COVID-19 mortality with regard to healthcare services availability, health risks, and socio-spatial factors at department level in France: A spatial cross-sectional analysis

Impact on Fatality Rates and Years of Life Lost During the COVID-19 Pandemic: The Experience of the Mexican Public Health Incident Management Command

BACKGROUND

The SARS-CoV-2 pandemic challenged health systems worldwide. In Mexico, the Public Health Incident Management Command (COISS) strategy was implemented to improve health care for patients with COVID-19 who required hospitalization.

AIM

To evaluate the impact of the COISS strategy on case fatality rates (CFR) and years of life lost (YLL) in hospitalized patients with COVID-19.

MATERIALS AND METHODS

The COISS strategy included eight actions implemented in states with high epidemic risk (COISS states). A secondary analysis of the public database from the Mexican Ministry of Health was performed considering patients with confirmed diagnoses of SARS-CoV-2 infection. The COISS strategy effectiveness was evaluated by its impact on in-hospital CFR and YLL at the beginning (T0) and end (T1) of the third wave, and at the end of the fourth wave (T2) and compared to states without intervention (non-COISS states).

RESULTS

At T0, COISS states showed a higher CFR for hospitalized patients than non-COISS states, which decreased after the strategy implementation. After correction for baseline conditions, lower relative CFR at T1 and T2, compared to T0, and a protective effect in different age groups, especially in those ≥65 years, were found in hospitalized patients in COISS states. The COISS strategy was associated with lower CFR in hospitalized patients with COVID-19 at both T1 and T2. At T0, YLLs were higher in COISS states, but there were no significant differences at T1 and T2.

CONCLUSIONS

COISS interventions effectively reduced CFR in hospitalized patients with COVID-19, providing protection to vulnerable patients and reducing the YLL gap.

Molecular Bases and Specificity behind the Activation of the Immune System OAS/RNAse L Pathway by Viral RNA

The first line of defense against invading pathogens usually relies on innate immune systems. In this context, the recognition of exogenous RNA structures is primordial to fight, notably, against RNA viruses. One of the most efficient immune response pathways is based on the sensing of RNA double helical motifs by the oligoadenylate synthase (OAS) proteins, which in turn triggers the activity of RNase L and, thus, cleaves cellular and viral RNA. In this contribution, by using long-range molecular dynamics simulations, complemented with enhanced sampling techniques, we elucidate the structural features leading to the activation of OAS by interaction with a model double-strand RNA oligomer mimicking a viral RNA. We characterize the allosteric regulation induced by the nucleic acid leading to the population of the active form of the protein. Furthermore, we also identify the free energy profile connected to the active vs. inactive conformational transitions in the presence and absence of RNA. Finally, the role of two RNA mutations, identified as able to downregulate OAS activation, in shaping the protein/nucleic acid interface and the conformational landscape of OAS is also analyzed.

Factors associated with maternal mortality in Kazakhstan: a pre- and during-pandemic comparison

Introduction

The maternal mortality indicator serves as a crucial reflection of a nation's overall healthcare, economic, and social standing. It is necessary to identify the variations in its impacts across diverse populations, especially those at higher risk, to effectively reduce maternal mortality and enhance maternal health. The global healthcare landscape has been significantly reshaped by the COVID-19 pandemic, pressing disparities and stalling progress toward achieving Sustainable Development Goals, particularly in maternal mortality reduction.

Methods

This study investigates the determinants of maternal mortality in Kazakhstan from 2019 to 2020 and maternal mortality trends in 17 regions from 2000 to 2020, employing data extracted from national statistical reports. Stepwise linear regression analysis is utilized to explore trends in maternal mortality ratios in relation to socioeconomic factors and healthcare service indicators.

Results

The national maternal mortality ratio in Kazakhstan nearly tripled from 13.7 in 2019 to 36.5 per 100,000 live births in 2020. A remarkable decrease was observed from 2000 until around 2015 with rates spiked by 2020. Significant factors associated with maternal mortality include antenatal care coverage and the number of primary healthcare units. Additionally, socioeconomic factors such as secondary education enrollment and cases of domestic violence against women emerged as predictors of MMR. Moreover, the impact of the pandemic was evident in the shift of coefficients for certain predictors, such as antenatal care coverage in our case. In 2020, predictors of MMR continued to include secondary education enrollment and reported cases of domestic violence.

Conclusion

Despite Kazakhstan's efforts and commitment toward achieving Sustainable Development Goals, particularly in maternal mortality reduction, the impact of the COVID-19 pandemic poses alarming challenges. Addressing these challenges and strengthening efforts to mitigate maternal mortality remains imperative for advancing maternal health outcomes in Kazakhstan.

