Under-reporting of deaths limits our understanding of true burden of covid-19

Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been performed regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844-264,075) and 24,546 deaths (CI 95% 2548-46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. In Spain, patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination. Our use of machine learning models provided a detailed estimation of the averted burden of the pandemic, demonstrating the effectiveness of vaccination at a population-wide level.

Regional inequalities in excess mortality and its community determinants during the early COVID-19 pandemic in South Korea

BACKGROUND

Excess mortality during the COVID-19 pandemic provides a comprehensive measure of disease burden, and its local variation highlights regional health inequalities. We investigated local excess mortality in 2020 and its determinants at the community level.

METHODS

We collected data from 250 districts in South Korea, including monthly all-cause mortality for 2015-2020 and community characteristics from 2019. Excess mortality rate was defined as the difference between observed and expected mortality rates. A Seasonal Autoregressive Integrated Moving Average model was applied to predict the expected rates for each district. Penalized regression methods were used to derive relevant community predictors of excess mortality based on the elastic net.

RESULTS

In 2020, South Korea exhibited significant variation in excess mortality rates across 250 districts, ranging from no excess deaths in 46 districts to more than 100 excess deaths per 100 000 residents in 30 districts. Economic status or the number of medical centres in the community did not correlate with excess mortality rates. The risk was higher in ageing, remote communities with limited cultural and sports infrastructure, a higher density of welfare facilities, and a higher prevalence of hypertension. Physical distancing policies and active social engagement in voluntary activities protected from excess mortality.

CONCLUSION

Substantial regional disparities in excess mortality existed within South Korea during the early stages of COVID-19 pandemic. Weaker segments of the community were more vulnerable. Local governments should refine their preparedness for future novel infectious disease outbreaks, considering community circumstances.

COVID-19 Death Determination Methods, Minnesota, USA, 2020-20221

Accurate and timely mortality surveillance is crucial for elucidating risk factors, particularly for emerging diseases. We compared use of COVID-19 keywords on death certificates alone to identify COVID-19 deaths in Minnesota, USA, during 2020-2022, with use of a standardized mortality definition incorporating additional clinical data. For analyses, we used likelihood ratio χ2 and median 1-way tests. Death certificates alone identified 96% of COVID-19 deaths confirmed by the standardized definition and an additional 3% of deaths that had been classified as non-COVID-19 deaths by the standardized definition. Agreement between methods was >90% for most groups except children, although agreement among adults varied by demographics and location at death. Overall median time from death to filing of death certificate was 3 days; decedent characteristics and whether autopsy was performed varied. Death certificates are an efficient and timely source of COVID-19 mortality data when paired with SARS-CoV-2 testing data.

Causes of preventable death among children of female sex worker mothers in low- and middle-income countries: A community knowledge approach investigation

Background

Female sex workers (FSW) in low- and middle-income countries (LMIC) are disproportionately vulnerable to poor health, social, and economic outcomes. The children of female sex workers (CFSW) experience health risks based on these challenging circumstances and the unique conditions to which they are exposed. Although country child mortality data exist, little is known about the causes of death among CFSW specifically, thereby severely limiting an effective public health response to the needs of this high-risk group of children.

Methods

The Community Knowledge Approach (CKA) was employed between January and October 2019 to survey a criterion sample of 1280 FSW participants across 24 cities in eight LMIC countries. Participants meeting pre-determined criteria provided detailed reports of deaths among the CFSW within their community of peers. Newborn deaths were gleaned from FSW maternal death reports where the infants also died following birth.

Results

Of the 668 child deaths reported, 589 were included in the analysis. Nutritional deficiencies comprised the leading cause of mortality accounting for 20.7% of deaths, followed closely by accidents (20.0%), particularly house fires, overdoses (19.4%), communicable diseases (18.5%), and homicides (9.8%). Other reported causes of death included neonatal conditions, respiratory illnesses, and suicides.

Conclusions

The causes of CFSW death in these eight countries are preventable with improved protections. Governments, intergovernmental organisations like the United Nations, nongovernmental stakeholder organisations (e.g. sex worker organisations), and funders can implement targeted policies and programmes to protect CFSW and assist vulnerable FSW who are pregnant and raising children. Further research is needed to identify effective child welfare safeguards for CFSW.

Induction of labour care in the UK: A cross-sectional survey of maternity units

OBJECTIVES

To explore local induction of labour pathways in the UK National Health Service to provide insight into current practice.

DESIGN

National survey.

SETTING

Hospital maternity services in all four nations of the UK.

SAMPLE

Convenience sample of 71 UK maternity units.

METHODS

An online cross-sectional survey was disseminated and completed via a national network of obstetrics and gynaecology specialist trainees (October 2021-March 2022). Results were analysed descriptively, with associations explored using Fisher's Exact and ANOVA.

MAIN OUTCOME MEASURES

Induction rates, criteria, processes, delays, incidents, safety concerns.

RESULTS

54/71 units responded (76%, 35% of UK units). Induction rate range 19.2%-53.4%, median 36.3%. 72% (39/54) had agreed induction criteria: these varied widely and were not all in national guidance. Multidisciplinary booking decision-making was not reported by 38/54 (70%). Delays reported 'often/always' in hospital admission for induction (19%, 10/54) and Delivery Suite transfer once induction in progress (63%, 34/54). Staffing was frequently reported cause of delay (76%, 41/54 'often/always'). Delays triggered incident reports in 36/54 (67%) and resulted in harm in 3/54 (6%). Induction was an area of concern (44%, 24/54); 61% (33/54) reported induction-focused quality improvement work.

CONCLUSIONS

There is substantial variation in induction rates, processes and policies across UK maternity services. Delays appear to be common and are a cause of safety concerns. With induction rates likely to increase, improved guidance and pathways are critically needed to improve safety and experience of care.

Learning From COVID-19: What Would It Take to Be Better Prepared in the Eastern Mediterranean Region?

