Evidence map›Paper›PMID 33278901›Full record

ArticleInternational journal of health geographics2020

Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala.

Ann C Miller, Peter Rohloff, Alexandre Blake, Eloin Dhaenens, Leah Shaw, Eva Tuiz, Francesco Grandesso, Carlos Mendoza Montano, Dana R Thomson

Open access · goldAbstract read
In one paragraph

Article in International journal of health geographics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 3 countries.

Ann C MillerDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Ave, Boston, MA, USA. Ann_miller@hms.harvard.edu.ORCID 0000-0001-6841-9439
Peter RohloffDepartment of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Ave, Boston, MA, USA.
Alexandre BlakeEpicentre, Paris, France.
Eloin DhaenensWuqu' Kawoq, Maya Health Alliance, Santiago Sacatepéquez, Guatemala.
Leah ShawWuqu' Kawoq, Maya Health Alliance, Santiago Sacatepéquez, Guatemala.
Eva TuizWuqu' Kawoq, Maya Health Alliance, Santiago Sacatepéquez, Guatemala.
Francesco GrandessoEpicentre, Paris, France.
Carlos Mendoza MontanoInstitute of Nutrition of Central America and Panama (Instituto de Nutrición de Centroamérica, INCAP), y Panamá, Guatemala City, Guatemala.
Dana R ThomsonDepartment of Social Statistics and Demography, University of Southampton, Southampton, England.
Probigua · GTBrigham and Women's Hospital · USHarvard University · USInstitute of Nutrition of Central America and Panama · GTUniversity of Southampton · GB

Funding

Building Capacity for Chronic Kidney Disease Research in Guatemala.R21TW010831 · FIC · BRIGHAM AND WOMEN'S HOSPITAL · PI MENDOZA, CARLOS, ROHLOFF, PETER · 2017 to 2018
$335k
FIC NIH HHS FIC R21 TW010831-02FIC NIH HHS R21 TW010831
6 · The paper itself

Abstract

backgroundPopulation-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre's Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala.

resultsWe successfully used Epicentre's Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h.

conclusionIn combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population-the unhoused, street dwellers or people living in vehicles.

Indexed as

Family CharacteristicsGeographic Information SystemsFeasibility StudiesGuatemalaHealth SurveysHumansRural PopulationSampling StudiesGuatemalaPopulation-representative studySample selectionSampling frameSimple random sample

Identifiers

PMID33278901
PMCPMC7718677
OpenAlexW3112583468

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.