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Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity

Independent Researcher, 27766 Stirrup Way, Los Altos Hills, CA 94022, USA
Global Medical Epidemiology, Worldwide Medical and Safety, Pfizer Inc., 235 E 42nd St., New York, NY 10017, USA
Worldwide Medical and Safety, Pfizer Inc., 235 E 42nd St., New York, NY 10017, USA
Author to whom correspondence should be addressed.
Healthcare 2022, 10(4), 603;
Received: 1 February 2022 / Revised: 16 March 2022 / Accepted: 17 March 2022 / Published: 23 March 2022
(This article belongs to the Special Issue Inequality in Health Systems)


Background: Wide disparities in health status exist in the United States across race and ethnicity, broadly driven by social determinants of health—most notably race and ethnic group differences in income, education, and occupational status. However, disparities in disease frequency or severity remain underappreciated for many individual diseases whose distribution in the population varies. Such information is not readily accessible, nor emphasized in treatment guidelines or reviews used by practitioners. Specifically, a summary on disease-specific evidence of disparities from population-based studies is lacking. Our goal was to summarize the published evidence for specific disease disparities in the United States so that this knowledge becomes more widely available “at the bedside”. We hope this summary stimulates health equity research at the disease level so that these disparities can be addressed effectively. Methods: A targeted literature review of disorders in Pfizer’s current pipeline was conducted. The 38 diseases included metabolic disorders, cancers, inflammatory conditions, dermatologic disorders, rare diseases, and infectious targets of vaccines under development. Online searches in Ovid and Google were performed to identify sources focused on differences in disease rates and severity between non-Hispanic Whites and Black/African Americans, and between non-Hispanic Whites and Hispanics. As a model for how this might be accomplished for all disorders, disparities in disease rates and disease severity were scored to make the results of our review most readily accessible. After primary review of each condition by one author, another undertook an independent review. Differences between reviewers were resolved through discussion. Results: For Black/African Americans, 29 of the 38 disorders revealed a robust excess in incidence, prevalence, or severity. After sickle cell anemia, the largest excesses in frequency were identified for multiple myeloma and hidradenitis suppurativa. For Hispanics, there was evidence of disparity in 19 diseases. Most notable were metabolic disorders, including non-alcoholic steatohepatitis (NASH). Conclusions: This review summarized recent disease-specific evidence of disparities based on race and ethnicity across multiple diseases, to inform clinicians and health equity research. Our findings may be well known to researchers and specialists in their respective fields but may not be common knowledge to health care providers or public health and policy institutions. Our hope is that this effort spurs research into the causes of the many disease disparities that exist in the United States.

1. Introduction

Increasing attention has been paid over recent years to the gaping disparities in health status seen in the United States among people of lower socioeconomic status, and members of groups subject to discrimination based on gender, race, or ethnicity [1,2,3]. These demographics, as well as many others, are associated with a larger group of social determinants of health, whose effects have been dramatically illustrated during the COVID-19 pandemic, and of which workers in the health sector became acutely aware [4,5]. Moreover, pre-existing differences in longevity by race and neighborhood became widely discussed [1,6]. However, disparities in disease incidence and prevalence or differences isn severity and outcomes remain less well appreciated at the level of individual diseases. At the “bedside,” where daily decisions are made about preventive and therapeutic interventions for individual patients with specific health conditions, information about such differences between groups in the natural history of disease, prognosis, responses to available treatments, and potential for adverse outcomes is not readily accessible, and too rarely informs treatment guidelines or reviews used most by practitioners. At the research and development level, such disease-specific information rarely informs clinical trials and other translational research, leading to overly homogenous trial populations and lost opportunities to confront health disparities.
Some data do exist, and some have been published. There are myriad reports reviewing and assessing such data for common disorders. Most have derived population-level estimates using aggregate health care claims data, registries, or surveys of U.S. population samples such as the National Health Interview Survey (NHIS) or the National Health and Nutrition Examination Survey (NHANES) [7,8]. For less prevalent conditions, reviews of case series or local registries provide information on disparities, although of variable generalizability to the larger U.S. population. From these published sources, it is possible to establish that differences between U.S. non-Hispanic Whites (NHWs) and Black/African Americans exist in rates of occurrence and/or severity/prognosis for many disorders. Increasingly, evidence of disparities for populations of Hispanic and Asian origin are being reported as well, with appreciation for the substantial ethnic diversity within these groups, resulting in uncertainty of estimates for less prevalent conditions [9,10].
There is presently a lack of an easily accessible reference for routine “bedside” use by practitioners, service planners, policy makers and the like who may be aware of the social factors confronting their patients/clientele but lack evidence specific to individual conditions being managed. Most disease guidelines or online reference manuals address demographic aspects of the disease cursorily if at all, effectively rendering the information inaccessible. In other words, there is presently no summary of evidence highlighting what is known about specific disease disparities and targeting the most relevant literature at the condition level. The goal of this paper was to demonstrate, using a convenient small sample of diseases, an approach tackling this challenge; in an ideal world, such information would appear in every textbook, treatment guideline, and online tools used by practitioners.
In this targeted literature review, we present one approach for addressing a gap in data on health disparities confronting Black/African Americans and Hispanics that could be addressed within the health care system, and we focus on the set of diseases in Pfizer’s preventive and therapeutic pipeline. Our primary goal in this paper is to capture and summarize the published evidence of racial and ethnic disparities in the United States for this set of conditions so that this knowledge becomes readily available “at the bedside.” Our hope is that this summary will stimulate new waves of health equity research at the disease level so that potential causes of disparities can be further identified, and these health disparities can be effectively addressed.

2. Materials and Methods

Disorders of interest were selected based upon Pfizer’s current therapeutic and vaccine development pipeline (; accessed on 1 March 2021), recognizing that there is nothing otherwise unique about these disorders for our illustrative purpose. Included were a group of common metabolic disorders and cancers, along with a spectrum of inflammatory conditions, dermatologic disorders, and some rare diseases. The infectious targets of vaccines under development were also explored. The 38 studied diseases are summarized in Table 1 below. We approached the process of covering a large swathe of clinical disorders as follows.
First, we conducted online searches in both Ovid and a general search in Google, the latter to capture public documents and reports that may not yet have appeared in the peer-reviewed literature. In Ovid, the following string of key words were used in conjunction with disease name: “disparities” AND “race OR ethnicity.” We restricted our search to sources in English, about humans, and published from 2010 through present (December 2021) to focus on the latest available data wherever possible. When necessary, we included reports that drew on data sources developed earlier. For a few diseases we extended the search to include older sources of high relevance identified in the selected papers; these pre-2010 publications are listed in the footnote of Table 1. From these results, we identified reports that focused on differences in the distribution of disease rates and outcomes between NHWs and Black/African Americans, and between NHWs and Hispanics. Four types of papers were considered of greatest relevance to the development of our “disparity matrix”:
Reports of large and comprehensive U.S. surveillance programs, of which SEER (The Surveillance, Epidemiology, and End Results) for cancer is the best example.
Studies using large and representative health care claims and electronic medical records data sets, often reporting on large fractions of the U.S. population, such as Medicare.
National surveys with standardized sampling strategies for extrapolation to the U.S. population, such as NHANES and NHIS.
Review articles that relied on one or more of these source-types.
Citations of the articles and reports serving as the primary sources of data are listed in Table 1 for each condition. Reviews that relied directly on other primary data sources, regional registries or surveys, and cohorts or case-series from a single institution or group of institutions, were available and were especially important for assessing differences in outcomes and severity for non-lethal conditions. Sources that failed to meet stricter criteria for being population-based are listed in the footnote of Table 1. In some instances, they were the only source of data (especially for rare diseases) or the only source for assessing differences in severity, regardless of disease prevalence.
To make the results of our review most readily accessible, we grouped our observations into a small number of categories. For incidence rates and point prevalence, we designated diseases as rare (incidence < 1 per 100,000 per year or prevalence <0.1%) with a score of “0”, uncommon (incidence 1 to <20 per 100,000 per year or prevalence between 0.1 and <1.0%) with a score of “1”, common (incidence 20–50 per 100,000 per year or prevalence 1–10%) with a score “2”, and very common (incidence >50 per 100,000 per year or prevalence >10%) with a score of “3”. For differences in disease rates between Black/African Americans and NHWs and between Hispanics and NHWs, we used a similar scale, using “0” for when the minority group had lower disease rates than NHWs, “1” for conditions in which the literature suggests comparable risk for Black/African Americans or Hispanics compared with NHWs and/or non-Hispanics, “2” when modest excess risk (i.e., not greater than two-fold) for the minority group appears established, and “3” when a significant excess risk (i.e., relative risk exceeds two-fold for rare diseases or greater than 20% excess for common ones) for the minority groups appears established.
We scored disparity in severity in the same general way, recognizing that this concept is intrinsically more complex than frequency, and incorporates differences in rates of complications, hospitalization, response to therapy, and survival. For these comparisons, “0” applies to those entities for which NHWs have been reported to do worse in some key measure than the minority group; “1” when reported severity was comparable between the minority group and NHWs; “2” when some suggestion emerges from the literature that the minority group has greater severity, and “3” when these differences are demonstrated quantitatively from a population-based source such as survival differences in SEER. If no data from the medical literature were identified for a given disease disparity, “ND” was entered into Table 1.
After primary review of each condition by one author, one other author undertook an independent review. Where differences in the interpretation of the literature emerged, agreement on a final score was reached after discussion.

