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Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations
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 sur...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The American Society of Tropical Medicine and Hygiene
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726942/ https://www.ncbi.nlm.nih.gov/pubmed/31436151 http://dx.doi.org/10.4269/ajtmh.18-1009 |
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author | Wild, Hannah Glowacki, Luke Maples, Stace Mejía-Guevara, Iván Krystosik, Amy Bonds, Matthew H. Hiruy, Abiy LaBeaud, A. Desiree Barry, Michele |
author_facet | Wild, Hannah Glowacki, Luke Maples, Stace Mejía-Guevara, Iván Krystosik, Amy Bonds, Matthew H. Hiruy, Abiy LaBeaud, A. Desiree Barry, Michele |
author_sort | Wild, Hannah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6726942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The American Society of Tropical Medicine and Hygiene |
record_format | MEDLINE/PubMed |
spelling | pubmed-67269422019-09-08 Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations Wild, Hannah Glowacki, Luke Maples, Stace Mejía-Guevara, Iván Krystosik, Amy Bonds, Matthew H. Hiruy, Abiy LaBeaud, A. Desiree Barry, Michele Am J Trop Med Hyg Articles 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. The American Society of Tropical Medicine and Hygiene 2019-09 2019-08-19 /pmc/articles/PMC6726942/ /pubmed/31436151 http://dx.doi.org/10.4269/ajtmh.18-1009 Text en © The American Society of Tropical Medicine and Hygiene This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Articles Wild, Hannah Glowacki, Luke Maples, Stace Mejía-Guevara, Iván Krystosik, Amy Bonds, Matthew H. Hiruy, Abiy LaBeaud, A. Desiree Barry, Michele Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title | Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title_full | Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title_fullStr | Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title_full_unstemmed | Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title_short | Making Pastoralists Count: Geospatial Methods for the Health Surveillance of Nomadic Populations |
title_sort | making pastoralists count: geospatial methods for the health surveillance of nomadic populations |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726942/ https://www.ncbi.nlm.nih.gov/pubmed/31436151 http://dx.doi.org/10.4269/ajtmh.18-1009 |
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