[Skip to Navigation]
Sign In
Figure.  Adjusted Odds Ratios and Incidence Rate Ratios of Timely Diagnosis and Number of Recommended Diagnostic Services by Race/Ethnicity, Individual Factors, and Contextual Factors
Adjusted Odds Ratios and Incidence Rate Ratios of Timely Diagnosis and Number of Recommended Diagnostic Services by Race/Ethnicity, Individual Factors, and Contextual Factors

Dotted line indicates the reference group estimate (White, male, mid–area deprivation index [ADI], and metropolitan). HC indicates high commute; LC, low commute.

Table 1.  Demographic Characteristics of California Medicare FFS Beneficiaries With Incident Diagnoses of Dementia or MCI
Demographic Characteristics of California Medicare FFS Beneficiaries With Incident Diagnoses of Dementia or MCI
Table 2.  Associations of Race/Ethnicity, Individual Factors, and Contextual Factors With Incident Diagnosis of Mild Cognitive Impairment vs Dementia
Associations of Race/Ethnicity, Individual Factors, and Contextual Factors With Incident Diagnosis of Mild Cognitive Impairment vs Dementia
Table 3.  Associations of Race/Ethnicity, Individual Factors, and Contextual Factors With the Number of Recommended Diagnostic Services Performed
Associations of Race/Ethnicity, Individual Factors, and Contextual Factors With the Number of Recommended Diagnostic Services Performed
1.
World Health Organization. Dementia: a public health priority. Published 2012. Accessed June 30, 2020. https://www.who.int/mental_health/publications/dementia_report_2012/en/
2.
Alzheimer’s Disease International. World Alzheimer report 2015: the global impact of dementia: an analysis of prevalence, incidence, cost and trends. Updated October 2015. Accessed June 30, 2020. https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf
3.
Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Accessed June 30, 2020. https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf
4.
US Department of Health and Human Services. National plan to address Alzheimer’s disease: 2019 update. Accessed August 12, 2020. https://aspe.hhs.gov/system/files/pdf/262601/NatlPlan2019.pdf
5.
Schneider  J, Jeon  S, Gladman  JT, Corriveau  RA, 2019. ADRD Summit 2019 report to the National Advisory Neurological Disorders and Stroke Council. Published September 4, 2019. Accessed August 12, 2020. https://www.ninds.nih.gov/sites/default/files/2019_adrd_summit_recommendations_508c.pdf
6.
Centers for Disease and Control Prevention. Advancing early detection: a healthy brain initiative issue map. Accessed August 12, 2020. https://www.cdc.gov/aging/healthybrain/pdf/Issue-Map-Early-Detection-508.pdf
7.
Cordell  CB, Borson  S, Boustani  M,  et al; Medicare Detection of Cognitive Impairment Workgroup.  Alzheimer’s Association recommendations for operationalizing the detection of cognitive impairment during the Medicare Annual Wellness Visit in a primary care setting.   Alzheimers Dement. 2013;9(2):141-150. doi:10.1016/j.jalz.2012.09.011 PubMedGoogle ScholarCrossref
8.
Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Accessed June 30, 2020. https://www.alz.org/media/homeoffice/facts%20and%20figures/facts-and-figures.pdf
9.
Dubois  B, Padovani  A, Scheltens  P, Rossi  A, Dell’Agnello  G.  Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges.   J Alzheimers Dis. 2016;49(3):617-631. doi:10.3233/JAD-150692 PubMedGoogle ScholarCrossref
10.
Tsai  RM, Boxer  AL.  Therapy and clinical trials in frontotemporal dementia: past, present, and future.   J Neurochem. 2016;138(suppl 1):211-221. doi:10.1111/jnc.13640 PubMedGoogle ScholarCrossref
11.
Schneider  J.  Research momentum brings hope for dementia in 2019.   Lancet Neurol. 2020;19(1):3-5. doi:10.1016/S1474-4422(19)30453-3 PubMedGoogle ScholarCrossref
12.
Heintz  H, Monette  P, Epstein-Lubow  G, Smith  L, Rowlett  S, Forester  BP.  Emerging collaborative care models for dementia care in the primary care setting: a narrative review.   Am J Geriatr Psychiatry. 2020;28(3):320-330. doi:10.1016/j.jagp.2019.07.015 PubMedGoogle ScholarCrossref
13.
Vos  SJ, Verhey  F, Frölich  L,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage.   Brain. 2015;138(pt 5):1327-1338. doi:10.1093/brain/awv029 PubMedGoogle ScholarCrossref
14.
Langa  KM, Levine  DA.  The diagnosis and management of mild cognitive impairment: a clinical review.   JAMA. 2014;312(23):2551-2561. doi:10.1001/jama.2014.13806 PubMedGoogle ScholarCrossref
15.
Petersen  RC, Lopez  O, Armstrong  MJ,  et al.  Practice guideline update summary: mild cognitive impairment: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology.   Neurology. 2018;90(3):126-135. doi:10.1212/WNL.0000000000004826 PubMedGoogle ScholarCrossref
16.
Arvanitakis  Z, Shah  RC, Bennett  DA.  Diagnosis and management of dementia.   JAMA. 2019;322(16):1589-1599. doi:10.1001/jama.2019.4782 PubMedGoogle ScholarCrossref
17.
US Census Bureau. Demographic turning points for the United States: population projections for 2020 to 2060. Accessed June 30, 2020. https://www.census.gov/content/dam/Census/library/publications/2020/demo/p25-1144.pdf
18.
Brewster  P, Barnes  L, Haan  M,  et al.  Progress and future challenges in aging and diversity research in the United States.   Alzheimers Dement. 2019;15(7):995-1003. doi:10.1016/j.jalz.2018.07.221 PubMedGoogle ScholarCrossref
19.
Wilkins  CH, Schindler  SE, Morris  JC.  Addressing health disparities among minority populations: why clinical trial recruitment is not enough.   JAMA Neurol. 2020;77(9):1063-1064. doi:10.1001/jamaneurol.2020.1614 PubMedGoogle ScholarCrossref
20.
Agency for Healthcare Research and Quality. 2018 National healthcare quality and disparities report: appendix A3: disparities: race and ethnicity. Accessed June 30, 2020. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr18/index.html
21.
Davis  MA, Guo  C, Sol  K, Langa  KM, Nallamothu  BK.  Trends and disparities in the number of self-reported healthy older adults in the United States, 2000 to 2014.   JAMA Intern Med. 2017;177(11):1683-1684. doi:10.1001/jamainternmed.2017.4357 PubMedGoogle ScholarCrossref
22.
Mayeda  ER, Glymour  MM, Quesenberry  CP, Whitmer  RA.  Inequalities in dementia incidence between six racial and ethnic groups over 14 years.   Alzheimers Dement. 2016;12(3):216-224. doi:10.1016/j.jalz.2015.12.007 PubMedGoogle ScholarCrossref
23.
Chen  C, Zissimopoulos  JM.  Racial and ethnic differences in trends in dementia prevalence and risk factors in the United States.   Alzheimers Dement (N Y). 2018;4:510-520. doi:10.1016/j.trci.2018.08.009 PubMedGoogle ScholarCrossref
24.
Babulal  GM, Quiroz  YT, Albensi  BC,  et al; International Society to Advance Alzheimer’s Research and Treatment, Alzheimer’s Association.  Perspectives on ethnic and racial disparities in Alzheimer’s disease and related dementias: update and areas of immediate need.   Alzheimers Dement. 2019;15(2):292-312. doi:10.1016/j.jalz.2018.09.009 PubMedGoogle ScholarCrossref
25.
Clark  PC, Kutner  NG, Goldstein  FC,  et al.  Impediments to timely diagnosis of Alzheimer’s disease in African Americans.   J Am Geriatr Soc. 2005;53(11):2012-2017. doi:10.1111/j.1532-5415.2005.53569.x PubMedGoogle ScholarCrossref
