JNCI Cancer Spectrum最新文献

Our study investigates the trends in prostate cancer screening amid the COVID-19 pandemic, particularly focusing on racial disparities between Black and White men. Utilizing data from the Behavioral Risk Factor Surveillance System from 2018, 2020, and 2022, we analyzed prostate-specific antigen screening rates in men aged 45-75 years. Our findings reveal initial declines in screening rates for both groups during the pandemic, with subsequent recovery; however, the pace of rebound differed statistically significantly between races. Whereas White men showed a notable increase in screening rates postpandemic, Black men's rates recovered more slowly. This disparity underscores the impact of socioeconomic factors, health-care access, and possibly systemic biases affecting health-care delivery. Our study highlights the need for targeted interventions to address these inequalities and ensure equitable access to prostate cancer preventive care in the aftermath of COVID-19.

Introduction: Studies suggest that many emergency department (ED) visits and hospitalizations for patients with cancer may be preventable. The Centers for Medicare & Medicaid Services has implemented changes to the hospital outpatient reporting program that targets acute care in-treatment patients for preventable conditions. Oncology urgent care centers aim to streamline patient care. Our cancer center developed an urgent care center called the direct referral unit in 2011.

Methods: We abstracted visits to our adjacent hospital ED and direct referral unit from January 2014 to June 2018. Patient demographics, cancer and visit diagnoses, visit charges, and 30-day therapy utilization were assessed.

Results: An analysis of 13 114 visits demonstrated that increased direct referral unit utilization was associated with decreased monthly ED visits (P < .001). Common direct referral unit visit diagnoses were dehydration, nausea and vomiting, abdominal pain, and fever. Patients receiving active cancer treatment more frequently presented to the direct referral unit (P < .001). The average charges were $2221 for the direct referral unit and $10 261 for the ED.

Conclusion: The association of decreased ED visits with increased direct referral unit utilization demonstrates the potential for urgent care centers to reduce acute care visits. Many patients presented to our direct referral unit with preventable conditions, and these visits were associated with considerable cost savings, supporting its use as a cost-effective method to reduce acute care costs.

Background: Young adult cancer survivors face medical financial hardships that may lead to delaying or forgoing medical care. This study describes the medical financial difficulties young adult cancer survivors in the United States experience in the post-Patient Protection and Affordable Care Act period.

Method: We identified 1009 cancer survivors aged 18 to 39 years from the National Health Interview Survey (2015-2022) and matched 963 (95%) cancer survivors to 2733 control individuals using nearest-neighbor matching. We used conditional logistic regression to examine the association between cancer history and medical financial hardship and to assess whether this association varied by age, sex, race and ethnicity, and region of residence.

Results: Compared with those who did not have a history of cancer, young adult cancer survivors were more likely to report material financial hardship (22.8% vs 15.2%; odds ratio = 1.65, 95% confidence interval = 1.50 to 1.81) and behavior-related financial hardship (34.3% vs 24.4%; odds ratio = 1.62, 95% confidence interval = 1.49 to 1.76) but not psychological financial hardship (52.6% vs 50.9%; odds ratio = 1.07, 95% confidence interval = 0.99 to 1.16). Young adult cancer survivors who were Hispanic or lived in the Midwest and South were more likely to report psychological financial hardship than their counterparts.

Conclusions: We found that young adult cancer survivors were more likely to experience material and behavior-related financial hardship than young adults without a history of cancer. We also identified specific subgroups of young adult cancer survivors that may benefit from targeted policies and interventions to alleviate medical financial hardship.

Purpose: Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment.

Methods: We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function.

Results: Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score.

Conclusions: Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.

Background: Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables.

Methods: We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers.

Results: Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group.

Conclusion: Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.

Background: Sleep problems following childhood cancer treatment may persist into adulthood, exacerbating cancer-related late effects and putting survivors at risk for poor physical and psychosocial functioning. This study examines sleep in long-term survivors and their siblings to identify risk factors and disease correlates.

Methods: Childhood cancer survivors (≥5 years from diagnosis; n = 12 340; 51.5% female; mean [SD] age = 39.4 [9.6] years) and siblings (n = 2395; 57.1% female; age = 44.6 [10.5] years) participating in the Childhood Cancer Survivor Study completed the Pittsburgh Sleep Quality Index (PSQI). Multivariable Poisson-error generalized estimating equation compared prevalence of binary sleep outcomes between survivors and siblings and evaluated cancer history and chronic health conditions (CHC) for associations with sleep outcomes, adjusting for age (at diagnosis and current), sex, race/ethnicity, and body mass index.

Results: Survivors were more likely to report clinically elevated composite PSQI scores (>5; 45.1% vs 40.0%, adjusted prevalence ratio [PR] = 1.20, 95% CI = 1.13 to 1.27), symptoms of insomnia (38.8% vs 32.0%, PR = 1.26, 95% CI = 1.18 to 1.35), snoring (18.0% vs 17.4%, PR = 1.11, 95% CI = 1.01 to 1.23), and sleep medication use (13.2% vs 11.5%, PR = 1.28, 95% CI = 1.12 to 1.45) compared with siblings. Within cancer survivors, PSQI scores were similar across diagnoses. Anthracycline exposure (PR = 1.13, 95% CI = 1.03 to 1.25), abdominal radiation (PR = 1.16, 95% CI = 1.04 to 1.29), and increasing CHC burden were associated with elevated PSQI scores (PRs = 1.21-1.48).

Conclusions: Among survivors, sleep problems were more closely related to CHC than diagnosis or treatment history, although longitudinal research is needed to determine the direction of this association. Frequent sleep-promoting medication use suggests interest in managing sleep problems; behavioral sleep intervention is advised for long-term management.