• In centers with robust clinical trial infrastructure and a strong culture of trials, clinical trial enrollment is high across demographics.

  • In our cohort, race, sex, and socioeconomic status were not predictors of trial participation, whereas age and diagnosis were predictive.

Abstract

Prior studies have demonstrated that certain populations including older patients, racial/ethnic minority groups, and women are underrepresented in clinical trials. We performed a retrospective analysis of patients with non-Hodgkin lymphoma (NHL) seen at MD Anderson Cancer Center (MDACC) to investigate the association between trial participation, race/ethnicity, travel distance, and neighborhood socioeconomic status (nSES). Using patient addresses, we ascertained nSES variables on educational attainment, income, poverty, racial composition, and housing at the census tract (CT) level. We also performed geospatial analysis to determine the geographic distribution of clinical trial participants and distance from patient residence to MDACC. We examined 3146 consecutive adult patients with NHL seen between January 2017 and December 2020. The study cohort was predominantly male and non-Hispanic White (NHW). The most common insurance types were private insurance and Medicare; only 1.1% of patients had Medicaid. There was a high overall participation rate of 30.5%, with 20.9% enrolled in therapeutic trials. In univariate analyses, lower participation rates were associated with lower nSES including higher poverty rates and living in crowded households. Racial composition of CT was not associated with differences in trial participation. In multivariable analysis, trial participation varied significantly by histology, and participation declined nonlinearly with age in the overall, follicular lymphoma, and diffuse large B-cell lymphoma (DLBCL) models. In the DLBCL subset, Hispanic patients had lower odds of participation than White patients (odds ratio, 0.36; 95% confidence interval, 0.21-0.62; P = .001). In our large academic cohort, race, sex, insurance type, and nSES were not associated with trial participation, whereas age and diagnosis were.

Non-Hodgkin lymphoma (NHL) is the seventh most common cancer and the ninth leading cause of cancer death for both men and women in the United States. In 2023, an estimated 80 550 people in the United States will be diagnosed with NHL, and 20 180 will die from NHL.1 Although the incidence of NHL in the United States has decreased and survival has improved in recent years,2 disparities on the basis of race, sex, age, insurance status, demographic location, socioeconomic status (SES), and other sociodemographic characteristics remain.3-9 There have been tremendous advancements in NHL therapies and outcomes in large part due to clinical trials, which are critical for advancing our understanding of cancer biology, evaluating the efficacy of novel treatments, and informing treatment decisions. Older individuals, certain minority groups (specifically Black and Hispanic patients), and women are recognized to be underrepresented in oncology clinical trials.10,11 A recent analysis of randomized controlled trials, which were crucial for the US Food and Drug Administration (FDA) approval of drugs for lymphoma treatment, has highlighted significant disparities in participant representation. Black patients, who make up 10.7% of NHL cases according to Surveillance, Epidemiology, and End Results Program statistics, were only 2.8% of the participants in these trials. Similarly, Hispanic patients, despite constituting 17.1% of patients with NHL, were underrepresented at 5.4% in these clinical trials.12 Underrepresentation of these groups in trials is detrimental to both these patient populations who do not receive equal access to novel therapies and to the trials that evaluate new approaches in participants who do not accurately reflect the patient populations in which the interventions are intended to be applied. In response to these findings, the US FDA now requires those seeking approval for late-stage clinical trials to submit a plan to ensure diversity among trial participants.13 

Understanding the influence of social context on clinical trial enrollment is vital to identify potential areas in which tailored interventions can effectively address participation barriers as well as identify patient populations at risk of underrepresentation. Individual level data on socioeconomic factors such as household income, educational attainment, and employment status are not prospectively collected and/or consistently documented, making it difficult to assess the effects of these variables on health outcomes and health care utilization. Neighborhood-level socioeconomic measures based on patient addresses may approximate individual level effects while also capturing contextual effects of an individual’s environment.6,9,14 The objective of our study was to investigate the associations between clinical trial participation patterns, patient demographics, and neighborhood socioeconomic status (nSES) factors determined by place of residence among patients with NHL seen at the University of Texas MD Anderson Cancer Center (MDACC) located in Houston, Texas. The Department of Lymphoma and Myeloma at MDACC is one of the largest multidisciplinary programs for lymphoma and myeloma with a robust portfolio of lymphoma clinic trials as well as programs to support clinical trial enrolment, such as dedicated research nurse coordinators, patient navigators, and financial assistance programs. Although this study reflects the practice of a large single center, this is, to our knowledge, the first study to apply this approach for a comprehensive evaluation of clinical enrolment in patients with NHL across subtypes.

Data source and study populations

We performed a retrospective cohort study of adult patients with NHL seen in The MDACC Department of Lymphoma and Myeloma in Houston, Texas. Our cohort consisted of patients aged >18 years who had a first visit at MDACC between January 2017 and December 2020. All patients without verified NHL histology were excluded. We used an MDACC cancer registry that records consecutive patients with newly diagnosed invasive cancer identified at our institution and the department Lymphoma Outcomes Database (LOD) that records all patients with lymphoma seen at MDACC. The LOD includes data extracted directly from patient charts with quality assurance checks by trained data abstractors. We identified patients who participated in clinical trials based on clinical trial enrollment tracked for the Cancer Center Support Grant report.

Outcomes and clinical trials definitions

The primary outcome of this study was the clinical trial participation, defined as enrollment and participation in any clinical trial including both interventional and noninterventional studies, from January 2017 to December 2020.

Clinical trials were any investigational studies performed prospectively involving human participants. Clinical trials are broadly categorized as interventional or noninterventional. Noninterventional trials were largely observational and involved no prospective intervention or alteration in the status of the participants. Interventional trials were trials in which participants were prospectively assigned to receive a specific intervention. This assignment could have been random for some studies. Participants received diagnostic, treatment, behavioral, or other types of interventions. Interventional therapeutic trials involved specific therapies or procedures with therapeutic intent. Interventional nontherapeutic trials involved interventions without a specific therapeutic purpose, including diagnostic imaging or tissue sampling performed without therapeutic components.

Demographics and neighborhood socioeconomic variables

Patient demographic information at the time of diagnosis was obtained via the MDACC Epic electronic heath record (EHR) system.15 These data included age, sex, insurance status (uninsured, Medicare, Medicaid, and private insurance), and self-reported race (Black, White, Asian, American Indian/Alaska Native, Native Hawaiian or other Pacific Islander, and other/unknown) and ethnicity (Hispanic or non-Hispanic), with categories following the US Census Bureau standards. We used the Census Geocoder available through the US Census Bureau website to geocode the last known patient addresses to obtain the corresponding census tracts using the Master Address File/Topologically Integrated Geographic Encoding and Referencing database.16,17 Based on the census tracts, we then extracted 32 nSES variables (listed in supplemental Tables) from the American Community Survey (2015-2020) and 2020 decennial census data.18,19 These variables were selected based on those previously described20,21 and those used in prior nSES indices such as the area deprivation index (ADI).14,22 The nSES variables represent domains such as educational attainment, neighborhood median income, poverty, crowding, race composition, home values, and essential home utilities.

In addition to the individual nSES variables, we also used ADI as a composite measure of neighborhood socioeconomic disadvantage. The ADI is a validated percentile rank of socioeconomic disadvantage determined at the census block group level that has been used in numerous studies.23-26 ADI is calculated using 17 indicators related to income level, income disparity, educational attainment, employment, and home values. Census block groups were ranked on the percentile scale (1-100) based on their deprivation relative to the national level, with higher ADI scores indicating higher deprivation. These rankings are published by the University of Wisconsin School of Medicine and Public Health.26 We categorized ADI scores into quartiles to facilitate interpretation.

Geospatial analysis and travel distance

Using Google Maps Geocoding Application Program Interface (API), we obtained geographic coordinates (longitude/latitude) of patient residential addresses. For patients residing in Texas, the R package “googleway” was used to interface with the Google Direction API to calculate driving distance (km) and time (hours) from patients' residence to MDACC at the Texas Medical Center (TMC).27 To visualize the geographic distribution of patient population, we aggregated the number of participants by county and ZIP code tabulated area (ZCTA) and created choropleth maps using “tmap” R package.28 Similarly, choropleth maps were also generated to visualize and compare the distributions of patients with no trial participation, nonintervention trial participation, and therapeutic trial participation. We chose ZCTA as the spatial unit of visualization for the protection of patient data privacy.

