Impact of the COVID-19 pandemic on breast cancer screening volumes and patient screening behaviors.

PURPOSE: In order to facilitate targeted outreach, we sought to identify patient populations with a lower likelihood of returning for breast cancer screening after COVID-19-related imaging center closures. METHODS: Weekly total screening mammograms performed throughout 2019 (baseline year) and 2020 (COVID-19-impacted year) were compared. Demographic and clinical characteristics, including age, race, ethnicity, breast density, breast cancer history, insurance status, imaging facility type used, and need for interpreter, were compared between patients imaged from March 16 to October 31 in 2019 (baseline cohort) and 2020 (COVID-19-impacted cohort). Census data and an online map service were used to impute socioeconomic variables and calculate travel times for each patient. Logistic regression was used to identify patient characteristics associated with a lower likelihood of returning for screening after COVID-19-related closures. RESULTS: The year-over-year cumulative difference in screening mammogram volumes peaked in week 21, with 2962 fewer exams in the COVID-19-impacted year. By week 47, this deficit had reduced by 49.4% to 1498. A lower likelihood of returning for screening after COVID-19-related closures was independently associated with younger age (odds ratio (OR) 0.78, p < 0.001), residence in a higher poverty area (OR 0.991, p = 0.014), lack of health insurance (OR 0.65, p = 0.007), need for an interpreter (OR 0.68, p = 0.029), longer travel time (OR 0.998, p < 0.001), and utilization of mobile mammography services (OR 0.27, p < 0.001). CONCLUSION: Several patient factors are associated with a lower likelihood of returning for screening mammography after COVID-19-related closures. Knowledge of these factors can guide targeted outreach to vulnerable patients to facilitate breast cancer screening.

An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19.

BACKGROUND: COVID-19 has caused an unprecedented global health emergency. The strains of such a pandemic can overwhelm hospital capacity. Efficient clinical decision-making is crucial for proper healthcare resource utilization in this crisis. Using observational study data, we set out to create a predictive model that could anticipate which COVID-19 patients would likely be admitted and developed a scoring tool that could be used in the clinical setting and for population risk stratification. METHODS: We retrospectively evaluated data from COVID-19 patients across a network of 6 hospitals in northeastern Pennsylvania. Analysis was limited to age, gender, and historical variables. After creating a variable importance plot, we chose a selection of the best predictors to train a logistic regression model. Variable selection was done using a lasso regularization technique. Using the coefficients in our logistic regression model, we then created a scoring tool and validated the score on a test set data. RESULTS: A total of 6485 COVID-19 patients were included in our analysis, of which 707 were hospitalized. The biggest predictors of patient hospitalization included age, a history of hypertension, diabetes, chronic heart disease, gender, tobacco use, and chronic kidney disease. The logistic regression model demonstrated an AUC of 0.81. The coefficients for our logistic regression model were used to develop a scoring tool. Low-, intermediate-, and high-risk patients were deemed to have a 3.5%, 26%, and 38% chance of hospitalization, respectively. The best predictors of hospitalization included age (odds ratio [OR] = 1.03, confidence interval [CI] = 1.02-1.03), diabetes (OR = 2.08, CI = 1.69-2.57), hypertension (OR = 2.36, CI = 1.90-2.94), chronic heart disease (OR = 1.53, CI = 1.22-1.91), and male gender (OR = 1.32, CI = 1.11-1.58). CONCLUSIONS: Using retrospective observational data from a 6-hospital network, we determined risk factors for admission and developed a predictive model and scoring tool for use in the clinical and population setting that could anticipate admission for COVID-19 patients.