The Typhoid Fever Surveillance in Africa Program: Geospatial Sampling Frames for Household-based Studies: Lessons Learned from a Multicountry Surveillance Network in Senegal, South Africa, and Sudan

Stephen Baker, Mohammad Ali, Jessica Fung Deerin, Muna Ahmed Eltayeb, Ligia Maria Cruz Espinoza, Nagla Gasmelseed, Justin Im, Ursula Panzner, Vera V. Kalckreuth, Karen H. Keddy, Gi Deok Pak, Jin Kyung Park, Se Eun Park, Arvinda Sooka, Amy Gassama Sow, Adama Tall, Stephen Luby, Christian G. Meyer, Florian Marks

Research output: Contribution to journalArticle

Abstract

Background: Robust household sampling, commonly applied for population-based investigations, requires sampling frames or household lists to minimize selection bias. We have applied Google Earth Pro satellite imagery to constitute structure-based sampling frames at sites in Pikine, Senegal; Pietermaritzburg, South Africa; and Wad-Medani, Sudan. Here we present our experiences in using this approach and findings from assessing its applicability by determining positional accuracy. Methods: Printouts of satellite imagery combined with Global Positioning System receivers were used to locate and to verify the locations of sample structures (simple random selection; weighted-stratified sampling). Positional accuracy was assessed by study site and administrative subareas by calculating normalized distances (meters) between coordinates taken from the sampling frame and on the ground using receivers. A higher accuracy in conjunction with smaller distances was assumed. Kruskal-Wallis and Dunn multiple pairwise comparisons were performed to evaluate positional accuracy by setting and by individual surveyor in Pietermaritzburg. Results: The median normalized distances and interquartile ranges were 0.05 and 0.03-0.08 in Pikine, 0.09 and 0.05-0.19 in Pietermaritzburg, and 0.05 and 0.00-0.10 in Wad-Medani, respectively. Root mean square errors were 0.08 in Pikine, 0.42 in Pietermaritzburg, and 0.17 in Wad-Medani. Kruskal-Wallis and Dunn comparisons indicated significant differences by low- and high-density setting and interviewers who performed the presented approach with high accuracy compared to interviewers with poor accuracy. Conclusions: The geospatial approach presented minimizes systematic errors and increases robustness and representativeness of a sample. However, the findings imply that this approach may not be applicable at all sites and settings; its success also depends on skills of surveyors working with aerial data. Methodological modifications are required, especially for resource-challenged sites that may be affected by constraints in data availability and area size.

Original languageEnglish (US)
Pages (from-to)S474-S482
JournalClinical Infectious Diseases
Volume69
DOIs
StatePublished - Oct 30 2019

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Satellite Imagery
Senegal
Typhoid Fever
South Africa
Interviews
Geographic Information Systems
Sudan
Selection Bias
Population
South Sudan

Keywords

  • geospatial sampling frame
  • positional accuracy
  • satellite imagery
  • sub-Saharan Africa

ASJC Scopus subject areas

  • Microbiology (medical)
  • Infectious Diseases

Cite this

The Typhoid Fever Surveillance in Africa Program : Geospatial Sampling Frames for Household-based Studies: Lessons Learned from a Multicountry Surveillance Network in Senegal, South Africa, and Sudan. / Baker, Stephen; Ali, Mohammad; Deerin, Jessica Fung; Eltayeb, Muna Ahmed; Cruz Espinoza, Ligia Maria; Gasmelseed, Nagla; Im, Justin; Panzner, Ursula; Kalckreuth, Vera V.; Keddy, Karen H.; Pak, Gi Deok; Park, Jin Kyung; Park, Se Eun; Sooka, Arvinda; Sow, Amy Gassama; Tall, Adama; Luby, Stephen; Meyer, Christian G.; Marks, Florian.

In: Clinical Infectious Diseases, Vol. 69, 30.10.2019, p. S474-S482.

