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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Proceedings of

The Use of Data in Achieving Health Equity for 

Individuals with Intellectual and Developmental 

Disabilities:

Developing the 2020-2030 Roadmap for Improving 

Data on Individuals with Intellectual and 

Developmental Disabilities

November 15, 2019

Washington, DC

Administration for Community Living

Health and Human Services Administration
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Participants

Alexandra Bonardi, Human Services Research Institute

Eric Carbone, National Center on Birth Defects and Developmental Disabilities, CDC, HHS

Kathy Cargill-Willis, Administration for Community Living

Samantha Crane, Autistic Self Advocacy Network

Daniel Davis, Administration for Community Living

Tawara Goode, Georgetown University Center for Excellence in Developmental Disabilities

Vicki Gottlich, Administration for Community Living

Susan Havercamp, Ohio State University Center for Excellence in Developmental Disabilities

Julie Hocker, Administration on Disabilities, Administration for Community Living

Theresa Nguyen, Administration on Disabilities, Administration for Community Living

Sherri Larson, University of Minnesota Center for Excellence in Developmental Disabilities 

Amie Lulinski, Coleman Institute for Cognitive Disabilities, University of Colorado

Meagan Khau, Office of Minority Health, Centers for Medicare and Medicaid Services

Barbara Kornblau, Coalition for Disability Health Equity, Florida A&M University

Gloria Krahn, College of Public Health & Human Sciences, Oregon State University

Bill Marton, Office of the Assistant Secretary for Planning and Evaluation, HHS

Donna Meltzer, National Association of Councils on Developmental Disabilities

Kimberly Mills, Virgin Islands University Center for Excellence in Developmental Disabilities

Andrew Morris, Administration for Community Living, HHS

Arun Natarajun, Center for Innovation & Partnership, Administration for Community Living, 

HHS

Maggie Nygren, American Association on Intellectual and Developmental Disabilities

Amanda Reichard, National Institute on Disability, Independent Living and Rehabilitation 

Research, Administration for Community Living, HHS

Lok Wong Samson, Office of the Assistant Secretary for Planning and Evaluation, HHS

Robin Troutman, National Association of Councils on Developmental Disabilities

Julie D. Weeks, National Center for Health Statistics, CDC, HHS

Nachama Wilker, National Disability Rights Network

Allison Cruz, Administration on Disabilities, Administration for Community Living, HHS
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Ann Sommers, National Council on Disability

Elizabeth Leef, Administration for Community Living, HHS

Heidi Eschenbacher, Institute on Community Integration, University of Minnesota

Lisa Grubb, National Council on Disability

Mallory Rapalyea, Colby-Sawyer College Health Studies Program: Public Health and 

Psychology, Intern: Coalition for Disability Health Equity

Sandra Pettingell, Institute on Community Integration, University of Minnesota

Wilma Roberts, Administration on Disabilities, Administration for Community Living, HHS

Jennifer Johnson, Administration on Disabilities, Administration for Community Living, HHS
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Welcome and overview

Jennifer Johnson, Administration on Disabilities, Administration for Community Living

“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.” 

- Surgeon General’s report in 2002 on health disparities in the ID/DD population.

Jennifer Johnson (Administration on Disabilities, Administration for Community Living, HHS)

provided the background to today’s meeting. ACL engaged in discussions more than 3 years 

ago on the need for updated prevalence rates for the ID/DD population, which evolved to 

improving data to assess health equity. Better data on health of the ID/DD population can 

help in targeting resources and development of policies and programs.

Previous work traces back to 2009, when a similar group outlined steps to get better data on 

ID/DD population. The five steps of the 2009-2019 plan are nearly achieved: (1) defining 

ID/DD for health surveillance (2) synthesize what has been done, (3) extend analyses of 

existing data, (4) conduct state or regional demonstrations and (5) expand national 

surveillance. CDC is currently supporting projects for multi-state demonstrations and ACL is 

partnering with several other agencies to expand national surveillance. 

A summit of multiple stakeholders in 2017 recommended next steps on further improving 

health surveillance data: (1) collaborate with NCHS to develop questions for national surveys 

that identify ID/DD respondents, and (2) examine opportunities in state and administrative 

data for a deeper understanding of health data. Two workgroups developed the following 

reports, each of which suggests potential next steps for improved data: 

• National surveys—agency collaborations and identification questions

https://aclprdep01.azureedge.net/cdn/ff/1iflcHhToo9FRTlfo47Yw-wct5dWXSf_RHQqY0XJhI/1569854188/public/Aging%20and%20Disability%20in%20America/National_Data

_Paper_AIDD-ACL_09.25.2019%20508%20compliant.pdf

• State level—administrative data and promising practices

https://aclprdep01.azureedge.net/cdn/ff/OATVJImFLs5gbVqdM6iUN0xHELxw4oogEah3HlsULA/1569854246/public/Aging%20and%20Disability%20in%20America/Final_State_Da

ta_Paper_09.25.2019%20word%20master%20508%20compliant.pdf

• Easy Read Summary

https://aclprdep01.azureedge.net/cdn/ff/iD18_yTvSrqXwljihVXNfKNANn5WjPXP9ySdpJZNQU

M/1569854262/public/Aging%20and%20Disability%20in%20America/Easy_Read_Final_Version--

How_to_improve_health_surveillance_of_people_with_IDD%209.25.2019%20508%20compliant.

pdf

In addition, AAIDD published a special journal issue on health surveillance and improving data 

for ID/DD population (https://www.aaiddjournals.org/toc/mere/57/5). Arising from these 

discussions, ACL is collaborating with NCHS to develop and pilot-test several questions to 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

identify the ID/DD population in NHIS. Today’s meeting is to provide an update on 

developments and to initiate planning of a road map for 2019-2029.

Panel I – Availability of data now and on the horizon

Moderator: Barbara Kornblau

Participants: Eric Carbone, Julie Weeks, Amanda Reichard, Bill Marton, Meagan Khau, 

Lok Wong Samson

Barbara Kornblau introduced the panel members and posed the questions of what data do 

they have? How are they using it? How to get data more available and shared?

