A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators.

Autor: Nilsen K; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK. Kristine.nilsen@soton.ac.uk., Tejedor-Garavito N; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Leasure DR; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Utazi CE; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Ruktanonchai CW; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Wigley AS; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Dooley CA; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK., Matthews Z; Department of Social Statistics and Demography, University of Southampton, Southampton, UK., Tatem AJ; WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
Jazyk: angličtina
Zdroj: BMC health services research [BMC Health Serv Res] 2021 Sep 13; Vol. 21 (Suppl 1), pp. 370. Date of Electronic Publication: 2021 Sep 13.
DOI: 10.1186/s12913-021-06370-y
Abstrakt: Background: Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples.
Methods: We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country's respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods.
Results: In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South.
Conclusions: Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.
(© 2021. The Author(s).)
Databáze: MEDLINE
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