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Background

Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India.

Methods

We used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths.

Results

The share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082).

Conclusions

A CBM with imputation can be used to predict excess deaths in an appropriate context.

Did COVID-19 ICU patient mortality risk increase as Colorado hospitals filled? A retrospective cohort study

OBJECTIVES

To assess whether increasing levels of hospital stress-measured by intensive care unit (ICU) bed occupancy (primary), ventilators in use and emergency department (ED) overflow-were associated with decreasing COVID-19 ICU patient survival in Colorado ICUs during the pre-Delta, Delta and Omicron variant eras.

DESIGN

A retrospective cohort study using discrete-time survival models, fit with generalised estimating equations.

SETTING

34 hospital systems in Colorado, USA, with the highest patient volume ICUs during the COVID-19 pandemic.

PARTICIPANTS

9196 non-paediatric SARS-CoV-2 patients in Colorado hospitals admitted once to an ICU between 1 August 2020 and 1 March 2022 and followed for 28 days.

OUTCOME MEASURES

Death or discharge to hospice.

RESULTS

For Delta-era COVID-19 ICU patients in Colorado, the odds of death were estimated to be 26% greater for patients exposed every day of their ICU admission to a facility experiencing its all-era 75th percentile ICU fullness or above, versus patients exposed for none of their days (OR: 1.26; 95% CI: 1.04 to 1.54; p=0.0102), adjusting for age, sex, length of ICU stay, vaccination status and hospital quality rating. For both Delta-era and Omicron-era patients, we also detected significantly increased mortality hazard associated with high ventilator utilisation rates and (in a subset of facilities) states of ED overflow. For pre-Delta-era patients, we estimated relatively null or even protective effects for the same fullness exposures, something which provides a meaningful contrast to previous studies that found increased hazards but were limited to pre-Delta study windows.

CONCLUSIONS

Overall, and especially during the Delta era (when most Colorado facilities were at their fullest), increasing exposure to a fuller hospital was associated with an increasing mortality hazard for COVID-19 ICU patients.

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis

BACKGROUND

The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information.

AIM

To identify key factors that may explain the variability in case lethality across countries.

METHODS

We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship.

RESULTS

The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR.

CONCLUSION

The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Excess Mortality Stratified by Age and Sex for Croatia and Croatian Counties during the 2020-2021 COVID-19 Pandemic

Excess mortality is often used to estimate the effect of a certain crisis on the population. It is defined as the number of deaths during a crisis exceeding the expected number based on historical trends. Here, we calculated excess mortality due to the COVID-19 pandemic for Croatia in the 2020-2021 period. The excess was calculated on the national and county level for different age and sex categories. In addition to the absolute number, the excess mortality was also expressed as a ratio of excess deaths to the predicted baseline and excess mortality rate. We showed that using both measures is necessary to avoid incorrect conclusions. The estimated excess mortality on the national level was 14,963, corresponding to an excess percentage of 14.3%. With respect to sex, there was a higher excess mortality rate for men compared to women. An exponential relationship was observed between age and the excess mortality rate.These trends wee representative of most counties as well, with large variations in the magnitude of the effect. However, there were also exceptions to the general rule. The reasons for these deviations were discussed in terms of between-county differences in demographic structure, population density and special events that took place during the pandemic.

Spatial patterns of COVID-19 and non-COVID-19 mortality across waves of infection in England, Wales, and Scotland

Recent studies have established the key individual-level risk factors of COVID-19 mortality such as age, gender, ethnicity, and socio-economic status. However, the spread of infectious diseases is a spatial and temporal process implying that COVID-19 mortality and its determinants may vary sub-nationally and over time. We investigate the spatial patterns of age-standardised death rates due to COVID-19 and their correlates across local authority districts in England, Wales, and Scotland across three waves of infection. Using a Spatial Durbin model, we explore within- and between-country variation and account for spatial dependency. Areas with a higher share of ethnic minorities and higher levels of deprivation had higher rates of COVID-19 mortality. However, the share of ethnic minorities and population density in an area were more important predictors of COVID-19 mortality in earlier waves of the pandemic than in later waves, whereas area-level deprivation has become a more important predictor over time. Second, during the first wave of the pandemic, population density had a significant spillover effect on COVID-19 mortality, indicating that the pandemic spread from big cities to neighbouring areas. Third, after accounting for differences in ethnic composition, deprivation, and population density, initial cross-country differences in COVID-19 mortality almost disappeared. COVID-19 mortality remained higher in Scotland than in England and Wales in the third wave when COVID-19 mortality was relatively low in all three countries. Interpreting these results in the context of higher overall (long-term) non-COVID-19 mortality in Scotland suggests that Scotland may have performed better than expected during the first two waves. Our study highlights that accounting for both spatial and temporal factors is essential for understanding social and demographic risk factors of mortality during pandemics.

