No abstract

Food insecurity can negatively impact adherence and receipt of high-quality cancer care. The purpose of the study was to (1) compare the prevalence of COVID-19 associated food insecurity by cancer history and (2) examine determinants associated with COVID-19 related food insecurity among cancer survivors.We used nationally-representative data from the COVID-19 Household Impact Survey (n = 10,760), collected at three time points: April 20-26, May 4-10, and May 30th -June 8th of 2020. Our primary exposure was cancer survivor status, based on participant’s self-report of a cancer diagnosis (n=854, 7.1%). Primary outcomes of food insecurity were categorized on how often participants reported the following: “We worried our food would run out before we got money to buy more” or “The food that we bought just didn’t last, and we didn’t have money to get more”; Respondents were categorized as food insecure if they chose often true or sometimes true. Multivariable Poisson regression was used to identify demographic determinants of food insecurity among cancer survivors.Thirty-two percent of cancer survivors were food insecure. Cancer survivors aged 30-44 years and those aged ≥60 were more likely to report being food insecure compared to respondents without a history of cancer in the same age categories (30-44 years, 59.9% versus 41.2% p = 0.01, ≥60 years 27.2% versus 20.2%, p = 0.01). Cancer survivors without a high school diploma were more likely to report food insecurity compared to adults with no history of cancer (87.0% versus 64.1%, p = 0.001). In multivariable models, uninsured cancer survivors (adjusted Prevalence Ratio (aPR) aPR: 2.39, 95% CI: 1.46-3.92) and those on Medicaid (aPR: 2.10, 95% CI: 1.40-3.17) were also more likely to report being food insecure.Food insecurity during the COVID-19 pandemic is vast but disparities persist. Among cancer survivors, differences in food insecurity were observed by age and SES.

Introduction: Food insecurity can negatively impact adherence and receipt of high-quality cancer care. The purpose of the study was to (1) compare the prevalence of COVID-19-associated food insecurity by cancer history and (2) examine determinants associated with COVID-19-related food insecurity among cancer survivors. Methods: We used nationally representative data from the 2020 COVID-19 Household Impact Survey ( n =10,760). Our primary exposure was participants' self-report of a cancer diagnosis ( n =854, 7.1%). Primary outcomes of food insecurity were categorized by the following questions: “We worried our food would run out before we got money to buy more” or “The food that we bought just didn't last, and we didn't have money to get more”; respondents were categorized as food insecure if they chose often true or sometimes true. Multivariable Poisson regression was used to identify demographic determinants of food insecurity among cancer survivors. Results: Thirty-two percent of cancer survivors were food insecure. Cancer survivors 30–44 years of age and those ≥60 years of age were more likely to report being food insecure compared to respondents without a history of cancer, respectively (30–44 years, 59.9% vs. 41.2% p =0.01, ≥60 years 27.2% vs. 20.2%, p =0.01). Cancer survivors without a high school diploma were more likely to report food insecurity compared to adults with no history of cancer (87.0% vs. 64.1%, p =0.001). In multivariable models, uninsured cancer survivors (adjusted prevalence ratio [aPR] aPR: 2.39, 95% CI: 1.46–3.92) and those on Medicaid (aPR: 2.10, 95% CI: 1.40–3.17) were also more likely to report being food insecure. Conclusion: Food insecurity during the COVID-19 pandemic is vast, but disparities persist. Among cancer survivors, differences in food insecurity were observed by age and socio economic status. Cancer survivors experiencing food insecurity more frequently reported mental health symptoms of depression, loneliness, and hopelessness compared to those who were food secure.

Parasitology and parasitic infections / International Journal of Infectious Diseases 101(S1) (2021) 419-436 thin blood smear pictures. This tool provides vital size/shape features, such as width, length and the perimeter of the T. cruzi and helps to attain a classification accuracy of 91.06% with the SVM classifier.Conclusion: This work proposes a soft-computing tool to detect, analyze and classify the T. cruzi existing in the thin blood smear microscopic images. This work achieved a classification accuracy of 91.06%, which confirms that the proposed tool has high clinical significance and it can be used for automated diagnosis of T. cruzi.