Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

Understanding excess mortality in 2022: The dual impact of COVID-19 and heatwaves on the Italian elderly population

In this study, we employ a comprehensive approach to model the concurrent effects of the COVID-19 epidemic and heatwaves on all-cause excess mortality. Our investigation uncovers distinct peaks in excess mortality, notably among individuals aged 80 years and older, revealing a strong positive correlation with excess temperatures (ET) during the summer of 2022 in Italy. Furthermore, we identify a notable role played by COVID-19 hospitalizations, exhibiting regional disparities, particularly during the winter months. Leveraging functional data regression, we offer robust and coherent insights into the excess mortality trends observed in Italy throughout 2022.

COVID-19 could accelerate the decline in recreational hunting: A natural experiment from Northern Italy

Although many studies highlighted the potential of COVID-19 to reshape existing models of wildlife management, empirical research on this topic has been scarce, particularly in Europe. We investigated the potential of COVID-19 pandemic to accelerate the ongoing decline in an aging population of recreational hunters in Italy. Namely, we modelled spatiotemporal trends between 2011 and 2021 in the number of recreational hunters in 50 Italian provinces with a varying incidence of COVID-19, and temporally delayed waves of infection. Compared to projections from 2011-2019 data, we detected a lower number of hunters who enrolled for the hunting season, both in 2020 (14 provinces) and in 2021 (15 provinces). The provinces with the highest incidence of COVID-19 in the Lombardy and Emilia-Romagna regions were also those experiencing the most marked decrease in hunting participation. Our findings revealed that a wildlife management system based on recreational hunting can be rapidly destabilized by epidemics and their associated public health measures, particularly when the average age of hunters is high, like in Italy. Considered the high incidence attained by COVID-19 in many European countries, where hunters are pivotal for the management of large ungulates and where they were already declining before the pandemic, our findings call for further large-scale research about the impact of COVID-19 on hunting participation.

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic

OBJECTIVES

Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic.

METHODS

We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017-2019 using a spatially discrete model.

RESULTS

Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions.

CONCLUSIONS

This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.

The unequal impact of the pandemic at subnational levels and educational attainment-related inequalities in COVID-19 mortality, Brazil, 2020-2021

OBJECTIVES

We estimated COVID-19 mortality indicators in 2020-2021 to show the epidemic's impact at subnational levels and to analyze educational attainment-related inequalities in COVID-19 mortality in Brazil.

STUDY DESIGN

This was an ecological study with secondary mortality information.

METHODS

Crude and age-standardized COVID-19 mortality rates were calculated by gender, major regions, and states. The COVID-19 proportional mortality (percentage) was estimated by gender and age in each region. Measures of education-related inequalities in COVID-19 mortality were calculated per state, in each of which the COVID-19 maternal mortality rate (MMR) was estimated by the number of COVID-19 maternal deaths per 100,000 live births (LBs).

RESULTS

The analysis of mortality rates at subnational levels showed critical regional differences. The North region proved to be the most affected by the pandemic, followed by the Center-West, with age-standardized COVID-19 mortality rates above 2 per 1000 inhabitants. The peak of COVID-19 mortality occurred in mid-March/April 2021 in all regions. Great inequality by educational level was found, with the illiterate population being the most negatively impacted in all states. The proportional mortality showed that males and females aged 50-69 years were the most affected. The MMR reached critical values (>100/100,000 LB) in several states of the North, Northeast, Southeast, and Center-West regions.

CONCLUSIONS

This study highlights stark regional and educational disparities in COVID-19 mortality in Brazil. Exacerbated by the pandemic, these inequalities reveal potential areas for intervention to reduce disparities. The results also revealed high MMRs in certain states, underscoring pre-existing healthcare access challenges that worsened during the pandemic.

Spatial Variation in Excess Mortality Across Europe: A Cross-Sectional Study of 561 Regions in 21 Countries

OBJECTIVE

To measure the burden of the COVID-19 pandemic in 2020 at the subnational level by estimating excess mortality, defined as the increase in all-cause mortality relative to an expected baseline mortality level.

METHODS

Statistical and demographic analyses of regional all-cause mortality data provided by the vital statistics systems of 21 European countries for 561 regions in Central and Western Europe. Life expectancy losses at ages 0 and 60 for males and females were estimated.

RESULTS

We found evidence of a loss in life expectancy in 391 regions, whilst only three regions exhibit notable gains in life expectancy in 2020. For 12 regions, losses of life expectancy amounted to more than 2 years and three regions showed losses greater than 3 years. We highlight geographical clusters of high mortality in Northern Italy, Spain and Poland, whilst clusters of low mortality were found in Western France, Germany/Denmark and Norway/Sweden.

CONCLUSIONS

Regional differences of loss of life expectancy are impressive, ranging from a loss of more than 4 years to a gain of 8 months. These findings provide a strong rationale for regional analysis, as national estimates hide significant regional disparities.

Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021)

Since its emergence in December 2019, the COVID-19 pandemic has resulted in a significant increase in deaths worldwide. This article presents a detailed analysis of the mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce age and sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a forecasting approach based on CP-splines that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). We also estimate that eight regions suffered losses exceeding 20 years of life per 1000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic's impact.

