Early Intervention: An Outcomes Based Evaluation of Disparity in Access

1. Early Intervention: An Outcomes Based Evaluation of Disparity in Access Taletha M. Derrington, M.A. and Beppie J. Shapiro, Ph.D. Center on Disability Studies, College of Education, University of Hawai`i www.seek.hawaii.edu, taletha@hawaii.edu, beppie@hawaii.edu,

2. Definitions • Early Intervention – Part C of IDEA, a mandated system of services for babies under age 3 with special needs (EI) • Child find – Efforts to ensure that babies with special needs are identified and referred to early intervention

3. Context • Required Child Find function • Community programs • No history of evaluation

4. Context • Infant Toddler Development Programs • Delays in 2 domains • Public Health Nursing Sections • Medical condition or single delay • Service areas • Geographically defined for rural areas • Parental choice for urban areas (2/3 state population)

5. Why Did We Study Disparity? • National focus on disparities in health care - Minority ethnicity - Low income - Recent immigrants - Limited English proficiency • Homelessness • Uninsured

6. What Demographics Predict Disparity? • Ethnicity vs. SES

7. Unfortunate Coincidence • Family demographics predict child delays • Same family demographics predict less access to services

8. Processes Studied for Equity in Access

9. Demographics Studied for Equity in Access • Enrollment • Referral • Low-Income • Uninsured • Immigrant • Limited English proficiency • Military • Homeless

10. Metric for Equity in Access • Ideally: compare # served with # in population (prevalence) • Problem: prevalence either unknown or based on # served • Assume: prevalence of EI eligible conditions evenly spread across all sub-populations % referred or enrolled = % in population

11. How we measured prevalence • Census is best population – wide data • But census does not give statistics for children aged 0 – 3 • So we had to estimate statistics for children 0 – 3 from Census statistics for children aged 0 – 18 or 0-5

12. Example: • 45,412 children aged 0 – 3/ 295,767 aged birth to 18 = .15 or 15% • If census reports 1000 children 0 – 18 are poor, we calculate 1000 X .15 = 150 children 0 – 3 are poor. • Note: new assumption – same % among poor as among total population • Expect 15% of babies referred to EI to be poor.

13. Data Sources • Intake records at EI programs (1997) • 4 ITDPs • 2 PHNs • Study-specific questions added to intake (1996-97) • 6 ITDPs • 5 PHNs • State information & referral line • Statewide EI management information system (1997)

14. Sample Sizes - Referral

15. Sample Sizes - Enrollment

16. Data Analysis • Determine if observed and population %’s differ using chi squared • If so, calculate the effect size using “Relative Risk”

17. And We Found…

18. Income/Public Insurance Referral Enrollment Poor Public Insurance Public Insurance

19. Uninsured Children Referral Enrollment

20. Immigrants Referral

21. Limited English Proficiency Referral Enrollment

22. Children in Military Families Referral Enrollment

23. Where Do We Go From Here? • Limitations • 1997 data; same in 2005? • Estimations for population comparison data

24. Uninsured Children • 56% less likely to be referred • 66% less likely to be enrolled • Disparity may be over-estimated • Still a cause for concern

25. Limited English Proficiency • Self-report a limitation for both study and population figures • Equity in referral • Disparity in enrollment possible for families who speak only some English • Need for interpreter not recognized by program staff? • What happens between referral & enrollment?

26. Children in Military Families • Equity in referral • Disparity in enrollment • Coordination with military Exceptional Family Member Program • What happens between referral & enrollment?

27. Homelessness • How can you study this without turning away needy families due to stigma? Data Privacy

28. Further Study • Multiple risk factors • Increased risk, over-representation, or over-referral?

29. Group Discussion • How can we address demographically based access barriers? • Uninsured • Limited English Proficiency • Military dependents • What can we do to address difficult-to-study demographics? • How can or should we use data collected several years before its publication?

30. MAHALO! Please complete an evaluation for this session

31. Contact & Reference Taletha M. Derrington, M.A. and Beppie J. Shapiro, Ph.D. Center on Disability Studies, College of Education, University of Hawai`i www.seek.hawaii.edu, taletha@hawaii.edu, beppie@hawaii.edu, Shapiro, B. & Derrington, T. (2004). Equity and Disparity in Access to Services: An Outcomes-Based Evaluation of Early Intervention Child Find. Topics in Early Childhood Special Education, 24(4), 199-212.