26.
Fitten  LJ, Ortiz  F, Pontón  M.  Frequency of Alzheimer’s disease and other dementias in a community outreach sample of Hispanics.   J Am Geriatr Soc. 2001;49(10):1301-1308. doi:10.1046/j.1532-5415.2001.49257.x PubMedGoogle ScholarCrossref
27.
Hill  CV, Pérez-Stable  EJ, Anderson  NA, Bernard  MA.  The National Institute on Aging Health Disparities Research Framework.   Ethn Dis. 2015;25(3):245-254. doi:10.18865/ed.25.3.245 PubMedGoogle ScholarCrossref
28.
Henning-Smith  C, Kozhimannil  K, Casey  M, Prasad  S, Moscovice  I.  Rural-urban differences in Medicare quality outcomes and the impact of risk adjustment.   Med Care. 2017;55(9):823-829. doi:10.1097/MLR.0000000000000761 PubMedGoogle ScholarCrossref
29.
Teel  CS.  Rural practitioners’ experiences in dementia diagnosis and treatment.   Aging Ment Health. 2004;8(5):422-429. doi:10.1080/13607860410001725018 PubMedGoogle ScholarCrossref
30.
Kirby  JB, Kaneda  T.  Neighborhood socioeconomic disadvantage and access to health care.   J Health Soc Behav. 2005;46(1):15-31. doi:10.1177/002214650504600103 PubMedGoogle ScholarCrossref
31.
Durfey  SNM, Kind  AJH, Buckingham  WR, DuGoff  EH, Trivedi  AN.  Neighborhood disadvantage and chronic disease management.   Health Serv Res. 2019;54(suppl 1):206-216. doi:10.1111/1475-6773.13092 PubMedGoogle ScholarCrossref
32.
Hunt  JFV, Buckingham  W, Kim  AJ,  et al.  Association of neighborhood-level disadvantage with cerebral and hippocampal volume.   JAMA Neurol. 2020;77(4):451-460. doi:10.1001/jamaneurol.2019.4501 PubMedGoogle ScholarCrossref
33.
Powell  WR, Buckingham  WR, Larson  JL,  et al.  Association of neighborhood-level disadvantage with Alzheimer disease neuropathology.   JAMA Netw Open. 2020;3(6):e207559. doi:10.1001/jamanetworkopen.2020.7559 PubMedGoogle Scholar
34.
US Census Bureau. State population by characteristics: 2010-2019. Accessed June 30, 2020. https://www.census.gov/content/census/en/data/datasets/time-series/demo/popest/2010s-state-detail.html
35.
Research Data Assistance Center. Find, request and use CMS data. Accessed June 30, 2020. https://www.resdac.org/
36.
Chronic Conditions Data Warehouse. Condition categories. Accessed June 30, 2020. https://www2.ccwdata.org/web/guest/condition-categories
37.
Filice  CE, Joynt  KE.  Examining race and ethnicity information in Medicare administrative data.   Med Care. 2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608 PubMedGoogle ScholarCrossref
38.
HealthCare.gov. Health coverage for American Indians & Alaska Natives. Accessed June 30, 2020. https://www.healthcare.gov/american-indians-alaska-natives/coverage/
39.
Taylor  DH  Jr, Fillenbaum  GG, Ezell  ME.  The accuracy of Medicare claims data in identifying Alzheimer’s disease.   J Clin Epidemiol. 2002;55(9):929-937. doi:10.1016/S0895-4356(02)00452-3 PubMedGoogle ScholarCrossref
40.
Taylor  DH  Jr, Østbye  T, Langa  KM, Weir  D, Plassman  BL.  The accuracy of Medicare claims as an epidemiological tool: the case of dementia revisited.   J Alzheimers Dis. 2009;17(4):807-815. doi:10.3233/JAD-2009-1099 PubMedGoogle ScholarCrossref
41.
Lee  E, Gatz  M, Tseng  C,  et al.  Evaluation of Medicare claims data as a tool to identify dementia.   J Alzheimers Dis. 2019;67(2):769-778. doi:10.3233/JAD-181005 PubMedGoogle ScholarCrossref
42.
Matthews  KA, Xu  W, Gaglioti  AH,  et al.  Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015-2060) in adults aged ≥65 years.   Alzheimers Dement. 2019;15(1):17-24. doi:10.1016/j.jalz.2018.06.3063 PubMedGoogle ScholarCrossref
44.
Drabo  EF, Barthold  D, Joyce  G, Ferido  P, Chang Chui  H, Zissimopoulos  J.  Longitudinal analysis of dementia diagnosis and specialty care among racially diverse Medicare beneficiaries.   Alzheimers Dement. 2019;15(11):1402-1411. doi:10.1016/j.jalz.2019.07.005 PubMedGoogle ScholarCrossref
45.
Elixhauser  A, Steiner  C, Harris  DR, Coffey  RM.  Comorbidity measures for use with administrative data.   Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004 PubMedGoogle ScholarCrossref
46.
US Department of Agriculture Economic Research Service. 2010 Rural-urban commuting area (RUCA) codes. Accessed December 15, 2019. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation/
47.
Kind  AJH, Buckingham  WR.  Making neighborhood-disadvantage metrics accessible—the Neighborhood Atlas.   N Engl J Med. 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313 PubMedGoogle ScholarCrossref
48.
University of Wisconsin Department of Medicine. Neighborhood atlas. Accessed December 10, 2015. https://www.neighborhoodatlas.medicine.wisc.edu/#about-anchor
49.
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2014. http://www.R-project.org/
50.
US Census Bureau. Asian alone or in any combination by selected groups: California. Accessed August 12, 2020. https://data.census.gov/cedsci/table?q=B02018&tid=ACSDT1Y2018.B02018&hidePreview=false
51.
Mayeda  ER, Glymour  MM, Quesenberry  CP  Jr, Whitmer  RA.  Heterogeneity in 14-year dementia incidence between Asian American subgroups.   Alzheimer Dis Assoc Disord. 2017;31(3):181-186. doi:10.1097/WAD.0000000000000189 PubMedGoogle ScholarCrossref
52.
US Census Bureau. Detailed languages spoken at home and ability to speak English for the population 5 years and over: 2009-2013. Accessed August 12, 2020. https://www.census.gov/data/tables/2013/demo/2009-2013-lang-tables.html
53.
Diamond  LC, Jacobs  EA.  Let’s not contribute to disparities: the best methods for teaching clinicians how to overcome language barriers to health care.   J Gen Intern Med. 2010;25(suppl 2):S189-S193. doi:10.1007/s11606-009-1201-8 PubMedGoogle ScholarCrossref
54.
Mehta  DH, Phillips  RS, Davis  RB, McCarthy  EP.  Use of complementary and alternative therapies by Asian Americans: results from the National Health Interview Survey.   J Gen Intern Med. 2007;22(6):762-767. doi:10.1007/s11606-007-0166-8 PubMedGoogle ScholarCrossref
55.
Mukadam  N, Cooper  C, Livingston  G.  A systematic review of ethnicity and pathways to care in dementia.   Int J Geriatr Psychiatry. 2011;26(1):12-20. doi:10.1002/gps.2484 PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    March 29, 2021

    Assessment of Racial/Ethnic Disparities in Timeliness and Comprehensiveness of Dementia Diagnosis in California

    Author Affiliations
    • 1Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco
    • 2Department of Neurology, University of California, San Francisco, San Francisco
    • 3Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas
    • 4Global Brain Health Institute, University of California, San Francisco, San Francisco
    • 5Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco
    • 6Associate Editor, JAMA Neurology
    • 7Division of Geriatrics, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison
    • 8Geriatrics Research Education and Clinical Center, Madison Veterans Affairs Hospital, Madison, Wisconsin
    JAMA Neurol. 2021;78(6):657-665. doi:10.1001/jamaneurol.2021.0399
    Key Points

    Question  Is race/ethnicity associated with timeliness of dementia diagnosis and comprehensiveness of dementia diagnostic workup?

