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Purpose: This study aimed to explore how race and the socioeconomic index (SES) of residential zip codes influenced COVID-19 testing rates in St. Louis during the early and peak phases of the pandemic. Method: An ecological study was conducted using COVID-19 testing data from March to November 2020 in St. Louis City, including 16,915 tests from 30 residential zip codes. Geographical mapping identified areas lacking testing, descriptive statistics characterized testing rates by race and SES, and robust linear regression modeled the association between testing rates and race, controlling for SES. Results: The overall testing rate was 303.97 per 10,000 population. Testing rates among Blacks, Whites, American Indians/Alaskan Natives, and Asians were 374.26, 247.77, 360.63, and 242.41 per 10,000, respectively. Higher rates were observed in low- and middle-SES zip codes compared with high-SES areas. Adjusted for SES, testing rates per 10,000 were higher by 125 (1.25%), 448 (4.48%), and 32 (0.32%) among Blacks, American Indians/Alaskan Natives, and Asians compared with Whites. Testing rates decreased by 56 (0.56%) per unit SES improvement. There was statistically significant SES finding and that of the difference between American Indians/Alaskan Natives and Whites. Geographical mapping showed higher rates in the inner core and lower rates in the periphery of St. Louis. Conclusions: Race and SES influence testing rates. Targeted interventions are needed in areas with low testing rates in St. Louis.

Abstract Background Survival differences between left‐sided colon cancer (LSCC) and right‐sided colon cancer (RSCC) has been previously reported with mixed results, with various study periods not accounting for other causes of mortality. Purpose We sought to assess the trends in colon cancer cause‐ specific survival (CSS) and overall survival (OS) based on sidedness. Method Fine‐Gray competing risk and Cox models were used to analyze Surveillance, Epidemiology, and End Results (SEER) population‐based cohort from 1975 to 2019. Various interval periods were identified based on the timeline of clinical adoption of modern chemotherapy (1975–1989, interval period A; 1990–2004, B; and 2005–2019, C). Results Of the 227,637 patients, 50.1% were female and 46.2% were RSCC. RSCC was more common for African Americans (51.5%), older patients (age ≥65; 51.4%), females (50.4%), while LSCC was more common among Whites (53.1%; p

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the coronavirus 19 (COVID‐19) pandemic have had a lasting impact on the care of cancer patients. The impact on patients with gastrointestinal (GI) malignancies remains incompletely understood. We aimed to assess the impact of COVID‐19 on mortality, length of stay (LOS), and cost of care among patients with GI malignancies, and identify differences in outcomes based on primary tumor site. Methods We analyzed discharge encounters collected from the National Inpatient Sample (NIS) between March 2020 and December 2020 using propensity score matching (PSM) and COVID‐19 as the treatment effect. Results Of the 87,684 patient discharges with GI malignancies, 1892 were positive for COVID‐19 (C+) and eligible for matching in the PSM model. Following PSM analysis, C+ with GI tumors demonstrated increased incidence of mortality compared to their COVID‐19‐negative (C‐) counterparts (21.3% vs. 11.9%, p

Abstract Background 25% of all breast cancer patients have HER-2 overexpression. Breast Cancer patients with HER-2 overexpression are typically treated with HER-2 inhibitors such as Trastuzumab. Trastuzumab is known to cause a decrease in left ventricular ejection fraction. The aim of this study is to create a cardiac risk prediction tool among women with Her-2 positive breast cancer to predict cardiotoxicity. Method Using a split sample design, we created a risk prediction tool using patient level data from electronic medical records. The study included women 18 years of age and older diagnosed with HER-2 positive breast cancer who received Trastuzumab. Outcome measure was defined as a drop in LVEF by more than 10% to less than 53% at any time in the 1-year study period. Logistic regression was used to test predictors. Results The cumulative incidence of cardiac dysfunction in our study was 9.4%. The sensitivity and specificity of the model are 46% and 84%, respectively. Given a cumulative incidence of cardiotoxicity of 9%, the negative predictive value of the test was 94%. This suggests that in a low-risk population, the interval of screening for cardiotoxicity may be performed less frequently. Conclusion Cardiac risk prediction tool can be used to identify Her-2 positive breast cancer patients at risk of developing cardiac dysfunction. Also, test characteristics in addition to disease prevalence may inform a rational strategy in performing cardiac ultrasound in Her-2 breast cancer patients. We have developed a cardiac risk prediction model with high NPV in a low-risk population which has an appealing cost-effectiveness profile.