2.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

3.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

4.  Most data interpretation requires context – a comparison group. ◦ Same group compared over time; ◦ Different groups compared within same timeframe; ◦ Different groups compared over time.  Without a comparison, the likelihood that findings are due to factors other than the hypothesized cause cannot be assessed.  Selection of study participants;  Chance;  Other factors or trends.

5.  A basic epidemiologic tool because they allow for appropriate comparisons. ◦ Comparing counts can be misleading. # of events in a specific time period Rate = -------------------------------------------------- x 10 n Avg. pop during that time period …per 100 (%) …per 1000 …per 100,000

6.  There were 1,765 heart disease deaths in Flushing, Queens in 2002 and 882 in Pelham Bay, Bronx.

7. A. Flushing, Queens B. Pelham Bay, Bronx

8. A. Flushing, Queens 354/100,000 pop B. Pelham Bay, Bronx 361/100,000 pop

9.  Because Flushing (n = 498,318) has a larger population than Pelham Bay (n = 244,452).  Same as saying 25 miles-per-hour is faster than 50 miles-per-day: ◦ 25 miles 50 miles 1 hour 1 day (24 hours)

10.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

11.  The process of inferring from your data whether an observed difference is likely due to chance.  Commonly, significance set at 0.05 (5%): 95% sure that the association is not due to chance.  sig=0.01 (1%): 99% sure.  sig=.10 (10%): 90% sure.  The smaller the sample, the more difficult it is to find a significant difference. ◦ In larger samples, it is often easy to find significance – but is it meaningful?

12.  Statistical significance ≠ importance  Not significant ≠ no association  Statistical significance ≠ causation

13.  An interval or range of values that reflects the precision of an estimate of a population parameter.  Statistically, how confident are we that the number is real?  E.g., Smoking prevalence (2010): 14.0% (12.9, 15.3)  The more confidence you want (90% vs. 95% vs. 99%), the wider the interval.

14.  What does it mean if 2 CIs overlap? –Prevalence of smoking among: Men: 16.1% (14.3%-18.1%) Women: 12.2% (10.8%-13.7%) –Prevalence of diabetes among: Men: 9.4% (8.3%-10.8%) Women: 9.1% (8.2%-10.2%)  What does it mean if a CI includes 0?

15.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

16.  A third factor that influences the relationship between exposure and disease.  If you are interested in actual differences in prevalence across populations, confounders are not that important.  However, if you are interested in assessing risk differences, confounders can and should be controlled for in analyses. 16

17.  Example: When comparing cardiac disease between men and women, what other factor may confound the relationship between sex and illness?  Age! If we don’t adjust for age, and find a higher prevalence among women, it might be due to the fact that in the general population, women are (on average) older than men.  Age-adjustment is one way to limit confounding.  Ensures that any differences you see between groups are NOT due to age.

18.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

19.  Cross-sectional ◦ Select a sample from the population and measure predictor and outcome variables at the same time.  Yields prevalence;  Cannot talk about incidence or risk of developing a disease;  Cannot establish sequence of events;  Cannot infer causation;  Can be generalizable.  Case-control ◦ Select two samples from the population - one with disease and one without, then look back and measure predictor variable.  Yields odds ratio (measure of association);  Cannot talk about incidence or risk of developing a disease;  Can be generalizable.

20.  Prospective cohort ◦ Select a sample from the population, measure predictor variable (presence or absence), then follow up and measure the outcome variable.  Yields incidence, relative risk;  Can be generalizable.  Randomized Control Trial (RCT) ◦ Randomly assign people to treatment or control (exposure), then follow up and measure outcome.  Can be generalizable;  STRONGEST STUDY DESIGN FOR CAUSATION.

21.  Ecologic Study ◦ Unit of analysis is a population, rather than an individual. For example, looking at rates of disease across countries.  Can’t infer anything about individuals;  Cannot infer causality.  Qualitative Study ◦ Aims to gather an in-depth understanding; ◦ Includes focus groups, in-depth interviews; ◦ Subjects are not systematically chosen to represent a target population.  Data cannot be generalized.

22.  Time sequence of events  Biological plausibility  Consistency and replications  Rule out confounding

23.  Size of study ◦ The bigger the study, the more power you have to detect findings and the more generalizable it will be.  New knowledge vs. replicated finding ◦ First study ever finding this result? ◦ Scientific method requires ability to replicate findings.

24.  Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence do we have in them?  Can anything else explain this association?  What can (and can’t) this study tell us?  How should findings be accurately presented?

25.  Provide clear context of literature base and importance of findings. ◦ How big is the population that these findings apply to and what population exactly is referenced?  Always source the data clearly, providing link to/information on original research for audience.  Question researchers on limitations to their data. ◦ Researcher “headlines” (titles/abstracts) can be misleading!

26.  Best answered by qualitative data (focus groups, interviews).  Speculation vs. Evidence.  Reporting “could be” rather than “is.”

27.  Anecdotes can make data come alive, but… ◦ “Anecdotal evidence” is an oxymoron.  Anecdotes should not be the only “counterfactual” argument against data. ◦ “Fairness” in reporting must insist on data (with stated limitations) from both sides.

28.  Anecdotes must be presented in the context of the data. ◦ Source says “Everyone does X” vs. data showing that 35% of people do X.

29.  EpiQuery ◦ Web-based, interactive data tool ◦ Multiple data sources  My Community’s Health: Data and Statistics ◦ www.nyc.gov/health