Management of advanced-stage non-small cell lung cancer (NSCLC) has changed significantly over the past two decades with the development of numerous systemic treatments, including targeted therapies. However, a high proportion of advanced-stage patients are untreated. The role that health insurance plays in receipt of systemic treatments is unclear.

Using California Cancer Registry data (2012-2014), we developed multivariable Poisson regression models to assess the independent effect of health insurance type on systemic treatment utilization among patients with stage IV NSCLC. Systemic treatment information was manually abstracted from treatment text fields.

A total of 17,310 patients were evaluated. Patients with Medicaid/other public insurance were significantly less likely to receive any systemic treatments [risk ratio (RR), 0.78; 95% confidence interval (CI), 0.75-0.82], bevacizumab combinations (RR, 0.57; 95% CI, 0.45-0.71), or tyrosine kinase inhibitors (RR, 0.70; 95% CI, 0.60-0.82) compared with the privately insured. Patients with Medicare or dual Medicare-Medicaid insurance were not significantly different from the privately insured in their likelihood of receiving systemic treatments.

Substantial disparities in the use of systemic treatments for stage IV NSCLC exist by source of health insurance in California. Patients with Medicaid/other public insurance were significantly less likely to receive systemic treatments compared with their privately insured counterparts.

Source of health insurance influences care received. Further research is warranted to better understand barriers to treatment that patients with Medicaid face.

Uninsured adolescents and young adults (AYAs) and those with publicly-funded health insurance are more likely to be diagnosed with cancer at later stages. However, prior population-based studies have not distinguished between AYAs who were continuously uninsured from those who gained Medicaid coverage at the time of cancer diagnosis.

AYA patients (ages 15-39) with nine common cancers diagnosed from 2005-2014 were identified using California Cancer Registry data. This cohort was linked to California Medicaid enrollment files to determine continuous enrollment, discontinuous enrollment or enrollment at diagnosis, with other types of insurance determined from registry data. Multivariable logistic regression evaluated factors associated with later stages at diagnosis.

The majority of 52,774 AYA cancer patients had private/military insurance (67.6%), followed by continuous Medicaid (12.4%), Medicaid at diagnosis (8.5%), discontinuous Medicaid (3.9%), other public insurance (1.6%), no insurance (2.9%) or unknown insurance (3.1%). Of the 13,069 with Medicaid insurance, 50.1% were continuously enrolled. Compared to those who were privately insured, AYAs who enrolled in Medicaid at diagnosis were 2.2-2.5 times more likely to be diagnosed with later stage disease, while AYAs discontinuously enrolled were 1.7-1.9-times and AYAs continuously enrolled were 1.4-1.5-times more likely to be diagnosed with later stage disease. Males, those residing in lower socioeconomic neighborhoods, and AYAs of Hispanic or Black race/ethnicity (vs non-Hispanic White) were more likely to be diagnosed at a later stage, independent of insurance.

Our findings suggest that access to continuous medical insurance is important for decreasing the likelihood of late stage cancer diagnosis.

Population-based cancer registries have treatment information for all patients making them an excellent resource for population-level monitoring. However, specific treatment details, such as drug names, are contained in a free-text format that is difficult to process and summarize. We assessed the accuracy and efficiency of a text-mining algorithm to identify systemic treatments for lung cancer from free-text fields in the California Cancer Registry.

The algorithm used Perl regular expressions in SAS 9.4 to search for treatments in 24,845 free-text records associated with 17,310 patients in California diagnosed with stage IV non-small cell lung cancer between 2012 and 2014. Our algorithm categorized treatments into six groups that align with National Comprehensive Cancer Network guidelines. We compared results to a manual review (gold standard) of the same records.

Percent agreement ranged from 91.1% to 99.4%. Ranges for other measures were 0.71-0.92 (Kappa), 74.3%-97.3% (sensitivity), 92.4%-99.8% (specificity), 60.4%-96.4% (positive predictive value), and 92.9%-99.9% (negative predictive value). The text-mining algorithm used one-sixth of the time required for manual review.

SAS-based text mining of free-text data can accurately detect systemic treatments administered to patients and save considerable time compared to manual review, maximizing the utility of the extant information in population-based cancer registries for comparative effectiveness research.

We investigated body size and survival by race/ethnicity in 11,351 breast cancer patients diagnosed from 1993 to 2007 with follow-up through 2009 by using data from questionnaires and the California Cancer Registry. We calculated hazard ratios and 95% confidence intervals from multivariable Cox proportional hazard model-estimated associations of body size (body mass index (BMI) (weight (kg)/height (m)(2)) and waist-hip ratio (WHR)) with breast cancer-specific and all-cause mortality. Among 2,744 ascertained deaths, 1,445 were related to breast cancer. Being underweight (BMI <18.5) was associated with increased risk of breast cancer mortality compared with being normal weight in non-Latina whites (hazard ratio (HR) = 1.91, 95% confidence interval (CI): 1.14, 3.20), whereas morbid obesity (BMI ≥ 40) was suggestive of increased risk (HR = 1.43, 95% CI: 0.84, 2.43). In Latinas, only the morbidly obese were at high risk of death (HR = 2.26, 95% CI: 1.23, 4.15). No BMI-mortality associations were apparent in African Americans and Asian Americans. High WHR (quartile 4 vs. quartile 1) was associated with breast cancer mortality in Asian Americans (HR = 2.21, 95% CI: 1.21, 4.03; P for trend = 0.01), whereas no associations were found in African Americans, Latinas, or non-Latina whites. For all-cause mortality, even stronger BMI and WHR associations were observed. The impact of obesity and body fat distribution on breast cancer patients' risk of death may vary across racial/ethnic groups.