The COVID-19 transmission in the Eastern Mediterranean Region (EMR) was influenced by various factors such as conflict, demographics, travel and social restrictions, migrant workers, weak health systems, and mass gatherings. The countries that responded well to COVID-19 had high-level political commitment, multisectoral coordination, and existing infrastructures that could quickly mobilize. However, some EMR countries faced challenges due to political instability and fragile health systems, which hindered their response strategies. The pandemic highlighted the region's weak health systems and preparedness, fragmented surveillance systems, and lack of trust in information sharing. COVID-19 exposed the disruption of access and delivery of essential health services as a major health system fragility. In 2020, the World Health Organization (WHO) conducted a global pulse survey, which demonstrated that the EMR experienced the highest disruption in health services compared to other WHO regions. However, thanks to prioritization by the WHO and its member states, significant improvement was observed in 2021 during the second round of the WHO's National Pulse Survey. The pandemic underscored the importance of political leadership, community engagement, and trust and emphasized that investing in health security benefits everyone. Increasing vaccine coverage, building regional capacities, strengthening health systems, and working toward universal health coverage and health security are all priorities in the EMR. Emergency public health plays a key role in preparing for and responding to pandemics and biological threats. Integrating public health into primary care and investing in public health workforce capacity building is essential to reshaping public health and health emergency preparedness.

Causes and costs of global COVID-19 vaccine inequity

Despite the rapid development of safe and effective COVID-19 vaccines and the widely recognized health and economic benefits of vaccination, there exist stark differences in vaccination rates across country income groups. While more than 70% of the population is fully vaccinated in high-income countries, vaccination rates in low-income countries are only around 30%. The paper reviews the factors behind global COVID-19 vaccine inequity and the health, social, and economic costs triggered by this inequity. The main contributors to vaccine inequity include vaccine nationalism, intellectual property rights, constraints in manufacturing capacity, poor resilience of healthcare systems, and vaccine hesitancy. Vaccine inequity has high costs, including preventable deaths and cases of illnesses in low-income countries, slow economic recovery, and large learning losses among children. Increasing vaccination rates in low-income countries is in the self-interest of higher-income countries as it may prevent the emergence of new variants and continuous disruptions to global supply chains.

Lessons learnt from COVID-19 to reduce mortality and morbidity in the Global South: addressing global vaccine equity for future pandemics

COVID-19, which killed more than 6 million people, will not be the last pandemic. Vaccines are key to preventing and ending pandemics. Therefore, it is critical to move now, before the next pandemic, towards global vaccine equity with shared goals, intermediate steps and long-term advocacy goals. Scientific integrity, ethical development, transparency, accountability and communication are critical. Countries can draw on lessons learnt from their response to the HIV pandemics, which has been at the vanguard of ensuring equitable access to rights-based services, to create shared goals and engage communities to increase access to and delivery of safe, quality vaccines. Access can be increased by: fostering the spread of mRNA intellectual property (IP) rights, with mRNA vaccine manufacturing on more continents; creating price transparency for vaccines; creating easily understandable, accessible and transparent data on vaccines; creating demand for a new international legal framework that allows IP rights to be waived quickly once a global pandemic is identified; and drawing on scientific expertise from around the world. Delivery can be improved by: creating strong public health systems that can deliver vaccines through the lifespan; creating or strengthening national regulatory agencies and independent national scientific advisory committees for vaccines; disseminating information from reliable, transparent national and subnational surveillance systems; improving global understanding that as more scientific data become available, this may result in changes to public health guidance; prioritising access to vaccines based on scientific criteria during an epidemic; and developing strategies to vaccinate those at highest risk with available vaccines.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

An overview of COVID-19 global epidemiology and discussion of potential drivers of variable global pandemic impacts

With a WHO-estimated excess mortality burden of 14.9 million over the course of 2020 and 2021, the COVID-19 pandemic has had a major human impact so far. It has also affected a range of disciplines, systems and practices from mathematical modelling to behavioural sciences, pharmaceutical development to health system management. This article explores these developments and, to set the scene, this paper summarizes the global epidemiology of COVID-19 from January 2020 to June 2021 and considers some potential drivers of variation.

Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia

Estimating the impact of vaccination and non-pharmaceutical interventions on COVID-19 incidence is complicated by several factors, including successive emergence of SARS-CoV-2 variants of concern and changing population immunity from vaccination and infection. We develop an age-structured multi-strain COVID-19 transmission model and inference framework to estimate vaccination and non-pharmaceutical intervention impact accounting for these factors. We apply this framework to COVID-19 waves in French Polynesia and estimate that the vaccination programme averted 34.8% (95% credible interval: 34.5-35.2%) of 223,000 symptomatic cases, 49.6% (48.7-50.5%) of 5830 hospitalisations and 64.2% (63.1-65.3%) of 1540 hospital deaths that would have occurred in a scenario without vaccination up to May 2022. We estimate the booster campaign contributed 4.5%, 1.9%, and 0.4% to overall reductions in cases, hospitalisations, and deaths. Our results suggest that removing lockdowns during the first two waves would have had non-linear effects on incidence by altering accumulation of population immunity. Our estimates of vaccination and booster impact differ from those for other countries due to differences in age structure, previous exposure levels and timing of variant introduction relative to vaccination, emphasising the importance of detailed analysis that accounts for these factors.

A global perspective on the intrinsic dimensionality of COVID-19 data

We develop a novel global perspective of the complexity of the relationships between three COVID-19 datasets, the standardised per-capita growth rate of COVID-19 cases and deaths, and the Oxford Coronavirus Government Response Tracker COVID-19 Stringency Index (CSI) which is a measure describing a country's stringency of lockdown policies. We use a state-of-the-art heterogeneous intrinsic dimension estimator implemented as a Bayesian mixture model, called Hidalgo. Our findings suggest that these highly popular COVID-19 statistics may project onto two low-dimensional manifolds without significant information loss, suggesting that COVID-19 data dynamics are generated from a latent mechanism characterised by a few important variables. The low dimensionality imply a strong dependency among the standardised growth rates of cases and deaths per capita and the CSI for countries over 2020-2021. Importantly, we identify spatial autocorrelation in the intrinsic dimension distribution worldwide. The results show how high-income countries are more prone to lie on low-dimensional manifolds, likely arising from aging populations, comorbidities, and increased per capita mortality burden from COVID-19. Finally, the temporal stratification of the dataset allows the examination of the intrinsic dimension at a more granular level throughout the pandemic.

Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city

Globally, excess deaths during 2020-21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India-an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% (95% confidence interval: 27-33%). Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0-13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting.

