Analysis and Optimization of Equitable US Cancer Clinical Trial Center Access by Travel Time Article Swipe
Hassal Lee
,
Alexander Shakeel Bates
,
Shawneequa Callier
,
Michael Chan
,
Nyasha Chambwe
,
Andrea Marshall
,
Mary Beth Terry
,
Karen M. Winkfield
,
Tobias Janowitz
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1001/jamaoncol.2023.7314
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1001/jamaoncol.2023.7314
Importance Racially minoritized and socioeconomically disadvantaged populations are currently underrepresented in clinical trials. Data-driven, quantitative analyses and strategies are required to help address this inequity. Objective To systematically analyze the geographical distribution of self-identified racial and socioeconomic demographics within commuting distance to cancer clinical trial centers and other hospitals in the US. Design, Setting, and Participants This longitudinal quantitative study used data from the US Census 2020 Decennial and American community survey (which collects data from all US residents), OpenStreetMap, National Cancer Institute–designated Cancer Centers list, Nature Index of Cancer Research Health Institutions, National Trial registry, and National Homeland Infrastructure Foundation-Level Data. Statistical analyses were performed on data collected between 2006 and 2020. Main Outcomes and Measures Population distributions of socioeconomic deprivation indices and self-identified race within 30-, 60-, and 120-minute 1-way driving commute times from US cancer trial sites. Map overlay of high deprivation index and high diversity areas with existing hospitals, existing major cancer trial centers, and commuting distance to the closest cancer trial center. Results The 78 major US cancer trial centers that are involved in 94% of all US cancer trials and included in this study were found to be located in areas with socioeconomically more affluent populations with higher proportions of self-identified White individuals (+10.1% unpaired mean difference; 95% CI, +6.8% to +13.7%) compared with the national average. The top 10th percentile of all US hospitals has catchment populations with a range of absolute sum difference from 2.4% to 35% from one-third each of Asian/multiracial/other (Asian alone, American Indian or Alaska Native alone, Native Hawaiian or Other Pacific Islander alone, some other race alone, population of 2 or more races), Black or African American, and White populations. Currently available data are sufficient to identify diverse census tracks within preset commuting times (30, 60, or 120 minutes) from all hospitals in the US (N = 7623). Maps are presented for each US city above 500 000 inhabitants, which display all prospective hospitals and major cancer trial sites within commutable distance to racially diverse and socioeconomically disadvantaged populations. Conclusion and Relevance This study identified biases in the sociodemographics of populations living within commuting distance to US-based cancer trial sites and enables the determination of more equitably commutable prospective satellite hospital sites that could be mobilized for enhanced racial and socioeconomic representation in clinical trials. The maps generated in this work may inform the design of future clinical trials or investigations in enrollment and retention strategies for clinical trials; however, other recruitment barriers still need to be addressed to ensure racial and socioeconomic demographics within the geographical vicinity of a clinical site can translate to equitable trial participant representation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1001/jamaoncol.2023.7314
- https://jamanetwork.com/journals/jamaoncology/articlepdf/2816796/jamaoncology_lee_2024_br_230025_1710866110.17559.pdf
- OA Status
- hybrid
- Cited By
- 14
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393037672
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393037672Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1001/jamaoncol.2023.7314Digital Object Identifier
- Title
-
Analysis and Optimization of Equitable US Cancer Clinical Trial Center Access by Travel TimeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-21Full publication date if available
- Authors
-
Hassal Lee, Alexander Shakeel Bates, Shawneequa Callier, Michael Chan, Nyasha Chambwe, Andrea Marshall, Mary Beth Terry, Karen M. Winkfield, Tobias JanowitzList of authors in order
- Landing page
-
https://doi.org/10.1001/jamaoncol.2023.7314Publisher landing page
- PDF URL
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https://jamanetwork.com/journals/jamaoncology/articlepdf/2816796/jamaoncology_lee_2024_br_230025_1710866110.17559.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://jamanetwork.com/journals/jamaoncology/articlepdf/2816796/jamaoncology_lee_2024_br_230025_1710866110.17559.pdfDirect OA link when available
- Concepts
