Diverse Research Teams and Underrepresented Groups in Clinical Studies

Ashank Bains; Pawarissara Osathanugrah; Nayan Sanjiv; Cedrick Chiu; Marissa G Fiorello; Nicole H Siegel; Crandall E Peeler; Alberto G Distefano; Hyunjoo J Lee; Steven Ness; Manishi A Desai; Jenna R Titelbaum; Tony Pira; Kara C LaMattina; Stephen P Christiansen; Howard J Cabral; Manju L Subramanian

doi:10.1001/jamaophthalmol.2023.4638

. 2023 Oct 19;141(11):1037–1044. doi: 10.1001/jamaophthalmol.2023.4638

Diverse Research Teams and Underrepresented Groups in Clinical Studies

Ashank Bains ¹, Pawarissara Osathanugrah ², Nayan Sanjiv ², Cedrick Chiu ³, Marissa G Fiorello ², Nicole H Siegel ^2,⁴, Crandall E Peeler ^2,⁴, Alberto G Distefano ^2,⁴, Hyunjoo J Lee ^2,⁴, Steven Ness ^2,⁴, Manishi A Desai ^2,⁴, Jenna R Titelbaum ^2,⁴, Tony Pira ^2,⁴, Kara C LaMattina ^2,⁴, Stephen P Christiansen ^2,^4,⁵, Howard J Cabral ⁶, Manju L Subramanian ^2,^4,^✉

¹Department of Ophthalmology, University of Washington, Seattle

²Department of Ophthalmology, Boston Medical Center, Boston, Massachusetts

³Boston College, Morrissey College of Arts and Sciences, Boston, Massachusetts

⁴Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts

⁵Department of Pediatrics, Boston Medical Center, Boston, Massachusetts

⁶Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts

Accepted for Publication: August 26, 2023.

Published Online: October 19, 2023. doi:10.1001/jamaophthalmol.2023.4638

^✉

Corresponding Author: Manju L. Subramanian, MD, Department of Ophthalmology, Boston Medical Center, 85 East Concord St, #8813, Boston, MA 02118 (manju.subramanian@bmc.org).

Author Contributions: Dr Subramanian had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bains, Fiorello, Siegel, Peeler, Distefano, Christiansen, Cabral, Subramanian.

Acquisition, analysis, or interpretation of data: Bains, Osathanugrah, Sanjiv, Chiu, Fiorello, Peeler, Lee, Ness, Desai, Titelbaum, Pira, LaMattina, Cabral, Subramanian.

Drafting of the manuscript: Bains, Sanjiv, Chiu, Fiorello, Peeler, Desai, Titelbaum, Christiansen.

Critical review of the manuscript for important intellectual content: Bains, Osathanugrah, Sanjiv, Fiorello, Siegel, Peeler, Distefano, Lee, Ness, Desai, Titelbaum, Pira, LaMattina, Cabral, Subramanian.

Statistical analysis: Bains, Sanjiv, Cabral.

Administrative, technical, or material support: Bains, Chiu, Fiorello, Peeler, Ness, Desai, Titelbaum, Pira, Christiansen.

Supervision: Osathanugrah, Fiorello, Siegel, Peeler, Distefano, Christiansen, Subramanian.

Conflict of Interest Disclosures: Dr Ness reported receiving grants from the National Institutes of Health (NIH) and the Diabetic Retinopathy Clinical Research (DRCR) Network and receiving personal fees from the DRCR Network outside the submitted work. Dr Christiansen reported serving on the scientific advisory board for iVeena Delivery Systems, receiving grants from the NIH, and being a consultant for Laboratoires Thea outside the submitted work. Dr Subramanian reported receiving grants from the NIH and research support from Novartis and DRCR.net. No other disclosures were reported.

Data Sharing Statement: See Supplement 2.

^✉

Corresponding author.

PMCID: PMC10587823 PMID: 37856135

This cohort study assesses whether demographic factors of potential research participants and research personnel are associated with rates of patient participation in prospective ophthalmic clinical studies.

Key Points

Question

What demographic factors of potential research participants and research personnel are associated with rates of patient participation in prospective ophthalmic clinical studies?

Findings

In this cohort study including 1380 potential participants, patients from racial and ethnic minority groups and those with lower socioeconomic status were less likely to consent to participate in ophthalmic clinical studies. Concordance of race and ethnicity between patients and research staff was associated with improved enrollment.

Meaning

Diverse clinical research teams may be associated with increased racial and ethnic minority patient enrollment and improved representation in ophthalmic clinical studies.

Abstract

Importance

Several ophthalmic diseases disproportionately affect racial and ethnic minority patients, yet most clinical trials struggle to enroll cohorts that are demographically representative of disease burden; some barriers to recruitment include time and transportation, language and cultural differences, and fear and mistrust of research due to historical abuses. Incorporating diversity within the research team has been proposed as a method to increase trust and improve engagement among potential study participants.

Objective

To examine how demographic factors of potential research participants and personnel may be associated with patient consent rates to participate in prospective ophthalmic clinical studies.

Design, Setting, and Participants

This retrospective cohort study included patients from an urban, academic hospital who were approached for consent to participate in prospective ophthalmic clinical studies conducted between January 2015 and December 2021.

Main Outcomes and Measures

Multivariable logistic regression assessing associations between patient and research personnel demographics and rates of affirmative consent to participate was used.

Results

In total, 1380 patients (mean [SD] age, 58.6 [14.9] years; 50.3% male) who were approached for consent to participate in 10 prospective ophthalmic clinical studies were included. Of prospective patients, 566 (43.5%) were Black; 327 (25.1%), Hispanic or Latino; 373 (28.6%), White; 36 (2.8%), other race and ethnicity; and 78 (5.8%) declined to answer. Black patients (odds ratio [OR], 0.32; 95% CI, 0.24-0.44; P < .001) and Hispanic or Latino patients (OR, 0.31; 95% CI, 0.20-0.47; P < .001) were less likely to consent compared with White patients. Patients with lower socioeconomic status were less likely to consent than patients with higher socioeconomic status (OR, 0.43; 95% CI, 0.33-0.53; P < .001). Concordance between patient and research staff race and ethnicity was associated with increased odds of affirmative consent (OR, 2.72; 95% CI, 1.99-3.73; P < .001).

Conclusions and Relevance

In this cohort study, patients from underrepresented racial and ethnic groups and those with lower socioeconomic status were less likely to participate in ophthalmic clinical studies. Concordance of race and ethnicity between patients and research staff was associated with improved participant enrollment. These findings underscore the importance of increasing diversity in clinical research teams to improve racial and ethnic representation in clinical studies.

Introduction

Several ophthalmic diseases have been found to disproportionately affect racial and ethnic minority patients,^1,2,3 and these disparities may persist in treatment outcomes.^4,5,6 Clinical trials have struggled to capture demographically diverse samples; studies have shown that racial and ethnic minority patients consent to participate in clinical trials at a lower rate across a variety of medical specialties.^7,8,9,10 This disparity may be associated with various factors, such as time and travel burden, language barriers, and historical and structural inequities, including prior mistreatment and other unethical practices in medical research. These factors likely continue to have a lasting impact^11,12; Corbie-Smith et al¹³ surveyed 1000 patients regarding medical research participation and found that compared with White patients, Black patients were more likely to feel that their physicians would expose them to unnecessary risk or include them in a research experiment without their consent.

Understanding ways to improve recruitment of racially and ethnically underrepresented patients in clinical research is critical. To provide the best care for all patients, clinical trials should involve diverse patient cohorts that match the demographics and disease burden of the populations served. This study examined how the demographic factors of potential research participants and personnel may be associated with patient consent rates to participate in prospective ophthalmic clinical studies. We hypothesized that participation of racially and ethnically underrepresented patients in clinical studies would increase with diverse research teams and the ability to communicate with patients in their primary language.