Alternative epidemic indicators for COVID-19 in three settings with incomplete death registration systems

Not all COVID-19 deaths are officially reported, and particularly in low-income and humanitarian settings, the magnitude of reporting gaps remains sparsely characterized. Alternative data sources, including burial site worker reports, satellite imagery of cemeteries, and social media-conducted surveys of infection may offer solutions. By merging these data with independently conducted, representative serological studies within a mathematical modeling framework, we aim to better understand the range of underreporting using examples from three major cities: Addis Ababa (Ethiopia), Aden (Yemen), and Khartoum (Sudan) during 2020. We estimate that 69 to 100%, 0.8 to 8.0%, and 3.0 to 6.0% of COVID-19 deaths were reported in each setting, respectively. In future epidemics, and in settings where vital registration systems are limited, using multiple alternative data sources could provide critically needed, improved estimates of epidemic impact. However, ultimately, these systems are needed to ensure that, in contrast to COVID-19, the impact of future pandemics or other drivers of mortality is reported and understood worldwide.

On the forecastability of food insecurity

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BACKGROUND

The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.

METHODS

This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.

RESULTS

For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.

CONCLUSIONS

We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

Vascular access in critically ill patients with COVID-19: A scoping review protocol

OBJECTIVE

The objective of this scoping review is to examine the extent of vascular access research undertaken in the critically ill COVID-19 population.

INTRODUCTION

Two fundamental supportive invasive interventions in the critical care environment are mechanical ventilation and intravenous therapy. Ventilation research has dominated the literature since the pandemic began, with little research on vascular access devices, despite these interventions existing almost codependently. The systematic proning of this cohort of patients increases the risk of dressing infiltration and infection. Vascular access devices, and the coagulopathic manifestations of COVID-19, place these patients at a heightened risk of complications. Vascular access device insertion, care, and maintenance in the critically ill COVID-19 population must be understood to investigate whether this population is at an increased risk of vascular access device complications and vessel health compromise.

INCLUSION CRITERIA

All study designs will be eligible for inclusion in the review. The intensive care unit will be the main focus of this scoping review. Results will be limited to adults with disease progression severe enough to require admission to critical care.

METHODS

A search of Embase, MEDLINE (Ovid), Web of Science, and PubMed will be conducted. Clinical trial data will also be sought. As recommended by JBI, a 3-step search process will be followed. Data will be extracted using a data extraction instrument based on a template from JBI. The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and results will be presented in a PRISMA flow diagram. Publication dates will be filtered from 2019 to the present; only English-language results will be included.

REVIEW REGISTRATION NUMBER

Not applicable.

[Methodological proposal for evaluation of death records from COVID-19]

The limitation of studies on the coverage and completeness of death records is one of the main problems regarding vital statistics in Brazil. In 2020, the number of information systems on death records in Brazil increased due to the COVID-19 pandemic, generating more uncertainties about the quality of death records. This study proposed an evaluation of the quality of death records due to COVID-19. Three methodological stages were considered: estimation of deaths under-registration; redistribution of deaths from nonspecific causes (Garbage Codes), and redistribution of deaths from ill-defined causes to COVID-19 data. The proposal was applied in the State of Paraíba, Brazil, and its municipalities in 2020, by using the official records of the Brazilian Mortality Information System of the Brazilian Ministry of Health. In total, 1,281 deaths were retrieved, besides the 3,426 deaths officially recorded for Paraíba State, an increase of 37.4% in deaths from COVID-19. The proposal was effective, easy to apply, and can be used by managers of governmental spheres and people interested in it as a tool to assess the quality of death records for any geographic space, thus, contributing to a better understanding of the real effect of the pandemic.

Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland

The direct and indirect impact of the COVID-19 pandemic on population-level mortality is of concern to public health but challenging to quantify. Using data for 2011-2019, we applied Bayesian models to predict the expected number of deaths in Switzerland and compared them with laboratory-confirmed COVID-19 deaths from February 2020 to April 2022 (study period). We estimated that COVID-19-related mortality was underestimated by a factor of 0.72 (95% credible interval [CrI]: 0.46-0.78). After accounting for COVID-19 deaths, the observed mortality was -4% (95% CrI: -8 to 0) lower than expected. The deficit in mortality was concentrated in age groups 40-59 (-12%, 95%CrI: -19 to -5) and 60-69 (-8%, 95%CrI: -15 to -2). Although COVID-19 control measures may have negative effects, after subtracting COVID-19 deaths, there were fewer deaths in Switzerland during the pandemic than expected, suggesting that any negative effects of control measures were offset by the positive effects. These results have important implications for the ongoing debate about the appropriateness of COVID-19 control measures.

Assessment of COVID-19 as the Underlying Cause of Death Among Children and Young People Aged 0 to 19 Years in the US

Importance

COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death.

Objective

To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US.

Design, Setting, and Participants

This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non-COVID-19 causes in 2019, 2020, and 2021.

Main Outcomes and Measures

Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years).

Results

There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CYP aged 0 to 19 years in the US, ranking eighth among all causes of deaths, fifth in disease-related causes of deaths (excluding unintentional injuries, assault, and suicide), and first in deaths caused by infectious or respiratory diseases when compared with 2019. COVID-19 deaths constituted 2% of all causes of death in this age group.

Conclusions and Relevance

The findings of this study suggest that COVID-19 was a leading cause of death in CYP. It caused substantially more deaths in CYP annually than any vaccine-preventable disease historically in the recent period before vaccines became available. Various factors, including underreporting and not accounting for COVID-19's role as a contributing cause of death from other diseases, mean that these estimates may understate the true mortality burden of COVID-19. The findings of this study underscore the public health relevance of COVID-19 to CYP. In the likely future context of sustained SARS-CoV-2 circulation, appropriate pharmaceutical and nonpharmaceutical interventions (eg, vaccines, ventilation, air cleaning) will continue to play an important role in limiting transmission of the virus and mitigating severe disease in CYP.

Analysis of neutralizing antibodies to COVID-19 inactivated or subunit recombinant vaccines in hospitalized patients with liver dysfunction

Background

The neutralizing antibodies (NAbs) response after COVID-19 vaccination after liver dysfunction is unclear. In this study, we evaluated the NAbs response after COVID-19 vaccination in hospitalized patients suffering from liver dysfunction.

Methods

In this cross-sectional study with longitudinal follow-up, we enrolled eligible patients with liver dysfunction and healthy volunteers with full-course COVID-19 vaccination. Blood samples were collected for the NAbs testing at the time of admission and after treatment. Multiple regression analysis to assess independent risk factors affecting NAbs response.

