How Should Low- and Middle-Income Countries Motivate Equity in Cancer Prevention and Control?

Cancer continues to be a prominent cause of morbidity and mortality in low- and middle-income countries (LMICs). Many LMICs, however, lack adequate data to better understand and respond to trends in cancer incidence. This article highlights crucial roles that government and public-private coalitions can play in cancer surveillance in LMICs. In particular, local and global investment in LMICs can build essential structures for cancer prevention and early detection, including public health surveillance systems and cancer control coalitions. Using examples from LMICs that show the promises and pitfalls of these approaches, this article argues that comprehensive cancer control can motivate health equity.

Disparities in the Use of Programmed Death 1 Immune Checkpoint Inhibitors.

Amid growing excitement for immune checkpoint inhibitors of programmed death protein 1 (anti-PD1 agents), little is known about whether race- or sex-based disparities exist in their use. In this observational study, we constructed a large and mostly community-based cohort of patients with advanced stage cancers, including melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma, to compare the odds of receiving systemic treatment with or without anti-PD1 agents by race and by sex. In multivariable models that adjusted for age, stage, and number of prior anticancer therapies, we found no significant race-based disparities in anti-PD1 treatment. However, among patients with NSCLC, males had significantly higher odds of receiving anti-PD1 treatment compared with females (odds ratio 1.13, 95% confidence interval 1.02-1.24, p = .02). This finding suggests that as anti-PD1 agents enter the market to transform patient care, it will be critical to monitor for disparities in the use of these drugs.

Speed of Adoption of Immune Checkpoint Inhibitors of Programmed Cell Death 1 Protein and Comparison of Patient Ages in Clinical Practice vs Pivotal Clinical Trials.

Importance: The US Food and Drug Administration (FDA) is increasing its pace of approvals for novel cancer therapeutics, including for immune checkpoint inhibitors of programmed cell death 1 protein (anti-PD-1 agents). However, little is known about how quickly anti-PD-1 agents agents reach eligible patients in practice or whether such patients differ from those studied in clinical trials that lead to FDA approval (pivotal clinical trials). Objectives: To assess the speed with which anti-PD-1 agents agents reached eligible patients in practice and to compare the ages of patients treated in clinical practice with the ages of those treated in pivotal clinical trials. Design, Setting, and Participants: This retrospective cohort study, performed from January 1, 2011, through August 31, 2016, included patients from the Flatiron Health Network who were eligible for anti-PD-1 agents treatment of selected cancer types, which included melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma (RCC). Main Outcomes and Measures: Cumulative proportions of eligible patients receiving anti-PD-1 agents treatment and their age distributions. Results: The study identified 3089 patients who were eligible for anti-PD-1 agents treatment (median age, 66 [interquartile range, 56-75] years for patients with melanoma, 66 [interquartile range, 58-72] years for patients with RCC, and 67 [interquartile range, 59-74] years for patients with NSCLC; 1742 male [56.4%] and 1347 [43.6%] female; 2066 [66.9%] white). Of these patients, 2123 (68.7%) received anti-PD-1 agents treatment, including 439 eligible patients with melanoma (79.1%), 1417 eligible patients with NSCLC (65.6%), and 267 eligible patients with RCC (71.2%). Within 4 months after FDA approval, greater than 60% of eligible patients in each cohort had received anti-PD-1 agents treatment. Overall, similar proportions of older and younger patients received anti-PD-1 agents treatment during the first 9 months after FDA approval. However, there were significant differences in age between clinical trial participants and patients receiving anti-PD-1 agents treatment in clinical practice, with more patients being older than 65 years in clinical practice (range, 327 of 1365 [60.6%] to 46 of 72 [63.9%]) than in pivotal clinical trials (range, 38 of 120 [31.7%] to 223 of 544 [41.0%]; all P < .001). Conclusions and Relevance: Anti-PD-1 agents rapidly reached patients in clinical practice, and patients treated in clinical practice differed significantly from patients treated in pivotal clinical trials. Future actions are needed to ensure that rapid adoption occurs on the basis of representative trial evidence.

Factors Associated With Cancer Disparities Among Low-, Medium-, and High-Income US Counties.