Statistical analysis

We performed descriptive analysis to examine the distribution of demographic variables for the total population and those enrolled in clinical trials. We performed univariate analysis using the χ2 test for categorical variables and a 2-sample t test for continuous variables to assess the differences in clinical trial enrollment patterns by sex, race, age, histology type, insurance type, nSES variables, and driving distance to MDACC TMC. To address potential nonnormality in our data, we included the Mann-Whitney test for continuous variables, which is a nonparametric test and robust to skewed distributions. After a consideration of variables of interest and independence among them, we modeled the likelihood of clinical trial participation using generalized additive logistic regression. This model considered various factors such as LOD diagnosis group, age, sex, race, ADI national rank (quartiles), and census tract level education attainment (percentage of people aged ≥25 years with less than a high school education). The model included a penalized spline to accommodate the nonlinear association between participation and age. In assessing the differences across discrete variables, we used Dunnett-adjusted contrasts, referencing specific categories for comparison. An expanded model that included interaction between age and ADI rank quantile was explored but ruled out due to worse Akaike Information Criterion. We applied the same analytical procedure to subgroups with diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). To account for multiple testing of the same data among these models, we used adjusted alpha of 0.05/3=0.017 as the criteria for significance in these models. All analyses were performed using R statistical software version 4.2.1.29 

Institutional Review Board approval obtained for this study denoted ICD2022-0443.

Patient characteristics and clinical trial participation patterns

A total of 3448 patients met the inclusion criteria of age ≥18 years with a histologically confirmed diagnosis of NHL treated at MDACC between January 2017 and December 2020. Of the patients identified, 130 had international addresses, and 172 did not have complete address information to be assigned to a census tract and were thus excluded from the final analysis. Therefore, the final cohort consisted of 3146 patients (Table 1). To assess for inadvertent selection bias, we compared the baseline characteristics between the final cohort and those excluded due to incomplete address information. No significant difference was detected, except in age (median age is 65 years for the study population and 68 years for the excluded patients; P < .0001). There was also a notable difference in the types of insurance between the study cohort and the excluded group; however, private insurance and Medicare were predominant in >90% of both cohorts. Importantly, overall clinical trial enrollment rate and the types of trials participated were comparable between the excluded patients and the final study cohort, as summarized in Table 2.

Table 1.

Patient characteristics and census tract level nSES

TotalWhiteHispanicBlackAsianOther/unknown
3146 2356 (74.9%) 411 (13.1%) 189 (6%) 116 (3.7%) 74 (2.4%) 
Sex       
Female 1274 913 (71.7%) 175 (13.7%) 93 (7.3%) 57 (4.5%) 36 (2.8%) 
Male 1872 1443 (77.1%) 236 (12.6%) 96 (5.13%) 59 (3.1%) 38 (2%) 
Age       
Mean (SD) 62.6 (14.6) 64.2 (13.9) 57.4 (15.7) 57.8 (15.6) 57.9 (16.5) 60.2 (13.3) 
Median 64.6 66.2 59.1 59.3 60.3 60.8 
Range 18.1-100.7 18.6-100.7 18.1-96.2 21.9-98.6 18.7-91.3 30.4-83.2 
Diagnosis       
DLBCL 917 642 (70%) 143 (15.6%) 65 (7.1%) 47 (5.1%) 20 (2.2%) 
FL 698 540 (77.4%) 87 (12.5%) 34 (4.9%) 20 (2.9%) 17 (2.4%) 
MCL 417 364 (87.3%) 28 (6.7%) 8 (1.9%) 6 (1.44%) 11 (2.6%) 
MZL 287 214 (74.6%) 36 (12.5%) 18 (6.3%) 9 (3.1%) 10 (3.5%) 
TCL 259 164 (63.3%) 43 (16.6%) 33 (12.7%) 13 (5%) 6 2.3%) 
Misc. 235 191 (81.3%) 19 (8.1%) 14 (6%) 5 (2.1%) 6 (2.6%) 
High-grade BCL 205 141 (68.8%) 42 (20.5%) 8 (3.9%) 11 (5.4%) 3 (1.5%) 
Composite 118 91 (77.12%) 13 (11%) 9 (7.6%) 4 (3.4%) 1 (0.9%) 
Gray zone lymphoma 10 9 (90%) 0 (0%) 0 (0%) 1 (10%) 0 (0%) 
ADI (Quartiles)       
Q1 (0-25) 774 615 (79.5%) 52 (6.7%) 23 (3%) 58 (7.5%) 26 (3.4%) 
Q2 (26-50) 889 700 (78.7%) 97 (10.9%) 48 (5.4%) 30 (3.4%) 14 (1.6%) 
Q3 (51-75) 753 544 (73.6%) 123 (16.3%) 52 (6.9%) 14 (1.9%) 20 (2.7%) 
Q4 (76-100) 472 (16.3%) 295 (62.5%) 112 (23.7%) 52 (11%) 5 (1.1%) 8 (1.7%) 
ADI (Continuous)       
Range 1-100 1-100 2-100 1-100 1-93 1-95 
Mean (SD) 45.5 (26.2) 43.4 (25.4) 57.2 (25.8) 57.9 (26) 30.2 (23.1) 39.8 (27.1) 
Median 44 41 59.5 58 22 38 
TotalWhiteHispanicBlackAsianOther/unknown
3146 2356 (74.9%) 411 (13.1%) 189 (6%) 116 (3.7%) 74 (2.4%) 
Sex       
Female 1274 913 (71.7%) 175 (13.7%) 93 (7.3%) 57 (4.5%) 36 (2.8%) 
Male 1872 1443 (77.1%) 236 (12.6%) 96 (5.13%) 59 (3.1%) 38 (2%) 
Age       
Mean (SD) 62.6 (14.6) 64.2 (13.9) 57.4 (15.7) 57.8 (15.6) 57.9 (16.5) 60.2 (13.3) 
Median 64.6 66.2 59.1 59.3 60.3 60.8 
Range 18.1-100.7 18.6-100.7 18.1-96.2 21.9-98.6 18.7-91.3 30.4-83.2 
Diagnosis       
DLBCL 917 642 (70%) 143 (15.6%) 65 (7.1%) 47 (5.1%) 20 (2.2%) 
FL 698 540 (77.4%) 87 (12.5%) 34 (4.9%) 20 (2.9%) 17 (2.4%) 
MCL 417 364 (87.3%) 28 (6.7%) 8 (1.9%) 6 (1.44%) 11 (2.6%) 
MZL 287 214 (74.6%) 36 (12.5%) 18 (6.3%) 9 (3.1%) 10 (3.5%) 
TCL 259 164 (63.3%) 43 (16.6%) 33 (12.7%) 13 (5%) 6 2.3%) 
Misc. 235 191 (81.3%) 19 (8.1%) 14 (6%) 5 (2.1%) 6 (2.6%) 
High-grade BCL 205 141 (68.8%) 42 (20.5%) 8 (3.9%) 11 (5.4%) 3 (1.5%) 
Composite 118 91 (77.12%) 13 (11%) 9 (7.6%) 4 (3.4%) 1 (0.9%) 
Gray zone lymphoma 10 9 (90%) 0 (0%) 0 (0%) 1 (10%) 0 (0%) 
ADI (Quartiles)       
Q1 (0-25) 774 615 (79.5%) 52 (6.7%) 23 (3%) 58 (7.5%) 26 (3.4%) 
Q2 (26-50) 889 700 (78.7%) 97 (10.9%) 48 (5.4%) 30 (3.4%) 14 (1.6%) 
Q3 (51-75) 753 544 (73.6%) 123 (16.3%) 52 (6.9%) 14 (1.9%) 20 (2.7%) 
Q4 (76-100) 472 (16.3%) 295 (62.5%) 112 (23.7%) 52 (11%) 5 (1.1%) 8 (1.7%) 
ADI (Continuous)       
Range 1-100 1-100 2-100 1-100 1-93 1-95 
Mean (SD) 45.5 (26.2) 43.4 (25.4) 57.2 (25.8) 57.9 (26) 30.2 (23.1) 39.8 (27.1) 
Median 44 41 59.5 58 22 38 

BCL, B-cell lymphoma; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma; SD, standard deviation; TCL, T-cell lymphoma.