Results

A total of 137 patients and 134 healthy controls (HC) were enrolled. Both seropositivity (65.7% vs 80.6%, p<0.01) and titer (3.95 vs 4.94 log₂ AU/ml, p<0.001) of NAbs in patients were significantly lower than that in HC. The decrease of antibody titer in patients was significantly faster than that in HC. After adjusting for potential confounding factors, males (odds ratio [OR]: 0.17; 95% confidence interval [CI]: 0.06, 0.46; p<0.001) and severe liver damage (OR: 0.30; 95% CI: 0.12, 0.71; p<0.01) were significantly associated with reduction of the probability of NAbs seropositivity in the multiple regression analysis. Males (β =-1.18; 95% CI: -1.73,-0.64) and chronic liver diseases (β =-1.45; 95% CI: -2.13, -0.76) were significantly associated with lower NAbs titers. In 26 patients with liver failure, both antibody seropositivity (53.8% vs 84.6%, p<0.05) and titer (3.55 vs 4.32 log₂ AU/ml, p<0.001) did not decrease but increased after artificial liver plasmapheresis.

Conclusions

NAbs response to COVID-19 inactivated or subunit recombinant vaccines was waning in patients with liver dysfunction. Moreover, patients with male sex, severe liver injury and chronic liver diseases have an increased risk of poor antibody responses.

Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A systematic review and meta-analysis

Background and aim

With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.

Methods

We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.

Results

Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06-0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38-1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07-1.30, p < 0.00001). There was "very low" certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.

Interpretation

The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

COVID-19 vaccine intentions in Aotearoa New Zealand: Behaviour, risk perceptions, and collective versus individual motivations

The global SARS-CoV-2 (COVID-19) pandemic presents a pressing health challenge for all countries, including Aotearoa New Zealand (NZ). As of early 2022, NZ public health measures have reduced impacts of the pandemic, but ongoing efforts to limit illness and fatalities will be significantly aided by widescale uptake of available vaccines including COVID-19 booster doses. Decades of research have established a broad range of demographic, social, cognitive, and behavioural factors which influence peoples' uptake of vaccinations, including a large amount of research in the last two years focused on COVID-19 vaccination in particular. In this study, we surveyed people in New Zealand (N = 660) in May and June of 2021, at which point the vaccine had been made available to high-risk groups. We explored individual versus collective motivations, finding that people who were hesitant about COVID-19 vaccination scored lower on independent self-construals (how people define themselves) but higher on community identity, weaker but still positive perceived social norms, lower general risk of COVID-19 to New Zealanders and higher vaccine risk for both themselves and others, and lower response-efficacy both for personal and collective benefits. Overall, the findings suggest some benefit of collective over individual appeals, but that generally messaging to encourage vaccination should focus on conveying social norms, risk from COVID-19 broadly, and vaccine safety and efficacy.

Comparative efficacy and safety of pharmacological interventions for severe COVID-19 patients: An updated network meta-analysis of 48 randomized controlled trials

BACKGROUND

To date, there has been little agreement on what drug is the "best" drug for treating severe COVID-19 patients. This study aimed to assess the efficacy and safety of different medications available at present for severe COVID-19.

METHODS

We searched databases for randomized controlled trials (RCTs) published up to February 28, 2022, with no language restrictions, of medications recommended for patients (aged 16 years or older) with severe COVID-19 infection. We extracted data on trials and patient characteristics, and the following primary outcomes: all-cause mortality (ACM), and treatment-emergent adverse events (TEAEs).

RESULTS

We identified 4021 abstracts and of these included 48 RCTs comprising 9147 participants through database searches and other sources. For decrease in ACM, we found that ivermectin/doxycycline, C-IVIG (i.e., a hyperimmune anti-COVID-19 intravenous immunoglobulin), methylprednisolone, interferon-beta/standard-of-care (SOC), interferon-beta-1b, convalescent plasma, remdesivir, lopinavir/ritonavir, immunoglobulin gamma, high dosage sarilumab (HS), auxora, and imatinib were effective when compared with placebo or SOC group. We found that colchicine and interferon-beta/SOC were only associated with the TEAEs of severe COVID-19 patients.

CONCLUSION

This study suggested that ivermectin/doxycycline, C-IVIG, methylprednisolone, interferon-beta/SOC, interferon-beta-1b, convalescent plasma (CP), remdesivir, lopinavir/ritonavir, immunoglobulin gamma, HS, auxora, and imatinib were efficacious for treating severe COVID-19 patients. We found that most medications were safe in treating severe COVID-19. More large-scale RCTs are still needed to confirm the results of this study.

The notable global heterogeneity in the distribution of COVID-19 cases and the association with pre-existing parasitic diseases

BACKGROUND

The coronavirus Disease 2019 (COVID-19) is a respiratory disease that has caused extensive ravages worldwide since being declared a pandemic by the World Health Organization (WHO). Unlike initially predicted by WHO, the incidence and severity of COVID-19 appeared milder in many Low-to-Middle-Income Countries (LMIC). To explain this noticeable disparity between countries, many hypotheses, including socio-demographic and geographic factors, have been put forward. This study aimed to estimate the possible association of parasitic diseases with COVID-19 as either protective agents or potential risk factors.

METHODS/PRINCIPAL FINDINGS

A country-level ecological study using publicly available data of countries was conducted. We conceptualized the true number of COVID-19 infections based on a function of test positivity rate (TPR) and employed linear regression analysis to assess the association between the outcome and parasitic diseases. We considered demographic, socioeconomic, and geographic confounders previously suggested. A notable heterogeneity was observed across WHO regions. The countries in Africa (AFRO) showed the lowest rates of COVID-19 incidence, and the countries in the Americas (AMRO) presented the highest. The multivariable model results were computed using 165 countries, excluding missing values. In the models analyzed, lower COVID-19 incidence rates were consistently observed in malaria-endemic countries, even accounting for potential confounding variables, Gross Domestic Product (GDP) per capita, the population aged 65 and above, and differences in the duration of COVID-19. However, the other parasitic diseases were not significantly associated with the spread of the pandemic.

CONCLUSIONS/SIGNIFICANCE

This study suggests that malaria prevalence is an essential factor that explains variability in the observed incidence of COVID-19 cases at the national level. Potential associations of COVID-19 with schistosomiasis and soil-transmitted helminthiases (STHs) are worthy of further investigation but appeared unlikely, based on this analysis, to be critical factors of the variability in COVID-19 epidemic trends. The quality of publicly accessible data and its ecological design constrained our research, with fundamental disparities in monitoring and testing capabilities between countries. Research at the subnational or individual level should be conducted to explore hypotheses further.

