Temporal Changes in Treatment Patterns for Rural and Urban Patients With Early-Stage Non-Small Cell Lung Cancer.

BACKGROUND Our objectives were to evaluate geographic access to lung cancer treatment modalities in North Carolina and to characterize how practice patterns are changing over time. We hypothesized that rural patients would be less likely to undergo treatment compared to urban patients, with widening disparities over time.METHODS We identified patients with Stage I non-small cell lung cancer (NSCLC) from 2006 to 2015 using the North Carolina Central Cancer Registry linked with Medicaid, Medicare, and private insurance claims. The primary outcome was first-course treatment: surgery, radiation, or no treatment. Calendar years were split into earlier (2006-2010) and later (2011-2015) periods. We estimated the adjusted odds ratio (OR) of rural/urban status and time period with 1) surgery and 2) any treatment (surgery or radiation) using multivariable logistic regression.RESULTS Among 5504 patients, 3206 (58%) underwent surgery as initial therapy, 1309 (24%) received radiation as initial therapy, and 989 (18%) had no therapy. There were no rural-urban disparities in treatment patterns. For rural and urban patients, the odds of surgery decreased over time and the odds of radiation increased. We also found that only 48% of those receiving no treatment ever reached a surgeon or radiation oncologist.LIMITATIONS This was an insured, single-state population. Treatment preferences are unknown.CONCLUSIONS Among all treated patients, whether urban or rural, there was increasing use of radiation and decreasing use of surgery over time. Many patients without treatment never had a consultation with a surgeon/radiation oncologist, and this is an actionable target for improving treatment access for early-stage NSCLC.

Understanding and identifying immortal-time bias in surgical health services research: An example using surgical resection of stage IV breast cancer.

Surgical health services researchers are increasingly utilizing observational data to assess associations between treatments and outcomes, especially since some procedures are unable to be evaluated through randomized controlled trials. However, the results of many of these studies may be affected by the presence of immortal-time bias, which exists when treatment does not occur on Day 0 of the study. This bias can result in researchers overestimating a treatment benefit, or even observe a treatment benefit when none exists. In this paper, we describe what immortal-time bias is, the challenges it presents, and how to recognize and address it using the real-world example of surgical resection of the primary tumor for stage IV breast cancer throughout. In our example, we guide researchers and illustrate how the early studies, which did not account for immortal-time bias, suggested a protective benefit of surgery, and how these results were supplanted by more recent studies through identifying and addressing immortal-time bias in their design and analyses.