Eric Carbone (National Center on Birth Defects and Developmental Disabilities, 

NCBDDD/CDC/HHS) provided an overview of related work at CDC. This includes the network 

of 19 state disability and health programs that include (but are not limited to) a focus on 

adults with ID; two national centers – National Center for Health, Physical Activity and 

Disability (NCHPAD) and Special Olympics Healthy Athletes; and the multi-state Medicaid data 

project where 10 states are examining their states’ Medicaid data. The Medicaid multi-state 

data project is intended to: 1) identify patterns of health care inequities and underutilization 

for people with ID/DD, and estimate costs of potentially avoidable emergency room use 

through better management of diabetes, epilepsy, cardiovascular disease and other 

conditions; 2) improve access to services and evidence-based interventions through informing 

Special Olympics and the network of CDC-funded state programs (e.g., health promotion 

videos about self-management of diabetes); and 3) provide evidence to support policies and 

leverage resources, e.g., possible designation of ID/DD as a medically underserved population

(MUP).

Challenges have included getting multi-state approvals and clearances for data use and

publications, concerns about data quality and validity for ID populations, (e.g., diagnostic 

miscoding, variations across state programs), and coordination across CDC centers and 

institutes and across federal HHS agencies. Future directions include looking at different 

states’ Medicaid policies impacting people with disabilities, increased attention to cooccurring mental health conditions and chronic illnesses, developing tools and resources to 

train health care providers and caregivers, and leveraging CDC Notice of Funding 

Opportunities (NOFOs) across centers and institutes to address health and access disparities of 

the ID/DD population.

Julie Weeks (National Center for Health Statistics, NCHS/CDC/HHS) affirmed the lack of

current, national population-based data on people with ID/DD. The last comprehensive data 

came from the 1994-5 NHIS Disability supplement, a massive effort that would now be costprohibitive to repeat. The issue is then: “what can we add to national surveys that will 

identify this population and provide information about them from other data in surveys?” 

Challenges include that people with ID/DD are a very small target population; NCHS has never 

cognitively tested questions for this population; there are varying definitions of ID/DD; and 

living arrangements make it difficult to find people with ID/DD. Cognitive testing is needed to 

determine how they or their proxies understand the questions, and what quality of data will 

result. A partnership between ACL and NCHS’s Collaborating Center on Question Design and 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Evaluation Research (CCQDER) has developed key questions that are being cognitively tested 

now, as well as work to better understand their responses to all other questions on the 

survey. The planners started with the legislative definitional elements of DD from the 

Developmental Disabilities Act, considered what elements are already included in the NHIS 

questions, and added questions like age of onset. 

The project of developing and testing questions is itself a project that will be an additional 

product. The staff are considering a series of questions: how do respondents understand the 

question? In what context of their daily life? Do they bring different understanding based on 

their living arrangements? Are there linguistic variations? What are the methods of recalling 

information and forming answers? Recruitment of adults with ID/DD is one of the biggest 

challenges faced by CCQDER. ACL has funded inclusion of age-of-onset question in 2020 NHIS, 

so survey data will be available even as the question’s cognitive validity is being tested.

Amanda Reichard (National Institute on Disability, Independent Living and Rehabilitation 

Research, NIDILRR/ACL/HHS) reported on findings based on collaborations across CMS and ACL 

to examine Medicare claims data for people with ID/DD. The goal was to provide 

demographics and health outcomes for ID/DD and compare them with the non-ID/DD

population. They combined 3 CMS data sets – Chronic Conditions Data Warehouse, Master 

Beneficiaries summary file, and Geographic Variations data for 2016 for fee-for-service 

recipients. This included 30 million persons supported by Medicare fee-for-service with both 

Parts A and B, and 1.56% (almost 484,000 people) were designated ID/DD. The ID/DD

population was identified through a CMS algorithm of ICD codes that included 5 diagnostic 

subgroups – autism spectrum disorder, cerebral palsy, intellectual disability, learning 

disabilities, and other developmental delays and compared them with adults who qualified for 

Medicare due to age (over 65). About 3/4 of ID/DD population was under age 65. 

Findings indicated a high prevalence of chronic conditions and of multiple chronic conditions.

Co-occurring mental health conditions were common, with 59% of the ID/DD population 

having at least one mental health condition and 17% had 3 or more. Most common were

anxiety, major depressive disease, and schizophrenia and other psychotic disorders. For 

example, the general population shows about a 3% prevalence of schizophrenia, whereas for 

the ID/DD population it’s 24% and we know so little about why that is. The study showed 

value of using Medicare claims data as a complement to other methods for health 

surveillance. Separate information for the ID/DD population shows prevalence and 

demographics along with health outcomes such as overuse of emergency rooms and 

hospitalizations for ambulatory care sensitive conditions. Next steps will drill down further 

into the CMS databases to examine questions like what mental health conditions are most 

prevalent among ASD population. CMS is refining ICD codes, and then we can re-run analyses

for even more information, and inclusion of questions in the Medicare Current Beneficiary 

Survey to identify ID/DD populations.

Bill Marton (Office of the Assistant Secretary for Planning and Evaluation, ASPE/HHS)

provided an update on work with Helen Lamont. They are interested in collecting disability 

data using internet panels. These data are crucial for policy-makers and would replace doorto-door household surveys which have strengths of representativeness but are very expensive

and not as timely. Internet panels offer data collection in a new way; they can be a platform 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

for collecting information on any topic — voting, consumer preferences, program 

participation, and other topics. Survey administrators can send out surveys instantaneously to 

15-20,000 respondents. The current work looked at quality of information — validity, 

reliability — compared to traditional panels as benchmarks. They specifically examined GFK 

and NORC AmeriSpeak panel surveys. GFK is address-based, constructed from large roster of 

addresses, respondents recruited and then responses weighted; NORC is recruited by mail, 

with follow-up in person for representation. GFK is around 50,000 respondents, including 

Hispanic panels for targeted surveys; NORC is around 25-30,000 respondents and growing. 