Detecting and mitigating simultaneous waves of COVID-19 infections

The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

Predictors of Mortality among COVID-19 Patients Admitted to Intensive Care Units: A Single-Center Study in Tehran, Iran

BACKGROUND

Iran was one of the first countries to become an epicenter of the coronavirus disease 2019 (COVID-19) epidemic. However, there is a dearth of data on the outcomes of COVID-19 and predictors of death in intensive care units (ICUs) in Iran. We collected extensive data from patients admitted to the ICUs of the one of the tertiary referral hospitals in Tehran, Iran, to investigate the predictors of ICU mortality.

METHODS

The study population included 290 COVID-19 patients who were consecutively admitted to the ICUs of the Sina hospital from May 5, 2021, to December 6, 2021, a period that included the peak of the epidemic of the delta (δ) variant. Demographic data, history of prior chronic diseases, laboratory data (including markers of inflammation), radiologic data, and medication data were collected.

RESULTS

Of the 290 patients admitted to the ICUs, 187 (64.5%) died and 103 (35.5%) survived. One hundred forty-one (141, 48.6%) were men, and the median age (10th percentile, 90th percentile) was 60 (41, 80). Using logistic regression models, older age, history of hypertension, high levels of inflammatory markers, low oxygen saturation, substantial lung involvement in computed tomography (CT) scans, and gravity of the disease as indicated by the WHO 8-point ordinal scale were primary predictors of mortality at ICU. The use of remdesivir and imatinib was associated with a statistically non-significant reduction in mortality. The use of tocilizumab had almost no effect on mortality.

CONCLUSION

The findings are consistent with and add to the currently existing international literature. The findings may be used to predict risk of mortality from COVID-19 and provide some guidance on potential treatments.

What can we learn from rapidly developed patient decision aids produced during the covid-19 pandemic?

Supporting people to make decisions during the covid-19 pandemic created new opportunities for shared decision making and rapid development of patient decision aids, say Michael Barry and colleagues

COVID-19 in Africa: Underreporting, demographic effect, chaotic dynamics, and mitigation strategy impact

The epidemic of COVID-19 has shown different developments in Africa compared to the other continents. Three different approaches were used in this study to analyze this situation. In the first part, basic statistics were performed to estimate the contribution of the elderly people to the total numbers of cases and deaths in comparison to the other continents; Similarly, the health systems capacities were analysed to assess the level of underreporting. In the second part, differential equations were reconstructed from the epidemiological time series of cases and deaths (from the John Hopkins University) to analyse the dynamics of COVID-19 in seventeen countries. In the third part, the time evolution of the contact number was reconstructed since the beginning of the outbreak to investigate the effectiveness of the mitigation strategies. Results were compared to the Oxford stringency index and to the mobility indices of the Google Community Mobility Reports.

Compared to Europe, the analyses show that the lower proportion of elderly people in Africa enables to explain the lower total numbers of cases and deaths by a factor of 5.1 on average (from 1.9 to 7.8). It corresponds to a genuine effect. Nevertheless, COVID-19 numbers are effectively largely underestimated in Africa by a factor of 8.5 on average (from 1.7 to 20. and more) due to the weakness of the health systems at country level. Geographically, the models obtained for the dynamics of cases and deaths reveal very diversified dynamics. The dynamics is chaotic in many contexts, including a situation of bistability rarely observed in dynamical systems. Finally, the contact number directly deduced from the epidemiological observations reveals an effective role of the mitigation strategies on the short term. On the long term, control measures have contributed to maintain the epidemic at a low level although the progressive release of the stringency did not produce a clear increase of the contact number. The arrival of the omicron variant is clearly detected and characterised by a quick increase of interpeople contact, for most of the African countries considered in the analysis.

Estimating deaths averted and cost per life saved by scaling up mRNA COVID-19 vaccination in low-income and lower-middle-income countries in the COVID-19 Omicron variant era: a modelling study

OBJECTIVES

While almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries (LICs) has received a full primary vaccine series, compared with over 70% of the population of high-income nations.

DESIGN

We used economic and epidemiological models, parameterised with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programmes in LICs and lower-middle-income countries (LMICs) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2.

SETTING

Low-income and lower-middle-income nations.

MAIN OUTCOME MEASURES

Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs.

FINDINGS

Globally, universal vaccination in LIC/LMIC with three doses of an mRNA vaccine would result in an estimated 1.5 million COVID-19 deaths averted with a total estimated cost of US$61 billion and an estimated cost-per-COVID-19 death averted of US$40 800 (sensitivity analysis range: US$7400-US$81 500). Lower estimated infection fatality ratios, higher cost-per-dose and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis.

CONCLUSIONS

Scaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life. Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders

BACKGROUND

The focus of many studies is to estimate the effect of risk factors on outcomes, yet results may be dependent on the choice of other risk factors or potential confounders to include in a statistical model. For complex and unexplored systems, such as the COVID-19 spreading process, where a priori knowledge of potential confounders is lacking, data-driven empirical variable selection methods may be primarily utilized. Published studies often lack a sensitivity analysis as to how results depend on the choice of confounders in the model. This study showed variability in associations of short-term air pollution with COVID-19 mortality in Germany under multiple approaches accounting for confounders in statistical models.

METHODS

Associations between air pollution variables PM_2.5, PM₁₀, CO, NO, NO₂, and O₃ and cumulative COVID-19 deaths in 400 German districts were assessed via negative binomial models for two time periods, March 2020-February 2021 and March 2021-February 2022. Prevalent methods for adjustment of confounders were identified after a literature search, including change-in-estimate and information criteria approaches. The methods were compared to assess the impact on the association estimates of air pollution and COVID-19 mortality considering 37 potential confounders.

RESULTS

Univariate analyses showed significant negative associations with COVID-19 mortality for CO, NO, and NO₂, and positive associations, at least for the first time period, for O₃ and PM_2.5. However, these associations became non-significant when other risk factors were accounted for in the model, in particular after adjustment for mobility, political orientation, and age. Model estimates from most selection methods were similar to models including all risk factors.

