Community Health Assessment: Data Analysis

1. Community Health Assessment:Data Analysis LHD TA Learning Session # 2 • October 24, 2012 Barbara LaClair, MHAKansas Health Institute

2. The data are collected – now what?

3. Turning Data into Information • Goals: • Accurate representation of community health issues and health status • Easy to identify areas where community does well, and not so well • Data are understandable to stakeholders and community members

4. Assessment Should Include: • Demographics • Health issues, including disparities • Contributing causes • Community resources and assets to address health issues • Primary andsecondary data sources • A possible mix of quantitative and qualitative data

5. Assessment Questions • What makes your community unique? • How does your community compare to others in terms of the health indicators selected? • What are your community’s major health risks and problems? • Why are the rates so high, or so low? • What health disparities exist? • What gaps exist in your community’s health care system?

6. Organize and Display the Data • Narrative • Tables • Charts and Graphs • Narrative explanations should accompany numbers • Definition of the indicator • Significance of the measure • Document sources for all data

7. Charts and Graphs

8. Pie Charts

9. Pie Charts

10. Stacked Bar Graph (Each bar totals to 100%)

11. Scatterplot

12. Line Graph

13. Bar Graph

14. Stacked Bar Graph

15. A Few Simple Rules • Include titles and labels for axes • Generally, Y axis should include 0 • Equal distance between years in time series • Caution with 3-D graphs – tend to distort perceptions of data, harder to interpret • Keep it simple

16. Don’t Mislead the Audience

17. Don’t Mislead the Audience

18. Interpreting Your Data • Where is your community different than the comparison group(s)? • What has changed over time? • Which differences are meaningful? • What issues…. • Affect a lot of people? • Greatly impact the entire community? • Have a solution? • What health disparities exist? • What issues/themes repeatedly emerge from the data?

19. Making Comparisons • Adjustment for differences in population • Confidence Intervals • p-values

20. Crude vs. Age-Adjusted Rates • Rate = # of events/population of interest • Population-based rates compensate for differences in population size • But, populations may also vary in composition • Adjustment to a standard population can compensate for differences, such as age, to make more valid comparisons between different populations

21. What is a Confidence Interval? • Degree of certainty around a number or estimate • 95% CI means that you can be 95% confident that the true value falls between the upper and lower confidence limits • Helpful in determining whether differences in frequencies or rates are real or due to sampling error • Confidence intervals that do not overlap suggest statistically significant differences, not due to chance

22. What is a p-value? • A probability, with values between 0 and 1 • Indicates the probability that an observed difference between two samples is due to chance • Smaller the p-value, the more likely a difference is statistically significant • Generally, p < 0.05 is considered significant

23. Rates Based on Small Numbers • Estimates based on samples from a population are subject to error due to sampling variability • Rates based on full population counts arealso subject to random error • Random error may be substantial when the number of events in the numerator is small (< 20), or when denominator is small (< 30) • The larger the numerator of the observed rate, the better it will estimate the underlying “true” rate

24. Infant Mortality Rates

25. Combining Data for Greater Precision • Combine several years of data • Drawback: Harder to assess trends or measure progress • Combine geographic areas • Drawback: May mask variability within the region. Regional rate may not accurately represent the local rate.

26. Assessing Differences • Trends – changes over time • Comparisons between groups • Practical vs. statistical significance • If “n”is large enough, almost any difference can be statistically significant • Statistical significant differences may or may not be meaningful in terms of practical or clinical significance • Preponderance of evidence around an issue

27. Example: Low Birth Weight

28. Low Birth Weight

29. Trends • Changes in an indicator over time • Need data from several points in time (5-10 years if annual rates) • Look for improvement, decline or steady state • Easiest to illustrate using graphs

30. Example: Child Poverty • Do you see… • A shift (8 or more points on one side of mean)? • A trend (6 consecutive jumps in same direction)? • A pattern (pattern recurs 8 or more times in row)?

31. Qualitative Data • Based on information that cannot be counted or quantified, and is expressed in text • Opinions • Perceptions • Observations • Answers to open questions

32. Qualitative Data Sources • Surveys • Key informant interviews • Focus groups • Photo voice projects • Etc.

33. Analyzing Qualitative Data • Look for • Recurrent themes in comments • General opinions about quality of life in the community • Perceived health problems • Perceived causes of health problems • Community strengths and assets • Survey results can be summarized as frequencies

34. The Data Profile • Facts only • Documentation of methods • Visual display of data helpful • Include comparison groups, trends over time • Understandable by broad stakeholder audience • Data sources cited

35. Community Survey Data • Report methods, especially sampling (If convenience sample, interpret with caution) • Use demographic data to describe sample population • Compare sample population to target population • Summarize important findings • Assess the degree to which community perceptions agree with secondary data

36. Questions?

37. Kansas Health Institute Information for policy makers. Health for Kansans.