Our findings show that the risk of death is significantly lower when care is provided in a trauma center than in a non-trauma center and argue for continued efforts at regionalization.

Importance Time to surgery (TTS) is of concern to patients and clinicians, but controversy surrounds its impact on breast cancer survival. There remains little national data evaluating the association. Objective To investigate the relationship between the time from diagnosis to breast cancer surgery and survival, using separate analyses of two of the largest cancer databases in the United States. Design Two independent population-based studies of prospectively-collected national data utilizing the Surveillance Epidemiology and End Results (SEER)-Medicare-linked database (SMDB), and the National Cancer Database (NCDB). Setting The SMDB cohort included Medicare patients >65 years of age, and the NCDB cohort included patients cared for at Commission on Cancer-accredited facilities throughout the United States. Each analysis assessed overall survival as a function of time between diagnosis and surgery by evaluating intervals encompassing ≤30, 31–60, 61–90, 91–120, and 121–180 days in length, and disease-specific survival at 60-day intervals. Participants All patients were diagnosed with noninflammatory, nonmetastatic, invasive breast cancer and underwent surgery as initial treatment. Main Outcomes and Measures Overall and disease-specific survival as a function of time between diagnosis and surgery, after adjusting for patient, demographic and tumor-related factors. Results The SMDB cohort had 94,544 patients ≥66 years old, diagnosed 1992 – 2009. With each interval delay increase, overall survival was lower overall (hazard ratio [HR] 1.09, p<0.001), and in stage I (HR 1.13, p<0.001) and II (HR 1.06, p=0.010) patients. Breast cancer-specific mortality increased with each 60-d interval (subhazard ratio [sHR] 1.26, p= 0.03). The NCDB study evaluated 115,790 patients ≥18 years old, diagnosed 2003 – 2005. The overall mortality HR was 1.10 (p<0.001) for each increasing interval, significant in stages I (HR 1.16, p<0.001) and II (1.09, p<0.001) only, adjusting for demographic, tumor and treatment factors. Conclusions and Relevance Greater TTS confers lower overall and disease-specific survival, and a shortened delay is associated with benefits comparable to some standard therapies. Although time is required for preoperative evaluation and consideration of some options such as reconstruction, efforts to reduce TTS should be pursued where possible to enhance survival.

Purpose To estimate the overall survival (OS) impact from increasing time to treatment initiation (TTI) for patients with head and neck squamous cell carcinoma (HNSCC). Methods Using the National Cancer Data Base (NCDB), we examined patients who received curative therapy for the following sites: oral tongue, oropharynx, larynx, and hypopharynx. TTI was the number of days from diagnosis to initiation of curative treatment. The effect of TTI on OS was determined by using Cox regression models (MVA). Recursive partitioning analysis (RPA) identified TTI thresholds via conditional inference trees to estimate the greatest differences in OS on the basis of randomly selected training and validation sets, and repeated this 1,000 times to ensure robustness of TTI thresholds. Results A total of 51,655 patients were included. On MVA, TTI of 61 to 90 days versus less than 30 days (hazard ratio [HR], 1.13; 95% CI, 1.08 to 1.19) independently increased mortality risk. TTI of 67 days appeared as the optimal threshold on the training RPA, statistical significance was confirmed in the validation set (P ≤ .001), and the 67-day TTI was the optimal threshold in 54% of repeated simulations. Overall, 96% of simulations validated two optimal TTI thresholds, with ranges of 46 to 52 days and 62 to 67 days. The median OS for TTI of 46 to 52 days or fewer versus 53 to 67 days versus greater than 67 days was 71.9 months (95% CI, 70.3 to 73.5 months) versus 61 months (95% CI, 57 to 66.1 months) versus 46.6 months (95% CI, 42.8 to 50.7 months), respectively (P < .001). In the most recent year with available data (2011), 25% of patients had TTI of greater than 46 days. Conclusion TTI independently affects survival. One in four patients experienced treatment delay. TTI of greater than 46 to 52 days introduced an increased risk of death that was most consistently detrimental beyond 60 days. Prolonged TTI is currently affecting survival.

The risk of death from index- and nonindex-cancers varies widely among primary sites. Risk of noncancer deaths now surpasses that of cancer deaths, particularly for young patients in the year after diagnosis.

Background Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. Methods We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. Results We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. Conclusions Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.