They examined traditional survey measures against 2 internet panels and noted significant 

variations among different panel types, not just with internet panels. Some difference arise 

from mode of survey — telephone vs in-person data collection, and so much depends on types 

of questions fielded. A technical expert panel reviewed findings and suggested continuation 

of this technical work to address statistical questions and methodologies, e.g., sample 

recruitment, use of proxies, loss to follow-up, and recruitment procedures. They are now 

working with NCHS to develop a series of questions via both telephone and internet panel to 

look at mode effects, qualitative analysis of results, look for differences from NHIS, and for 

differences in subgroups.

Meagan Khau (Office of Minority Health, Centers for Medicare and Medicaid Services, HHS), 

provided an overview of the new Transformed Medicaid Statistical Information System (TMSIS) that is being rolled out. It represents a collection of utilization and claims data to 

provide robust statistical information for monitoring and oversight of Medicaid and CHIP for 

73 million Americans. Currently covering data from 2014-16, it includes beneficiary eligibility 

and enrollment (demographic data that includes disability), provider enrollment, service 

utilization, service claims and managed care data. CMS has just created a series of data sets 

for analytics for T-MSIS with beneficiary-level data available under data user agreement for 

external data users. 

Of interest to this group, the disability-need supplemental file includes beneficiaries who 

require long-term services and support, became eligible in the conception-to-birth option, 

received other government program assistance, or had a home and community-based services 

(HCBS) chronic condition, lock-in provider, disabling condition, or third-party liability

coverage. HCBS includes age, physical, intellectual, developmental or other disability,

autism, mental illness, brain injury, HIV/AIDS, or technologically dependent/medically 

fragile. The 2016 data on race and ethnicity has 20.3% missing data nationally, an 

improvement from the previous year of 30% missing data. Data from 2017 and 2018 should be 

available in 2020. External researchers apply for access through CMS ResDAC (Research Data 

Assistance Center). CMS will release documentation to guide researchers to access data.

Lok Wung Samson (Office of the Assistant Secretary for Planning and Evaluation, HHS)

described a project to update and expand existing claims algorithms to identify people with 

functional disabilities including ID/DD. Project goals are to build on existing Medicaid and 

Medicare claims algorithms to better identify patients’ functional status and to expand to 

include information from electronic health records (EHR) that will allow better capture of 

clinical information. It builds on work done by CMS in establishing Chronic Conditions 

Warehouse administrative claims data sets. Persons with functional disabilities are at higher
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

risk for poorer health outcomes, but lack of data limits targeting of interventions, 

implementing proactive services, and evaluation of effectiveness. These data are especially 

needed as value-based purchasing trends will incentivize providers for measures of outcomes 

instead of services. 

Persons with functional disabilities have great care needs and drive a lot of costs; we want to 

improve measures to adjust for functional status. ASPE is establishing a project advisory task 

force and looking for expert input. The current focus is a validation study looking at postacute care assessment data linked to Medicare claims data, focusing on dually enrolled 

beneficiaries. Post-acute care data already includes much functional information, especially 

for people using skilled nursing facilities and home health care. We will validate with 

electronic health records (EHRs) including standardized assessments, screening tools, with the 

intent of making predictive analytics more usable for this population. Frailty, also known as

geriatric syndrome, emerges through reductions in abilities such as walking speed, grip 

strength, and clinical markers for aging. Predictive algorithms have been developed for 

decline in function. The Chronic Conditions Warehouse includes ID/DD, and they are seeking 

input on other conditions not already included and other data sources that could increase 

data available. For example, mental health and behavioral disorders often co-occur but are 

not addressed by clinicians. 

Responses—Perspectives from the territories, the federal 

government, and the states

Moderator: Barbara Kornblau

Participants: Kimberly Mills, Jennifer Johnson, Arun Natarajan

Kimberley Mills (Virgin Islands University Center for Excellence in Developmental Disabilities)

reported that the Virgin Islands (VI) is one of five US territories with human populations. US VI 

were transferred to the US in 1917. “Territory” refers to land purchased by another country, 

with each territory having a different history of ownership and colonization after indigenous 

settlement. Territorials are usually US citizens (except for American Samoa), but non-voting 

in Presidential elections. Territories have local government with the operating agreement 

through the Organic Act. Medicaid in the territories is not funded in the same way as in the 

states, but territories saw increased funding under the Affordable Care Act. 

Dr. Mills noted that the purpose of data collection is not just to conduct interesting analyses,

but its purpose is to change people’s lives. Despite the VI not having massive data sets, they 

still use data to create programs and change lives. One example was using clinical data and 

working with parents of autistic children and a senator to get funding for autism treatment in 

VI. Another example used data from her own dissertation to sponsor a Crime Reduction 

Symposium showing that people with disabilities are disproportionately involved in the 

juvenile justice system. This led to legislation that reduces sentences in return for increasing 

education level. In terms of disability data sources, VI uses some T-MSIS data, 2010 and 2020 

census, Cornell University’s Disability Status Report, Kids Count, and other data sets that they 

gather. With a total population of 100,000, everyone knows everyone, so all it takes is a few 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

people to get things done. She stated that people from VI want their data included with the 

rest of the states — they don’t want to be an addendum.

Jennifer Johnson (Administration on Disabilities, Administration for Community Living, HHS)

noted that this meeting was convened because people with ID/DD are invisible in data, and 

we want to use existing data or create new data to make people with ID/DD visible. From a 

federal perspective, ACL will focus on 3 areas. The first is federal leadership in ensuring

equity in data. The ACL leadership role is ensuring equity for ID/DD population through all of 

the federal government. They are charged with oversight of programs authorized under the 

Developmental Disabilities Assistance and Bill of Rights Act and improving quality of life. But 

work is done in the absence of good information on prevalence of the DD population and their 

health status. Knowing that health is vital to everyday life, it’s remarkable that we don’t 

invest more in understanding their health. We need to recommit to ensuring health equity in 

data sets that include people with ID/DD. We need to see this population as part of the 

broader population and their right to participate as part of the national population. ACL 

currently invests in longitudinal studies about where people live, their employment status, 

and their long-term services and supports; but don’t invest in longitudinal data on health. ACL 

should be held accountable for lack of health data equity. 

