Proposing standardised geographical indicators of physical access to emergency obstetric and newborn care in low-income and middle-income countries

Ebener, Steeve; Stenberg, Karin; Brun, Michel; Monet, Jean-Pierre; Ray, Nicolas; Sobel, Howard; Roos, Nathalie; Gault, Patrick; Conlon, Claudia Morrissey; Bailey, Patsy; Moran, Allisyn C.; Ouedraogo, Léopold; Kitong, Jacqueline; Ko, Eunyoung; Sanon, Djenaba; Jega, Farouk; Azogu, Olajumoke; Ouedraogo, Boureima; Osakwe, Chidude; Chanza, Harriet Chimwemwe; Steffen, Mona Mehta; Hamadi, Imed Ben; Tib, Hayat; Asaad, Ahmed Haj; Torres, Tessa Tan

doi:10.1136/bmjgh-2018-000778

Cited by 36 publications

(48 citation statements)

References 19 publications

(25 reference statements)

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“…First, a friction surface was computed. Several geospatial datasets (land coverage types, boundaries of restricted areas, roads infrastructure, navigable river networks, and topography) were used to construct a surface (i.e., raster or grid), as it was constructed in previous studies (22,24,25), of a given spatial resolution (i.e., 30 m per pixel) where the value of each pixel (or cell) contains the time required to travel one meter in that given area. Secondly, this friction surface and the geolocation of the health facilities were used to infer the travel time to the most proximate (shortest travel time) health facility using a cumulative cost function.…”

Section: Methodsmentioning

confidence: 99%

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

Carrasco-Escobar

Manrique

Tello-Lizarraga

et al. 2020

Front. Public Health

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To better estimate the travel time to the most proximate health care facility (HCF) and determine differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach. Geospatial data of 145,134 cities and villages and 8,067 HCF were gathered with land coverage types, roads and river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to HCFs across Peru. This study estimated important variations in travel time to HCFs between urban and rural settings and major land coverage types in Peru. The median travel time to primary, secondary, and tertiary HCFs was 1.9-, 2.3-, and 2.2-fold higher in rural than urban settings, respectively. This study provides a new methodology to estimate the travel time to HCFs as a tool to enhance the understanding and characterization of the profiles of accessibility to HCFs in low-and middle-income countries.

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Section: Methodsmentioning

confidence: 99%

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

Carrasco-Escobar

Manrique

Tello-Lizarraga

et al. 2020

Front. Public Health

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“…A number of techniques have been developed to measure access to healthcare namely: gravity model [44], population provider ratio, travel time model [45] and network analysis [46]. Compared to the other methods, travel time model has been credited to be the most efficient in SSA [47,48] and also recommended by WHO as it represents the near real world reality in accessing care [49]. Travel time model was selected as it captures and integrate corrections due to different land cover surface and terrain [28,29,50], it reflects most probable decision care seekers make, is intuitive and comparable across different countries [29,51].…”

Section: Generating Travel Time Estimates Using Geographic Accessibilmentioning

confidence: 99%

Geographical accessibility in assessing bypassing behaviour for inpatient neonatal care, Bungoma County-Kenya

Ocholla

Agutu

Ouma

et al. 2020

BMC Pregnancy Childbirth

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Background: Neonatal mortality rate in Kenya continues to be unacceptably high. In reducing newborn deaths, inequality in access to care and quality care have been identified as current barriers. Contributing to these barriers are the bypassing behaviour and geographical access which leads to delay in seeking newborn care. This study (i) measured geographical accessibility of inpatient newborn care, and (ii), characterized bypassing behaviour using the geographical accessibility of the inpatient newborn care seekers. Methods: Geographical accessibility to the inpatient newborn units was modelled based on travel time to the units across Bungoma County. Data was then collected from 8 inpatient newborn units and 395 mothers whose newborns were admitted in the units were interviewed. Their spatial residence locations were geo-referenced and were used against the modelled travel time to define bypassing behaviour. Results: Approximately 90% of the sick newborn population have access to nearest newborn units (< 2 h). However, 36% of the mothers bypassed their nearest inpatient newborn facility, with lack of diagnostic services (28%) and distrust of health personnel (37%) being the major determinants for bypassing. Approximately 75% of the care seekers preferred to use the higher tier facilities for both maternal and neonatal care in comparison to sub-county facilities which mostly were bypassed and remained underutilised. Conclusion: Our findings suggest that though majority of the population have access to care, sub-county inpatient newborn facilities have high risk of being bypassed. There is need to improve quality of care in maternal care, to reduce bypassing behaviour and improving neonatal outcome.

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“…This is likely due to a previous lack of spatial data at these smaller administrative levels. Additionally, they do not consider the accessibility of services, regarding the time taken to reach a hospital or the actual population at risk, for example, WoCBA, pregnancies, or births [17][18][19]. Using high-resolution estimates of population and pregnancies [29][30][31], hospital locations, and their associated travel times [32], we estimate the availability and geographical accessibility of services across SSA and assess the suitability of these indicators for monitoring maternal health targets.…”

Section: Introductionmentioning

confidence: 99%

Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa

Wigley

Tejedor‐Garavito

Alegana

et al. 2020

BMC Med

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Background With universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes. Methods Availability was estimated using the World Health Organization guidelines for monitoring emergency obstetric care, defined as the number of hospitals per 500,000 population. Geographical accessibility was estimated using the Lancet Commission on Global Surgery, defined as the proportion of pregnancies within 2 h of the nearest hospital. These were calculated using geo-located hospital data for sub-Saharan Africa, with their associated travel times, along with small area estimates of population and pregnancies. The results of the availability analysis were then compared to the results of the accessibility analysis, to highlight differences between the availability and geographical accessibility of services. Results Despite most countries meeting the targets at the national level, we identified substantial sub-national variation, with 58% of the countries having at least one administrative unit not meeting the availability target at province level and 95% at district level. Similarly, 56% of the countries were found to have at least one province not meeting the accessibility target, increasing to 74% at the district level. When comparing both availability and accessibility within countries, most countries were found to meet both targets; however sub-nationally, many countries fail to meet one or the other. Conclusion While many of the countries met the targets at the national level, we found large within-country variation. Monitoring under the current guidelines, using national averages, can mask these areas of need, with potential consequences for vulnerable women and children. It is imperative therefore that indicators for monitoring the availability and geographical accessibility of health care reflect this need, if targets for universal health coverage are to be met by 2030.

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Proposing standardised geographical indicators of physical access to emergency obstetric and newborn care in low-income and middle-income countries

Cited by 36 publications

References 19 publications

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

Geographical accessibility in assessing bypassing behaviour for inpatient neonatal care, Bungoma County-Kenya

Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa

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