3. Results

A total of 38 conditions were reviewed for potential disparities comparing Black/African Americans vs. NHWs and Hispanics vs. NHWs. The list included 8 cancers, 11 inflammatory diseases, 6 common medical disorders, 8 rare and/or congenital diseases, and 5 vaccine-targeted conditions from the Pfizer pipeline. The list is presented along with the results in Table 1.
For Black/African Americans, 29 of the 38 disorders revealed an excess in incidence, prevalence, or severity, or suggestion of such a disparity. Aside from sickle cell anemia, Black–White differences were identified for multiple myeloma [11,12], hidradenitis suppurative [13], and lupus [14,15,16,17,18,19,20] among others. None of the conditions assessed had evidence of less severity for Black/African Americans [13]. As shown in Table 1, there was evidence of disparities for Hispanics in 19 entities. Notable differences from NHWs include NASH [21], obesity [22,23,24,25], and diabetes [26,27,28,29,30].
For Hispanics, there are larger gaps in knowledge of disease disparities than with Blacks. We found no substantive data on the incidence, prevalence, or disease severity comparing Hispanics to NHWs for 17 conditions, suggesting the possibility of further disparities being uncovered with additional research.
Table 1. Disease Areas of Pfizer’s Portfolio: Heat Map of Racial and Ethnic Disparities in Incidence, Prevalence and Disease Severity *.
Table 1. Disease Areas of Pfizer’s Portfolio: Heat Map of Racial and Ethnic Disparities in Incidence, Prevalence and Disease Severity *.
DiseaseIncidence/PrevalenceBlack/AA Disparity
Black/AA Disparity in SeverityHispanic Disparity Incidence/PrevalenceHispanic Disparity in SeverityCitations
Prostate Cancer32201[31,32,33,34,35]
Lung Cancer32200[35,36,37]
Breast Cancer30201[35,38,39,40,41,42,43]
Bladder Cancer20212[44,45]
Colorectal Cancer22201[35,46,47,48]
Head and Neck Cancer222ND2[49,50,51,52,53]
Multiple Myeloma13121[11,12,56]
Inflammation and Immunology
Ankylosing Spondylitis10312[57,58,59,60]
Rheumatoid Arthritis11102[61,62,63,64,65]
Inflammatory Bowel Disease10100[75,76,77,78,79,80,81,82,83]
Atopic Dermatitis32222[84,85,86,87,88,89]
Hidradenitis Suppurativa132ND2[13,93,94,95,96]
Alopecia Areata23212[101,102]
Stasis dermatitis2020ND[103,104]
Systemic Lupus Erythematosus13222[14,15,16,17,18,19,20]
Internal Medicine
Diabetes Mellitus22322[26,27,28,29,30]
Obesity32ND3ND [23,24,25]
Nonalcoholic Steatohepatitis20121[21,115,116,117,118,119]
Pulmonary Arterial Hypertension01111[120,121,122]
Rare Disease
LMNA-Related Dilated Cardiomyopathy03NDNDND[123]
Growth Hormone Deficiency0121ND[124,125]
Duchenne Muscular Dystrophy00NDND1[128,129,130]
Focal Segmental Glomerulosclerosis131NDND[131,132,133]
Idiopathic Thrombocytopenic Purpura002NDND[136,137,138]
Sickle Cell Anemia03ND0ND[139,140]
Lyme Disease10ND0ND[141,142,143]
Clostridium Difficile Colitis20210[144,145]
Streptococcus Pneumoniae2212ND[146,147,148,149,150,151]
Group B Streptococcus2222ND[152,153,154]
Meningococcal Meningitis022NDND[149,155,156]
Abbreviations: AA = African American; ND = no data; NHW = non-Hispanic White; PY = person-year. Scoring Legend: Incidence (cancers and vaccines): 0 = <1 per 100,000 PYs; 1 = 1 to <20; 2 = 20 to 50 per 100,000 PYs; 3 = >50 per 100,000 PYs. Prevalence (all other diseases): 0 = <0.1%; 1 = 0.1 to <1%; 2 = 1 to 10%; 3 = >10%. Incidence/prevalence disparity: 0 = Lower than NHW; 1 = Equal to NHW; 2 = Modest excess compared to NHW; 3 = Significant excess compared to NHW. Survival disparity: 0 = Greater than NHW; 1 = Equal to NHW; 2 = Modestly worse than NHW; 3 = Significantly worse than NHW. Severity disparity: 0 = Lower than NHW; 1 = Equal to NHW; 2 = Suggestion in the literature more severely impacted than NHW; 3 = Quantitative evidence of greater severity than NHW.* For cancers, severity was assessed based on survival. For non-cancers, severity based on rates of complications, hospitalization, response to therapy, etc. (Source did not meet criteria for generalizability: [13,14,15,16,17,18,19,20,22,27,29,30,38,43,48,49,50,51,56,57,59,63,65,66,67,68,69,70,71,72,73,75,77,81,83,84,85,86,93,94,95,96,97,98,99,100,101,102,104,105,106,107,108,110,111,112,113,115,119,120,121,122,123,124,125,129,131,132,133,135,137,140,142,146,147,148,149,150,151,152,154,156]). (Source published prior to 2010: [104,131,135,142,149,154]).

4. Discussion

While the broad pattern of disease disparities by race and ethnicity in the United States has received increasing attention across the medical literature in recent years, and especially since the COVID-19 pandemic [157,158,159,160,161], few papers highlighting disparities for individual disease occurrence or severity have appeared in prominent journals aimed at health care providers, the institutions supporting them, and other parties in the health sector. The explicit purpose of this paper was to delineate disparities across a sample of diseases that are the targets for drug development and day-to-day medical care. We used a visual map to evidence differences in both frequency of occurrence and severity of diseases, comparing Black/African Americans with NHW and Hispanics with NHW populations, noting that we have not explored other important populations such as Native Americans, Asians, or Pacific-Islanders, each known to have unique risks as well. While many of the observations summarized in Table 1 are well known to researchers in their respective fields and specialists who focus on the diagnosis and treatment of the diseases with disparities, the darker cells on the heat map may not be common knowledge to health care providers and academic medical centers, public health institutions, and policy makers. The latter group, including those with responsibility for resource and facilities planning, as well as communication with the public is the core target audience.
Despite achieving our goal of visually displaying health disparities affecting Black/African American and Hispanic populations, we must recognize several limitations. First, we did not conduct a systematic review or a meta-analysis of any single disease. For the latter, the summary of broad evidence includes all available datasets meeting pre-specified criteria. While this approach is ideal for the establishment of the best available evidence for the effect of an exposure or intervention on an outcome, our aim here was solely to identify the best descriptive information available and did not lend itself to a meta-analytic approach. Contrasts between this paper’s findings and reports in the literature are most likely attributable to non-representative sampling or misclassification of the race or ethnicity being assessed as opposed to analytic methods or choices more indicative of meta-analytic reviews. Likewise, systematic reviews have become a standard approach to comprehensive literature reviews, with pre-specified criteria applied to an exhaustive exploration of available reports, taking a critical approach to each source. As our purpose here was to identify evidence of population-level disparities for easy accessibility by providers and service planners, systematic review was not warranted. As only large population-level sources satisfy the need for generalizable inference about disparities, most other available studies are of limited value for this purpose. Granular clinical details, which are of enormous value for follow-up investigations into the root causes of the identified disparities, are less valuable for the purposes of the current paper than the size and representativeness of the populations described. To answer the questions at hand, we needed to stand back from the proverbial elephant before attempting to analyze it. From a more practical perspective, very few rigorous population-based studies have been conducted for many diseases, especially those that are less common. Many disorders reported here are, in fact, “orphans” of this sort, but we have attempted to make sense of what is available, reserving the category “ND” for those rare instances when there is literally nothing to draw inference from.
While reports on disease rate differences based on population-level sampling were available for most conditions, including rare ones, studies of disease severity differences between groups proved more challenging. Large representative registries such as SEER provide quantifiable severity data by race and ethnicity (e.g., stage, survival), but most other sources are cross-sectional and lack standardized criteria for severity comparisons between groups. To assess such differences in less lethal conditions, such as lupus or hidradenitis suppurative, it was necessary to avail reports that did not meet the more stringent population-based criteria, but which represented reports from large clinical registries, regional or local surveys, cohort and panel studies, and, as a last resort, large case series. For obvious reasons, these sources sometimes differed in their findings. Whenever such conflicts arose, we erred on the side of increased sensitivity to detect a disparity, which future research may demonstrate was an over-reach, to limit the likelihood of missing disparities likely to emerge from future investigations. In each case we relied on such sources, we marked the references in Table 1 with an obelisk symbol to distinguish it from studies using population-based sampling.
Data on Hispanics were far more limited than for Black/African Americans, perhaps in part because “Hispanic” ethnicity, whether deemed of White or Black/African American race, comprises a particularly complex population with recent ancestries from multiple and diverse regions of the Americas, which complicates interpretability of results. The same problem makes inference difficult for Asian Americans and the group presently lumped together as Native Americans, Hawaiians, and Pacific Islanders, for whom data were deemed too sparse even to attempt review. It is important to note there is also genetic and cultural heterogeneity within the Black/African American category used in most health equity research. Compared with U.S. born individuals of African ancestry, foreign-born persons of African descent have been reported to have a different psychosocial context and different sociocultural determinants that influence their health status and disease risk [162]. For example, previous research has shown that Black Caribbean immigrants to the U.S. differ from African Americans on multiple measures of physical health status [163].
A final limitation was the scope of our work. We limited our review to a convenient sample of 38 disorders from the development pipeline of Pfizer. While these conditions range across a wide swathe of diseases and disease categories, our findings only reflect observations on those considered.
Notwithstanding these limitations, our approach combines a sharp focus on national population-based estimates, relying as little as possible on sources derived from local or regional observations, and a method that is reproducible for other diseases. We illustrate a pathway by which similar and extended information could be gleaned for every medical condition under care in the U.S.’s highly diverse population.
Recognition is only the first step in the path to amelioration of disparities. For few of the conditions assessed here has sufficient evidence emerged to identify root causes that can be remedied. For example, the study on the biology of hypertension in NHWs and African Americans has led to certain race-directed algorithms for pharmacologic interventions based on the likely population-level differences in genetic mechanisms [164,165]. With the advent of pharmacogenetics, other drugs that target population differences for some disorders will likely lead to an expansion of such testing, based on population-level risk of carrying a variant genotype, as has evolved for anticoagulants [166,167]. However, the evidence suggests genetic differences are likely to explain only a small portion of the observed disparities for many of these diseases. Although many would view the search for genetic differences extremely valuable for identifying new targets for therapy and more personalized treatment approaches, others may view it as a failure in assuming responsibility for the lower-hanging fruit: differential access to care and differing quality of care, and uneven distribution of resources; differences in patient knowledge and trust; and differential behaviors and social and physical environments. These factors have been identified as social determinants of health, which the WHO defines as “the conditions in which people are born, grow, live, work, and age” [168]. These social determinants, rather than group-level genetic or biologic differences, appear to be the main drivers of the extreme race and ethnic group disparities observed in COVID-19 outcomes [157,158,159,160,161]. That said, it is likely that at the individual disease level, the contributions of behavior, and social and physical environment will vary greatly. We have undertaken this review for the purpose of highlighting how to identify disease targets for more in-depth disparities research.