Our second focus is to engage with people with lived experiences. We do a disservice to the 

community by not engaging them in developing their own future. Third, is the importance of 

data to federal agencies — we need better data to make more informed decisions. Just 

consider: what could we as a federal government do? Agencies could spend money more 

wisely and effectively. With quality measurement in health care, we could use data on 

critical incidents and follow-up; it could inform our debate on home and community-based 

services; and determine the true cost of care in achieving better health outcomes. But we 

don’t have the data. The federal government is making an effort but we need resources; 

various agencies support some of the work but we need an infrastructure that coordinates. 

And good data needs to change over time as our systems change.

Arun Natarajan (Center for Innovation and Partnership, Administration for Community 

Living, HHS and Office of the National Coordinator Health IT) provided insights from his 

former position in managing Washington DC’s waiver program for services for people with 

ID/DD. Funding primarily comes via Medicaid and in home and community-based services, 

using 3 authorities: 1915 (c) waiver, 1915 (i) state plan amendment, or receiving state plan 

services under 1115 (c) demonstration. 

Mr. Natarajan noted that there is lots of data in multiple systems around assessments to 

qualify for Medicaid, authorization for paid and unpaid services, critical incidents, 

information produced by state-level systems of adult and child protective services, schools, 

rehabilitation services. Records may be on paper or electronic and may be hard to access. 

Data is often stuck in data silos – like little “data lakes” that are not interoperable—and don’t

create a national picture. On the other hand, almost every waiver program in the US has 

“point of intake” data in electronic format; this includes assessment, service planning, 

eligibility determination, access and outreach, individual selection of providers for paid and 

unpaid services. Additional data are collected on providers and provider processes, and a 

survey of beneficiary experience of care. The Health IT Stack graphic shows elements
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

required for a functioning health IT ecosystem from the federal government’s view. A 

challenge is that state Medicaid agencies often develop systems in isolation from ID/DD

programs. Use cases provide a view into longitudinal care for health care outcomes —

longitudinal care management, episodic care management, improved service coordination 

among providers, outreach and preventative services, and quality measurements for basis of 

payment. Screening processes can provide new data.

Discussion on availability of data

One focus was on what’s needed to have better interfaces across the isolated data sets and 

the importance of including human services in these frameworks. A concern is that many 

current measures document process and not outcomes, reflecting the purpose of data 

collection to document compliance with federal requirements. Participants noted a shift in 

thinking at national and state level to measuring individual outcomes, and infrastructure 

shifts to building a culture of data collection. Another issue is state data capacity—findings of 

a focus group study showed states don’t have staff for proper data collection and may not 

understand what they are actually measuring. States lost over 200,000 positions in the recent 

recession and have still not recovered. Further, at the state level, the DD division is often in a 

different department from Medicaid, and the complexity of state systems is hard to navigate 

when people aren’t working together. While National Core Indicators has 46 states’ 

participation, there is high staff turnover so impact of staff training could be limited. At the 

national health data level – are we still invisible there?

Potential strategies included attending to the measurement tools, ability to aggregate data 

across states, and explaining data to federal agencies. Can we be more proactive and change 

what the federal agencies ask states to collect? Should we be looking at international data 

and develop more consensus on which outcome data to collect? Are there best practice 

models? The need was raised for a national center for health data about people with ID/DD

that would assist states in using data. We need creativity on infrastructure, because states 

don’t have resources. T-MSIS may be useful for comparative analytics of claims eligibility as 

potential areas for national benchmarking.

Summary of issues from the sessions on data availability:

Gloria Krahn (Oregon State University) summarized key themes from the morning that can 

inform next steps on roadmap for better data.

The first relates to excitement about the availability of new data sources, especially through 

Medicare and Medicaid, and including those with dual eligibility. There is a need to 

understand how the populations included in these programs overlap and where they differ

(like Venn diagrams), as we try to build a data mosaic that paints the picture on health of 

people with ID/DD.

A second issue is the variability of data across states, quality of data at state and national 

levels, comparability in quality of data, and currency of data. Comparability of samples across 

data sets emerged as in issue in the November 2017 summit and came forward again now.

These data are described as “data lakes” and our task will be to portage across these lakes. 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

A third issue related to interoperability of data and how that may change in the future. 

A fourth issue as subtext for today’s discussion relates to health. Once we shift to a focus on 

health of the ID/DD population, we get engaged with a new set of systems (health records, 

health services administration research) and matching up with new data elements. As we 

investigate health of people with ID/DD we encounter issues of co-occurring health 

conditions, including emerging findings on mental health data. These are powerful data that, 

in some states, are only now being considered together. Human services that address social 

determinants of health need to be included in these considerations.

A fifth issue is definitional around ID and DD—when are they the same, and when do they 

differ? Following the DD Act definition of DD, a substantial group of people with ID do not 

qualify as DD because they don’t meet the criterion of substantial difficulties in at least three 

areas. 

The sixth area relates to “what can we learn from other countries? The special issue of ID/DD

on health surveillance includes a paper from Western Australia and Manitoba (Canada) that 

demonstrates linking administrative data on health and other services for people with 

intellectual disabilities. The UK and reports from its Learning Observatory have additional

approaches to offer, noting that this work is all based on some type of national health system.

Finally, collaboration across agencies is essential. ACL is currently coordinating efforts in HHS

to pursue equity in health in data by leveraging relationships across agencies, finding common 

ground and common outcomes to pursue. A decade ago when a group similar to today’s met in 

Kingston Ontario Canada, they felt similarly challenged for developing a roadmap for 

improved data. Those efforts were highly successful in developing and then achieving the 

steps of the roadmap; we can do the same and advance a roadmap for the next decade.
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Panel II - Reaching beyond the research community: Enhancing 

the utilization, dissemination and knowledge translation of data

Moderator: Tawara Goode

Participants: Samantha Crane, Donna Meltzer, Tawara Goode

Samantha Crane (Autistic Self Advocacy Network--ASAN) described ASAN’s expertise in 

creating resources to make complex topics accessible to people with diverse disabilities.

Making data accessible increases community engagement, makes it more useful for advocates, 

complies with 504/508 requirements, and helps data become more accessible to others with 

literacy challenges, limited advocacy experience, and limited English proficiency. The 

meaning of data should be clearly explained and be relevant to our lives – not just prevalence 

of disability, but quality of life items like access to housing, employment, autonomy. A good 

example of relevant data is National Core Indicators that includes information on people with 

ID/DD disaggregated by race, employment, residence and type of care. How variables are

operationalized is important – e.g., employment isn’t just ‘yes’ or ‘no’ but ideally provides

for distinction of work below minimum wage and distinguishes non-competitive employment. 

