Ten-Year Single Institutional Analysis of Geographic and Demographic Characteristics of Patients Treated With Stereotactic Body Radiation Therapy for Localized Prostate Cancer.
SBRT (stereotactic body radiation therapy)
disparities (health)
machine learning
prostate cancer
racial
travel distance
treatment burden
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
10
2020
accepted:
23
12
2020
entrez:
15
3
2021
pubmed:
16
3
2021
medline:
16
3
2021
Statut:
epublish
Résumé
Stereotactic Body Radiation Therapy (SBRT) offers definitive treatment for localized prostate cancer with comparable efficacy and toxicity to conventionally fractionated radiotherapy. Decreasing the number of treatment visits from over 40 to five may ease treatment burden and increase accessibility for logistically challenged patients. Travel distance is one factor that affects a patient's access to treatment and is often related to geographic location and socioeconomic status. In this study, we review the demographic and geographic factors of patients treated with SBRT for prostate cancer for a single institution with over a decade of experience. Patient zip codes from one thousand and thirty-five patients were derived from a large, prospectively maintained quality of life database for patients treated for prostate cancer with SBRT from 2008 to 2017. The geospatial distance between the centroid of each zip code to our institution was calculated using the R package Geosphere. Characteristics for seven hundred and twenty-one patients were evaluated at the time of analysis including: race, age, and insurance status. To assess the geographic reach of our institution, we evaluated the demographic features of each zip code using US Census data. Statistical comparisons for these features and their relation to distance traveled for treatment was performed using the Mann-Whitney U test. Finally, an unsupervised learning algorithm was performed to identify distinct clusters of patients with respect to median income, racial makeup, educational level, and rural residency. Patients traveled from 246 distinct zip codes at a median distance of 11.35 miles. Forty percent of patients were African American, 6.9% resided in a rural region, and 22% were over the age of 75. Using K-means cluster analysis, four distinct patient zip-code groups were identified based on the aforementioned demographic features: Suburban/high-income (45%), Urban (30%), Suburban/low-income (17%), and Rural (8%). For each of the clusters, the average travel distance for SBRT was significantly different at 11.17, 9.26, 11.75, and 40.2 miles, respectively (p-value: <0.001). Distinct demographic features are related to travel distance for prostate SBRT. In our large cohort, travel distance did not prevent uptake of prostate SBRT in African American, elderly or rural patient populations. Prostate SBRT offers a diverse population modern treatment for their localized prostate cancer and particularly for those who live significant distances from a treatment center.
Identifiants
pubmed: 33718117
doi: 10.3389/fonc.2020.616286
pmc: PMC7947279
doi:
Types de publication
Journal Article
Langues
eng
Pagination
616286Informations de copyright
Copyright © 2021 Aghdam, Carrasquilla, Wang, Pepin, Danner, Ayoob, Yung, Collins, Kumar, Suy, Collins and Lischalk.
Déclaration de conflit d'intérêts
SC, BC, and JL serve as clinical consultants to Accuray Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Int Neurourol J. 2016 Nov;20(Suppl 2):S112-119
pubmed: 27915474
JAMA. 2002 Jan 2;287(1):111
pubmed: 11754721
Front Oncol. 2017 Jul 24;7:157
pubmed: 28791252
J Community Health. 2013 Oct;38(5):976-93
pubmed: 23543372
Appl Health Econ Health Policy. 2014 Aug;12(4):391-408
pubmed: 25022451
Med Care Res Rev. 2009 Oct;66(5):542-60
pubmed: 19454624
J Oncol Pract. 2012 May;8(3 Suppl):e31s-7s
pubmed: 22942832
Ann Surg. 2008 Oct;248(4):675-86
pubmed: 18936581
Cancer. 2015 Nov 1;121(21):3885-93
pubmed: 26218755
Ann Oncol. 2014 Mar;25(3):564-577
pubmed: 24285020
Cancer. 2017 Aug 1;123(15):2945-2954
pubmed: 28301689
JAMA Oncol. 2016 Jan;2(1):137-9
pubmed: 26468994
Lancet. 2019 Aug 3;394(10196):385-395
pubmed: 31227373
J Clin Oncol. 2011 Jul 10;29(20):2821-6
pubmed: 21632508
J Clin Oncol. 2013 Oct 20;31(30):3749-57
pubmed: 24043731
Gates Open Res. 2019 Jul 2;3:1503
pubmed: 31701090
Oncologist. 2015 Dec;20(12):1378-85
pubmed: 26512045
J Natl Cancer Inst. 2000 Feb 2;92(3):269-71
pubmed: 10655446
JAMA Netw Open. 2019 Feb 1;2(2):e188006
pubmed: 30735235
Cancer. 2018 Mar 15;124(6):1141-1149
pubmed: 29231964