    Findings  In this cross-sectional study of 10 472 California Medicare fee-for-service beneficiaries, Asian, Black, and Hispanic beneficiaries were less likely than White beneficiaries to receive a diagnosis of incident mild cognitive impairment vs dementia. Asian beneficiaries were also less likely to receive key elements of a diagnostic evaluation compared with White beneficiaries.

    Meaning  A lower likelihood of early diagnosis of dementia and comprehensive diagnostic workup among beneficiaries from racial/ethnic minority groups highlights key areas for public health interventions to reduce health disparities among older adults.

    Abstract

    Importance  The US aging population is rapidly becoming more racially and ethnically diverse. Early diagnosis of dementia is a health care priority.

    Objective  To examine the associations between race/ethnicity and timeliness of dementia diagnosis and comprehensiveness of diagnostic evaluation.

    Design, Setting, and Participants  This retrospective cross-sectional study used 2013-2015 California Medicare fee-for-service data to examine the associations of race/ethnicity, individual factors, and contextual factors with the timeliness and comprehensiveness of dementia diagnosis. Data from 10 472 unique beneficiaries were analyzed. The sample was selected on the basis of the following criteria: presence of 1 or more claims; no diagnoses of dementia or mild cognitive impairment in 2013 to 2014; continuous enrollment in Medicare Parts A and B; Asian, Black, Hispanic, or White race/ethnicity; and incident diagnoses of dementia or mild cognitive impairment in January through June 2015. Data analyses were conducted from November 1, 2019, through November 10, 2020.

    Main Outcomes and Measures  Timeliness of diagnosis, defined as incident diagnosis of mild cognitive impairment vs dementia, and comprehensiveness of diagnostic evaluation, defined as presence of the following services in claims within 6 months before or after the incident diagnosis date: specialist evaluation, laboratory testing, and neuroimaging studies.

    Results  The sample comprised 10 472 unique Medicare beneficiaries with incident diagnoses of dementia or mild cognitive impairment (6504 women [62.1%]; mean [SD] age, 82.9 [8.0] years) and included 993 individuals who identified as Asian (9.5%), 407 as Black (3.9%), 1255 as Hispanic (12.0%), and 7817 as White (74.6%). Compared with White beneficiaries, those who identified as Asian (odds ratio, 0.46; 95% CI, 0.38-0.56), Black (odds ratio, 0.73; 95% CI, 0.56-0.94), or Hispanic (odds ratio, 0.62; 95% CI, 0.52-0.72) were less likely to receive a timely diagnosis. Asian beneficiaries (incidence rate ratio, 0.81; 95% CI, 0.74-0.87) also received fewer diagnostic evaluation elements. These associations remained significant after adjusting for age, sex, comorbidity burden, neighborhood disadvantage, and rurality.

    Conclusions and Relevance  These findings highlight substantial disparities in the timeliness and comprehensiveness of dementia diagnosis. Public health interventions are needed to achieve equitable care for people living with dementia across all racial/ethnic groups.

    Introduction

    Dementia is a syndrome characterized by declines in cognitive, behavioral, social, and functional domains and is one of the leading causes of disability and loss of independence in older adults worldwide.1 With increasing longevity, the prevalence and burden of dementia are increasing exponentially,1,2 particularly with regard to public health costs.3 Early diagnosis of dementia is a health care priority,4-7 and its benefits include opportunities to identify causes, to inform and coordinate medical care, to enable planning for the future, to address potential safety issues, to connect families with interventions, and to identify appropriate candidates for clinical trials of potentially disease-modifying therapies that are anticipated to benefit patients in early disease stages.8-12 Mild cognitive impairment (MCI) is largely recognized as a prodromal phase of neurodegenerative disease,13 and diagnostic practice recommendations focus on diagnosis of MCI as an early stage of dementia based on results of comprehensive workup.14-16 Although specific elements of diagnostic evaluations may vary by setting and the needs of each patient, a comprehensive workup typically includes evaluation by a clinician with dementia specialty expertise and laboratory and neuroimaging studies to help identify underlying causes, including potential nonneurodegenerative causes.14-16

    Quiz Ref IDAge is the main risk factor for dementia, and, in the US, the aging population is rapidly becoming more racially/ethnically diverse.17 At the same time, racial/ethnic minority status has been associated with numerous social and economic inequalities, including poverty, lower quality of education, poorer general health, discrimination, and reduced health care access and quality.18-20 Moreover, recent findings suggest that racial/ethnic gaps in older adults’ self-reported health have widened during the past 2 decades.21 Within dementia research, a growing body of evidence highlights racial/ethnic disparities in dementia incidence22 and prevalence,23 as well as social and medical risk factors for dementia.24 In addition, undiagnosed dementia may be more common among racially/ethnically diverse individuals, particularly African American individuals25 and Hispanic and Latino individuals26 compared with White individuals. In addition, beyond race/ethnicity, there is growing recognition that social determinants of health, comorbid medical conditions, and variability in health behavior patterns are likely associated with these inequalities.3,18,19 A deeper understanding of the multidimensional mechanisms underlying health care disparities in late life is a public research priority as outlined in the National Institute on Aging Health Disparities Framework.27 Among these mechanisms, rurality has been associated with reduced access to medical care,28 including limited access to specialists and lack of community support for diagnosis and treatment of dementia among rural clinicians.29 Similarly, greater neighborhood disadvantage has been associated with limited access to care,30 poorer management of chronic disease,31 reduced cerebral gray matter volumes,32 and increased risk of Alzheimer disease neuropathologic findings among older adults in the US.33 Taken together, these findings highlight significant associations of race/ethnicity, as well as social determinants of health factors, with health care disparities among older adults. However, to our knowledge, little is known about potential racial/ethnic differences with regard to timeliness and comprehensiveness of dementia diagnosis. Addressing this gap is of critical value for informing public health and policy interventions.