Methods

This retrospective cohort study was exempted with limited review by the institutional review board of Boston University Chobanian & Avedisian School of Medicine and Boston Medical Center (BMC) because the research involved the collection or study of existing data that were originally recorded by the investigator in such a manner that patients could not be identified, directly or through identifiers linked to the patients. We examined screening logs from 10 prospective ophthalmic clinical studies conducted between January 2015 and December 2021 (Table 1), from the Department of Ophthalmology at BMC, an urban, academic hospital that serves a diverse and largely underserved patient population.²⁵ Informed consent was waived, as the institutional review board determined that this study was of minimal risk to patients. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Table 1. Description of Included Prospective Studies.

Trial	Principal investigator	Patients approached for consent, No.	Consent rate, %	Design	Reimbursement	Description
Oral Vs Intravenous Sedation for Ocular Procedures^14,15,16,17	M.L.S.	777	41.4	Interventional	Yes	Single-center clinical trial comparing patient satisfaction with oral vs intravenous sedation for ocular surgery
Eye Biomarkers Study¹⁸	M.L.S.	210	29.5	Interventional	Yes	Single-center study sampling levels of Alzheimer disease–related proteins (amyloid-β, tau) in vitreous humor, aqueous, and tear secretions
Pediatric Myopia	J.R.T.	93	75.3	Noninterventional (pediatric)	No	Single-center study analyzing the potential myopic progression during the COVID-19 remote learning period in pediatric patients aged 3-17 y
Ocular Technologies	T.P.	77	90.0	Noninterventional	No	Single-center validation study for a novel stereo video slitlamp microscopy platform for examination of the anterior segment of the eye
Automated Pupillometry in Opioid Use Disorder¹⁹	C.E.P.	67	40.3	Noninterventional	No	Single-center study examining automated pupillometry in grading opioid withdrawal, patients receiving opioid agonists for withdrawal, and measurements taken via pupillometry pre- and postopioid administration
Multifunctional OCT^20,21^a	H.J.L., S.N., and M.A.D.	57	38.6	Noninterventional	Subset received	Single-center imaging study of patients with retinal, corneal, or conjunctival abnormalities to evaluate an experimental visible-light OCT technology; some participants were compared with healthy controls via multifunctional OCT and/or conventional OCT
Novo Focus²²	N.H.S.	42	50.0	Interventional	Yes	Multicenter collaborative study with department of endocrinology assessing the effect of semaglutide on diabetic retinopathy and diabetic macular edema in patients with type 2 diabetes
Pediatric Eye Disease Investigator Group IXT5²³	S.P.C.	27	22.2	Interventional (pediatric)	Yes	Multicenter study analyzing children with intermittent exotropia; treatment with overminus glasses vs placebo (regular glasses)
Thyroid Eye Disease	A.G.D.	17	64.7	Interventional	No	Study comparing outcomes for patients taking teprotumumab vs other management strategies in the treatment of thyroid eye disease
Novartis LKA651²⁴	M.L.S.	13	46.2	Interventional	Yes	Randomized multicenter clinical trial comparing intravitreal LKA651 alone vs coadministered with ranibizumab; displayed clinical efficacy and safety in patients with diabetic macular edema

Open in a new tab

Abbreviation: OCT, optical coherence tomography.

^{^a}

Included a limited subsample of the first 57 of 167 patients chronologically approached, as the decision to consent was not documented for all subsequent patients after the first 57.

During recruitment for the clinical studies, screening logs were used to document patients who were approached by research personnel for study participation. These patients were preidentified as having met initial inclusion criteria. The log recorded each patient’s decision to participate or decline, their medical record number, basic demographic information, other data points that varied among studies, and the research staff member who approached the patient.

Studies were categorized as interventional or noninterventional in design, with interventional studies being defined as those in which participants received 1 or more study-specific intervention. Some studies included financial reimbursement for time, parking, and travel. Two studies involved pediatric patients in which case consent was obtained from parents or legal guardians, and the parent or legal guardian’s demographic information was used in place of the pediatric patient’s information. Studies were excluded if information on the decision to participate was unclear or if documentation was insufficient to complete analysis (eTable in Supplement 1).

The electronic medical record (Epic Systems Corporation) was used to gather the following patient data: age, sex (patient-reported), race, ethnicity, address, primary or preferred language, and primary insurance. If a patient was approached for consent at their initial visit (if the patient was new to the practice or new to the physician), this was also recorded. Research personnel demographic data were gathered via voluntary survey, and variables collected included age, sex (self-reported), race and ethnicity, and all languages spoken. Race and ethnicity data for patients were gathered from the hospital’s electronic medical record, which uses an intake questionnaire that allows patients to select the demographic information with which they most closely self-identify. The BMC intake questionnaire separates race and ethnicity. The questionnaire defines race groups as American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Middle Eastern, Native Hawaiian or Pacific Islander, and White; ethnicity is defined as either Hispanic or Latino or non-Hispanic or non-Latino. In this study, race and ethnicity were combined into a single variable; patients who identified as Hispanic or Latino ethnicity were included in the Hispanic or Latino cohort regardless of race designation, while other racial groups included only patients who did not also identify as Hispanic or Latino. American Indian or Alaskan Native, Asian, Middle Eastern, and Native Hawaiian or Pacific Islander categories were combined as other race and ethnicity because of small samples.

Informed consent for prospective clinical studies was obtained by the clinical research team. The clinical research team included full-time and part-time research assistants (RA), the clinical research manager, and the principal investigator and coinvestigators. Most RAs were full-time undergraduate students from Northeastern University’s Cooperative Education program, an experiential learning program with alternating periods of full-time study and full-time work. Research assistants participated in recruitment, data collection and collation, institutional review board protocol writing and submission, language translation, and research compliance. After an initial period of training, the clinical research manager directly observed the RAs obtaining informed consent prior to allowing them to independently obtain consent. Throughout the consent process, an investigator was available to participate in the discussion. Consent forms were translated to the patient’s primary language. Research assistants who were fluent in the patient’s primary language became certified to obtain consent in that language. If the RA did not speak the patient’s primary language, an official hospital interpreter was used. In general, attempts were made to match patients with RAs who spoke their primary language. Patients who were recruited into studies requiring additional testing, such as ophthalmic imaging or blood sample obtainment, were preferentially matched with RAs who had been trained in those procedures. The RAs were not matched to patients based on demographic factors such as sex or race.

Socioeconomic status (SES) was included in the analysis using a surrogate measure, the Area Deprivation Index (ADI). The ADI score was developed by the US Health Resources & Services Administration and ranks neighborhoods by socioeconomic disadvantage by factoring in 17 neighborhood-level markers, including median income, educational levels, employment levels, and housing quality. The ADI was obtained using the University of Wisconsin School of Medicine’s Neighborhood Atlas website.²⁶ The patients’ addresses were used to determine their census block group, which was assigned an ADI score ranked from 1 to 10, with a higher number indicating a more disadvantaged block group. In accordance with other studies,²⁷ patients were divided into a higher SES group (ADI of ≤5) and a lower SES group (ADI of ≥6).

Statistical Analysis

Statistical analysis was performed using SPSS, version 27 (IBM Corp) in collaboration with a biostatistician (H.J.C.). In the unadjusted, bivariate analyses, categorical variables were examined using χ² tests of independence or Fisher exact tests, and continuous variables were examined using 2-sample t tests. Two-sided P < .05 was considered significant. Research personnel characteristics, including age, sex, race, ethnicity, and languages spoken, were analyzed for congruence with the same patient characteristics. The primary multivariable analysis consisted of binary logistic regression with decision to consent as the dependent variable. Odds ratios (ORs) and 95% CIs were computed based on these models. Studies that included pediatric patients were excluded from any analyses examining patient age or insurance status since a disproportionate percentage of pediatric patients at BMC receive Medicaid. Individual screening logs in which the documentation on which RA approached the patient was unclear were excluded from the concordance analysis.

