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Published in final edited form as: Disabil Health J. 2021 Feb 18;14(3):101072. doi: 10.1016/j.dhjo.2021.101072

A brief report of the prevalence of chronic and acute health conditions among blind American adults

Nazanin M Heydarian 1,*, Yessenia Castro 1, Osvaldo F Morera 2
PMCID: PMC8516085  NIHMSID: NIHMS1675079  PMID: 33640309

Abstract

Background.

Prior research demonstrates disparities in the prevalence of certain chronic and acute health conditions among persons who are blind (PWB) compared to non-blind persons, such as diabetes and infectious diseases. However, a comprehensive understanding of the prevalence of chronic and acute health conditions among PWB is currently lacking.

Objective.

The present study addressed this gap by examining the prevalence of chronic and acute conditions among blind persons, and examining differences by gender.

Methods.

The present study surveyed 410 PWB residing in the U.S. about their health conditions and activities. Lifetime prevalence for eight chronic and six acute health conditions were estimated separately for men and women. Engagement in physical activity, regular use of medication, and satisfaction with health were also estimated.

Results.

We found that men more often reported their health conditions interfered with daily activities compared to women, as well as higher prevalence of stroke and arthritis compared to women.

Conclusion.

The current study contributes information that is vital to understanding the burden of specific health conditions on this population and necessary to understand the extent to which this burden disproportionately affects PWB.

Keywords: Blindness, chronic disease, acute condition, health behaviors, health disparities

Introduction

Persons with disabilities comprise the largest minority group in the U.S., at approximately a quarter of the U.S. population,1,2 of which 17% are persons who are blind (PWB; inclusive of blind and low vision persons). It is expected that there will be nearly 13 million PWB in the U.S. by the year 2050.3 PWB experience numerous environmental barriers and social disadvantages that likely contribute to the observed health disparities among PWB. Despite this, PWB remain a drastically understudied population and little is known about the rates of most health problems among PWB.

The limited published surveillance data indicate PWB have a higher prevalence of diabetes,2,4 arthritis,2,4 and stroke2,4 compared to non-blind persons. There are elevated rates of heart disease and hypertension among blind older adults compared with non-disabled older adults.5 PWB also experience disparities in health promoting behaviors. For example, PWB spend less time than non-blind persons engaging in health promoting behaviors such as exercise, and are more likely to report fair-to-poor health compared to non-disabled persons.5 Thus, the extant data characterizing the prevalence of health conditions and health behaviors among PWB has done much to demonstrate that PWB experience a disproportionate burden of illness. However, there remains numerous medical conditions for which prevalence data among PWB is currently lacking. For example, the prevalence data on head injuries, migraine headaches, encephalitis and meningitis, epilepsy, heart attack and bypass surgery, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease are unknown among the general population of PWB. Furthermore, prior research characterizing prevalence of chronic and acute health conditions among PWB calls for specifying prevalence by demographic characteristics such as gender, given that there are gender differences observed for chronic and acute health conditions in the general population.4

Such information that is vital to understanding the burden of specific health conditions on this population and necessary to understand the extent to which PWB are disproportionately burdened. As such, the purpose of the current study was to estimate the lifetime prevalence of health conditions as well as health events and behaviors among a sample PWB residing in the United States and to specify prevalence by gender.

Methods

Participants and Procedures

The current study is a secondary analysis of data combined from three separate studies, each of which represented a separate phase in the development of a scale to assess interactions of blind persons with healthcare providers6. Inclusion criteria and data collection procedures were the same across the three studies. Inclusion criteria were: identifying as blind/low vision/visually disabled defined as endorsement of the question: “Are you blind or have low vision?”; being over the age of 18, residing in the U.S., and ability to communicate in English (with accommodations if applicable). The single exclusion criterion was having already participated in one of the three studies. Thus, all participants across the studies are unique. Participants were recruited from email lists of stakeholder organizations, social media groups targeted at PWB, and word-of-mouth where the first author’s connections in the blind community and past research participants were asked to share information about study participation with their social networks.

Interested participants could either follow a link to an online survey available in recruitment materials or contact the researchers to complete the survey over the phone. Online participants read the informed consent form and indicated consent by choosing to proceed with the survey. Telephone participants were consented by study staff and provided verbal consent. Upon completion of the survey, participants had the option to enter into one of three raffle drawings for a $100 Amazon gift card. All questionnaires were administered in an accessible format.7 All procedures were approved by the Institutional Review Board of The University of Texas at El Paso.