Keep narrative in ‘plain language;’ use simple words like the top 1000 words of English. Avoid 

undefined terms, passive voice, and complex sentence structure. Each element should have

its own sentence and its own line; illustrations could be included for each line.

Example

Using online tools, try to keep reading level at Grade 3. ASAN publishes resources in EasyRead format – plain language, separated into individual lines, illustrations on each line that 

orient the reader to where they are in the document and what each line is about. Use graphs 

like people graphs for demographics; graphs using color blocks don’t work for color-blind 

people. Include text-only description for people who have visual disability or learning 

disability for visual data. Provide information in a variety of formats to communicate to 

people with different needs, e.g., text-only, Easy-Read, visual-heavy, narrated videos with 

captions. “Easy Read” relates to this specific format, while “plain language” refers to 8th -12th

grade reading level. Their website AutisticAdvocacy.org has tool kits and examples.

Donna Meltzer (National Association of Councils on Developmental Disabilities -- NACDD)

NACDD is a non-profit, non-partisan national organization that supports 56 Councils on DD in 

the U.S. Their vision is that people with DD are full participants in their communities, enjoy 

“The percentage of state respondents for whom a dual diagnosis was reported 

ranged from 34% to 64%.”

Replace with: 

“In one state 34% of people with developmental disability are dually diagnosed. 

That’s 34 out of every 100 or one third. In another state it was 64% of people.

That’s 64 out of every 100, almost two thirds. In every other state, the number 

was between 34 and 64 out of every 100 people.”
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

opportunities to achieve their potential, make their own decisions and exercise their civil 

rights. NACDD’s three functions are federal advocacy, technical assistance for member 

Councils, and addressing trends and developing projects/workgroups so that Councils can 

serve as incubators for innovative solutions. Sixty percent of Council members must be 

persons with ID/DD or family members; members are appointed by governors. Data are crucial 

in reporting to governors, state legislatures, federal funders, and to evaluate Council’s own 

progress. 

Much of the data Councils use come from the three longitudinal data sets funded by ACL, 

National Core Indicators (NCI), and Case for Inclusion reports. Councils also conduct their own 

surveys. e.g., the Ohio Council is conducting needs surveys of medical professionals, business, 

and people with lived experiences. Trends in data sharing include more data tied to 

outcomes, less about outputs; more visual support in data presentations with less narrative

and more icons. Prevalence data are crucial—consistent numbers will drive services and 

funding. If we don’t have accurate prevalence, we’re not getting appropriate funding and not 

reaching our target population. Presently we have conflicting numbers for how many persons 

there are with DD in the U.S. based on statistics from the disability supplement to the 

National Health Interview Survey (NHIS-D 1994/95), as well as more current data from CDC

and Special Olympics. Each have their own numbers. Finally, we support efforts to ensure 

data from territories are included in national data sets, and that people with ID/DD are 

considered in every type of data collection

Tawara Goode (Georgetown University Center for Excellence in Developmental Disabilities)

presented 2018 ACS data on culturally and linguistically diverse populations in the US, noting

that 5% self-identify as ‘some other race’, and 3.4% self-identify as ‘two or more races.’ 

Another factor is languages spoken in US. In particular, she emphasized ‘limited Englishspeaking households’ in census data (households in which no one over age 14 speaks only 

English or speaks it very well). Usually associated with Hispanic households, the highest rates 

of limited English-speaking households are actually among Asian/Pacific Islanders. Other 

cultural factors beyond racial, ethnic and linguistic diversity affect design and dissemination 

of information. She recommended looking at the audience’s understanding of ‘data’, their 

health literacy and numeracy literacy. Be prepared to address distrust of government sources, 

including questions of “how will the data be used? Will it be accurate? Will it portray my 

group negatively? Who’s collecting and analyzing data?” Understand the characteristics of 

your intended audiences—a one-size-fits-all template does not work in presenting 

information. How does it fit with the inherent communal knowledge as you work to achieve 

knowledge translation. One strategy is to identify voices in the community that can bridge the 

gap between communal and formal knowledge to assist it in becoming part of ‘big picture.’

Consider community expectations on the timeline for gathering and disseminating 

information, and note what issues are of interest to them. Share the consequences of refusing

to participate in data collection—they need to know what’s on the line by not participating. 

Know how information is disseminated in a community to enable the data project and engage 

community members and other stakeholders to facilitate it. Consider various methods of 

dissemination delivery – plain language document, video, and a variety of formats and 

graphics, knowing that the most effective methods may not be documents or websites.

Budget for these dissemination methods. Challenges to engaging communities in acquiring and 

disseminating data include fiscal and personnel resources, adequate time, and the willingness 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

of academics, researchers and policy makers to value community expertise and engage 

reciprocally with awareness of the power differentials.

Discussion on utilization, dissemination and knowledge translation

Groups who need to be considered are self-advocates or those with lived experience with 

ID/DD from culturally, linguistically or racially diverse communities for whom health 

disparities and mental health are significant; people who are deaf or hard-of-hearing; and 

people who are blind. In this, we need to consider the influence of different service systems.

One dissemination goal needs to be making people with ID/DD more visible in data. Creating 

visibility means explaining why it’s important to include everyone, including people with 

ID/DD. This means bringing forward the issues of those whose views are often not included—

people with limited communication and cognition. Speak in plain language so that everyone 

can see the value of participating.

DD Councils can be intermediaries for identifying the questions for analyses and use of 

stories. There was general discussion on the importance of collaborating with DD Councils 

around data collation and analyses for longer term trends, including examining and reporting 

on data to reflect systems changes.