CONCLUSION

Results highlight the importance of adequately accounting for high-impact confounders when analyzing associations of air pollution with COVID-19 and show that it can be of help to compare multiple selection approaches. This study showed how model selection processes can be performed using different methods in the context of high-dimensional and correlated covariates, when important confounders are not known a priori. Apparent associations between air pollution and COVID-19 mortality failed to reach significance when leading selection methods were used.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00657-5.

Collecting mortality data via mobile phone surveys: A non-inferiority randomized trial in Malawi

Despite the urgent need for timely mortality data in low-income and lower-middle-income countries, mobile phone surveys rarely include questions about recent deaths. Such questions might a) be too sensitive, b) take too long to ask and/or c) generate unreliable data. We assessed the feasibility of mortality data collection using mobile phone surveys in Malawi. We conducted a non-inferiority trial among a random sample of mobile phone users. Participants were allocated to an interview about their recent economic activity or recent deaths in their family. In the group that was asked mortality-related questions, half of the respondents completed an abridged questionnaire, focused on information necessary to calculate recent mortality rates, whereas the other half completed an extended questionnaire that also included questions about symptoms and healthcare. The primary trial outcome was the cooperation rate, i.e., the number of completed interviews divided by the number of mobile subscribers invited to participate. Secondary outcomes included self-reports of negative feelings and stated intentions to participate in future interviews. We called more than 7,000 unique numbers and reached 3,054 mobile subscribers. In total, 1,683 mobile users were invited to participate. The difference in cooperation rates between those asked to complete a mortality-related interview and those asked to answer questions about economic activity was 0.9 percentage points (95% CI = -2.3, 4.1), which satisfied the non-inferiority criterion. The mortality questionnaire was non-inferior to the economic questionnaire on all secondary outcomes. Collecting mortality data required 2 to 4 additional minutes per reported death, depending on the inclusion of questions about symptoms and healthcare. More than half of recent deaths elicited during mobile phone interviews had not been registered with the National Registration Bureau. Including mortality-related questions in mobile phone surveys is feasible. It might help strengthen the surveillance of mortality in countries with deficient civil registration systems. Registration: AEA RCT Registry, #0008065 (14 September 2021).

COVID-19 Vaccine Hesitancy Associated With Vaccine Inequity Among Healthcare Workers in a Low-Income Fragile Nation

Objectives

Preventing severe disease and acquiring population immunity to COVID-19 requires global immunization coverage through mass vaccination. While high-income countries are battling vaccine hesitancy, low-income and fragile nations are facing the double dilemma of vaccine hesitancy and lack of access to vaccines. There is inadequate information on any correlation between vaccine hesitancy and access to vaccines. Our study in a low-income nation aimed to fill this gap.

Methods

In the backdrop of a severe shortage of COVID-19 vaccines in Yemen, a low-income fragile nation, we conducted a nation-wide cross-sectional survey among its healthcare workers (HCWs), between 6 July and 10 August 2021. We evaluated factors influencing agreement to accept a COVID-19 vaccine and any potential correlation between vaccine acceptance and lack of access to vaccines.

Results

Overall, 61.7% (n = 975) of the 1,581 HCWs agreed to accept a COVID-19 vaccine. Only 45.4% of the participants agreed to have access to a COVID-19 vaccine, with no sex dependent variations. Although several determinants of vaccine acceptance were identified, including, having a systemic disease, following the updates about COVID-19 vaccines, complying with preventive guidelines, having greater anxiety about contracting COVID-19, previous infection with COVID-19, believing COVID-19 to be a severe disease, and lower concern about the side effects of COVID-19, the strongest was access to vaccines (OR: 3.18; 95% CI: 2.5–4.03; p-value: 0.001).

Conclusion

The immediate and more dangerous threat in Yemen toward achieving population immunity is the severe shortage and lack of access to vaccines, rather than vaccine hesitancy, meaning, improving access to vaccines could lead to greater acceptance.

Myocarditis and Cardiac Complications Associated With COVID-19 and mRNA Vaccination: A Pragmatic Narrative Review to Guide Clinical Practice

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is likely to remain endemic globally despite widespread vaccination. There is increasing concern for myocardial involvement and ensuing cardiac complications due to COVID-19, however, the available evidence suggests these risks are low. Pandemic publishing has resulted in rapid manuscript availability though pre-print servers. Subsequent article retractions, a lack of standardised definitions, over-reliance on isolated troponin elevation and the heterogeneity of studied patient groups (i.e. severe vs. symptomatic vs all infections) resulted in early concern for high rates of myocarditis in patients with and recovering from COVID-19. The estimated incidence of myocarditis in COVID-19 infection is 11 cases per 100,000 infections compared with an estimated 2.7 cases per 100,000 persons following mRNA vaccination. For substantiated cases, the clinical course of myocarditis related to COVID-19 or mRNA vaccination appears mild and self-limiting, with reports of severe/fulminant myocarditis being rare. There is limited data available on the management of myocarditis in these settings.

Clinical guidance for appropriate use of cardiac investigations and monitoring in COVID-19 is needed for effective risk stratification and efficient use of cardiac resources in Australia. An amalgamation of national and international position statements and guidelines is helpful for guiding clinical practice. This paper reviews the current available evidence and guidelines and provides a summary of the risks and potential use of cardiac investigations and monitoring for patients with COVID-19.

Learning from the pandemic: mortality trends and seasonality of deaths in Australia in 2020

AIM

To assess whether the observed numbers and seasonality of deaths in Australia during 2020 differed from expected trends based on 2015-19 data.

METHODS

We used provisional death data from the Australian Bureau of Statistics, stratified by state, age, sex and cause of death. We compared 2020 deaths with 2015-19 deaths using interrupted time series adjusted for time trend and seasonality. We measured the following outcomes along with 95% confidence intervals: observed/expected deaths (rate ratio: RR), change in seasonal variation in mortality (amplitude ratio: AR) and change in week of peak seasonal mortality (phase difference: PD).

RESULTS

Overall 4% fewer deaths from all causes were registered in Australia than expected in 2020 [RR 0·96 (0·95-0·98)] with reductions across states, ages and sex strata. There were fewer deaths from respiratory illness [RR 0·79 (0·76-0·83)] and dementia [RR 0·95 (0·93-0·98)] but more from diabetes [RR 1·08 (1·04-1·13)]. Seasonal variation was reduced for deaths overall [AR 0·94 (0·92-0·95)], and for deaths due to respiratory illnesses [AR 0·78 (0·74-0·83)], dementia [AR 0.92 (0.89-0.95)] and ischaemic heart disease [0.95 (0.90-0.97)].