    Quiz Ref IDAccording to 2019 US Census estimates, California has the largest population of older adults of any state, with approximately 11% of the total national population aged 65 years or older, and it is one of the most diverse states with regard to racial/ethnic constitution of the older adult population.34 In this study, we leveraged a 100% sample of Medicare fee-for-service beneficiaries in California with incident diagnoses of dementia or MCI to examine the associations of race/ethnicity with the timeliness and comprehensiveness of dementia diagnosis. We also examined independent associations of individual factors (age, sex, and comorbidity burden) and contextual factors (rurality and neighborhood disadvantage) with outcome variables. Finally, we explored interactions between race/ethnicity and individual and contextual variables to elucidate any potential overlapping associations of these variables with the timeliness and comprehensiveness of dementia diagnosis. Based on prior work, we hypothesized that beneficiaries who identified as Asian, Black, and Hispanic would have a lower likelihood of having an incident diagnosis of MCI vs dementia and receiving a comprehensive diagnostic workup, and that the associations between race/ethnicity and diagnostic outcome variables would remain significant after controlling for individual and contextual factors.

    Methods
    Data Source

    We used the Centers for Medicare & Medicaid (CMS) administrative enrollment and claims data for 100% of Medicare beneficiaries enrolled in the fee-for-service program in California from 2013 through 2015 (N = 6 293 386). Data for individuals enrolled in Medicare Advantage were not available. We used data from all available claim types, including carrier, durable medical equipment, home health agency, hospice, inpatient, outpatient, and skilled nursing facility. Data were obtained and used with permission from and a data use agreement with the Research Data Assistance Center.35 Data use fully complied with the requirements of the Privacy Act, the Health Insurance Portability and Accountability Act Privacy Rule, and CMS data release policies. The study was approved by the University of California, San Francisco Committee on Human Research, and informed consent was waived because the use or disclosure of the requested information did not adversely affect the rights and welfare of the beneficiaries and involved no more than a minimal risk to their privacy.

    Study Population

    We included California Medicare fee-for-service beneficiaries with incident diagnoses of either dementia or MCI who were continuously enrolled in Medicare Parts A and B from January 1, 2013, through December 31, 2015 (eTable 1 in the Supplement). Incident diagnosis was established based on methods modeled after CMS Chronic Condition algorithms36 with data from January 1, 2013, through December 31, 2014, used as a 2-year lookback period to ensure absence of either cognitive diagnosis in past claims. The dates of incident diagnoses were limited to January 1 through June 30, 2015, and a 6-month prediagnosis and postdiagnosis window was used to identify diagnostic workup services. For individuals with more than 1 diagnosis in claims files between January 1 and June 30, 2015, the earliest recorded diagnosis within this period was used.

    The primary independent variable was race/ethnicity reported in the Master Beneficiary Summary Files, including “Asian,” “Black,” “Hispanic,” and “White.” In our primary analyses, we used the Research Triangle Institute race/ethnicity codes that are derived from an imputation algorithm based on surnames, given prior evidence of better representation of Asian and Hispanic beneficiaries using this coding system.37 In sensitivity analyses, we used the Medicare Enrollment Database race/ethnicity codes that are based on self-report.37 We excluded beneficiaries whose self-reported race/ethnicity was North American Native owing to low sample size (eTable 1 in the Supplement) and the possibility of poor representation of this population in Medicare claims data in light of the availability of alternative health care coverage programs.38

    Outcomes

    We defined “timeliness of diagnosis” as a dichotomous variable of incident diagnosis of MCI (dummy coded as 1) vs all-cause dementia (dummy coded as 0). The diagnoses of MCI and dementia were determined based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes widely used in prior studies39-42 (eTable 2 in the Supplement). Quiz Ref IDWe developed the algorithm for quantifying the comprehensiveness of diagnostic workup based on published practice recommendations14-16 and defined it as a count outcome (dummy coded as 0-3) based on the presence of the following recommended services: specialist evaluation, brain health laboratory testing, and neuroimaging studies. Specialist evaluation was determined based on the published clinician type taxonomy codes,43 as reported in existing literature,44 and included the following clinician types: geriatrician, geriatric psychiatrist, neurologist, and neuropsychologist (eTable 3 in the Supplement). Laboratory testing was based on the presence of a minimally recommended blood test workup, specifically vitamin B12 and thyrotropin studies14-16 (eTable 4 in the Supplement). Neuroimaging variables were based on the presence of either head computerized tomography or brain magnetic resonance imaging studies (eTable 4 in the Supplement). Each of the constituent metrics was dummy coded as either 1 (≥1 frequency of the service in base claims or revenue center data) or 0 (service not present in base claims or revenue center data) within 6 months before and after the incident diagnosis date.

    Covariates

    Individual characteristics, including age (in years), sex (dummy coded as 0 = male and 1 = female), and comorbidity burden were included as covariates. Comorbidity burden was estimated within 6 months before and after the incident diagnosis date and was based on the Elixhauser Comorbidity Index.45 Additional contextual covariates were rurality and neighborhood disadvantage. Rurality was established using 2010 Rural-Urban Commuting Area codes46 based on zip+4 codes reported in Master Beneficiary Summary Files and was coded into 5 categories: metropolitan, micropolitan high commute, micropolitan low commute, small town or rural high commute, and small town or rural low commute46 (eTable 5 in the Supplement). Neighborhood disadvantage was measured by the area deprivation index (ADI), a publicly available composite metric of neighborhood characteristics incorporating 17 measures of education, employment, housing quality, and poverty.47 We used the state-level ADI decile scores coded as high ADI (deciles 9-10; most disadvantage), middle ADI (deciles 3-8; intermediate disadvantage), and low ADI (deciles 1-2; least disadvantage).48

    Statistical Analysis

    Statistical analyses were conducted from November 1, 2019, to November 10, 2020. Logistic regression models were performed to investigate the association between race/ethnicity and timeliness of diagnosis (dichotomous outcome: MCI vs dementia). Models were performed without (unadjusted) and with (fully adjusted) covariates, which included age, sex, comorbidity burden, rurality, and neighborhood disadvantage. To facilitate the interpretation of findings, we calculated odds ratios (ORs) based on unadjusted and fully adjusted models. Similarly, we fit unadjusted and fully adjusted Poisson regression models to examine the association between race/ethnicity and number of recommended diagnostic services (count outcome: 0-3). We calculated incidence rate ratios (IRRs) based on the unadjusted and fully adjusted models to facilitate interpretation of findings. In addition, marginal effects of race/ethnicity were estimated for fully adjusted models to further facilitate a quantitative interpretation of the results while keeping other covariates fixed.

    In supplementary analyses, we tested interaction terms between race/ethnicity and each of the other demographic and geographical factors for the outcomes with fully adjusted models. To examine whether an evaluation with a dementia specialist modified the results, we repeated our fully adjusted logistic and Poisson models only for beneficiaries with a specialist encounter. In sensitivity analyses, we repeated all primary logistic and Poisson models using the Medicare Enrollment Database race/ethnicity codes.

    All analyses were performed in R, version 4.0.2 (R Foundation for Statistical Computing).49 All analyses were 2-sided, and significance was set at P < .05 using the false discovery rate (FDR) approach to account for multiple comparisons. All models were checked for overdispersion, influential values, and multicollinearity, and Hosmer-Lemeshow tests were performed to ensure goodness of fit of multivariable models.