Table 2.

Population differences of study cohort vs excluded patients and clinical trial participants vs nonparticipants

Excluded NHL lacking census tract (%)Total study cohort populationP value
(α = .05)
Clinical Trial
participants (%)
Nonparticipants (%)P value (α = .05)
172 3146  950 (30.2) 2196 (69.8)  
Sex       
Female 74 (43) 1274 (40.5) .52 348 (36.6) 926 (42.2) .004 
Male 98 (57) 1872 (59.5)  602 (63.4) 1270 (57.8)  
Age       
Mean 67 63 <.0001 61 63 .002 
SD 11.1 14.6  14.2 14.7  
Median 68 65  63 65  
Ethnicity       
Hispanic/Latino 25 (14.5) 411 (13.1) .90 110 (11.6) 301 (13.7) .12 
Non-Hispanic 143 (83.1) 2660 (84.6)  822 (86.5) 1838 (83.7)  
Unknown 4 (2.3) 75 (2.4)  18 (1.9) 57 (2.6)  
Race       
NHW 132 (76.7) 2356 (74.9) .40 742 (78.1) 1614 (73) .11 
Hispanic/Latino 25 (14.5) 411 (13.1)  110 (11.6) 301 (13.7)  
Non-Hispanic Black/AA 8 (4.7) 189 (6)  50 (5.3) 139(6.3)  
Non-Hispanic Asian 2 (1.2) 116 (3.7)  29 (3.1) 87 (3)  
Other/unknown 5 (2.9) 74 (2.4)  19 (2) 55 (2.5)  
Diagnosis       
DLBCL 55 (32) 917 (29.1) .89 259 (27.3) 658 (30.0) .0005 
FL 41 (23.8) 698 (22.2)  154 (16.2) 544 (24.8)  
MCL 18 (10.5) 417 (13.3)  228 (24) 189 (8.6)  
MZL 16 (9.3) 287(9.1)  55 (5.8) 214 (10.6)  
TCL 16 (9.3) 259 (8.2)  102 (10.7) 157 (7.1)  
Misc. 12 (7) 235 (7.5)  49 (5.2) 186 (8.5)  
High-grade BCL 8 (4.7) 205 (6.5)  63 (6.6) 142 (6.5)  
Composite 6 (3.5) 118 (3.8)  39 (4.1) 79 (3.6)  
Gray zone lymphoma 0 (0) 10 (0.3)  1 (0.1) 9 (0.4)  
Clinical trial type       
Any 50 (29.1) 950 (30.2) .95 950 (30.2) N/A  
Noninterventional 19 (11) 403 (12.8) .8 403 (12.8) N/A  
Interventional, nontherapeutic 3 (1.7) 90 (2.9) .69 90 (2.9) N/A  
Interventional, therapeutic 35 (20.3) 646 (20.5) .92 646 (20.5) N/A  
Insurance type       
Private insurance/managed care 68 (39.5) 1504 (47.8) .011 472 (49.7) 1032 (47) .40 
Medicare 101 (58.7) 1452 (46.2)  425 (44.7) 1027 (46.8)  
Medicaid 0 (0) 36 (1.1)  11 (1.2) 25 (1.1)  
Self-pay 0 (0) 80 (2.5)  18 (1.8) 62 (2.8)  
Government/other 3 (1.7) 74 (2.4)  24 (2.5) 50 (2.3)  
ADI (quartiles)    n = 879 n = 2009  
Q1 (0-25) N/A N/A N/A 260 (29.6) 514 (25.6) .043 
Q2 (26-50) N/A N/A  279 (31.7) 610 (30.4)  
Q3 (51-75) N/A N/A  208 (23.7) 545 (27.1)  
Q4 (76-100) N/A N/A  132 (15) 340 (16.9)  
ADI (Continuous)       
Mean N/A N/A N/A 44 46 0.009 
SD N/A N/A  26.1 26.2  
Median N/A N/A  42 45  
Excluded NHL lacking census tract (%)Total study cohort populationP value
(α = .05)
Clinical Trial
participants (%)
Nonparticipants (%)P value (α = .05)
172 3146  950 (30.2) 2196 (69.8)  
Sex       
Female 74 (43) 1274 (40.5) .52 348 (36.6) 926 (42.2) .004 
Male 98 (57) 1872 (59.5)  602 (63.4) 1270 (57.8)  
Age       
Mean 67 63 <.0001 61 63 .002 
SD 11.1 14.6  14.2 14.7  
Median 68 65  63 65  
Ethnicity       
Hispanic/Latino 25 (14.5) 411 (13.1) .90 110 (11.6) 301 (13.7) .12 
Non-Hispanic 143 (83.1) 2660 (84.6)  822 (86.5) 1838 (83.7)  
Unknown 4 (2.3) 75 (2.4)  18 (1.9) 57 (2.6)  
Race       
NHW 132 (76.7) 2356 (74.9) .40 742 (78.1) 1614 (73) .11 
Hispanic/Latino 25 (14.5) 411 (13.1)  110 (11.6) 301 (13.7)  
Non-Hispanic Black/AA 8 (4.7) 189 (6)  50 (5.3) 139(6.3)  
Non-Hispanic Asian 2 (1.2) 116 (3.7)  29 (3.1) 87 (3)  
Other/unknown 5 (2.9) 74 (2.4)  19 (2) 55 (2.5)  
Diagnosis       
DLBCL 55 (32) 917 (29.1) .89 259 (27.3) 658 (30.0) .0005 
FL 41 (23.8) 698 (22.2)  154 (16.2) 544 (24.8)  
MCL 18 (10.5) 417 (13.3)  228 (24) 189 (8.6)  
MZL 16 (9.3) 287(9.1)  55 (5.8) 214 (10.6)  
TCL 16 (9.3) 259 (8.2)  102 (10.7) 157 (7.1)  
Misc. 12 (7) 235 (7.5)  49 (5.2) 186 (8.5)  
High-grade BCL 8 (4.7) 205 (6.5)  63 (6.6) 142 (6.5)  
Composite 6 (3.5) 118 (3.8)  39 (4.1) 79 (3.6)  
Gray zone lymphoma 0 (0) 10 (0.3)  1 (0.1) 9 (0.4)  
Clinical trial type       
Any 50 (29.1) 950 (30.2) .95 950 (30.2) N/A  
Noninterventional 19 (11) 403 (12.8) .8 403 (12.8) N/A  
Interventional, nontherapeutic 3 (1.7) 90 (2.9) .69 90 (2.9) N/A  
Interventional, therapeutic 35 (20.3) 646 (20.5) .92 646 (20.5) N/A  
Insurance type       
Private insurance/managed care 68 (39.5) 1504 (47.8) .011 472 (49.7) 1032 (47) .40 
Medicare 101 (58.7) 1452 (46.2)  425 (44.7) 1027 (46.8)  
Medicaid 0 (0) 36 (1.1)  11 (1.2) 25 (1.1)  
Self-pay 0 (0) 80 (2.5)  18 (1.8) 62 (2.8)  
Government/other 3 (1.7) 74 (2.4)  24 (2.5) 50 (2.3)  
ADI (quartiles)    n = 879 n = 2009  
Q1 (0-25) N/A N/A N/A 260 (29.6) 514 (25.6) .043 
Q2 (26-50) N/A N/A  279 (31.7) 610 (30.4)  
Q3 (51-75) N/A N/A  208 (23.7) 545 (27.1)  
Q4 (76-100) N/A N/A  132 (15) 340 (16.9)  
ADI (Continuous)       
Mean N/A N/A N/A 44 46 0.009 
SD N/A N/A  26.1 26.2  
Median N/A N/A  42 45  

Boldface values denote statistical significance at the P value < .05 level.

