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,

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

Context

Required Child Find function

Community programs

No history of evaluation

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)

Why Did We Study Disparity?

National focus on disparities in health care - Minority ethnicity

- Low income- Recent immigrants- Limited English proficiency

– Homelessness– Uninsured

What Demographics Predict Disparity?

Ethnicity vs. SES

Unfortunate Coincidence

Family demographics predict child delays

Same family demographics predict less access to services

Processes Studied for Equity in Access

Public Awareness: Media campaigns, brochures, public education

Identification: Parent or professional notices a child’s need

Referral to an EI program: First EI record created

Intake process at EI program Program contact with parent

Intake record created

Eligibility determination

Enrollment If eligible and parent agrees

Service record created

Demographics Studied for

Equity in Access

EnrollmentReferral

Low-Income Uninsured Immigrant Limited English proficiency Military Homeless

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

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

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.

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)

Process

Sub-Group IntakeStudy-Specific

MIS

Referral

Low-Income161 286 No data

Uninsured161 No data No data

ImmigrantNo data 286 No data

Limited English proficiency

No data 286 No data

MilitaryNo data 286 No data

HomelessNo data No data No data

Sample Sizes - Referral

Sample Sizes - Enrollment

Process Sub-Group IntakeStudy-Specific

MIS

Enrollment

Low-Income96 No data 911

Uninsured96 No data 911

ImmigrantNo data 286 No data

Limited English proficiency

No data No data 911

MilitaryNo data No data 911

HomelessNo data No data No data

Data Analysis

Determine if observed and population %’s differ using chi squared

If so, calculate the effect size using “Relative Risk”

And We Found…

Income/Public Insurance

0

10

20

30

40

05

101520253035404550

Referral

Public InsurancePoor

0

10

20

30

40

50

60

70

Expected % Observed %

Public Insurance

Enrollment

Uninsured Children

Referral Enrollment

0

1

2

3

4

5

6

7

Expected %

Observed %

0

1

2

3

4

5

6

7

Immigrants

0

2

4

6

8

10

12

Expected %

Observed %

Referral

Limited English Proficiency

0

1

2

3

4

5

6

7

Not at All Not Well orAt All

Expected %

Observed %

0

1

2

3

4

5

6

Not At All Not Well + At All

Referral Enrollment

Children in Military Families

10

12

14

Expected %Observed %

0

2

4

6

8

10

12

Referral Enrollment

Where Do We Go From Here?

Limitations– 1997 data; same in 2005?– Estimations for population comparison

data

Uninsured Children

56% less likely to be referred66% less likely to be enrolled Disparity may be over-estimatedStill a cause for concern

Limited English Proficiency

Self-report a limitation for both study and population figures

Equity in referralDisparity in enrollment possible for

families who speak only some English– Need for interpreter not recognized by

program staff?– What happens between referral &

enrollment?

Children in Military Families

Equity in referralDisparity in enrollment

– Coordination with military Exceptional Family Member Program

– What happens between referral & enrollment?

Homelessness

How can you study this without turning away needy families due to stigma?

Data Privacy

Further Study

Multiple risk factorsIncreased risk, over-

representation, or over-referral?

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?

February 28, 2005

MAHALO!

Please complete an evaluation for this session

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.