Learning lessons from field surveys in humanitarian contexts: a case study of field surveys conducted in North Kivu, DRC 2006-2008

Author(s)
Grais, R. F., Luquero, F. J., Grellety, E., Pham, H., Coghlan, B. and Salignon, P.
Publication language
English
Pages
8pp
Date published
10 Sep 2009
Publisher
Conflict and Health
Type
Articles
Keywords
Conflict, violence & peace, Health
Countries
Democratic Republic of the Congo

Survey estimates of mortality and malnutrition are commonly used to guide humanitarian decisionmaking.
Currently, different methods of conducting field surveys are the subject of debate among
epidemiologists. Beyond the technical arguments, decision makers may find it difficult to
conceptualize what the estimates actually mean. For instance, what makes this particular situation
an emergency? And how should the operational response be adapted accordingly. This brings into
question not only the quality of the survey methodology, but also the difficulties epidemiologists
face in interpreting results and selecting the most important information to guide operations. As a
case study, we reviewed mortality and nutritional surveys conducted in North Kivu, Democratic
Republic of Congo (DRC) published from January 2006 to January 2009. We performed a PubMed/
Medline search for published articles and scanned publicly available humanitarian databases and
clearinghouses for grey literature. To evaluate the surveys, we developed minimum reporting
criteria based on available guidelines and selected peer-review articles. We identified 38 reports
through our search strategy; three surveys met our inclusion criteria. The surveys varied in
methodological quality. Reporting against minimum criteria was generally good, but presentation of
ethical procedures, raw data and survey limitations were missed in all surveys. All surveys also failed
to consider contextual factors important for data interpretation. From this review, we conclude
that mechanisms to ensure sound survey design and conduct must be implemented by operational
organisations to improve data quality and reporting. Training in data interpretation would also be
useful. Novel survey methods should be trialled and prospective data gathering (surveillance)
employed wherever feasible.