AA, African American; BCL, B-cell lymphoma; Misc., miscellaneous; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma; N/A, not applicable; SD, standard deviation; TCL, T-cell lymphoma.

The study population consisted of 59.5% male and 74.9% non-Hispanic white (NHW), with 13.1% Hispanic, 6% Black, and 3.7% Asian patients. The most prevalent histology groups were DLBCL at 29.1%, FL at 22.2%, mantle cell at 13.9%, marginal zone at 9.1%, T-cell at 8.2%, and high-grade B-cell lymphoma at 6.5%. The most common insurance type was managed care (private preferred provider organization [PPO]/health maintenance organization [HMO] plans) at 47.8%, and Medicare at 46.2%; 2.5% of patients were self-pay, 1.1% had Medicaid, and 2.4% had other government insurance.

Within our cohort of 3146 patients, a subset of 1819 (58%) resided in Texas. The analysis of geographical distribution revealed that a substantial proportion of the patients were from Harris County (n = 558 [30.6%]), where the city of Houston is located (Figure 1A). Furthermore, the map of ZCTA sample size in Harris County (Figure 1B) indicated that most patients were from suburban areas, with an average driving distance of 34.3 km and travel time of 30.2 minutes to the medical center. Additionally, the geographic patterns of patients’ residencies differed in relation to the types of trials in which patients participated, as depicted in Figure 1C-F. This suggests that trial participation may correlate with geographic factors.

Figure 1.

Geospatial distribution of patients’ residencies by trial participation. (A) Distribution of all patients within Texas. (B) All patients proximate to TMC. (C) Patients with any trial participation. (D) Patients with no trial participation. (E) Patients in noninterventional trials (with overlap). (F) Patients in therapeuric trials.

Figure 1.

Geospatial distribution of patients’ residencies by trial participation. (A) Distribution of all patients within Texas. (B) All patients proximate to TMC. (C) Patients with any trial participation. (D) Patients with no trial participation. (E) Patients in noninterventional trials (with overlap). (F) Patients in therapeuric trials.

Close modal
Figure 2.

Depiction of trial participation by age. (A) Entire study cohort. (B) DLBCL subcohort. (C) FL subcohort.

Figure 2.

Depiction of trial participation by age. (A) Entire study cohort. (B) DLBCL subcohort. (C) FL subcohort.

Close modal

Characteristics of clinical trial participants and geospatial and univariate analysis

Clinical trial participants were 63.4% male, 78.1% NHW, 11.6% Hispanic, 5.3% Black, and 3.1% Asian. Clinical trial participants were slightly younger than nonparticipants (median age, 63 vs 65 years; P = .001). There was a high overall clinical trial participation rate of 30.3%, with the majority of patients being enrolled in therapeutic trials (20.9% of the study cohort). Patients also participated in noninterventional (12.7%) and interventional nontherapeutic (2.9%; Table 2). Many patients participated in >1 type of trial; thus, there was notable overlap predominantly between noninterventional and interventional therapeutic trials. There were higher participation rates among men than women (32.2% vs 27.6%; odds ratio [OR], 1.26; P = .006). In the total cohort, participation rates were similar across race/ethnicity. Most patients had managed care (private HMO/PPO plans; 47%) or Medicare (45.7%), and few patients had Medicaid (1.2%) or other government insurance. Participation rates did not differ by insurance type in this cohort. There was significant variation in clinical trial participation by histology type (P = .0005). The trial participation rates for DLBCL and FL were 28.2% and 22%, respectively. The highest trial participation rates were observed among patients with mantle cell lymphoma (54.6%) and T-cell lymphoma (39.4%), which contrasted with the lower rates among patients with marginal zone lymphoma (19.2%) and other types (10%).

In univariate analyses, 3 of the 31 nSES variables showed a significant association with clinical trial participation. Specifically, lower participation rates were correlated with areas with a higher percentage of the overall populations living below the poverty level (P = .007), a higher percent of populations aged 19 to 64 years living below the poverty level (P = .008), and a higher percent of populations living in crowded households (>1 person per room; P = .002). Further analysis using the Mann-Whitney test to address skewed data identified other significant associations. There are lower participation rates in areas with a higher percentage of the population aged >25 years with less than a high school diploma (P = .018), a lower percentage of the population aged >25 years with a bachelor’s degree (P = .048), a lower median household income (P = .047), a lower percentage of households with incomes above $200 000 (P = .004), and lower home values (P = .003). Moreover, the association between participation and ADI was significant (P = .043), and even stronger when analyzing only the participants in interventional/therapeutic trials (P = .007), as summarized in Table 2 and Table 3. Notably, there was no significant association between the racial composition of each census tract and clinical trial participation. There was also no significant association between clinical trial participation and either driving distance (OR, 1.23; confidence interval [CI], 0.83-1.84; P = .31) or driving time (OR, 1.03; CI, 0.94-1.14; P = .52).

Table 3.

Comparison of demographic factors between non-participates vs participation by type of trial

