An algorithm to assess methodological quality of nutrition and mortality cross-sectional surveys: development and application to surveys conducted in Darfur, Sudan.

Background: Nutrition 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.

Methods: We 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.

Results: The 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.

Conclusion: Our 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.