Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.
Tatem, Andrew J.; Noor, Abdisalan M.; Hay, Simon I.
Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery Journal Article
In: Remote Sensing of Environment, vol. 93, no. 1, pp. 42-52, 2004, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture
@article{TATEM200442,
title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},
author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},
doi = {https://doi.org/10.1016/j.rse.2004.06.014},
issn = {0034-4257},
year = {2004},
date = {2004-01-01},
journal = {Remote Sensing of Environment},
volume = {93},
number = {1},
pages = {42-52},
abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},
keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},
pubstate = {published},
tppubtype = {article}
}