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      Survival After Cancer Treatment at Top-Ranked US Cancer Hospitals vs Affiliates of Top-Ranked Cancer Hospitals

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          Key Points

          Question

          Are there differences in survivorship between top-ranked cancer hospitals and affiliates that share a top-ranked hospital’s brand?

          Findings

          In this cohort study of 119 834 patients who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, and bladder cancer, risk of 90-day mortality after complex cancer treatment was higher and long-term survival was inferior at affiliate hospitals.

          Meaning

          These findings suggest that quality improvement efforts are needed to address important differences in survival between top-ranked cancer hospitals and brand-sharing affiliate hospitals.

          Abstract

          This cohort study examines short-term survival outcomes among patients who receive cancer treatment at top-ranked cancer hospitals compared with those who receive cancer treatment at affiliate hospitals.

          Abstract

          Importance

          Hospital networks formed around top-ranked cancer hospitals represent an opportunity to optimize complex cancer care in the community.

          Objective

          To compare the short- and long-term survival after complex cancer treatment at top-ranked cancer hospitals and the affiliates of top-ranked hospitals.

          Design, Setting, and Participants

          This cohort study was conducted using data from the unabridged version of the National Cancer Database. Included patients were individuals 18 years or older who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, or bladder cancer diagnosed between January 1, 2012, and December 31, 2016. Patient outcomes after complex surgical procedures for cancer at top-ranked cancer hospitals (as ranked in top 50 by US News and World Report) were compared with outcomes at affiliates of top-ranked cancer hospitals (affiliation listed in American Hospitals Association survey and confirmed by search of internet presence). Data were analyzed from July through December 2019.

          Exposures

          Undergoing complex cancer treatment at a top-ranked cancer hospital or an affiliated hospital.

          Main Outcomes and Measures

          The association of affiliate status with short-term survival (ie, 90-day mortality) was compared using logistic regression, and the association of affiliate status with long-term survival was compared using time-to-event models, adjusting for patient demographic, payer, clinical, and treatment factors.

          Results

          Among 119 834 patients who underwent surgical treatment for cancer, 79 981 patients (66.7%) were treated at top-ranked cancer hospitals (median [interquartile range] age, 66 [58-74] years; 40 910 [54.9%] men) and 39 853 patients (33.3%) were treated at affiliate hospitals (median [interquartile range] age, 69 [60-77] years; 19 004 [50.0%] men). In a pooled analysis of all cancer types, adjusted perioperative mortality within 90 days of surgical treatment was higher at affiliate hospitals compared with top-ranked hospitals (odds ratio, 1.67 [95% CI, 1.49-1.89]; P < .001). Adjusted long-term survival following cancer treatment at affiliate hospitals was only 77% that of top-ranked hospitals (time ratio, 0.77 [95% CI, 0.72-0.83]; P < .001). The survival advantage was not fully explained by differences in annual surgical volume, with both long- and short-term survival remaining superior at top-ranked hospitals even after models were adjusted for volume.

          Conclusions and Relevance

          These findings suggest that short- and long-term survival after complex cancer treatment were superior at top-ranked hospitals compared with affiliates of top-ranked hospitals. Further study of cancer care within top-ranked cancer networks could reveal collaborative opportunities to improve survival across a broad contingent of the US population.

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          Most cited references27

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          Time to Surgery and Breast Cancer Survival in the United States.

          Time to surgery (TTS) is of concern to patients and clinicians, but controversy surrounds its effect on breast cancer survival. There remains little national data evaluating the association.
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            • Record: found
            • Abstract: not found
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            Comparison of Commission on Cancer–Approved and –Nonapproved Hospitals in the United States: Implications for Studies That Use the National Cancer Data Base

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              • Article: not found

              Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution.

              The widely used Cox proportional hazards regression model for the analysis of censored survival data has limited utility when either hazard functions themselves are of primary interest, or when relative times instead of relative hazards are the relevant measures of association. Parametric regression models are an attractive option in situations such as this, although the choice of a particular model from the available families of distributions can be problematic. The generalized gamma (GG) distribution is an extensive family that contains nearly all of the most commonly used distributions, including the exponential, Weibull, log normal and gamma. More importantly, the GG family includes all four of the most common types of hazard function: monotonically increasing and decreasing, as well as bathtub and arc-shaped hazards. We present here a taxonomy of the hazard functions of the GG family, which includes various special distributions and allows depiction of effects of exposures on hazard functions. We applied the proposed taxonomy to study survival after a diagnosis of clinical AIDS during different eras of HIV therapy, where proportionality of hazard functions was clearly not fulfilled and flexibility in estimating hazards with very different shapes was needed. Comparisons of survival after AIDS in different eras of therapy are presented in terms of both relative times and relative hazards. Standard errors for these and other derived quantities are computed using the delta method and checked using the bootstrap. Description of standard statistical software (Stata, SAS and S-Plus) for the computations is included and available at http://statepi.jhsph.edu/software.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                26 May 2020
                May 2020
                26 May 2020
                : 3
                : 5
                : e203942
                Affiliations
                [1 ]Section of Thoracic Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut
                [2 ]American College of Surgeons Cancer Programs, National Cancer Database, Chicago, Illinois
                [3 ]Cancer Outcomes Public Policy and Effectiveness Research Center, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [4 ]Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [5 ]Department of Thoracic Surgery, Geisinger Heart Institute, Danville, Pennsylvania
                [6 ]American College of Surgeons Cancer Programs, Chicago, Illinois
                [7 ]Abramson Cancer Center, University of Pennsylvania, Philadelphia
                Author notes
                Article Information
                Accepted for Publication: March 1, 2020.
                Published: May 26, 2020. doi:10.1001/jamanetworkopen.2020.3942
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Boffa DJ et al. JAMA Network Open.
                Corresponding Author: Daniel J. Boffa, MD, Section of Thoracic Surgery, Department of Surgery, Yale School of Medicine, Box 208062, New Haven CT 06520-8062 ( daniel.boffa@ 123456yale.edu ).
                Author Contributions: Dr Mallin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Boffa, Mallin, Herrin, Resio, Facktor, Shulman.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Boffa, Mallin, Resio, Salazar.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Mallin, Herrin, Salazar.
                Obtained funding: Boffa.
                Administrative, technical, or material support: Boffa, Resio, Palis, McCabe, Shulman.
                Supervision: Boffa, Facktor, McCabe, Nelson, Shulman.
                Conflict of Interest Disclosures: Dr Boffa reported receiving nonfinancial support from Epic Sciences outside the submitted work. Dr Shulman reported receiving grants from Celgene outside the submitted work. No other disclosures were reported.
                Funding/Support: This study was supported in part by the donations of patients of the Yale Thoracic Oncology Program.
                Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Additional Contributions: Andres Monsalve, MD, provided assistance in investigating the nature of affiliations between hospitals via internet presence as part of his work as a research associate in thoracic oncology at Yale University School of Medicine.
                Article
                zoi200188
                10.1001/jamanetworkopen.2020.3942
                7251445
                32453382
                6dbd6c5b-f04a-467b-b53b-dce2b6759ff6
                Copyright 2020 Boffa DJ et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 12 December 2019
                : 1 March 2020
                Categories
                Research
                Original Investigation
                Online Only
                Oncology

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