Measures

Participants completed a demographics questionnaire including age, gender, education, and ethnicity. Participants were also asked about their health behaviors, health events, and health conditions8,9. Overall health was measured by three Likert-type items: 1) Compared to other people your own age, how would you rate your physical health? (1 = much worse to 5 = much better), 2) How satisfied are you with your present health? (1 = Not at all satisfied to 5 = extremely satisfied), and 3) How often do health problems stand in the way of your doing the things you want to do? (1 = Never to 5 = Always). Participants were asked whether they take medications regularly and whether they exercise regularly. Participants were asked if they had experienced significant medical events including loss of consciousness due to a head injury, number of recent (i.e., within the last five years) bone fractures, surgeries, and hospitalizations. Finally, participants indicated whether they, currently or in the past, had any of the following acute and chronic conditions: Chronic migraine headaches, Diabetes, Encephalitis or Meningitis, Epilepsy, Heart attack or bypass surgery, Multiple sclerosis, Parkinson’s disease, Rheumatoid arthritis or other autoimmune disorders, Osteoarthritis, Stroke, Alzheimer’s disease, and Dementia or other memory disorder.

Data Analysis

Proportions of missing data for each variable were computed, missing data analyses were conducted, and multiple imputation procedures were utilized to account for missing data10,11. Among variables with missing data, proportions of missing data ranged between 18.6% and 24.4%. Maximum likelihood estimation was utilized to generate 20 imputed datasets, and data was imputed for all variables with any missing data regardless of the amount of missingness. Consistent with preliminary diagnostics, each dataset was saved after 17,800 iterations of the imputation algorithm. All reported results are estimates pooled across the 20 imputed datasets.

The variables gender (1 = female, 0 = male), significant health events (i.e., head injury, bone fractures, recent surgeries, and recent hospitalizations), and all medical condition variables were treated as binary categorical variables where 1 indicates having experienced the significant medical event or health condition and 0 indicates never experiencing the event or condition. The variables of age, education, comparative health, satisfaction with health, and degree to which health problems posed barriers were treated as continuous variables. Participant characteristics were estimated for the overall sample. Health behaviors, medical events, and medical conditions were estimated separately for men and women. Estimates consisted of proportions or means as appropriate, and their corresponding 95% confidence intervals (95% CI). Analyses were conducted using Mplus software.12

Results

A total of 409 participants completed the study (273 women, 114 men, 22 with missing data on gender). See Table 1 for sociodemographic characteristics, overall health ratings, health behaviors, health events, and health conditions. Estimated proportions and 95% Cis are reported by gender.

Table 1.

