Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia

Research output: Contribution to journalArticle

Abstract

Objectives To describe our experience using satellite image-based sampling to conduct a health survey of children in an urban area of Lusaka, Zambia, as an approach to sampling when the population is poorly characterized by existing census data or maps. Methods Using a publicly available Quickbird™ image of several townships, we created digital records of structures within the residential urban study area using ArcGIS 9.2. Boundaries were drawn to create geographic subdivisions based on natural and man-made barriers (e.g. roads). Survey teams of biomedical research students and local community health workers followed a standard protocol to enrol children within the selected structure, or to move to the neighbouring structure if the selected structure was ineligible or refused enrolment. Spatial clustering was assessed using the K-difference function. Results Digital records of 16 105 structures within the study area were created. Of the 750 randomly selected structures, six (1%) were not found by the survey teams. A total of 1247 structures were assessed for eligibility, of which 691 eligible households were enroled. The majority of enroled households were the initially selected structures (51%) or the first selected neighbour (42%). Households that refused enrolment tended to cluster more than those which enroled. Conclusions Sampling from a satellite image was feasible in this urban African setting. Satellite images may be useful for public health surveillance in populations with inaccurate census data or maps and allow for spatial analyses such as identification of clustering among refusing households.

Original languageEnglish (US)
Pages (from-to)70-78
Number of pages9
JournalTropical Medicine and International Health
Volume14
Issue number1
DOIs
StatePublished - Jan 2009

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Health Surveys
Censuses
Cluster Analysis
Public Health Surveillance
Spatial Analysis
Population
Biomedical Research
Students
Surveys and Questionnaires

Keywords

  • Geographic information systems
  • Health survey
  • Sampling
  • Satellite imagery
  • Zambia

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Infectious Diseases
  • Parasitology

Cite this

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title = "Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia",
abstract = "Objectives To describe our experience using satellite image-based sampling to conduct a health survey of children in an urban area of Lusaka, Zambia, as an approach to sampling when the population is poorly characterized by existing census data or maps. Methods Using a publicly available Quickbird™ image of several townships, we created digital records of structures within the residential urban study area using ArcGIS 9.2. Boundaries were drawn to create geographic subdivisions based on natural and man-made barriers (e.g. roads). Survey teams of biomedical research students and local community health workers followed a standard protocol to enrol children within the selected structure, or to move to the neighbouring structure if the selected structure was ineligible or refused enrolment. Spatial clustering was assessed using the K-difference function. Results Digital records of 16 105 structures within the study area were created. Of the 750 randomly selected structures, six (1{\%}) were not found by the survey teams. A total of 1247 structures were assessed for eligibility, of which 691 eligible households were enroled. The majority of enroled households were the initially selected structures (51{\%}) or the first selected neighbour (42{\%}). Households that refused enrolment tended to cluster more than those which enroled. Conclusions Sampling from a satellite image was feasible in this urban African setting. Satellite images may be useful for public health surveillance in populations with inaccurate census data or maps and allow for spatial analyses such as identification of clustering among refusing households.",
keywords = "Geographic information systems, Health survey, Sampling, Satellite imagery, Zambia",
author = "Lowther, {Sara A.} and Curriero, {Frank C} and Shields, {Timothy M} and Saifuddin Ahmed and Mwaka Monze and Moss, {William J}",
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AU - Curriero, Frank C

AU - Shields, Timothy M

AU - Ahmed, Saifuddin

AU - Monze, Mwaka

AU - Moss, William J

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N2 - Objectives To describe our experience using satellite image-based sampling to conduct a health survey of children in an urban area of Lusaka, Zambia, as an approach to sampling when the population is poorly characterized by existing census data or maps. Methods Using a publicly available Quickbird™ image of several townships, we created digital records of structures within the residential urban study area using ArcGIS 9.2. Boundaries were drawn to create geographic subdivisions based on natural and man-made barriers (e.g. roads). Survey teams of biomedical research students and local community health workers followed a standard protocol to enrol children within the selected structure, or to move to the neighbouring structure if the selected structure was ineligible or refused enrolment. Spatial clustering was assessed using the K-difference function. Results Digital records of 16 105 structures within the study area were created. Of the 750 randomly selected structures, six (1%) were not found by the survey teams. A total of 1247 structures were assessed for eligibility, of which 691 eligible households were enroled. The majority of enroled households were the initially selected structures (51%) or the first selected neighbour (42%). Households that refused enrolment tended to cluster more than those which enroled. Conclusions Sampling from a satellite image was feasible in this urban African setting. Satellite images may be useful for public health surveillance in populations with inaccurate census data or maps and allow for spatial analyses such as identification of clustering among refusing households.

AB - Objectives To describe our experience using satellite image-based sampling to conduct a health survey of children in an urban area of Lusaka, Zambia, as an approach to sampling when the population is poorly characterized by existing census data or maps. Methods Using a publicly available Quickbird™ image of several townships, we created digital records of structures within the residential urban study area using ArcGIS 9.2. Boundaries were drawn to create geographic subdivisions based on natural and man-made barriers (e.g. roads). Survey teams of biomedical research students and local community health workers followed a standard protocol to enrol children within the selected structure, or to move to the neighbouring structure if the selected structure was ineligible or refused enrolment. Spatial clustering was assessed using the K-difference function. Results Digital records of 16 105 structures within the study area were created. Of the 750 randomly selected structures, six (1%) were not found by the survey teams. A total of 1247 structures were assessed for eligibility, of which 691 eligible households were enroled. The majority of enroled households were the initially selected structures (51%) or the first selected neighbour (42%). Households that refused enrolment tended to cluster more than those which enroled. Conclusions Sampling from a satellite image was feasible in this urban African setting. Satellite images may be useful for public health surveillance in populations with inaccurate census data or maps and allow for spatial analyses such as identification of clustering among refusing households.

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