Delayed Medical Care And Unmet Care Needs Due To The COVID-19 Pandemic Among Adults With Disabilities In The US

More than sixty-one million Americans have a disability, constituting more than 18 percent of the US population.¹ Americans with disabilities experience disparities in health outcomes and health care caused by systemic barriers to care, disadvantages with respect to social determinants of health, and widespread ableism among health care professionals.^2–5 Many factors related to the COVID-19 pandemic exacerbated these inequities, including severe disruptions in access to nonemergency medical services,⁶ accessible transportation,⁷ and home health care and personal assistance services^8,9 and shortages in personal protective equipment.^9,10 Adults with disabilities also have significantly higher rates of underlying medical conditions such as coronary heart disease, respiratory disease, hypertension, diabetes, and obesity compared with their nondisabled peers.^5,11–13 These comorbid conditions, coupled with COVID-19 pandemic–related disruptions in access to medical and support services,^6–10,14 have placed adults with disabilities at greater risk for severe illness and death from COVID-19.¹⁵ Adults with intellectual or developmental disabilities and mental health conditions,^16–20 as well as adults who need support to perform activities of daily living (ADLs), such as bathing, dressing, eating, and using the toilet, and instrumental activities of daily living (IADLs), such as shopping, meal preparation, and light housework,²¹ have been particularly disadvantaged by reduced access to care and support.

Prepandemic research on health care access shows that adults with disabilities frequently delay needed medical care,¹⁶ including preventive, dental, mental health, and other specialty care services; skip medications; and forgo needed durable medical equipment or assistive technologies^{2,4,5,17–20,22–24} for a variety of reasons, such as inadequate health insurance coverage,^18,25 cost concerns,¹⁶ disability bias,²⁶ lack of disability competency among providers,²⁷ inadequate transportation, and lack of accessible health care facilities and medical diagnostic equipment.⁵

Although pandemic-related disruptions in services used by adults with disabilities have been well documented, there is little empirical research on their self-reported experiences with regard to delayed medical care and unmet needs for medical care because of the pandemic. A few studies have found disproportionate COVID-19 mortality rates among adults with intellectual and developmental disabilities and adults with mobility disabilities but did not investigate the underlying reasons.^28–30 In this study we examined unmet needs for medical care and delayed medical care because of the COVID-19 pandemic during the second half of 2020 and explored variations among adults with different types of disabilities. We hypothesized that delaying care or not receiving needed medical services because of the pandemic would be more common among adults with each type of disability than among adults with no disabilities.

Study Data And Methods

Data Source And Study Population

We analyzed data from the 2020 National Health Interview Survey (NHIS).³¹ In 2020 quarter 3 (Q3) and Q4, the NHIS added new questions specifically about participants’ COVID-19 experiences, including questions about COVID-19 diagnosis, testing, use of virtual medical appointments, and delayed and unmet need for medical care.³¹ Because of the pandemic, in March 2020 the NHIS switched from its standard practice of in-person interviewing to conducting all interviews by telephone.³² Although in-person interviewing resumed in July 2020, most interviews continued to be conducted by telephone through December 2020. The shift from in-person to all-telephone interviews resulted in a reduction in the household response rate from 60.0 percent in Q1 to 42.7 percent in Q2; the sample adult response rate declined from 57.9 percent in Q1 to 41.1 percent in Q2.³² Because of the low response rates during 2020 Q2, NHIS administrators invited approximately 20,000 adults who had completed the 2019 NHIS to also answer the 2020 NHIS questionnaire during Q3 and Q4.³³ Detailed information on the design of the 2020 NHIS is available elsewhere.³¹

Study Outcomes

The study outcomes included self-reported experiences, because of the COVID-19 pandemic, of delaying medical care, not getting needed medical care for something other than COVID-19, and not getting needed medical care at home from a health professional. They were measured as binary outcomes based on yes or no answers to the following questions: ”Was there any time when [you] DELAYED getting medical care because of the coronavirus pandemic?” “Was there any time when [you] needed medical care for something other than coronavirus but DID NOT GET IT because of the coronavirus pandemic?” ”Was there any time when [you] needed care at home from a nurse or other health professional but DID NOT GET IT because of the coronavirus pandemic?”