Nonparticipants (%)Interventional therapeutic trial all (%)P value (α = unadjusted)Interventional
nontherapeutic only
P value (α = unadjusted)Noninterventional
trial only
P value (α = unadjusted)
2196 646  69  235  
Sex        
Female 926 (42.2) 228 (35.3) .75 33 (47.8) .39 87 (37) .81 
Male 1270 (57.8) 418 (64.7)  36 (52.2)  148 (63)  
Age        
Mean 63 61 .001 60 .062 63 .86 
SD 14.7 13.3  14.6  16.4  
Median 65 62  62  65  
Ethnicity        
Hispanic/Latino 301 (13.7) 74 (11.5) .17 8 (11.6) .74 28 (11.9) .67 
Non-Hispanic (NH) 1838 (83.7) 560 (86.7)  60 (87)  202 (86)  
Unknown 57 (2.6) 12 (1.9)  1 (1.4)  5 (2.1)  
Race        
NH White 1614 (73) 505 (78.2) .22 55 (79.7) .63 182 (77.4) .76 
Hispanic/Latino 301 (13.7) 74 (11.5)  8 (11.6)  28 (11.9)  
Non-Hispanic Black/AA 139 (6.3) 33 (5.1)  4 (5.8)  13 (5.5)  
Non-Hispanic Asian 87 (3) 20 (3.1)  2 (2.9)  7 (3)  
Other/unknown 55 (2.5) 14 (2.2)  0 (0)  5 (2.1)  
Diagnosis        
DLBCL 658 (30.0) 162 (25) .0005 18 (26) 0.58 79 (33.6) .0005 
FL 544 (24.8) 114 (17.6)  21 (30.4)  19 (8.1)  
MCL 189 (8.6) 207 (32)  4 (5.8)  17 (7.2)  
MZL 214 (10.6) 24 (3.7)  10 (14.5)  21 (8.9)  
TCL 157 (7.1) 79 (12.2)  4 (5.8)  19 (8.1)  
Misc. 186 (8.5) 10 (1.5)  8 (11.6)  31 (13.2)  
High Grade BCL 142 (6.5) 26 (4)  4 (5.8)  33 (14)  
Composite 79 (3.6) 23 (3.6)  0 (0)  16 (6.8)  
Gray Zone lymphoma 9 (0.4) 1 (0.2)  0 (0)  0 (0)  
Insurance type        
Private insurance / managed Care 1032 (47) 332 (51.4) .37 40 (58) .25 100 (42.6) .19 
Medicare 1027 (46.8) 278 (43)  28 (40)  119 (50.6)  
Medicaid 25 (1.1) 7 (1.1)  0 (0)  4 (1.7)  
Self-pay 62 (2.8) 14 (2.2)  1 (1.4)  3 (1.3)  
Government/other 50 (2.3) 15 (2.3)  0 (0)  9 (3.8)  
ADI (quartiles) n = 2009 n = 598    N = 219  
Q1 (0-25) 514 (25.6) 191 (31.9) .007 18 (29) .85 51 (23.3) .52 
Q2 (26-50) 610 (30.4) 185 (30.9)  20 (32.2)  74 (33.8)  
Q3 (51-75) 545 (27.1) 141 (23.6)  14 (22.6)  53 (24.2)  
Q4 (76-100) 340 (16.9) 81 (13.5)  10 (16.1)  41 (18.7)  
ADI (continuous)        
Mean 46 42 0.001 44 0.54 47.3 0.86 
SD 26.2 25.9  26  26.5  
Median 45 41  40  44  
Nonparticipants (%)Interventional therapeutic trial all (%)P value (α = unadjusted)Interventional
nontherapeutic only
P value (α = unadjusted)Noninterventional
trial only
P value (α = unadjusted)
2196 646  69  235  
Sex        
Female 926 (42.2) 228 (35.3) .75 33 (47.8) .39 87 (37) .81 
Male 1270 (57.8) 418 (64.7)  36 (52.2)  148 (63)  
Age        
Mean 63 61 .001 60 .062 63 .86 
SD 14.7 13.3  14.6  16.4  
Median 65 62  62  65  
Ethnicity        
Hispanic/Latino 301 (13.7) 74 (11.5) .17 8 (11.6) .74 28 (11.9) .67 
Non-Hispanic (NH) 1838 (83.7) 560 (86.7)  60 (87)  202 (86)  
Unknown 57 (2.6) 12 (1.9)  1 (1.4)  5 (2.1)  
Race        
NH White 1614 (73) 505 (78.2) .22 55 (79.7) .63 182 (77.4) .76 
Hispanic/Latino 301 (13.7) 74 (11.5)  8 (11.6)  28 (11.9)  
Non-Hispanic Black/AA 139 (6.3) 33 (5.1)  4 (5.8)  13 (5.5)  
Non-Hispanic Asian 87 (3) 20 (3.1)  2 (2.9)  7 (3)  
Other/unknown 55 (2.5) 14 (2.2)  0 (0)  5 (2.1)  
Diagnosis        
DLBCL 658 (30.0) 162 (25) .0005 18 (26) 0.58 79 (33.6) .0005 
FL 544 (24.8) 114 (17.6)  21 (30.4)  19 (8.1)  
MCL 189 (8.6) 207 (32)  4 (5.8)  17 (7.2)  
MZL 214 (10.6) 24 (3.7)  10 (14.5)  21 (8.9)  
TCL 157 (7.1) 79 (12.2)  4 (5.8)  19 (8.1)  
Misc. 186 (8.5) 10 (1.5)  8 (11.6)  31 (13.2)  
High Grade BCL 142 (6.5) 26 (4)  4 (5.8)  33 (14)  
Composite 79 (3.6) 23 (3.6)  0 (0)  16 (6.8)  
Gray Zone lymphoma 9 (0.4) 1 (0.2)  0 (0)  0 (0)  
Insurance type        
Private insurance / managed Care 1032 (47) 332 (51.4) .37 40 (58) .25 100 (42.6) .19 
Medicare 1027 (46.8) 278 (43)  28 (40)  119 (50.6)  
Medicaid 25 (1.1) 7 (1.1)  0 (0)  4 (1.7)  
Self-pay 62 (2.8) 14 (2.2)  1 (1.4)  3 (1.3)  
Government/other 50 (2.3) 15 (2.3)  0 (0)  9 (3.8)  
ADI (quartiles) n = 2009 n = 598    N = 219  
Q1 (0-25) 514 (25.6) 191 (31.9) .007 18 (29) .85 51 (23.3) .52 
Q2 (26-50) 610 (30.4) 185 (30.9)  20 (32.2)  74 (33.8)  
Q3 (51-75) 545 (27.1) 141 (23.6)  14 (22.6)  53 (24.2)  
Q4 (76-100) 340 (16.9) 81 (13.5)  10 (16.1)  41 (18.7)  
ADI (continuous)        
Mean 46 42 0.001 44 0.54 47.3 0.86 
SD 26.2 25.9  26  26.5  
Median 45 41  40  44  

Bold values denote statistical significance at the P value < .05 level.

AA, African American; BCL, B-cell lymphoma; MCL, mantle cell lymphoma; MZL, Marginal zone lymphoma; TCL, T-cell lymphoma.

Multivariable analysis

Multivariable analysis revealed a nonlinear inverse correlation between increasing age and the likelihood of clinical trial participation (P = .001), indicating the odds of clinical trial participation decrease as age increases (Figures 1-2). Additionally, we found histology type was significantly associated with the odds of clinical trial participation. Specifically, patients with T-cell lymphoma (OR, 1.62; P = .12) and mantle cell lymphoma (OR, 2.93; P < .001) were more likely to participate in clinical trials than those with DLBCL, whereas patients with marginal zone lymphoma had lower participation rates (OR, 0.56; P =.009) than those with DLBCL. There was no significant difference in participation based on sex, race/ethnicity, ADI, or the nSES variable related to education attainment (percentage population aged >25 years with no high school diploma). Interestingly, the observed significant association between trial participation and ADI in univariant analysis was no longer present in the covariate-adjusted logistic regression models. In these adjusted models, age appeared to show the strongest association with participation. When we adjusted the same covariates except histology, a separate model for the DLBCL subgroup showed that Hispanic patients were significantly less likely to enroll in trials (OR, 0.36; P = .001) than their NHW counterparts (Figure 1B). In the FL subgroup analysis, age demonstrated significant inverse association with the odds of clinical trial participation (estimated degrees of freedom = 2.60; P = .002) (Figure 1C). Regarding noninterventional trials and interventional nontherapeutic trials, the analysis revealed no significant associations across any demographic, nSES variables, ADI, or travel distance. The geospatial distribution of patients’ residencies by trial participation is shown in Figure 1.

It is estimated that only 2% to 3% of adult patients with cancer participate in clinical trials.10,30-34 Participation rates differ significantly by care setting, with much higher participation rates at academic sites (∼16%) than community sites (∼7%).34 It is also important to note that 85% of patients receive treatment in the community, compared with only ∼15% in larger academic centers.34 Barriers to clinical trial recruitment vary by institutional setting, and a large meta-analysis found that 56% of patients with cancer lacked local trial availability, a significant structural barrier.34 This study also found that structural factors such as trial availability and clinical factors (including eligibility criteria) together precluded 77.1% of patients from having the option to participate in trials.34 The underrepresentation of minority patients has been reported to be even more pronounced in hematologic malignancies, with 1 meta-analysis showing that Black patients represented 1.5% of participants in the phase 3 DLBCL trials examined.33 Our analysis explored clinical trial enrollment patterns among patients with lymphoma at a single high-volume institution and examined the association with race, ethnicity, and neighborhood socioeconomic variables. Participation rates in our cohort were quite high across NHL subtypes, with an overall clinical trial participation rate of 30% and 21% in interventional therapeutic clinical trials. Although clinical trial participants in our cohort were predominantly NHW and male, the racial composition of our cohort’s trial participants largely reflected the patient population treated at the institution. The only significant difference detected was among patients with DLBCL, among whom Hispanic patients had 63% lower odds of enrolling in trials than their NHW counterparts.

Although we identified associations between various nSES variables and clinical trial participation, the most significant associations were with age and histology. Interestingly, enrollment in clinical trials did not vary significantly by individual race or neighborhood racial composition, which stands in contrast to previously described lower participation rates among these groups in other studies.31,35-38 It is important to note that although clinical trial participation at this single center was representative of the patient population at the institution, this patient population does not entirely reflect the broader population of patients with NHL in the local community. There are many potential reasons for the observed differences that may provide some insight into the differences seen in representativeness in clinical trial populations. Firstly, MDACC attracts patients from all across the globe, with ∼43% of the new patients with lymphoma residing outside of Texas and 4% being international patients. It is important to note that patients who can travel to receive care are likely to have more resources and less likely to live in neighborhoods with the sociodemographic characteristics that we identified to be associated with lower clinical trial participation.

Another notable feature of our study population was that 93% of patients had private insurance or Medicare insurance, which is markedly different from the local community. According to the US Census Bureau data, Texas leads the country in the number of uninsured residents (18%).39 Moreover, in 2020 in Harris County, 20.4% of all residents were uninsured, including 26.9% of individuals between the ages of 19 to 64 who were without insurance.40,41 Medicaid accounted for 15.3% of insurance coverage in Harris County, but only 1.2% of our study population had Medicaid as their primary insurance.41 Other hospital systems within Harris County provide care for patients with other insurance types such as Harris Health that provides care for indigent, uninsured, Medicaid, and underinsured patients. Expanding access to clinical trials for patients with lymphoma with these insurance types will require dedicated efforts to increase the quantity and variety of clinical trials in locations where these patients receive care.

