JTCVS Open (Dec 2022)

Time to treatment of esophageal cancer in Ontario: A population-level cross-sectional studyCentral MessagePerspective

  • Nader M. Hanna, MBBS, MSc,
  • Paul Nguyen, PhD,
  • Wiley Chung, MD,
  • Patti A. Groome, PhD

Journal volume & issue
Vol. 12
pp. 430 – 449

Abstract

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Objective: Timely cancer treatment improves survival and anxiety for some sites. Patients with esophageal cancer require specific workup before treatment, which can prolong the time from diagnosis to treatment (treatment interval [TI]). The geographical variation of this interval remains uninvestigated in patients with esophageal cancer. Methods: This retrospective population-level study conducted in Ontario used linked administrative health care databases. Patients treated for esophageal cancer between 2013 and 2018 were included. The TI was time from diagnosis to treatment. Patients were assigned a geographical Local Health Integration Network on the basis of postal code. Covariates included patient, disease, and diagnosing physician characteristics. Quantile regression modeled TI length at the 50th and 90th percentile and identified associated factors. Results: Of 7509 patients, 78% were male and most were aged between 60 and 69 years. The 50th and 90th percentile TI was 36 (interquartile range, 22-55) and 77 days, respectively. The difference between the Local Health Integration Network with the longest and shortest TI at the 50th and 90th percentile was 18 and 25 days, respectively. Older age (P < .0001), greater comorbidity (P = .0005), greater material deprivation (P = .001), rurality (P = .03), histology (P = .02), and treatment group (P < .0001) were associated with a longer median TI. Older age (P = .03), greater comorbidity (P = .003), greater material deprivation (P = .005), rurality (P = .04), and treatment group (P < .0001) were associated with a longer 90th percentile TI. Conclusions: Geographic variability of time to treatment exists across Ontario. Investigation of facility-level differences is warranted. Patient and disease factors are associated with longer wait times. These results might inform future health care policy and resource allocation.

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