Disability Status

We identified disability status and disability types as measured by the NHIS, based on difficulties reported by respondents related to the following functions: seeing; hearing; walking or climbing steps; remembering or concentrating; self-care, such as washing all over or dressing (which are ADLs); and doing errands alone, such as visiting a doctor’s office or shopping (which are IADLs) (see online appendix exhibit 1 for the NHIS questions used to measure disability status).³⁴ Respondents who reported “a lot of difficulty” or “cannot do this at all” to any of the functional areas were classified as having a disability; respondents who reported “no difficulty” or “some difficulty” were classified as not having a disability. Similarly, we created variables for each disability type (vision, hearing, physical, cognitive, ADL limitation, and IADL limitation), which were independent variables of interest in separate analyses that examined unmet needs for medical care and medical care that was delayed because of the COVID-19 pandemic among adults by specific types of disabilities. The variables for each disability type were not mutually exclusive. We included respondents who reported having more than one disability type in each corresponding disability group. Because of the small sample size and concern over statistical power, for the main analysis we grouped vision or hearing disability into the sensory disability group and ADL or IADL limitations into a single ADL/IADL limitation group. Nevertheless, we conducted exploratory analyses to assess whether the experiences of those with ADL limitations were similar to the experiences of those with IADL limitations.

Covariates

Covariates included age, race and ethnicity, sex, marital status, geographic classification, education, household income, employment status, health insurance status, and whether respondents had any virtual medical appointments (either video or telephone visits) because of the COVID-19 pandemic. The latter was measured as binary outcomes (yes or no) based on an affirmative answer to the following COVID-19-related question: “Were any of your appointments done by video or by phone because of reasons related to the coronavirus pandemic?” All covariates were treated as categorical.

Statistical Analysis

We compared distribution of the covariates among adults in each disability group with distribution among nondisabled adults, using chi-square tests. We calculated the prevalence for each of the outcome indicators among adults with and without disabilities, including by disability type. We used a stepwise approach in multivariable regressions. To avoid overadjustment bias,³⁵ we first adjusted for demographic and socioeconomic characteristics including age, race and ethnicity, sex, marital status, geographic classification, education, household income, employment status, and health insurance status (model 1). In the next set of multivariable models (model 2), we added whether respondents had virtual medical appointments because of the COVID-19 pandemic. We assumed that having a virtual appointment would reduce delayed medical care and unmet needs for medical care that could result from pandemic-related declines in the availability of accessible public transportation⁷ and in the availability of care at home by nurses or health professionals.^8,9 We assessed the influence of the added covariates in each step of multivariable regressions. Because a number of model covariates (marital status, 3.10 percent; education, 0.47 percent; and insurance, 0.30 percent) had missing values, consistent with best practices,^36,37 we conducted ten multiple imputations by chained equations to impute values for the variables with missing data. All analyses adhered to the methodological standards for research using the NHIS.³¹ Specifically, NHIS adult sample weight and variance estimation variables were applied to the sample data for all analyses to create national estimates. We used Stata, version 16, for all analyses.

Limitations

Our study had some limitations. First, NHIS data are self-reported and may be influenced by recall and social desirability biases. Second, because of the shift in the NHIS survey methodology from in-person interviewing to all-telephone interviewing in 2020 Q3 and Q4, adults with vision, hearing, cognitive, or intellectual disabilities might not be well represented in the data. Underrepresentation of these populations could be due in part to a lack of accommodations needed for effective telephonic communication. Third, analysis of the disability types was limited by small sample sizes of adults in each disability group. Given the diversity in the population of people with disabilities, future research should include larger sample sizes for each of these disability groups. Increased sample sizes would provide sufficient statistical power to assess whether certain disability groups, such as adults with ADL limitations versus those with IADL limitations or blind versus deaf adults, might have greater risk of delaying or not getting needed medical care. One option for increasing sample sizes would be to combine multiple years of survey data. Fourth, as with many secondary data analyses, missing data for several covariates used in the regression analysis could have produced biased estimates because of case-wise deletion of observations with missing values for covariates. We used Rubin’s approach^36,37 and performed multiple imputations to reduce bias due to missing data.