Results

Patient Demographics

A total of 10 prospective studies^{14,15,16,17,18,19,20,21,22,23,24} involving 1380 total patients who were approached for consent met inclusion criteria; 3 cohort studies have not been published (J.R.T., recruitment closed, ongoing data analysis May 2023; T.P., ongoing recruitment, September 2023; and A.G.D., recruitment suspended temporarily due to change in principal investigator, September 2023). Table 1 describes the included studies. The eTable in Supplement 1 cites all studies that were excluded. The mean (SD) age of participants was 58.6 (14.9) years; 686 (49.7%) identified as female, and 694 (50.3%) identified as male. Of 1380 patients, 566 (43.5%) were Black; 327 (25.1%), Hispanic or Latino; 373 (28.6%), White; 36 (2.8%), other race and ethnicity; and 78 (5.8%) declined to answer. English was the most common primary language (892 [64.6%]), followed by Spanish (345 [25.1%]), Haitian Creole (112 [8.1%]), and other languages (Cape Verdean or Portuguese Creole, Vietnamese, Portuguese, Albanian, Amharic or Ethiopian, Arabic, French, and Tigrinya) (31 [2.2%]) (Table 2). The mean (SD) ADI was 5.5 (2.2).

Table 2. Patient Baseline Characteristics, Visit History, and Study Design of Trial by Decision to Consent in Prospective Ophthalmic Studies.

Characteristic	Patients, No. (%)		Difference, No. (%)	P value
Characteristic	Consented (n = 617)	Declined (n = 763)	Difference, No. (%)	P value
Age, mean (SD), y	58.2 (14.7)	58.9 (15.0)	0.7	.17
Sex
Female	283 (41.3)	403 (58.7)	120 (−17.4)	.01
Male	334 (48.1)	360 (51.9)	26 (−3.8)	.01
Race and ethnicity
Black	209 (36.9)	357 (63.1)	148 (−26.2)	<.001
Hispanic or Latino	134 (41.0)	193 (59.0)	59 (−18.0)
White	224 (60.1)	149 (39.9)	75 (20.2)
Other^a	15 (41.7)	21 (58.3)	6 (−16.6)
Language
English	410 (46.0)	482 (54.0)	72 (−8.0)	.23
Haitian Creole	40 (35.7)	72 (64.3)	32 (−28.6)
Spanish	154 (44.6)	191 (55.4)	37 (10.8)
Other^b	13 (41.9)	18 (58.2)	5 (−16.3)
Area Deprivation Index^c
High	210 (32.5)	436 (67.5)	226 (−35.0)	<.001
Low	337 (51.3)	320 (48.7)	17 (−2.6)	<.001
Insurance
Medicare	163 (33.5)	323 (66.5)	160 (−33.0)	<.001
Medicaid	254 (45.9)	299 (54.1)	45 (−8.2)
Private	49 (42.2)	67 (57.8)	18 (−15.6)
Uninsured	4 (26.7)	11 (73.3)	7 (−46.6)
First visit with clinician
Yes	165 (43.7)	213 (56.3)	48 (−12.6)	.65
No	450 (45.0)	550 (55.0)	100 (−10.0)	.65
First visit to eye clinic
Yes	100 (39.7)	152 (60.3)	52 (−21.0)	.08
No	515 (45.7)	611 (54.3)	96 (−8.6)	.08
Study design
Interventional	422 (39.8)	637 (60.2)	215 (−20.4)	<.001
Noninterventional	195 (60.9)	125 (39.1)	70 (21.8)	<.001

Open in a new tab

^{^a}

Other includes American Indian or Alaskan Native, Asian, Middle Eastern, and Native Hawaiian or Pacific Islander.

^{^b}

Other includes Cape Verdean or Portuguese Creole, Vietnamese, Portuguese, Albanian, Amharic or Ethiopian, Arabic, French, and Tigrinya.

^{^c}

Scores range from 1 to 10, with a score of 5 or lower (low Area Deprivation Index) indicating higher socioeconomic status and a score of 6 or higher (high Area Deprivation Index) indicating lower socioeconomic status.

RA Demographics

A total of 20 of 27 RAs (74.1%) completed the survey. The mean (SD) age of research personnel was 23.4 (2.6) years; 16 (80.0%) identified as female, and 4 (20.0%) identified as male. A total of 7 RAs (35.0%) identified as Asian, 2 (10.0%) as Black, 5 (25.0%) as Hispanic or Latino, and 6 (30.0%) as White. All RAs spoke English; 5 (25.0%), also spoke Spanish; 1 (5.0%), Cantonese; 1 (5.0%), Korean; and 2 (10.0%), Hindi. Consent rates among RAs were all within 1 SD of the mean.

Study Participant Demographics and Decision to Consent

In our bivariate analyses, the rate of consent among male patients (48.1% [334 of 694]) was higher than that among female patients (41.3% [283 of 686]) (P = .01). Consent rates were higher for White patients (60.1% [224 of 373]) compared with Black patients (36.9% [209 of 566]) and Hispanic or Latino patients (41.0% [134 of 327]) (P < .001). Patients with higher SES were found to consent to study participation at higher rates than those with lower SES (51.3% vs 32.5%; P < .001). The mean consent rate for all interventional studies was lower at 39.8% (range, 22.2%-64.7%) compared with 60.9% (range, 38.6%-90.0%) for noninterventional studies (P < .001) (Table 1). Patient age, preferred language, and whether or not the patient was approached for consent at their initial visit were not associated with the decision to consent (Table 2). Multivariable logistic regression models were subsequently used to further investigate these associations (Table 3) while controlling for patient sex, race and ethnicity, language, ADI, insurance, and study design. With these controls, Black patients (OR, 0.32; 95% CI, 0.24-0.44; P < .001) and Hispanic or Latino patients (OR, 0.31; 95% CI, 0.20-0.47; P < .001) were less likely to consent compared with White patients. Patients from the lower SES cohort were less likely to consent compared with patients from the higher SES cohort (OR, 0.43; 95% CI, 0.33-0.55; P < .001), while those insured through Medicaid were more likely to consent compared with patients insured through Medicare (OR, 1.80; 95% CI, 1.36-2.39; P < .001). Consent rates in interventional compared with noninterventional study designs did not differ in the multivariable model. Notably, patients’ primary language was not found to be associated with the decision to consent in this model.

Table 3. Factors Associated With a Decision to Participate in Prospective Ophthalmic Studies Based on Patient Characteristics^a.

Factor	Odds ratio (95% CI)	P value
Sex
Female	1 [Reference]	NA
Male	1.30 (1.01-1.67)	.04
Race and ethnicity
Black	0.32 (0.24-0.44)	<.001
Hispanic or Latino	0.31 (0.20-0.47)	<.001
White	1 [Reference]	NA
Other^b	0.57 (0.25-1.28)	.17
Language
English	1 [Reference]	NA
Haitian Creole	1.04 (0.64-1.68)	.89
Spanish	1.42 (0.96-2.10)	.08
Other^c	1.20 (0.46-3.12)	.71
Area Deprivation Index^d
Low	1 [Reference]	NA
High	0.43 (0.33-0.55)	<.001
Insurance
Medicare	1 [Reference]	NA
Medicaid	1.80 (1.36-2.39)	<.001
Private	1.36 (0.87-2.12)	.18
Uninsured	0.66 (0.19-2.27)	.51
Study design
Interventional	1 [Reference]	NA
Noninterventional	1.54 (0.99-2.38)	.06

Open in a new tab

Abbreviation: NA, not applicable.

^{^a}

Multivariable regression analysis adjusted for sex, race and ethnicity, language, Area Deprivation Index, insurance status, and study design.

^{^b}

Other includes American Indian or Alaskan Native, Asian, Middle Eastern, and Native Hawaiian or Pacific Islander.

^{^c}

Other includes Cape Verdean or Portuguese Creole, Vietnamese, Portuguese, Albanian, Amharic or Ethiopian, Arabic, French, and Tigrinya.

