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      COVID-19 Emergency Sick Leave Has Helped Flatten The Curve In The United States : Study examines the impact of emergency sick leave on the spread of COVID-19.

      1 , 2 , 3
      Health Affairs
      Health Affairs (Project Hope)

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          Abstract

          <p class="first" id="d2736045e85">This analysis examines whether the coronavirus disease 2019 (COVID-19) emergency sick leave provision of the bipartisan Families First Coronavirus Response Act (FFCRA) reduced the spread of the virus. Using a difference-in-differences strategy, we compared changes in newly reported COVID-19 cases in states where workers gained the right to take paid sick leave (treatment group) versus in states where workers already had access to paid sick leave (control group) before the FFCRA. We adjusted for differences in testing, day-of-the-week reporting, structural state differences, general virus dynamics, and policies such as stay-at-home orders. Compared with the control group and relative to the pre-FFCRA period, states that gained access to paid sick leave through the FFCRA saw around 400 fewer confirmed cases per state per day. This estimate translates into roughly one prevented case per day per 1,300 workers who had newly gained the option to take up to two weeks of paid sick leave. </p>

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          Clinical Characteristics of Coronavirus Disease 2019 in China

          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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            Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program

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              Designing Difference in Difference Studies: Best Practices for Public Health Policy Research

              The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.
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                Author and article information

                Journal
                Health Affairs
                Health Affairs
                Health Affairs (Project Hope)
                0278-2715
                1544-5208
                October 15 2020
                : 10.1377/hlthaff
                Affiliations
                [1 ]Stefan Pichler is a research associate at KOF Swiss Economic Institute, ETH Zurich, in Zurich, Switzerland.
                [2 ]Katherine Wen is a PhD student in policy analysis and management at Cornell University, in Ithaca, New York.
                [3 ]Nicolas R. Ziebarth () is an associate professor in the Department of Policy Analysis and Management, Cornell University.
                Article
                10.1377/hlthaff.2020.00863
                33058691
                eb85e17c-70cd-4afe-995f-89f4607ef3a6
                © 2020
                History

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