A few unique features about our environment include the patient population, the staff, available resources, and the institution itself. It is important to note that The Department of Lymphoma and Myeloma at MDACC is 1 of the largest multidisciplinary programs with 34 research and clinical faculty and >250 department members and trainees. It houses a robust portfolio of lymphoma clinical trials with dozens of active therapeutic trials at any given time and dedicated programs to support clinical trial enrollment such as specialized research nurse coordinator, patient navigators, and financial assistance programs. Additionally, many patients are drawn to the institution specifically for novel therapies, often with specific trials in mind, which results in a patient population that is inherently more inclined to participate in clinical trials. Additionally, there are specialized departments such as the behavioral science and cancer health disparities, which champions several programs aimed at improving diversity of clinical trial participants and surmounting numerous obstacles that might impede access. The institution invests in resources such as a learning center site, which provides information about clinical trials through various media, including education reading material and informative videos to provide multimedia modes of information dissemination. There are also programs matching patients with similar diagnoses to facilitate peer support groups, which can help patients learn from the experiences of other patients, including those previously participated in trials. Lastly, there have been significant strategic investments in programs such as the growing patient navigator program that provides trained research navigators to help patients understand clinical trial process and overcome barriers to participation including some financial challenges.

Despite its strengths, this study has other limitations. Due to the retrospective nature of our study, we were unable to control for all possible confounders including selection bias arising from our eligibility criteria. nSES may not serve as a surrogate for individual-level SES (iSES), which measures different dimensions of social determinants of health that can also affect clinical trial participation and outcomes. Although nSES provides insight about the communities that patients come from, discrepancies between iSES and nSES and complex interactions between the 2 are not well accounted for in our study design. These measures have been shown useful when evaluated jointly and are not mutually exclusive.42-44 The patient address and insurance status at the time of diagnosis are used as static measures but may change over the course of a patients’ care, which was not captured in our analysis. Although we focused on primary insurance, it is important to note that many patients may have multiple insurance providers. For example, patients aged ≥65 years may have both Medicare and other supplementary insurance plans including Medicaid dual enrollment or private insurance. Our results may not be generalizable to the US population because the highly selected patient population seen at MDACC differs from patients receiving care in other health care settings. However, these data do indicate that in clinical environments with strong infrastructure and a focus on clinical trials, similar clinical trial participation across racial and ethnic groups can be achieved.

It is important to note that the current categorization of race and ethnicities and how we collect these data are intrinsically flawed. Although self-reported race and ethnicity are generally regarded as more reliable than chart-abstracted groupings, current categories are too simplistic. Race is a social construct that groups people of common ancestry by means of physical characteristics, such as hair type, color of eyes and skin, and stature. However, these groupings, based on limited biologic basis, can have significant social consequences. Ethnicity, on the other hand, refers to a large group of people with a shared culture, language, history, set of traditions, and other features. These 2 concepts, although often reported together, represent very different concepts and simplify the categorization of diverse and heterogenous populations. For example, the current category of Asian (non-Hispanic Asian) inadequately captures a very diverse groups of ethnicities from various regions of a continent, including people of Japanese, Chinese, Indian, and Vietnamese heritage, to name a few, with different languages and cultures. Analogous inaccuracies occur for other groups. It is important to interpret the results of our findings in this context.

Much of the literature about accrual to clinical trials has focused on the patient’s willingness to participate. Prospective attitude surveys and recent meta-analysis studies show high level of willingness to participate in clinical trials when offered the opportunity, with more than half of patients agreeing with no difference in acceptance rates by race.45-47 Future efforts to improve lymphoma trial participation should shift from solely focusing on patient willingness to other factors. For example, programs such as the incorporation of oncology nurse navigation have been shown to be an efficacious and cost-effective strategy for increasing clinical participation among minority patients with cancer.48 

To improve enrollment rates across diverse settings, it is important to evaluate the effectiveness of potential interventions such as financial assistance based on income level/poverty status, housing support, clinical trial educational resources, and additional counseling services. Moreover, proactively screening patients based on their address may identify patients from socioeconomically disadvantaged neighborhoods who may benefit from these additional services. Prospectively evaluating the relationships between individual SES and nSES and clinical trial participation also will be important in developing future interventions.

Academic centers with robust trial portfolios still face challenges with recruiting participants. Several studies have shown that stringent eligibility criteria in lymphoma clinical trials such as criteria for stage, organ function, HIV status, history of other malignancy, self-reported comorbidity burden, and other laboratory cut offs may unnecessarily exclude potentially eligible patients without a significant impact on trial outcomes.49-51 These criteria may preferentially exclude patients with high-risk disease in need of urgent treatment, older patients, or minority patients who might have a higher burden of other cancers and comorbidities.52 Modifications to eligibility criteria to balance between safety and access could significantly expand trial accessibility, facilitate enrollment of a clinically diverse study population, and ensure that patients are not unnecessarily excluded from trial participation.50 Recommendations to modernize eligibility criteria for cancer clinical trials have been suggested in previous publications.53,54 Accessibility to trial sites is another critical factor. Barriers to access comprehensive cancer centers for low-income patients can be reduced by expanding coverage and clinical trials availability at community cancer centers where many receive care or implementing decentralized clinical trials. Trial sponsors should develop policies to expand their selection of sites, taking into consideration the diversity of patients treated across sites.

For future prospective trials, it will be critical to capture data on social determinants of health to better assess how well study populations reflect the demographics affected by a particular disease. Relying solely on race, ethnicity, and currently collected demographic data may not fully capture the socioeconomic variation in clinical trial participation and neglect important factors contributing to disparities. These data will be crucial in informing future efforts and trial design considerations to ensure meaningful representation in trials. Additionally, incorporating pharmacogenomics data in future prospective trials will provide valuable insights into the efficacy of novel therapies among patients of different ancestries, offering a more nuanced understanding than the often biologically limited categories of race and ethnicity.

There are numerous strategies used at MDACC to improve access to clinical trials. These include systematic screening of new and return patients for trials, offering a clinical study to all new patients, the broad availability of observational and biomarker studies, and the use of patient navigation for clinical trials and clinical trial nurse coordinators. It is also notable that physicians at this site have trial leadership roles as principal investigators and are active in recruiting patients to clinical trials. This study shows that it is possible to enroll patients across all racial/ethnic groups at rates similar to the demographics of the overall patient population and provides proof of principle for using these methods. It will be important to prospectively examine the efficacy of the previously mentioned strategies in improving enrollment rates of all patients and minority patients to lymphoma clinical trials and to extrapolate findings from these studies to other contexts.

iSES and nSES have been identified as independent and joint determinants of cancer disparities.3-9 In this large single-center academic cohort, multivariable models identified age and diagnosis as independent predictors of clinical trial participation, whereas race, sex, travel distance, and nSES were not significant factors. Additional challenges faced by potential cancer clinical trial participants such as frequent office visits and financial concerns are frequently cited as limiting factors, particularly for vulnerable populations. Recognizing this deficiency, the FDA offers guidance to help achieve proportional representation on clinical trials including evaluation for and elimination of restrictive eligibility criteria, increasing community engagement, and decreasing financial, travel, and other burdens on participants that could be barriers to participation.55,56 The National Academies of Science, Engineering and Medicine 2022 report provides 17 system-level recommendations with complete policy assessments to improve the representation of underrepresented and excluded populations in clinical trials and achieve lasting change.57 These include increasing accessibility and transparency of data on trial participation and recruitment efforts, journals and publishers requiring information on the representativeness of trials and studies for submissions, increasing federal incentives to institutions and pharmaceutical companies for increasing diversity in trials, ensuring that trials provide adequate compensation for research participants, increasing the diversity of the research workforce and leadership, and investing in community research infrastructure as well as strategic partnerships.

