Birth Hospitals’ Role in
Access to Early Intervention
Services among
Drug-Exposed Infants
Taletha Derrington, PhD & Milton Kotelchuck, PhD, MPH
141st APHA Annual Meeting
November 4, 2013 ● Boston, MA
Policy Context
 2003
Keeping Children and Families Safe Act
(better known by it’s precursor law, CAPTA –
Child Abuse Prevention and Treatment Act)
 2004 Individuals with Disabilities Education
Improvement Act (IDEA)
2
Study Question 1
 What
are the rates and trends of Early
Intervention (EI) referrals by hospitals among
drug-exposed infants (DEI) born from 19982005?
3
Study Question 2
 Are
•
•
•
•
•
•
any of the following predictors of referral?
Neonatal abstinence syndrome (NAS) diagnosis
Toxicology screen results
Insurance type
Maternal race/ethnicity
Hospital maternity level of care
Birth hospital discharge status
4
Drug Exposed Infant Identification Algorithm
(DEIIA)
Pregnancy to Early Live Longitudinal (PELL) Data System
Maternal prenatal records
(DOB – Gestational Age)
Non-birth Hospital Discharge
CORE
Birth
Certificate
Hospital Discharge
Delivery (Mother)
Observational Stays
Emergency Department
Child post-birth records
(to age 3)
Hospital Discharge
Birth (Child)
7,348 DEI
(1.2% of births)
624,269 live births
from 1998-2005
Early Intervention Service
Records 1998-2008
4,436 referrals
(60.9% of DEI)
5
Analytic Methods
 Hospital
referral source
 Pre- to Post-Mandate differences in referral
• Chi squared
• Time series
6
Analytic Methods
 Predictors of referral
• Generalized estimating equations (GEE) logistic
regression
• Interaction analyses with “Ai-Norton” corrections for
NAS and toxicology screens
• Difference in differences to model interaction effects
7
Hospital Referrals of DEI
Pre- to Post-Mandate Differences in DEI Births Referred to EI
Percent
100%
50%
59%
66%
Pre: 1998-2003
Post: 2004-2005
17%
12%
0%
DEI Births All Referral
Sources*
DEI Births Hospital
Referral
Source*
* Chi Squared P < .01
21%
25%
DEI Referrals Hospital
Referral
Source*
8
Hospital Referrals of DEI
Pre- and Post-Mandate Time Series of DEI
Births, Referrals, & Hospital Referrals
140
Count
120
100
80
60
40
20
0
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Birth Month & Year
Mandate
Births
Referrals
Hospital Referrals
9
Predictors of Referral
Good or expected outcome
Disparity for reference group
Disparity for comparison group
Adjusted Odds Ratio a
NAS Diagnosis vs.
None***
Positive vs.
Negative Tox**
Public vs.
Private Ins***
No vs.
Private Ins*
Non-Hispanic
Black vs. NHW
Hispanic vs. NHW
Asian/Pacific
Islander vs. NHW**
Other vs. NHW
*** P < .001
** P < .01
* P < .05
0.0
0.5
1.0
1.5
2.0
2.5
Ins = insurance; NAS = neonatal abstinence syndrome; NHW =
Non-Hispanic White; Tox = Toxicology Screen
10
Predictors of Referral
Good or expected outcome
Disparity for reference group
Disparity for comparison group
Adjusted Odds Ratio a
Well-baby nursery
vs. NICU*
Special Care Nursery
vs. NICU
Transferred vs. PC***
Home Health
Discharge vs. PC***
0.0
*** P < .001
** P < .01
* P < .05
0.5
1.0
1.5
2.0
2.5
NICU = Neonatal Intensive Care Unit; PC = Parental care
a
Adjusted for: birth weight, gestational age, clinical risk factors for EI eligibility, conditions establishing EI eligibility (e.g.,
Down syndrome), maternal characteristics (age, education, and nativity), maternal custody of infant, region of residence,
rural/urban residence, and neighborhood poverty
11
NAS Diagnosis Interaction
Predicted Percent Referred
Differences in Predicted % DEI Referred by Insurance
90%
80%
80%
70%
60%
50%
24.4
11.2
No NAS
66%
55%
56%
55%
19.6
NAS Dx
40%
35%
30%
None
Public
Private
Difference in differences: None vs. Pvt. = -8.4 (P < .0001)
Pub. vs. Pvt. = 4.8 (not significant)
12
NAS Diagnosis Interaction
Predicted Percent Referred
Differences in Predicted % DEI Referred by
Birth Hospital Maternity Level
90%
80%
78%
70%
23.3
60% 18.2
25.8
No NAS
65%
50%
40%
81%
NAS
55%
55%
47%
30%
Well-Baby
Special
Care
Neonatal
Intensive Care
Difference in differences: Well-baby vs. NICU = -7.6 (P < .05)
Special Care vs. NICU = -2.5 (not significant)
13
Toxicology Screen Interaction
Predicted Percent Referred
Differences in Predicted % DEI Referred by Insurance
80%
77%
70%
60%
50%
16.8
4.3
60%
Negative
60%
Postive
56%
40%
- 3.7
40%
36%
30%
None
Public
Private
Difference in differences: None vs. Pvt. = 8.0 (not significant)
Pub. vs. Pvt. = 20.5 (P < .0001)
14
Toxicology Screen Interaction
Predicted Percent Referred
Differences in Predicted % DEI Referred by
Birth Hospital Maternity Level
80%
70%
60%
76%
15.5
62%
13.4
14.4
74%
Negative
60%
60%
50%
40%
Postive
48%
30%
Well-Baby
Special
Care
Neonatal
Intensive Care
Difference in differences: Well-baby vs. NICU = -1.1 (P < .01)
Special Care vs. NICU = 1.0 (P < .05)
15
Conclusions
 DEI
access to EI is suboptimal
• 34% of post-mandate births not referred
 Hospitals
could identify and refer most DEI
• Referred only 17% of post-mandate births
• General program improvement for all birth
hospitals needed to accelerate the weak upward
trend in referrals
16
Conclusions
 Referrals of
DEI with NAS or positive
toxicology screens should not vary across
non-clinical factors
• All children with NAS or positive toxicology
screens should be referred
• Type of insurance should not be related
• Targeted program improvement needed for
well-baby hospitals
17
Limitations

