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

Abstract

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.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

“…Data on population distribution is the main denominator for almost all public health interventions. The effectiveness of evidence-based health planning, such as the distribution of health facilities or the implementation of vaccination campaigns, largely depends on accurate population estimates 54 , 55 to calculate resource needs and measure the impact of interventions 56 , 57 . Moreover, the SDGs and other international health targets are based on indicators that reflect the proportion of the population that has access to certain services.…”

“…In terms of fitness for use, population datasets that constrain population to settled areas, based on high-resolution settlement data (i.e., HRSL, WorldPop top–down constrained), are more suited for accessibility modeling assuming acceptable levels of accuracy 57 . Most accessibility models need to consider the population at their place of residence (i.e., de jure/de facto population) 20 , because the aim is to capture the complexity of the patient’s journey to reach a health facility from their home, so that health system improvements can be targeted, and microplanning of outreach is possible 54 , 55 .…”

Small area population denominators for improved disease surveillance and response

“…While sample sizes are often small, building models upon recent enumeration data, rather than linear projections from census baselines many decades ago in settings where massive changes have occurred provides more confidence in outputs ( Wardrop et al, 2018 ). A growing amount of anecdotal and quantitative feedback from field teams and national statistical offices on the accuracy of estimates adds to statistical evidence from model cross-validation, as well as assessments on the use of data in deriving metrics or in health delivery campaigns adds to this ( Nilsen et al, 2021 , Leasure et al, 2020 , GRID3, 2021b , Boo et al, 2022 , GRID3, 2020 , Thomson et al, 2021 , Ali et al, 2020 ). Moreover, the explicit measurement and communication of uncertainty in predicted population estimates provides users with quantitative insights on where confidence in predictions is higher or lower, taking small area population estimates a step forward beyond the opacity of many top-down model outputs ( Leasure et al, 2020 ).…”

Small area population denominators for improved disease surveillance and response

“…1 highlights how different a selection of commonly used open model estimates can be at the scale of health zones. These will in turn result in differing surveillance indicators, denominators for health metrics ( Nilsen et al, 2021 ), and target populations for interventions. Fig.…”

“…While sample sizes are often small, building models upon recent enumeration data, rather than linear projections from census baselines many decades ago in settings where massive changes have occurred provides more confidence in outputs ( Wardrop et al, 2018 ). A growing amount of anecdotal and quantitative feedback from field teams and national statistical offices on the accuracy of estimates adds to statistical evidence from model cross-validation, as well as assessments on the use of data in deriving metrics or in health delivery campaigns adds to this ( Nilsen et al, 2021 , Leasure et al, 2020 , GRID3, 2021b , GRID3, 2020 , Boo et al, 2022 , Thomson et al, 2021 , Ali et al, 2020 ). Moreover, the explicit measurement and communication of uncertainty provides users with quantitative insights on where confidence in estimates is higher or lower, taking small area population estimates a step forward beyond the opacity of many top-down model outputs ( Leasure et al, 2020 ).…”