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Published on: February 12, 2017
Association Between Social Vulnerability Index and Time to Treatment in Resectable Non-Small Cell Lung Cancer
Arsalan A Khan1, Savan K Shah1, Wara Naeem1
1Department of Cardiovascular and Thoracic Surgery, Rush University Medical Center, Chicago, Illinois.
Background:
Social determinants of health, including race, education, and insurance status, have been linked to delays in non-small cell lung cancer (NSCLC) treatment, although quantification has been challenging. This study investigated whether the social vulnerability index (SVI), a quantitative measure of social determinants of health, is associated with time to treatment.
Methods:
Patients with surgically resected NSCLC at a single institution from 2010 to 2021 were identified. Non-Illinois residents, patients with incomplete addresses, and patients receiving neoadjuvant therapy were excluded. Time to treatment was defined as the interval from chest computed tomographic scan prompting surgery to the surgery date. SVI, ranging from 0 to 100, was calculated using the Centers for Disease Control and Prevention census-tract-level tool. Univariable and multivariable linear regressions assessed the relationship between SVI and time to treatment, with adjustment for demographics and comorbidities.
Results:
Of 890 patients, 55.6% were female and 78.8% were White, with a median age of 71 years (interquartile range [IQR], 64-76 years). The median SVI was 0.31 (IQR, 0.15-0.60), and median time to treatment was 35 days (IQR, 23-67 days). Overall, 13% of patients experienced 30-day major morbidity or mortality, 36% died during the follow-up period, and 26% experienced disease recurrence. Multivariable analysis confirmed that higher SVI (β = 1.18; P < .001), dual Medicaid and Medicare coverage (β = 43.85; P < .001), and lack of insurance (β = 79.21; P = .003) independently predicted delays in time to treatment.
Conclusions:
Higher SVI, Medicaid and Medicare coverage, and uninsured status are associated with increased time to treatment. SVI may help to identify patients at risk for delays in treatment.
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