^{^d}

Patient and RA Concordance Analysis

The characteristics of the research team member who initially approached the patient for consent were analyzed for concordance with patient characteristics (Table 4). Of the individual screening logs, 169 (12%) were excluded based on unclear documentation of the research team member who approached the patient. Of the remaining 1211 screening logs, concordance in race and ethnicity between RAs and patients was associated with improved rates of participation, with 65.1% (136 of 209) of patients with race and ethnicity concordance consenting to participate compared with 39.9% (400 of 1002) of patients with discordant race and ethnicity (P < .001). Research personnel speaking the same language as the patient was not associated with a patient’s decision to enroll in a study. In the multivariable analysis (Table 5), concordance in race and ethnicity (OR, 2.72; 95% CI, 1.99-3.73; P < .001) was associated with increased odds of patients participating in studies regardless of language or sex concordance. Sex concordance and language concordance between patients and research personnel were not found to be associated with decision to participate.

Table 4. Concordance vs Discordance Between Patient and Research Personnel Characteristics by Decision to Participate in Prospective Ophthalmic Studies^a.

Concordance status	Patient and research personnel pairs, No. (%)^a			P value
Concordance status	Consent (n = 536)	Decline (n = 675)		P value
Sex
Concordant	251 (41.9)		346 (58.1)	.12
Discordant	285 (46.4)		329 (53.6)	.12
Race and ethnicity
Concordant	136 (65.1)		73 (34.9)	<.001
Discordant	400 (39.9)		602 (60.1)	<.001
Language
Concordant	368 (45.8)		436 (54.2)	.14
Discordant	168 (41.3)		239 (58.7)	.14

Open in a new tab

^{^a}

Percentages are across rows.

Table 5. Factors Associated With the Decision to Participate in Prospective Ophthalmic Studies Based on Congruence With Research Personnel Characteristics^a.

Concordance factor	Odds ratio (95% CI)	P value
Sex	0.86 (0.68-1.08)	.20
Race and ethnicity	2.72 (1.99-3.73)	<.001
Language	1.12 (0.88-1.44)	.35

Open in a new tab

^{^a}

Multivariable regression analysis adjusted for sex, race and ethnicity, and language concordance.

Discussion

Several key ophthalmic studies, including the Baltimore Eye Survey,^28,29 the Proyecto VER study,³⁰ and the Los Angeles Latino Eye Study,^31,32 have found that rates of ocular disease can vary among different racial and ethnic groups. Hamid et al³³ evaluated the demographics of patients in ophthalmic clinical studies from 1993 to 2017 and found substantial underenrollment of Asian, Black, and Hispanic patients, similar to most other fields of medicine.³⁴ Furthermore, Black and Hispanic patients were found to be underrepresented in ophthalmic clinical trials leading to US Food and Drug Administration drug approvals.³⁵ In a meta-analysis of over 100 clinical trials for primary open-angle glaucoma, Allison et al³⁶ found underrepresentation of Black and Hispanic patients in relation to disease burden; 16.8% of patients recruited were Black, 3.4% were Hispanic, and 70.7% were White. In contrast, in the US, Black individuals have nearly 3 times the prevalence of primary open-angle glaucoma compared with White individuals³⁷ and are nearly 7 times more likely experience progression to blindness.³⁶ Due to the disparity in patient recruitment compared with disease burden, there is a pressing need for novel strategies to increase enrollment of racial and ethnic minority patients into prospective clinical studies.

Our study found that patients who were approached by research personnel of the same race and ethnicity were more likely to consent for participation in prospective clinical studies. This association remained true even after adjusting for other potential confounding factors, including sex concordance and language concordance. To our knowledge, this is the first study to find that racial and ethnic concordance between patients and research personnel was associated with improved consent rates for in-person study recruitment.

Our finding that participants being approached by a research team member of a shared race and ethnicity were positively associated with successful recruitment underscores the need to increase the diversity of research teams to improve racial and ethnic minority patient representation in clinical studies. This is supported by existing literature in which the importance of a diverse physician team in the context of patient care has been well documented.^38,39,40,41 Black patients have not only reported greater satisfaction when cared for by a Black physician but were also more likely to agree to invasive health screenings.⁴² Research teams have already adopted recruitment strategies to maximize the diversity of research staff, with the aim of increasing recruitment of underrepresented patients.^43,44 Moorman et al⁴⁵ explored rates of participation in telephone interviews of 889 women in the Carolina Breast Cancer Study and found that race concordance was associated with increased participation among Black patients by 6% to 15%. Alternatively, a telephone survey study by Gadegbeku et al⁴⁶ found that only a small percentage of patients reported that their decision to decline participation was based on the perceived race (7%) and sex (5%) of research personnel. Both studies were conducted by telephone and included limited demographics, with only female patients and only Black patients, respectively.

To best capture the patient perspective and experience interacting with research personnel, our study looked specifically at racial and ethnic concordance between research participants and the RA who approached the patient to obtain consent. These initial face-to-face conversations are often quite comprehensive and lengthy, and patients may be more likely to form an impression of the research team from their interaction with this team member than perhaps the principal investigator. Inclusion of a diverse team of RAs may therefore foster increased patient confidence and trust in the research process, leading to improved recruitment, but this is just a start. Even more challenging will be ensuring representation within the whole team, including research managers and investigators who hold the power to make critical decisions in research design.

Patients who lived in areas of more economic deprivation, as measured through a higher ADI score, were found to have lower rates of enrollment, which is in alignment with prior reports.⁴⁷ Low SES can contribute to a decreased ability to participate in medical research through various factors, including decreased time and transportation availability^48,49 and insufficient medical coverage.^50,51,52 Neighborhood disadvantage adds more difficulties, such as increased crime rates, housing instability, and limited access and exposure to health care,⁵³ all of which are direct barriers to patient recruitment. Although ADI was measured separately from race and ethnicity in this study, it is important to note that through the lasting effects of structural racism, geographic economic deprivation more disproportionately affects racial and ethnic minority groups, adding to the challenges of diverse patient recruitment.^49,52,53

With regard to insurance status, our study showed that those with Medicaid coverage were more likely to consent than those with Medicare coverage, which appears to be contradictory since Medicaid primarily provides coverage for patients with limited income and resources. Reasons for this inconsistency are unclear but may be related to limitations in how information on insurance status was collected (only primary insurance was included for those with dual eligibility) and the disproportionately large percentage of patients with Medicaid coverage at BMC compared with other neighboring hospital systems. Additionally, it is important to note that Massachusetts has had universal health care coverage since 2006, and many third-party private payers offer health plans through the state-funded Health Connector. Therefore, our study’s conclusions regarding participation based on insurance status should be interpreted with discretion.

Limitations

This study has several limitations. First, the patient’s hospital intake questionnaire from which race and ethnicity information was collected contained some inaccurate descriptions, such as Hispanic or Latino as a race option and the absence of mixed race as an option. Additionally, 169 (12%) of the individual screening logs were excluded from the concordance analysis based on unclear documentation of which RA approached the patient. However, demographic characteristics of these patients did not differ from those of the larger group. It is also important to note that interactions between the research participant and the RA occurred in the context of perceived race and ethnicity. Research assistants were not asked or required to disclose their race or ethnicity to the research participant. As our study included patients from a variety of clinical studies, there may have been additional confounding variables or selection biases influencing the decision to consent. For example, some studies targeted certain populations such as patients with opioid use disorder or pediatric patients whose guardians may have had unique reasons for agreeing or declining participation. Most studies offered reimbursement for time and travel, and therefore, this was not considered in our analysis (Table 1). In addition, the majority of the study population came from a single large clinical trial (Oral Vs Intravenous Sedation for Ocular Procedures trial^14,15,16,17), but when the analysis was repeated with only the trial’s study participants, the outcomes were similar. Furthermore, the representation of SES in our model is limited by several factors, including binary categorization of the ADI score as low or high (although the mean ADI in the study population was in alignment with the cutoff used) and restrictions inherent in the US Census Bureau database, such as incomplete accounting for undocumented immigrant populations. Finally, we acknowledge that race and gender are social constructs based on physical characteristics and are concepts created from human interactions. Attitudes toward both continue to change over time.

Conclusions

In this cohort study, patients from underrepresented racial and ethnic groups and those with lower SES status were less likely to consent to participate in ophthalmic clinical studies. We found that concordance of race and ethnicity between patients and research staff was associated with greater likelihood of patient consent. Increasing diversity in research personnel may help to improve racial and ethnic patient representation in clinical studies. Further work is warranted to investigate the effects of other potential interventions, such as decreasing the burden of travel time and cost, promoting community support, and outreach efforts to improve equity in patient recruitment.