Estimates and 95% Confidence Intervals for Men and Women

Men
 Women
Proportion (95% Confidence Interval)
Sociodemographics
 Age (in years)1 M = 50.27, (48.39, 52.15) M = 52.6, (49.70, 55.50)
 Education (in years) M = 17.10 (16.054, 18.15) M = 16.05 (14.42, 17.67)
 Race/Ethnicity
  White* 74.0 (68.4, 79.0) 79.6 (71.1, 86.1)
  Hispanic/Latino 7.2 (4.7, 11.0) 4.4 (1.8, 10.0%)
  Black/African American 8.4 (5.6, 12.4) 6.5 (3.1, 13.1)
Other Race 9.9 (6.9, 14.1) 8.6 (4.5, 15.8%)
 Health Behaviors and Overall Health
 Overall health rating 3.32 (3.19, 3.44) 3.41 (3.22, 3.59)
 Satisfaction with overall health 3.57 (3.44, 3.70) 3.57 (3.37, 3.78)
 Interference of health problems* 2.58 (2.46, 2.70) 2.24 (2.06, 2.42)
 Health Conditions
 Head Injury 6.8 (4.4, 10.5) 9.3 (5.3, 16.1)
 Encephalitis or meningitis 7.9 (4.9, 12.5) 7.6 (3.6, 15.1)
 Heart attack/bypass surgery 11.2 (7.5, 16.4) 6.7 (2.9, 14.7)
 Stroke* 10.0 (6.5, 15.0) 7.5 (3.61, 5.0)
 Migraine headaches* 41.7 (35.8, 47.9) 20.1 (12.8, 30.0)
 Diabetes 24.1 (19.0, 29.9) 32.4 (24.0, 42.1)
 Epilepsy 16.1 (11.5, 22.1) 12.2 (7.0, 20.6)
 Multiple sclerosis 5.7 (2.8, 11.3) 0.4 (0.0, 100.0)
 Rheumatoid Arthritis* 24.6 (19.2, 31.0) 8.8 (4.2, 17.6)
 Osteoarthritis* 30.9 (25.5, 36.8) 12.9 (7.3, 22.0)
 Alzheimer’s disease 5.3 (2.6, 10.6) 5.0 (2.0,12.0)
 Other Dementia 9.4 (6.2, 14.1) 7.3 (3.1, 16.0)
 Parkinson’s disease 4.7 (2.5, 8.6) 2.0 (0.3, 12.8)
 Health Behaviors
 Engages in exercise 15.5 (11.7, 20.3) 15.5 (9.9, 23.5)
 Takes medications 91.0 (87.0, 93.8) 84.6 (76.9, 90.1)
 Health Events
 Bone Fractures
  None 83.8 (79.0, 87.7) 87.1 (79.8, 92.1)
  Once 10.5 (7.4, 14.7) 9.3 (5.2, 16.0)
  Twice 3.2 (1.7, 6.0) 1.8 (0.5, 7.1)
  3–5 times 2.5 (1.2, 5.2) 1.7 (0.4, 6.5)
 Surgeries
  None 51.6 (45.7, 57.5) 61.5 (52.3, 69.9)
  Once 28.4 (23.3, 34.0) 20.1 (13.7, 28.4)
  Twice 12.0 (8.6, 16.3) 12.5 (7.5, 20.0)
  3–5 times 7.2 (4.7, 10.9) 5.1(2.3, 11.0)
  More than 5 0.8 (0.2, 3.1) 0.8 (0.1, 5.6)
 Hospitalizations
  None 55.5 (49.5, 61.3) 64.2 (55.1, 72.3)
  Once* 21.3 (16.9, 26.5) 7.2 (3.6, 13.8)
  Twice 11.1 (7.8, 15.5) 15.5 (10.0, 23.3)
  3–5 times 10.0 (7.0, 14.2) 7.9 (4.2, 14.4)
  6–10 times 0.7 (0.2, 2.8) 5.0 (2.3, 10.8)
  More than 10 times 1.3 (0.5, 3.7) 0.0 (0.0, 100.0)
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*

indicates those prevalence rates that are significantly different between men and women.

1.

The standard deviation for age is 15.774 and the range is 18–90 years.

Of note, there were four health conditions and one health event wherein men and women significantly differed. Among the health conditions, significantly more men (10%; 95% CI = 6.5–15.0) than women (7.5%; 95% CI = 3.61–5.0) had experienced a stroke, and significantly more men experienced migraine headaches (10.0%; 95% CI = 6.5–15.0) than women (7.5%; 95% CI = 3.61–5.0). Men experienced a greater prevalence of rheumatoid arthritis (24.6%; 95% CI = 19.2–31.0) compared to women (8.8%; 95% CI = 4.2–17.6). Men also had a significantly higher prevalence of osteoarthritis (30.9%; 95% CI = 25.5–36.8) than women (12.9%; 95% CI = 7.3–22.0). There was a greater prevalence of one recent hospitalization among men (21.3%; 95% CI = 16.9 – 26.5) compared to women (7.2%; 95% CI = 3.6–13.8).

Discussion

This study adds to the literature prevalence rates of a breadth of chronic and acute health conditions observed among PWB, specified by gender (as called for by Crews et al., 2017). The prevalence reported in this paper offers complementary and novel information.

Participant Characteristics and Overall Health

Participants demographically reflect the characteristics of the wider U.S. blind population in gender, age, and race and ethnicity.13 However, participants in this study had higher educational attainment compared to the general disability population.14,15 Furthermore, prior research shows that nearly half of PWB report fair to poor health, and poorer health when compared to non-disabled peers.2 However, in the present study, participants reported slightly positive satisfaction with their overall health and having equivalent-to-slightly better health when compared to their peers. Furthermore, participants reported that health problems moderately interfered with overall activities and men reported significantly more interference compared to women. Husser and Roberto found that women with CVD and additional chronic conditions or disabilities reported less interference in multiple domains (e.g., work, leisure, everyday activities, social activities, and interactions with friends and family) from CVD than those with CVD alone.16 Future research should characterize perceived interference of chronic and acute conditions in various domains of activity for those with disabilities and compare with those without disabilities.