COVID-19 mortality and its associated factors in Nepal: A cross-sectional study

Objectives

Reports from other countries have indicated that severe forms and fatal cases of COVID-19 in older adults and people with underlying comorbidities. The aim of this study was to assess the risk factors associated with COVID-19 mortality in Nepal.

Methods

A cross-sectional study was conducted from April 12 to July 23, 2021 to identify the underlying factors associated with COVID-19 deaths. Our sample included all cases diagnosed and registered as COVID-19-related deaths at 30 hospitals of Nepal.

Results

A total of 1459 COVID-19 hospital-based death records were collected from 30 hospitals. Mean age at death was 60.2 (±15.6) years. One-third of cases were admitted with fever, cough, and shortness of breath. The computerized tomography Severity Score showed that 7.3% of the individuals who underwent high-resolution computerized tomography chest had a severe form of lung involvement, and 3.6% had mild to moderate involvement. The most common comorbidities were hypertension (43.7%) followed by diabetes mellitus (25.8%). Among the deceased, 37.7% were diagnosed as cases of COVID-19 pneumonia. The most common recorded causes of death were respiratory failure followed by cardio-pulmonary arrest.

Conclusions

Individuals with comorbidities including hypertension and diabetes mellitus were at greater risk of developing complications and had a higher rate of mortality.

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

Impacts of the COVID-19 pandemic on life expectancy at birth in Asia

OBJECTIVE

To investigate the impact of the COVID-19 pandemic on life expectancy at birth (e0) for 51 Asian countries and territories from January 1, 2020 to December 31, 2021.

METHOD

Based on age-sex-specific mortality used for estimating the changes in e0 for years 2019, 2020, and 2021 from the 2022 revision of the World Population Prospects, we employed Arriaga's discrete method to decompose changes in e0 into both absolute and relative contributions of changes in age-specific death rate, and further obtained the age-sex-specific contribution to changes in e0 by country/territory and period (i.e., 2019-2020 and 2020-2021) for Asia.

FINDINGS

The COVID-19 pandemic reduced 1.66 years in e0 of the Asian population from 2019 to 2021, slightly lower than the world average of 1.74 years. South Asia had a high loss of 3.01 years, whereas Eastern Asia had almost no changes. Oman, Lebanon, India, Armenia, Azerbaijan, Indonesia, and the Philippines experienced a high loss of above 2.5 years in e0. Despite significant national and territorial variations, the decline of e0 in Asia was mostly from the age group of 60-79 years, followed by age groups of 80 + and 45-59 years; and age groups of children contributed little (i.e., 0-4 and 5-14 years old). Males suffered more losses than females in this pandemic. Asian nations saw less loss in e0 in the second year of the pandemic, i.e., 2020-2021, than in the first year, i.e., 2019-2020, but this recovery trend was not observed in Southern Asia and South-Eastern Asia. Countries from Central Asia and Western Asia, such as Kazakhstan, Armenia, Azerbaijan, Lebanon, and Oman, had extraordinarily more losses in e0 in the first year at ages around 70.

CONCLUSION

The COVID-19 pandemic had significantly affected e0 of Asian populations, and most contribution to the reduction of e0 came from the three older age groups, 60-79 years, 80 + years, and 45-59 years, with great variations across countries/territories. Our findings could have important implications for development of more resilient public health systems in Asian societies with better policy interventions for vulnerable demographic groups.

The Effects of Race/Ethnicity, Age, and Area Deprivation Index (ADI) on COVID-19 Disease Early Dynamics: Washington, D.C. Case Study