An issue of particular concern among communities of color is distrust—of disability disclosure, 

data gathering and its dissemination. In preparation for the 2020 census, there were 

discussions with the ADA coordinator on why measured prevalence of disability has reduced

over the last decade. One factor may be non-responding or not identifying as having a 

disability. The possibility of launching an awareness campaign has been considered to explain 

the importance of answering questions accurately. Work on health disparities might offer 

strategies such as partnerships with communities, engendering trust via federally funded 

projects, and engaging communities to find out their interests, needs, and fears. Explain to 

persons with lived experiences that “because you filled out that survey, real changes

happened” – we collect stories from you and bring stories back to you. NCHS recognized their

need of communities to help tell the story of the data that they collect. Others suggested the 

need to build bridges to distill findings from surveys for use by end users with ID/DD. Ideas 

include creating and sharing tools for engaging participants. NCI is concerned about getting a 

representative group of participants. Strategies tried in some states are to prepare userfriendly version of results with indicators of interest for specific communities while still 

making the documents broadly appropriate. There is a need to explain purposes of data 

collection to representative groups so the products reflect lived experience. All data briefs 

should have a plain-language introduction and summary. The discussion concluded with the 

suggestion of adapting a truth and reconciliation model for communities that have been 

discriminated against. This includes offering an apology offered, naming the hurt, and 

acknowledging it was wrong.
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Panel III - Mapping out the future of data analysis in achieving 

health equity

Moderator: Maggie Nygren, AAIDD

Participants: Alixe Bonardi, Susan Havercamp, Julie Hocker, Gloria Krahn

Maggie Nygren (Executive Director of AAIDD) noted that the journal IDD published a special 

issue featuring papers for this meeting’s topic, with papers available online at aaidd.org.

https://www.aaiddjournals.org/toc/mere/57/. This panel was structured with moderator 

questions and panelists responding with interactive discussion

I. What would health equity look like for the ID/DD population?

Julie Hocker (Administration on Disabilities, Administration for Community Living, HHS)

stated that health equity is an important issue for AOD/ACL who has established their firstever health equity team. One focus is on education of health care providers, so that people 

with ID/DD are afforded the respect and attention that others expect via quality of care 

assessments. In the long term, ACL wants to close the life expectancy gap between the ID/DD

and general population, and will look for short-term outputs to drive long-term change. 

Alixe Bonardi (Human Services Research Institute) stated that health equity would mean that 

we would know about health and health needs of people with ID; that we would see that 

conditions that are preventable (and not related to the underlying disability) are equivalent 

and well-managed. She agreed on equitable life expectancy as a goal post, and also looking 

toward excellent medical care and quality support systems to help achieve that. Further, 

people with ID/DD and their key supports would be informed and empowered to engage in 

healthy behaviors.

Gloria Krahn (College of Public Health and Human Sciences, Oregon State University) added 

that with health equity, people with ID/DD are a desirable patient group that providers feel 

competent to treat; and for whom they are incentivized to provide excellent care. She 

noted that quality care should be incentivized so that providers and systems would provide 

quality care for persons with ID/DD – not just because it’s the right thing or the required 

thing to do, but because it’s the rewarded thing to do—that good care aligns with the 

incentives of the health system, like monitoring and reimbursement rates.

Susan Havercamp (Nisonger Center University Center for Excellence in Developmental 

Disabilities, The Ohio State University) noted the need for benchmark data: “how many 

people have a specific concern?” and then “how can we develop better projections on how to

achieve equity?” She stated the need to look at high-level benchmarks such as life expectancy 

but also at upstream issues that may affect access to supports and services that affect 

people’s health outcomes. When we look at social determinants of health, there are 

significant disparities in access to home and community-based services, with variations based 

on racial, ethnic, linguistic differences or differences in functional support needs. Finally, 

health equity means that people with ID/DD and their key supports are informed and 

empowered to engage in healthy behaviors.
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Panelists noted that engaging participants in their own care should be part of shaping health 

care design and system, and this will require specific additional targeted support. Health care 

providers may lack holistic knowledge about what it’s like for persons with disabilities to live

in their community. There was agreement that medical care is only a very small part of living 

with ID/DD. Focusing on health data equity, there is a need for disability and ID/DD

identifiers in all data sets. “Disability inclusion in data sets is a civil rights issue.” When 

disability identifiers are available, data should routinely be examined for disability 

disparities.

II. What are the metrics/indicators for measuring health equity for the 

ID/DD population, including social determinants of health?

Panelists noted that data are a tool to help us see that there are health problems at a 

population level (not individual level), where the problems are, and how big of a problem 

they are. Analyses can inform us if programs or policies are making a difference at a 

population level. In these ways, quality data are vital to good program planning, policy 

development, and fiscal projections.

We need to start by considering who’s responsible for gathering information and how data

might be available. While we need data with ID/DD indicators, we need to be mindful that 

administrative data are often collected for purposes other than health surveillance. We need 

to respect states’ right to organize their data collection and make sure metrics aren’t 

increasing burden to states.

There will be new metrics available from TMSIS data and states’ data that will allow us to

look for benchmarks relative to the general population on standard health equity measure 

(e.g., diabetes control). It’s heartening to see the progress that has been made, but there is 

still a long way to go. We may need new data collection initiatives, like longitudinal or

extended surveys. In considering healthcare assets and core outcomes, we should consider not 

only life expectancy but also incidence of big-ticket chronic diseases like diabetes, asthma, 

cardiovascular. These all really compromise life expectancy. And we need to look at social 

determinants of health like employment, neighborhood, sense of safety, access to internet. 

We need to know what the community’s own issues are (e.g., sexually transmitted 

infections). For example, findings from the deaf community indicate lower rates of 

mammograms related to their being less exposed to general information about the 

importance of mammograms.

We should consider adopting a standard public health framework to organize factors, 

including environmental exposures; and create models showing the relationship between 

individual decisions and long-term life expectancy. Access to dental care is another 

preventable issue that ties into social determinants and life expectancy. Highlight these

barriers and others, such as transportation. Special Olympics takes a holistic approach by 

including physical fitness, dental, podiatric, vision, and other areas. Our approach also needs 

to address health promotion education, measuring how we can help people live longer 

healthier lives. Inclusion of health promotion and social determinants extends where we have 

to look for data. Data for pure outcomes are still valuable but they need to be put in context 

of multiple viewpoints on health equity.
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

III. Extending the recommendations of the workgroups, what are the 

current issues, gaps and best practices and how can they be addressed 

to inform practice and policy to help achieve health equity?