5. Conclusions

We hope that our paper will generate, first, greater knowledge and appreciation among frontline health care workers and their institutions of disease-specific race and ethnic group disparities, and second, more research documenting these disparities and identifying their root causes. From this perspective, Table 1 should be viewed as a “map” highlighting fruitful areas of focus for translational scientists, clinical investigators, and those who provide and pay for available and future interventions for these disorders. It is our hope that others will use the approach we have taken to continue to address gaps in our knowledge of the disparities landscape, while bolder colleagues begin the critical deep dives into the ripest areas for interventions to lessen these disparities.
Yet even with good science, solutions to address these disparities, short of radical redistribution of society’s resources, will likely depend first and foremost on recognition and acceptance of the inequity issue by the entire health industry—health care providers, insurers, payors, health care organizations, device and drug manufacturers, drug retail stores, and public policy makers all have potential roles. Pfizer, which undertook and supported this review, fully embraces this broad public responsibility, and invites other entities to examine disparities across its portfolios to identify knowledge gaps and motivate unified societal action.

Author Contributions

Conceptualization, M.R.C., L.J.R., A.R.L. and A.H.; methodology, M.R.C., L.J.R. and A.R.L.; validation, L.J.R., A.R.L., D.M.B. and Y.A.; formal analysis, M.R.C.; writing—original draft preparation, M.R.C., L.J.R. and A.R.L.; writing—review and editing, M.R.C., L.J.R., A.R.L., D.M.B., Y.A. and A.H. All authors have read and agreed to the published version of the manuscript.


M.R.C. was a paid consultant to Pfizer. A.R.L., L.J.R., D.M.B., Y.A. and A.H. are full-time employees of Pfizer. A.H. is currently on leave from her position as a Professor of Medicine at Stanford University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