Supplement 1.

eTable. Prospective Trials Excluded in Analysis by Principal Investigator (PI) Initials, Study Design, and Reason for Exclusion

Click here for additional data file.^{(104.8KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(13.7KB, pdf)}

References

1.Malhotra NA, Greenlee TE, Iyer AI, Conti TF, Chen AX, Singh RP. Racial, ethnic, and insurance-based disparities upon initiation of anti-vascular endothelial growth factor therapy for diabetic macular edema in the US. Ophthalmology. 2021;128(10):1438-1447. doi: 10.1016/j.ophtha.2021.03.010 [DOI] [PubMed] [Google Scholar]
2.Emanuele N, Sacks J, Klein R, et al. ; Veterans Affairs Diabetes Trial Group . Ethnicity, race, and baseline retinopathy correlates in the Veterans Affairs diabetes trial. Diabetes Care. 2005;28(8):1954-1958. doi: 10.2337/diacare.28.8.1954 [DOI] [PubMed] [Google Scholar]
3.Stein JD, Kim DS, Niziol LM, et al. Differences in rates of glaucoma among Asian Americans and other racial groups, and among various Asian ethnic groups. Ophthalmology. 2011;118(6):1031-1037. doi: 10.1016/j.ophtha.2010.10.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Osathanugrah P, Sanjiv N, Siegel NH, Ness S, Chen X, Subramanian ML. The impact of race on short-term treatment response to bevacizumab in diabetic macular edema. Am J Ophthalmol. 2021;222:310-317. doi: 10.1016/j.ajo.2020.09.042 [DOI] [PubMed] [Google Scholar]
5.Al-Moujahed A, Vail D, Do DV. Racial differences in anti-VEGF intravitreal injections among commercially insured beneficiaries. Ophthalmic Surg Lasers Imaging Retina. 2021;52(4):208-217. doi: 10.3928/23258160-20210330-05 [DOI] [PubMed] [Google Scholar]
6.Wadhwa SD, Higginbotham EJ. Ethnic differences in glaucoma: prevalence, management, and outcome. Curr Opin Ophthalmol. 2005;16(2):101-106. doi: 10.1097/01.icu.0000156137.28193.48 [DOI] [PubMed] [Google Scholar]
7.Noah BA. The participation of underrepresented minorities in clinical research. Am J Law Med. 2003;29(2-3):221-245. doi: 10.1017/S0098858800002823 [DOI] [PubMed] [Google Scholar]
8.Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA. 2004;291(22):2720-2726. doi: 10.1001/jama.291.22.2720 [DOI] [PubMed] [Google Scholar]
9.Sardar MR, Badri M, Prince CT, Seltzer J, Kowey PR. Underrepresentation of women, elderly patients, and racial minorities in the randomized trials used for cardiovascular guidelines. JAMA Intern Med. 2014;174(11):1868-1870. doi: 10.1001/jamainternmed.2014.4758 [DOI] [PubMed] [Google Scholar]
10.Schneider MG, Swearingen CJ, Shulman LM, Ye J, Baumgarten M, Tilley BC. Minority enrollment in Parkinson’s disease clinical trials. Parkinsonism Relat Disord. 2009;15(4):258-262. doi: 10.1016/j.parkreldis.2008.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Seto B. History of medical ethics and perspectives on disparities in minority recruitment and involvement in health research. Am J Med Sci. 2001;322(5):248-250. doi: 10.1097/00000441-200111000-00002 [DOI] [PubMed] [Google Scholar]
12.Freimuth VS, Quinn SC, Thomas SB, Cole G, Zook E, Duncan T. African Americans’ views on research and the Tuskegee Syphilis Study. Soc Sci Med. 2001;52(5):797-808. doi: 10.1016/S0277-9536(00)00178-7 [DOI] [PubMed] [Google Scholar]
13.Corbie-Smith G, Thomas SB, St George DMM. Distrust, race, and research. Arch Intern Med. 2002;162(21):2458-2463. doi: 10.1001/archinte.162.21.2458 [DOI] [PubMed] [Google Scholar]
14.Peeler CE, Villani CM, Fiorello MG, Lee HJ, Subramanian ML; Oral Versus Intravenous Sedation Study Group. Patient satisfaction with oral versus intravenous sedation for cataract surgery: a randomized clinical trial. Ophthalmology. 2019;126(9):1212-1218. [DOI] [PubMed] [Google Scholar]
15.Siegel NH, Fiorello MG, Ness S, et al. Patient satisfaction with oral vs intravenous sedation for vitrectomy surgery: a randomized, noninferiority clinical trial. J Vitreoretin Dis. 2021;6(3):201-209. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Lee HN, Desai MA, Sadlak N, Fiorello MG, Githere WG, Subramanian ML; Oral Versus Intravenous Sedation Study Group. Oral sedation is non-inferior to intravenous sedation for cornea and glaucoma surgery: a randomized controlled trial. Clin Ophthalmol. 2022;16:2105-2117. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Sadlak N, Fiorello MG, Cabral HJ, Subramanian ML, Desai MA, Lee HJ. Poor correlation of provider and patient satisfaction with anesthesia in ophthalmic surgeries: a secondary analysis of a clinical trial. Clin Ophthalmol. 2022;16:677-683. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Vig V, Garg I, Tuz-Zahra F, et al. Vitreous humor biomarkers reflect pathological changes in the brain for Alzheimer's disease and chronic traumatic encephalopathy. J Alzheimers Dis. 2023;93:(3):1181-1193. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Peeler CE, Gorgy M, Sadlak N, et al. A pilot study of automated pupillometry in the treatment of opioid use disorder. J Addict Med. 2021;15:(6):477-483. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Wang J, Baker A, Subramanian NK, et al. Simultaneous visible light optical coherence tomography and near infrared OCT angiography in retinal pathologies: a case study. Exp Biol Med (Maywood). 2022;247:(5):377-384. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Song W, Zhou L, Zhang S, Ness S, Desai M, Yi J. Fiber-based visible and near infrared optical coherence tomography (vnOCT) enables quantitative elastic light scattering spectroscopy in human retina. Biomed Opt Express. 2018;9(7):3464-3480. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.A research study to look at how semaglutide compared to placebo affects diabetic eye disease in people with type 2 diabetes (FOCUS). ClinicalTrials.gov identifier: NCT03811561. Updated August 22, 2023. Accessed September 7, 2023. https://clinicaltrials.gov/study/NCT03811561
23.Chen AM, Erzurum SA, Chandler DL, et al.; Pediatric Eye Disease Investigator Group. Overminus lens therapy for children 3 to 10 years of age with intermittent exotropia: a randomized clinical trial. JAMA Ophthalmol. 2021;139(4):464-476. doi: 10.1001/jamaophthalmol.2021.0082 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Multiple dose safety and efficacy of LKA651 in patients with diabetic macular edema. ClinicalTrials.gov identifier: NCT03927690. Updated June 15, 2023. Accessed September 7, 2023. https://clinicaltrials.gov/study/NCT03927690
25.Boston Medical Center. About BMC: a new kind of excellence in healthcare. Accessed December 25, 2022. https://www.bmc.org/about-bmc
26.Center for Health Disparities Research, University of Wisconsin School of Medicine and Public Health. Neighborhood atlas. Accessed December 25, 2022. https://www.neighborhoodatlas.medicine.wisc.edu/
27.Mora J, Krepline AN, Aldakkak M, et al. Adjuvant therapy rates and overall survival in patients with localized pancreatic cancer from high Area Deprivation Index neighborhoods. Am J Surg. 2021;222(1):10-17. doi: 10.1016/j.amjsurg.2020.12.001 [DOI] [PubMed] [Google Scholar]
28.Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J. Racial variations in the prevalence of primary open-angle glaucoma: the Baltimore Eye Survey. JAMA. 1991;266(3):369-374. doi: 10.1001/jama.1991.03470030069026 [DOI] [PubMed] [Google Scholar]
29.Friedman DS, Katz J, Bressler NM, Rahmani B, Tielsch JM. Racial differences in the prevalence of age-related macular degeneration: the Baltimore Eye Survey. Ophthalmology. 1999;106(6):1049-1055. doi: 10.1016/S0161-6420(99)90267-1 [DOI] [PubMed] [Google Scholar]
30.Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol. 2001;119(12):1819-1826. doi: 10.1001/archopht.119.12.1819 [DOI] [PubMed] [Google Scholar]
31.Varma R, Torres M, Peña F, Klein R, Azen SP; Los Angeles Latino Eye Study Group . Prevalence of diabetic retinopathy in adult Latinos: the Los Angeles Latino Eye Study. Ophthalmology. 2004;111(7):1298-1306. doi: 10.1016/j.ophtha.2004.03.002 [DOI] [PubMed] [Google Scholar]
32.Varma R, Ying-Lai M, Francis BA, et al. ; Los Angeles Latino Eye Study Group . Prevalence of open-angle glaucoma and ocular hypertension in Latinos: the Los Angeles Latino Eye Study. Ophthalmology. 2004;111(8):1439-1448. doi: 10.1016/j.ophtha.2004.01.025 [DOI] [PubMed] [Google Scholar]
33.Hamid M, Orlov S, De Lott L, Ling JJ, Woodward MA. Reporting and enrollment of women and racial minorities in ophthalmic clinical trials. Investig Ophthalmol Vis Sci. 2019;60(9):5469. [Google Scholar]
34.King TE Jr. Racial disparities in clinical trials. N Engl J Med. 2002;346(18):1400-1402. doi: 10.1056/NEJM200205023461812 [DOI] [PubMed] [Google Scholar]
35.Berkowitz ST, Groth SL, Gangaputra S, Patel S. Racial/ethnic disparities in ophthalmology clinical trials resulting in US Food and Drug Administration drug approvals from 2000 to 2020. JAMA Ophthalmol. 2021;139(6):629-637. doi: 10.1001/jamaophthalmol.2021.0857 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Allison K, Patel DG, Greene L. Racial and ethnic disparities in primary open-angle glaucoma clinical trials: a systematic review and meta-analysis. JAMA Netw Open. 2021;4(5):e218348. doi: 10.1001/jamanetworkopen.2021.8348 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Friedman DS, Wolfs RC, O’Colmain BJ, et al. ; Eye Diseases Prevalence Research Group . Prevalence of open-angle glaucoma among adults in the United States. Arch Ophthalmol. 2004;122(4):532-538. doi: 10.1001/archopht.122.4.532 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Alsan M, Garrick O, Graziani G. Does diversity matter for health? Experimental evidence from Oakland. Am Econ Rev. 2019;109(12):4071-4111. doi: 10.1257/aer.20181446 [DOI] [Google Scholar]
39.Gomez LE, Bernet P. Diversity improves performance and outcomes. J Natl Med Assoc. 2019;111(4):383-392. [DOI] [PubMed] [Google Scholar]
40.Rosenkranz KM, Arora TK, Termuhlen PM, et al. Diversity, equity and inclusion in medicine: why it matters and how do we achieve it? J Surg Educ. 2021;78(4):1058-1065. doi: 10.1016/j.jsurg.2020.11.013 [DOI] [PubMed] [Google Scholar]
41.Simms C. Voice: the importance of diversity in healthcare. Int J Clin Pract. 2013;67(5):394-396. doi: 10.1111/ijcp.12134 [DOI] [PubMed] [Google Scholar]
42.Laveist TA, Nuru-Jeter A. Is doctor-patient race concordance associated with greater satisfaction with care? J Health Soc Behav. 2002;43(3):296-306. doi: 10.2307/3090205 [DOI] [PubMed] [Google Scholar]
43.Sankaré IC, Bross R, Brown AF, et al. Strategies to build trust and recruit African American and Latino community residents for health research: a cohort study. Clin Transl Sci. 2015;8(5):412-420. doi: 10.1111/cts.12273 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Taani MH, Zabler B, Fendrich M, Schiffman R. Lessons learned for recruitment and retention of low-income African Americans. Contemp Clin Trials Commun. 2020;17:100533. doi: 10.1016/j.conctc.2020.100533 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Moorman PG, Newman B, Millikan RC, Tse CKJ, Sandler DP. Participation rates in a case-control study: the impact of age, race, and race of interviewer. Ann Epidemiol. 1999;9(3):188-195. doi: 10.1016/S1047-2797(98)00057-X [DOI] [PubMed] [Google Scholar]
46.Gadegbeku CA, Stillman PK, Huffman MD, Jackson JS, Kusek JW, Jamerson KA. Factors associated with enrollment of African Americans into a clinical trial: results from the African American study of kidney disease and hypertension. Contemp Clin Trials. 2008;29(6):837-842. doi: 10.1016/j.cct.2008.06.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Henshall C, Narendran P, Andrews RC, et al. Qualitative study of barriers to clinical trial retention in adults with recently diagnosed type 1 diabetes. BMJ Open. 2018;8(7):e022353. doi: 10.1136/bmjopen-2018-022353 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Borno HT, Zhang L, Siegel A, Chang E, Ryan CJ. At What cost to clinical trial enrollment? A retrospective study of patient travel burden in cancer clinical trials. Oncologist. 2018;23(10):1242-1249. doi: 10.1634/theoncologist.2017-0628 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Ford JG, Howerton MW, Lai GY, et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer. 2008;112(2):228-242. doi: 10.1002/cncr.23157 [DOI] [PubMed] [Google Scholar]
50.Liu AJ, Friedman DS, Collins ME. Strategies to address racial and ethnic disparities in vision care research. JAMA Ophthalmol. 2020;138(11):1119-1120. doi: 10.1001/jamaophthalmol.2020.3969 [DOI] [PubMed] [Google Scholar]
51.Abdus S, Mistry KB, Selden TM. Racial and ethnic disparities in services and the Patient Protection and Affordable Care Act. Am J Public Health. 2015;105(Suppl 5)(suppl 5):S668-S675. doi: 10.2105/AJPH.2015.302892 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.George S, Duran N, Norris K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health. 2014;104(2):e16-e31. doi: 10.2105/AJPH.2013.301706 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Tung EL, Peek ME, Rivas MA, Yang JP, Volerman A. Association Of neighborhood disadvantage with racial disparities in COVID-19 positivity in Chicago. Health Aff (Millwood). 2021;40(11):1784-1791. doi: 10.1377/hlthaff.2021.00695 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable. Prospective Trials Excluded in Analysis by Principal Investigator (PI) Initials, Study Design, and Reason for Exclusion