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Medicine, Socioeconomic status, Demography, Health equity, Population, Cancer, Gerontology, Census, Ethnic group, Cancer registry, Environmental health, Public health, Internal medicine, Nursing, Anthropology, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
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2025: 8, 2024: 6Per-year citation counts (last 5 years)
- References (count)
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data | 61, 74, 107, 284 |
| abstract_inverted_index.each | 247, 314 |
| abstract_inverted_index.from | 62, 75, 135, 241, 245, 301 |
| abstract_inverted_index.help | 21 |
| abstract_inverted_index.high | 143, 147 |
| abstract_inverted_index.maps | 388 |
| abstract_inverted_index.mean | 211 |
| abstract_inverted_index.more | 199, 273, 367 |
| abstract_inverted_index.need | 416 |
| abstract_inverted_index.race | 125, 267 |
| abstract_inverted_index.site | 433 |
| abstract_inverted_index.some | 265 |
| abstract_inverted_index.that | 175, 374 |
| abstract_inverted_index.this | 23, 188, 391 |
| abstract_inverted_index.used | 60 |
| abstract_inverted_index.were | 104, 190 |
| abstract_inverted_index.with | 150, 197, 202, 219, 234 |
| abstract_inverted_index.work | 392 |
| abstract_inverted_index.+6.8% | 215 |
| abstract_inverted_index.1-way | 131 |
| abstract_inverted_index.2020. | 112 |
| abstract_inverted_index.Black | 275 |
| abstract_inverted_index.Data. | 101 |
| abstract_inverted_index.Index | 87 |
| abstract_inverted_index.Other | 261 |
| abstract_inverted_index.Trial | 94 |
| abstract_inverted_index.White | 207, 280 |
| abstract_inverted_index.above | 317 |
| abstract_inverted_index.areas | 149, 196 |
| abstract_inverted_index.could | 375 |
| abstract_inverted_index.found | 191 |
| abstract_inverted_index.index | 145 |
| abstract_inverted_index.list, | 85 |
| abstract_inverted_index.major | 154, 170, 327 |
| abstract_inverted_index.other | 47, 266, 412 |
| abstract_inverted_index.range | 236 |
| abstract_inverted_index.sites | 330, 361, 373 |
| abstract_inverted_index.still | 415 |
| abstract_inverted_index.study | 59, 189, 345 |
| abstract_inverted_index.times | 134, 295 |
| abstract_inverted_index.trial | 44, 138, 156, 165, 173, 329, 360, 438 |
| abstract_inverted_index.which | 321 |
| abstract_inverted_index.(Asian | 250 |
| abstract_inverted_index.(which | 72 |
| abstract_inverted_index.7623). | 309 |
| abstract_inverted_index.Alaska | 255 |
| abstract_inverted_index.Cancer | 81, 83, 89 |
| abstract_inverted_index.Census | 65 |
| abstract_inverted_index.Health | 91 |
| abstract_inverted_index.Indian | 253 |
| abstract_inverted_index.Native | 256, 258 |
| abstract_inverted_index.Nature | 86 |
| abstract_inverted_index.alone, | 251, 257, 264, 268 |
| abstract_inverted_index.biases | 347 |
| abstract_inverted_index.cancer | 42, 137, 155, 164, 172, 183, 328, 359 |
| abstract_inverted_index.census | 290 |
| abstract_inverted_index.design | 396 |
| abstract_inverted_index.ensure | 421 |
| abstract_inverted_index.future | 398 |
| abstract_inverted_index.higher | 203 |
| abstract_inverted_index.inform | 394 |
| abstract_inverted_index.living | 353 |
| abstract_inverted_index.preset | 293 |
| abstract_inverted_index.racial | 34, 380, 422 |
| abstract_inverted_index.sites. | 139 |
| abstract_inverted_index.survey | 71 |
| abstract_inverted_index.tracks | 291 |
| abstract_inverted_index.trials | 184, 400 |
| abstract_inverted_index.within | 38, 126, 292, 331, 354, 426 |
| abstract_inverted_index.(+10.1% | 209 |
| abstract_inverted_index.+13.7%) | 217 |
| abstract_inverted_index.African | 277 |
| abstract_inverted_index.Centers | 84 |
| abstract_inverted_index.Design, | 52 |
| abstract_inverted_index.Pacific | 262 |
| abstract_inverted_index.Results | 167 |
| abstract_inverted_index.address | 22 |
| abstract_inverted_index.analyze | 28 |
| abstract_inverted_index.between | 109 |
| abstract_inverted_index.center. | 166 |
| abstract_inverted_index.centers | 45, 174 |
| abstract_inverted_index.closest | 163 |
| abstract_inverted_index.commute | 133 |
| abstract_inverted_index.display | 322 |
| abstract_inverted_index.diverse | 289, 336 |
| abstract_inverted_index.driving | 132 |
| abstract_inverted_index.enables | 363 |
| abstract_inverted_index.indices | 122 |
| abstract_inverted_index.located | 194 |
| abstract_inverted_index.overlay | 141 |
| abstract_inverted_index.races), | 274 |
| abstract_inverted_index.trials. | 12, 386 |
| abstract_inverted_index.trials; | 410 |
| abstract_inverted_index.American | 69, 252 |
| abstract_inverted_index.Hawaiian | 259 |
| abstract_inverted_index.Homeland | 98 |
| abstract_inverted_index.Islander | 263 |
| abstract_inverted_index.Measures | 116 |
| abstract_inverted_index.National | 80, 93, 97 |
| abstract_inverted_index.Outcomes | 114 |
| abstract_inverted_index.Racially | 1 |
| abstract_inverted_index.Research | 90 |
| abstract_inverted_index.Setting, | 53 |
| abstract_inverted_index.US-based | 358 |