  1. Arias, E.; Tejada-Vera, B.; Ahmad, F. Provisional Life Expectancy Estimates for January through June, 2020; Vital Statistics Rapid Release; National Center for Health Statistics: Hyattsville, MD, USA, 2021.
  2. Carratala, S.; Maxwell, C. Health Disparities by Race and Ethnicity: Center for American Progress Action Fund. 2020. Available online: (accessed on 7 January 2022).
  3. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Population Health and Public Health Practice. The State of Health Disparities in the United States. In Communities in Action: Pathways to Health Equity; Baciu, A., Negussie, Y., Geller, A., Eds.; National Academies Press: Washington, DC, USA, 2017. [Google Scholar]
  4. Social Determinants of Health. U.S. Department of Health and Human Services: Office of Disease Prevention and Health Promotion. 2021. Available online: (accessed on 7 January 2022).
  5. Artiga, S.; Hinton, E. Beyond Health Care: The Role of Social Determinants in Promoting Health and Health Equity. 10 May 2018. Available online: (accessed on 7 January 2022).
  6. Cantu, P.A.; Hayward, M.D.; Hummer, R.A.; Chiu, C.T. New estimates of racial/ethnic differences in life expectancy with chronic morbidity and functional loss: Evidence from the National Health Interview Survey. J. Cross Cult. Gerontol. 2013, 28, 283–297. [Google Scholar] [CrossRef][Green Version]
  7. About the National Health and Nutrition Examination Survey: Centers for Disease Control and Prevention, National Center for Health Statistics. 2017. Available online: (accessed on 7 January 2022).
  8. About the National Health Interview Survey Centers for Disease Control and Prevention, National Center for Health Statistics. 2020. Available online: (accessed on 7 January 2022).
  9. Hastings, K.G.; Jose, P.O.; Kapphahn, K.I.; Frank, A.T.; Goldstein, B.A.; Thompson, C.A.; Eggleston, K.; Cullen, M.R.; Palaniappan, L.P. Leading Causes of Death among Asian American Subgroups (2003–2011). PLoS ONE 2015, 10, e0124341. [Google Scholar] [CrossRef] [PubMed][Green Version]
  10. Vega, W.A.; Rodriguez, M.A.; Gruskin, E. Health disparities in the Latino population. Epidemiol. Rev. 2009, 31, 99–112. [Google Scholar] [CrossRef] [PubMed][Green Version]
  11. Cancer Stat Facts: Myeloma: National Cancer Institute: Surveillance, Epidemiology, and End Results Program. 2021. Available online: (accessed on 7 January 2022).
  12. Marinac, C.R.; Ghobrial, I.M.; Birmann, B.M.; Soiffer, J.; Rebbeck, T.R. Dissecting racial disparities in multiple myeloma. Blood Cancer J. 2020, 10, 19. [Google Scholar] [CrossRef] [PubMed]
  13. Udechukwu, N.S.; Fleischer, A.B., Jr. Higher Risk of Care for Hidradenitis Suppurativa in African American and Non-Hispanic Patients in the United States. J. Natl. Med. Assoc. 2017, 109, 44–48. [Google Scholar] [CrossRef]
  14. Arnaud, L.; Tektonidou, M.G. Long-term outcomes in systemic lupus erythematosus: Trends over time and major contributors. Rheumatology 2020, 59 (Suppl. 5), v29–v38. [Google Scholar] [CrossRef]
  15. Crosslin, K.L.; Wiginton, K.L. The impact of race and ethnicity on disease severity in systemic lupus erythematosus. Ethn. Dis. 2009, 19, 301–307. [Google Scholar]
  16. Lim, S.S.; Helmick, C.G.; Bao, G.; Hootman, J.; Bayakly, R.; Gordon, C.; Drenkard, C. Racial Disparities in Mortality Associated with Systemic Lupus Erythematosus-Fulton and DeKalb Counties, Georgia, 2002–2016. MMWR Morb. Mortal Wkly. Rep. 2019, 68, 419–422. [Google Scholar] [CrossRef][Green Version]
  17. Maningding, E.; Dall’Era, M.; Trupin, L.; Murphy, L.B.; Yazdany, J. Racial and Ethnic Differences in the Prevalence and Time to Onset of Manifestations of Systemic Lupus Erythematosus: The California Lupus Surveillance Project. Arthritis Care Res. 2020, 72, 622–629. [Google Scholar] [CrossRef]
  18. Pons-Estel, G.J.; Alarcón, G.S.; Scofield, L.; Reinlib, L.; Cooper, G.S. Understanding the epidemiology and progression of systemic lupus erythematosus. Semin. Arthritis Rheum. 2010, 39, 257–268. [Google Scholar] [CrossRef][Green Version]
  19. Roberts, M.H.; Erdei, E. Comparative United States autoimmune disease rates for 2010–2016 by sex, geographic region, and race. Autoimmun. Rev. 2020, 19, 102423. [Google Scholar] [CrossRef] [PubMed]
  20. Yen, E.Y.; Singh, R.R. Brief Report: Lupus-An Unrecognized Leading Cause of Death in Young Females: A Population-Based Study Using Nationwide Death Certificates, 2000–2015. Arthritis Rheumatol. 2018, 70, 1251–1255. [Google Scholar] [CrossRef] [PubMed][Green Version]
  21. Samji, N.S.; Snell, P.D.; Singal, A.K.; Satapathy, S.K. Racial Disparities in Diagnosis and Prognosis of Nonalcoholic Fatty Liver Disease. Clin. Liver Dis. 2020, 16, 66–72. [Google Scholar] [CrossRef] [PubMed]
  22. Bleich, S.N.; Thorpe, R.J.; Jr Sharif-Harris, H.; Feshazion, R.; LaVeist, T. Social context explains race disparities in obesity among women. J. Epidemiol. Community Health 2010, 64, 465–469. [Google Scholar] [CrossRef][Green Version]
  23. Byrd, A.S.; Toth, A.T.; Stanford, F.C. Racial Disparities in Obesity Treatment. Curr. Obes. Rep. 2018, 7, 130–138. [Google Scholar] [CrossRef]
  24. Petersen, R.; Pan, L.; Blanck, H.M. Racial and Ethnic Disparities in Adult Obesity in the United States: CDC’s Tracking to Inform State and Local Action. Prev. Chronic Dis. 2019, 16, E46. [Google Scholar] [CrossRef][Green Version]
  25. Sen, B. Using the Oaxaca-Blinder decomposition as an empirical tool to analyze racial disparities in obesity. Obesity 2014, 22, 1750–1755. [Google Scholar] [CrossRef]
  26. Centers for Medicare & Medicaid Services. Racial and Ethnic Disparities in Diabetes Prevalence, Self-Management, and Health Outcomes among Medicare Beneficiaries CMS OMH Data Highlight; Centers for Medicare & Medicaid Services: Baltimore, MD, USA, 2017.
  27. Egede, L.E.; Gebregziabher, M.; Hunt, K.J.; Axon, R.N.; Echols, C.; Gilbert, G.E.; Mauldin, P.D. Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes. Diabetes Care 2011, 34, 938–943. [Google Scholar] [CrossRef][Green Version]
  28. Larsen, B.A.; Martin, L.; Strong, D.R. Sedentary behavior and prevalent diabetes in Non-Latino Whites, Non-Latino Blacks and Latinos: Findings from the National Health Interview Survey. J. Public Health 2015, 37, 634–640. [Google Scholar] [CrossRef][Green Version]
  29. Piccolo, R.S.; Duncan, D.T.; Pearce, N.; McKinlay, J.B. The role of neighborhood characteristics in racial/ethnic disparities in type 2 diabetes: Results from the Boston Area Community Health (BACH) Survey. Soc. Sci. Med. 2015, 130, 79–90. [Google Scholar] [CrossRef][Green Version]
  30. Spanakis, E.K.; Golden, S.H. Race/ethnic difference in diabetes and diabetic complications. Curr. Diab. Rep. 2013, 13, 814–823. [Google Scholar] [CrossRef] [PubMed][Green Version]
  31. Siegel, D.A.; O’Neil, M.E.; Richards, T.B.; Dowling, N.F.; Weir, H.K. Prostate Cancer Incidence and Survival, by Stage and Race/Ethnicity-United States, 2001–2017. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 1473–1480. [Google Scholar] [CrossRef] [PubMed]
  32. Rebbeck, T.R. Prostate Cancer Disparities by Race and Ethnicity: From Nucleotide to Neighborhood. Cold Spring Harb. Perspect. Med. 2018, 8, a030387. [Google Scholar] [CrossRef] [PubMed]
  33. Wen, W.; Luckenbaugh, A.N.; Bayley, C.E.; Penson, D.F.; Shu, X.O. Racial disparities in mortality for patients with prostate cancer after radical prostatectomy. Cancer 2021, 127, 1517–1528. [Google Scholar] [CrossRef]
  34. Underwood, W., 3rd. Racial Regional Variations in Prostate Cancer Survival Must Be Viewed in the Context of Overall Racial Disparities in Prostate Cancer. JAMA Netw. Open 2020, 3, e201854. [Google Scholar] [CrossRef][Green Version]
  35. Zavala, V.A.; Bracci, P.M.; Carethers, J.M.; Carvajal-Carmona, L.; Coggins, N.B.; Cruz-Correa, M.R.; Davis, M.; de Smith, A.J.; Dutil, J.; Feiredo, J.C.; et al. Cancer health disparities in racial/ethnic minorities in the United States. Br. J. Cancer 2021, 124, 315–332. [Google Scholar] [CrossRef]
  36. Soneji, S.; Tanner, N.T.; Silvestri, G.A.; Lathan, C.S.; Black, W. Racial and Ethnic Disparities in Early-Stage Lung Cancer Survival. Chest 2017, 152, 587–597. [Google Scholar] [CrossRef]
  37. Schabath, M.B.; Cress, D.; Munoz-Antonia, T. Racial and Ethnic Differences in the Epidemiology and Genomics of Lung Cancer. Cancer Control. 2016, 23, 338–346. [Google Scholar] [CrossRef][Green Version]