Click here for additional data file.^{(104.8KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(13.7KB, pdf)}

[eoi230058r1] 1.Malhotra NA, Greenlee TE, Iyer AI, Conti TF, Chen AX, Singh RP. Racial, ethnic, and insurance-based disparities upon initiation of anti-vascular endothelial growth factor therapy for diabetic macular edema in the US. Ophthalmology. 2021;128(10):1438-1447. doi: 10.1016/j.ophtha.2021.03.010 [DOI] [PubMed] [Google Scholar]

[eoi230058r2] 2.Emanuele N, Sacks J, Klein R, et al. ; Veterans Affairs Diabetes Trial Group . Ethnicity, race, and baseline retinopathy correlates in the Veterans Affairs diabetes trial. Diabetes Care. 2005;28(8):1954-1958. doi: 10.2337/diacare.28.8.1954 [DOI] [PubMed] [Google Scholar]

[eoi230058r3] 3.Stein JD, Kim DS, Niziol LM, et al. Differences in rates of glaucoma among Asian Americans and other racial groups, and among various Asian ethnic groups. Ophthalmology. 2011;118(6):1031-1037. doi: 10.1016/j.ophtha.2010.10.024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r4] 4.Osathanugrah P, Sanjiv N, Siegel NH, Ness S, Chen X, Subramanian ML. The impact of race on short-term treatment response to bevacizumab in diabetic macular edema. Am J Ophthalmol. 2021;222:310-317. doi: 10.1016/j.ajo.2020.09.042 [DOI] [PubMed] [Google Scholar]

