COVID-19 mortality in the United States: It's been two Americas from the start.

During the summer of 2021, a narrative of "two Americas" emerged: one with high demand for the COVID-19 vaccine and the second with widespread vaccine hesitancy and opposition to masks and vaccines. We analyzed "excess mortality" rates (the difference between total deaths and what would have been expected based on earlier time periods) prepared by the CDC for the United States from January 3, 2020 to September 26, 2021. Between Jan. 3, 2020 and Sept. 26, 2021, there were 895,693 excess deaths associated with COVID-19, 26% more than reported as such. The proportion of deaths estimated by the excess mortality method that was reported as COVID-19 was highest in the Northeast (92%) and lowest in the West (72%) and South (76%). Of the estimated deaths, 43% occurred between Oct. 4, 2020 and Feb. 27, 2021. Before May 31, 2020, approximately 56% of deaths were in the Northeast, where 17% of the population resides. Subsequently, 48% of deaths were in the South, which makes up 38% of the population. Since May 31, 2020, the South experienced COVID-19 mortality 26% higher than the national rate, whereas the Northeast's rate was 42% lower. If each region had the same mortality rate as the Northeast, more than 316,234 COVID-19 deaths between May 31, 2020 and Sept. 26, 2021 were "avoidable." More than half (63%) of the avoidable deaths occurred between May 31, 2020 and February, 2021, and more than half (60%) were in the South. Regional differences in COVID-19 mortality have been strong throughout the pandemic. The South has had higher mortality rates than the rest of the U.S. since May 31, 2020, and experienced 62% of the avoidable deaths. A comprehensive COVID-19 policy, including population-based restrictions as well as vaccines, is needed to control the pandemic.

The impact of health policies and vaccine rollout on the COVID-19 pandemic waves in Italy.

Background: Over the course of the COVID-19 pandemic in Italy, different response measures were taken to contain the spread of the virus. These include a variety of non-pharmaceutical interventions and a mass vaccination campaign. While not definitive, epidemiological measures provide some indication of the impact of such measures on the dynamics of the pandemic and lessons to better prepare for future emergencies. Objective: To describe the impact of vaccine rollout and health policies on the evolution of the COVID-19 pandemic in Italy from March 2020 to October 2021 using a set of epidemiological indicators. Methods: We performed a time-trend analysis of new confirmed COVID-19 cases, patients in hospital, and deaths. Using line charts, we informally assessed the relationship of these indicators with the immunization campaign and other health policies. Daily aggregate data were gathered from GitHub repositories of certified data from Italy's Government and Civil Protection. Results: The immunization coverage increased starting in March 2021, with a parallel decrease in COVID-19 infections, hospitalizations, and deaths. Despite different implementation approaches, the vaccine coverage growth rate had a similar pattern across regions. A comprehensive approach including measures such as requiring face masks and a "Green Pass" to enter indoor places also helped contain the pandemic. Conclusions: The vaccine rollout had a major effect on COVID-19 in Italy, especially on hospitalizations and deaths. Before the vaccine was available, however, other non-pharmaceutical interventions also helped to contain the spread of the virus and mitigate its effect on the population.

COVID-19 data are messy: analytic methods for rigorous impact analyses with imperfect data.

BACKGROUND: The COVID-19 pandemic has led to an avalanche of scientific studies, drawing on many different types of data. However, studies addressing the effectiveness of government actions against COVID-19, especially non-pharmaceutical interventions, often exhibit data problems that threaten the validity of their results. This review is thus intended to help epidemiologists and other researchers identify a set of data issues that, in our view, must be addressed in order for their work to be credible. We further intend to help journal editors and peer reviewers when evaluating studies, to apprise policy-makers, journalists, and other research consumers about the strengths and weaknesses of published studies, and to inform the wider debate about the scientific quality of COVID-19 research. RESULTS: To this end, we describe common challenges in the collection, reporting, and use of epidemiologic, policy, and other data, including completeness and representativeness of outcomes data; their comparability over time and among jurisdictions; the adequacy of policy variables and data on intermediate outcomes such as mobility and mask use; and a mismatch between level of intervention and outcome variables. We urge researchers to think critically about potential problems with the COVID-19 data sources over the specific time periods and particular locations they have chosen to analyze, and to choose not only appropriate study designs but also to conduct appropriate checks and sensitivity analyses to investigate the impact(s) of potential threats on study findings. CONCLUSIONS: In an effort to encourage high quality research, we provide recommendations on how to address the issues we identify. Our first recommendation is for researchers to choose an appropriate design (and the data it requires). This review describes considerations and issues in order to identify the strongest analytical designs and demonstrates how interrupted time-series and comparative longitudinal studies can be particularly useful. Furthermore, we recommend that researchers conduct checks or sensitivity analyses of the results to data source and design choices, which we illustrate. Regardless of the approaches taken, researchers should be explicit about the kind of data problems or other biases that the design choice and sensitivity analyses are addressing.

Adjusting COVID-19 Seroprevalence Survey Results to Account for Test Sensitivity and Specificity.

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic-and narrower-credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys.

A National Framework to Improve Mortality, Morbidity, and Disparities Data for COVID-19 and Other Large-Scale Disasters.

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report's recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.

Interpreting COVID-19 Test Results in Clinical Settings: It Depends!

Tests for Coronavirus disease 2019 (COVID-19) are intended for a disparate and shifting range of purposes: (1) diagnosing patients who present with symptoms to inform individual treatment decisions; (2) organizational uses such as "cohorting" potentially infected patients and staff to protect others; and (3) contact tracing, surveillance, and other public health purposes. Often lost when testing is encouraged is that testing does not by itself confer health benefits. Rather, testing is useful to the extent it forms a critical link to subsequent medical or public health interventions. Such interventions might be individual level, like better diagnosis, treatment, isolation, or quarantine of contacts. They might aid surveillance to understand levels and trends of disease within a defined population that enables informed decisions to implement or relax social distancing measures. In this article, we describe the range of available COVID-19 tests; their accuracy and timing considerations; and the specific clinical, organizational, and public health considerations that warrant different testing strategies. Three representative clinical scenarios illustrate the importance of appropriate test use and interpretation. The reason a patient seeks testing is often a strong indicator of the pretest probability of infection, and thus how to interpret test results. In addition, the level of population spread of the virus and the timing of testing play critical roles in the positive or negative predictive value of the test. We conclude with practical recommendations regarding the need for testing in various contexts, appropriate tests and testing methods, and the interpretation of test results.

SARS-CoV-2/COVID-19 Testing: The Tower of Babel.

BACKGROUND AND AIM: Testing represents one of the main pillars of public health response to SARS-CoV-2/COVID-19 pandemic. This paper shows how accuracy and utility of testing programs depend not just on the type of tests, but on the context as well. METHODS: We describe the testing methods that have been developed and the possible testing strategies; then, we focus on two possible methods of population-wide testing, i.e., pooled testing and testing with rapid antigen tests. We show the accuracy of split-pooling method and how, in different pre-test probability scenarios, the positive and negative predictive values vary using rapid antigen tests. RESULTS: Split-pooling, followed by retesting of negative results, shows a higher sensitivity than individual testing and requires fewer tests. In case of low pre-test probability, a negative result with antigen test could allow to rule out the infection, while, in case of a positive result, a confirmatory molecular test would be necessary. CONCLUSIONS: Test performance alone is not enough to properly choose which test to use; goals and context of the testing program are essential. We advocate the use of pooled strategies when planning population-wide screening, and the weekly use of rapid tests for close periodic monitoring in low-prevalence populations.