  38. Chlebowski, R.T.; Chen, Z.; Anderson, G.L.; Rohan, T.; Aragaki, A.; Lane, D.; Dolan, N.C.; Paskett, E.D.; McTiernan, A.; Hubbell, F.A.; et al. Ethnicity and breast cancer: Factors influencing differences in incidence and outcome. J. Natl. Cancer Inst. 2005, 97, 439–448. [Google Scholar] [CrossRef][Green Version]
  39. Ademuyiwa, F.O.; Edge, S.B.; Erwin, D.O.; Orom, H.; Ambrosone, C.B.; Underwood, W., 3rd. Breast cancer racial disparities: Unanswered questions. Cancer Res. 2011, 71, 640–644. [Google Scholar] [CrossRef][Green Version]
  40. Yedjou, C.G.; Sims, J.N.; Miele, L.; Noubissi, F.; Lowe, L.; Fonseca, D.D.; Alo, R.A.; Payton, M.; Tchounwou, P.B. Health and Racial Disparity in Breast Cancer. Adv. Exp. Med. Biol. 2019, 1152, 31–49. [Google Scholar] [CrossRef] [PubMed]
  41. Doepker, M.P.; Holt, S.D.; Durkin, M.W.; Chu, C.H.; Nottingham, J.M. Triple-Negative Breast Cancer: A Comparison of Race and Survival. Am. Surg. 2018, 84, 881–888. [Google Scholar] [CrossRef]
  42. Aizer, A.A.; Wilhite, T.J.; Chen, M.H.; Graham, P.L.; Choueiri, T.K.; Hoffman, K.E.; Martin, N.E.; Trin, Q.D.; Hu, J.C.; Nguyen, P.L. Lack of reduction in racial disparities in cancer-specific mortality over a 20-year period. Cancer 2014, 120, 1532–1539. [Google Scholar] [CrossRef] [PubMed]
  43. Silber, J.H.; Rosenbaum, P.R.; Clark, A.S.; Giantonio, B.J.; Ross, R.N.; Teng, Y.; Wang, M.; Niknam, B.A.; Ludwig, J.M.; Wang, W.; et al. Characteristics associated with differences in survival among black and white women with breast cancer. J. Am. Med. Assoc. 2013, 310, 389–397. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. Wang, Y.; Chang, Q.; Li, Y. Racial differences in Urinary Bladder Cancer in the United States. Sci. Rep. 2018, 8, 12521. [Google Scholar] [CrossRef]
  45. Yee, D.S.; Ishill, N.M.; Lowrance, W.T.; Herr, H.W.; Elkin, E.B. Ethnic differences in bladder cancer survival. Urology 2011, 78, 544–549. [Google Scholar] [CrossRef][Green Version]
  46. Augustus, G.J.; Ellis, N.A. Colorectal Cancer Disparity in African Americans: Risk Factors and Carcinogenic Mechanisms. Am. J. Pathol. 2018, 188, 291–303. [Google Scholar] [CrossRef]
  47. Jackson, C.S.; Oman, M.; Patel, A.M.; Patel, A.M.; Vega, K.J. Health disparities in colorectal cancer among racial and ethnic minorities in the United States. J. Gastrointest Oncol. 2016, 7 (Suppl. 1), S32–S43. [Google Scholar] [CrossRef]
  48. White, A.; Vernon, S.W.; Franzini, L.; Franzini, L.; Du, X.L. Racial disparities in colorectal cancer survival: To what extent are racial disparities explained by differences in treatment, tumor characteristics, or hospital characteristics? Cancer 2010, 116, 4622–4631. [Google Scholar] [CrossRef][Green Version]
  49. Settle, K.; Posner, M.R.; Schumaker, L.M.; Tan, M.; Suntharalingam, M.; Goloubeva, O.; Strome, S.E.; Haddad, R.I.; Patel, S.S.; Cambell, E.V., 3rd; et al. Racial survival disparity in head and neck cancer results from low prevalence of human papillomavirus infection in black oropharyngeal cancer patients. Cancer Prev. Res. 2009, 2, 776–781. [Google Scholar] [CrossRef][Green Version]
  50. Mukherjee, A.; Idigo, A.J.; Ye, Y.; Wiener, H.W.; Paluri, R.; Nabell, L.M.; Shrestha, S. Geographical and Racial Disparities in Head and Neck Cancer Diagnosis in South-Eastern United States: Using Real-World Electronic Medical Records Data. Health Equity 2020, 4, 43–51. [Google Scholar] [CrossRef] [PubMed]
  51. Albert, A.; Giri, S.; Kanakamedala, M.; Mangana, S.; Bhanat, E.; Shenoy, V.; Thomas, T.V.; Joseph, S.; Gonzalez, M.; Shalaby, A.; et al. Racial disparities in tumor features and outcomes of patients with squamous cell carcinoma of the tonsil. Laryngoscope 2019, 129, 643–654. [Google Scholar] [CrossRef] [PubMed]
  52. Schrank, T.P.; Han, Y.; Weiss, H.; Resto, V.A. Case-matching analysis of head and neck squamous cell carcinoma in racial and ethnic minorities in the United States--possible role for human papillomavirus in survival disparities. Head Neck 2011, 33, 45–53. [Google Scholar] [CrossRef] [PubMed]
  53. Oh, M.S.; Jiang, R.; Liu, Y.; Zhu, X.; Saba, N.F.; Henriquez, O.A. Patterns of head and neck cancer incidence, mortality, and survival in the U.S. Hispanic population. J. Clin. Oncol. 2017, 35 (Suppl. 15), e13084. [Google Scholar] [CrossRef]
  54. Centers for Disease Control and Prevention. US Cancer Statistics Data Brief: Melanoma Incidence and Mortality, United States–2012–2016; Centers for Disease Control and Prevention, US Department of Health and Human Services: Atlanta, GA, USA, 2019.
  55. Ward-Peterson, M.; Acuña, J.M.; Alkhalifah, M.K. Association Between Race/Ethnicity and Survival of Melanoma Patients in the United States Over 3 Decades: A Secondary Analysis of SEER Data. Medicine 2016, 95, e3315. [Google Scholar] [CrossRef]
  56. Kaur, G.; Mejia Saldarriaga, M.; Shah, N.; Catamero, D.D.; Yue, L.; Ashai, N.; Goradia, N.; Heisler, J.; Xiao, Z.; Ghalib, N.; et al. Multiple Myeloma in Hispanics: Incidence, Characteristics, Survival, Results of Discovery, and Validation Using Real-World and Connect MM Registry Data. Clin. Lymphoma Myeloma Leuk. 2021, 21, e384–e397. [Google Scholar] [CrossRef]
  57. Singh, D.K.; Magrey, M.N. Racial Differences in Clinical Features and Comorbidities in Ankylosing Spondylitis in the United States. J. Rheumatol. 2020, 47, 835–838. [Google Scholar] [CrossRef]
  58. Reveille, J.D.; Hirsch, R.; Dillon, C.F.; Carroll, M.D.; Weisman, M.H. The prevalence of HLA-B27 in the US: Data from the US National Health and Nutrition Examination Survey, 2009. Arthritis Rheum. 2012, 64, 1407–1411. [Google Scholar] [CrossRef][Green Version]
  59. Jamalyaria, F.; Ward, M.M.; Assassi, S.; Learch, T.J.; Lee, M.; Gensler, L.S.; Brown, M.A.; Diekman, L.; Tahanan, A.; Rahbar, M.H.; et al. Ethnicity and disease severity in ankylosing spondylitis a cross-sectional analysis of three ethnic groups. Clin. Rheumatol. 2017, 36, 2359–2364. [Google Scholar] [CrossRef]
  60. Reveille, J.D.; Witter, J.P.; Weisman, M.H. Prevalence of axial spondylarthritis in the United States: Estimates from a cross-sectional survey. Arthritis Care Res. 2012, 64, 905–910. [Google Scholar] [CrossRef][Green Version]
  61. Adigweme, O.; Nelson, C.L.; Watkins-Castillo, S.I. Arthritis, 4 ed. The Burden of Musculoskeletal Diseases in the United States. 2014. Available online: (accessed on 7 January 2022).
  62. Bolen, J.; Schieb, L.; Hootman, J.M.; Helmick, C.G.; Theis, K.; Murphy, L.B.; Langmaid, G. Differences in the prevalence and severity of arthritis among racial/ethnic groups in the United States, National Health Interview Survey, 2002, 2003, and 2006. Prev. Chronic Dis. 2010, 7, A64. [Google Scholar] [PubMed]
  63. Greenberg, J.D.; Spruill, T.M.; Shan, Y.; Reed, G.; Kremer, J.M.; Potter, J.; Yazici, Y.; Ogedegbe, G.; Harrold, L.R. Racial and ethnic disparities in disease activity in patients with rheumatoid arthritis. Am. J. Med. 2013, 126, 1089–1098. [Google Scholar] [CrossRef] [PubMed][Green Version]
  64. Obana, K.K.; Davis, J. Racial Disparities in the Prevalence of Arthritis among Native Hawaiians and Pacific Islanders, Whites, and Asians. Hawaii J. Med. Public Health 2016, 75, 155–161. [Google Scholar]
  65. Kawatkar, A.A.; Portugal, C.; Chu, L.; R., I. Racial/Ethnic Trends in Incidence and Prevalence of Rheumatoid Arthritis in a Large Multi-Ethnic Managed Care Population. In Proceedings of the 2012 ACR/ARHP Annual Meeting, Washington, DC, USA, 9–14 November 2012. [Google Scholar]
  66. Li, S.; Schwartz, A.V.; LaValley, M.P.; Wang, N.; Desai, N.; Sun, X.; Neogi, T.; Nevitt, M.; Lewis, C.E.; Guermazi, A.; et al. Association of Visceral Adiposity With Pain but Not Structural Osteoarthritis. Arthritis Rheumatol. 2020, 72, 1103–1110. [Google Scholar] [CrossRef]
  67. McClendon, J.; Essien, U.R.; Youk, A.; Ibrahim, S.A.; Vina, E.; Kwoh, C.K.; Hausmann, L.R.M. Cumulative Disadvantage and Disparities in Depression and Pain Among Veterans with Osteoarthritis: The Role of Perceived Discrimination. Arthritis Care Res. 2021, 73, 11–17. [Google Scholar] [CrossRef] [PubMed]
  68. Pierson, E.; Cutler, D.M.; Leskovec, J.; Mullainathan, S.; Obermeyer, Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat. Med. 2021, 27, 136–140. [Google Scholar] [CrossRef] [PubMed]
