Evidence map›Paper›PMID 41275301›Full record

ArticleInternational journal of health geographics2025

Assessment of a gridded population sample frame for a household survey of refugee populations in Uganda, 2021.

Shannon M Farley, Stephen Delgado, Stephen McCracken, Dana R Thomson, Qixuan Chen, Giles Reid, Sandra Baptista, Sam Biraro, Andrew Kabala, Herbert Mulindwa and 16 more

Abstract read
In one paragraph

Article in International journal of health geographics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

26 authors.

Shannon M FarleyICAP at Columbia University, New York, NY, USA. smf2026@cumc.columbia.edu.
Stephen Delgado *ICAP at Columbia University, New York, NY, USA.
Stephen McCrackenDivision of Global HIV & TB, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Dana R ThomsonCIESIN at Columbia University, New York, NY, USA.
Qixuan ChenMailman School of Public Health, Department of Biostatistics, Columbia University, New York, NY, USA.
Giles ReidICAP at Columbia University, New York, NY, USA.
Sandra BaptistaCIESIN at Columbia University, New York, NY, USA.
Sam Biraro *ICAP at Columbia University, Kampala, Uganda.
Andrew Kabala *ICAP at Columbia University, Kampala, Uganda.
Herbert Mulindwa *ICAP at Columbia University, Kampala, Uganda.
David Okimait *ICAP at Columbia University, Kampala, Uganda.
Veronicah Mugisha *ICAP at Columbia University, Kampala, Uganda.
Hannah ChungICAP at Columbia University, New York, NY, USA.
Brittany GianettiDivision of Global HIV & TB, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Sam SendagalaDivision of Global HIV & TB, U.S. Centers for Disease Control and Prevention (CDC), Entebbe, Uganda.
Jennifer NelDivision of Global HIV & TB, U.S. Centers for Disease Control and Prevention (CDC), Entebbe, Uganda.
Dustin CurrieDivision of Global HIV & TB, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Christine A WestDivision of Global HIV & TB, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Charles Herbert MatovuUnited Nations High Commission On Refugees, Kampala, Uganda.
Ronald NyakoojoUnited Nations High Commission On Refugees, Kampala, Uganda.
Julius KasoziUnited Nations High Commission On Refugees, Kampala, Uganda.
Hillary MumbereUnited Nations High Commission On Refugees, Kampala, Uganda.
Wilford KirungiMinistry of Health, Kampala, Uganda.
Joshua MusinguziMinistry of Health, Kampala, Uganda.
David HoosICAP at Columbia University, New York, NY, USA.
Wafaa M El-SadrICAP at Columbia University, New York, NY, USA.

Funding

CGH CDC HHS NU2GGH002173
6 · The paper itself

Abstract

backgroundTo date, few HIV-related population-based data are available for refugee populations. Household surveys typically require reliable population counts and well-defined geographic areas, which are often not available for refugee settlements. We describe the gridded population sampling approach as an option for conducting such a survey in Uganda and describe its application for a household survey in Uganda and assess its utility among refugee populations.

methodsThe Uganda Refugee Population-based HIV Impact Assessment (RUPHIA) 2021 was a cross-sectional, population-based HIV survey among refugee households in Ugandan settlements, excluding Kampala. We collected shapefiles and population counts for the refugee settlements. These shapefiles from the various geographic areas of interest represented the aggregated refugee settlement zones (including all settlements with available zone shapefiles) and served as the base for creating the sample frame. The sample frame was constructed by disaggregating United Nations High Commission for Refugees population counts from large refugee settlement zones into 100 × 100 m grid cells using WorldPop's peanutButter-Disaggregate app that uses building footprint information to distribute the population into the grid cells. We then utilized a gridded population sampling approach which redistributed the population into manageable-sized areas of contiguous grid cells based on their estimated population size, forming enumeration area-like sampling units using the publicly available GridEZ algorithm.

resultsThe resulting gridded population dataset had 43,193 100 m x 100 m cells with an estimated mean of 31 people per cell and a range from 2 to 1028. The final gridded population sample frame had 2636 GridEZ units with an average population of 500 ranging from 178 to 1531. The sample frame performed well for survey activities, with few issues encountered in the field, although the size measures for number of households had some inaccuracies, due to issues such as compounds having multiple structures.

conclusionsGridded population sampling was successfully utilized for this refugee study, saving time and money that would have been needed if enumeration of all the refugee settlements had been required. Gridded population sampling is a useful tool when census data are outdated or unavailable or when the population is dynamic, such as with refugees or other mobile or at-risk populations for surveillance or as part of a humanitarian response.

Indexed as

Family CharacteristicsHIV InfectionsPopulation SurveillanceRefugeesCross-Sectional StudiesFemaleHumansMaleUgandaAlternative sample frameGridded population samplingHousehold surveyRefugee populationSatellite-based population modellingUganda

Identifiers

PMID41275301
PMCPMC12750948

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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.