The COVID-19 pandemic and its associated mitigation strategies have significant psychosocial, behavioral, socioeconomic, and health impacts, particularly in vulnerable US populations. Different factors have been identified as influencers of the transmission rate; however, the effects of area deprivation index (as a measure of social determinants of health, SDoH) as a factor on COVID-19 disease early dynamics have not been established. We determined the effects of area deprivation index (ADI) and demographic factors on COVID-19 outcomes in Washington, D.C. This retrospective study used publicly available data on COVID-19 cases and mortality of Washington, D.C., during March 31st-July 4th, 2020. The main predictors included area deprivation index (ADI), age, and race/ethnicity. The ADI of each census block groups in D.C. (n=433) were obtained from Neighborhood Atlas map. Using a machine learning-based algorithm, the outcome variables were partitioned into time intervals: time duration (Pi, days), rate of change coefficient (Ei), and time segment load (Pi×Ei) for transmission rate and mortality. Correlation analysis and multiple linear regression models were used to determine associations between predictors and outcome variables. COVID-19 early transmission rate (E1) was highly correlated with ADI (SDoH; r= 0.88, p=0.0044) of the Washington, D.C. community. We also found positive association between ADI, age (0-17 years, r=0.91, p=0.0019), and race (African American/Black, r=0.86; p=0.0068) and COVID-19 outcomes. There was high variability in early transmission across the geographic regions (i.e., wards) of Washington, D.C., and this variability was driven by race/ethnic composition and ADI. Understanding the association of COVID-19 disease early transmission and mortality dynamics and key socio-demographic risk factors such as age, race, and ADI, as a measure of social determinants, will contribute to health equity/equality and distribution of economic resources/assistance and is essential for future predictive modeling of the COVID-19 pandemic to limit morbidity and mortality.

Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions

Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.

Geographical variations and district-level factors associated with COVID-19 mortality in Indonesia: a nationwide ecological study

BACKGROUND

Ensuring health equity, especially for vulnerable populations in less developed settings with poor health system is essential for the current and future global health threats. This study examined geographical variations of COVID-19 mortality and its association with population health characteristics, health care capacity in responding pandemic, and socio-economic characteristics across 514 districts in Indonesia.

METHODS

This nationwide ecological study included aggregated data of COVID-19 cases and deaths from all 514 districts in Indonesia, recorded in the National COVID-19 Task Force database, during the first two years of the epidemic, from 1 March 2020 to 27 February 2022. The dependent variable was district-level COVID-19 mortality rate per 100,000 populations. The independent variables include district-level COVID-19 incidence rate, population health, health care capacity, and socio-demographics data from government official sources. We used multivariable ordinal logistic regression to examine factors associated with higher mortality rate.

RESULTS

Of total 5,539,333 reported COVID-19 cases, 148,034 (2.7%) died, and 5,391,299 (97.4%) were recovered. The district-level mortality rate ranged from 0 to 284 deaths per 100,000 populations. The top five districts with the highest mortality rate were Balikpapan (284 deaths per 100,000 populations), Semarang (263), Madiun (254), Magelang (250), and Yogyakarta (247). A higher COVID-19 incidence (coefficient 1.64, 95% CI 1.22 to 1.75), a higher proportion of ≥ 60 years old population (coefficient 0.26, 95% CI 0.06 to 0.46), a higher prevalence of diabetes mellitus (coefficient 0.60, 95% CI 0.37 to 0.84), a lower prevalence of obesity (coefficient -0.32, 95% CI -0.56 to -0.08), a lower number of nurses per population (coefficient -0.27, 95% CI -0.50 to -0.04), a higher number of midwives per population (coefficient 0.32, 95% CI 0.13 to 0.50), and a higher expenditure (coefficient 0.34, 95% CI 0.10 to 0.57) was associated with a higher COVID-19 mortality rate.

CONCLUSION

COVID-19 mortality rate in Indonesia was highly heterogeneous and associated with higher COVID-19 incidence, different prevalence of pre-existing comorbidity, healthcare capacity in responding the pandemic, and socio-economic characteristics. This study revealed the need of controlling both COVID-19 and those known comorbidities, health capacity strengthening, and better resource allocation to ensure optimal health outcomes for vulnerable population.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Modeling the Enzymatic Mechanism of the SARS-CoV-2 RNA-Dependent RNA Polymerase by DFT/MM-MD: An Unusual Active Site Leading to High Replication Rates

Viral infection relies on the hijacking of cellular machineries to enforce the reproduction of the infecting virus and its subsequent diffusion. In this context, the replication of the viral genome is a key step performed by specific enzymes, i.e., polymerases. The replication of SARS-CoV-2, the causative agent of the COVID-19 pandemics, is based on the duplication of its RNA genome, an action performed by the viral RNA-dependent RNA polymerase. In this contribution, by using highly demanding DFT/MM-MD computations coupled to 2D-umbrella sampling techniques, we have determined the chemical mechanisms leading to the inclusion of a nucleotide in the nascent viral RNA strand. These results highlight the high efficiency of the polymerase, which lowers the activation free energy to less than 10 kcal/mol. Furthermore, the SARS-CoV-2 polymerase active site is slightly different from those usually found in other similar enzymes, and in particular, it lacks the possibility to enforce a proton shuttle via a nearby histidine. Our simulations show that this absence is partially compensated by lysine whose proton assists the reaction, opening up an alternative, but highly efficient, reactive channel. Our results present the first mechanistic resolution of SARS-CoV-2 genome replication at the DFT/MM-MD level and shed light on its unusual enzymatic reactivity paving the way for the future rational design of antivirals targeting emerging RNA viruses.

