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OBJECTIVES/GOALS: Due to clinical advances, cancer survivors are living longer but have an increased risk of a second primary cancer (SPC). This cross-sectional study aims to examine SPC prevalence in immigrant women and compare healthcare use (HCU) and cancer screening in immigrants with SPC versus (1) immigrants with a single cancer and (2) US-born women with SPC. METHODS/STUDY POPULATION: The study population will include adult women with breast/gynecologic primary cancer (PC) from the 2005, 2008, 2010, 2013, and 2015 National Health Interview Survey. First-generation immigrant or US-born status will be defined by region of birth. SPC includes diagnosis with a second cancer type ≥1 year after the initial PC diagnosis. We will compare the prevalence of ≥1 SPC in immigrant and US-born women. To evaluate HCU and cancer screening differences, we will assess sociodemographic and socioeconomic factors, risk behaviors, length of US residence, and citizenship status with descriptive statistics. In regression analyses, we will compare number of provider visits and cancer screening rates in immigrant women with SPC versus immigrants with PC alone and US-born women with SPC after matching by age and PC type. RESULTS/ANTICIPATED RESULTS: Disparities in cancer diagnosis, quality of care, receipt of recommended treatment, and screening rates among immigrants in the US are well documented. Therefore, we hypothesize that immigrant cancer survivors will have similar or higher rates of SPC compared to women born in the US with variations based on health status. We further hypothesize that immigrants with SPC will report lower rates of HCU after diagnosis of their first cancer and cancer screening compared to US-born women. However, we expect that immigrants with SPC will report similar or higher rates of HCU and cancer screening compared to immigrant women with PC alone. DISCUSSION/SIGNIFICANCE: To our knowledge, this study will be the first to describe SPC among immigrant cancer survivors in the US. This research will inform interventions to improve cancer care delivery and ultimately reduce SPC in immigrants with cancer.

Introduction Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology. Methods We utilized PubMed to search for peer‐reviewed research articles published between 2016 and 2021 with the query type “(“deep learning” or “machine learning” or “neural network” or “artificial intelligence”) and (“neoplas $” or “cancer$ ” or “tumor $” or “tumour$ ”).” We included clinical trials and original research studies and excluded reviews and meta‐analyses. Oncology‐related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies. Results Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%–93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. “White” vs. “non‐White” or “White” vs. “Black”). Conclusions We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non‐White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology. A minority of oncologic studies utilizing AI analyzed in this review reported the racial and ethnic demographic data of participants included in their training and validation sets. In studies that did report racial demographic data, few non‐white patients were included in the datasets.

Background Hospitalized patients with cancer often experience a high symptom burden, which may impact care satisfaction and healthcare utilization. Methods We prospectively enrolled patients with cancer and unplanned hospitalizations from September 2014 to April 2017. Upon admission, we assessed patients’ care satisfaction (FAMCARE items: satisfaction with care coordination and speed with which symptoms are treated) and physical (Edmonton Symptom Assessment System [ESAS]) and psychological (Patient Health Questionnaire-4 [PHQ-4]) symptoms. We used regression models to identify factors associated with care satisfaction and associations of satisfaction with symptom burden and hospital length of stay (LOS). Results Among 1,576 participants, most reported being “satisfied”/ “very satisfied” with care coordination (90%) and speed with which symptoms are treated (89%). Older age (coordination: B  < 0.01, P  = 0.02, speed: B  = 0.01, P  < 0.01) and admission to a dedicated oncology service ( B  = 0.20, P  < 0.01 for each) were associated with higher satisfaction. Higher satisfaction with care coordination was associated with lower ESAS-physical ( B  =  − 1.28, P  < 0.01), ESAS-total ( B  =  − 2.73, P  < 0.01), PHQ4-depression ( B  =  − 0.14, P  = 0.02), and PHQ4-anxiety ( B  =  − 0.16, P  < 0.01) symptoms. Higher satisfaction with speed with which symptoms are treated was associated with lower ESAS-physical ( B  =  − 1.32, P  < 0.01), ESAS-total ( B  =  − 2.46, P  < 0.01), PHQ4-depression ( B  =  − 0.14, P  = 0.01), and PHQ4-anxiety ( B  =  − 0.17, P  < 0.01) symptoms. Satisfaction with care coordination ( B  =  − 0.48, P  = 0.04) and speed with which symptoms are treated ( B  =  − 0.44, P  = 0.04) correlated with shorter LOS. Conclusions Hospitalized patients with cancer report high care satisfaction, which correlates with older age and admission to a dedicated oncology service. Significant associations among higher care satisfaction, lower symptom burden, and shorter hospital LOS highlight the importance of improving symptom management and care coordination in this population.