Finally, to protect confidentiality, the NHIS does not provide state-level characteristics, thus precluding consideration of potentially important contextual variables in the analysis, such as differences in state policies and COVID-19 pandemic mitigation measures undertaken during the pandemic’s first year. Inclusion of state-level pandemic precautions could provide insight into the mediating effects of such policies on virtual medical care or delayed care. Future research could build on our study by conducting additional analyses to account for state-level contextual variables, such as state policies on social distancing and masking, and state spending allocations for long-term services and supports overall and by specific type of service or support (Medicaid home and community-based services versus institutional care). Such analyses could provide insight into whether certain state policies are conducive to continuous access to medical care during a pandemic.

Study Results

A total of 17,635 noninstitutionalized adults ages eighteen and older answered the COVID-19-related NHIS questions in 2020 Q3 and Q4 (see appendix exhibit 2 for a flowchart showing analytic sample selection),³⁴ including 15,784 nondisabled and 1,851 disabled adults.

Compared with nondisabled adults, adults with any disability and in each disability group were more likely to be older, less educated, poor, and unemployed and were less likely to be married (exhibit 1). A higher proportion of adults with any disability and in each disability group had public health insurance, a lower proportion were uninsured, and a higher proportion had at least one virtual medical care appointment because of the COVID-19 pandemic compared with their nondisabled peers.

Again compared with findings among nondisabled adults, a higher proportion of adults with any disability and in each disability group reported, because of the pandemic, delaying medical care, not getting medical care for something other than COVID-19 when they needed it, and not getting medical care at home from a nurse or other health professional when they needed it (exhibit 2, appendix exhibit 3).³⁴ The strength of association between disability status and each of the outcome indicators is reported as unadjusted and adjusted prevalence ratios, with any prevalence ratio greater than 1.00 indicating higher risk. When compared with results among nondisabled adults, the unadjusted prevalence ratios in adults with any disability were 44 percent higher (PR: 1.44) for delaying medical care, 78 percent higher (PR: 1.78) for not getting needed medical care for something other than COVID-19, and more than seven times higher (PR: 7.06) for not getting needed medical care at home from a nurse or other health professional. When we adjusted only for sociodemographic characteristics (model 1), the prevalence ratios slightly increased for delaying medical care (PR: 1.48) and slightly decreased for not getting needed medical care for something other than COVID-19 (PR: 1.73) and for not getting needed medical care at home from a nurse or other health professional (PR: 5.87).

After further adjustment for the likely pathway variable, having any medical appointments by video or telephone because of the COVID-19 pandemic (model 2), all of the prevalence ratios for delayed care and unmet care needs decreased. However, even with this adjustment, disabled adults still had roughly a 30 percent higher prevalence ratio (PR: 1.28) than nondisabled adults for delaying medical care, close to 50 percent higher (PR: 1.45) for not getting needed medical care for something other than COVID-19, and almost five times higher (PR: 4.90) for not getting needed medical care at home from a nurse or other health professional. Similar patterns were observed in each disability group when compared with nondisabled adults. Prevalence ratios for not getting needed medical care for something other than COVID-19 because of the pandemic under model 2 were highest for people with cognitive disability (PR: 1.62), followed by the prevalence ratios for those with physical disability (PR: 1.50), ADL/IADL limitation (PR: 1.45), and sensory disability (PR: 1.33). We also found significant differences by disability type in prevalence ratios for not getting needed medical care at home from a nurse or other health professional because of the pandemic. After we adjusted for all covariates, the prevalence ratios by disability type were as high as nine times (PR: 8.99) for physical disability, more than six times (PR: 6.39) for ADL/IADL limitation, nearly five times (PR: 4.59) for cognitive disability, and more than four times for sensory disability (PR: 4.42). The fully adjusted model in our exploratory analysis showed that adults with ADL limitation had a higher prevalence ratio than those with IADL limitation for delaying medical care because of the pandemic (PR: 1.34 versus 1.29) and not getting needed medical care at home from a nurse or other health professional because of the pandemic (PR: 9.89 versus 6.50) (see appendix exhibit 3).³⁴

