To identify geographic and socioeconomic variables predictive of residential proximity to neovascular age-related macular degeneration (nAMD) clinical trial locations.
Retrospective, cross-sectional study.
Census tract–level data from public datasets and trial-level data from ClinicalTrials.gov were analyzed. We calculated the driving distance (>60 miles) and time (>60 minutes) from the population-weighted US census tract centroid to the nearest clinical trial site.
We identified 42 trials studying nAMD across 829 unique clinical trial sites in the United States. In a multivariable model, driving distance >60 miles had a significant association with rural location (adjusted odds ratio [aOR] 5.54; 95% confidence interval [CI] 3.86-7.96, P < .0001) and with Midwest (aOR 2.30; 95% CI 1.21-4.38, P = .01) and South (aOR 2.43; 95% CI 1.21-4.91, P = .01) as compared to the Northeast region, and with some college or an associate’s degree, as compared to a bachelor’s degree (aOR 1.02; 95% CI 1.01-1.04, P = .0007, and aOR 1.05; 95% CI 1.00-1.10, P = .04, respectively). Lower odds of traveling >60 miles to the nearest nAMD trial site were associated with census tracts with a higher percentage of blacks (aOR 0.98; 95% CI 0.97-0.99, P < .0001), Hispanics (aOR 0.97; 95% CI 0.95-0.99, P = .002), and Asians (aOR 0.90; 95% CI 0.88-0.93, P < .0001), as compared to whites, and with a lower percentage of the population <200% of the federal poverty level. Similar predictors were found in time traveled >60 minutes.
There are geographic access disparities of clinical trial sites for nAMD in the United States.
W ith an estimated prevalence of 6.5% among those aged 40 years and older, age-related macular degeneration (AMD) presents a mounting public health challenge as the United States (US) population ages. Projections suggest that by 2050, late-stage AMD, including neovascular AMD (nAMD), will affect nearly 5.5 million individuals in the US. In the last 2 decades, the advent of intravitreal anti–vascular endothelial growth factor (anti-VEGF) therapy in treating nAMD has ushered forth an expanding pipeline of novel therapeutics aimed at further reducing the burden of disease and improving visual outcomes.
In the United States, regulatory approval and clinical adoption of novel therapeutics rely critically on robust evidence generated by clinical trials ensuring safe and efficacious use. The quality of clinical trial evidence depends not only on inherent study design features, but also on enrollment of appropriate study populations. In general, there are well-described disparities in clinical trial enrollment across racial, socioeconomic, and age groups with typically lower rates of participation among minority and elderly populations. , Promoting broad participation in clinical trials has important data validity and social equity implications, including improved data generalizability, increased accrual, and greater exposure of medically underserved populations to innovative care.
While clustering clinical trials to large urban areas offers advantages for efficient enrollment and retention of patients, unequal geographic distribution of clinical trial sites is a well-recognized barrier to clinical trial diversity. Clustering of trial sites in large urban centers may exacerbate disparities by tending to exclude certain groups, for whom costs associated with travel may represent too high a burden. In nAMD, where there are known disparities in disease burden and treatment delivery, , understanding limitations of existing clinical trial data may help inform future trial planning to maximize access and minimize exclusion of certain high-risk groups. Geographic access to nAMD clinical trials has not yet been previously described.
Using multisource, publicly available data, we conducted a retrospective, cross-sectional study examining the geographic distribution of nAMD-related clinical trials in the United States and determined predictors of geographic accessibility to identify populations at risk for noninclusion in nAMD trials. We defined geographic accessibility in terms of ground travel distance and travel time from the population-weighted geographic centroid of individual US census tracts to the nearest nAMD clinical trial site.
The institutional review board at Wills Eye Hospital designated the current cross-sectional, retrospective study a nonhuman subjects research study. The research adhered to the tenets of the Declaration of Helsinki .
Using ClinicalTrials.gov, we constructed a database of US nAMD trials initiating enrollment in 2017 or later. ClinicalTrials.gov is the largest public web-based international registry of industry and non-industry-sponsored clinical trials. In accordance with the legal mandate of the Food and Drug Administration (FDA) Amendments Act of 2007, all clinical trials evaluating FDA-regulated therapeutics must be registered on ClinicalTrials.gov. Subsequent policy by the National Institutes of Health (NIH) has since expanded the registration mandate to include all NIH-sponsored investigations. We limited our analysis to studies beginning in 2017 or later to ensure a more universal sampling of trials, given recent tightening of registration enforcement stringency beginning in 2017.
We searched ClinicalTrials.gov to identify registrations of clinical trials studying “eye diseases” and then further limited our analysis to trials listed as evaluating “neovascular AMD,” “wet AMD,” or “exudative AMD.” We excluded registrations of observational studies, in addition to trials that were not yet recruiting, suspended, terminated, or withdrawn. International clinical trials without US locations were additionally excluded.
