Decline in Colorectal Cancer Screening Rates in Federally Qualified Health Centers in Los Angeles County From 2019 to 2020

Introduction: Federally Qualified Health Centers (FQHCs) are funded by the Health Resources and Services Administration (HRSA) to provide primary care services to low-income and underinsured individuals. Los Angeles County (LAC) is a large, diverse county with greater than 10.2 million residents and 8 distinct Service Planning Areas (SPAs) that represent specific geographic regions with variable resources. We aimed to describe colorectal cancer (CRC) screening rates (CRCSR) and the screening rate change (SRCs) in LAC overall and for each SPA between 2019 and 2020 to determine where resources are most needed for CRCSR recovery following the COVID-19 pandemic. Method(s): Our data source was the Uniform Data System (UDS), which includes quality data for the FQHCs funded by HRSA. We determined 2019 and 2020 CRCSR for LAC FQHCs overall and for each FQHC, including average-risk patients age 50-74. We then separated FQHCs into quartiles based on SRC and performed mixed-effects logistic regression to determine FQHC-level characteristics associated with the largest decline in CRCSR from 2019 to 2020 (i.e., predictors of category SRC Q1). Lastly, we determined SRC for each SPA in LAC. Result(s): In 2019, there were 58 FQHCs in LAC with 326,473 patients eligible for CRC screening. In 2020, there were 59 FQHCs with 350,405 eligible patients. The median 2020 CRCSR in LAC FQHCs was 37.3%, down from 48.0% in 2019 (2020 median SRC= -9.6%) (Table). In the regression model among all LAC FQHCs, those with higher proportions of patients preferring a non-English language had significantly higher odds of having the largest decline in CRCSR from 2019 to 2020 (SRC Q1) (aOR=3.25, 95% CI=1.22-8.65;data not shown). CRCSR decreased from 2019 to 2020 in all SPAs with SRC ranging from -17.0% (South Bay) to -1.4% (West LA) (Figure). Conclusion(s): In Los Angeles County FQHCs, CRC screening rates were higher than the national FQHC average in 2019 however declined considerably between 2019 and 2020. The decline in CRC screening rates was highest in FQHCs serving a higher proportion of patients with a preference for a non-English language and varied by county region. Our findings highlight the need for targeted measures, including language-appropriate resources, to improve CRC screening uptake in FQHCs that provide care to some of the most historically marginalized individuals.

Patient and provider experiences with virtual care during the COVID-19 pandemic: A mixed methods study

The COVID-19 pandemic prompted the rapid uptake of Virtual Care (VC). Positive patient outcomes with VC are previously reported but little is known about the experiences of patients and providers using VC during the pandemic. We aimed to describe patient and primary care provider experiences, satisfaction, perceptions, and attitudes to VC during the COVID-19 pandemic that might explain adoption of VC across the continuum of care and inform sustained uptake. We conducted a sequential explanatory mixed methods study using online surveys and virtual interviews with a convenience sample of primary care providers and patients in a Canadian province (July – December 2020). Eligible participants included patients and primary care providers using VC during the COVID-19 pandemic. Survey responses and interviews were analyzed using descriptive statistics and thematic analysis, respectively. Overall satisfaction was compared using the Mann-Whitney U test. Eighty-five patients and 94 primary care providers responded to the surveys. Patients reported higher overall satisfaction with VC than primary care providers (median [interquartile range]: 4.4 [4.0-4.7] and 3.7 [3.4-3.9] p < 0.001). Ten patients and 11 primary care providers were interviewed. Both groups strongly appreciated VC’s increased access and convenience, identified the lack of compensation as a pre-pandemic barrier to providing VC, and reported willingness to continue VC post-COVID-19 pandemic. The COVID-19 pandemic provided an opportunity for patients and primary care providers to rapidly adopt VC with high satisfaction. Patients and primary care providers viewed VC positively due to its convenience and accessibility;both intend to continue using VC post-pandemic. © The Author(s), 2022.

DECLINE IN COLORECTAL CANCER SCREENING RATES IN FEDERALLY QUALIFIED HEALTH CENTERS IN THE UNITED STATES BETWEEN 2019 AND 2020

Introduction Screening for colorectal cancer (CRC) varies significantly by sociodemographic factors. The Health Resources and Services Administration (HRSA) provides primary care services, including CRC screening, to over 30 million medically underserved individuals at Federally Qualified Health Centers (FQHCs) in the United States (US). Given known disparities in CRC screening utilization and the national decline in screening due to the COVID- 19 pandemic, we aimed to determine the change in screening rates in FQHCs between 2019 and 2020 and factors associated with changes in rates. Methods This repeated cross-sectional analysis was conducted using 2019 and 2020 data from the Uniform Data System (UDS), which includes FQHC quality data for all US FQHCs. We ed CRC screening rates for each FQHC and for each state (FQHCs only) for patients age 50-75 for the years 2019 and 2020. We then calculated the change in screening (2020 rate minus 2019 rate) for each FQHC and for each state. To compare FQHC characteristics, we separated FQHCs into quartiles based on the 2020 screening rate and used ANOVA to compare FQHC characteristics between quartiles. Lastly, we performed a multivariable logistic regression to determine FQHC-level characteristics (2020 data) associated with an increase vs. decrease in screening rate from 2019 to 2020. Results In the 50 states, there were 1308 FQHCs and 7,132,411 FQHC patients eligible for CRC screening in 2020. Change in screening rates by state ranged from -11.1% (North Carolina) to +6.71% (Alaska) (mean= -3.55%) (Figure). The mean change in screening rates in FQHCs was -3.6% (range -62% to +58%) (Table). FQHCs with the lowest screening rates in 2020 (quartile 1, Table) had higher percentages of Black (p<0.001), male (p=0.018), homeless (p<0.001), uninsured (p<0.001), and low-income (p<0.001) patients, and were more likely to be in urban settings (p<0.001). FQHCs with the highest screening rates (quartile 4, Table) had a higher percentage of White (p<0.001) patients. When controlling for FQHC characteristics (including number of patients and 2019 CRC screening rate), each one point increase in the percentage of White patients served in a FQHC was associated with lower odds (aOR 0.71;95%CI=0.56-0.91) of experiencing a decrease in CRC screening rates in 2020 compared to 2019 (data not shown). Discussion FQHCs in the US have below-average CRC screening rates and saw notable declines in CRC screening utilization during the COVID-19 pandemic. Extent of decline varied broadly by state and FQHC, and declines were greater in FQHCs that served a higher proportion of (Figure Presented) Figure. Percent change in colorectal cancer (CRC) screening rate among adults age 50 to 74 at Health Resources and Services Administration-funded FQHCs between 2019 and 2020, by US state. (Table Presented) Table. FQHC characteristics (2020 data) and CRC screening rates (2019 and 2020) for HRSA-funded FQHCs in the US overall and by 2020 CRC screening rate quartiles.

An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight. Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.