Alixe identified the need for credible prevalence rates for people with ID/DD that starts with 

agreeing on definitions of ID and DD so that we can speak with one voice to make the case for 

better programs and policies. Definitions based on diagnostic categories vs functional 

limitations, and varying degrees of severity all contribute to inconsistency.

Susan added concerns about the very real limitations on interoperability of state-level data. 

There are mountains of data collected but often only usable at state or even county or 

provider levels. We have some records for health indicators but we won’t be able to look at 

access and social determinants from state-level data. There are limitations in capacity at 

state level to use data. Virginia State University is an example of linking state-collected 

Medicaid data with outcome records from NCI and Supports Intensity Scale. She noted that

proxy reporting is a very significant for this population. How can we handle proxy reporting 

vs. self-reported health status? We need to emphasize meaningful participant engagement –

people with lived experiences participating in a meaningful way.

Gloria added that we have many “pockets of data” but we need a trained workforce to 

harness data, the idea of ‘super-users’ at multiple levels who know how to link and analyze 

data. The capacity to compare findings across different data sets, depends on similar

definitions, sample populations and statistical controls. We need strategies and resources to 

compare or harmonize findings across large data sets such as NCI, Special Olympics, and 

administrative data, and for how to handle proxy-vs self-reporting. The CDC-sponsored 

Medicaid Multi-State process provided a nice example of comparing across states. Do we need 

a national center to support strategies and training? Further, accessible communications need 

to become more routine and standardized. We may need workforce training to standardize 

proxy vs. self- reporting and development of accessible materials.

Susan affirmed the importance of communicating clearly with constituents; to not get caught 

up in our ideas but hear what constituents want to know. Providing information is not about 

our own vanity in our work, but in what the recipients need or want to hear. We can learn 

from proxies on how to interpret information.

Julie noted that we need to address gaps simultaneously with getting better data; we can’t 

wait for perfect data before taking action to achieve change. We need to get to the change 

we want to see, but also need to keep sight of data while making changes as we go. One 

strategy is to remove silos among our workers for greater access and understanding of data,

reduce delays in changing course, and inform best practices. As AOD connects more with the 

medical community, it informs our decisions on what data to collect and what’s still missing. 

Data and knowledge should inform policy and programs, where money goes, and connect us 

with other arms of HHS.
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Discussion on future of data analysis

Themes in the discussion included the lack of a consistent definition of ID/DD that contributes 

to inconsistent numbers. Numbers from school data indicate around 15-19% of all children and 

youth are identified with disability but drops down substantially to 3-5% as adults. The 

workgroup used the DD Act definition of disability that focuses on functional limitations and is 

pretty conservative. In many other countries, universal health identifiers make tracking 

easier. The six question identifiers are now being asked on community surveys to uncover 

missing populations, but many interviewees with ID/DD remain completely invisible—

uncounted, unserved, and unrecognized by states. We hope that the added functional 

questions to the NHIS and other surveys will pick up some hidden ID/DD community members. 

A participant suggested that data consistency might be partially addressed by asking three 

questions of adults— “Do you have ID? DD? or ASD?” From NHIS data, we know that about 85% 

of ID/DD adults live with family members their whole lives, and household surveys can access 

this group. Another participant noted that some parts of DD Act legislative definition may 

never be operationalized in data collection, but we have to resolve on a measurement 

definition that everyone can agree with. 

Another theme was the need for a definition of health equity. The definition of Paula 

Braveman (2017) was recommended: “health equity means reducing and ultimately

eliminating disparities in health and its determinants that adversely affect excluded or 

marginalized groups”

https://www.healthaffairs.org/do/10.1377/hblog20170622.060710/full/). The range of 

disparities include availability, accessibility, acceptability, utilization, and quality. Disparities 

in health care system are not just due to ignorance or lack of experience, but can express 

implicit biases that must be addressed.

Third, there was considerable discussion on “where can people with ID/DD show up in data?” 

One participant observed that as the parent of six children with disabilities, she now realized 

that they won’t be counted anywhere – not in Medicaid or Medicare, because they are all 

working to some extent. It was suggested that they would be included in All-Payer Claims 

databases and could be identifiable if the medical notes include condition in the chart (e.g., 

CP, XYY chromosome). Another participant noted that some data are a catalyst for very fast 

change — we should look for data sets that might have powerful local effects and larger ripple 

effects, e.g., if everyone who is incarcerated gets a psychological review, it will be shocking 

to discover how many have hidden psychological illness in prison. Further, the move to 

managed care means that more of the ID/DD population will leave no footprint in 

governmental data. We will need to access All Payers Claims database to find them. The 

largest provider organizations have recently published powerful papers on autism spectrum 

disorder. Private databases are important into the future.

There was a call for getting disability and ID/DD identifiers included in other data collection

systems. There was advocacy for ID/DD being included as a demographic variable in the 

decennial census because those data are highly influential for policy and funding. The ID/DD

population is at much higher risk of poverty and other vulnerable characteristics - how can we

get them counted in the census? Others questioned whether the census is the right place to 

identify ID/DD population because it is so small relative to national population. We only have 

6 questions to address all disabilities, with nothing on psycho-social or some cognitive 
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

disabilities. With questions on the National Health Interview Survey (NHIS), you don’t have to 

do it every year and you can still get a lot of useful data. National data resources are as 

important as state data.

With respect to state data, one participant cautioned that that we assume that state agencies 

are keeping records. But some may not be, or records may be paper records retained in local 

offices and not readily accessed for aggregation. This was also noted in the workgroup report.

When looking at outcomes for people who had left state-run institutions, they were provided 

handwritten data on papers taped together. States need to do a better job, especially for 

these populations. Vital statistics records belong to the states, and the federal government 

collates that data by buying it from states. The federal government train states on how to 

collect and record information and upgrade systems. That raises the question: “what 

partnership could we create for DD data to be collected and aggregated as analogous to this 

system?”

In looking for indicators, it was generally agreed that the first step needs to be asking what 

the community wants, and then work backward to achieve it, e.g., community’s complaint 

may be that they can’t get to work or find a place to live, or “I just want a friend.” Mental 

health was cited repeatedly as an important issue to include in health surveillance data.

