Journal of the National Cancer Institute. Monographs最新文献

{"title":"Adapting a model of cervical carcinogenesis to self-identified Black women to evaluate racial disparities in the United States.","authors":"Jennifer C Spencer, Emily A Burger, Nicole G Campos, Mary Caroline Regan, Stephen Sy, Jane J Kim","doi":"10.1093/jncimonographs/lgad015","DOIUrl":"10.1093/jncimonographs/lgad015","url":null,"abstract":"<p><strong>Background: </strong>Self-identified Black women in the United States have higher cervical cancer incidence and mortality than the general population, but these differences have not been clearly attributed across described cancer care inequities.</p><p><strong>Methods: </strong>A previously established microsimulation model of cervical cancer was adapted to reflect demographic, screening, and survival data for Black US women and compared with a model reflecting data for all US women. Each model input with stratified data (all-cause mortality, hysterectomy rates, screening frequency, screening modality, follow-up, and cancer survival) was sequentially replaced with Black-race specific data to arrive at a fully specified model reflecting Black women. At each step, we estimated the relative contribution of inputs to observed disparities.</p><p><strong>Results: </strong>Estimated (hysterectomy-adjusted) cervical cancer incidence was 8.6 per 100 000 in the all-race model vs 10.8 per 100 000 in the Black-race model (relative risk [RR] = 1.24, range = 1.23-1.27). Estimated all-race cervical cancer mortality was 2.9 per 100 000 vs 5.5 per 100 000 in the Black-race model (RR = 1.92, range = 1.85-2.00). We found the largest contributors of incidence disparities were follow-up from positive screening results (47.3% of the total disparity) and screening frequency (32.7%). For mortality disparities, the largest contributor was cancer survival differences (70.1%) followed by screening follow-up (12.7%).</p><p><strong>Conclusion: </strong>To reduce disparities in cervical cancer incidence and mortality, it is important to understand and address differences in care access and quality across the continuum of care. Focusing on the practices and policies that drive differences in treatment and follow-up from cervical abnormalities may have the highest impact.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637021/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Racial disparities in prostate cancer mortality: a model-based decomposition of contributing factors.","authors":"Roman Gulati, Yaw A Nyame, Jane M Lange, Jonathan E Shoag, Alex Tsodikov, Ruth Etzioni","doi":"10.1093/jncimonographs/lgad018","DOIUrl":"10.1093/jncimonographs/lgad018","url":null,"abstract":"<p><p>To investigate the relative contributions of natural history and clinical interventions to racial disparities in prostate cancer mortality in the United States, we extended a model that was previously calibrated to Surveillance, Epidemiology, and End Results (SEER) incidence rates for the general population and for Black men. The extended model integrated SEER data on curative treatment frequencies and cancer-specific survival. Starting with the model for all men, we replaced up to 9 components with corresponding components for Black men, projecting age-standardized mortality rates for ages 40-84 years at each step. Based on projections in 2019, the increased frequency of developing disease, more aggressive tumor features, and worse cancer-specific survival in Black men diagnosed at local-regional and distant stages explained 38%, 34%, 22%, and 8% of the modeled disparity in mortality. Our results point to intensified screening and improved care in Black men as priority areas to achieve greater equity.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.","authors":"Yuliia Sereda, Fernando Alarid-Escudero, Nina A Bickell, Su-Hsin Chang, Graham A Colditz, Chin Hur, Hawre Jalal, Evan R Myers, Tracy M Layne, Shi-Yi Wang, Jennifer M Yeh, Thomas A Trikalinos","doi":"10.1093/jncimonographs/lgad021","DOIUrl":"10.1093/jncimonographs/lgad021","url":null,"abstract":"<p><strong>Background: </strong>We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism.</p><p><strong>Methods: </strong>Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population.