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

Conclusions

(go to Outline)

Are the conclusions appropriately based on the survey results or other data presented in the discussion section? It is very difficult to prove cause-effect based on the results of a single survey. Finding both a high cumulative prevalence of diarrheal disease and a high prevalence of acute malnutrition does not necessarily mean that the diarrheal disease caused the malnutrition. Authors who state that their survey does this are probably over interpreting their results. In addition, the conclusions in many survey reports seem to have little relationship to the data gathered by the survey and the results of data analysis.

Are the results of a survey extended beyond the sampling universe? A survey done with a sample taken from one province cannot be generalized to a neighbouring province. The survey report authors may point out reasons why the neighbouring province may have a similar health or nutritional status as the surveyed province, but they should not try to reach absolute conclusions.

Are the conclusions consistent with known scientific fact? Many things are already very well known about malnutrition and other health indicators. If the survey seems to contradict these well-known principals, perhaps there is something wrong with the survey. For example, if a survey shows a very high prevalence of acute malnutrition in young children in the midst of a dysentery outbreak, but a mortality rate in young children which is below normal, there is probably something wrong. We know that malnutrition, especially when combined with disease outbreaks, kills children. If there is a lot of both malnutrition and disease, children are probably dying at an elevated rate, and the survey results should reflect this.

Do the conclusions seem to be motivated more by politics than survey results? This is a very common problem. No one is completely neutral. Survey managers must be careful to prevent their opinions from influencing their survey results or conclusions. A survey report that shows this type of influence is automatically suspect.

Are the results put into a larger context? Because of the inherent limitations of surveys, survey results must be supplemented by other information which can explain the results and provide program guidance. The results should be compared to those of prior surveys or surveys done in similar populations.