BACKGROUND: Cancer incidence and death rates in the United States are often published at the county or statelevels; examining cancer statistics at the congressional district (CD) level allows decision makers to better understand how cancer is impacting the specific populations they represent. METHODS: Cancer incidence data were obtained from the Centers for Disease Control and Prevention's National Program of Cancer Registries and the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. Mortality data were obtained from the National Center for Health Statistics. CD rates were estimated by assigning the county-level age-adjusted rates to the census block and weighting those by the block population proportion of the CD. Those weighted rates were then aggregated over the blocks within the CD to estimate the district rate. Incidence rate estimates for 406 CDs and death rate estimates for 436 CDs were reported according to the boundaries for the 115th Congress of the United States. Maps showing rate estimates for all cancers combined, lung/bronchus, colorectal, female breast, cervical, and prostate cancer are presented by sex and race/ethnicity. RESULTS: The distribution of cancer incidence and death rates by CDs show similar patterns to those that have been observed at the county and state levels, with the highest cancer incidence and death rates observed in CDs in the South and Eastern regions. CONCLUSION: This examination of cancer rates at the CD-level provides data that can be used to inform cancer control strategies at the local and national levels. Displaying the data with the Data Visualizations tool makes it easily accessible to the public and decision makers.

INTRODUCTION: In 2020, the COVID-19 pandemic led to significant declines in cancer screening, including among women served by the National Breast and Cervical Cancer Early Detection Program (NBCCEDP). This study examined the spatial association between state-based COVID-19 test percent positivity and proportional change in NBCCEDP screening volume. METHODS: Using the COVID-19 Diagnostic Laboratory Testing dataset, we calculated state-based monthly COVID-19 test percent positivity from July through December 2020 and categorized rates into low, medium, and high groups. We used data from 48 NBCCEDP state awardees to calculate the state-based monthly proportional change in screening volume and compared data for July-December 2020 with the previous 5-year average for those months. We categorized changes in screening volume into large decrease, medium decrease, and minimal change and created maps of the associations between variable subgroups by using bivariate mapping in QGIS. RESULTS: Bivariate relationships between COVID-19 test percent positivity and proportional change in cancer screening volume varied over time and geography. In 5 of 6 months, 4 states had high COVID-19 test percent positivity and minimal change in breast or cervical cancer screening volume; 2 states had high COVID-19 test percent positivity and minimal change in breast and cervical cancer screening volume. CONCLUSION: Some states maintained pre-COVID-19 screening volumes despite high COVID-19 test percent positivity. Follow-up research will be conducted to determine how these states differ from those with consistent decreases in screening volume and identify factors that may have contributed to differences. This information could be useful for planning to maximize NBCCEDP awardees' ability to maintain screening volume during future public health emergencies.

Approximately 15.5 million cancer survivors were alive in the United States in 2016 with expected growth to 26.1 million by 2040 (1). Cancer survivors are living longer because of advances in early detection and treatment, but face psychosocial, cognitive, financial, and physical challenges (1,2). Physical challenges include cardiovascular complications, partly because cancer and cardiovascular disease (CVD) share some cumulative risk factors including tobacco use, physical inactivity, obesity, poor diet, hypertension, diabetes, and dyslipidemia (3). In addition, many cancer treatments damage the heart, and some cancer types increase risk for developing CVD (4). The recognition and management of heart disease in cancer survivors has given rise to the discipline of cardio-oncology, which focuses on the cardiovascular health of this population (5). CVD risk has been previously estimated using prediction models, and studies suggest that physician-patient communication using predicted heart age rather than predicted 10-year risk has led to a more accurate perception of excess heart age, encouraged actions to adopt a healthy lifestyle, and improved modifiable CVD risk factors (6,7). Using the nonlaboratory-based Framingham Risk Score (FRS) to estimate 10-year risk for developing CVD, predicted heart age is estimated from the 10-year risk of CVD (predicted by age, sex, diabetes status, smoking status, systolic blood pressure, hypertension treatment status, and body mass index); it is the age of an otherwise healthy person with the same predicted risk, with all other risk factors included in the prediction model at the normal level (systolic blood pressure of 125 mmHg, no hypertension treatment, body mass index of 22.5, nonsmoker, and nondiabetic) (6). Using data from the Behavioral Risk Factor Surveillance System (BRFSS), this study estimates predicted heart age, excess heart age (difference between predicted heart age and actual age), and racial/ethnic and sociodemographic disparities in predicted heart age among U.S. adult cancer survivors and noncancer participants aged 30-74 years using previously published methods (7). A total of 22,759 men and 46,294 women were cancer survivors with a mean age of 48.7 and 48.3 years, respectively. The predicted heart age and excess heart age among cancer survivors were 57.2 and 8.5 years, respectively, for men and 54.8 and 6.5 years, respectively, for women, and varied by age, race/ethnicity, education and income. The use of predicted heart age by physicians to encourage cancer survivors to improve modifiable risk factors and make heart healthy choices, such as tobacco cessation, regular physical activity, and a healthy diet to maintain a healthy weight, can engage survivors in informed cancer care planning after diagnosis.

