Dataset Information

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.

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.

SUBMITTER: Baker S

PROVIDER: S-EPMC6821174 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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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 S Ali Mohammad M Deerin Jessica Fung JF Eltayeb Muna Ahmed MA Cruz Espinoza Ligia Maria LM Gasmelseed Nagla N Im Justin J Panzner Ursula U Kalckreuth Vera V VV Keddy Karen H KH Pak Gi Deok GD Park Jin Kyung JK Park Se Eun SE Sooka Arvinda A Sow Amy Gassama AG Tall Adama A Luby Stephen S Meyer Christian G CG Marks Florian F

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20191001 Suppl 6

<h4>Background</h4>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.<h4>Methods ...[more]

PMID: 31665783

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Project description:The 2014 outbreak of Ebola virus disease (EVD) in West Africa was the largest ever recorded. Starting in September 2014, International Medical Corps (IMC) managed 5 Ebola treatment units (ETUs) in Liberia and Sierra Leone, which cumulatively cared for about 2,500 patients. We conducted a retrospective cohort study of patient data collected at the 5 ETUs over 1 year of operations.To collect clinical and epidemiological data from the patient care areas, each chart was either manually copied across the fence between the high-risk zone and low-risk zone, imaged across the fence, or imaged in the high-risk zone. Each ETU's data were entered into a separate electronic database, and these were later combined into a single relational database. Lot quality assurance sampling was used to ensure data quality, with reentry of data with high error rates from imaged records.The IMC database contains records on 2,768 patient presentations, including 2,351 patient admissions with full follow-up data. Of the patients admitted, 470 (20.0%) tested positive for EVD, with an overall case fatality ratio (CFR) of 57.0% for EVD-positive patients and 8.1% for EVD-negative patients. Although more men were admitted than women (53.4% vs. 46.6%), a larger proportion of women were diagnosed EVD positive (25.6% vs. 15.2%). Diarrhea, red eyes, contact with an ill person, and funeral attendance were significantly more common in patients with EVD than in those with other diagnoses. Among EVD-positive patients, age was a significant predictor of mortality: the highest CFRs were among children under 5 (89.1%) and adults over 55 (71.4%).While several prior reports have documented the experiences of individual ETUs, this study is the first to present data from multiple ETUs across 2 countries run by the same organization with similar clinical protocols. Our experience demonstrates that even in austere settings under difficult conditions, it is possible for humanitarian organizations to collect high-quality clinical and epidemiologic data during a major infectious disease outbreak.

Project description:ObjectiveTo explore healthcare policy and system improvements that would more proactively respond to future penetration of counterfeit cancer medications in the USA drug supply chain using geospatial analysis.DesignA statistical and geospatial analysis of areas that received notices from the Food and Drug Administration (FDA) about the possibility of counterfeit Avastin penetrating the US drug supply chain. Data from FDA warning notices were compared to data from 44 demographic variables available from the US Census Bureau via correlation, means testing and geospatial visualisation. Results were interpreted in light of existing literature in order to recommend improvements to surveillance of counterfeit medicines.Setting/participantsThis study analysed 791 distinct healthcare provider addresses that received FDA warning notices across 30,431 zip codes in the USA.OutcomesStatistical outputs were Pearson's correlation coefficients and t values. Geospatial outputs were cartographic visualisations. These data were used to generate the overarching study outcome, which was a recommendation for a strategy for drug safety surveillance congruent with existing literature on counterfeit medication.ResultsZip codes with greater numbers of individuals age 65+ and greater numbers of ethnic white individuals were most correlated with receipt of a counterfeit Avastin notice. Geospatial visualisations designed in conjunction with statistical analysis of demographic variables appeared more capable of suggesting areas and populations that may be at risk for undetected counterfeit Avastin penetration.ConclusionsThis study suggests that dual incorporation of statistical and geospatial analysis in surveillance of counterfeit medicine may be helpful in guiding efforts to prevent, detect and visualise counterfeit medicines penetrations in the US drug supply chain and other settings. Importantly, the information generated by these analyses could be utilised to identify at-risk populations associated with demographic characteristics. Stakeholders should explore these results as another tool to improve on counterfeit medicine surveillance.

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Dataset Information

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.

Publications

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.

Similar Datasets

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