Our study examined an academic institution with policies, procedures, and a predominantly insured patient population. In this setting, we identified age as the predominant factor associated with decreased participation in lymphoma clinical trials. Examination of multicenter data sets including community practices in which underinsured patients received care and evaluation of interventions to improve access to clinical trials are needed to fully understand and address the broader spectrum of barriers.

This work was supported in part by a research grant agreement with the Leukemia and Lymphoma Society IMPACT program, MorphoSys US Inc (PI: C.R.F.); the Cancer Prevention and Research Institute of Texas (RR190079), where C.R.F. is a CPRIT Scholar in Cancer Research; and the National Cancer Institute of the National Institutes of Health under award number K24CA208132. This work was supported in part by a research funding from Conquer Cancer Foundation of ASCO Young Investigator Award and the American Society of Hematology Minority Hematology Fellow Awards received by C.N. Additionally, this work was also supported by the Center for Clinical and Translational Research, an MD Anderson resource.

Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily represent the official views of the National Institutes of Health, American Society of Clinical Oncology, Conquer Cancer, or the American Society of Hematology.

Contribution: C.N. and C.R.F. conceptualized and designed the study; all authors were responsible for data acquisition; C.N., C.R.A., A.A.A., C.X.B., and C.R.F. analyzed the data; C.N., C.R.A., L.J.N., and C.R.F. interpreted the data; C.N., C.R.A., A.A.A., L.J.N., and C.R.F. drafted the manuscript; and all authors critically reviewed and revised the manuscript, have approved the manuscript as submitted, and agreed to the published version of the manuscript.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Chijioke Nze, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77006; email: [email protected].