</p><p><strong>Discussion: </strong>The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11009510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Some water in the data desert: the Cancer Intervention and Surveillance Modeling Network's capacity to guide mitigation of cancer health disparities.","authors":"Robert A Winn, Katherine Y Tossas, Chyke Doubeni","doi":"10.1093/jncimonographs/lgad032","DOIUrl":"10.1093/jncimonographs/lgad032","url":null,"abstract":"<p><p>Despite significant progress in cancer research and treatment, a persistent knowledge gap exists in understanding and addressing cancer care disparities, particularly among populations that are marginalized. This knowledge deficit has led to a \"data divide,\" where certain groups lack adequate representation in cancer-related data, hindering their access to personalized and data-driven cancer care. This divide disproportionately affects marginalized and minoritized communities such as the U.S. Black population. We explore the concept of \"data deserts,\" wherein entire populations, often based on race, ethnicity, gender, disability, or geography, lack comprehensive and high-quality health data. Several factors contribute to data deserts, including underrepresentation in clinical trials, poor data quality, and limited access to digital technologies, particularly in rural and lower-socioeconomic communities.The consequences of data divides and data deserts are far-reaching, impeding equitable access to precision medicine and perpetuating health disparities. To bridge this divide, we highlight the role of the Cancer Intervention and Surveillance Modeling Network (CISNET), which employs population simulation modeling to quantify cancer care disparities, particularly among the U.S. Black population. We emphasize the importance of collecting quality data from various sources to improve model accuracy. CISNET's collaborative approach, utilizing multiple independent models, offers consistent results and identifies gaps in knowledge. It demonstrates the impact of systemic racism on cancer incidence and mortality, paving the way for evidence-based policies and interventions to eliminate health disparities. We suggest the potential use of voting districts/precincts as a unit of aggregation for future CISNET modeling, enabling targeted interventions and informed policy decisions.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Health disparities across the cancer care continuum and implications for microsimulation modeling.","authors":"Chyke A Doubeni, Zinzi D Bailey, Robert A Winn","doi":"10.1093/jncimonographs/lgad031","DOIUrl":"10.1093/jncimonographs/lgad031","url":null,"abstract":"","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11009501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A health equity framework to support the next generation of cancer population simulation models.","authors":"Christina Chapman, Jinani Jayasekera, Chiranjeev Dash, Vanessa Sheppard, Jeanne Mandelblatt","doi":"10.1093/jncimonographs/lgad017","DOIUrl":"10.1093/jncimonographs/lgad017","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10846912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using simulation modeling to guide policy to reduce disparities and achieve equity in cancer outcomes: state of the science and a road map for the future.","authors":"Jeanne Mandelblatt, Rafael Meza, Amy Trentham-Dietz, Brandy Heckman-Stoddard, Eric Feuer","doi":"10.1093/jncimonographs/lgad033","DOIUrl":"10.1093/jncimonographs/lgad033","url":null,"abstract":"","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11009490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population simulation modeling of disparities in US breast cancer mortality.","authors":"Jeanne S Mandelblatt, Clyde B Schechter, Natasha K Stout, Hui Huang, Sarah Stein, Christina Hunter Chapman, Amy Trentham-Dietz, Jinani Jayasekera, Ronald E Gangnon, John M Hampton, Linn Abraham, Ellen S O'Meara, Vanessa B Sheppard, Sandra J Lee","doi":"10.1093/jncimonographs/lgad023","DOIUrl":"10.1093/jncimonographs/lgad023","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.","authors":"Jinani Jayasekera, Safa El Kefi, Jessica R Fernandez, Kaitlyn M Wojcik, Jennifer M P Woo, Adaora Ezeani, Jennifer L Ish, Manami Bhattacharya, Kemi Ogunsina, Che-Jung Chang, Camryn M Cohen, Stephanie Ponce, Dalya Kamil, Julia Zhang, Randy Le, Amrita L Ramanathan, Gisela Butera, Christina Chapman, Shakira J Grant, Marquita W Lewis-Thames, Chiranjeev Dash, Traci N Bethea, Allana T Forde","doi":"10.1093/jncimonographs/lgad020","DOIUrl":"10.1093/jncimonographs/lgad020","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Muscle loss during cancer therapy is associated with poor outcomes in advanced ovarian cancer.","authors":"Clarissa Polen-De,&nbsp;Smith Giri,&nbsp;Priyal Fadadu,&nbsp;Amy Weaver,&nbsp;Michaela E Mcgree,&nbsp;Michael Moynagh,&nbsp;Naoki Takahashi,&nbsp;Aminah Jatoi,&nbsp;Nathan K Lebrasseur,&nbsp;William Cliby,&nbsp;Grant Williams,&nbsp;Amanika Kumar","doi":"10.1093/jncimonographs/lgad007","DOIUrl":"https://doi.org/10.1093/jncimonographs/lgad007","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73988,"journal":{"name":"Journal of the National Cancer Institute. Monographs","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10031547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}