Background: Addressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in a community sample drawn from Santa Clara County. Methods: On April 3-4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a sample of individuals living within the county by demographic and geographic characteristics. We estimate weights to adjust our sample to match the zip code, sex, and race/ethnicity distribution within the county. We report both the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We also adjust for test performance characteristics by combining data from 16 independent samples obtained from manufacturer’s data, regulatory submissions, and independent evaluations: 13 samples for specificity (3,324 specimens) and 3 samples for sensitivity (157 specimens). Results: The raw prevalence of antibodies to SARS-CoV-2 in our sample was 1.5% (exact binomial 95CI 1.1-2.0%). Test performance specificity in our data was 99.5% (95CI 99.2-99.7%) and sensitivity was 82.8% (95CI 76.0-88.4%). The unweighted prevalence adjusted for test performance characteristics was 1.2% (95CI 0.7-1.8%). After weighting for population demographics of Santa Clara County, the prevalence was 2.8% (95CI 1.3-4.7%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 54,000 (95CI 25,000 to 91,000 using weighted prevalence; 23,000 with 95CI 14,000-35,000 using unweighted prevalence) people were infected in Santa Clara County by early April, many more than the approximately 1,000 confirmed cases at the time of the survey. Conclusions: The estimated population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection may be much more widespread than indicated by the number of confirmed cases. More studies are needed to improve precision of prevalence estimates. Locally-derived population prevalence estimates should be used to calibrate epidemic and mortality projections.

Objectives. The authors sought to determine the effects of cold-weather periods on budgets and nutritional outcomes among poor American families. Methods. The Consumer Expenditure Survey was used to track expenditures on food and home fuels, and the Third National Health and Nutrition Examination Survey was used to track calorie consumption, dietary quality, vitamin deficiencies, and anemia. Results. Both poor and richer families increased fuel expenditures in response to unusually cold weather. Poor families reduced food expenditures by roughly the same amount as their increase in fuel expenditures, whereas richer families increased food expenditures. Conclusions. Poor parents and their children spend less on and eat less food during cold-weather budgetary shocks. Existing social programs fail to buffer against these shocks.

Background and Aims: The most restrictive nonpharmaceutical interventions (NPIs) for controlling the spread of COVID‐19 are mandatory stay‐at‐home and business closures. Given the consequences of these policies, it is important to assess their effects. We evaluate the effects on epidemic case growth of more restrictive NPIs (mrNPIs), above and beyond those of less‐restrictive NPIs (lrNPIs).Methods: We first estimate COVID‐19 case growth in relation to any NPI implementation in subnational regions of 10 countries: England, France, Germany, Iran, Italy, Netherlands, Spain, South Korea, Sweden and the United States. Using first‐difference models with fixed effects, we isolate the effects of mrNPIs by subtracting the combined effects of lrNPIs and epidemic dynamics from all NPIs. We use case growth in Sweden and South Korea, 2 countries that did not implement mandatory stay‐at‐home and business closures, as comparison countries for the other 8 countries (16 total comparisons).Results: Implementing any NPIs was associated with significant reductions in case growth in 9 out of 10 study countries, including South Korea and Sweden that implemented only lrNPIs (Spain had a nonsignificant effect). After subtracting the epidemic and lrNPI effects, we find no clear, significant beneficial effect of mrNPIs on case growth in any country. In France, for example, the effect of mrNPIs was +7% (95% CI: −5%‐19%) when compared with Sweden and + 13% (−12%‐38%) when compared with South Korea (positive means pro‐contagion). The 95% confidence intervals excluded 30% declines in all 16 comparisons and 15% declines in 11/16 comparisons.Conclusions: While small benefits cannot be excluded, we do not find significant benefits on case growth of more restrictive NPIs. Similar reductions in case growth may be achievable with less‐restrictive interventions.

Welfare-Enhancing Technological Change and the Growth of Obesity by Darius Lakdawalla, Tomas Philipson and Jay Bhattacharya. Published in volume 95, issue 2, pages 253-257 of American Economic Review, May 2005

Recent innovations in biomedicine seem poised to revolutionize medical practice. At the same time, disease and disability are increasing among younger populations. This paper considers how these confluent trends will affect the elderly's health status and health care spending over the next thirty years. Because healthier people live longer, cumulative Medicare spending varies little with a beneficiary's disease and disability status upon entering Medicare. On the other hand, ten of the most promising medical technologies are forecast to increase spending greatly. It is unlikely that a “silver bullet” will emerge to both improve health and dramatically reduce medical spending.