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Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations
Author(s) -
Hannah Wild,
Luke Glowacki,
Stace Maples,
Iván MejíaGuevara,
Amy Krystosik,
Matthew H. Bonds,
Abiy Hiruy,
A. Desirée LaBeaud,
Michèle Barry
Publication year - 2019
Publication title -
american journal of tropical medicine and hygiene
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.015
H-Index - 151
eISSN - 1476-1645
pISSN - 0002-9637
DOI - 10.4269/ajtmh.18-1009
Subject(s) - pastoralism , geospatial analysis , sampling frame , geography , population , census , sampling (signal processing) , environmental health , socioeconomics , medicine , cartography , computer science , forestry , computer vision , sociology , livestock , filter (signal processing)
Nomadic pastoralists are among the world's hardest-to-reach and least served populations. Pastoralist communities are difficult to capture in household surveys because of factors including their high degree of mobility over remote terrain, fluid domestic arrangements, and cultural barriers. Most surveys use census-based sampling frames which do not accurately capture the demographic and health parameters of nomadic populations. As a result, pastoralists are "invisible" in population data such as the Demographic and Health Surveys (DHS). By combining remote sensing and geospatial analysis, we developed a sampling strategy designed to capture the current distribution of nomadic populations. We then implemented this sampling frame to survey a population of mobile pastoralists in southwest Ethiopia, focusing on maternal and child health (MCH) indicators. Using standardized instruments from DHS questionnaires, we draw comparisons with regional and national data finding disparities with DHS data in core MCH indicators, including vaccination coverage, skilled birth attendance, and nutritional status. Our field validation demonstrates that this method is a logistically feasible alternative to conventional sampling frames and may be used at the population level. Geospatial sampling methods provide cost-affordable and logistically feasible strategies for sampling mobile populations, a crucial first step toward reaching these groups with health services.

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