Health Behaviors and Health Events

Consistent with extant research that finds persons with disabilities spend more time per day engaging in medication self-management compared to non-disabled persons,17 nearly all participants in the present study reported taking medications regularly. In contrast, self-report of physical activity was very low. Notably, the estimate observed here was lower than that observed in a previous study,4 despite a younger sample. The measure used by Crews and colleagues was more liberal than the one used in the present study (“do you participate in any regular form of exercise or activity?”). They classified participants who reported participation in light or moderate leisure-time physical activities at least once per year as engaging in physical activity. Crews and colleagues’ more liberal measure may be more appropriate for assessing older adult physical activity, but is likely prone to overreporting, particularly in a younger sample. The results from this study are significant because they reflect a qualitatively different sample than Crews and colleagues’ in that they are younger and, as such, may be more represent of those with congenital or early onset blindness as opposed to an older sample which is more likely to contain those who lost vision later on in life. There may be differences in health behaviors, chronic and acute health conditions between people with congenital/early onset disabilities compared to those who acquired disabilities later on in life.

Health Conditions

Our results contribute additional information on the prevalence of a number of chronic and acute conditions as well as specified prevalence by gender for PWB. For instance, the prevalence of stroke, diabetes, and heart attack in the present study was similar to prior reported prevalence for PWB and reflected a similar pattern by gender observed in the general population.2,4,18 In the present study, we identified that men experience a significantly more strokes than women. Compared to the general population results from the present study reflected lower overall prevalence of head injuries,19 and similar prevalence of Parkinson’s disease.2021 We found that prevalence of epilepsy among PWB is equivalent to prevalence in people with intellectual disabilities22 and greater than that reported for the general population.23 Common vascular mechanisms between diabetic retinopathy and cardiovascular disease24,25 could account for these prevalence rates observed in our sample. Also, the current sample is younger, on average, than the prior sample of PWB older adults.

There were three health conditions wherein gender differences in prevalence rates were observed among PWB. Not only was the prevalence of migraines found in the present study higher than prevalence in the general population, the gender difference was observed in the opposite direction compared to the general population26 such that male PWB have double the prevalence of female PWB. The prevalence of osteo- and rheumatoid arthritis in the present study were lower than previously reported estimates for PWB.2,4 But also, estimated rates were higher for men compared women; this pattern is opposite what has been observed in the general population (18.9% among women and 15.2% among men).2

Our results offer novel insights into the prevalence of chronic and acute conditions with no prior published prevalence for PWB. For example, to the best of our knowledge, there is no published prevalence of encephalitis or meningitis for PWB. The prevalence of meningitis and encephalitis in the present study are much higher than has been reported in the general population.27,28 To the best of our knowledge, the present study is also the first to report prevalence of multiple sclerosis and Alzheimer’s disease among PWB. We found prevalence of multiple sclerosis among PWB to be much higher than prevalence in the United States population.29 We found that Alzheimer’s disease is over three times as prevalent among PWB compared to the general population.30

Health Disparities

Secondary conditions may develop in PWB due to a lack of timely access to quality healthcare, which may exacerbate small health problems.31,32 Considering that persons with disabilities spend more time seeking healthcare from healthcare providers33 and engaging in treatment adherence,17 it may be the case that there are social and structural factors that obstruct access to health information and high-quality health care. Much of prior research primarily focuses on biological mechanisms of health conditions that may cause vision loss. Risk and prevalence of these health conditions have yet to be examined with the lens of the minority stress model of health disparities, distress and social inequality associated with minority status (e.g., having a disability) predispose a population for higher prevalence of chronic and acute health conditions.34 According to this model, distress and social inequality experienced by persons with disabilities may increase their risk of health conditions (both chronic and acute). Examining health disparities from this perspective may allow for a more complete and nuanced understanding of the association among disability and chronic disease, and will lead to more effective chronic and acute condition prevention and management intervention.

Limitations and Future Directions

The current study has some limitations. Participants were not randomly sampled, thus it is possible that those who have experienced health conditions self-selected to participate in this study, leading to an overestimation of acute and chronic conditions. Minority participants were underrepresented in the sample while White participants were overrepresented, thus we were unable to examine differences by race and ethnicity. This study did not gather information on sexual and gender identity, those with marginalized sexual and gender identities are more prevalent in the disability population compared to the general population.35,36 The current study cannot differentiate the cases wherein the visual disability was caused by a reported illness (e.g., a visual disability due to multiple sclerosis or due to other causes). Additional breaking down of characteristics would further contribute to the understanding of the intersectional experience of disability with health condition. Future research may examine the origin of the disability (acquired versus congenital or early onset) or degree of usable vision by prevalence of chronic and acute health conditions. Furthermore, we were unable to separate reports of heart attack and bypass surgery. In order to preserve participant anonymity, geographical data regarding the participants’ residences was not collected. Future research may collect geographic data such as on the state-level to both preserve participant anonymity while also accounting for variability in health conditions observed across states.37,38 Data on employment and income was also not collected in the current study, but should be considered in future work to help to understand and account for socioeconomic factors that contribute to risk for health conditions. Finally, the current study is not a comprehensive survey of health conditions. Future research may include a more exhaustive list of acute and chronic conditions.