| abstract_inverted_index.absolute | 238 |
| abstract_inverted_index.affluent | 200 |
| abstract_inverted_index.analyses | 15, 103 |
| abstract_inverted_index.average. | 222 |
| abstract_inverted_index.barriers | 414 |
| abstract_inverted_index.centers, | 157 |
| abstract_inverted_index.clinical | 11, 43, 385, 399, 409, 432 |
| abstract_inverted_index.collects | 73 |
| abstract_inverted_index.compared | 218 |
| abstract_inverted_index.distance | 40, 160, 333, 356 |
| abstract_inverted_index.enhanced | 379 |
| abstract_inverted_index.existing | 151, 153 |
| abstract_inverted_index.hospital | 372 |
| abstract_inverted_index.however, | 411 |
| abstract_inverted_index.identify | 288 |
| abstract_inverted_index.included | 186 |
| abstract_inverted_index.involved | 177 |
| abstract_inverted_index.minutes) | 300 |
| abstract_inverted_index.national | 221 |
| abstract_inverted_index.racially | 335 |
| abstract_inverted_index.required | 19 |
| abstract_inverted_index.unpaired | 210 |
| abstract_inverted_index.vicinity | 429 |
| abstract_inverted_index.American, | 278 |
| abstract_inverted_index.Currently | 282 |
| abstract_inverted_index.Decennial | 67 |
| abstract_inverted_index.Objective | 25 |
| abstract_inverted_index.Relevance | 343 |
| abstract_inverted_index.addressed | 419 |
| abstract_inverted_index.available | 283 |
| abstract_inverted_index.catchment | 232 |
| abstract_inverted_index.collected | 108 |
| abstract_inverted_index.community | 70 |
| abstract_inverted_index.commuting | 39, 159, 294, 355 |
| abstract_inverted_index.currently | 8 |
| abstract_inverted_index.diversity | 148 |
| abstract_inverted_index.equitable | 437 |
| abstract_inverted_index.equitably | 368 |
| abstract_inverted_index.generated | 389 |
| abstract_inverted_index.hospitals | 48, 230, 303, 325 |
| abstract_inverted_index.inequity. | 24 |
| abstract_inverted_index.mobilized | 377 |
| abstract_inverted_index.one-third | 246 |
| abstract_inverted_index.performed | 105 |
| abstract_inverted_index.presented | 312 |
| abstract_inverted_index.registry, | 95 |
| abstract_inverted_index.retention | 406 |
| abstract_inverted_index.satellite | 371 |
| abstract_inverted_index.translate | 435 |
| abstract_inverted_index.120-minute | 130 |
| abstract_inverted_index.Conclusion | 341 |
| abstract_inverted_index.Importance | 0 |
| abstract_inverted_index.Population | 117 |
| abstract_inverted_index.commutable | 332, 369 |
| abstract_inverted_index.difference | 240 |
| abstract_inverted_index.enrollment | 404 |
| abstract_inverted_index.hospitals, | 152 |
| abstract_inverted_index.identified | 346 |
| abstract_inverted_index.percentile | 226 |
| abstract_inverted_index.population | 269 |
| abstract_inverted_index.strategies | 17, 407 |
| abstract_inverted_index.sufficient | 286 |
| abstract_inverted_index.Statistical | 102 |
| abstract_inverted_index.deprivation | 121, 144 |
| abstract_inverted_index.difference; | 212 |
| abstract_inverted_index.individuals | 208 |
| abstract_inverted_index.minoritized | 2 |
| abstract_inverted_index.participant | 439 |
| abstract_inverted_index.populations | 6, 201, 233, 352 |
| abstract_inverted_index.proportions | 204 |
| abstract_inverted_index.prospective | 324, 370 |
| abstract_inverted_index.recruitment | 413 |
| abstract_inverted_index.residents), | 78 |
| abstract_inverted_index.Data-driven, | 13 |
| abstract_inverted_index.Participants | 55 |
| abstract_inverted_index.demographics | 37, 425 |
| abstract_inverted_index.distribution | 31 |
| abstract_inverted_index.geographical | 30, 428 |
| abstract_inverted_index.inhabitants, | 320 |
| abstract_inverted_index.longitudinal | 57 |
| abstract_inverted_index.populations. | 281, 340 |
| abstract_inverted_index.quantitative | 14, 58 |
| abstract_inverted_index.Institutions, | 92 |
| abstract_inverted_index.determination | 365 |
| abstract_inverted_index.disadvantaged | 5, 339 |
| abstract_inverted_index.distributions | 118 |
| abstract_inverted_index.socioeconomic | 36, 120, 382, 424 |
| abstract_inverted_index.Infrastructure | 99 |
| abstract_inverted_index.OpenStreetMap, | 79 |
| abstract_inverted_index.investigations | 402 |
| abstract_inverted_index.representation | 383 |
| abstract_inverted_index.systematically | 27 |
| abstract_inverted_index.representation. | 440 |
| abstract_inverted_index.self-identified | 33, 124, 206 |
| abstract_inverted_index.Foundation-Level | 100 |
| abstract_inverted_index.underrepresented | 9 |
| abstract_inverted_index.sociodemographics | 350 |
| abstract_inverted_index.socioeconomically | 4, 198, 338 |
| abstract_inverted_index.Institute–designated | 82 |
| abstract_inverted_index.Asian/multiracial/other | 249 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5070960728 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I1302444339, https://openalex.org/I901861585 |
| citation_normalized_percentile.value | 0.96649186 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |