Racial and Socioeconomic Disparities in Colorectal Cancer Screening During the COVID-19 Pandemic

INTRODUCTION: Colorectal cancer (CRC) screening has reduced CRC mortality. The COVID-19 pandemic led to a reduction in screening volume. We sought to evaluate whether specific populations or socioeconomic groups were disproportionately impacted by the reduced access to care. METHOD(S): Patients eligible for CRC screening in a large integrated healthcare system, who had a primary care visit between January 2016 and April 2022, were evaluated. Trends in CRC screening were assessed by age, race, gender, insurance type, and geographic delineation by state and classification of urban or rural areas. Multilevel logistic regression models evaluated region-level cluster effects of CRC screening by patient demographics, insurance, and social vulnerability index (SVI), including socioeconomic status, household composition and disability, minority status and language, and housing and transportation domains. The interaction between trend in CRC screening and race was also investigated. RESULT(S): A total of 654,386 patients were screeneligible between January 2016 and April 2022. The cohort screening rate peaked at 70% in 2019 with a subsequent downtrend to a nadir of 63.6% through the first part of 2022. Whereas the Native American population is consistently the least screened population, the Asian population demonstrated the most significant decrease in screening during and after the COVID-19 pandemic, falling from a peak at 69.1% in 2019 to 59.3% in 2021;this remains low in 2022 at 58.9%. Further, older patients, males, location in an urban area, White ethnicity and use of commercial insurance were significantly associated with higher odds of CRC screening (p< 0.001). Conversely, patients living in more vulnerable census tracts based on the SVI socioeconomic status and housing/transportation domain had lower odds of having CRC screening (p< 0.001). Finally, there was a significant interaction between trend in CRC screening and race. The CRC screening rate increased between 2016 and 2019 and then decreased for all races, but Asian patients had the most significant decrease in CRC screening between 2020 and 2021 (68.3% versus 60.2%, p< 0.001;Figure 1). CONCLUSION(S): This is the first study to demonstrate that the COVID-19 pandemic led to a population-wide decrease in CRC screening volume that disproportionately affected the Asian population and those of lower socioeconomic status. We are currently evaluating whether this impacted stage migration and mortality. (Figure Presented).

Impact of COVID-19 on the productivity of ornamental fish farmers: a case of ornamental fish farms in Colombo district in Sri Lanka

Ornamental fish production in Sri Lanka has become a valuable foreign exchange earner during recent past years. However, COVID-19 pandemic has been explored worldwide and it has affected the world trade and economy from different perspectives. This study was conducted to explore the impact of the COVID-19 pandemic on ornamental fish production., Moreover, study on present status of ornamental fish farmers' major constraints and suggestions to overcome those constraints of ornamental fish farmers were also conducted. In Colombo district, four important Divisional Secretariat (DS) divisions were purposely selected for the study, as they are the main ornamental fish production areas of the district. The data was collected from a randomly selected 60 farmers using a field survey from July to October 2020. Descriptive statistics, two sample Wilcoxon signed-rank test and chi-square test were used to analyses the data. Results revealed that, there are both negative and positive impacts on ornamental fish production due to the COVID-19 pandemic. Ornamental fish farmers have to face mainly production and marketing problems. Some of them have engaged in coping strategies and resilience methods during COVID-19 period. Coping strategies have shown a significant association with the income and the production capacity of the farms. However, the majority of ornamental fish farmers haven't engaged in resilience methods during the COVID-19 pandemic. Other than the COVID-19 pandemic, the major constraints of ornamental fish production were lack of market information about the changes in the overseas markets and demand, the low price given for the product and lack of communication between farmers and exporters. Ideal "ORNAFISBIZ" model is suggested for overcome those constraints in order to protect the ornamental fish farmers in this area.

A Hybrid Approach for Detection of Fake News in Sinhala Text

The impact of technology on people's lives has grown continuously. The consumption of online news is one of the important trends as the share of population with internet access grows rapidly over time. Global statistics have shown that the internet and social media usage has an increasing trend. Recent developments like the Covid 19 pandemic have amplified this trend even more. However, the credibility of online news is a very critical issue to consider since it directly impacts the society and the people's mindsets. Majority of users tend to instinctively believe what they encounter and come into conclusions based upon them. It is essential that the consumers have an understanding or prior knowledge regarding the news and its source before coming into conclusions. This research proposes a hybrid model to predict the accuracy of a particular news article in Sinhala text. The model combines the general news content based analysis techniques using machine learning/ deep learning classifiers with social network related features of the news source to make predictions. A scoring mechanism is utilized to provide an overall score to a given news item where two independent scores- Accuracy Score (by analyzing the news content) and Credibility Score (by a scoring mechanism on social network features of the news source) are combined. The hybrid model containing the Passive Aggressive Classifier has shown the highest accuracy of 88%. Also, the models containing deep neural netWorks has shown accuracy around 75-80%. These results highlight that the proposed method could efficiently serve as a Fake News Detection mechanism for news content in Sinhala Language. Also, since there's no publicly available dataset for Fake News detection in Sinhala, the datasets produced in this work could also be considered as a contribution from this research. © 2022 IEEE.