People with ID/DD are in mental health wards at hospitals and prisons. The staff are generally 

not trained to know how to provide care and services for them--data could help by showing 

them the range of issues they might anticipate.

The Division at CDC has materials that highlight health disparities among ID populations, 

including cardiovascular, obesity, and the relationship between mental and physical health. 

We see higher disparities in mental health. We need better research on why disparities arise, 

the role of stigma, and what can we do about them? Another participant noted that if you’re 

disabled and looking for a job for many years but can’t communicate, you’re going to get 

depressed; “we must fight to get hospitals to ask questions of ID/DD patients.”

Finally, there was considerable discussion on need for health care provider education. Adults 

with ID/DD get trapped in pediatric practices because they can’t find health care personnel in 

adult systems who understand that having a disability is part of who you are. It’s important to 

educate medical professionals so that non-pediatric ID/DD populations can get their flu shot 

from their GP or discuss their healthcare in an informative and comforting way. This is an 

even bigger problem in rural communities. One study using an iterative survey with about 100 

people with DD, parents, advocates, healthcare professionals found 95% agreement on six 

competencies for health care providers (provide link). Topics specifically mentioned included

reproductive health and mental health.

Final thoughts from all participants

In presenting data, don’t lump everyone into one category, disaggregate by racial, cultural, 

service systems.

Find ways to engage more directly with the ID/DD communities:

• Always speak from an inclusion perspective
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

• Say why supplying data is important— elicit stories and share stories of how the 

information had an impact 

• Learn from what state DD Councils are doing

• Build bridges to use advocates and professionals with disabilities who can be 

consultants to this work

• Issues of trust come up in most contexts

• Consider use of truth and reconciliation model — “name the hurt”; apology offered, 

and acknowledging errors made and changes to make (people have been excluded 

from data collection previously)

Improve ways of presenting data to non-research audiences:

• Provide information back to the ID/DD communities that participate — create 

pamphlets or infographics on questions, or basic data, and mail back to them

• Present material that is largely stripped of methods; put technical material into an 

appendix

• Not all indicators are important to all the groups; customize your materials

• Let communities and participants see their contribution

• All data presentations need to have plain language introduction and summary
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Summary: Where do we go next?

Jennifer Johnson noted that today’s meeting is the beginning for developing a roadmap for 

improved health equity data. What are thoughts for moving forward, starting with 

conversations from today’s conference to develop the roadmap through future discussions?

who else should we consult? What “clusters” of work should we consider?

The discussion identified key issues that begin to define the roadmap:

A caution

One caution raised was the need to prioritize and collaborate. Given the many issues

identified, this initiative will have to set priorities: What is most important and to whom?

What are key issues? What are populations to include? What time frame to use? What are 

intended outcomes of the initiative? Participants have discussed concrete ideas for 

establishing prevalence. Some of the other topics are already being discussed elsewhere –

what are the specific goals for this initiative and how to collaborate with related efforts?

Who needs to be part of this conversation now?

• People with DD and families, SABE: ACL is committed to start with the end and 

prioritize their needs and wants

• Diverse communities, understanding roles for better participation in data collection

• Disability groups arranged around particular communities

• Advocacy entities like Arc, Special Olympics or UCP with individual chapters that have 

connections with people in more rural environments

• State agencies and NASDDDS, Medicaid directors’ group

• Health care providers

• Health care industry, they are paying more attention to ID/D issues because of cost of 

care - e.g., All Claims Care data.

• Foundations

• Other HHS agencies; integration of disability into conversations with other agencies in 

HHS on investing in data and bringing it forward in HHS

What are the Emerging Areas to Work in?

1. Working toward a core measurement definition of ID/DD

• We have AAIDD but we need APA and some other organizations involved in 

diagnostic categorization at the table

• Has someone already done this work? If so, use their knowledge

• Look at 2017-8 NHIS and Washington Group questions on cognition – 2017 data 

showed 5% of those who reported difficulty in concentrating, remembering, 

making decisions, attributed it to ID/DD, but in 2018 this fell to 1% because 

‘decision-making’ was removed from list – this is incremental progress
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

2. For Indicators:

• Ask persons with lived experience to get indicators — use information as you go 

to create impact

• In the advocacy communities, there are different ideas from data community 

about what the communities’ issues are. Attend a CCD (Consortium of Citizens 

with Disabilities) meeting to hear their views and ask some targeted questions on 

what issues they see — useful set of inputs, how data might be used on the 

ground incrementally as it’s developed

• Return to question of what we want the data for — end-user question, what do 

they need from us? Numbers and prevalence matter to us, not to person who 

needs services, can’t get a job or education, or are facing health disparities

• Mental health has come up repeatedly today

• Sometimes we don’t know what we don’t know (e.g., it’s hard to remember that 

we didn’t know that ID/DD had much higher risk for diabetes). Examine what we 

have in available data sets 

• We may need to clean up core data and get ID/DD identifiers into data sets, 

while also looking at critical questions to ask of the data

3. For Data:

• T-MSIS opens new vistas

Both qualitative and quantitative information from the community – e.g. recent 

pain survey got 8000 responses

• Prevalence data is important

• Also longitudinal work, think creatively and don’t just add on to other surveys—

do we need panel studies?

• The data puzzle is a metaphor, we need to find and lay out all the pieces on the 

table at the same time, find overlaps and gaps across available data sets. This is 

some of the real work in finding the big picture, e.g. measuring in Medicaid, 

Medicare, state services — where and what are we not measuring?

• As we move toward managed care, how will we get data? If it’s not available via 

the states, access will be problematic.

4. For Utilization and Dissemination:

• Recognize that it is our role to give them information. We’re listening to them, 

their stories are powerful

• Communicating for different audiences in different way, with plain language as 

standard

5. For Analytics:

• Use IT expertise that already exists for interoperability of data; 

• Big Data scientists who look at data analytics in a different way could help us 

approach data management issues

• Analytic workforce capacity is a major issue at state and national levels; this 

includes interoperability, linking data sets, harmonizing findings
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Proceedings — Roadmap for Health Equity Data for People with ID/DD, November 15, 2019

Meeting Adjourned — Thank you to participants and organizers