    Results
    Sample Characteristics

    Of the 1 892 633 California Medicare beneficiaries with no prior diagnoses of dementia or MCI from 2013 through 2014 (eTable 1 in the Supplement), 10 472 (0.6%) received an incident diagnosis in January through June 2015, with no significant group differences across racial/ethnic groups (Table 1). In the incident diagnosis study cohort, (62.1% women and 37.9% men; mean [SD] age, 82.9 [8.0] years), 9.5% of the individuals identified as Asian, 3.9% as Black, 12.0% as Hispanic, and 74.6% as White. A minority of beneficiaries received a timely diagnosis with elements of a comprehensive evaluation: 21.2% of beneficiaries received a diagnosis of incident MCI (vs dementia), 34.6% received a specialist evaluation, 16.2% had recommended laboratory testing, and 28.7% had neuroimaging studies. Compared with White beneficiaries, those who identified as Asian were more likely to reside in metropolitan areas (97.9% vs 86.1%) and were less likely to receive an incident MCI diagnosis (12.3% vs 23.3%), a specialist evaluation (28.8% vs 35.4%), or laboratory tests (10.2% vs 17.2%) (Table 1). Compared with White beneficiaries, Black beneficiaries were younger (mean [SD] age, 80.4 [8.4] vs 83.4 [8.1] years), more likely to reside in highly disadvantaged neighborhoods (24.8% vs 15.2%) and metropolitan areas (96.8% vs 86.1%), had a greater comorbidity burden (mean [SD] Elixhauser score, 5.2 [3.0] vs 4.8 [2.9]), and were less likely to receive an incident MCI diagnosis (18.2% vs 23.3%). Compared with White beneficiaries, Hispanic beneficiaries were also younger (mean [SD] age, 80.9 [7.7] vs 83.4 [8.1] years), more likely to reside in highly disadvantaged neighborhoods (34.6% vs 15.2%) and metropolitan areas (89.4% vs 86.1%), had a greater comorbidity burden (mean [SD] Elixhauser score, 5.1 [3.0] vs 4.8 [2.9]), and were less likely to receive an incident MCI diagnosis (15.8% vs 23.3%).

    Racial/Ethnic Differences in Timeliness of Diagnosis

    Results of unadjusted and fully adjusted logistic regression models are presented in Table 2. Area deprivation index values could not be linked for 3.9% of beneficiaries and were excluded from fully adjusted models. Compared with White beneficiaries, those who identified as Asian (OR, 0.46; 95% CI, 0.38-0.56; P < .001, using the FDR approach), Black (OR, 0.73; 95% CI, 0.56-0.94; P = .02, using the FDR approach), and Hispanic (OR, 0.62; 95% CI, 0.52-0.72; P < .001, using the FDR approach) were less likely to receive an incident diagnosis of MCI vs dementia. After adjustment for demographic and geographical factors, these differences remained significant, with Asian beneficiaries having the lowest likelihood of an incident MCI diagnosis (OR, 0.45; 95% CI, 0.37-0.55; P < .001, using the FDR approach), followed by Hispanic (OR, 0.65; 95% CI, 0.55-0.77; P ≤ .001, using the FDR approach) and Black (OR, 0.70; 95% CI, 0.53-0.91; P = .01, using the FDR approach) beneficiaries (Figure). The estimated mean marginal effects of race/ethnicity on incident diagnosis of MCI were −11.0% (95% CI, −13.2% to −8.8%; P < .001, using the FDR approach) for Asian beneficiaries, −6.6% (95% CI, −8.9% to −4.2%; P < .001, using the FDR approach) for Hispanic beneficiaries, and −5.6% (95% CI, −9.4% to −1.7%; P = .01, using the FDR approach) for Black beneficiaries. Other variables associated with lower likelihood of an incident MCI diagnosis were increasing age (OR for every additional 5 years, 0.80; 95% CI, 0.77-0.82; P < .001, using the FDR approach), residence in a highly disadvantaged neighborhood (OR, 0.73; 95% CI, 0.63-0.84; P < .001, using the FDR approach), and greater comorbidity burden (OR for every additional comorbid condition, 0.96; 95% CI, 0.94-0.98; P < .001, using the FDR approach) (Figure).

    Racial/Ethnic Differences in Comprehensiveness of Diagnostic Workup

    Table 3 summarizes the results of unadjusted and fully adjusted Poisson models. Compared with White beneficiaries, individuals who identified as Asian were less likely to receive recommended diagnostic workup services (IRR, 0.81; 95% CI, 0.74-0.87; P < .001, using the FDR approach). In fully adjusted models for individual and contextual covariates, the associations of race/ethnicity for Asian individuals remained significant (IRR, 0.81; 95% CI, 0.75-0.87; P < .001, using the FDR approach). Marginal effects analyses showed a mean effect of −15.7% (95% CI, −21.2% to −10.2%; P < .001, using the FDR approach) of receiving recommended diagnostic services for Asian beneficiaries. Other variables associated with lower likelihood of diagnostic workup services were older age (IRR for every additional 5 years, 0.98; 95% CI, 0.98-0.99; P < .001, using the FDR approach) and greater neighborhood disadvantage (IRR, 0.91; 95% CI, 0.86-0.96; P = .003, using the FDR approach) (Figure).

    Supplementary and Sensitivity Analyses

    We found significant interactions between Black race/ethnicity and greater neighborhood disadvantage with regard to timeliness of diagnosis (B = 0.713, SE = 0.318; P = .02, using the FDR approach), indicating that Black beneficiaries residing in highly disadvantaged neighborhoods had a lower likelihood of receiving a diagnosis of MCI. We also found significant interactions between Hispanic ethnicity and greater neighborhood disadvantage (B = 0.133, SE = 0.050 P = .03, using the FDR approach) in association with the number of recommended diagnostic services, such that the likelihood of receiving a recommended diagnostic workup was lower for Hispanic individuals residing in highly disadvantaged neighborhoods.

    Among individuals who received a specialist evaluation, beneficiaries who identified as Asian (OR, 0.40; 95% CI, 0.28-0.55; P < .001, using the FDR approach) or Hispanic (OR, 0.60; 95% CI, 0.46-0.78; P < .001, using the FDR approach) were less likely than those who identified as White to receive a diagnosis of incident MCI. The comprehensiveness of diagnostic services performed (laboratory and neuroimaging studies) did not differ by racial/ethnic group (full models in eTable 6 in the Supplement). Sensitivity analyses using the Medicare Enrollment Database race/ethnicity codes showed equivalent results with small differences in unstandardized coefficients (full models in eTables 7-9 in the Supplement).

    Discussion

    Early diagnosis of dementia is a public health priority4-9 that will become even more urgent as potential disease-modifying treatments become available.10,11 Consistent with our hypotheses, beneficiaries who identified as belonging to a racial/ethnic minority group were less likely than White beneficiaries to receive a timely diagnosis (defined as incident diagnosis of MCI vs dementia) or a comprehensive evaluation. Quiz Ref IDWhereas 23.3% of White beneficiaries received MCI as their first diagnosis, incident MCI diagnosis was less common among Asian (12.3%), Hispanic (15.8%), and Black (18.2%) beneficiaries, even though Black and Hispanic beneficiaries, on average, were younger at incident diagnosis than White beneficiaries.Quiz Ref ID Furthermore, we found that beneficiaries who identified as Asian received a less comprehensive diagnostic evaluation.

    After adding demographic and geographical factors to the models, we found that greater neighborhood disadvantage and older age were each independently associated with later diagnosis and a less comprehensive evaluation. Greater comorbidity burden was also independently associated with later diagnosis. Significant interactions indicated that Black beneficiaries residing in disadvantaged neighborhoods were less likely to receive a timely diagnosis and that Hispanic beneficiaries in disadvantaged neighborhoods were less likely to receive comprehensive evaluations. Asian and Hispanic beneficiaries who had a specialist evaluation did so at a later stage of disease, but no significant racial/ethnic inequalities in the comprehensiveness of the diagnostic evaluation were found among beneficiaries with a specialist visit. These results point to the complex mechanisms underlying health care disparities that must be understood to tailor effective solutions.