Background: There are concerns about the degree to which county income level might underlie the stark disparities in cancer death rates among US counties; at the same time, there is uncertainty about the factors that may mediate the disparities. Objectives: To assess county-level cancer death rates and to identify possible mediators of the association between county-level median incomes and cancer death rates. Design, Setting, and Participants: Cross-sectional study using death records from the National Center for Health Statistics in 2014, with data collected and analyzed between October 1, 2016, and July 31, 2017. All US counties and county equivalents were included. Exposures: County-level median household income. Main Outcomes and Measures: County-level age-standardized cancer death rate. Results: In 3135 counties, median incomes ranged from $22 126 to $121 250 per year. Low-income counties (median income, $33 445) vs high-income counties (median income, $55 780) had higher proportions of residents who were non-Hispanic black, lived in rural areas, or reported poor or fair health. The mean (SD) cancer death rate was 185.9 (24.4) per 100 000 person-years in high-income counties, compared with 204.9 (26.3) and 229.7 (32.9) per 100 000 person-years in medium- and low-income counties, respectively. In mediation models, health risk behaviors (smoking, obesity, and physical inactivity); clinical care factors (unaffordable care and low-quality care); health environments (food insecurity); and health policies (state smoke-free laws and Medicaid payment rates) in aggregate accounted for more than 80% of the income-related disparity. The strongest possible mediators were food insecurity (explaining 19.1% [95% CI, 12.5%-26.5%] of the association between county incomes and cancer deaths), low-quality care (17.9%; 95% CI, 14.0%-21.8%), smoking (12.7%; 95% CI, 9.4%-15.6%), and physical inactivity (12.2%; 95% CI, 9.4%-15.6%). Conclusions and Relevance: There are wide gaps in cancer death rates between low-, medium-, and high-income counties. Future studies are needed to assess whether targeting the possible mediators might ameliorate the substantial socioeconomic cancer disparities.

Measuring financial toxicity as a clinically relevant patient-reported outcome: The validation of the COmprehensive Score for financial Toxicity (COST).

BACKGROUND: Cancer and its treatment lead to increased financial distress for patients. To the authors' knowledge, to date, no standardized patient-reported outcome measure has been validated to assess this distress. METHODS: Patients with AJCC Stage IV solid tumors receiving chemotherapy for at least 2 months were recruited. Financial toxicity was measured by the COmprehensive Score for financial Toxicity (COST) measure. The authors collected data regarding patient characteristics, clinical trial participation, health care use, willingness to discuss costs, psychological distress (Brief Profile of Mood States [POMS]), and health-related quality of life (HRQOL) as measured by the Functional Assessment of Cancer Therapy: General (FACT-G) and the European Organization for Research and Treatment of Cancer (EORTC) QOL questionnaires. Test-retest reliability, internal consistency, and validity of the COST measure were assessed using standard-scale construction techniques. Associations between the resulting factors and other variables were assessed using multivariable analyses. RESULTS: A total of 375 patients with advanced cancer were approached, 233 of whom (62.1%) agreed to participate. The COST measure demonstrated high internal consistency and test-retest reliability. Factor analyses revealed a coherent, single, latent variable (financial toxicity). COST values were found to be correlated with income (correlation coefficient [r] = 0.28; P<.001), psychosocial distress (r = -0.26; P<.001), and HRQOL, as measured by the FACT-G (r = 0.42; P<.001) and by the EORTC QOL instruments (r = 0.33; P<.001). Independent factors found to be associated with financial toxicity were race (P = .04), employment status (P<.001), income (P = .003), number of inpatient admissions (P = .01), and psychological distress (P = .003). Willingness to discuss costs was not found to be associated with the degree of financial distress (P = .49). CONCLUSIONS: The COST measure demonstrated reliability and validity in measuring financial toxicity. Its correlation with HRQOL indicates that financial toxicity is a clinically relevant patient-centered outcome. Cancer 2017;123:476-484. © 2016 American Cancer Society.

Financial toxicity in cancer care.

The cost of cancer care is increasing, with important implications for the delivery of high-quality, patent-centered care. In the clinical setting, patents and physicians express a desire to discuss out-of-pocket costs. Nevertheless, both groups feel inadequately prepared to participate in these discussions, and perhaps not surprisingly, the integration of these discussions into clinical practice seems to be the exception rather than the rule. The resulting neglect of financial issues has the potential to cause unnecessary suffering for oncology patents. In this paper, we review the most relevant literature on financial toxicity in cancer care. In addition, we discuss potential predictors of financial toxicity, and the recent development of instruments to help clinicians and researchers quantify financial burden.