[eoi230058r5] 5.Al-Moujahed A, Vail D, Do DV. Racial differences in anti-VEGF intravitreal injections among commercially insured beneficiaries. Ophthalmic Surg Lasers Imaging Retina. 2021;52(4):208-217. doi: 10.3928/23258160-20210330-05 [DOI] [PubMed] [Google Scholar]

[eoi230058r6] 6.Wadhwa SD, Higginbotham EJ. Ethnic differences in glaucoma: prevalence, management, and outcome. Curr Opin Ophthalmol. 2005;16(2):101-106. doi: 10.1097/01.icu.0000156137.28193.48 [DOI] [PubMed] [Google Scholar]

[eoi230058r7] 7.Noah BA. The participation of underrepresented minorities in clinical research. Am J Law Med. 2003;29(2-3):221-245. doi: 10.1017/S0098858800002823 [DOI] [PubMed] [Google Scholar]

[eoi230058r8] 8.Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: race-, sex-, and age-based disparities. JAMA. 2004;291(22):2720-2726. doi: 10.1001/jama.291.22.2720 [DOI] [PubMed] [Google Scholar]

[eoi230058r9] 9.Sardar MR, Badri M, Prince CT, Seltzer J, Kowey PR. Underrepresentation of women, elderly patients, and racial minorities in the randomized trials used for cardiovascular guidelines. JAMA Intern Med. 2014;174(11):1868-1870. doi: 10.1001/jamainternmed.2014.4758 [DOI] [PubMed] [Google Scholar]

[eoi230058r10] 10.Schneider MG, Swearingen CJ, Shulman LM, Ye J, Baumgarten M, Tilley BC. Minority enrollment in Parkinson’s disease clinical trials. Parkinsonism Relat Disord. 2009;15(4):258-262. doi: 10.1016/j.parkreldis.2008.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r11] 11.Seto B. History of medical ethics and perspectives on disparities in minority recruitment and involvement in health research. Am J Med Sci. 2001;322(5):248-250. doi: 10.1097/00000441-200111000-00002 [DOI] [PubMed] [Google Scholar]

[eoi230058r12] 12.Freimuth VS, Quinn SC, Thomas SB, Cole G, Zook E, Duncan T. African Americans’ views on research and the Tuskegee Syphilis Study. Soc Sci Med. 2001;52(5):797-808. doi: 10.1016/S0277-9536(00)00178-7 [DOI] [PubMed] [Google Scholar]

[eoi230058r13] 13.Corbie-Smith G, Thomas SB, St George DMM. Distrust, race, and research. Arch Intern Med. 2002;162(21):2458-2463. doi: 10.1001/archinte.162.21.2458 [DOI] [PubMed] [Google Scholar]

[eoi230058r14] 14.Peeler CE, Villani CM, Fiorello MG, Lee HJ, Subramanian ML; Oral Versus Intravenous Sedation Study Group. Patient satisfaction with oral versus intravenous sedation for cataract surgery: a randomized clinical trial. Ophthalmology. 2019;126(9):1212-1218. [DOI] [PubMed] [Google Scholar]

[eoi230058r15] 15.Siegel NH, Fiorello MG, Ness S, et al. Patient satisfaction with oral vs intravenous sedation for vitrectomy surgery: a randomized, noninferiority clinical trial. J Vitreoretin Dis. 2021;6(3):201-209. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r16] 16.Lee HN, Desai MA, Sadlak N, Fiorello MG, Githere WG, Subramanian ML; Oral Versus Intravenous Sedation Study Group. Oral sedation is non-inferior to intravenous sedation for cornea and glaucoma surgery: a randomized controlled trial. Clin Ophthalmol. 2022;16:2105-2117. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r17] 17.Sadlak N, Fiorello MG, Cabral HJ, Subramanian ML, Desai MA, Lee HJ. Poor correlation of provider and patient satisfaction with anesthesia in ophthalmic surgeries: a secondary analysis of a clinical trial. Clin Ophthalmol. 2022;16:677-683. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r18] 18.Vig V, Garg I, Tuz-Zahra F, et al. Vitreous humor biomarkers reflect pathological changes in the brain for Alzheimer's disease and chronic traumatic encephalopathy. J Alzheimers Dis. 2023;93:(3):1181-1193. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r19] 19.Peeler CE, Gorgy M, Sadlak N, et al. A pilot study of automated pupillometry in the treatment of opioid use disorder. J Addict Med. 2021;15:(6):477-483. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r20] 20.Wang J, Baker A, Subramanian NK, et al. Simultaneous visible light optical coherence tomography and near infrared OCT angiography in retinal pathologies: a case study. Exp Biol Med (Maywood). 2022;247:(5):377-384. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r21] 21.Song W, Zhou L, Zhang S, Ness S, Desai M, Yi J. Fiber-based visible and near infrared optical coherence tomography (vnOCT) enables quantitative elastic light scattering spectroscopy in human retina. Biomed Opt Express. 2018;9(7):3464-3480. [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r22] 22.A research study to look at how semaglutide compared to placebo affects diabetic eye disease in people with type 2 diabetes (FOCUS). ClinicalTrials.gov identifier: NCT03811561. Updated August 22, 2023. Accessed September 7, 2023. https://clinicaltrials.gov/study/NCT03811561

[eoi230058r23] 23.Chen AM, Erzurum SA, Chandler DL, et al.; Pediatric Eye Disease Investigator Group. Overminus lens therapy for children 3 to 10 years of age with intermittent exotropia: a randomized clinical trial. JAMA Ophthalmol. 2021;139(4):464-476. doi: 10.1001/jamaophthalmol.2021.0082 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r24] 24.Multiple dose safety and efficacy of LKA651 in patients with diabetic macular edema. ClinicalTrials.gov identifier: NCT03927690. Updated June 15, 2023. Accessed September 7, 2023. https://clinicaltrials.gov/study/NCT03927690

[eoi230058r25] 25.Boston Medical Center. About BMC: a new kind of excellence in healthcare. Accessed December 25, 2022. https://www.bmc.org/about-bmc

[eoi230058r26] 26.Center for Health Disparities Research, University of Wisconsin School of Medicine and Public Health. Neighborhood atlas. Accessed December 25, 2022. https://www.neighborhoodatlas.medicine.wisc.edu/

[eoi230058r27] 27.Mora J, Krepline AN, Aldakkak M, et al. Adjuvant therapy rates and overall survival in patients with localized pancreatic cancer from high Area Deprivation Index neighborhoods. Am J Surg. 2021;222(1):10-17. doi: 10.1016/j.amjsurg.2020.12.001 [DOI] [PubMed] [Google Scholar]

[eoi230058r28] 28.Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J. Racial variations in the prevalence of primary open-angle glaucoma: the Baltimore Eye Survey. JAMA. 1991;266(3):369-374. doi: 10.1001/jama.1991.03470030069026 [DOI] [PubMed] [Google Scholar]

[eoi230058r29] 29.Friedman DS, Katz J, Bressler NM, Rahmani B, Tielsch JM. Racial differences in the prevalence of age-related macular degeneration: the Baltimore Eye Survey. Ophthalmology. 1999;106(6):1049-1055. doi: 10.1016/S0161-6420(99)90267-1 [DOI] [PubMed] [Google Scholar]

[eoi230058r30] 30.Quigley HA, West SK, Rodriguez J, Munoz B, Klein R, Snyder R. The prevalence of glaucoma in a population-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol. 2001;119(12):1819-1826. doi: 10.1001/archopht.119.12.1819 [DOI] [PubMed] [Google Scholar]

[eoi230058r31] 31.Varma R, Torres M, Peña F, Klein R, Azen SP; Los Angeles Latino Eye Study Group . Prevalence of diabetic retinopathy in adult Latinos: the Los Angeles Latino Eye Study. Ophthalmology. 2004;111(7):1298-1306. doi: 10.1016/j.ophtha.2004.03.002 [DOI] [PubMed] [Google Scholar]