  69. Deshpande, B.R.; Katz, J.N.; Solomon, D.H.; Yelin, E.H.; Hunter, D.J.; Messier, S.P.; Suter, L.G.; Losina, E. Number of Persons with Symptomatic Knee Osteoarthritis in the US: Impact of Race and Ethnicity, Age, Sex, and Obesity. Arthritis Care Res. 2016, 68, 1743–1750. [Google Scholar] [CrossRef]
  70. Gaskin, D.J.; Karmarkar, T.D.; Maurer, A.; Bucay-Harari, L.; Casillas, G.; Gittens, A.; Jones, L.C.; Thorpe, R.J., Jr.; Tolbert, E.; Wood, J.E. Potential Role of Cost and Quality of Life in Treatment Decisions for Arthritis-Related Knee Pain in African American and Latina Women. Arthritis Care Res. 2020, 72, 692–698. [Google Scholar] [CrossRef]
  71. Thompson, K.A.; Terry, E.L.; Sibille, K.T.; Gossett, E.W.; Ross, E.N.; Bartley, E.J.; Glover, T.L.; Vaughn, I.A.; Cardoso, J.S.; Sotolongo, A.; et al. At the Intersection of Ethnicity/Race and Poverty: Knee Pain and Physical Function. J. Racial Ethn. Health Disparities 2019, 6, 1131–1143. [Google Scholar] [CrossRef]
  72. Cruz-Almeida, Y.; Sibille, K.T.; Goodin, B.R.; Petrov, M.E.; Bartley, E.J.; Riley, J.L., 3rd; King, C.D.; Glover, T.L.; Sotolongo, A.; Herbert, M.S.; et al. Racial and ethnic differences in older adults with knee osteoarthritis. Arthritis Rheumatol. 2014, 66, 1800–1810. [Google Scholar] [CrossRef]
  73. Vaughn, I.A.; Terry, E.L.; Bartley, E.J.; Schaefer, N.; Fillingim, R.B. Racial-Ethnic Differences in Osteoarthritis Pain and Disability: A Meta-Analysis. J. Pain 2019, 20, 629–644. [Google Scholar] [CrossRef] [PubMed]
  74. Centers for Disease Control and Prevention (CDC). Prevalence of doctor-diagnosed arthritis and arthritis-attributable effects among Hispanic adults, by Hispanic subgroup—United States, 2002, 2003, 2006, and 2009. MMWR Morb. Mortal. Wkly. Rep. 2011, 60, 167–171. [Google Scholar]
  75. Agrawal, M.; Cohen-Mekelburg, S.; Kayal, M.; Axelrad, J.; Galati, J.; Tricomi, B.; Kamal, K.; Faye, A.S.; Abrudescu, P.; Scherl, E.; et al. Disability in inflammatory bowel disease patients is associated with race, ethnicity and socio-economic factors. Aliment. Pharmacol. Ther. 2019, 49, 564–571. [Google Scholar] [CrossRef] [PubMed]
  76. Xu, F.; Wheaton, A.G.; Liu, Y.; Lu, H.; Greenlund, K.J. Hospitalizations for Inflammatory Bowel Disease Among Medicare Fee-for-Service Beneficiaries—United States, 1999–2017. MMWR Morb. Mortal. Wkly. Rep. 2019, 68, 1134–1138. [Google Scholar] [CrossRef] [PubMed][Green Version]
  77. Aniwan, S.; Harmsen, W.S.; Tremaine, W.J.; Loftus, E.V., Jr. Incidence of inflammatory bowel disease by race and ethnicity in a population-based inception cohort from 1970 through 2010. Therap. Adv. Gastroenterol. 2019, 12, 1756284819827692. [Google Scholar] [CrossRef] [PubMed][Green Version]
  78. Barnes, E.L.; Kochar, B.; Long, M.D.; Pekow, J.; Ananthakrishnan, A.; Anyane-Yeboa, A.; Martin, C.; Galanko, J.; Herfarth, H.H.; Kappelman, M.D.; et al. Lack of Difference in Treatment Patterns and Clinical Outcomes Between Black and White Patients with Inflammatory Bowel Disease. Inflamm. Bowel Dis. 2018, 24, 2634–2640. [Google Scholar] [CrossRef]
  79. Montgomery, S.R., Jr.; Butler, P.D.; Wirtalla, C.J.; Collier, K.T.; Hoffman, R.L.; Aarons, C.B.; Damrauer, S.M.; Kelz, R.R. Racial disparities in surgical outcomes of patients with Inflammatory Bowel Disease. Am. J. Surg. 2018, 215, 1046–1050. [Google Scholar] [CrossRef]
  80. Misra, R.; Faiz, O.; Munkholm, P.; Burisch, J.; Arebi, N. Epidemiology of inflammatory bowel disease in racial and ethnic migrant groups. World J. Gastroenterol. 2018, 24, 424–437. [Google Scholar] [CrossRef]
  81. Walker, C.; Allamneni, C.; Orr, J.; Yun, H.; Fitzmorris, P.; Xie, F.; Malik, T.A. Socioeconomic Status and Race are both Independently associated with Increased Hospitalization Rate among Crohn’s Disease Patients. Sci. Rep. 2018, 8, 4028. [Google Scholar] [CrossRef]
  82. Nguyen, G.C.; Chong, C.A.; Chong, R.Y. National estimates of the burden of inflammatory bowel disease among racial and ethnic groups in the United States. J. Crohns. Colitis 2014, 8, 288–295. [Google Scholar] [CrossRef][Green Version]
  83. Dotson, J.L.; Cho, M.; Bricker, J.; Kappelman, M.D.; Chisolm, D.J.; Tomer, G.; Crandall, W.V. Race Differences in Initial Presentation, Early Treatment, and 1-year Outcomes of Pediatric Crohn’s Disease: Results from the ImproveCareNow Network. Inflamm. Bowel Dis. 2017, 23, 767–774. [Google Scholar] [CrossRef] [PubMed][Green Version]
  84. Kim, Y.; Blomberg, M.; Rifas-Shiman, S.L.; Camargo, C.A., Jr.; Gold, D.R.; Thyssen, J.P.; Litonjua, A.A.; Oken, E.; Asgari, M.M. Racial/Ethnic Differences in Incidence and Persistence of Childhood Atopic Dermatitis. J. Investig. Dermatol. 2019, 139, 827–834. [Google Scholar] [CrossRef][Green Version]
  85. McKenzie, C.; Silverberg, J.I. The prevalence and persistence of atopic dermatitis in urban United States children. Ann. Allergy Asthma Immunol. 2019, 123, 173–178.e1. [Google Scholar] [CrossRef] [PubMed]
  86. Wan, J.; Oganisian, A.; Spieker, A.J.; Hoffstad, O.J.; Mitra, N.; Margolis, D.J.; Takeshita, J. Racial/Ethnic Variation in Use of Ambulatory and Emergency Care for Atopic Dermatitis among US Children. J. Investig. Dermatol. 2019, 139, 1906–1913.e1. [Google Scholar] [CrossRef] [PubMed]
  87. Fischer, A.H.; Shin, D.B.; Margolis, D.J.; Takeshita, J. Racial and ethnic differences in health care utilization for childhood eczema: An analysis of the 2001–2013 Medical Expenditure Panel Surveys. J. Am. Acad. Dermatol. 2017, 77, 1060–1067. [Google Scholar] [CrossRef]
  88. Shaw, T.E.; Currie, G.P.; Koudelka, C.W.; Simpson, E.L. Eczema prevalence in the United States: Data from the 2003 National Survey of Children’s Health. J. Investig. Dermatol. 2011, 131, 67–73. [Google Scholar] [CrossRef][Green Version]
  89. Silverberg, J.I.; Hanifin, J.M. Adult eczema prevalence and associations with asthma and other health and demographic factors: A US population-based study. J. Allergy Clin. Immunol. 2013, 132, 1132–1138. [Google Scholar] [CrossRef]
  90. WHO. Global Report on Psoriasis; World Health Organization: Geneva, Switzerland, 2016. [Google Scholar]
  91. Alexis, A.F.; Blackcloud, P. Psoriasis in skin of color: Epidemiology, genetics, clinical presentation, and treatment nuances. J. Clin. Aesthet. Dermatol. 2014, 7, 16–24. [Google Scholar]
  92. Rachakonda, T.D.; Schupp, C.W.; Armstrong, A.W. Psoriasis prevalence among adults in the United States. J. Am. Acad. Dermatol. 2014, 70, 512–516. [Google Scholar] [CrossRef]
  93. Price, K.N.; Hsiao, J.L.; Shi, V.Y. Race and Ethnicity Gaps in Global Hidradenitis Suppurativa Clinical Trials. Dermatology 2021, 237, 97–102. [Google Scholar] [CrossRef]
  94. Lee, D.E.; Clark, A.K.; Shi, V.Y. Hidradenitis Suppurativa: Disease Burden and Etiology in Skin of Color. Dermatology 2017, 233, 456–461. [Google Scholar] [CrossRef] [PubMed]
  95. Garg, A.; Lavian, J.; Lin, G.; Strunk, A.; Alloo, A. Incidence of hidradenitis suppurativa in the United States: A sex- and age-adjusted population analysis. J. Am. Acad. Dermatol. 2017, 77, 118–122. [Google Scholar] [CrossRef]
  96. Vlassova, N.; Kuhn, D.; Okoye, G.A. Hidradenitis suppurativa disproportionately affects African Americans: A single-center retrospective analysis. Acta Derm. Venereol. 2015, 95, 990–991. [Google Scholar] [CrossRef] [PubMed]
  97. Krüger, C.; Schallreuter, K.U. A review of the worldwide prevalence of vitiligo in children/adolescents and adults. Int. J. Dermatol. 2012, 51, 1206–1212. [Google Scholar] [CrossRef] [PubMed]
  98. Zhang, Y.; Cai, Y.; Shi, M.; Jiang, S.; Cui, S.; Wu, Y.; Gao, X.H.; Chen, H.D. The Prevalence of Vitiligo: A Meta-Analysis. PLoS ONE 2016, 11, e0163806. [Google Scholar] [CrossRef]
  99. Silverberg, J.I.; Reja, M.; Silverberg, N.B. Regional variation of and association of US birthplace with vitiligo extent. JAMA Dermatol. 2014, 150, 1298–1305. [Google Scholar] [CrossRef][Green Version]
  100. Sheth, V.M.; Guo, Y.; Qureshi, A.A. Comorbidities associated with vitiligo: A ten-year retrospective study. Dermatology 2013, 227, 311–315. [Google Scholar] [CrossRef]
  101. Lee, H.; Jung, S.J.; Patel, A.B.; Thompson, J.M.; Qureshi, A.; Cho, E. Racial characteristics of alopecia areata in the United States. J. Am. Acad. Dermatol. 2020, 83, 1064–1070. [Google Scholar] [CrossRef]