The purpose of this study is to present a variety of measures that quantify equity in cancer outcomes, demonstrate how the measures perform in various cancer types, and identify counties, or Bright Spots, that meet the criteria of those measures. Using county-level age adjusted death rates for 2007-2016 from the National Center for Health Statistics, we determined counties that had both equitable and optimal outcomes for the black and white death rates across five cancer types, lung/bronchus, prostate, breast, colorectal, and liver cancers. The number of counties that met the criteria ranged from 0 to 442 depending on cancer type and measure used, and prostate and male liver cancer consistently had the lowest number of Bright Spots with a maximum of 3 counties meeting the most lenient criteria. This study presents several ways to examine equity, using rate ratios and standard error measures, in cancer mortality outcomes. It highlights areas with positive progress towards equity and areas potential need for equity-focused cancer control planning. Examining local areas of positive deviance can inform cancer control programming and planning around health equity.

Background Scant data are available about global patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread and global epidemiology of early confirmed cases of COVID-19 outside mainland China. We describe the global spread of SARS-CoV-2 and characteristics of COVID-19 cases and clusters before the characterisation of COVID-19 as a pandemic. METHODS: Cases of COVID-19 reported between Dec 31, 2019, and March 10, 2020 (ie, the prepandemic period), were identified daily from official websites, press releases, press conference transcripts, and social media feeds of national ministries of health or other government agencies. Case characteristics, travel history, and exposures to other cases were abstracted. Countries with at least one case were classified as affected. Early cases were defined as those among the first 100 cases reported from each country. Later cases were defined as those after the first 100 cases. We analysed reported travel to affected countries among the first case reported from each country outside mainland China, demographic and exposure characteristics among cases with age or sex information, and cluster frequencies and sizes by transmission settings. FINDINGS: Among the first case reported from each of 99 affected countries outside of mainland China, 75 (76%) had recent travel to affected countries; 60 (61%) had travelled to China, Italy, or Iran. Among 1200 cases with age or sex information, 874 (73%) were early cases. Among 762 early cases with age information, the median age was 51 years (IQR 35-63); 25 (3%) of 762 early cases occurred in children younger than 18 years. Overall, 21 (2%) of 1200 cases were in health-care workers and none were in pregnant women. 101 clusters were identified, of which the most commonly identified transmission setting was households (76 [75%]; mean 2·6 cases per cluster [range 2-7]), followed by non-health-care occupational settings (14 [14%]; mean 4·3 cases per cluster [2-14]), and community gatherings (11 [11%]; mean 14·2 cases per cluster [4-36]). INTERPRETATION: Cases with travel links to China, Italy, or Iran accounted for almost two-thirds of the first reported COVID-19 cases from affected countries. Among cases with age information available, most were among adults aged 18 years and older. Although there were many clusters of household transmission among early cases, clusters in occupational or community settings tended to be larger, supporting a possible role for physical distancing to slow the progression of SARS-CoV-2 spread. FUNDING: None.

Ocular melanoma is a rare form of melanoma (less than 5% of melanoma cases) arising from melanocytes of the uveal tract, conjunctiva, or orbit; however, up to 50% of patients will develop metastatic disease [1, 2]. Causes of ocular melanoma are unclear, but some possible risk factors include type B ultraviolet radiation, light eye color, ocular melanocytosis, and ubiquitin carboxyl-terminal hydrolase BAP1 mutations [1]. A previously published study using National Cancer Institute’s Surveillance, Epidemiology, and End Results Program data examined 4999 cases and found a statistically significant increase of 0.5% in uveal melanoma (the most common type of ocular melanoma) incidence rates among whites from 1973 to 2013 [3]. The purpose of this study is to provide an update and overview of the incidence of ocular melanoma for the entire United States (US).

INTRODUCTION: Burden of disease is often defined by using epidemiologic measures. However, there may be latent aspects of disease burden that are not factored into these types of estimates. This study quantified environmental burden of disease by using population health indicators and exploratory factor analysis at the county level across the United States. METHODS: Ninety-nine variables drawn from public use data sets from 2010 to 2016 were used to create a multifactor index - the burden index. We applied principal components analysis with promax rotation to allow the factors to correlate. Correlation coefficients for each factor and the outcome of interest, age-adjusted cancer death rate, were calculated. We used both unadjusted and adjusted linear regression techniques. RESULTS: The final additive county-level index included 9 factors that explained 68.3% of the variance in the counties and county equivalents. The burden index had a moderate association with the age-adjusted cancer death rates (r =.48, P <.001), and adjusted linear regression with all 9 factors explained 34% of the variance in the age-adjusted cancer death rate. Results were mapped, and the geographic distribution of both the burden index and age-adjusted cancer mortality were assessed. There are distinct geospatial patterns for both. CONCLUSIONS: Results from this study show potential areas of need, as well as the importance of including environmental variables in the study of cancer etiology. Future studies can aim to validate these findings by quantifying burden as it relates to overall cancer mortality by using epidemiologic measures, along with other confirmatory statistical methods.