Discussion

This study contributes new data on the impact of the COVID-19 pandemic on the timeliness of medical care and unmet care needs among people with disabilities in the US during the first year of the pandemic. In the second half of 2020, adults with disabilities were much more likely than those without disabilities to report delaying medical care, not getting needed medical care for something other than COVID-19, and not getting needed medical care at home from a nurse or other health professional because of the pandemic. These findings pertained to people with any disability as well as across disability types.

Our findings are troubling in light of the increased COVID-19-associated morbidity and mortality among people with disabilities reported in previous studies.^{29,30,38–40} Factors contributing to inequities in delaying medical care and unmet needs for medical care services among people with disabilities during the COVID-19 pandemic could include disparities in access to technology and broadband internet; economic and employment insecurities that made medical care unaffordable; reduced availability of accessible public transportation⁷ and care at home by nurses and other health professionals;^8,9 and lack of real-time communication about COVID-19 information and resources in accessible formats (such as in American Sign Language, closed captioning, and Braille text) for people with communication disabilities.⁴¹

Lack of data on disability in routinely collected public health or surveillance systems^1,42 limits understanding of the impact of the COVID-19 pandemic, making it difficult to identify the types of problems and underlying reasons for delayed medical care and unmet needs for medical care services among people with different types of disabilities. COVID-19-related health care indicators are primarily reported by age, race, and sex but are reported infrequently by disability status and not at all by type of disability.⁴² It is therefore critical to improve data collection on health and health care indicators by disability status and type of disability at all levels, including the federal, state, and health care provider levels.

Implications For Policy And Practice

Our findings add to the emerging evidence that people with disabilities were disproportionately affected by the COVID-19 pandemic.

Our findings add to the emerging evidence that people with disabilities were disproportionately affected by the COVID-19 pandemic. People with disabilities have been overlooked and have been often an afterthought in pandemic response and mitigation efforts in the US.⁴¹ Concerted efforts must be made to address the existing inequities in delayed and unmet need for medical care services among people with disabilities both during and after the COVID-19 pandemic.⁴³ These efforts will require addressing the root causes of structural inequities affecting people with disabilities, including the social, environmental, economic, and cultural determinants of health.⁴⁴

Surveillance is one of the main pillars of public health. Without a comprehensive health care surveillance system, which collects data in real time, including on outpatient and inpatient health care use, morbidity, and mortality by disability status and disability type, it will continue to be a challenge to document the full impact of the COVID-19 pandemic and future pandemics on people with disabilities. Having a disability-inclusive surveillance system would enable decision and policy makers to develop response measures to address the unique needs of each disability group during and after the pandemic. Therefore, there is an urgent need to adopt the National Council on Disability’s recommendation¹ to designate people with disabilities as a Special Medically Underserved Population under the Public Health Services Act of 1944. So designating people with disabilities would require that the National Institutes of Health establish centers of excellence for research, education, and training and engage in health disparities research by including disability status, similar to race and ethnicity and sex, as a demographic indicator.¹ Such research would help identify barriers to medical care specifically for people with disabilities and provide critical information for policy and decision makers to develop program and policy responses. To advance a disability-inclusive approach to research, electronic health record systems should be modified to incorporate standardized data on disability status, types of disabilities, and needed accommodations. These data should be included in outpatient, inpatient, pharmacy, and other medical and billing records.

Conclusion

Adults with disabilities, including those in each disability category, experienced significant disparities in delayed and unmet need for medical care in the US during the first year of the COVID-19 pandemic. Improvements in data collection on disabled Americans according to disability status and type of disability, designating people with disabilities as a Special Medically Underserved Population, and modifying electronic health record systems to incorporate standardized disability data would inform policies, programs, and interventions to achieve equitable access to high-quality medical care services that meet the needs of all people with disabilities during the COVID-19 pandemic and beyond.

