Data Analysis: Predictors of Short-term and Long-term Retention in the DP Demonstration Project

1. SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 2 Meeting 1 Data Analysis: Predictors of Short-term and Long-term Retention in the DP Demonstration Project November 2011

2. Retention Analyses • Methods • Participants • Measures • Statistical Analysis • Results • Conclusions • Discussion

3. SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 2 Meeting 1 Methods

4. Participants • Short-term retention analyses • N = 2,638 • Participants whose start date was before 8/1/2008 • Included data through 7/31/2009 • Needed to have sufficient number of responses to provider annual questionnaire (at site level) • Long-term retention analyses • N = 2,552 • Same criteria as short-term analyses • Did not include participants who never started DPP classes

5. Measures: Outcomes • Short-term retention • Attending all 16 DPP classes • Long-term retention • Amount of time before becoming inactive

6. Measures: Participant-Level Predictors • Sociodemographics • Age • Gender • Education status • Employment status • Marital status • Annual household income

7. Measures: Participant-Level Predictors • Clinical Indicators • Fasting blood glucose (FBG) • Body mass index (BMI) • Systolic blood pressure (SBP) • Diastolic blood pressure (DBP) • Low-density lipoprotein (LDL) • High-density lipoprotein (HDL) • Triglycerides • Comorbidity (self-reported)

8. Measures: Participant-Level Predictors • Smoking status • Stages of change (diet and exercise) • Presence of family support person • Positive Family Support Scale • Kessler Distress Scale • Rapid Assessment of Physical Activity – Aerobic (RAPA) • Pain Disability Index • Pain Visual Assessment • Print literacy • Numeracy • Sociobehavioral Factors

9. Measures: Program-Level Predictors • Site Characteristics • Organization type (IHS vs. Tribal) • User population size • Total participants accrued (as of July 31, 2008) • Average age of staff members • Proportion of staff who are female • Proportion of staff who have completed graduate or professional school

10. Measures: Program-Level Predictors • Staff Opinions about the SDPI-DP Program • Teamwork and leadership • Belief and knowledge about the program • Time and effort burden on staff • Staff Experience Retaining Participants • Participant lack of interest • Appropriateness of content and focus • Participant lack of transportation, childcare or eldercare • Staff Experience with Other Staff in the Organization • Lack of support from the organization • Staff turnover

11. Statistical Analysis • Short-term retention analyses • Bivariate associations between retention and participant-level predictor variables were assessed using χ2 tests and t-tests • Bivariate associations between retention and program-level predictor variables were assessed using χ2 tests and generalized estimation equation (GEE) models (in order to control for clustering of participants within sites) • Final multivariate GEE model was constructed by: (1) entering all predictors with a bivariate p value of < .20 and (2) eliminating terms that did not remain significant at p < .25 with an iterative stepwise procedure

12. Statistical Analysis • Long-term retention analyses • Survival analyses were conducted and hazard ratios were calculated • Final multivariate Cox Regression model was constructed by: (1) entering all predictors with a bivariate p value of < .20 and (2) eliminating terms that did not remain significant at p < .25 with an iterative stepwise procedure • Models controlled for clustering of participants within sites

13. SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 2 Meeting 1 Results

14. Reasons Participants Became Inactive

16. Participant-Level Predictors of Short-Term Retention

21. Program-Level Predictors of Short-Term Retention

24. Cautions – Interpreting “Box” Graphs • All graphs are designed so that an odds ratio or a hazard ratio of greater than 1 signifies a greater risk for becoming inactive • Because the predictor variables were not standardized prior to analysis, no direct comparisons between predictors can be made based on the magnitude of odds ratios or hazard ratios

25. Odds Ratio (of becoming inactive)

26. Short-Term Retention Final Model

27. Odds Ratio (of becoming inactive) Income Reference: ≥$50k 3.135

28. Participant-Level Predictors of Long-Term Retention

29. Hazard Ratio (of becoming inactive) Education Status Reference: < HS Employment Status Reference: Employed Marital Status Reference: Married Income Reference: < $15k

30. Hazard Ratio (of becoming inactive)

31. Hazard Ratio (of becoming inactive)

32. Program-Level Predictors of Long-Term Retention

33. Hazard Ratio (of becoming inactive) User Population Size Reference: Small

34. Hazard Ratio (of becoming inactive)

35. Long-Term Retention Final Model

36. Hazard Ratio (of becoming inactive) Employment Status Reference: Unemployed/student Marital Status Reference: Never married User Population Size Reference: Large

37. SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 2 Meeting 1 Conclusions

38. What Participant-Level Factors Predict Short-Term Retention Success? • Age (older)# • Gender (female)# • Higher education • Employed or retired • Higher income# • Higher comorbidity# • Non smoker • Presence of family support person# • Less pain# • Higher print literacy • Greater numeracy # Significant when included in the final multivariate model

39. What Program-Level Factors Predict Short-Term Retention Success? • Medium user population size • Total participants accrued ≤ 50 • Average age of staff members ≥ 40 years • Proportion female staff ≤ 70% • Proportion of staff completing graduate or professional school ≥ 50% • Staff believing participants have interest in program # Significant when included in the final multivariate model

40. What Participant-Level Factors Predict Long-Term Retention Success? • Age (older)# • Gender (female) • Higher education • Employed# or retired • Married / live together • Higher income • Lower FBG# • Lower BMI • Presence of family support person • Lower Kessler Distress • Less pain# • Action stage for exercise # Significant when included in the final multivariate model

41. What Program-Level Factors Predict Long-Term Retention Success? • Small user population size# • Average age of staff members ≥ 40 years • Staff believing participants had adequate transportation, childcare or eldercare# • Staff believing participants have interest in program # Significant when included in the final multivariate model

42. Discussion