  102. Thompson, J.M.; Park, M.K.; Qureshi, A.A.; Cho, E. Race and Alopecia Areata amongst US Women. J. Investig. Dermatol. Symp. Proc. 2018, 19, S47–S50. [Google Scholar] [CrossRef][Green Version]
  103. Dua, A.; Desai, S.S.; Heller, J.A. The Impact of Race on Advanced Chronic Venous Insufficiency. J. Vasc. Surg. Venous Lymphat. Disord. 2015, 3, 126. [Google Scholar] [CrossRef]
  104. Criqui, M.H.; Jamosmos, M.; Fronek, A.; Denenberg, J.O.; Langer, R.D.; Bergan, J.; Golomb, B.A. Chronic venous disease in an ethnically diverse population: The San Diego Population Study. Am. J. Epidemiol. 2003, 158, 448–456. [Google Scholar] [CrossRef] [PubMed]
  105. Lin, S.X.; Carnethon, M.; Szklo, M.; Bertoni, A. Racial/ethnic differences in the association of triglycerides with other metabolic syndrome components: The Multi-Ethnic Study of Atherosclerosis. Metab. Syndr. Relat. Disord. 2011, 9, 35–40. [Google Scholar] [CrossRef] [PubMed][Green Version]
  106. Ye, X.; Kong, W.; Zafar, M.I.; Chen, L.L. Serum triglycerides as a risk factor for cardiovascular diseases in type 2 diabetes mellitus: A systematic review and meta-analysis of prospective studies. Cardiovasc. Diabetol. 2019, 18, 48. [Google Scholar] [CrossRef] [PubMed][Green Version]
  107. Rodriguez, C.J.; Daviglus, M.L.; Swett, K.; González, H.M.; Gallo, L.C.; Wassertheil-Smoller, S.; Giachello, A.L.; Teng, Y.; Schneiderman, N.; Talavera, G.A.; et al. Dyslipidemia patterns among Hispanics/Latinos of diverse background in the United States. Am. J. Med. 2014, 127, 1186–1194.e1. [Google Scholar] [CrossRef] [PubMed][Green Version]
  108. Essilfie, G.; Shavelle, D.M.; Tun, H.; Platt, K.; Kobayashi, R.; Mehra, A.; Matthews, R.V.; Clavijo, L.; Gaglia, M.A., Jr. Association of elevated triglycerides and acute myocardial infarction in young Hispanics. Cardiovasc. Revasc. Med. 2016, 17, 510–514. [Google Scholar] [CrossRef] [PubMed]
  109. Carroll, M.; Kit, B.; Lacher, D. Trends in Elevated Triglyceride in Adults: United States, 2001–2012; NCHS Data Brief; Centers for Disease Control and Prevention: Hyattsville, MD, USA, 2015; p. 198.
  110. Haffner, S.M.; D’Agostino, R., Jr.; Goff, D.; Howard, B.; Festa, A.; Saad, M.F.; Mykkänen, L. LDL size in African Americans, Hispanics, and non-Hispanic whites: The insulin resistance atherosclerosis study. Arterioscler. Thromb. Vasc. Biol. 1999, 19, 2234–2240. [Google Scholar] [CrossRef] [PubMed][Green Version]
  111. Booker, S.; Cardoso, J.; Cruz-Almeida, Y.; Sibille, K.T.; Terry, E.L.; Powell-Roach, K.L.; Riley, J.L., 3rd; Goodin, B.R.; Bartley, E.J.; Addison, A.S.; et al. Movement-evoked pain, physical function, and perceived stress: An observational study of ethnic/racial differences in aging non-Hispanic Blacks and non-Hispanic Whites with knee osteoarthritis. Exp. Gerontol. 2019, 124, 110622. [Google Scholar] [CrossRef]
  112. Vina, E.R.; Ran, D.; Ashbeck, E.L.; Kwoh, C.K. Natural history of pain and disability among African-Americans and Whites with or at risk for knee osteoarthritis: A longitudinal study. Osteoarthr. Cartil. 2018, 26, 471–479. [Google Scholar] [CrossRef][Green Version]
  113. Hollingshead, N.A.; Ashburn-Nardo, L.; Stewart, J.C.; Hirsh, A.T. The Pain Experience of Hispanic Americans: A Critical Literature Review and Conceptual Model. J. Pain 2016, 17, 513–528. [Google Scholar] [CrossRef][Green Version]
  114. Zelaya, C.E.; Dahlhamer, J.M.; Lucas, J.W.; Connor, E.M. Chronic Pain and High-impact Chronic Pain Among, U.S. Adults, 2019; NCHS Data Brief; Centers for Disease Control and Prevention: Hyattsville, MD, USA, 2020; pp. 1–8.
  115. Rich, N.E.; Oji, S.; Mufti, A.R.; Browning, J.D.; Parikh, N.D.; Odewole, M.; Singal, A.G. Racial and Ethnic Disparities in Nonalcoholic Fatty Liver Disease Prevalence, Severity, and Outcomes in the United States: A Systematic Review and Meta-analysis. Clin. Gastroenterol. Hepatol. 2018, 16, 198–210.e2. [Google Scholar] [CrossRef][Green Version]
  116. Arshad, T.; Golabi, P.; Henry, L.; Younossi, Z.M. Epidemiology of Non-alcoholic Fatty Liver Disease in North America. Curr. Pharm. Des. 2020, 26, 993–997. [Google Scholar] [CrossRef] [PubMed]
  117. Kabbany, M.N.; Conjeevaram Selvakumar, P.K.; Watt, K.; Lopez, R.; Akras, Z.; Zein, N.; Carey, W.; Alkhouri, N. Prevalence of Nonalcoholic Steatohepatitis-Associated Cirrhosis in the United States: An Analysis of National Health and Nutrition Examination Survey Data. Am. J. Gastroenterol. 2017, 112, 581–587. [Google Scholar] [CrossRef] [PubMed]
  118. Sherif, Z.A.; Saeed, A.; Ghavimi, S.; Nouraie, S.M.; Laiyemo, A.O.; Brim, H.; Ashktorab, H. Global Epidemiology of Nonalcoholic Fatty Liver Disease and Perspectives on US Minority Populations. Dig. Dis. Sci. 2016, 61, 1214–1225. [Google Scholar] [CrossRef] [PubMed][Green Version]
  119. Bambha, K.; Belt, P.; Abraham, M.; Wilson, L.A.; Pabst, M.; Ferrell, L.; Unalp-Arida, A.; Bass, N. Ethnicity and nonalcoholic fatty liver disease. Hepatology 2012, 55, 769–780. [Google Scholar] [CrossRef] [PubMed][Green Version]
  120. Al-Naamani, N.; Paulus, J.K.; Roberts, K.E.; Pauciulo, M.W.; Lutz, K.; Nichols, W.C.; Kawut, S.M. Racial and ethnic differences in pulmonary arterial hypertension. Pulm. Circ. 2017, 7, 793–796. [Google Scholar] [CrossRef][Green Version]
  121. National Organization for Rare Disorders. Pulmonary Arterial Hypertension. 2021. Available online: (accessed on 7 January 2022).
  122. Medrek, S.; Sahay, S.; Zhao, C.; Selej, M.; Frost, A. Impact of race on survival in pulmonary arterial hypertension: Results from the REVEAL registry. J. Heart Lung Transplant. 2020, 39, 321–330. [Google Scholar] [CrossRef][Green Version]
  123. Naso, P.; Falco, L.; Porcari, A.; Di Lenarda, A.; Lardieri, G. Epidemiology. In Dilated Cardiomyopathy: From Genetics to Clinical Management; Sinagra, G., Merlo, M., Pinamonti, B., Eds.; Springer: Cham, Switzerland, 2019; pp. 11–16. [Google Scholar]
  124. National Organization for Rare Disorders. Growth Hormone Deficiency. 2016. Available online: (accessed on 7 January 2022).
  125. Grimberg, A.; Lindberg, A.; Wajnrajch, M.; Cucchiara, A.J.; Camacho-Hübner, C. Racial/Ethnic Disparities in US Pediatric Growth Hormone Treatment. Horm. Res. Paediatr. 2018, 90, 102–108. [Google Scholar] [CrossRef]
  126. Soucie, J.M.; Miller, C.H.; Dupervil, B.; Le, B.; Buckner, T.W. Occurrence rates of haemophilia among males in the United States based on surveillance conducted in specialized haemophilia treatment centres. Haemophilia 2020, 26, 487–493. [Google Scholar] [CrossRef] [PubMed]
  127. Data and Statistics on Hemophilia: Centers for Disease Control and Prevention 2020. Available online: (accessed on 7 January 2022).
  128. Salzberg, D.C.; Mann, J.R.; McDermott, S. Differences in Race and Ethnicity in Muscular Dystrophy Mortality Rates for Males under 40 Years of Age, 2006–2015. Neuroepidemiology 2018, 50, 201–206. [Google Scholar] [CrossRef]
  129. Crisafulli, S.; Sultana, J.; Fontana, A.; Salvo, F.; Messina, S.; Trifirò, G. Global epidemiology of Duchenne muscular dystrophy: An updated systematic review and meta-analysis. Orphanet. J. Rare Dis. 2020, 15, 141. [Google Scholar] [CrossRef] [PubMed]
  130. Romitti, P.A.; Zhu, Y.; Puzhankara, S.; James, K.A.; Nabukera, S.K.; Zamba, G.K.; Ciafaloni, E.; Cunniff, C.; Druschel, C.M.; Mathews, K.D.; et al. Prevalence of Duchenne and Becker muscular dystrophies in the United States. Pediatrics 2015, 135, 513–521. [Google Scholar] [CrossRef] [PubMed][Green Version]
  131. Kitiyakara, C.; Kopp, J.B.; Eggers, P. Trends in the epidemiology of focal segmental glomerulosclerosis. Semin. Nephrol. 2003, 23, 172–182. [Google Scholar] [CrossRef] [PubMed][Green Version]
  132. Guruswamy Sangameswaran, K.D.; Baradhi, K.M. Focal Segmental Glomerulosclerosis; StatPearls Publishing: Treasure Island, FL, USA, 2021. [Google Scholar]
  133. Sim, J.J.; Batech, M.; Hever, A.; Harrison, T.N.; Avelar, T.; Kanter, M.H.; Jacobsen, S.J. Distribution of Biopsy-Proven Presumed Primary Glomerulonephropathies in 2000–2011 Among a Racially and Ethnically Diverse US Population. Am. J. Kidney Dis. 2016, 68, 533–544. [Google Scholar] [CrossRef] [PubMed][Green Version]
  134. Coi, A.; Santoro, M.; Garne, E.; Pierini, A.; Addor, M.C.; Alessandri, J.L.; Bergman, J.E.H.; Bianchi, F.; Boban, L.; Braz, P.; et al. Epidemiology of achondroplasia: A population-based study in Europe. Am. J. Med. Genet. A 2019, 179, 1791–1798. [Google Scholar] [CrossRef] [PubMed]
