Abstract
BackgroundNutrition and mortality surveys are the main tools whereby evidence on the health status of populations affected by disasters and armed conflict is quantified and monitored over time. Several reviews have consistently revealed a lack of rigor in many surveys. We describe an algorithm for analyzing nutritional and mortality survey reports to identify a comprehensive range of errors that may result in sampling, response, or measurement biases and score quality. We apply the algorithm to surveys conducted in Darfur, Sudan.MethodsWe developed an algorithm based on internationally agreed upon methods and best practices. Penalties are attributed for a list of errors, and an overall score is built from the summation of penalties accrued by the survey as a whole. To test the algorithm reproducibility, it was independently applied by three raters on 30 randomly selected survey reports. The algorithm was further applied to more than 100 surveys conducted in Darfur, Sudan.ResultsThe Intra Class Correlation coefficient was 0.79 for mortality surveys and 0.78 for nutrition surveys. The overall median quality score and range of about 100 surveys conducted in Darfur were 0.60 (0.12-0.93) and 0.675 (0.23-0.86) for mortality and nutrition surveys, respectively. They varied between the organizations conducting the surveys, with no major trend over time.ConclusionOur study suggests that it is possible to systematically assess quality of surveys and reveals considerable problems with the quality of nutritional and particularly mortality surveys conducted in the Darfur crisis.
Highlights
The prevalence of acute malnutrition and mortality rates are crucial indicators to benchmark the severity of a crisis, to track trends, and to inform funding and operational decisions [1,2]
We describe an algorithm based on systematic and comparable criteria for analyzing nutritional and mortality survey reports to identify a comprehensive range of errors that may result in sampling, response, or measurement biases and score the quality of a survey as a whole
Reproducibility of the algorithm The algorithm was independently applied by three raters on 30 randomly selected survey reports from 11 non-governmental organizations (NGOs), including mortality surveys and nutrition surveys
Summary
The prevalence of acute malnutrition and mortality rates are crucial indicators to benchmark the severity of a crisis, to track trends, and to inform funding and operational decisions [1,2]. Despite recent improvements in standardization of nutrition and mortality survey methodology and analysis [5,6,7], errors in the field application of survey methods persist, potentially resulting in biased data and harmful operational decisions. Reviews of surveys carried out in various crisis settings have consistently revealed a lack of rigor in many nutritional [8,9,10,11,12] and most mortality surveys [10,11]. Several reviews have consistently revealed a lack of rigor in many surveys. We describe an algorithm for analyzing nutritional and mortality survey reports to identify a comprehensive range of errors that may result in sampling, response, or measurement biases and score quality. We apply the algorithm to surveys conducted in Darfur, Sudan
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