1. Mother and Child Health: Research Methods G.J.Ebrahim Editor Journal of Tropical Pediatrics, Oxford University Press.

2. Bias Bias means “different” 3 types of bias: –Selection Bias –Information Bias –Confounding

3. Selection Bias Examples Patients referred for specialist care are different from those in the community Migration bias. People with chronic lung disease tend to move out of urban areas; those with psychiatric problems seek the anonymity of cities High dropout rates. Those who drop out of a study tend to be different from those continuing

4. Information Bias Examples Response Bias occurs when subjects give inaccurate responses. Measurement Bias occurs when instruments are faulty Observer error A process tends to show improvement when being observed. (Hawthorne Effect)

5. Strategies for Avoiding Bias Have clear and precise definitions (e.g. for cases; controls;exposure;criteria for inclusion/exclusion) “Blinding” where appropriate Reduce measurement error by ‘quality control” careful check of study design; choice of subjects; ascertainment of disease and exposure;planning of questionnaires; methods of data collection.

6. Confounders Confounders act by being associated with both a risk factor and outcome in a way that makes the two seem related. Poor Maternal Nutrition Low Birth Weight Low Socioecono mic Class

7. Dealing with Confounders - 1 Think about possible confounders at the design stage, and gather data on all possible confounders. A quick test about a possible confounder is to check whether it is unevenly distributed between study and comparison groups. Suspect confounding if the odds ratio gets altered after adjusting for another factor.

8. Method of Checking for a Possible Confounder First calculate Odds Ratio for the exposure variable. Next calculate odds ratio for different strata of the confounding variable If the odds ratios are not materially different then there is no confounding.

9. Strategies for dealing with Confounding Design Stage – Strict inclusion criteria – Matching – Randomization Analysis Stage – Do analysis by adjusting for several strata of the confounding variable – Multiple regression analysis

10. Validity Are the conclusions true? Common threats to validity –Selection bias –Measurement bias –Differential loss of subjects –Confounders –Unexpected events –Hawthorne effect

11. Strategies for ensuring validity Have a control group. Helps against confounding, unexpected events, Hawthorne effect. Random assignment of subjects to different groups. Before / After measurements. Carefully prepared research designs. Quality control of equipment Knowledge of environmental events especially if the study is of long duration. Unobtrusive methods of observation.