    We identified significant inequities in diagnostic care among Asian American individuals. The Asian population in California and the US in general is extremely heterogeneous with regard to ethnicity, culture, and language characteristics.50,51 About half (49%) of Asian residents of California aged 5 years or older report having limited English proficiency52; for older Asian individuals who do not speak English, language barriers are associated with reduced access to preventive medical care, poor understanding of instructions, and increased risk of misdiagnosis.53 In addition, Asian American individuals may be more likely to rely on complementary and alternative medicine treatment for chronic conditions.54 Moreover, the stigma associated with dementia and the cultural values and beliefs that emphasize personal or family responsibility in caring for a person with dementia could delay bringing the patient’s condition to medical attention.55 Because of delayed and inadequate diagnostic evaluations, Asian American older adults may appear to have a lower prevalence,42 and, to some degree, a lower incidence22 of dementia.

    Although our primary findings concern racial/ethnic disparities in dementia diagnosis, our results also provide important insights into overall dementia diagnostic practices in California. In particular, less than half of beneficiaries who received a diagnosis of dementia or MCI have received recommended diagnostic services, including a specialist evaluation (34.6%), brain health laboratory testing (16.2%), and structural neuroimaging studies (28.7%). Moreover, our analyses revealed that these services were less likely to be performed for individuals who were older and had a greater comorbidity burden, which underlines the importance of supporting efforts to optimize dementia diagnostic pathways, particularly in general practice settings.

    Our study has important implications for health care policy and future research. Specifically, it highlights the need for training and supporting nonspecialist health care professionals who serve patients from racial/ethnic minority groups and disadvantaged communities, strengthening community education and awareness of dementia and its early signs, and outreach by specialists to facilitate referrals from underserved communities at an early stage of disease. Future research should focus on further understanding the causes of the observed disparities, including both individual characteristics and contextual factors, to address health care inequality among older adults. Future studies should also examine long-term social and economic outcomes of delayed diagnosis and poorer comprehensiveness of diagnostic services among racially/ethnically diverse older adults to inform policy interventions as dementia prevalence continues to increase. Population-based studies of dementia prevalence and incidence by race/ethnicity must consider the association of delayed diagnosis with findings.

    Limitations and Strengths

    The limitations of this study are associated with the use of secondary claims data (ie, nonstandard diagnostic approaches and inability to identify preclinical cases), a restricted lookback window to establish incident diagnoses, and limited access to individual-level socioeconomic characteristics (eg, educational level). We relied on the race/ethnicity variables reported in the administrative data that have previously been shown to have limited accuracy in representing individuals who identify as non-White.37 Also, our sample was limited to Medicare fee-for-service beneficiaries in California, and owing to unavailability of data on individuals enrolled in Medicare Advantage plans or who are uninsured, our estimates of racial/ethnic groups likely underrepresent the diversity of the older adult population in California.

    Our study also has some strengths, including the fact that we found equivalent results using Research Triangle Institute and Medicare Enrollment Database coding. Another major strength of this study is that this is, to our knowledge, the first empirical analysis of racial/ethnic disparities with regard to the timeliness and comprehensiveness of dementia diagnosis among US older adults. Inclusion of other demographic and geographical factors has also strengthened our findings through identification of additional areas of vulnerability that underlie dementia diagnostic practices.

    Conclusions

    We found that Asian, Black, and Hispanic Medicare beneficiaries received a less timely diagnosis and that Asian beneficiaries received a less comprehensive diagnostic evaluation compared with White beneficiaries. Our findings highlight substantial gaps in diagnostic care among racially diverse older adults that are likely associated with underrepresentation in clinical trials and inequities in treatment. Major policy and practice efforts are necessary to address these gaps via targeted interventions for vulnerable populations.

    Back to top
    Article Information

    Accepted for Publication: December 29, 2020.

    Published Online: March 29, 2021. doi:10.1001/jamaneurol.2021.0399

    Corresponding Author: Katherine L. Possin, PhD, Memory and Aging Center, Department of Neurology, University of California, San Francisco, 675 Nelson Rising Ln, San Francisco, CA 94158 (katherine.possin@ucsf.edu).

    Author Contributions: Drs Tsoy and Possin had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Tsoy, Guterman, Tee, Dorsman, Lanata, Miller, Kind, Possin.

    Acquisition, analysis, or interpretation of data: Tsoy, Kiekhofer, Guterman, Windon, Lanata, Rabinovici, Possin.

    Drafting of the manuscript: Tsoy, Guterman, Miller.

    Critical revision of the manuscript for important intellectual content: Tsoy, Kiekhofer, Guterman, Tee, Windon, Dorsman, Lanata, Rabinovici, Kind, Possin.

    Statistical analysis: Tsoy, Guterman, Kind.

    Obtained funding: Possin.

    Administrative, technical, or material support: Kiekhofer, Dorsman, Lanata, Miller, Kind.

    Supervision: Tsoy, Guterman, Lanata, Kind, Possin.

    Conflict of Interest Disclosures: Dr Guterman reported receiving grant funding from the National Institute of Neurological Disorders and Stroke (1K23NS116128-01), the National Institute on Aging (5R01AG056715), and the American Academy of Neurology; and receiving consulting fees from Marinus Pharmaceuticals Inc and honoraria from Knowledge to Practice. Dr Rabinovici reported receiving grants from Avid Radiopharmaceuticals, Eli Lilly, GE Healthcare, Life Molecular Imaging; and personal fees from Eisai, Johnson & Johnson, GE Healthcare, Axon Neurosciences, Eisai, Roche, Genetech, and Merck; and research funding from the National Institutes of Health (NIH), the Rainwater Charitable Foundation, and the Alzheimer’s Association outside the submitted work. Dr Miller reported serving on the Cambridge National Institute for Health Research Biomedical Research Centre advisory committee and its subunit, the Biomedical Research Unit in Dementia; serving as a board member for the American Brain Foundation; serving on John Douglas French Alzheimer’s Foundation board of directors; serving on the Safely You board of directors; serving as scientific director for the Tau Consortium; serving as medical advisor for and receiving a grant from The Bluefield Project for Frontotemporal Dementia Research; serving as a consultant for Rainwater Charitable Foundation, Stanford Alzheimer’s Disease Research Center, Buck Institute SAB, Larry L. Hillblom Foundation, University of Texas Center for Brain Health, University of Washington Alzheimer’s Disease Research Center EAB, and Harvard University Alzheimer’s Disease Research Center EAB; receiving royalties from Guilford Press, Cambridge University Press, Johns Hopkins Press, and Oxford University Press; serving as editor for Neurocase; serving as section editor for Frontiers in Neurology; and receiving grants P30 AG062422, P01 AG019724, R01 AG057234, and T32 AG023481 from the NIH. Dr Kind reported receiving grants from the NIH during the conduct of the study; grants from the NIH, the US Department of Veterans Affairs, and the Commonwealth Foundation outside the submitted work; and consulting fees from the University of Wisconsin–Health’s Isthmus Project. Dr Possin reported receiving grants from the National Institute on Aging and the National Institute of Neurological Disorders and Stroke during the conduct of the study; grants from Global Brain Health Institute, Quest Diagnostics, Administration for Community Living, Rainwater Charitable Trust, and Merck Foundation outside the submitted work; and personal fees from ClearView Health Partners and Vanguard. No other disclosures were reported.