Conclusion

The present study adds to data on prevalence rates for health conditions previously studied in PWB, and estimates prevalence for health conditions not previously available for PWB. Furthermore, this study offers prevalence specified by gender-something that has not yet been done for PWB. Understanding the prevalence of chronic and acute conditions in PWB is a critical step towards informing treatment and care to support chronic and acute health condition self-management, independence, and life satisfaction.

Acknowledgements.

The authors thank Carmel Heydarian, Mary Uribe and other research associates for their assistance with data collection. Also, the authors thank Ashley Bangert, Angela Frederick, Daniel Jones, and other dissertation committee members for their feedback and comments.

Funding. The National Heart, Lung, and Blood Institute, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Federation of the Blind national and Texas state scholarship programs.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

No conflicts of interest to disclose

References

  • 1.Brault M. People with Disabilities. Curr Popul Rep US Census Bur. Published online 2012:70–131. [Google Scholar]
  • 2.Centers for Disease Control and Prevention. National Center on Birth Defects and Developmental Disabilities, Division of Human Development and Disability. Disability and Health Data System (DHDS) Data [online]. Published 2020. Accessed April 13, 2020. https://dhds.cdc.gov
  • 3.National Eye Institute. All Vision Impairment Data and Statistics | National Eye Institute. Published 2019. Accessed September 2, 2020. https://www.nei.nih.gov/learn-about-eye-health/resources-for-health-educators/eye-health-data-and-statistics/all-vision-impairment-data-and-statistics [Google Scholar]
  • 4.Crews JE, Chou C-F, Sekar S, Saaddine JB. The prevalence of chronic conditions and poor health among people with and without vision impairment, aged≥ 65 years, 2010–2014. Am J Ophthalmol. 2017;182:18–30. [DOI] [PubMed] [Google Scholar]
  • 5.Crews JE, Campbell VA. Vision impairment and hearing loss among community-dwelling older Americans: implications for health and functioning. Am J Public Health. 2004;94(5):823–829. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Authors.
  • 7. Authors.
  • 8.Bangert AS, Reuter-Lorenz PA, Walsh CM, Schachter AB, Seidler RD. Bimanual coordination and aging: neurobehavioral implications. Neuropsychologia. 2010;48(4):1165–1170. doi: 10.1016/j.neuropsychologia.2009.11.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Fling BW, Walsh CM, Bangert AS, Reuter-Lorenz PA, Welsh RC, Seidler RD. Differential callosal contributions to bimanual control in young and older adults. J Cogn Neurosci. 2011;23(9):2171–2185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Enders CK. Applied Missing Data Analysis. Guilford press; 2010. [Google Scholar]
  • 11.Little TD, Baraldi AN, Enders CK. Missing Data Methods. Oxford University Press; 2013. doi: 10.1093/oxfordhb/9780199934898.013.0027 [DOI] [Google Scholar]
  • 12.Muthén B, Muthén BO. Statistical Analysis with Latent Variables. Wiley New York; 2009. [Google Scholar]
  • 13.Varma R, Vajaranant TS, Burkemper B, et al. Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050. JAMA Ophthalmol. 2016;134(7):802–809. doi: 10.1001/jamaophthalmol.2016.1284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.National Federation of the Blind. Blindness Statistics. Accessed December 29, 2020. https://www.nfb.org/resources/blindness-statistics
  • 15.Erickson W, Lee C, von Schrader S. Disability Statistics from the American Community Survey (ACS). Ithaca, NY: Cornell University Yang-Tan Institute (YTI).; 2017. [Google Scholar]
  • 16.Husser EK, Roberto KA. Older women with cardiovascular disease: perceptions of initial experiences and long-term influences on daily life. J Women Aging. 2009;21(1):3–18. [DOI] [PubMed] [Google Scholar]
  • 17.Anand P, Ben-Shalom Y. How do working-age people with disabilities spend their time? New evidence from the American Time Use Survey. Demography. 2014;51(6):1977–1998. [DOI] [PubMed] [Google Scholar]
