Lives saved and lost in the first six month of the US COVID-19 pandemic: A retrospective cost-benefit analysis

Exploring the Complexities of Long COVID

Since the emergence of the SARS-CoV-2 virus in 2019, nearly 700 million COVID-19 cases and 7 million deaths have been reported globally. Despite most individuals recovering within four weeks, the Center for Disease Control (CDC) estimates that 7.5% to 41% develop post-acute infection syndrome (PAIS), known as 'Long COVID'. This review provides current statistics on Long COVID's prevalence, explores hypotheses concerning epidemiological factors, such as age, gender, comorbidities, initial COVID-19 severity, and vaccine interactions, and delves into potential mechanisms, including immune responses, viral persistence, and gut dysbiosis. Moreover, we conclude that women, advanced age, comorbidities, non-vaccination, and low socioeconomic status all appear to be risk factors. The reasons for these differences are still not fully understood and likely involve a complex relationship between social, genetic, hormonal, and other factors. Furthermore, individuals with Long COVID-19 seem more likely to endure economic hardship due to persistent symptoms. In summary, our findings further illustrate the multifaceted nature of Long COVID and underscore the importance of understanding the epidemiological factors and potential mechanisms needed to develop effective therapeutic strategies and interventions.

Epidemiological approaches to multivariable models of health inequity: A study of race, rurality, and occupation during the COVID-19 pandemic

PURPOSE

Methods for assessing the structural mechanisms of health inequity are not well established. This study applies a phased approach to modeling racial, occupational, and rural disparities on the county level.

METHODS

Rural counties with disparately high rates of COVID-19 incidence or mortality were randomly paired with in-state control counties with the same rural-urban continuum code. Analysis was restricted to the first six months of the pandemic to represent the baseline structural reserves for each county and reduce biases related to the disruption of these reserves over time. Conditional logistic regression was applied in two phases-first, to examine the demographic distribution of disparities and then, to examine the relationships between these disparities and county-level social and structural reserves.

RESULTS

In over 200 rural county pairs (205 for incidence, 209 for mortality), disparities were associated with structural variables representing economic factors, healthcare infrastructure, and local industry. Modeling results were sensitive to assumptions about the relationships between race and other social and structural variables measured at the county level, particularly in models intended to reflect effect modification or mediation.

CONCLUSIONS

Multivariable modeling of health disparities should reflect the social and structural mechanisms of inequity and anticipate interventions that can advance equity.

COVID-19's death transfer to Sub-Saharan Africa

The COVID-19 spread very quickly around the world following its discovery in China, in December 2019. Lockdowns implemented in China and the Global North to control the propagation of the virus and to save human lives have resulted in a global recession. The transmission of the recessionary effects from the Global North to the Global South is reflected in the decline in sub-Saharan Africa's (SSA) GDP and the associated increase in poverty. The purpose of this paper is to illustrate how the recession induced in China and the Global North by COVID-19 lockdowns may have had indirect effects on SSA mortality that are higher than those directly attributed to the pandemic itself. Our methodology relies on a three-step relationship: (i) the impact of lockdowns on the recession in the North, (ii) the impact of the recession in the North on income in SSA countries, and (iii) the impact of a decline in income on mortality in SSA. We show that COVID-19-induced lockdowns in the Global North, through the severe recessions they induced in the Global South, resulted in the transfer of between 538,000 and 679,000 deaths in one year to SSA, including the deaths of 140,000 to 177,000 children aged 0-5 years. This corresponds to a 6-7% increase in the crude death rate and a 5-6% increase in under-5 mortality. These figures are much higher than the number of deaths directly attributable to COVID-19 in SSA. Thus, policymakers must not lose sight of the indirect excess mortality caused by global economic recession triggered by the pandemic. Our results reveal the need to increase the resilience of SSA countries to exogenous shocks, including COVID-19, which, in addition to increasing poverty, may induce excessive mortality due to the high sensitivity of mortality in SSA countries to economic recession.

Policy analysis and data mining tools for controlling COVID-19 policies

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.

Sputnik V Effectiveness against Hospitalization with COVID-19 during Omicron Dominance

Mass vaccination campaigns against COVID-19 affected more than 90% of the population in most developed countries. The new epidemiologic wave of COVID-19 has been ongoing since the end of 2021. It is caused by a virus variant B.1.1.529, also known as “Omicron” and its descendants. The effectiveness of major vaccines against Omicron is not known. The purpose of this study is to evaluate the efficacy of the Sputnik V vaccine. The main goal is to assess its protection against hospitalization in the period of Omicron dominance. We conducted our study based on a large clinical center in Moscow (Russia) where 1112 patients were included. We used the case-population method to perform the calculations. The data we obtained indicate that the Omicron variant causes at least 90% of infections in the studied cohort. The effectiveness of protection against hospitalization with COVID-19 in our study was 85.9% (95% CI 83.0–88.0%) for those who received more than one dose. It was 87.6% (95% CI 85.4–89.5%) and 97.0% (95% CI 95.9–97.8%) for those who received more than two or three doses. The effectiveness in cases of more severe forms was higher than for less severe ones. Thus, present study indicates the high protective efficacy of vaccination against hospitalization with COVID-19 in case of Omicron lineage.