Research output: Contribution to journalArticle

Baker, S, Ali, M, Deerin, JF, Eltayeb, MA, Cruz Espinoza, LM, Gasmelseed, N, Im, J, Panzner, U, Kalckreuth, VV, Keddy, KH, Pak, GD, Park, JK, Park, SE, Sooka, A, Sow, AG, Tall, A, Luby, S, Meyer, CG & Marks, F 2019, 'The Typhoid Fever Surveillance in Africa Program: Geospatial Sampling Frames for Household-based Studies: Lessons Learned from a Multicountry Surveillance Network in Senegal, South Africa, and Sudan', Clinical Infectious Diseases, vol. 69, pp. S474-S482. https://doi.org/10.1093/cid/ciz755
Baker, Stephen ; Ali, Mohammad ; Deerin, Jessica Fung ; Eltayeb, Muna Ahmed ; Cruz Espinoza, Ligia Maria ; Gasmelseed, Nagla ; Im, Justin ; Panzner, Ursula ; Kalckreuth, Vera V. ; Keddy, Karen H. ; Pak, Gi Deok ; Park, Jin Kyung ; Park, Se Eun ; Sooka, Arvinda ; Sow, Amy Gassama ; Tall, Adama ; Luby, Stephen ; Meyer, Christian G. ; Marks, Florian. / The Typhoid Fever Surveillance in Africa Program : Geospatial Sampling Frames for Household-based Studies: Lessons Learned from a Multicountry Surveillance Network in Senegal, South Africa, and Sudan. In: Clinical Infectious Diseases. 2019 ; Vol. 69. pp. S474-S482.
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abstract = "Background: Robust household sampling, commonly applied for population-based investigations, requires sampling frames or household lists to minimize selection bias. We have applied Google Earth Pro satellite imagery to constitute structure-based sampling frames at sites in Pikine, Senegal; Pietermaritzburg, South Africa; and Wad-Medani, Sudan. Here we present our experiences in using this approach and findings from assessing its applicability by determining positional accuracy. Methods: Printouts of satellite imagery combined with Global Positioning System receivers were used to locate and to verify the locations of sample structures (simple random selection; weighted-stratified sampling). Positional accuracy was assessed by study site and administrative subareas by calculating normalized distances (meters) between coordinates taken from the sampling frame and on the ground using receivers. A higher accuracy in conjunction with smaller distances was assumed. Kruskal-Wallis and Dunn multiple pairwise comparisons were performed to evaluate positional accuracy by setting and by individual surveyor in Pietermaritzburg. Results: The median normalized distances and interquartile ranges were 0.05 and 0.03-0.08 in Pikine, 0.09 and 0.05-0.19 in Pietermaritzburg, and 0.05 and 0.00-0.10 in Wad-Medani, respectively. Root mean square errors were 0.08 in Pikine, 0.42 in Pietermaritzburg, and 0.17 in Wad-Medani. Kruskal-Wallis and Dunn comparisons indicated significant differences by low- and high-density setting and interviewers who performed the presented approach with high accuracy compared to interviewers with poor accuracy. Conclusions: The geospatial approach presented minimizes systematic errors and increases robustness and representativeness of a sample. However, the findings imply that this approach may not be applicable at all sites and settings; its success also depends on skills of surveyors working with aerial data. Methodological modifications are required, especially for resource-challenged sites that may be affected by constraints in data availability and area size.",
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T2 - Geospatial Sampling Frames for Household-based Studies: Lessons Learned from a Multicountry Surveillance Network in Senegal, South Africa, and Sudan

AU - Baker, Stephen

AU - Ali, Mohammad

AU - Deerin, Jessica Fung

AU - Eltayeb, Muna Ahmed

AU - Cruz Espinoza, Ligia Maria

AU - Gasmelseed, Nagla

AU - Im, Justin

AU - Panzner, Ursula

AU - Kalckreuth, Vera V.

AU - Keddy, Karen H.

AU - Pak, Gi Deok

AU - Park, Jin Kyung

AU - Park, Se Eun

AU - Sooka, Arvinda

AU - Sow, Amy Gassama

AU - Tall, Adama

AU - Luby, Stephen

AU - Meyer, Christian G.

AU - Marks, Florian

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N2 - Background: Robust household sampling, commonly applied for population-based investigations, requires sampling frames or household lists to minimize selection bias. We have applied Google Earth Pro satellite imagery to constitute structure-based sampling frames at sites in Pikine, Senegal; Pietermaritzburg, South Africa; and Wad-Medani, Sudan. Here we present our experiences in using this approach and findings from assessing its applicability by determining positional accuracy. Methods: Printouts of satellite imagery combined with Global Positioning System receivers were used to locate and to verify the locations of sample structures (simple random selection; weighted-stratified sampling). Positional accuracy was assessed by study site and administrative subareas by calculating normalized distances (meters) between coordinates taken from the sampling frame and on the ground using receivers. A higher accuracy in conjunction with smaller distances was assumed. Kruskal-Wallis and Dunn multiple pairwise comparisons were performed to evaluate positional accuracy by setting and by individual surveyor in Pietermaritzburg. Results: The median normalized distances and interquartile ranges were 0.05 and 0.03-0.08 in Pikine, 0.09 and 0.05-0.19 in Pietermaritzburg, and 0.05 and 0.00-0.10 in Wad-Medani, respectively. Root mean square errors were 0.08 in Pikine, 0.42 in Pietermaritzburg, and 0.17 in Wad-Medani. Kruskal-Wallis and Dunn comparisons indicated significant differences by low- and high-density setting and interviewers who performed the presented approach with high accuracy compared to interviewers with poor accuracy. Conclusions: The geospatial approach presented minimizes systematic errors and increases robustness and representativeness of a sample. However, the findings imply that this approach may not be applicable at all sites and settings; its success also depends on skills of surveyors working with aerial data. Methodological modifications are required, especially for resource-challenged sites that may be affected by constraints in data availability and area size.

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