The use of epidemiological tools in conflict-affected populations: open-access educational resources for policy-makers

Table of Contents

1. Welcome
2. Introduction: Epidemiology in crises
3. Ethical issues in data collection
4. Need for epidemiologic competence
5. Surveys - Introduction
6. Surveys - Description of sampling methods
7. Surveys - Sampling error, bias, accuracy, precision, & sample size
8. Surveys - Resources required for surveys
9. Surveys - Critiquing survey reports
   1. Introduction
   2. Objectives
   3. Justification for survey
   4. General procedures
   5. Outcomes and risk factors
   6. Sample size
   7. Sampling method
   8. Data analysis
   9. Data analysis
   10. Discussion
   11. Conclusions
   12. Recommendations
10. Surveillance - When to do surveillance
11. Surveillance - Methods
12. Surveillance - Common problems
13. Programme data
14. Rapid assessment
15. Mortality - Indicators and their measurement
16. Mortality - Data sources
17. Mortality - Interpretation and action
18. Nutrition - Introduction and background
19. Nutrition - Indicators and their measurement
20. Nutrition - Data sources
21. Nutrition - Interpretation and action
22. Health services
23. Vaccination programmes
24. Water supply, sanitation, and shelter
25. Violence
26. Presentation of results
27. Formulating conclusions and recommendations
28. Dissemination and action

Data analysis

(go to Outline)

Was the sampling done correctly? If the sampling is described in enough detail, the reader can judge whether or not there are potential biases injected by the incorrect sampling. Certainly, the reader should be able to judge if the sampling was done randomly or by convenience.

Were the data analysis techniques appropriate to the sampling method? If a cluster sample was done, did the calculation of confidence intervals take this into account? If not, the confidence intervals calculated are narrower than they should be, leading readers to believe that the survey results are more precise than they really are. This is a very frequent mistake that demonstrates that the survey managers did not know as much about survey statistics as they should have known.

Did all the survey subjects have the same likelihood of selection; if not, was different likelihood accounted for in the analysis? If the same sample size was selected in one refugee camp with 1,000 households and in another refugee camp with 5,000 households, the likelihood of selection is very different. Analyzing these data together without appropriate analytic techniques can potentially introduce sampling bias.