For each study, we collected data on enrollment status, intervention, phase, sponsorship, and number and location of trial sites. For each trial site, we determined the exact geographic coordinates of latitude and longitude.
Using the 2014-2018 US Census Bureau’s American Community Survey, we identified all census tracts (N = 72,450) in the contiguous 48 United States. Census tracts are unique geographically defined regions, established by the US Census Bureau, comprising approximately 6,000-11,000 residents; they are commonly employed in population-based statistical analyses because their population characteristics and composition remain relatively static over time. Census tracts in Alaska and Hawaii were excluded because air or water transport is often required to access trial sites in these states, which is beyond the scope of our road travel analysis.
For each census tract, we collected and categorized data on age (<18 years, 18-64 years, and ≥65 years), race/ethnicity (white, black/African American, Hispanic, American Indian/Alaskan Native, Asian, Native Hawaiian/Pacific Islander, or other), education level (less than high school, high school completion, some college, associate’s degree, or bachelor’s degree or higher), health insurance status, and income level (less than 200% of the federal poverty level by quartile). Census tracts are assigned Rural-Urban Commuting area (RUCA) codes by the US Department of Agriculture on a scale of 1-10 based on population density and commuting patterns, with “1” designating a “metropolitan core” and “10” a “rural area.” We categorized RUCA codes for each census tract as follows: urban (RUCA of 1-3) or rural (RUCA of 4-10). We further grouped census tracts by region as follows: Northeast, Midwest, South, and West.
Main Outcome Measures
The primary outcomes of our study were road travel distance (in miles) and travel time (in minutes) to the nearest nAMD clinical trial site. Using ArcGIS Pro 2.6 (Esri, Redlands, California, USA; 2020), we mapped the population-weighted geographic centroids of each census tract. The origin-destination cost-matrix analysis tool in ArcGIS finds the quickest route using street-level data between origins and destinations based on standard traffic patterns and using a personal vehicle. Using this software, we determined travel distance and travel time from each census tract centroid to the single nearest clinical trial site. Travel distance was categorized as follows: <30 miles, ≥30 but ≤60 miles, >60 miles but ≤120 miles, and >120 miles. Travel time was categorized as follows: <60 minutes or ≥60 minutes.
We examined the distribution of demographic and socioeconomic characteristics of US census tracts based on travel distance and travel time to identify census tract characteristics associated with greater travel burden.
We used descriptive statistics to characterize census tracts and clinical studies. Univariate and multivariate logistic regression analyses were performed to examine associations between variables of interest and the outcomes of traveling more than 60 miles and traveling longer than 60 minutes to the nearest clinical trial center. Variance inflation factors were used to determine multicollinearity among covariates. All covariates that were significant in univariate analyses at the P = .25 level were included in the multivariate model. Variables either significant at the P = .05 level in the multivariable model or whose removal caused greater than a 20% change in the coefficient of at least 1 other variable were allowed to remain in the multivariate model. All modeling was performed in SAS 9.4 using PROC GENMOD; all models were weighted by each census tract’s population and clustered by state.
Among 2,542 active clinical trials in ophthalmology beginning 2017 or later, we identified 42 trials studying nAMD across 829 unique clinical trial sites in the United States ( Figure 1 ). The majority (n = 30; 71.4%) of trials studied an anti-VEGF agent. Industry involvement was present in 85.7% (n = 36). Detailed characteristics of each clinical trial are described in Table 1 .
|Active, not recruiting||10 (23.8)|
|Enrolling by invitation||3 (7.1)|
|Type of intervention|
|Anti-VEGF intervention||30 (71.4)|
|Not applicable||1 (2.4)|
|Any industry sponsor/collaborator||36 (85.7)|
|Number of US sites per trial, median site number (IQR)||9 (2.75-34.5)|
The majority of urban census tracts (n = 39,057; 66.2%) are situated fewer than 30 miles from the nearest trial site, compared to just 3.4% (n = 440) of rural census tracts. Most rural census tracts (n = 9,818; 75.8%) were located >60 miles from the nearest nAMD clinical trial site; a minority of urban census tracts (n = 10,914; 18.4%) were located that far. Detailed data on travel distance and median travel time by location are listed in Table 2 .