Potential under-ascertainment of referral
• EI linkage rates 84%, similar to other studies
• DEI may have lower linkage rates due to greater adoption &
mobility

Validity of key measures
• Referral source in EI data
• Toxicology screen measure on birth certificate
18
Implications for Research & Policy
 Birth
hospitals as potential universal referral
source
• Encourage birth hospitals to refer – use DEIIA
• DEIIA – feasible screening tool & should undergo
further validation studies as a research tool
 More
longitudinally linked data systems are
needed for research to inform program
improvement and policy
19
Implications for Research & Policy
 Need additional
research on EI referrals by
hospitals
• Why are DEI born to mothers with private
insurance are not being referred as often?
• Why is private insurance related to different
referral patterns for children with NAS or positive
toxicology screens?
20
Acknowledgements
This study is dedicated to the memory of
Dr. Lorraine Vogel Klerman,
an inspirational mentor and champion of students
Dissertation Committee




Marji Erickson Warfield
Jody Hoffer Gittell
Dominic Hodgkin
Milton Kotelchuck
Dissertation funding support


Nancy Lurie Marks Institute on
Disability Policy Fellowship
Grants from the Heller Alumni
Association and the Office of the
Provost, Brandeis University
I have no financial interests or disclosures
21
Thank You!
E-mail: [email protected]
Web: http://dasycenter.org
REFERENCES
Ai, C & Norton, EC. Interaction terms in logit and probit models. Economics
Letters. 2003; 80(1):123-129.
Derrington, TM. Development of the Drug-Exposed Infant Identification
Algorithm (DEIIA) and Its Application to Measuring Part C Early
Intervention Referral and Eligibility in Massachusetts, 1998–2005.
Maternal & Child Health Journal. 2012; 10.1007/s10995-012-1157-x
Norton, EC, Wang, H & Ai, C. Computing interaction effects and standard
errors in logit and probit models. State Journal. 2004; 4(2): 154-167.
22