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COVID-19 pandemic has affected people’s daily life dramatically since December 2019. More than 211 million cases and 4.42 million deaths have been reported and confirmed all over the world. Long-term care facilities are taking the biggest hit during this pandemic, even after the spread-out of the vaccines. Globally, residents in long-term care facilities have experienced disproportionately high morbidity and mortality from COVID-19. Elderlies residing in long-term care facilities have the greatest susceptibility to COVID-19 and the poorest outcomes from infections. This chapter overviewed the insight, impact, and challenges of COVID-19 on the residential care homes in UK, US, and Australia and provided possible implications for the long-term care market post-pandemic.
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This paper presents a study on 80 countries that evaluates the socioeconomic factors in containing the spread and mortality of COVID-19. Our results show that the long-term social factors such as lower personal freedom, better education in science, and past coronavirus outbreak experience are more effective than the economic factors such as higher healthcare-associated factors per 1000 population and larger GDP. However, using GDP per capita as the instrumental variable, we also find that the richer countries with a high degree of personal freedom have a higher number of infection or death cases per million population because they would be less likely to adhere to and implement the policy of the movement restrictions to restrict their access to goods and services.
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We show that overconfident individuals are likely to be arrested for public intoxication by using arrest records from a university town police log. This relationship is robust to various control variables such as risk aversion, time discounting, present bias, self-control, selfishness, loss aversion, and socializing with peers arrested for public intoxication. However, this relationship is no longer significant using only self-reported arrest data. We hypothesize that overconfident individuals are likely to underreport their arrests. This result has important implications for the use of self-reported data on public intoxication arrests rather than actual arrest records.
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Selecting the appropriate, reasonable, and affordable health insurance plan becomes a very important question to solve for both employers and employees. Our research tries to locate the factors determining private sector health insurance plan enrolment decision, and also provides a guideline to both private companies and employees on health insurance plan selection strategies. By using Kaiser Family Foundation Annual Employer Health Benefits Survey (KFF EHBS) data, we apply random decision forest machine learning methodology to study the determinants of employees' health insurance selection, as well as to compare the prediction accuracy among different methodologies. The results indicate: 1) the employees at large firms and the firms with higher eligible rate would tend to choose PPO plan; 2) employees who need family coverage would have different choices comparing employees who seek for single coverage only; 3) employer's contribution and annual total contribution to the health insurance plan are the most important determinants on employees' insurance selection. The conclusion also can provide some suggestions to insurance companies on health insurance package design for different types of employers and employees.
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