|<30 Miles||30-60 Miles||>60 Miles to 120 Miles||>120 Miles||All Distance Categories Median (IQR)||Total Number of Census Tracts||Population|
|Median (IQR)||18.77 (12.44, 26.57)||51.51 (44.32, 58.81)||88.19 (76.67, 101.74)||153.22 (133.74, 188.78)|
|Rural, n (% of rural census tracts)||440 (3.4)||2,675 (20.7)||5,481 (42.3)||4,337 (33.5)||97.17 (67.80, 141.72)||12,933||50,142,010|
|Urban, n (% of urban census tracts)||39,057 (66.2)||9,014 (15.3)||6,917 (11.7)||3,977 (6.7)||26.57 (15.56, 52.68)||58,965||270,484,922|
|Total, n (% of total census tracts)||39,497 (54.9)||11,689 (16.3)||12,398 (17,2)||8,314 (11.6)||33.74 (17.62, 76.10)||71,898||320,626,932|
In multivariable regression analysis, those traveling >60 miles to the nearest nAMD clinical trial had significantly higher odds (adjusted odds ratio [aOR] 5.54; 95% confidence interval [CI] 3.86-7.96, P < .0001) of being from a rural census tract site and living in the Midwest (aOR 2.30; 95% CI 1.21-4.38, P = .01) and South (aOR 2.43; 95% CI 1.21-4.9, P = .01), compared to the Northeast. Those traveling >60 miles had a lower odds of residing in census tracts with a higher proportion of black residents (aOR 0.98; 95% CI 0.97-0.99, P < .0001), Hispanic residents (aOR 0.97; 95% CI 0.95-0.99, P = .002), Asian residents (aOR 0.90; 95% CI 0.88-0.93, P < .0001), or other race (aOR 0.81; 95% CI 0.75-0.87, P < .0001), compared to the white proportion. Those traveling >60 miles to the nearest nAMD clinical trial had lower odds of residing in a census tract, with higher proportions of people aged 18 through 64 (unadjusted OR 0.97; 95% CI 0.96-0.99, P < .0001), although the multivariate analysis did not find a significant association with age distributions. Those traveling >60 miles also had significantly higher odds of living in a census tract where more residents completed some college or completed an associate’s degree, as compared to a bachelor’s degree (aOR 1.02; 95% CI 1.01-1.04, P = .0007 and aOR 1.05; 95% CI 1.00-1.10, P = .04, respectively). Finally, those traveling >60 miles to the nearest nAMD trial site had lower odds of residing in a wealthier census tract ( Table 3 ).
|Census Tract Characteristic||Univariate Model Unadjusted OR (95% CI)||P Value||Multivariate Model Adjusted OR (95% CI)||P Value|
|Rural vs urban|
|Rural||13.26 (9.37-18.76)||<.0001 *||5.54 (3.86-7.96)||<.0001 *|
|Midwest||3.94 (1.87-8.31)||.0003 *||2.30 (1.21-4.38)||.01 *|
|South||2.75 (1.23-6.17)||.01 *||2.43 (1.21-4.91)||.01 *|
|West||1.13 (0.44-2.92)||.79||1.37 (0.73-2.56)||.32|
|Age group in years a|
|18-64||0.97 (0.96-0.99)||<.0001 *||0.99 (0.98-1.00)||.12|
|65+||1.02 (0.99-1.06)||.14||0.99 (0.95-1.02)||.44|
|Black||0.99 (0.98-0.997)||.0068 *||0.98 (0.97-0.99)||<.0001 *|
|American Indian/Alaskan Native||1.08 (1.00-1.18)||.06||1.02 (0.99-1.06)||.23|
|Asian||0.80 (0.77-0.83)||<.0001 *||0.90 (0.88-0.93)||<.0001 *|
|Native Hawaiian/Pacific Islander||0.95 (0.83-1.09)||.44||0.92 (0.81-1.06)||.24|
|Hispanic||0.98 (0.97-0.99)||<.0001 *||0.97 (0.95-0.99)||.002 *|
|Other race||0.73 (0.66-0.80)||<.0001 *||0.81 (0.75-0.87)||<.0001 *|
|Two races||0.98 (0.93-1.04)||.51||0.97 (0.91-1.03)||.27|
|Less than high school||1.00 (0.98-1.02)||.98||1.02 (1.00-1.05)||.08|
|High school completion||1.07 (1.05-1.09)||<.0001 *||1.00 (0.99-1.02)||.31|
|Some college||1.04 (1.02-1.06)||.0001 *||1.02 (1.01-1.04)||.0007 *|
|Associate’s degree||1.08 (1.03-1.12)||.0008 *||1.05 (1.00-1.10)||.04 *|
|Bachelor’s degree or higher||[Reference]|
|No health insurance a||1.01 (0.99-1.03)||0.58||Not included|
|Percentage of population below federal poverty level, by quartile a|
|Quartile 1 (0-6.5%)||0.37 (0.28-0.49)||<.0001 *||0.33 (0.24-0.46)||0.0001 *|
|Quartile 2 (6.5%-12%)||0.79 (0.64-0.99)||.04 *||0.50 (0.38-0.65)||<.0001 *|
|Quartile 3 (12%-20.7%)||1.12 (0.96-1.31)||.16||0.67 (0.55-0.82)||<.0001 *|
|Quartile 4 (20.7%-100%)||[Reference]|