Journal of the National Cancer Institute. Monographs最新文献

Over the past 2 decades, population simulation modeling has evolved as an effective public health tool for surveillance of cancer trends and estimation of the impact of screening and treatment strategies on incidence and mortality, including documentation of persistent cancer inequities. The goal of this research was to provide a framework to support the next generation of cancer population simulation models to identify leverage points in the cancer control continuum to accelerate achievement of equity in cancer care for minoritized populations. In our framework, systemic racism is conceptualized as the root cause of inequity and an upstream influence acting on subsequent downstream events, which ultimately exert physiological effects on cancer incidence and mortality and competing comorbidities. To date, most simulation models investigating racial inequity have used individual-level race variables. Individual-level race is a proxy for exposure to systemic racism, not a biological construct. However, single-level race variables are suboptimal proxies for the multilevel systems, policies, and practices that perpetuate inequity. We recommend that future models designed to capture relationships between systemic racism and cancer outcomes replace or extend single-level race variables with multilevel measures that capture structural, interpersonal, and internalized racism. Models should investigate actionable levers, such as changes in health care, education, and economic structures and policies to increase equity and reductions in health-care-based interpersonal racism. This integrated approach could support novel research approaches, make explicit the effects of different structures and policies, highlight data gaps in interactions between model components mirroring how factors act in the real world, inform how we collect data to model cancer equity, and generate results that could inform policy.

Background: Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence.

Methods: Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy.

Results: Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness.

Conclusion: Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.

Purpose: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research.

Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States.

Results: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents.

Conclusions: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.

Data evaluating change in body composition during treatment of advanced cancer are limited. Here we evaluated computed tomography (CT)-based changes in muscle mass during treatment for advanced ovarian cancer (OC) and association with outcomes. We analyzed the preoperative and posttreatment skeletal muscle index (SMI), skeletal muscle area normalized for height of 109 patients with advanced OC who underwent primary surgery and platinum-based chemotherapy from 2006 to 2016. Based on an SMI less than 39 cm2/m2, 54.1% of patients were never sarcopenic, 24.8% were sarcopenic on both CT scans, and 21.1% were newly sarcopenic upon treatment completion. Patients who lost muscle during treatment had the worst survival of the 3 groups identified: median survival 2.6 years vs 4.6 years if sarcopenic on both CT scans and 4.8 years if never sarcopenic. Loss of muscle portends a poor prognosis among patients with OC. Additional research is needed to better understand and best mitigate these changes.

Intermittent fasting entails restricting food intake during specific times of day, days of the week, religious practice, or surrounding clinically important events. Herein, the metabolic and circadian rhythm mechanisms underlying the proposed benefits of intermittent fasting for the cancer population are described. We summarize epidemiological, preclinical, and clinical studies in cancer published between January 2020 and August 2022 and propose avenues for future research. An outstanding concern regarding the use of intermittent fasting among cancer patients is that fasting often results in caloric restriction, which can put patients already prone to malnutrition, cachexia, or sarcopenia at risk. Although clinical trials do not yet provide sufficient data to support the general use of intermittent fasting in clinical practice, this summary may be useful for patients, caregivers, and clinicians who are exploring intermittent fasting as part of their cancer journey for clinical outcomes and symptom management.

Obesity is a chronic, relapsing, progressive disease of excess adiposity that increases the risk of developing at least 13 types of cancer. This report provides a concise review of the current state of the science regarding metabolic and bariatric surgery and obesity pharmacotherapy related to cancer risk. Meta-analyses of cohort studies report that metabolic and bariatric surgery is independently associated with a lower risk of incident cancer than nonsurgical obesity care. Less is known regarding the cancer-preventive effects of obesity pharmacotherapy. The recent approval and promising pipeline of obesity drugs will provide the opportunity to understand the potential for obesity therapy to emerge as an evidence-based cancer prevention strategy. There are myriad research opportunities to advance our understanding of how metabolic and bariatric surgery and obesity pharmacotherapy may be used for cancer prevention.

Cancer cells cannot proliferate without sufficient energy to generate biomass for rapid cell division, as well as to fuel their functions at baseline. For this reason, many recent observational and interventional studies have focused on increasing energy expenditure and/or reducing energy intake during and after cancer treatment. The impact of variance in diet composition and in exercise on cancer outcomes has been detailed extensively elsewhere and is not the primary focus of this review. Instead, in this translational, narrative review we examine studies of how energy balance impacts anticancer immune activation and outcomes in triple-negative breast cancer (TNBC). We discuss preclinical, clinical observational, and the few clinical interventional studies on energy balance in TNBC. We advocate for the implementation of clinical studies to examine how optimizing energy balance-through changes in diet and/or exercise-may optimize the response to immunotherapy in people with TNBC. It is our conviction that by taking a holistic approach that includes energy balance as a key factor to be considered during and after treatment, cancer care may be optimized, and the detrimental effects of cancer treatment and recovery on overall health may be minimized.