Nonsmall cell lung cancer (NSCLC) is the leading cause of cancer deaths. Lung cancer screening (LCS) reduces NSCLC mortality; however, a lack of diversity in LCS studies may limit the generalizability of the results to marginalized groups who face higher risk for and worse outcomes from NSCLC. Identifying sources of inequity in the LCS pipeline is essential to reduce disparities in NSCLC outcomes. The authors searched 3 major databases for studies published from January 1, 2010 to February 27, 2020 that met the following criteria: 1) included screenees between ages 45 and 80 years who were current or former smokers, 2) written in English, 3) conducted in the United States, and 4) discussed socioeconomic and race-based LCS outcomes. Eligible studies were assessed for risk of bias. Of 3721 studies screened, 21 were eligible. Eligible studies were evaluated, and their findings were categorized into 3 themes related to LCS disparities faced by Black and socioeconomically disadvantaged individuals: 1) eligibility; 2) utilization, perception, and utility; and 3) postscreening behavior and care. Disparities in LCS exist along racial and socioeconomic lines. There are several steps along the LCS pipeline in which Black and socioeconomically disadvantaged individuals miss the potential benefits of LCS, resulting in increased mortality. This study identified potential sources of inequity that require further investigation. The authors recommend the implementation of prospective trials that evaluate eligibility criteria for underserved groups and the creation of interventions focused on improving utilization and follow-up care to decrease LCS disparities.
Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths in the US and worldwide. In particular, vulnerable populations such as those of low socioeconomic status (SES) are at the highest risk for and suffer the highest mortality from NSCLC. Although lung cancer screening (LCS) has been demonstrated to be a powerful tool to lower NSCLC mortality, it is underutilized by eligible smokers, and disparities in screening are likely to contribute to inequities in NSCLC outcomes. It is imperative that we collect and analyze LCS data focused on individuals of low socioeconomic position to identify and address barriers to LCS utilization and help close the gaps in NSCLC mortality along socioeconomic lines.Toward this end, this review aims to examine published studies that have evaluated the impact of income and education on LCS utilization, eligibility, and outcomes. We searched the PubMed, Ovid MEDLINE, and CINAHL Plus databases for all studies published from January 1, 2010, to October 21, 2020, that discussed socioeconomic-based LCS outcomes. The review reveals that income and education have impact on LCS utilization, eligibility, false positive rates and smoking cessation attempts; however, there is a lack of studies evaluating the impact of SES on LCS follow-up, stage at diagnosis, and treatment. We recommend the intentional inclusion of lower SES participants in LCS studies in order to clarify appropriate eligibility criteria, risk-based metrics and outcomes in this high-risk group. We also anticipate that low SES smokers and their providers will require increased support and education regarding smoking cessation and shared decision-making efforts.
Background: Poor patients often reside in neighborhoods of lower socioeconomic status (SES) with high levels of airborne pollutants. They also have higher mortality from non–small cell lung cancer (NSCLC) than those living in wealthier communities. We investigated whether living in polluted neighborhoods is associated with somatic mutations linked with lower survival rates, i.e., TP53 mutations. Methods: In a retrospective cohort of 478 patients with NSCLC treated at a comprehensive cancer center between 2015 and 2018, we used logistic regression to assess associations between individual demographic and clinical characteristics, including somatic TP53 mutation status and environmental risk factors of annual average particulate matter (PM2.5) levels, and neighborhood SES. Results: 277 patients (58%) had somatic TP53 mutations. Of those, 45% lived in neighborhoods with “moderate” Environmental Protection Agency–defined PM2.5 exposure, compared with 39% of patients without TP53 mutations. We found significant associations between living in neighborhoods with “moderate” versus “good” PM2.5 concentrations and minority population percentage [OR, 1.06; 95% confidence interval (CI), 1.04–1.08]. There was a significant association between presence of TP53 mutations and PM2.5 exposure (moderate versus good: OR, 1.66; 95% CI, 1.02–2.72) after adjusting for patient characteristics, other environmental factors, and neighborhood-level SES. Conclusions: When controlling for individual- and neighborhood-level confounders, we find that the odds of having a TP53-mutated NSCLC are increased in areas with higher PM2.5 exposure. Impact: The link between pollution and aggressive biology may contribute to the increased burden of adverse NSCLC outcomes in individuals living in lower SES neighborhoods.
Background: Given that media coverage can shape healthcare expectations, it is essential that we understand how the media frames “personalized medicine” (PM) in oncology, and whether information about unproven technologies is widely disseminated. Methods: We conducted a content analysis of 396 news reports related to cancer and PM published between 1 January 1998 and 31 December 2011. Two coders independently coded all the reports using a pre-defined framework. Determination of coverage of “standard” and “non-standard” therapies and tests was made by comparing the media print/broadcast date to the date of Federal Drug Administration approval or incorporation into clinical guidelines. Results: Although the term “personalized medicine” appeared in all reports, it was clearly defined only 27% of the time. Stories more frequently reported PM benefits than challenges (96% vs. 48%, p < 0.001). Commonly reported benefits included improved treatment (89%), prediction of side effects (30%), disease risk prediction (33%), and lower cost (19%). Commonly reported challenges included high cost (28%), potential for discrimination (29%), and concerns over privacy and regulation (21%). Coverage of inherited DNA testing was more common than coverage of tumor testing (79% vs. 25%, p < 0.001). Media reports of standard tests and treatments were common; however, 8% included information about non-standard technologies, such as experimental medications and gene therapy. Conclusion: Confusion about personalized cancer medicine may be exacerbated by media reports that fail to clearly define the term. While most media stories reported on standard tests and treatments, an emphasis on the benefits of PM may lead to unrealistic expectations for cancer genomic care.
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