    Disclaimer: Dr Rabinovici is associate editor of JAMA Neurology, but was not involved in any of the decisions regarding review of the manuscript or its acceptance.

    Funding/Support: This study was supported by the Department of Health and Human Services Centers for Medicare & Medicaid Services (grant 1C1CMS331346), the National Institute of Neurological Disorders and Stroke (grant UG3 NS105557-01), the National Institute on Aging (grant 5R01AG056715), and the Global Brain Health Institute.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    World Health Organization. Dementia: a public health priority. Published 2012. Accessed June 30, 2020. https://www.who.int/mental_health/publications/dementia_report_2012/en/
    2.
    Alzheimer’s Disease International. World Alzheimer report 2015: the global impact of dementia: an analysis of prevalence, incidence, cost and trends. Updated October 2015. Accessed June 30, 2020. https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf
    3.
    Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Accessed June 30, 2020. https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf
    4.
    US Department of Health and Human Services. National plan to address Alzheimer’s disease: 2019 update. Accessed August 12, 2020. https://aspe.hhs.gov/system/files/pdf/262601/NatlPlan2019.pdf
    5.
    Schneider  J, Jeon  S, Gladman  JT, Corriveau  RA, 2019. ADRD Summit 2019 report to the National Advisory Neurological Disorders and Stroke Council. Published September 4, 2019. Accessed August 12, 2020. https://www.ninds.nih.gov/sites/default/files/2019_adrd_summit_recommendations_508c.pdf
    6.
    Centers for Disease and Control Prevention. Advancing early detection: a healthy brain initiative issue map. Accessed August 12, 2020. https://www.cdc.gov/aging/healthybrain/pdf/Issue-Map-Early-Detection-508.pdf
    7.
    Cordell  CB, Borson  S, Boustani  M,  et al; Medicare Detection of Cognitive Impairment Workgroup.  Alzheimer’s Association recommendations for operationalizing the detection of cognitive impairment during the Medicare Annual Wellness Visit in a primary care setting.   Alzheimers Dement. 2013;9(2):141-150. doi:10.1016/j.jalz.2012.09.011 PubMedGoogle ScholarCrossref
    8.
    Alzheimer’s Association. 2018 Alzheimer’s disease facts and figures. Accessed June 30, 2020. https://www.alz.org/media/homeoffice/facts%20and%20figures/facts-and-figures.pdf
    9.
    Dubois  B, Padovani  A, Scheltens  P, Rossi  A, Dell’Agnello  G.  Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges.   J Alzheimers Dis. 2016;49(3):617-631. doi:10.3233/JAD-150692 PubMedGoogle ScholarCrossref
    10.
    Tsai  RM, Boxer  AL.  Therapy and clinical trials in frontotemporal dementia: past, present, and future.   J Neurochem. 2016;138(suppl 1):211-221. doi:10.1111/jnc.13640 PubMedGoogle ScholarCrossref
    11.
    Schneider  J.  Research momentum brings hope for dementia in 2019.   Lancet Neurol. 2020;19(1):3-5. doi:10.1016/S1474-4422(19)30453-3 PubMedGoogle ScholarCrossref
    12.
    Heintz  H, Monette  P, Epstein-Lubow  G, Smith  L, Rowlett  S, Forester  BP.  Emerging collaborative care models for dementia care in the primary care setting: a narrative review.   Am J Geriatr Psychiatry. 2020;28(3):320-330. doi:10.1016/j.jagp.2019.07.015 PubMedGoogle ScholarCrossref
    13.
    Vos  SJ, Verhey  F, Frölich  L,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage.   Brain. 2015;138(pt 5):1327-1338. doi:10.1093/brain/awv029 PubMedGoogle ScholarCrossref
    14.
    Langa  KM, Levine  DA.  The diagnosis and management of mild cognitive impairment: a clinical review.   JAMA. 2014;312(23):2551-2561. doi:10.1001/jama.2014.13806 PubMedGoogle ScholarCrossref
    15.
    Petersen  RC, Lopez  O, Armstrong  MJ,  et al.  Practice guideline update summary: mild cognitive impairment: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology.   Neurology. 2018;90(3):126-135. doi:10.1212/WNL.0000000000004826 PubMedGoogle ScholarCrossref
    16.
    Arvanitakis  Z, Shah  RC, Bennett  DA.  Diagnosis and management of dementia.   JAMA. 2019;322(16):1589-1599. doi:10.1001/jama.2019.4782 PubMedGoogle ScholarCrossref
    17.
    US Census Bureau. Demographic turning points for the United States: population projections for 2020 to 2060. Accessed June 30, 2020. https://www.census.gov/content/dam/Census/library/publications/2020/demo/p25-1144.pdf
    18.
    Brewster  P, Barnes  L, Haan  M,  et al.  Progress and future challenges in aging and diversity research in the United States.   Alzheimers Dement. 2019;15(7):995-1003. doi:10.1016/j.jalz.2018.07.221 PubMedGoogle ScholarCrossref
    19.
    Wilkins  CH, Schindler  SE, Morris  JC.  Addressing health disparities among minority populations: why clinical trial recruitment is not enough.   JAMA Neurol. 2020;77(9):1063-1064. doi:10.1001/jamaneurol.2020.1614 PubMedGoogle ScholarCrossref
    20.
    Agency for Healthcare Research and Quality. 2018 National healthcare quality and disparities report: appendix A3: disparities: race and ethnicity. Accessed June 30, 2020. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr18/index.html
    21.
    Davis  MA, Guo  C, Sol  K, Langa  KM, Nallamothu  BK.  Trends and disparities in the number of self-reported healthy older adults in the United States, 2000 to 2014.   JAMA Intern Med. 2017;177(11):1683-1684. doi:10.1001/jamainternmed.2017.4357 PubMedGoogle ScholarCrossref
    22.
    Mayeda  ER, Glymour  MM, Quesenberry  CP, Whitmer  RA.  Inequalities in dementia incidence between six racial and ethnic groups over 14 years.   Alzheimers Dement. 2016;12(3):216-224. doi:10.1016/j.jalz.2015.12.007 PubMedGoogle ScholarCrossref
    23.
    Chen  C, Zissimopoulos  JM.  Racial and ethnic differences in trends in dementia prevalence and risk factors in the United States.   Alzheimers Dement (N Y). 2018;4:510-520. doi:10.1016/j.trci.2018.08.009 PubMedGoogle ScholarCrossref
    24.
    Babulal  GM, Quiroz  YT, Albensi  BC,  et al; International Society to Advance Alzheimer’s Research and Treatment, Alzheimer’s Association.  Perspectives on ethnic and racial disparities in Alzheimer’s disease and related dementias: update and areas of immediate need.   Alzheimers Dement. 2019;15(2):292-312. doi:10.1016/j.jalz.2018.09.009 PubMedGoogle ScholarCrossref
    25.
    Clark  PC, Kutner  NG, Goldstein  FC,  et al.  Impediments to timely diagnosis of Alzheimer’s disease in African Americans.   J Am Geriatr Soc. 2005;53(11):2012-2017. doi:10.1111/j.1532-5415.2005.53569.x PubMedGoogle ScholarCrossref
