Study Description
Title
Sub-Saharan Public Hospitals Geo-coded database
Production Statement
Producer/Author: Ouma, PO; Okiro, EA and Snow, RW
Version Statement
Version:
1
Version
Responsibility:
Snow, RW; Ouma, PO; Okiro, EA and/or KWTRP Data Repository
Bibliographic Citation
Ouma, P; Okiro, EA; Snow, RW, 2017, "Sub-Saharan Public Hospitals Geo-coded database", doi:10.7910/DVN/JTL9VY, Harvard
Dataverse, V1
Study Scope
Subject Information
Timely access to emergency care can significantly reduce mortality. International benchmarks
for access to emergency hospital care have been established to guide ambitions for universal
health care by 2030. However, there is no complete geo-coded inventory of hospital services
in Africa in relation to how populations might access these services.
Abstract:
We assembled a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan
Africa from 100 different sources. A cost distance algorithm based on the location of 4908 public hospitals,
population distributions and road networks were used to compute the proportion of populations living within
a combined walking and motorised travel time of 2 hours to emergency hospital services. We estimate that
286 million (29%) people and 64 million (28%) women of child bearing age are located more than 2 hours
from the nearest hospital. Marked differences were observed within and between countries. Only 17 countries
reached the international benchmark of more than 80% of their populations living within a 2-hour travel time
of the nearest hospital.
Summary
Data Description
Data
collection
Date:
Start:
End:
Cycle:
Geographic
Coverage:
Africa
Georgraphical
Unit:
Country
Unit of analysis:
Administrative Units
Kind of Data:
Raw Data
N/A
N/A
N/A
Methodology and Processing
Data Collection Methodology
Sources
Statement:
For the first time in Africa, we present a composite geo-coded assembly of public sector hospital services across
the continent south of the Sahara. We use this spatial platform to interrogate general population access to
hospital care metrics, and specifically for women of child bearing age (WoCBA) who would be at highest risk of
maternal mortality if located far from obstetric emergency care. Our audit focused on public hospitals, which are
managed by governments at national levels or locally at municipality (e.g. Zimbabwe and South Africa) or local
authority (e.g. Kenya and Tanzania), faith based (FBO) and non-governmental organisations (NGOs).
Data Acess
Dataset Availability
Location:
https://dataverse.harvard.edu/dataverse/population-health
Extent of
Collection:
1 data files (xlsx) + codebook/data dictionary (PDF) + Readme File
Data Use Statement
None. Dataset and documentation is made available under open access. See section on Notes below for terms of
use/license
Restrictions:
Citation
Requirements:
Publications based on this data collection should acknowledge this source by means of bibliographic citation. To
ensure that such source attributions are captured for bibliographic utilities, citations must appear in footnotes or
in the reference section of publications. The bibliographic citation for this data collection is:"Ouma, P; Okiro, EA;
Snow, RW, 2017, "Sub-Saharan Public Hospitals Geo-coded database", doi:10.7910/DVN/JTL9VY, Harvard
Dataverse, V1"
Notes
This data is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/legalcode. Publications based on these data should
acknowledge this source by means of bibliographic citations. For more information
on these data, please contact the authors: Snow, RW (rsnow@kemri-wellcome.org); Ouma, PO (pouma@kemriwellcome.org); Okiro, EA (eokiro@kemri-wellcome.org) or the data governance office via this email address:
dgc@kemri-wellcome.org
Data File Description
File-by-File Description
File Name:
Sub-Saharan Public Hospitals Geo-coded database
File Structure
File Dimensions:
Type of file:
No. of observations:
4908
No. of Variables:
10
Size:
1,457,676
Records per case:
Multiple (with reference to variable 'Country')
Raw Data
Notes
A PDF codebook and Readme file accompanies the data, which provides complete information for all variables.
Related Files
Dataset(s):
None
Publication(s):
Access to emergency hospital care provided by the public sector in sub-Saharan Africa (Manuscript)
Variable Description & Frequency
File-by-File Description
File Name:
Sub-Saharan Public Hospitals Geo-coded database
Variable Codebook
Variable Name
Description
Country
Country
String
Region
Region
String
Zone
Zone
String
FacilityName
Name of health facility
String
FacilityType
Type of health facility
Type of health facility (Derived
from FacilityType)
Renamedfacilitytype
Owner
Institution that owns/runs the
health facility
Lat
Latitude
Long
Longitude
LLSource
Source of information on
Latitude and Longitude
Value
Label
Format
String
FBO
Faith based organisation
Govt
Government
NGO
Non-governmental organisation
Local Govt
Local Government
String
Numeric
Numeric
Combination
A combination of Google Earth,
Geonames, open streetmaps and also
String
includes coordinates in original lists which
were checked with additional sources
Encarta
A web mapping application
Fallingrain
A directory of cities and towns
Google Earth
A web mapping application
GPS
Global Positioning System
Other
Location derived primarily from shapefile
centroids and checked in google earth