ACKNOWLEDGMENTS

Sowmya Rao reports receiving financial support from Massachusetts General Hospital, Gallaudet University, the Bedford Veterans Affairs Medical Center, and the World Health Organization. Monika Mitra reports receiving financial support from Elsevier Publishing Company. This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work, for commercial use, provided the original work is properly cited. See http://creativecommons.org/licenses/by/4.0/.

NOTES

• 1 National Council on Disability. Health equity framework for people with disabilities [Internet]. Washington (DC): NCD; 2022 Feb [cited 2022 Aug 2]. Available from: https://ncd.gov/sites/default/files/NCD_Health_Equity_Framework.pdf Google Scholar
• 2 Krahn GL, Walker DK, Correa-De-Araujo R. Persons with disabilities as an unrecognized health disparity population. Am J Public Health. 2015;105(Suppl 2):S198–206. Crossref, Medline, Google Scholar
• 3 Janz HL. Ableism: the undiagnosed malady afflicting medicine. CMAJ. 2019;191(17):e478–9. Crossref, Medline, Google Scholar
• 4 Lagu T, Iezzoni LI, Lindenauer PK. The axes of access—improving care for patients with disabilities. N Engl J Med. 2014;370(19):1847–51. Crossref, Medline, Google Scholar
• 5 National Council on Disability. The current state of health care for people with disabilities [Internet]. Washington (DC): NCD; 2009 [cited 2022 Aug 2]. Available from: https://www.ncd.gov/publications/2009/Sept302009 Google Scholar
• 6 Santoli JM, Lindley MC, DeSilva MB, Kharbanda EO, Daley MF, Galloway Let al. Effects of the COVID-19 pandemic on routine pediatric vaccine ordering and administration—United States, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(19):591–3. Crossref, Medline, Google Scholar
• 7 Cochran AL. Impacts of COVID-19 on access to transportation for people with disabilities. Transp Res Interdiscip Perspect. 2020;8:100263. Google Scholar
• 8 Schwartz AE, Munsell EGS, Schmidt EK, Colón-Semenza C, Carolan K, Gassner DL. Impact of COVID-19 on services for people with disabilities and chronic health conditions. Disabil Health J. 2021;14(3):101090. Crossref, Medline, Google Scholar
• 9 Tyler D, Hunter M, Mulmule N, Porter K. COVID-19 intensifies home care workforce challenges [Internet]. Washington (DC): Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation; 2021 May 31 [cited 2022 Aug 2]. Available from: https://aspe.hhs.gov/reports/covid-19-intensifies-home-care-workforce-challenges Google Scholar
• 10 National Council on Disability. The impact of COVID-19 on people with disabilities [Internet]. Washington (DC): NCD; 2021 Oct 29 [cited 2022 Aug 2]. Available from: https://ncd.gov/sites/default/files/NCD_COVID-19_Progress_Report_508.pdf Google Scholar
• 11 Liao P, Vajdic C, Trollor J, Reppermund S. Prevalence and incidence of physical health conditions in people with intellectual disability—a systematic review. PLoS One. 2021;16(8):e0256294. Crossref, Medline, Google Scholar
• 12 Cooper SA, McLean G, Guthrie B, McConnachie A, Mercer S, Sullivan Fet al. Multiple physical and mental health comorbidity in adults with intellectual disabilities: population-based cross-sectional analysis. BMC Fam Pract. 2015;16(1):110. Crossref, Medline, Google Scholar
• 13 Dixon-Ibarra A, Horner-Johnson W. Disability status as an antecedent to chronic conditions: National Health Interview Survey, 2006–2012. Prev Chronic Dis. 2014;11:130251. Crossref, Medline, Google Scholar
• 14 Sama SR, Quinn MM, Galligan CJ, Karlsson ND, Gore RJ, Kriebel Det al. Impacts of the COVID-19 pandemic on home health and home care agency managers, clients, and aides: a cross-sectional survey, March to June, 2020. Home Health Care Manage Pract. 2021;33(2):125–9. Crossref, Google Scholar
