Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach.

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Inhaled Water and Salt Suppress Respiratory Droplet Generation and COVID-19 Incidence and Death on US Coastlines

Dry air alters salt and water balance in the upper airways and increases the risks of COVID-19 among other respiratory diseases. We explored whether such upper airway variations in salt and water balance might alter respiratory droplet generation and potentially contribute to observed impacts of airway hydration on respiratory disease. In a randomized 4-arm study of 21 healthy human subjects we found that the breathing of humid air, the wearing of cotton masks, and the delivery of (sodium, calcium, and magnesium chloride) salt droplets sized to deposit in the nose, trachea, and main bronchi similarly reduce the exhalation of respiratory droplets by approximately 50% (P < 0.05) within 10 minutes following hydration. Respiratory droplet generation returns to relatively high baseline levels within 60–90 minutes on return to dry air in all cases other than on exposure to divalent (calcium and magnesium) salts, where suppression continues for 4–5 hours. We also found via a preliminary ecological regression analysis of COVID-19 cases in the United States between January 2020 and March 2021 that exposure to elevated airborne salt on (Gulf and Pacific) US coastlines appears to suppress by approximately 25%–30% (P < 0.05) COVID-19 incidence and deaths per capita relative to inland counties — accounting for ten potential confounding environmental, physiological, and behavioral variables including humidity. We conclude that the hydration of the upper airways by exposure to humidity, the wearing of masks, or the breathing of airborne salts that deposit in the upper airways diminish respiratory droplet generation and may reduce the risks of COVID-19 incidence and symptoms.

COVID-19 severity determinants inferred through ecological and epidemiological modeling.

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.