Including People with ID in
National Health Surveillance
Julie Bershadsky, PhD
A 2010 Research Topic of Interest (RTOI), awarded by the Association
of University Centers on Disabilities (AUCD) and funded through a
cooperative agreement with the Centers for Disease Control and
Prevention (CDC) National Center on Birth Defects and
Developmental Disabilities (NCBDDD)
Team Members
Human Services Research Institute (HSRI)
Val Bradley, MA
Sarah Taub, MA
Julie Bershadsky, PhD
UMMS (CWM) Center for Developmental Disabilities
Evaluation and Research (CDDER)
Alexandra Bonardi, OTR/L, MHA (PI)
Emily Lauer, MPH
Steven Staugaitis, PhD
Courtney Noblett, MPH
People with Intellectual Disability Living in the U.S.
• Prevalence: 1.0-1.5% (~4 mil. people) of intellectual/
developmental disability (ID) in the non-institutionalized
U.S. population.
• Population shifting to community based since 1970’s
• Ageing population, increasing prevalence of chronic
conditions
Health of People with ID: What we know
People with ID are more likely to:
• Live with complex health conditions
• Have poorly managed chronic conditions, such as epilepsy
• Have limited access to quality health care and health
promotion programs
• Receive some cancer screenings at lower rates than the
general population
• Be obese
• Have undetected vision and hearing problems
• Have mental health problems and potential over-use of
psychotropic medications
Public Health Surveillance and
Health Disparities
“We’re invisible in the data. We can’t make
people believe we need more services if we
don’t have data to back us up.”
Participant – Surgeon General’s Conference on Health Disparities and
Mental Retardation 2001
Surveillance Gaps
• Rate of people with ID in population falls dramatically from school-age
to post-school age young adults.
• Institutionalized populations are not usually included in national
surveys. People with ID who live in larger group settings or in prisons
are excluded.
• Information on ID is not systematically collected in US health surveys.
When people with ID live in the community, they are not identifiable in
the population. Specific disparities they experience cannot be
detected.
• People with mild ID (IQ, 50-70) may be underestimated because they
receive services outside of traditional monitoring systems.
• Operational definition for ID varies across data systems, limited
capacity for aggregation or benchmarking
• People with ID may be reluctant to self-identify as having ID
Key public health questions
1.What is the relative health status of adults with
ID?
2.What are their major health risks, and how do the
risks vary for different subgroups of this
population?
3.How do access to and quality of health care relate
to health outcomes for this population?
AUCD/NCBDDDS Cooperative Agreement RTOI
Health Surveillance of Adults with ID
1. Define ID (“Who are we talking about?”)
2. Compile and synthesize a knowledge base (“What
are we talking about?”)
3. Extend past analyses of existing data sources that
capture health information for people with an ID…
4. Pilot state or regional demonstrations
5. Develop sustainable approaches to expand
surveillance
2010 RTOI: Health Surveillance in
Adults with Intellectual Disability
1.Operational definition of ID
2.Compendium of sources of data, including a
critical review of reliability, validity, and utility,
3.Expand knowledge about the population not
included in current sampling frames
Input and guidance from the Project Advisory Group
Who are we talking about?
• Eligible for state DD/ID specific services (NCI)
• Self-identify on national health survey (NHIS)
• Receive SSI/SSDI because of an intellectual
disability (SSA)
• A diagnosis of a ‘related condition’ ~ Autism, Down
Syndrome… (Medicaid/Medicare)
• People who were identified with ‘MR’ in school?