CONCLUSIONS

The observed reductions in respiratory and dementia deaths and the reduced seasonality in ischaemic heart disease deaths may reflect reductions in circulating respiratory (non-SARS-CoV-2) pathogens resulting from the public health measures taken in 2020. The observed increase in diabetes deaths is unexplained and merits further study.

How the COVID-19 Pandemic Alters the Landscapes of the HIV and Tuberculosis Epidemics in South Africa: A Case Study and Future Directions

South Africa has long grappled with one of the highest HIV and tuberculosis (TB) burdens in the world. The COVID-19 pandemic poses challenges to the country's already strained health system. Measures to contain COVID-19 virus may have further hampered the containment of HIV and TB in the country and further widened the socioeconomic gap. South Africa's handling of the pandemic has led to disruptions to HIV/TB testing and treatment. It has, furthermore, influenced social risk factors associated with increased transmission of these diseases. Individuals living with HIV and/or TB also face higher risk of developing severe COVID-19 disease. In this case study, we contextualize the HIV/TB landscape in South Africa and analyze the direct and indirect impact of the COVID-19 pandemic on the country's efforts to combat these ongoing epidemics.

Impacto de la COVID-19 en la mortalidad en Perú mediante la triangulación de múltiples fuentes de datos

Se realizó un estudio transversal analítico, con el objetivo de cuantificar el impacto de la COVID-19 en la mortalidad del Perú. Se calculó el exceso de mortalidad total, por causas naturales y externas. El impacto directo e indirecto de la COVID-19 fue estimado en dos momentos: cuando las muertes por COVID-19 eran confirmadas mediante una prueba de laboratorio y cuando eran confirmadas por criterios más amplios (muertes reclasificadas). Esta comparación se hizo en general, por sexo, edad y geografía. Se calculó la sensibilidad de los criterios de laboratorio y certificado de defunción para la confirmación de una muerte por COVID-19 utilizando las muertes reclasificadas como estándar de oro.

Desde la semana epidemiológica 10 del 2020 hasta la 23 del 2021 ocurrieron 349 756 muertes, determinando un exceso de 183 237 muertes, principalmente por causas naturales. Considerando aquellas muertes confirmadas por pruebas de laboratorio se encontró 100 955 muertes; sin embargo, con los criterios de la reclasificación subieron a 188 708. Las pruebas de laboratorio tuvieron una sensibilidad del 53,3%, siendo menor al inicio de la pandemia (10.6%) y durante la primera ola (37,8%). La sensibilidad del certificado de defunción fue mayor que la prueba de laboratorio (41,7% vs 23,9%) solo durante los meses de baja disponibilidad de pruebas. Estos datos evidenciaron que en Perú el impacto de la COVID-19 en la mortalidad fue principalmente directo. Asimismo, en períodos con acceso limitado de pruebas de laboratorio, los certificados de defunción fueron una fuente de información útil para determinar las muertes causadas directamente por la COVID-19.

Excess mortality associated with the COVID-19 pandemic in Latvia: a population-level analysis of all-cause and noncommunicable disease deaths in 2020

Background

Age-standardised noncommunicable disease (NCD) mortality and the proportion of the elderly population in Latvia are high, while public health and health care systems are underresourced. The emerging COVID-19 pandemic raised concerns about its detrimental impact on all-cause and noncommunicable disease mortality in Latvia. We estimated the timing and number of excess all-cause and cause-specific deaths in 2020 in Latvia due to COVID-19 and selected noncommunicable diseases.

Methods

A time series analysis of all-cause and cause-specific weekly mortality from COVID-19, circulatory diseases, malignant neoplasms, diabetes mellitus, and chronic lower respiratory diseases from the National Causes of Death Database from 2015 to 2020 was used by applying generalised additive modelling (GAM) and joinpoint regression analysis.

Results

Between weeks 14 and 52 (from 1 April to 29 December) of 2020, a total of 3111 excess deaths (95% PI 1339 – 4832) were estimated in Latvia, resulting in 163.77 excess deaths per 100 000. Since September 30, with the outbreak of the second COVID-19 wave, 55% of all excess deaths have occurred. Altogether, COVID-19-related deaths accounted for only 28% of the estimated all-cause excess deaths. A significant increase in excess mortality was estimated for circulatory diseases (68.91 excess deaths per 100 000). Ischemic heart disease and cerebrovascular disease were listed as the underlying cause in almost 60% of COVID-19-contributing deaths.

Conclusions

All-cause mortality and mortality from circulatory diseases significantly increased in Latvia during the first pandemic year. All-cause excess mortality substantially exceeded reported COVID-19-related deaths, implying COVID-19-related mortality during was significantly underestimated. Increasing mortality from circulatory diseases suggests a negative cumulative effect of COVID-19 exposure and reduced access to healthcare services for NCD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13491-4.

A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data

The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries.

Is there an association between COVID-19 mortality and Human development index? The case study of Nigeria and some selected countries

OBJECTIVES

We assessed Case Fatality Rate (CFR) of COVID-19 as an indicator to situate the performance of Nigeria relative to other selected countries. We obtained case fatality rates of different countries from data sets available from open-sources. The CFRs were calculated as the rate of deaths compared with total cases. The values were compared with Nigeria's COVID-19 CFR. Other relevant statistical comparisons were also conducted.

RESULTS

The worst performing countries with regards to CFR in descending order were Yemen (19.5%), Peru (9.0%) Mexico (7.6%), Sudan (7.4%) and Ecuador (6.3%) while the best performing nations were Bhutan (0.11%), Burundi (0.19%), Iceland (0.20%), Laos (0.21%) and Qatar (0.25%). The CFR of Nigeria was 1.39% which falls below the 50th percentile. Other comparison done showed significant difference in the CFR values between countries similar to Nigeria and countries that are dissimilar when HDI is used. (Mann-Whitney U test 126.0, p = 0.01). The trend of the CFR in Nigeria showed a steady decline and flattening of the CFR curve which does not seem to be affected by the spikes in the daily declared cases.

