Nome do Projeto
Geospatial analyses of health indicators using national health surveys from low- and middle-income countries: analyses of reproductive, maternal, newborn and child health
Data inicial - Data final
01/10/2019 - 28/02/2022
Unidade de Origem
Coordenador Atual
Área CNPq
Ciências da Saúde - Saúde Coletiva
In light of the agenda defined by the Sustainable Development Goals, improving data quality and availability is crucial to ensure countries are in the right direction towards reducing preventable maternal and newborn deaths. Assessing geographic inequalities is challenging since the main sources of information on low- and middle-income countries are not designed to be analyzed spatially. Recent improvements in computer processing power and the increased availability of ancillary data sources have contributed to the development of modeling approaches that aim to provide estimates at finer resolutions than provided by the administrative divisions commonly used in surveys. This project involves using these techniques of geospatial modeling to investigate geographic inequalities by generating high resolution estimates of health indicators. The estimates will be evaluated in two different scenarios: i) aligned with the most recent surveys, map the composite coverage index, a measure of universal health coverage based on essential reproductive, maternal and child health interventions, at second administrative level (e.g the equivalent to district or counties in different countries) and at pixel level (e.g 5 x 5km or 10x 10km); and ii) assess time trends using a geographic perspective to identify spatial patterns of increase in coverage of two vaccines since their introduction in Peru, in the early 2000s.

Objetivo Geral

General objective
Investigate how geographic inequalities can be assessed through geospatial methodology.

Specific objectives

1. Based on findings of the literature review, we aim to:
a) Compare the spatial modelling methodologies utilized for small area estimation.
b) Discuss spatial modeling suitability for given outcomes.
c) Identify gaps and opportunities for further studies in RMNCH.
d) Discuss how health policy makers can benefit from spatial modeling.
2. Model universal health coverage through the composite coverage index using DHS and
ancillary data sources:
a) Determine the best available analytical approach for the task.
b) Generate gridded surface maps for environmental associated factors.
c) Explore the possibilities for data imputation on missing CCI interventions to improve
data availability.
3. Understand the spatial patterns of vaccination coverage and how they change over time:
a) Explore how coverage of Hib and rotavirus vaccinesincreased geographically over the
years in both countries given different implementation strategies.
b) Identify geographical barriers delaying expansion of vaccination coverage.

Equipe do Projeto

NomeCH SemanalData inicialData final
FERNANDO PIRES HARTWIG101/10/201928/02/2022
LEONARDO ZANINI FERREIRA401/10/201928/02/2022

Fontes Financiadoras

Sigla / NomeValorAdministrador
Fundação Bill & Melinda GatesR$ 124.000,00

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