  135. Waller, D.K.; Correa, A.; Vo, T.M.; Wang, Y.; Hobbs, C.; Langlois, P.H.; Pearson, K.; Romitti, P.A.; Shaw, G.M.; Hecht, J.T. The population-based prevalence of achondroplasia and thanatophoric dysplasia in selected regions of the US. Am. J. Med. Genet. A 2008, 146A, 2385–2389. [Google Scholar] [CrossRef]
  136. Landgren, O.; Gridley, G.; Fears, T.R.; Caporaso, N. Immune thrombocytopenic purpura does not exhibit a disparity in prevalence between African American and White veterans. Blood 2006, 108, 1111–1112. [Google Scholar] [CrossRef][Green Version]
  137. Kim, T.O.; Grimes, A.B.; Kirk, S.E.; Gilbert, M.M.; Reed, H.D.; Staggers, K.A.; Walker, L.A.; Arulselvan, A.; Cohen, A.S.; Lambert, M.P.; et al. Racial variation in ITP prevalence and chronic disease phenotype suggests biological differences. Blood 2020, 136, 640–643. [Google Scholar] [CrossRef]
  138. Moulis, G.; Palmaro, A.; Montastruc, J.L.; Godeau, B.; Lapeyre-Mestre, M.; Sailler, L. Epidemiology of incident immune thrombocytopenia: A nationwide population-based study in France. Blood 2014, 124, 3308–3315. [Google Scholar] [CrossRef][Green Version]
  139. Ojodu, J.; Hulihan, M.M.; Pope, S.N.; Grant, A.M. Incidence of sickle cell trait—United States, 2010. MMWR Morb. Mortal. Wkly. Rep. 2014, 63, 1155–1158. [Google Scholar]
  140. Solovieff, N.; Hartley, S.W.; Baldwin, C.T.; Klings, E.S.; Gladwin, M.T.; Taylor, J.G.t.; Kato, G.J.; Farrer, L.A.; Steinberg, M.H.; Sebastiani, P. Ancestry of African Americans with sickle cell disease. Blood Cells Mol. Dis. 2011, 47, 41–45. [Google Scholar] [CrossRef][Green Version]
  141. Springer, Y.P.; Johnson, P.T.J. Large-scale health disparities associated with Lyme disease and human monocytic ehrlichiosis in the United States, 2007–2013. PLoS ONE 2018, 13, e0204609. [Google Scholar] [CrossRef] [PubMed]
  142. Fix, A.D.; Peña, C.A.; Strickland, G.T. Racial differences in reported Lyme disease incidence. Am. J. Epidemiol. 2000, 152, 756–759. [Google Scholar] [CrossRef] [PubMed]
  143. Lyme Disease Charts and Figures: Most Recent Year: Centers for Disease Control and Prevention 2021. Available online:,%20%20592%20%2014%20more%20rows%20 (accessed on 7 January 2022).
  144. Argamany, J.R.; Delgado, A.; Reveles, K.R. Clostridium difficile infection health disparities by race among hospitalized adults in the United States, 2001 to 2010. BMC Infect. Dis. 2016, 16, 454. [Google Scholar] [CrossRef][Green Version]
  145. Mao, E.J.; Kelly, C.R.; Machan, J.T. Racial Differences in Clostridium difficile Infection Rates Are Attributable to Disparities in Health Care Access. Antimicrob. Agents Chemother. 2015, 59, 6283–6287. [Google Scholar] [CrossRef] [PubMed][Green Version]
  146. Soto, K.; Petit, S.; Hadler, J.L. Changing disparities in invasive pneumococcal disease by socioeconomic status and race/ethnicity in Connecticut, 1998–2008. Public Health Rep. 2011, 126 (Suppl. 3), 81–88. [Google Scholar] [CrossRef] [PubMed][Green Version]
  147. De St Maurice, A.; Grijalva, C.G.; Fonnesbeck, C.; Schaffner, W.; Halasa, N.B. Racial and Regional Differences in Rates of Invasive Pneumococcal Disease. Pediatrics 2015, 136, e1186–e1194. [Google Scholar] [CrossRef] [PubMed]
  148. Wortham, J.M.; Zell, E.R.; Pondo, T.; Harrison, L.H.; Schaffner, W.; Lynfield, R.; Thomas, A.; Reingold, A.; Bennett, N.M.; Petit, S.; et al. Racial disparities in invasive Streptococcus pneumoniae infections, 1998–2009. Clin. Infect. Dis. 2014, 58, 1250–1257. [Google Scholar] [CrossRef]
  149. Clarke, C.; Mallonee, S. State-based surveillance to determine trends in meningococcal disease. Public Health Rep. 2009, 124, 280–287. [Google Scholar] [CrossRef]
  150. Redelings, M.D.; Sorvillo, F.; Simon, P. A population-based analysis of pneumococcal disease mortality in California, 1989–1998. Public Health Rep. 2005, 120, 157–164. [Google Scholar] [CrossRef]
  151. American Lung Association. Trends in Pneumonia and Influenza Morbidity and Mortality; American Lung Association: Chicago, IL, USA, 2015. [Google Scholar]
  152. Spiel, M.H.; Hacker, M.R.; Haviland, M.J.; Mulla, B.; Roberts, E.; Dodge, L.E.; Young, B.C. Racial disparities in intrapartum group B Streptococcus colonization: A higher incidence of conversion in African American women. J. Perinatol. 2019, 39, 433–438. [Google Scholar] [CrossRef]
  153. Group B Strep (GBS) Clinical Overview: Centers for Disease Control and Prevention. 2020. Available online: (accessed on 7 January 2022).
  154. Espinosa, M.L.; Lio, P. Skin Issues that Affect Patients with Skin of Color. Dermatol. Times 2019, 4. Available online: (accessed on 7 January 2022).
  155. MacNeil, J.R.; Blain, A.E.; Wang, X.; Cohn, A.C. Current Epidemiology and Trends in Meningococcal Disease-United States, 1996–2015. Clin. Infect. Dis. 2018, 66, 1276–1281. [Google Scholar] [CrossRef] [PubMed]
  156. Mbaeyi, S.A.; Blain, A.; Whaley, M.J.; Wang, X.; Cohn, A.C.; MacNeil, J.R. Epidemiology of Meningococcal Disease Outbreaks in the United States, 2009–2013. Clin. Infect. Dis. 2019, 68, 580–585. [Google Scholar] [CrossRef] [PubMed]
  157. Health Equity Considerations and Racial and Ethnic Minority Groups Centers for Disease Control and Prevention. 2021. Available online: (accessed on 7 January 2022).
  158. Chowkwanyun, M.; Reed, A.L., Jr. Racial Health Disparities and Covid-19—Caution and Context. N. Engl. J. Med. 2020, 383, 201–203. [Google Scholar] [CrossRef]
  159. Lopez, L., 3rd; Hart, L.H., 3rd; Katz, M.H. Racial and Ethnic Health Disparities Related to COVID-19. J. Am. Med. Assoc. 2021, 325, 719–720. [Google Scholar] [CrossRef]
  160. Reitsma, M.B.; Claypool, A.L.; Vargo, J.; Shete, P.B.; McCorvie, R.; Wheeler, W.H.; Rocha, D.A.; Myers, J.F.; Murray, E.L.; Bregman, B.; et al. Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California. Health Aff. 2021, 40, 870–878. [Google Scholar] [CrossRef]
  161. Wen, L.S.; Sadeghi, N.B. Addressing Racial Health Disparities in the COVID-19 Pandemic: Immediate and Long-Term Policy Solutions. Health Aff. Forefr. 2020, 10, 594. [Google Scholar]
  162. Amuta-Jimenez, A.O.; Jacobs, W.; Smith, G. Health Disparities and the Heterogeneity of Blacks/African Americans in the United States: Why Should We Care? Health Promot. Pract. 2020, 21, 492–495. [Google Scholar] [CrossRef]
  163. Williams, D.R.; Haile, R.; González, H.M.; Neighbors, H.; Baser, R.; Jackson, J.S. The mental health of Black Caribbean immigrants: Results from the National Survey of American Life. Am. J. Public Health 2007, 97, 52–59. [Google Scholar] [CrossRef]
  164. Williams, S.K.; Ravenell, J.; Seyedali, S.; Nayef, S.; Ogedegbe, G. Hypertension Treatment in Blacks: Discussion of the U.S. Clinical Practice Guidelines. Prog. Cardiovasc. Dis. 2016, 59, 282–288. [Google Scholar] [CrossRef][Green Version]
  165. Zilbermint, M.; Hannah-Shmouni, F.; Stratakis, C.A. Genetics of Hypertension in African Americans and Others of African Descent. Int. J. Mol. Sci. 2019, 20, 1081. [Google Scholar] [CrossRef] [PubMed][Green Version]
  166. Daly, A.K. Pharmacogenomics of anticoagulants: Steps toward personal dosage. Genome Med. 2009, 1, 10. [Google Scholar] [CrossRef] [PubMed][Green Version]
  167. Raymond, J.; Imbert, L.; Cousin, T.; Duflot, T.; Varin, R.; Wils, J.; Lamoureux, F. Pharmacogenetics of Direct Oral Anticoagulants: A Systematic Review. J. Pers. Med. 2021, 11, 37. [Google Scholar] [CrossRef] [PubMed]
  168. World Health Organization. Social Determinants of Health. 2021. Available online: (accessed on 7 January 2022).
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Cullen, M.R.; Lemeshow, A.R.; Russo, L.J.; Barnes, D.M.; Ababio, Y.; Habtezion, A. Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity. Healthcare 2022, 10, 603.

AMA Style

Cullen MR, Lemeshow AR, Russo LJ, Barnes DM, Ababio Y, Habtezion A. Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity. Healthcare. 2022; 10(4):603.

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Cullen, Mark R., Adina R. Lemeshow, Leo J. Russo, David M. Barnes, Yaa Ababio, and Aida Habtezion. 2022. "Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity" Healthcare 10, no. 4: 603.

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