[eoi230058r32] 32.Varma R, Ying-Lai M, Francis BA, et al. ; Los Angeles Latino Eye Study Group . Prevalence of open-angle glaucoma and ocular hypertension in Latinos: the Los Angeles Latino Eye Study. Ophthalmology. 2004;111(8):1439-1448. doi: 10.1016/j.ophtha.2004.01.025 [DOI] [PubMed] [Google Scholar]

[eoi230058r33] 33.Hamid M, Orlov S, De Lott L, Ling JJ, Woodward MA. Reporting and enrollment of women and racial minorities in ophthalmic clinical trials. Investig Ophthalmol Vis Sci. 2019;60(9):5469. [Google Scholar]

[eoi230058r34] 34.King TE Jr. Racial disparities in clinical trials. N Engl J Med. 2002;346(18):1400-1402. doi: 10.1056/NEJM200205023461812 [DOI] [PubMed] [Google Scholar]

[eoi230058r35] 35.Berkowitz ST, Groth SL, Gangaputra S, Patel S. Racial/ethnic disparities in ophthalmology clinical trials resulting in US Food and Drug Administration drug approvals from 2000 to 2020. JAMA Ophthalmol. 2021;139(6):629-637. doi: 10.1001/jamaophthalmol.2021.0857 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r36] 36.Allison K, Patel DG, Greene L. Racial and ethnic disparities in primary open-angle glaucoma clinical trials: a systematic review and meta-analysis. JAMA Netw Open. 2021;4(5):e218348. doi: 10.1001/jamanetworkopen.2021.8348 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r37] 37.Friedman DS, Wolfs RC, O’Colmain BJ, et al. ; Eye Diseases Prevalence Research Group . Prevalence of open-angle glaucoma among adults in the United States. Arch Ophthalmol. 2004;122(4):532-538. doi: 10.1001/archopht.122.4.532 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r38] 38.Alsan M, Garrick O, Graziani G. Does diversity matter for health? Experimental evidence from Oakland. Am Econ Rev. 2019;109(12):4071-4111. doi: 10.1257/aer.20181446 [DOI] [Google Scholar]

[eoi230058r39] 39.Gomez LE, Bernet P. Diversity improves performance and outcomes. J Natl Med Assoc. 2019;111(4):383-392. [DOI] [PubMed] [Google Scholar]

[eoi230058r40] 40.Rosenkranz KM, Arora TK, Termuhlen PM, et al. Diversity, equity and inclusion in medicine: why it matters and how do we achieve it? J Surg Educ. 2021;78(4):1058-1065. doi: 10.1016/j.jsurg.2020.11.013 [DOI] [PubMed] [Google Scholar]

[eoi230058r41] 41.Simms C. Voice: the importance of diversity in healthcare. Int J Clin Pract. 2013;67(5):394-396. doi: 10.1111/ijcp.12134 [DOI] [PubMed] [Google Scholar]

[eoi230058r42] 42.Laveist TA, Nuru-Jeter A. Is doctor-patient race concordance associated with greater satisfaction with care? J Health Soc Behav. 2002;43(3):296-306. doi: 10.2307/3090205 [DOI] [PubMed] [Google Scholar]

[eoi230058r43] 43.Sankaré IC, Bross R, Brown AF, et al. Strategies to build trust and recruit African American and Latino community residents for health research: a cohort study. Clin Transl Sci. 2015;8(5):412-420. doi: 10.1111/cts.12273 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r44] 44.Taani MH, Zabler B, Fendrich M, Schiffman R. Lessons learned for recruitment and retention of low-income African Americans. Contemp Clin Trials Commun. 2020;17:100533. doi: 10.1016/j.conctc.2020.100533 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r45] 45.Moorman PG, Newman B, Millikan RC, Tse CKJ, Sandler DP. Participation rates in a case-control study: the impact of age, race, and race of interviewer. Ann Epidemiol. 1999;9(3):188-195. doi: 10.1016/S1047-2797(98)00057-X [DOI] [PubMed] [Google Scholar]

[eoi230058r46] 46.Gadegbeku CA, Stillman PK, Huffman MD, Jackson JS, Kusek JW, Jamerson KA. Factors associated with enrollment of African Americans into a clinical trial: results from the African American study of kidney disease and hypertension. Contemp Clin Trials. 2008;29(6):837-842. doi: 10.1016/j.cct.2008.06.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r47] 47.Henshall C, Narendran P, Andrews RC, et al. Qualitative study of barriers to clinical trial retention in adults with recently diagnosed type 1 diabetes. BMJ Open. 2018;8(7):e022353. doi: 10.1136/bmjopen-2018-022353 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r48] 48.Borno HT, Zhang L, Siegel A, Chang E, Ryan CJ. At What cost to clinical trial enrollment? A retrospective study of patient travel burden in cancer clinical trials. Oncologist. 2018;23(10):1242-1249. doi: 10.1634/theoncologist.2017-0628 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r49] 49.Ford JG, Howerton MW, Lai GY, et al. Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer. 2008;112(2):228-242. doi: 10.1002/cncr.23157 [DOI] [PubMed] [Google Scholar]

[eoi230058r50] 50.Liu AJ, Friedman DS, Collins ME. Strategies to address racial and ethnic disparities in vision care research. JAMA Ophthalmol. 2020;138(11):1119-1120. doi: 10.1001/jamaophthalmol.2020.3969 [DOI] [PubMed] [Google Scholar]

[eoi230058r51] 51.Abdus S, Mistry KB, Selden TM. Racial and ethnic disparities in services and the Patient Protection and Affordable Care Act. Am J Public Health. 2015;105(Suppl 5)(suppl 5):S668-S675. doi: 10.2105/AJPH.2015.302892 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r52] 52.George S, Duran N, Norris K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am J Public Health. 2014;104(2):e16-e31. doi: 10.2105/AJPH.2013.301706 [DOI] [PMC free article] [PubMed] [Google Scholar]

[eoi230058r53] 53.Tung EL, Peek ME, Rivas MA, Yang JP, Volerman A. Association Of neighborhood disadvantage with racial disparities in COVID-19 positivity in Chicago. Health Aff (Millwood). 2021;40(11):1784-1791. doi: 10.1377/hlthaff.2021.00695 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Diverse Research Teams and Underrepresented Groups in Clinical Studies

Ashank Bains, MD

Pawarissara Osathanugrah, MD

Nayan Sanjiv, MD

Cedrick Chiu, BS

Marissa G Fiorello, BS

Nicole H Siegel, MD

Crandall E Peeler, MD

Alberto G Distefano, MD

Hyunjoo J Lee, MD, PhD

Steven Ness, MD

Manishi A Desai, MD

Jenna R Titelbaum, OD

Tony Pira, MD

Kara C LaMattina, MD

Stephen P Christiansen, MD

Howard J Cabral, PhD

Manju L Subramanian, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Table 1. Description of Included Prospective Studies.

Statistical Analysis

Results

Patient Demographics

Table 2. Patient Baseline Characteristics, Visit History, and Study Design of Trial by Decision to Consent in Prospective Ophthalmic Studies.

RA Demographics

Study Participant Demographics and Decision to Consent

Table 3. Factors Associated With a Decision to Participate in Prospective Ophthalmic Studies Based on Patient Characteristicsa.

Patient and RA Concordance Analysis

Table 4. Concordance vs Discordance Between Patient and Research Personnel Characteristics by Decision to Participate in Prospective Ophthalmic Studiesa.

Table 5. Factors Associated With the Decision to Participate in Prospective Ophthalmic Studies Based on Congruence With Research Personnel Characteristicsa.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 3. Factors Associated With a Decision to Participate in Prospective Ophthalmic Studies Based on Patient Characteristics^a.

Table 4. Concordance vs Discordance Between Patient and Research Personnel Characteristics by Decision to Participate in Prospective Ophthalmic Studies^a.

Table 5. Factors Associated With the Decision to Participate in Prospective Ophthalmic Studies Based on Congruence With Research Personnel Characteristics^a.