A rapid and reproducible picture of open access health facility data in Africa to support the COVID-19 response

South, Andy; Dicko, Alassane; Herringer, Mark; Macharia, Peter M.; Maina, Joseph; Okiro, Emelda A.; Snow, Robert W.; Anelda, Van der Walt

doi:10.12688/wellcomeopenres.16075.1

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…This is in addition to continental level analyses ( 11 , 12 , 22 , 23 ) where Kenya was included and other analyses at a much smaller geographical scale ( 24 , 25 , 28 ). These spatial accessibility analyses have relied on data that was assembled several years ago ( 12 , 20 , 21 , 29 ) and has not been updated to reflect recent changes in newly opened facilities, closed health facilities, changes in the designation of healthcare providers (such as upgrading level 2–3), improvement in geocoding techniques and data sources. More importantly all the previous attempts in Kenya suffer a significant drawback.…”

Section: Introductionmentioning

confidence: 99%

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis

Moturi

Alegana²,

Mumo³

et al. 2022

Front. Public Health

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ObjectivesTo achieve universal health coverage, adequate geographic access to quality healthcare services is vital and should be characterized periodically to support planning. However, in Kenya, previous assessments of geographic accessibility have relied on public health facility lists only, assembled several years ago. Here, for the first time we assemble a geocoded list of public and private health facilities in 2021 and make use of this updated list to interrogate geographical accessibility to all health providers.MethodsExisting health provider lists in Kenya were accessed, merged, cleaned, harmonized, and assigned a unique geospatial location. The resultant master list was combined with road network, land use, topography, travel barriers and healthcare-seeking behavior within a geospatial framework to estimate travel time to the nearest (i) private, (ii) public, and (iii) both (public and private-PP) health facilities through a travel scenario involving walking, bicycling and motorized transport. The proportion of the population within 1 h and outside 2-h was computed at 300 × 300 spatial resolution and aggregated at subnational units used for decision-making. Areas with a high disease prevalence for common infections that were outside 1-h catchment (dual burden) were also identified to guide prioritization.ResultsThe combined database contained 13,579 health facilities, both in the public (55.5%) and private-for-profit sector (44.5%) in 2021. The private health facilities' distribution was skewed toward the urban counties. Nationally, average travel time to the nearest health facility was 130, 254, and 128 min while the population within 1-h was 89.4, 80.5, and 89.6% for the public, private and PP health facility, respectively. The population outside 2-h were 6% for public and PP and 11% for the private sector. Mean travel time across counties was heterogeneous, while the population within 1-h ranged between 38 and 100% in both the public sector and PP. Counties in northwest and southeast Kenya had a dual burden.ConclusionContinuous updating and geocoding of health facilities will facilitate an improved understanding of healthcare gaps for planning. Heterogeneities in geographical access continue to persist, with some areas having a dual burden and should be prioritized toward reducing health inequities and attaining universal health coverage.

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Section: Introductionmentioning

confidence: 99%

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis

Moturi

Alegana²,

Mumo³

et al. 2022

Front. Public Health

Self Cite

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“…Our study helps fill this important evidence gap for older adults in the region and is, thus, of high relevance for informing countries' efforts to improve care for conditions that affect older adults, particularly chronic non-communicable diseases. Specifically, that study builds on the findings of existing studies 17 , 19 , 21 , 22 , 23 that have mapped physical access to health care in sub-Saharan Africa at a subnational level within countries. Ouma and colleagues investigated access to emergency hospital care in sub-Saharan Africa.…”

Section: Discussionmentioning

confidence: 99%

“…Other relevant studies have focused on the effect of physical access to a health-care facility on the probability of seeking care for a febrile episode in children, 19 estimating travel time to health-care facilities among populations at risk of viral haemorrhagic fevers, 21 and examining physical access to major district and regional hospitals. 22 In addition, while not focusing directly on physical access to care, South and colleagues 23 have mapped health-care facility locations in sub-Saharan Africa using a combination of OSM and MFL data as well as direct information from national ministries of health.…”

