Journal of the National Cancer Institute. Monographs最新文献

Stigma is a social process characterized by negative beliefs, attitudes, and stereotypes associated with a specific attribute or characteristic that leads to discrimination and social exclusion. Stigma manifests across the cancer control continuum and remains a key challenge for cancer prevention and control worldwide. In this commentary, we provide an overview of the U.S. National Cancer Institute's (NCI) Global Cancer Stigma Research Workshop, a multi-disciplinary international conference held virtually in September 2022, which focused on the intersection of cancer and stigma. The meeting was unique in its convening of researchers, advocates, clinicians, and non-governmental and governmental organizations, who-as a collective-provided overarching topics, cross-cutting considerations, and future directions for the cancer stigma research community to consider, which we describe herein. In summary, studying cancer stigma comprehensively requires a holistic, adaptive, and multifaceted approach-and should consider interrelated factors and their intersection within diverse cultural and social contexts worldwide. Collectively, there was a call for: an inclusive approach, encouraging researchers and practitioners to identify and measure cancer stigma as a driver for cancer health inequities globally; an expansion of existing research methodology to include diversity of experiences, contexts, and perspectives; and collaborations among diverse stakeholders to develop more effective strategies for reducing stigma and improving cancer outcomes. Such efforts are essential to cultivating effective and equitable approaches to preventing and treating cancer worldwide.

Effective communication about cancer diagnosis and prognosis in sub-Saharan African oncology settings is often challenged by the cancer-related shame and stigma patients and families experience. Enhancing empathic communication between health care providers, including physicians and nurses, and oncology patients and their families can not only reduce cancer stigma but also improve patient engagement, treatment satisfaction, and quality of life. To reduce lung cancer stigma, we adapted an evidence-based empathic communication skills training intervention to reduce patients' experience of stigma in Nigeria and conducted a pilot study examining the feasibility and acceptability of the empathic communication skills training. Thirty health care providers, recruited from University College Hospital, Ibadan, and Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, participated in a 2.25-hour didactic and experiential training session. Participant surveys were completed before and after the training. Overall, participants reported highly favorable training evaluations, with at least 85% of health care providers agreeing or strongly agreeing to survey items assessing training relevance, novelty, clarity, and facilitator effectiveness. Self-efficacy to communicate empathically with patients increased significantly from before-training (Mean [SD] = 3.93 [0.28]) to after-training (Mean [SD] = 4.55 [0.15]; t29 = 3.51, P < .05). Significant improvements were observed in health care provider reports of empathy toward lung cancer survivors and attitude toward lung cancer care as well as significant reductions in lung cancer blame were noted. The empathic communication skills training was feasible, well received by oncology clinicians in Nigeria, and demonstrated improvements in health care provider-reported outcomes from before- to after-training.

Persons with HIV-associated Kaposi's sarcoma (KS) experience three co-existing stigmatizing health conditions: skin disease, HIV, and cancer, which contribute to a complex experience of stigmatization and to delays in diagnosis and treatment. Despite the importance of stigma among these patients, there are few proven stigma-reduction strategies for HIV-associated malignancies. Using qualitative methods, we explore how people with HIV-associated KS in western Kenya between August 2022 and 2023 describe changes in their stigma experience after participation in a multicomponent navigation strategy, which included 1) physical navigation and care coordination, 2) video-based education with motivational survivor stories, 3) travel stipend, 4) health insurance enrollment assistance, 5) health insurance stipend, and 6) peer mentorship. A purposive sample of persons at different stages of chemotherapy treatment were invited to participate. Participants described how a multicomponent navigation strategy contributed to increased knowledge and awareness, a sense of belonging, hope to survive, encouragement, and social support, which served as stigma mitigators, likely counteracting the major drivers of intersectional stigma in HIV-associated KS.

The US Black population has higher colorectal cancer (CRC) incidence rates and worse CRC survival than the US White population, as well as historically lower rates of CRC screening. The Surveillance, Epidemiology, and End Results incidence rate data in people diagnosed between the ages of 20 and 45 years, before routine CRC screening is recommended, were analyzed to estimate temporal changes in CRC risk in Black and White populations. There was a rapid rise in rectal and distal colon cancer incidence in the White population but not the Black population, and little change in proximal colon cancer incidence for both groups. In 2014-2018, CRC incidence per 100 000 was 17.5 (95% confidence interval [CI] = 15.3 to 19.9) among Black individuals aged 40-44 years and 16.6 (95% CI = 15.6 to 17.6) among White individuals aged 40-44 years; 42.3% of CRCs diagnosed in Black patients were proximal colon cancer, and 41.1% of CRCs diagnosed in White patients were rectal cancer. Analyses used a race-specific microsimulation model to project screening benefits, based on life-years gained and lifetime reduction in CRC incidence, assuming these Black-White differences in CRC risk and location. The projected benefits of screening (via either colonoscopy or fecal immunochemical testing) were greater in the Black population, suggesting that observed Black-White differences in CRC incidence are not driven by differences in risk. Projected screening benefits were sensitive to survival assumptions made for Black populations. Building racial disparities in survival into the model reduced projected screening benefits, which can bias policy decisions.

Background: Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American ("Black") people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities.

Methods: We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences.

Results: Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort.

Conclusions: After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. Data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change.

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.

Background: 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.

Methods: 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.

Results: 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%).

Conclusion: 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.

Background: 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.

Methods: 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.

Discussion: 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.

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.