“Surveillance demands uniformity, simplicity,
and brevity”
(MMWR vol. 39/No. RR13)
1. Defining Intellectual Disability for
Health Surveillance
To date:
Review of current definitions
Summit: Developing an Operational Definition
of ID
Draft definition
Presentation at AUCD Health Frontiers
conference in May
Comment period
Definitions of Intellectual Disability
ADMINISTRATIVE (used for eligibility / funding)
• AAIDD
• Developmental Disabilities Act (DD Act)
• ICD-9, ICD-10
• DSM-IV, DSM-V
• SSI/SSDI
SURVEY
• Current Population Survey and American Community Survey
• National Health Interview Survey
• Behavioral Risk Factor Surveillance System
• Survey of Income and Program Participation
Additional sample frames
• ICF
• Special Olympics
• Metropolitan Atlanta DD Surveillance program
• Pomona Project (range)
Expert Summit to Define ID
• Multi-stakeholder participation:
Self Advocates and family members
Researchers
State ID/DD service providers
Clinicians
Input from Federal agencies
• Pre-summit materials (review of current
definitions of ID)
• One day summit to develop draft (4-13-11)
• Summit notes and draft definition
Assumptions
1. Focus on adults with intellectual disability.
2. Focus on health surveillance, recognizing that
health is one of many important areas of quality
of life.
3. Health surveillance demands a representative
population.
4. Ethical issues of labeling must be considered.
5. Definition primarily for health surveillance. Not
intended for eligibility.
Guiding Principles
 Applicable now and in the future
 Include a functional component (not solely based upon IQ
score)
 Go beyond service eligibility definitions
 To the degree possible, identify (neurodevelopmental)
cause of ID
 Refrain from putting a negative value on people with
intellectual disabilities
 Mindful of current, ongoing, and any new data collection
effrorts
Working Draft Definition for Health
Surveillance
[#1]
AND [#2]
AND [#3]
AND [#4]
IQ score approximately 70 or below,
OR a clinician has told the person that they have an ID,
OR “related condition” with support need
[Intellectual Abilities and Related conditions]
needs formal or informal support [Adaptive behavior]
diagnosed in the ‘developmental period’ [Age of onset]
expected to need some help for their entire life because
of intellectual limitations [Life-long]
OR [#5] person is eligible for State or Federal public support
programs because of intellectual disability [Support needs]
Application of the Definition
Apply working definition across data sources to
identify population by:
 Mining current administrative or survey data
and/or
 Tweaking current collection of information and/or
 New data collection (e.g. de novo survey)
Pathways to identifying the
population with ID
 1.Eligible for services because of ID?
 2.Test of intelligence, or determination by
clinician?
 3.Related condition and difficulties in learning?
 4.Special education?
Draft ‘Pathways’ model
1 State or
Federal assistance or supports
because of ID?
N
N
3 ‘Related Condition’?
N
2
Clinician determination of ID?
Y
‘Needs Support
for ADL / IADL’?
4 Received
Special Education
services?
Y
Y
?Y?
Y
Onset during
developmental
period?
N
Reason?
Y
ID (MR) or ‘Multiple Disability
including ID’?
(IDEA definition)
YES
Autism, severe learning disability, or
other related condition?
? YES ?
Temporary
Condition?
N
Population with ID for the purpose of Health Surveillance
2. Compiling a Knowledge Base
Round 1: close to 120 data sources
reviewed, both general and ID/DD specific










ID/DD group uniquely identifiable?
Included in larger disability group?
Fields that identify or potentially identify ID/DD
Possible to separate ID from DD?
Inclusion and exclusion criteria
Health-related measures
Sample methodology
Sample size
Data collection methodology
Known and potential linkages
2. Compiling a Knowledge Base
End of Round 1: narrowed to approximately
40 data sources for further critical review
Round 2: critical review








Biases in sampling?
Power?
Response rate?
Biases in health measures?
Applicability of health measures
Primary data: info reliable? Proxy used?
Secondary data: inclusion/exclusion criteria? Info reliable?
Usefulness? Potential for adaptation? Methodology? Linkage?
2. Compiling a Knowledge Base
Currently at end of Round 2.
Next steps:
Compendium of existing data sources that can
be utilized for health surveillance
Recommendations on methodology for new
surveillance efforts
Contact Information
Alixe Bonardi
Alexandra.Bonardi@umassmed.edu
Emily Lauer
Lauer.Emily@gmail.com
Julie Bershadsky
Jbershadsky@hsri.org
Sarah Taub
Staub@hsri.org