Environmental factors are associated to hospital outcomes in COVID-19 patients during lockdown and post-lockdown in 2020: A nationwide study

Association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, China

To date, a limited number of studies have assessed the impact of individual and combined PM2.5 components on mumps incidence. Between 2013 and 2019, we collected data on 6270 mumps cases in Lanzhou, along with corresponding PM2.5 and its components, to analyze their temporal and spatial distributions. A generalized additive mixed model was constructed to examine the association between PM2.5 components and mumps incidence. Additionally, Bayesian kernel machine regression was used to evaluate the combined and interactive effects of co-exposure to PM2.5 components on mumps incidence and to identify key contributing components. A significant linear correlation was found between PM2.5 and mumps incidence at lag 1 month, with a relative risk (RR) of 1.85 (95 % CI: 1.14, 3.02) for each unit increase in PM2.5 (log-transformed PM2.5 concentration). Organic matter (OM) at lag 0 and 1 month, as well as black carbon (BC) at lag 1 month, were significantly positively correlated with mumps incidence. Furthermore, the joint exposure-effect curve for PM2.5 components and mumps incidence displayed an approximate V-shape. The effects of PM2.5 and its components on mumps incidence were more pronounced during the warm season. These findings suggest a significant association between short-term exposure to PM2.5 and its components and mumps incidence in Lanzhou, with potential variations in effect depending on the specific components of PM2.5.

A national population-based study of mortality and risk factors in COVID-19-hospitalized patients in Spain (2020-2021)

Objectives

The study aimed to analyze in-hospital mortality (IHM) among all COVID-19 patients hospitalized in Spain between March 1, 2020, and December 31, 2021, and to compare two distinct periods: the prevaccination period (March 1, 2020, to January 31, 2021) and the vaccination period (February 1, 2021, to December 31, 2021). The objective was to assess the impact of vaccination on IHM and identify associated risk factors, using data from Spain's national hospitalization registry.

Methods

This retrospective analysis used data from the Spanish National Surveillance System for Hospital Data. The primary outcome was in-hospital mortality (IHM). Multivariate logistic regression identified risk factors across the overall study period, as well as during the prevaccination and vaccination periods. Risk factors included age (in 20-year intervals), sex, comorbidities (e.g., hypertension, diabetes, chronic kidney failure, obesity, neurodegenerative disorders, and others), and admission to the intensive care unit.

Results

A total of 524,314 COVID-19 hospitalizations were recorded in Spain, with 329,690 during the prevaccination period and 194,624 during the vaccination period. Hospitalization rates dropped from 697/100,000 people to 411/100,000, and in-hospital mortality (IHM) decreased from 16.2 to 11.5% (adjusted odds ratio [AOR]: 0.71, 95% CI: 0.70-0.73, p < 0.001). IHM rose with age, from 0.8% in patients aged 18-39 to 31.7% in those ≥80 years (p < 0.001), but significant decreases were observed across all age groups after vaccination, especially in those ≥80 years (AOR: 0.76, 95% CI: 0.75-0.79, p < 0.001). Risk factors for IHM remained consistent, with leukemia, neoplasm, and lymphoma posing the highest risks, while female sex (AOR: 0.75, 95% CI: 0.74-0.77, p < 0.001) and dyslipidemia (AOR: 0.85, 95% CI: 0.32-0.86, p < 0.001) were protective factors.

Conclusion

During the vaccination period, the risk of in-hospital mortality (IHM) was 29% lower than in the prevaccination period, after adjusting for sex, age, and comorbidities. This reduced risk was observed across sexes, age groups, and comorbidities. The risk factors for IHM remained consistent between the two periods, with age as the main risk factor, while female sex and dyslipidemia were identified as protective factors.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.