  • 18.Huang Y-Y, Kung P- T, Chiu L- T, Tsai W- C. Related factors and incidence risk of acute myocardial infarction among the people with disability: A national population-based study. Res Dev Disabil. 2015;36:366–375. [DOI] [PubMed] [Google Scholar]
  • 19.Schneider AL, Wang D, Ling G, Gottesman RF, Selvin E. Prevalence of self-reported head injury in the United States. N Engl J Med. 2018;379(12):1176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson’s disease: A systematic review and meta-analysis. Mov Disord. 2014;29(13):1583–1590. [DOI] [PubMed] [Google Scholar]
  • 21.Hamedani AG, Abraham DS, Maguire MG, Willis AW. Visual Impairment Is More Common in Parkinson’s Disease and Is a Risk Factor for Poor Health Outcomes. Mov Disord. Published online 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Robertson J, Hatton C, Emerson E, Baines S. Prevalence of epilepsy among people with intellectual disabilities: a systematic review. Seizure. 2015;29:46–62. [DOI] [PubMed] [Google Scholar]
  • 23.Epilepsy Foundation. Epilepsy Statistics. Epilepsy Foundation. Accessed September 9, 2020. https://www.epilepsy.com/learn/about-epilepsy-basics/epilepsy-statistics [Google Scholar]
  • 24.Klein BE, Klein R, McBride PE, et al. Cardiovascular disease, mortality, and retinal microvascular characteristics in type 1 diabetes: Wisconsin epidemiologic study of diabetic retinopathy. Arch Intern Med. 2004;164(17):1917–1924. [DOI] [PubMed] [Google Scholar]
  • 25.Xie J, Ikram MK, Cotch MF, et al. Association of diabetic macular edema and proliferative diabetic retinopathy with cardiovascular disease: a systematic review and meta-analysis. JAMA Ophthalmol. 2017;135(6):586–593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Burch R, Rizzoli P, Loder E. The prevalence and impact of migraine and severe headache in the United States: figures and trends from government health studies. Headache J Head Face Pain. 2018;58(4):496–505. [DOI] [PubMed] [Google Scholar]
  • 27.Dubey D, Pittock SJ, Kelly CR, et al. Autoimmune encephalitis epidemiology and a comparison to infectious encephalitis. Ann Neurol. 2018;83(1):166–177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Brouwer MC, Tunkel AR, van de Beek D. Epidemiology, diagnosis, and antimicrobial treatment of acute bacterial meningitis. Clin Microbiol Rev. 2010;23(3):467–492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Graves J, Balcer LJ. Eye disorders in patients with multiple sclerosis: natural history and management. Clin Ophthalmol Auckl NZ. 2010;4:1409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Alzheimers Dement. 2018;14(3):367–429. [Google Scholar]
  • 31.Hwang K, Johnston M, Tulsky D, Wood K, Dyson-Hudson T, Komaroff E. Access and coordination of health care service for people with disabilities. J Disabil Policy Stud. 2009;20(1):28–34. [Google Scholar]
  • 32.Krahn GL, Walker DK, Correa-De-Araujo R. Persons with disabilities as an unrecognized health disparity population. Am J Public Health. 2015;105(S2):S198–S206. doi: 10.2105/AJPH.2014.302182 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.McColl MA, Shortt S, Gignac M, Lam M. Disentangling the effects of disability and age on health service utilisation. Disabil Rehabil. 2011;33(13–14):1253–1261. [DOI] [PubMed] [Google Scholar]
  • 34.Dentato MP. The minority stress perspective. Published online 2012. [Google Scholar]
  • 35.Center for American Progress. LGBT People with Disabilities.; 2019:4.
  • 36.James S, Herman J, Rankin S, Keisling M, Mottet L, Anafi M. The report of the 2015 US transgender survey. Published online 2016.
  • 37.Welch HG, Sharp SM, Gottlieb DJ, Skinner JS, Wennberg JE. Geographic variation in diagnosis frequency and risk of death among Medicare beneficiaries. Jama. 2011;305(11):1113–1118. doi: 10.1001/jama.2011.307 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Centers for Medicare and Medicaid Services. Interactive Atlas of Chronic Conditions. Published 2017. Accessed December 23, 2020. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Chronic-Conditions/Interactive_Atlas

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