The influence of democracy, governance and government policies on the COVID-19 pandemic mortality

The paper analyses the determinants of mortality during the Corona pandemic. In a first step, possible causes are subjected to a global comparison.The focus is on political, institutional, economic, demographic and health policy factors. It is shown that, contrary to the assumption, democratic countries have a higher Covid-19 mortality. In a second step, the developed democracies are then analysed to explain this puzzle. Here, more detailed information is used than in the global comparison. It turns out that, measured by the Oxford Stringency Index, government action has been largely unsuccessful. Also, the party-political composition of the government does not play a role.The most important factors are a country's governance structures, the level of corona incidence, a country's burden of high-risk patients and its health system. In addition, cultural factors and the vaccination rate seem to have an influence on mortality.

Trust, Science Education and Vaccines

The issue of trust in science has come to the fore in recent years. I focus on vaccines, first looking at what is known about trust in vaccines and then concentrating on whether what science education teaches about vaccines can be trusted. I present an argument to connect the phenomenon of vaccine hesitancy to the issue of trust and then argue for what an education about vaccines in school science might look like that takes seriously the notion of respect for students, including students who hold views about vaccination with which science teachers might disagree. Trust in others (people and institutions) varies greatly, both between countries and within countries, and depends on the characteristics of both trustor and trustee, and there are great differences in the extent to which people trust vaccines. However, it is a mistake to think that people who do not trust vaccines are simply ill-informed. There are a range of reasons for rejecting what is often an unexamined narrative about vaccines, namely that vaccines are always desirable. Many people come from communities that have sound reasons for being suspicious of what they are told by governments, business and the medical establishment. COVID-19 and earlier reactions to vaccination health scares show how important high-quality education about vaccines is. Much of that education can take place out of school, but the foundations are laid in school. Vaccine rejection and hesitancy have major global public health implications. Good quality vaccine education should help students understand about relevant biology and the nature of science; it should also be respectful of all students, including those who come from families that reject vaccines or are hesitant about them.

COVID-19 Autopsies Reveal Underreporting of SARS-CoV-2 Infection and Scarcity of Co-infections

Coronavirus disease 2019 (COVID-19) mortality can be estimated based on reliable mortality data. Variable testing procedures and heterogeneous disease course suggest that a substantial number of COVID-19 deaths is undetected. To address this question, we screened an unselected autopsy cohort for the presence of SARS-CoV-2 and a panel of common respiratory pathogens. Lung tissues from 62 consecutive autopsies, conducted during the first and second COVID-19 pandemic waves in Switzerland, were analyzed for bacterial, viral and fungal respiratory pathogens including SARS-CoV-2. SARS-CoV-2 was detected in 28 lungs of 62 deceased patients (45%), although only 18 patients (29%) were reported to have COVID-19 at the time of death. In 23 patients (37% of all), the clinical cause of death and/or autopsy findings together with the presence of SARS-CoV-2 suggested death due to COVID-19. Our autopsy results reveal a 16% higher SARS-CoV-2 infection rate and an 8% higher SARS-CoV-2 related mortality rate than reported by clinicians before death. The majority of SARS-CoV-2 infected patients (75%) did not suffer from respiratory co-infections, as long as they were treated with antibiotics. In the lungs of 5 patients (8% of all), SARS-CoV-2 was found, yet without typical clinical and/or autopsy findings. Our findings suggest that underreporting of COVID-19 contributes substantially to excess mortality. The small percentage of co-infections in SARS-CoV-2 positive patients who died with typical COVID-19 symptoms strongly suggests that the majority of SARS-CoV-2 infected patients died from and not with the virus.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Underdispersion: A statistical anomaly in reported Covid data

Throughout the Covid-19 pandemic, we have become used to seeing daily numbers of cases and deaths go up and down. But in some countries, the reported numbers show very little movement over days and weeks - they are "underdispersed", says Dmitry Kobak, and this may be a sign that all is not right with the data.

Global landscape of SARS-CoV-2 genomic surveillance and data sharing

Genomic surveillance has shaped our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. We performed a global landscape analysis on SARS-CoV-2 genomic surveillance and genomic data using a collection of country-specific data. Here, we characterize increasing circulation of the Alpha variant in early 2021, subsequently replaced by the Delta variant around May 2021. SARS-CoV-2 genomic surveillance and sequencing availability varied markedly across countries, with 45 countries performing a high level of routine genomic surveillance and 96 countries with a high availability of SARS-CoV-2 sequencing. We also observed a marked heterogeneity of sequencing percentage, sequencing technologies, turnaround time and completeness of released metadata across regions and income groups. A total of 37% of countries with explicit reporting on variants shared less than half of their sequences of variants of concern (VOCs) in public repositories. Our findings indicate an urgent need to increase timely and full sharing of sequences, the standardization of metadata files and support for countries with limited sequencing and bioinformatics capacity.

High rates of undiagnosed diabetes mellitus among patients with active tuberculosis in Addis Ababa, Ethiopia

Background

Tuberculosis (TB) and diabetes mellitus (DM) have negative synergistic impact on each other. Global guidelines recommend collaborative efforts to address this synergy, but implementation has been slow. Part of the reason is lack of adequate evidence on the operational feasibility of existing tools and mechanisms of collaboration. The objective of this study was to assess the yield of DM screening among TB patients using risk scoring tools combined with blood tests as a feasible strategy for early detection to improve TB/DM treatment outcomes.

Methods

Between September 2020 and December 2021, we conducted a cross-sectional study among patients receiving TB treatment in public health facilities in Addis Ababa, Ethiopia. Trained health workers collected data on symptoms and risk scoring checklists before testing for random and fasting blood glucose levels. We used logistic regression analyses techniques to determine factors associated with increased DM detection. A receiver-operating characteristic curve was constructed to determine the performance of the risk scoring checklist.

Results

Of 2381 TB patients screened, 197 (8.3%) had DM of which 48.7% were newly diagnosed. Having a family history of DM predicted DM with Odds Ratio (OR) of 5.36 (95% Confidence Interval, [3.67, 7.83]) followed by age ≥ 45 years (OR = 4.64, [3.18, 6.76]). Having one or more “symptoms” of DM was a significant predictor (OR 2.88, 95% CI, 2.06–4.01). Combining risk scores with symptoms predicted DM diagnosis with sensitivity of 94.7%, but specificity was low at 29.4%. In patients with known treatment outcome status, death rate was high.

Conclusions

Almost a half of TB patients with DM did not know their status. A simple tool that combined risk factors with symptoms accurately predicted a subsequent diagnosis of DM. Such tools can help avoid high rates of death among TB patients suffering from DM through early detection.