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Colorectal cancer (CRC) is the third leading cause of cancer-related death in the United States, responsible for over 50,000 deaths per year. According to the American Cancer Society, regular screening for CRC through fecal occult blood stool tests (FOBT) and through relatively more invasive colonoscopy or sigmoidoscopy is highly effective: the five-year survival rate for colorectal cancers that are detected early is around 90% (American Cancer Society 2017). CRC screening for asymptomatic individuals age 50-74 has thus earned a grade of 'A' from the United States Preventive Services Task Force (USPSTF), indicating strong scientific consensus on the medical efficacy and appropriateness of CRC screening (Bibbins-Domingo et al., 2016). This is in noted contrast to the associated grade for screening for asymptomatic individuals for breast cancer (earning a grade of B for 50-74-year-olds), lung cancer (earning a grade of B for older adults with 30 years of smoking history), and skin cancer (earning a grade of I, or inconclusive).The unusually strong scientific consensus surrounding the efficacy of CRC screening for asymptomatic adults age 50-74 is also notable because population rates of such screenings lag well below recommended levels. For example, while
In the U.S., means-tested cash, in-kind assistance, and social insurance are part of a patchwork safety net, often run with substantial involvement of state and local governments. Take-up–participation among eligible persons in this system is incomplete. A large literature points to both neo-classical and behavioral science explanations for low take-up. In this paper, we explore the response of the safety net to COVID-19 using newly-collected survey data from one U.S. state–Utah. The rich Utah data ask about income and demographics as well as use of three social safety net programs which collectively provided a large share of relief spending: the Unemployment Insurance program, a social insurance program providing workers who lose their jobs with payments; the Supplemental Nutrition Assistance Program, which provides benefit cards for purchasing unprepared food at retailers; and Economic Impact Payments, which provided relatively universal relief payments to individuals. The data do not suffice to determine eligibility for all of the programs, so we focus on participation per capita. These data also collect information on several measures of hardship and why individuals did not receive any of the 3 programs. We test for explanations that differentiate need, lack of information, transaction costs/administrative burden, stigma, and lack of eligibility. We use measures of hardship to assess targeting. We find that lack of knowledge as well as difficulty applying, and stigma in the UI program each play a role as reasons for not participating in the programs.
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