• 15 Centers for Disease Control and Prevention. Underlying medical conditions associated with higher risk for severe COVID-19: information for healthcare professionals [Internet]. Atlanta (GA): CDC; 2020 Feb [last updated 2022 Jun 15; cited 2022 Aug 2]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/underlyingconditions.html Google Scholar
• 16 Reichard A, Stransky M, Phillips K, McClain M, Drum C. Prevalence and reasons for delaying and foregoing necessary care by the presence and type of disability among working-age adults. Disabil Health J. 2017;10(1):39–47. Crossref, Medline, Google Scholar
• 17 Lagu T, Hannon NS, Rothberg MB, Wells AS, Green KL, Windom MOet al. Access to subspecialty care for patients with mobility impairment: a survey. Ann Intern Med. 2013;158(6):441–6. Crossref, Medline, Google Scholar
• 18 Drainoni ML, Lee-Hood E, Tobias C, Bachman SS, Andrew J, Maisels L. Cross-disability experiences of barriers to health-care access: consumer perspectives. J Disabil Policy Stud. 2006;17(2):101–15. Crossref, Google Scholar
• 19 D’Addazio G, Santilli M, Sinjari B, Xhajanka E, Rexhepi I, Mangifesta Ret al. Access to dental care—a survey from dentists, people with disabilities, and caregivers. Int J Environ Res Public Health. 2021;18(4):1556. Crossref, Medline, Google Scholar
• 20 Iezzoni LI, Kurtz SG, Rao SR. Trends in Pap testing over time for women with and without chronic disability. Am J Prev Med. 2016;50(2):210–9. Crossref, Medline, Google Scholar
• 21 Department of Health and Human Services, Administration for Community Living. LongTermCare.gov: glossary [Internet]. Washington (DC): ACL [cited 2022 Aug 2]. Available from: https://acl.gov/ltc/glossary Google Scholar
• 22 Brown HK, Plourde N, Ouellette-Kuntz H, Vigod S, Cobigo V. Brief report: cervical cancer screening in women with intellectual and developmental disabilities who have had a pregnancy. J Intellect Disabil Res. 2016;60(1):22–7. Crossref, Medline, Google Scholar
• 23 Cobigo V, Ouellette-Kuntz H, Balogh R, Leung F, Lin E, Lunsky Y. Are cervical and breast cancer screening programmes equitable? The case of women with intellectual and developmental disabilities. J Intellect Disabil Res. 2013;57(5):478–88. Crossref, Medline, Google Scholar
• 24 Mitra M, Bogen K, Long-Bellil LM, Heaphy D. Unmet needs for home and community-based services among persons with disabilities in Massachusetts. Disabil Health J. 2011;4(4):219–28. Crossref, Medline, Google Scholar
• 25 Mahmoudi E, Meade MA. Disparities in access to health care among adults with physical disabilities: analysis of a representative national sample for a ten-year period. Disabil Health J. 2015;8(2):182–90. Crossref, Medline, Google Scholar
• 26 Gallegos AJ. Misperceptions of people with disabilities lead to low-quality care: how policy makers can counter the harm and injustice. Health Affairs Blog [blog on the Internet]. 2021 Apr 1 [cited 2022 Aug 2]. Available from: https://www.healthaffairs.org/do/10.1377/forefront.20210325.480382/full/ Google Scholar
• 27 Krahn GL, Drum CE. Translating policy principles into practice to improve health care access for adults with intellectual disabilities: a research review of the past decade. Ment Retard Dev Disabil Res Rev. 2007;13(2):160–8. Crossref, Medline, Google Scholar
• 28 Gleason J, Ross W, Fossi A, Blonsky H, Tobias J, Stephens M. The devastating impact of Covid-19 on individuals with intellectual disabilities in the United States. NEJM Catalyst [serial on the Internet]. 2021 Mar 5 [cited 2022 Aug 2]. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.21.0051 Google Scholar
• 29 Landes SD, Turk MA, Ervin DA. COVID-19 case-fatality disparities among people with intellectual and developmental disabilities: evidence from 12 US jurisdictions. Disabil Health J. 2021;14(4):101116. Crossref, Medline, Google Scholar
