1. Maximizing Retention in an Urban Prospective Cohort Study Elaina Murray, BS 1 ; Kate Beatty, MPH 1 ; Louise F. Flick, DrPH 1 ; Michael Elliott, PhD 1 ; Lisa V. John, PhD 2 ; Vetta Sanders Thompson, PhD 3 ; Allison King, MD, MPH 3 ; Laura Bernaix, PhD, RN 4 ; Candi LeDuc, RN 4 ; Elizabeth Lacy, RN 4 ; Kristi Helmkamp, RN 2 ; Amanda S. Harrod, MPH 1 ; Nikki Weinstein, MSW 2 1 Saint Louis University, 2 Battelle Memorial Institute, 3 Washington University in Saint Louis, and 4 Southern Illinois University-Edwardsville

2. Presenter Disclosures The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months: Elaina Murray No relationships to disclose

3. National Children’s Study (NCS) St. Louis City Location Background NCS will examine the effects of the environment on the growth, development, and health of children across the US, following them from before birth until age 21 St. Louis City: Recruiting door-to-door in randomly selected segments (neighborhoods) Segments, groups of 8 to 25 city blocks distributed around city Eligible women were pregnant or trying to conceive Retaining participants in multi-year prospective cohort studies presents challenges, especially in urban settings Early identification of participants at risk for attrition may enhance retention 3

4. Research Questions 4 Can we predict missed appointments from SES characteristics? Is the use of a subjective risk ranking better at predicting risk than SES characteristics alone?

5. Methods 5 Subjective Risk Ranking of participants Data collectors assigned participants to either low, medium, or high risk of loss to follow-up High risk characteristics: Chaotic family life Very busy Disengaged from the Study Financial or housing crisis

6. Methods Data Set SES variables pulled from larger NCS database (N =97) Race - White - Black Age - 18-24 - 25-34 - 35-44 Education - Less than HS or HS grad - Some college or more Risk Ranking Low/Medium High # missed appointments None One or more 6

7. Statistical Analysis 7 Used IBM SPSS Statistics 20 Preliminarily used Chi-square analysis to look for associations Binary logistic regression to develop propensity scores to summarize SES variables (DV=risk ranking, IV = race, age, & education) Binary logistic regression to develop 4 models (DV = missed appointments): Model 1: IV = Race, age, and education Model 2: IV = Risk only Model 3: IV = Race, age, education, and risk Model 4: IV = Propensity score and risk

8. Statistical Analysis Propensity score Typically used to match cases/controls Also used to control for demographic variables by creating one score to account for all the SES variables - Used when not interested in the effects of each SES variable alone - Good for small sample sizes, increases power 8

9. Statistical Analysis 9 Propensity score (cont.) Ran logistic regression using risk ranking as outcome - Included all SES/demographic variables as predictors of risk ranking Saved predicted probabilities to use as propensity score for each participant

10. Results Total (%)Missed Appointmentsχ2χ2 None (%)One or more (%) Race, N=67 White50.740.833.3χ 2 =4.307 df=1 * Black49.359.266.7 Age, N=67χ 2 =.572 df=2 18-2434.532.240.0 25-3450.052.544.0 35-4415.515.316.0 Education, N=67χ 2 =.116 df=1 <HS or HS Graduate 44.645.841.7 Some college or more 55.454.258.3 Risk Ranking, N=72χ 2 =8.698 df=1 ** Low/Medium75.984.552.4 High24.115.547.6 Table 1. χ 2 Analysis of Demographics & Risk with Number of Missed Appointments *p<.05; **p<.01; ***p<.001

11. Table 2. Logistic regression: Predicting risk ranking to develop propensity score (N = 67) *p<.05; **p<.01; ***p<.001 Results CovariatesModel Propensity Score a O.R. (95% CI) N67 Intercept.354 Race WhiteReference Black4.738 (.406 – 5.221) Age 18-24Reference 25-34.393 (.96 – 1.611) 35-441.915 (.265 – 13.856) Education <HS or HS GraduateReference Some college or more.282 (.070 – 1.142) -2 Log likelihood61.163 Model Χ 2 Χ 2 =15.885 df=4 ** LR Stat Nagelkerke R 2.306

12. Results CovariatesModel 1 Demographics Only a O.R. (95% CI) Model 2 Risk only c O.R. (95% CI) Model 3 Demographics and Risk a O.R. (95% CI) Model 4 Risk and propensity score a O.R. (95% CI) N677267 Intercept.295.224.121.224 Race WhiteReference---Reference--- Black2.787 (.913 - 8.508)---1.456 (.406 – 5.221)--- Age 18-24Reference---Reference--- 25-34.796 (.231 – 2.739)---1.076 (.257 – 4.507)--- 35-441.309 (.242 – 7.087)---1.105 (.144 – 8.483)--- Education <HS or HS GraduateReference---Reference--- Some college or more.973 (.289 – 3.276)---1.912 (.426 – 8.574)--- Propensity Score--- 1.566 (.066 – 37.334) Risk Ranking Low/Medium---Reference High---4.949 (1.626 – 15.062)**6.109 (1.495, 24.955)*4.782 (1.2 – 19.051)* -2 Log likelihood85.95583.45671.30272.336 Model Χ 2 Χ 2 =4.254 df=4 Χ 2 =8.035 df=1 **Χ 2 =8.603 df=5 Χ 2 =7.569 df=2 * Nagelkerke R 2.080.141.173.153 *p<.05; **p<.01; ***p<.001 Table 3. Hierarchical logistic regression of DV of missed appointments

13. Limitations Rating of risk done at one point in time, based on all experiences Length of time considered varied Based on a relatively low total number of visits (1 to 7) Ratings done by 3 nurses, do not have measure of inter-rater reliability 13

14. Conclusions Using the propensity score to control for demographics worked Using the subjective risk ranking is a better predictor of missed appointments than using demographics alone 14