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
<p><strong>Background</strong> 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 a...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2019
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| _version_ | 1797054283152097280 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Baker, S |
| collection | OXFORD |
| description | <p><strong>Background</strong>
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.</p>
<p><strong>Methods</strong>
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.</p>
<p><strong>Results</strong>
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.</p>
<p><strong>Conclusions</strong>
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.</p> |
| first_indexed | 2024-03-06T18:55:02Z |
| format | Journal article |
| id | oxford-uuid:118cc959-571f-42a1-8979-7b2574bed960 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:55:02Z |
| publishDate | 2019 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:118cc959-571f-42a1-8979-7b2574bed9602022-03-26T10:02:58ZThe 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 SudanJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:118cc959-571f-42a1-8979-7b2574bed960EnglishSymplectic ElementsOxford University Press2019Baker, SAli, MDeerin, JFEltayeb, MACruz Espinoza, LMGasmelseed, NIm, JPanzner, UKalckreuth, VVKeddy, KHPak, GDPark, JKPark, SESooka, ASow, AGTall, ALuby, SMeyer, CGMarks, F<p><strong>Background</strong> 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.</p> <p><strong>Methods</strong> 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.</p> <p><strong>Results</strong> 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.</p> <p><strong>Conclusions</strong> 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.</p> |
| spellingShingle | 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 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
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