1. KINE 4565: The epidemiology of injury prevention Case control and case crossover studies

2. Overview Odds ratios, relative risks and confidence intervals Study design: case control and case- crossover studies Two examples from the literature

3. 2X2 tables: the foundation Disease or other outcome No disease or other outcome Exposed ab Not exposed cd

4. 2X2 tables: estimating associations Disease or other outcome No disease or other outcome Exposed aba+b Not exposed cdc+d a+cb+d a+b+c+d

5. Odds ratios and relative risks Odds ratios (ad/bc) calculate the odds of an outcome given an exposure Relative risk (a/a+c)/b/b+d) calculates the relative risk of an outcome in exposed compared to non-exposed group Statistical packages calculate confidence intervals

6. Odds Ratios and Relative Risks Usually ORs and RRs are greater than 1 to indicate an increased risk of the outcome in those exposed In injury studies, OR and RR can be less than one to indicate a protective effect of the intervention

7. Confidence intervals Confidence intervals are used for hypothesis testing in 2X2 tables (and others) The width of a confidence interval is based on the variablility within the data and the sample size An OR or RR of 1 = no association A confidence interval that crosses 1 is NOT statistically significant The wider the confidence interval, the less precise the estimate

8. Direction of inquiry POPULATION Sample of people with the disease Sample of people without the disease Exposed Not exposed Exposed Not exposed Start with OUTCOME Go backwards Check for EXPOSURE Summary: Design of a case control study

9. Case control studies Cases Controls Eab Ēcd Rate of exposure in cases = a/a+c Rate of exposure in controls = b/b+d Odds of exposure in cases = a/c Odds of exposure in controls = b/d Odds ratio = ad/bc

10. Example: Case– control study death No death law1211151 No law 171413420 Odds Ratio = (121 x 13420) / (1151 x 1714) = 0.82 Assessing the association between booster seat laws and child death in car crashes

11. A value of OR > 1 means that the exposure is associated with an increased risk of developing the outcome A value of OR < 1 indicates that the exposure is associated with a reduced risk If the outcome is rare, the OR (in a case control study) is an estimate of the RR Interpreting the odds ratio

12. Selection of control group Roster (complete listing of the study base. Example: Annual residence list, electoral list) Injured controls with another injury Random Digit Dialing Neighborhood controls

13. Selection of control group Population control group Advantages: Drawn from the same source population as the cases Disadvantages: Inappropriate if a random sample is not possible Inconvenience (response, time) Recall bias (different in cases versus healthy controls) Less motivated than hospital controls

14. Selection of control group Hospital or disease registry controls Advantages Members of the same population Comparable availability of information Disadvantages Bias if exposure is associated with the disease that the control group has

15. Advantages Require short time when outcomes are delayed (e.g., cancer studies) Inexpensive Practical for study of rare diseases such as injuries Few ethical dilemmas since outcome and exposure are both in the past Case-control studies

16. Disadvantages Information bias (Recall bias) of the exposure (those who are injured may remember different things than those who are uninjured) High potential for sample selection bias depending on how controls are selected Case-control studies

17. Case-crossover studies: a variation In case-crossover studies each case acts as their own control Usually the ‘control’ is at a different time, but doing the same thing Can be very cost-effective (fewer subjects recruited) Controls for variation in other variables (potential confounders) eg: age, sex, risk taking, SES

18. Conclusions Case-control studies very common in injury epidemiology Very useful for rare outcomes (e.g., bicycle-related injuries, deaths) Very useful for studying effectiveness where it is unethical to randomize (e.g, bike helmets) But be cautious of the potential for bias