Hijacking of Cellular Functions by Severe Acute Respiratory Syndrome Coronavirus-2. Permeabilization and Polarization of the Host Lipid Membrane by Viroporins

Like all viral infections, SARS-CoV-2 acts at multiple levels, hijacking fundamental cellular functions and assuring its replication and immune system evasion. In particular, the viral 3' Open Reading Frame (ORF3a) codes for a hydrophobic protein, which embeds in the cellular membrane, where it acts as an ion viroporin and is related to strong inflammatory response. Here we report equilibrium and enhanced sampling molecular dynamic simulation of the SARS-CoV-2 ORF3a in a model lipid bilayer, showing how the protein permeabilizes the lipid membrane, via the formation of a water channel, which in turn assures ion transport. We report the free energy profile for both K+ and Cl- transfer from the cytosol to the extracellular domain. The important role of ORF3a in the viral cycle and its high conservation among coronaviruses may also make it a target of choice for future antiviral development, further justifying the elucidation of its mechanism at the atomistic level.

Health and Healthcare Variables Associated with Italy's Excess Mortality during the First Wave of the COVID-19 pandemic: An Ecological Study

Background

Healthcare factors have strongly influenced the propagation of COVID-19. This study aims to examine whether excess mortality during the first phase of the COVID-19 outbreak in Italy was associated with health, healthcare, demographic, and socioeconomic, provincial-level indicators.

Methods

This ecological study concerns the raw number of deaths reported from February 1 to April 30, 2020 and the mean number of deaths occurred during the same months from 2015 to 2019, per province. Information on socioeconomic factors and healthcare settings was extracted from updated databases on the Italian National Institute of Statistics (ISTAT) website. A multivariate model and four multilevel models were constructed to test the association between excess mortality and the analysed indicators across 107 Italian provinces.

Results

The hospitalization rate in long-term care wards and the cardiovascular disease mortality rate correlate positively with excess mortality (p <0.05), while higher densities of licensed physicians and of general practitioners are associated with lower excess mortality (p <0.05). After controlling for the COVID-19 cumulative incidence in each province, only the density of licensed physicians remains negatively associated with excess mortality (p <0.01).

Conclusion

Some health and healthcare variables (in particular, the density of physicians) are strongly associated with excess mortality during the first wave of the COVID-19 pandemic in Italy and should be targeted to increase the resilience of health systems.

Analysis of Deaths and Favorable Developments of Patients with SARS-CoV-2 Hospitalized in the Largest Hospital for Infectious Diseases and Pneumo-Phthisiology in the West of the Country

Purpose

Romania is one of the European countries that has been hit the hardest by the severe acute respiratory syndrome caused by the new coronavirus SARS-CoV-2, with over 1.91 million reported cases and over 59,257 deaths. The aim of this study was to identify the main predictors of death in hospitalized patients.

Patients and Methods

In the period from 1 March 2020 to 30 June 2021, an observational, retrospective, randomized, case-control study was conducted, which included a sample of 139 patients who died in hospital and another sample of 275 patients who had been discharged in an improved or healed condition. Confirmation of COVID-19 cases was performed by RT-PCR from nasopharyngeal and oropharyngeal exudates. Statistical data were analyzed by logistic regression, Cox regression and a comparison of survival curves by the log-rank (Mantel-Cox) test.

Results

The most powerful logistic regression model identified the following independent predictors of death: history of coagulopathy HR = 30.73 [1.94–487.09], p = 0.015; high percentage of neutrophils HR = 1.09 [1.01–1.19], p = 0.027; and decreased blood-oxygenation HR = 53881.97 [1762.24–1647489.44], p < 0.001. Cox regression identified the following factors that influenced the evolution of cases: history of coagulopathy HR = 2.44 [1.38–4.35], p = 0.000; O₂ saturation HR = 0.98 [0.96–0.99], p = 0.043; serum creatinine HR = 1.23 [1.08–1.39], p = 0.001; dyspnea on admission HR = 2.99 [1.42–6.30], p = 0.004; hospitalization directly in the ICU HR = 3.803 [1.97–7.33], p < 0.001; heart damage HR = 16.76 [1.49–188.56], p = 0.022; and decreased blood-oxygenation HR = 35.12 [5.92–208.19], p < 0.001.

Conclusion

Knowledge of the predictors of death in hospitalized patients allows for the future optimization of triage and therapeutic case management for COVID-19.