    26.
    Fitten  LJ, Ortiz  F, Pontón  M.  Frequency of Alzheimer’s disease and other dementias in a community outreach sample of Hispanics.   J Am Geriatr Soc. 2001;49(10):1301-1308. doi:10.1046/j.1532-5415.2001.49257.x PubMedGoogle ScholarCrossref
    27.
    Hill  CV, Pérez-Stable  EJ, Anderson  NA, Bernard  MA.  The National Institute on Aging Health Disparities Research Framework.   Ethn Dis. 2015;25(3):245-254. doi:10.18865/ed.25.3.245 PubMedGoogle ScholarCrossref
    28.
    Henning-Smith  C, Kozhimannil  K, Casey  M, Prasad  S, Moscovice  I.  Rural-urban differences in Medicare quality outcomes and the impact of risk adjustment.   Med Care. 2017;55(9):823-829. doi:10.1097/MLR.0000000000000761 PubMedGoogle ScholarCrossref
    29.
    Teel  CS.  Rural practitioners’ experiences in dementia diagnosis and treatment.   Aging Ment Health. 2004;8(5):422-429. doi:10.1080/13607860410001725018 PubMedGoogle ScholarCrossref
    30.
    Kirby  JB, Kaneda  T.  Neighborhood socioeconomic disadvantage and access to health care.   J Health Soc Behav. 2005;46(1):15-31. doi:10.1177/002214650504600103 PubMedGoogle ScholarCrossref
    31.
    Durfey  SNM, Kind  AJH, Buckingham  WR, DuGoff  EH, Trivedi  AN.  Neighborhood disadvantage and chronic disease management.   Health Serv Res. 2019;54(suppl 1):206-216. doi:10.1111/1475-6773.13092 PubMedGoogle ScholarCrossref
    32.
    Hunt  JFV, Buckingham  W, Kim  AJ,  et al.  Association of neighborhood-level disadvantage with cerebral and hippocampal volume.   JAMA Neurol. 2020;77(4):451-460. doi:10.1001/jamaneurol.2019.4501 PubMedGoogle ScholarCrossref
    33.
    Powell  WR, Buckingham  WR, Larson  JL,  et al.  Association of neighborhood-level disadvantage with Alzheimer disease neuropathology.   JAMA Netw Open. 2020;3(6):e207559. doi:10.1001/jamanetworkopen.2020.7559 PubMedGoogle Scholar
    34.
    US Census Bureau. State population by characteristics: 2010-2019. Accessed June 30, 2020. https://www.census.gov/content/census/en/data/datasets/time-series/demo/popest/2010s-state-detail.html
    35.
    Research Data Assistance Center. Find, request and use CMS data. Accessed June 30, 2020. https://www.resdac.org/
    36.
    Chronic Conditions Data Warehouse. Condition categories. Accessed June 30, 2020. https://www2.ccwdata.org/web/guest/condition-categories
    37.
    Filice  CE, Joynt  KE.  Examining race and ethnicity information in Medicare administrative data.   Med Care. 2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608 PubMedGoogle ScholarCrossref
    38.
    HealthCare.gov. Health coverage for American Indians & Alaska Natives. Accessed June 30, 2020. https://www.healthcare.gov/american-indians-alaska-natives/coverage/
    39.
    Taylor  DH  Jr, Fillenbaum  GG, Ezell  ME.  The accuracy of Medicare claims data in identifying Alzheimer’s disease.   J Clin Epidemiol. 2002;55(9):929-937. doi:10.1016/S0895-4356(02)00452-3 PubMedGoogle ScholarCrossref
    40.
    Taylor  DH  Jr, Østbye  T, Langa  KM, Weir  D, Plassman  BL.  The accuracy of Medicare claims as an epidemiological tool: the case of dementia revisited.   J Alzheimers Dis. 2009;17(4):807-815. doi:10.3233/JAD-2009-1099 PubMedGoogle ScholarCrossref
    41.
    Lee  E, Gatz  M, Tseng  C,  et al.  Evaluation of Medicare claims data as a tool to identify dementia.   J Alzheimers Dis. 2019;67(2):769-778. doi:10.3233/JAD-181005 PubMedGoogle ScholarCrossref
    42.
    Matthews  KA, Xu  W, Gaglioti  AH,  et al.  Racial and ethnic estimates of Alzheimer’s disease and related dementias in the United States (2015-2060) in adults aged ≥65 years.   Alzheimers Dement. 2019;15(1):17-24. doi:10.1016/j.jalz.2018.06.3063 PubMedGoogle ScholarCrossref
    44.
    Drabo  EF, Barthold  D, Joyce  G, Ferido  P, Chang Chui  H, Zissimopoulos  J.  Longitudinal analysis of dementia diagnosis and specialty care among racially diverse Medicare beneficiaries.   Alzheimers Dement. 2019;15(11):1402-1411. doi:10.1016/j.jalz.2019.07.005 PubMedGoogle ScholarCrossref
    45.
    Elixhauser  A, Steiner  C, Harris  DR, Coffey  RM.  Comorbidity measures for use with administrative data.   Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004 PubMedGoogle ScholarCrossref
    46.
    US Department of Agriculture Economic Research Service. 2010 Rural-urban commuting area (RUCA) codes. Accessed December 15, 2019. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation/
    47.
    Kind  AJH, Buckingham  WR.  Making neighborhood-disadvantage metrics accessible—the Neighborhood Atlas.   N Engl J Med. 2018;378(26):2456-2458. doi:10.1056/NEJMp1802313 PubMedGoogle ScholarCrossref
    48.
    University of Wisconsin Department of Medicine. Neighborhood atlas. Accessed December 10, 2015. https://www.neighborhoodatlas.medicine.wisc.edu/#about-anchor
    49.
    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2014. http://www.R-project.org/
    50.
    US Census Bureau. Asian alone or in any combination by selected groups: California. Accessed August 12, 2020. https://data.census.gov/cedsci/table?q=B02018&tid=ACSDT1Y2018.B02018&hidePreview=false
    51.
    Mayeda  ER, Glymour  MM, Quesenberry  CP  Jr, Whitmer  RA.  Heterogeneity in 14-year dementia incidence between Asian American subgroups.   Alzheimer Dis Assoc Disord. 2017;31(3):181-186. doi:10.1097/WAD.0000000000000189 PubMedGoogle ScholarCrossref
    52.
    US Census Bureau. Detailed languages spoken at home and ability to speak English for the population 5 years and over: 2009-2013. Accessed August 12, 2020. https://www.census.gov/data/tables/2013/demo/2009-2013-lang-tables.html
    53.
    Diamond  LC, Jacobs  EA.  Let’s not contribute to disparities: the best methods for teaching clinicians how to overcome language barriers to health care.   J Gen Intern Med. 2010;25(suppl 2):S189-S193. doi:10.1007/s11606-009-1201-8 PubMedGoogle ScholarCrossref
    54.
    Mehta  DH, Phillips  RS, Davis  RB, McCarthy  EP.  Use of complementary and alternative therapies by Asian Americans: results from the National Health Interview Survey.   J Gen Intern Med. 2007;22(6):762-767. doi:10.1007/s11606-007-0166-8 PubMedGoogle ScholarCrossref
    55.
    Mukadam  N, Cooper  C, Livingston  G.  A systematic review of ethnicity and pathways to care in dementia.   Int J Geriatr Psychiatry. 2011;26(1):12-20. doi:10.1002/gps.2484 PubMedGoogle ScholarCrossref
    ×