Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

Leonardo Z Ferreira,Fernando P Hartwig,Cauane Blumenberg,Kristine Nilsen,Andrew J Tatem,Aluisio J D Barros,C Edson Utazi

doi:10.1186/s12942-020-00239-9

Abstract

BackgroundGeospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies.MethodsTwo independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded.ResultsWe identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure.ConclusionsThe field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

Highlights

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs)
Information on RMNCH indicators is predominantly obtained from national health surveys in low- and middle-income countries (LMIC), which offer standardized and reliable estimates [5]
This study aims to discuss the core methodological aspects of geospatial estimation, including any specificities employed for each RMNCH outcome, in studies focused on producing fine spatial scale estimates

Summary

Introduction

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). Planning and implementation of essential health interventions, delivered by supporting organizations and governments, is mainly done at small administrative divisions such as districts, states, provinces, regions or counties [3] This requires geographically disaggregated information, which enables more precise adjustment of policies and targeting of resources [4]. Geospatial approaches have been widely used for estimating RMNCH outcomes for small areas using georeferenced survey data These methods derive indirect estimates from statistical models by ‘borrowing strength’ across space or from supplementary data, such as geospatial variables, censuses and administrative records [7]. The literature often uses the terms model-based geostatistics, small area estimation and (geo)spatial modeling interchangeably as model-based approaches to derive estimates for small geographies assisted by supplementary data

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