1.
Siegel
RL
,
Miller
KD
,
Wagle
NS
,
Jemal
A
.
Cancer statistics, 2023
. Jan.
CA Cancer J Clin
.
2023
;
73
(
1
):
17
-
48
.
2.
Teras
LR
,
DeSantis
CE
,
Cerhan
JR
,
Morton
LM
,
Jemal
A
,
Flowers
CR
.
2016 US lymphoid malignancy statistics by World Health Organization subtypes
.
CA A Cancer J Clin
.
2016
;
66
(
6
):
443
-
459
.
3.
DeRouen
MC
,
Schupp
CW
,
Koo
J
, et al
.
Impact of individual and neighborhood factors on disparities in prostate cancer survival
.
Cancer Epidemiol
.
2018
;
53
:
1
-
11
.
4.
Sposto
R
,
Keegan
TH
,
Vigen
C
, et al
.
The effect of patient and contextual characteristics on racial/ethnic disparity in breast cancer mortality
.
Cancer Epidemiol Biomarkers Prev
.
2016
;
25
(
7
):
1064
-
1072
.
5.
Cheng
I
,
Shariff-Marco
S
,
Koo
J
, et al
.
Contribution of the neighborhood environment and obesity to breast cancer survival: the California breast cancer survivorship consortium
.
Cancer Epidemiol Biomarkers Prev
.
2015
;
24
(
8
):
1282
-
1290
.
6.
Gomez
SL
,
Shariff-Marco
S
,
DeRouen
M
, et al
.
The impact of neighborhood social and built environment factors across the cancer continuum: current research, methodological considerations, and future directions
.
Cancer
.
2015
;
121
(
14
):
2314
-
2330
.
7.
Tao
L
,
Foran
JM
,
Clarke
CA
,
Gomez
SL
,
Keegan
TH
.
Socioeconomic disparities in mortality after diffuse large B-cell lymphoma in the modern treatment era
.
Blood
.
2014
;
123
(
23
):
3553
-
3562
.
8.
DeRouen
MC
,
Yang
J
,
Jain
J
,
Weden
MM
,
Gomez
SL
,
Shariff-Marco
S
.
Disparities in prostate cancer survival according to neighborhood archetypes, a population-based study
.
Urology
.
2022
;
163
:
138
-
147
.
9.
Aoki
RLF
,
Uong
SP
,
Gomez
SL
, et al
.
Individual- and neighborhood-level socioeconomic status and risk of aggressive breast cancer subtypes in a pooled cohort of women from Kaiser Permanente Northern California
.
Cancer
.
2021
;
127
(
24
):
4602
-
4612
.
10.
Murthy
VH
,
Krumholz
HM
,
Gross
CP
.
Participation in cancer clinical trials: race-sex-and age-based disparities
.
JAMA
.
2004
;
291
(
22
):
2720
-
2726
.
11.
Gopishetty
S
,
Kota
V
,
Guddati
AK
.
Age and race distribution in patients in phase III oncology clinical trials
.
Am J Transl Res
.
2020
;
12
(
9
):
5977
-
5983
.
12.
Casey
M
,
Odhiambo
L
,
Aggarwal
N
,
Shoukier
M
,
Islam
KM
,
Cortes
J
.
Representation of the population in need for pivotal clinical trials in lymphomas
.
Blood
.
2023
;
142
(
9
):
846
-
855
.
13.
Kozlov
M
.
FDA to require diversity plan for clinical trials
.
Nature
.
16 February 2023
Accessed 23 April 2024. .
14.
Kind
AJ
,
Jencks
S
,
Brock
J
, et al
.
Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study
.
Ann Intern Med
.
2014
;
161
(
11
):
765
-
774
.
15.
Epic
.
Epic electronic medical record
. 2023. Accessed 5 December 2023. https://www.epic.com/software/2023.
16.
US Census Bureau
.
TIGER/Line Geodatabases
. Accessed 5 December 2023. https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.html.
17.
US Census Bureau
.
Geocoder. C
. Accessed 5 December 2023. https://geocoding.geo.census.gov/geocoder/.
18.
US Census Bureau
.
American Community Survey Data
. Accessed 5 December 2023. https://www.census.gov/programs-surveys/acs/data.html.
19.
US Census Bureau
.
US Decennial Census 2020
. Accessed 5 December 2023. https://data.census.gov/cedsci/.
20.
Yost
K
,
Perkins
C
,
Cohen
R
,
Morris
C
,
Wright
W
.
Socioeconomic status and breast cancer incidence in California for different race/ethnic groups
.
Cancer Causes Control
.
2001
;
12
(
8
):
703
-
711
.
21.
Gomez
SL
,
Glaser
SL
,
McClure
LA
, et al
.
The California neighborhoods data system: a new resource for examining the impact of neighborhood characteristics on cancer incidence and outcomes in populations
.
Cancer Causes Control
.
2011
;
22
(
4
):
631
-
647
.
22.
Singh
GK
.
Area deprivation and widening inequalities in US mortality, 1969-1998
.
Am J Public Health
.
2003
;
93
(
7
):
1137
-
1143
.
23.
Fairfield
KM
,
Black
AW
,
Ziller
EC
, et al
.
Area deprivation index and rurality in relation to lung cancer prevalence and mortality in a rural state
.
JNCI Cancer Spectr
.
2020
;
4
(
4
):
pkaa011
.
24.
Ghirimoldi
FM
,
Schmidt
S
,
Simon
RC
, et al
.
Association of socioeconomic area deprivation index with hospital readmissions after colon and rectal surgery
.
J Gastrointest Surg
.
2021
;
25
(
3
):
795
-
808
.
25.
Lau
N
,
Bradford
MC
,
Steineck
A
, et al
.
Examining key sociodemographic characteristics of adolescents and young adults with cancer: a post hoc analysis of the promoting resilience in stress management randomized clinical trial
.
Palliat Med
.
2020
;
34
(
3
):
336
-
348
.
26.
Center for Health Disparities Research UoWSoMPH
.
2015 Area Deprivation Index, version 2.0
. Accessed 15 October 2022. https://www.neighborhoodatlas.medicine.wisc.edu/.
27.
Googleway
.
Accesses Google Maps APIs to Retrieve Data and Plot Maps
. Accessed 5 December 2022. https://CRAN.R-project.org/package=googleway.
28.
Tennekes
M
.
tmap: Thematic Maps in R
.
J Stat Softw
.
2018
;
84
(
6
):
1
-
39
.
29.
R Project
.
The R Project for Statistical Computing
. Accessed 5 December 2023. https://www.r-project.org.
30.
Tejeda
HA
,
Green
SB
,
Trimble
EL
, et al
.
Representation of African-Americans, Hispanics, and whites in national cancer institute cancer treatment trials
.
J Natl Cancer Inst
.
1996
;
88
(
12
):
812
-
816
.
31.
Sateren
WB
,
Trimble
EL
,
Abrams
J
, et al
.
How sociodemographics, presence of oncology specialists, and hospital cancer programs affect accrual to cancer treatment trials
.
J Clin Oncol
.
2002
;
20
(
8
):
2109
-
2117
.
32.
Institute of Medicine (US) Forum on Drug Discovery, Development, and Translation
.
Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary
.
National Academies Press (US)
;
2010
.
33.
Gopishetty
S
,
Kota
V
,
Guddati
AK
.
Age and race distribution in patients in phase III oncology clinical trials
.
Am J Transl Res
.
2020
;
12
(
9
):
5977
-
5983
.
34.
Unger
JM
,
Vaidya
R
,
Hershman
Dawn L
,
Minasian
Lori M
,
Fleury
Mark E
.
Systematic review and meta-analysis of the magnitude of structural, clinical, and physician and patient barriers to cancer clinical trial participation
.
J Natl Cancer Inst
.
2019
;
111
(
3
):
245
-
255
.
35.
Unger
JM
,
Gralow
JR
,
Albain
KS
,
Ramsey
SD
,
Hershman
DL
.
Patient income level and cancer clinical trial participation: a prospective survey study
.
JAMA Oncol
.
2016
;
2
(
1
):
137
-
139
.
36.
Sharrocks
K
,
Spicer
J
,
Camidge
DR
,
Papa
S
.
The impact of socioeconomic status on access to cancer clinical trials
.
Br J Cancer
.
2014
;
111
(
9
):
1684
-
1687
.
37.
Du
XL
,
Fang
S
,
Meyer
TE
.
Impact of treatment and socioeconomic status on racial disparities in survival among older women with breast cancer
.
Am J Clin Oncol
.
2008
;
31
(
2
):
125
-
132
.
38.
Hamel
LM
,
Penner
LA
,
Albrecht
TL
,
Heath
E
,
Gwede
CK
,
Eggly
S
.
Barriers to clinical trial enrollment in racial and ethnic minority patients with cancer
.
Cancer Control
.
2016
;
23
(
4
):
327
-
337
.
39.
US Census Bureau
.
Decline in share of people without health insurance driven by increase in public coverage in 36 states
. Accessed 5 December 2023. https://www.census.gov/library/stories/2022/09/uninsured-rate-declined-in-28-states.html.
40.
US Census Bureau. American community survey S2701
.
Selected characteristics of health insurance coverage in the Unites States
. Accessed 5 December 2023. https://data.census.gov/table/ACSST5Y2020.S2701?q=health%20insurance%20coverage&%20g=0100000US_0500000US48201&%20=.
41.
US Census Bureau
.
Small area health insurance estimates (SAHIE)
. Accessed 5 December 2023. https://www.census.gov/data-tools/demo/sahie/#/?s_statefips=48& s_year=2020& s_stcou=48201& s_racecat=0& s_agecat=1& s_measures=ui_snc.
42.
Cheng
E
,
Soulos
PR
,
Irwin
ML
, et al
.
Neighborhood and individual socioeconomic disadvantage and survival among patients with nonmetastatic common cancers
.
JAMA Netw Open
.
2021
;
4
(
12
):
e2139593
.
43.
Ribeiro
AI
,
Fraga
S
,
Severo
M
, et al
.
Association of neighbourhood disadvantage and individual socioeconomic position with all-cause mortality: a longitudinal multicohort analysis
.
Lancet Public Health
.
2022
;
7
(
5
):
e447
-
e457
.
44.
Wallar
LE
,
Rosella
LC
.
Individual and neighbourhood socioeconomic status increase risk of avoidable hospitalizations among Canadian adults: a retrospective cohort study of linked population health data
.
Int J Popul Data Sci
.
2020
;
5
(
1
):
1351
-
1366
.
45.
Unger
JM
,
Hershman
DL
,
Till
C
, et al
.
"When offered to participate": a systematic review and meta-analysis of patient agreement to participate in cancer clinical trials
.
.
2021
;
113
(
3
):
244
-
257
.
46.
National Cancer Institute
.
March HINTS Brief 48: Clinical Trial Participation Among U.S. Adults
. Accessed 5 December 2023. https://hints.cancer.gov/publications-reports/hints-briefs.aspx.
47.
Comis
RL
,
Miller
JD
,
Aldigé
CR
,
Krebs
L
,
Stoval
E
.
Public attitudes toward participation in cancer clinical trials
.
.
2003
;
21
:
830
-
835
.
48.
Holmes
DR
,
Major
J
,
Lyonga
DE
,
Alleyne
RS
,
Clayton
SM
.
Increasing minority patient participation in cancer clinical trials using oncology nurse navigation
.
Am J Surg
.
2012
;
203
(
4
):
415
-
422
.
49.
Luan
D
,
Fatola
T
,
Toure
A
, et al
.
Evaluating the impact of eligibility criteria in first-line clinical trials for follicular lymphoma: a MER/LEO cohort analysis
.
Blood Adv
.
2022
;
6
(
15
):
4413
-
4423
.
50.
Harkins
RA
,
Patel
SP
,
Lee
MJ
, et al
.
Improving eligibility criteria for first-line trials for patients with DLBCL using a US-based Delphi-method survey
.
Blood Adv
.
2022
;
6
(
9
):
2745
-
2756
.
51.
Khurana
A
,
Mwangi
R
,
Nowakowski
GS
, et al
.
Impact of organ function-based clinical trial eligibility criteria in patients with diffuse large B-cell lymphoma: who gets left behind?
.
J Clin Oncol
.
2021
;
39
(
15
):
1641
-
1649
.
52.
Khurana
A
,
Mwangi
R
,
Nastoupil
LJ
, et al
.
Evaluating the Impact of Lab-Based Eligibility Criteria By Race/Ethnicity in Frontline Clinical Trials for Diffuse Large B-Cell Lymphoma (DLBCL): A LEO Cohort Analysis
.
Blood
.
2022
;
140
(
Supplement 1
):
2056
-
2058
.
53.
Magnuson
A
,
Bruinooge
SS
,
Singh
H
, et al
.
Modernizing Clinical Trial Eligibility Criteria: Recommendations of the ASCO-Friends of Cancer Research Performance Status Work Group
.
Clin Cancer Res
.
2021
;
27
(
9
):
2424
-
2429
.
54.
Kim
ES
,
Bruinooge
SS
,
Roberts
S
, et al
.
Broadening eligibility criteria to make clinical trials more representative: American Society of Clinical Oncology and Friends of Cancer Research Joint Research Statement
.
.
2017
;
35
(
33
):
3737
-
3744
.
55.
US Food and Drug Administration
.
Diversity plans to improve enrollment of participants from underrepresented racial and ethnic populations in clinical trials; draft guidance for industry; availability
. 2022. Accessed 5 December 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/diversity-plans-improve-enrollment-participants-underrepresented-racial-and-ethnic-populations.
56.
US Food and Drug Administration
. Enhancing the Diversity of Clinical Trial Populations — Eligibility Criteria, Enrollment Practices, and Trial Designs Guidance for Industry. US-FDA-2019-D-1264.
2024
Accessed 23 April 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/enhancing-diversity-clinical-trial-populations-eligibility-criteria-enrollment-practices-and-trial.
57.
National Academies of Sciences E, and Medicine
. Improving representation in clinical trials and research: building research equity for women and underrepresented groups. In:
Bibbins-Domingo
K
,
Helman
A
, eds.
Improving Representation in Clinical Trials and Research: Building Research Equity for Women and Underrepresented Groups
.
The National Academies Collection: Reports funded by National Institutes of Health
;
2022
.

Author notes

Data can be shared with those who request it. Data will be shared upon reasonable request to the corresponding author, Chijioke Nze ([email protected]).

The full-text version of this article contains a data supplement.

Supplemental data