Section: Discussionmentioning

confidence: 99%

Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis

Geldsetzer¹,

Reinmuth²,

Ouma³

et al. 2020

The Lancet Healthy Longevity

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Background Severe acute respiratory syndrome coronavirus 2, the virus causing COVID-19, is rapidly spreading across sub-Saharan Africa. Hospital-based care for COVID-19 is often needed, particularly among older adults. However, a key barrier to accessing hospital care in sub-Saharan Africa is travel time to the nearest health-care facility. To inform the geographical targeting of additional health-care resources, we aimed to estimate travel time at a 1 km × 1 km resolution to the nearest hospital and to the nearest health-care facility of any type for adults aged 60 years and older in sub-Saharan Africa.Methods We assembled a dataset on the geolocation of health-care facilities, separately for hospitals and any type of health-care facility and including both private-sector and public-sector facilities, using data from the OpenStreetMap project and the Kenya Medical Research Institute-Wellcome Trust Programme. Population data at a 1 km × 1 km resolution were obtained from WorldPop. We estimated travel time to the nearest health-care facility for each 1 km × 1 km grid using a cost-distance algorithm. Findings 9•6% (95% CI 5•2-16•9) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time to the nearest hospital of 6 h or longer, varying from 0•0% (0•0-3•7) in Burundi and The Gambia to 40•9% (31•8-50•7) in Sudan. For the nearest health-care facility of any type (whether primary, secondary, or tertiary care), 15•9% (95% CI 10•1-24•4) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time of 2 h or longer, ranging from 0•4% (0•0-4•4) in Burundi to 59•4% (50•1-69•0) in Sudan. Most countries in sub-Saharan Africa contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of 12 h or longer and to the nearest health-care facility of any type of 6 h or longer. The median travel time to the nearest hospital for the fifth of adults aged 60 years or older with the longest travel times was 348 min (IQR 240-576; equal to 5•8 h) for the entire population of sub-Saharan Africa, ranging from 41 min (34-54) in Burundi to 1655 min (1065-2440; equal to 27•6 h) in Gabon.Interpretation Our high-resolution maps of estimated travel times to both hospitals and health-care facilities of any type can be used by policy makers and non-governmental organisations to help target additional health-care resources, such as makeshift hospitals or transport programmes to existing health-care facilities, to older adults with the least physical access to care. In addition, this analysis shows the locations of population groups most likely to under-report COVID-19 symptoms because of low physical access to health-care facilities. Beyond the COVID-19 response, this study can inform the efforts of countries to improve physical access to care for conditions that are common among older adults in the region, such as chronic non-communicable diseases.Funding Bill & Melinda Gates Foundation.

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“…Senegal has demonstrated regular use of facility-level information and cultivated strong demand for data use in health service planning; however, to the best of our knowledge, Senegal has never had a centralized MFL or comprehensive database of health facilities with directly linkable geolocated information 6 , 7 . Many potential use cases and applications for such a consolidated facility list have already been identified, such as strengthening strategic planning and monitoring of health program activities, optimizing resource deployment and logistics to health facilities, and streamlining health service referral systems.…”

Section: Background and Summarymentioning

confidence: 99%

A consolidated and geolocated facility list in Senegal from triangulating secondary data

Gueye,

Ly,

Gueye

et al. 2024

Sci Data

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Having a geolocated list of all facilities in a country – a “master facility list” (MFL) – can provide critical inputs for health program planning and implementation. To the best of our knowledge, Senegal has never had a centralized MFL, though many data sources currently exist within the broader Senegalese data landscape that could be leveraged and consolidated into a single database – a critical first step toward building a full MFL. We collated 12,965 facility observations from 16 separate datasets and lists in Senegal, and applied matching algorithms, manual checking and revisions as needed, and verification processes to identify unique facilities and triangulate corresponding GPS coordinates. Our resulting consolidated facility list has a total of 4,685 facilities, with 2,423 having at least one set of GPS coordinates. Developing approaches to leverage existing data toward future MFL establishment can help bridge data demands and inform more targeted approaches for completing a full facility census based on areas and facility types with the lowest coverage. Going forward, it is crucial to ensure routine updates of current facility lists, and to strengthen government-led mechanisms around such data collection demands and the need for timely data for health decision-making.

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A rapid and reproducible picture of open access health facility data in Africa to support the COVID-19 response

Cited by 11 publications

References 19 publications

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis

Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis

A consolidated and geolocated facility list in Senegal from triangulating secondary data

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