• 30 Turk MA, Landes SD, Formica MK, Goss KD. Intellectual and developmental disability and COVID-19 case-fatality trends: TriNetX analysis. Disabil Health J. 2020;13(3):100942. Crossref, Medline, Google Scholar
• 31 National Center for Health Statistics. National Health Interview Survey, 2020 survey description [Internet]. Hyattsville (MD): NCHS; 2021 Sep [cited 2022 Aug 2]. Available from: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2020/srvydesc-508.pdf Google Scholar
• 32 Dahlhamer JM, Bramlett MD, Maitland A, Blumberg SJ. Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April–June 2020 [Internet]. Hyattsville (MD): National Center for Health Statistics; 2021 Feb [cited 2022 Aug 2]. Available from: https://www.cdc.gov/nchs/data/nhis/earlyrelease/nonresponse202102-508.pdf Google Scholar
• 33 Minnesota Population Center. 2020 COVID-related NHIS study design changes, introduction of a longitudinal sample, and COVID-related content [Internet]. Minneapolis (MN): The Center; [cited 2022 Aug 2]. Available from: https://nhis.ipums.org/nhis/userNotes_covid.shtml Google Scholar
• 34 To access the appendix, click on the Details tab of the article online.
• 35 Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488–95. Crossref, Medline, Google Scholar
• 36 Rubin DB, Schenker N. Multiple imputation in health-care databases: an overview and some applications. Stat Med. 1991;10(4):585–98. Crossref, Medline, Google Scholar
• 37 Royston P, White IR. Multiple imputation by chained equations (MICE): implementation in Stata. J Stat Softw. 2011;45(4):1–20. Crossref, Google Scholar
• 38 Clift AK, Coupland CAC, Keogh RH, Hemingway H, Hippisley-Cox J. COVID-19 mortality risk in Down syndrome: results from a cohort study of 8 million adults. Ann Intern Med. 2021;174(4):572–6. Crossref, Medline, Google Scholar
• 39 Makary M. Risk factors for COVID-19 mortality among privately insured patients—a claims data analysis [Internet]. New York (NY): FAIR Health; 2020 Nov 11 [cited 2022 Aug 2]. Available from: https://s3.amazonaws.com/media2.fairhealth.org/whitepaper/asset/Risk%20Factors%20for%20COVID-19%20Mortality%20among%20Privately%20Insured%20Patients%20-%20A%20Claims%20Data%20Analysis%20-%20A%20FAIR%20Health%20White%20Paper.pdf Google Scholar
• 40 Bosworth ML, Ayoubkhani D, Nafilyan V, Foubert J, Glickman M, Davey Cet al. Deaths involving COVID-19 by self-reported disability status during the first two waves of the COVID-19 pandemic in England: a retrospective, population-based cohort study. Lancet Public Health. 2021;6(11):e817–25. Crossref, Medline, Google Scholar
• 41 Disability Rights Education and Defense Fund. DREDF calls for effective communication in COVID treatment [Internet]. Berkeley (CA): DREDF; 2020 May 29 [cited 2022 Aug 2]. Available from: https://dredf.org/2020/06/01/dredf-calls-for-effective-communication-in-covid-treatment/ Google Scholar
• 42 Reed NS, Meeks LM, Swenor BK. Disability and COVID-19: who counts depends on who is counted. Lancet Public Health. 2020;5(8):e423. Crossref, Medline, Google Scholar
• 43 White House. Fact sheet: administration announces new actions to address the needs of people with disabilities and older adults in response to and recovery from COVID-19 [Internet]. Washington (DC): White House; 2022 Feb 24 [cited 2022 Aug 2]. Available from: https://www.whitehouse.gov/briefing-room/statements-releases/2022/02/24/fact-sheet-administration-announces-new-actions-to-address-the-needs-of-people-with-disabilities-and-older-adults-in-response-to-and-recovery-from-covid-19/ Google Scholar
• 44 Weinstein JN, Geller A, Negussie Y, Baciu A, editors. Communities in action: pathways to health equity. Washington (DC): National Academies Press; 2017. Chapter 3, The root causes of health inequity; p. 99–184. Crossref, Google Scholar