Area-level excess mortality in times of COVID-19 in Switzerland: geographical, socioeconomic and political determinants

The coronavirus disease 2019 (COVID-19)-related excess mortality in Switzerland is well documented, but no study examined mortality at the small-area level. We analysed excess mortality in 2020 for 2141 Swiss municipalities using a Bayesian spatiotemporal model fitted to 2011-19 data. Areas most affected included the Ticino, the Romandie and the Northeast. Rural areas, municipalities within cross-border labour markets, of lower socioeconomic position and with less support for control measures in the popular vote on the COVID-19 Act had greater excess mortality. Particularly vulnerable municipalities require special efforts to mitigate the impact of pandemics.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Assessing the heterogeneity of the impact of COVID-19 incidence on all-cause excess mortality among healthcare districts in Lombardy, Italy, to evaluate the local response to the pandemic: an ecological study

OBJECTIVES

The fragmentation of the response to the COVID-19 pandemic at national, regional and local levels is a possible source of variability in the impact of the pandemic on society. This study aims to assess how much of this variability affected the burden of COVID-19, measured in terms of all-cause 2020 excess mortality.

DESIGN

Ecological retrospective study.

SETTING

Lombardy region of Italy, 2015-2020.

OUTCOME MEASURES

We evaluated the relationship between the intensity of the epidemics and excess mortality, assessing the heterogeneity of this relationship across the 91 districts after adjusting for relevant confounders.

RESULTS

The epidemic intensity was quantified as the COVID-19 hospitalisations per 1000 inhabitants. Five confounders were identified through a directed acyclic graph: age distribution, population density, pro-capita gross domestic product, restriction policy and population mobility.Analyses were based on a negative binomial regression model with district-specific random effects. We found a strong, positive association between COVID-19 hospitalisations and 2020 excess mortality (p<0.001), estimating that an increase of one hospitalised COVID-19 patient per 1000 inhabitants resulted in a 15.5% increase in excess mortality. After adjusting for confounders, no district differed in terms of COVID-19-unrelated excess mortality from the average district. Minimal heterogeneity emerged in the district-specific relationships between COVID-19 hospitalisations and excess mortality (6 confidence intervals out of 91 did not cover the null value).

CONCLUSIONS

The homogeneous effect of the COVID-19 spread on the excess mortality in the Lombardy districts suggests that, despite the unprecedented conditions, the pandemic reactions did not result in health disparities in the region.

Estimating subnational excess mortality in times of pandemic. An application to French départements in 2020

The COVID-19 pandemic's uneven impact on subnational regions highlights the importance of understanding its local-level mortality impact. Vital statistics are available for an increasing number of countries for 2020, 2021, and 2022, facilitating the computation of subnational excess mortality and a more comprehensive assessment of its burden. However, this calculation faces two important methodological challenges: it requires appropriate mortality projection models; and small populations imply considerable, though commonly neglected, uncertainty in the estimates. We address both issues using a method to forecast mortality at the subnational level, which incorporates uncertainty in the computation of mortality measures. We illustrate our approach by examining French départements (NUTS 3 regions, or 95 geographical units), and produce sex-specific estimates for 2020. This approach is highly flexible, allowing one to estimate excess mortality during COVID-19 in most demographic scenarios and for past pandemics.

Impact of COVID-19 on mortality in coastal Kenya: a longitudinal open cohort study

The mortality impact of COVID-19 in Africa remains controversial because most countries lack vital registration. We analysed excess mortality in Kilifi Health and Demographic Surveillance System, Kenya, using 9 years of baseline data. SARS-CoV-2 seroprevalence studies suggest most adults here were infected before May 2022. During 5 waves of COVID-19 (April 2020-May 2022) an overall excess mortality of 4.8% (95% PI 1.2%, 9.4%) concealed a significant excess (11.6%, 95% PI 5.9%, 18.9%) among older adults ( ≥ 65 years) and a deficit among children aged 1-14 years (-7.7%, 95% PI -20.9%, 6.9%). The excess mortality rate for January 2020-December 2021, age-standardised to the Kenyan population, was 27.4/100,000 person-years (95% CI 23.2-31.6). In Coastal Kenya, excess mortality during the pandemic was substantially lower than in most high-income countries but the significant excess mortality in older adults emphasizes the value of achieving high vaccine coverage in this risk group.

Variation in mortality burden of the COVID-19 pandemic across federal states in Germany

BACKGROUND

Intra-annual excess mortality is the most reliable measure of losses of lives due to short-term risk factors. The objectives of our study are (i) to estimate excess mortality across German states in the course of the coronavirus disease 2019 (COVID-19) pandemic years 2020 and 2021 and (ii) to identify possible regional-level determinants of spatial inequality in pandemic-related excess mortality.

METHODS

We use weekly mortality data series for the calculation of weekly death rates, standardized by age for each federal state of Germany. We estimate the expected level of mortality as state-specific mortality trends and excess mortality in 2020 and 2021. We explore ecological statistical relationships between excess mortality, COVID-19 morbidity, and selected regional socioeconomic indicators using fixed-effects regression models.

RESULTS

Our study shows that during the first pandemic year, there was South-to-North gradient in excess mortality in Germany, with excess mortality being higher in the South. Over the course of the second pandemic year 2021, this gradient changed to become an East-to-West gradient, with excess mortality being higher in the East. The results of the study show stronger effects of COVID-19 morbidity on excess mortality in East Germany. State-level indicators reflecting economic activity, employment, and capacity of intensive care units show significant correlations with excess mortality across the states.

CONCLUSIONS

The results show pronounced state-level differences in the magnitude of excess mortality during the COVID-19 pandemic in Germany. Economic activity, employment and capacity of intensive care units were the most important state-level characteristics associated with the observed spatial variations in excess mortality.

Impact of the COVID-19 pandemic on total, sex- and age-specific all-cause mortality in 20 countries worldwide during 2020: results from the C-MOR project

BACKGROUND

To understand the impact of the COVID-19 pandemic on mortality, this study investigates overall, sex- and age-specific excess all-cause mortality in 20 countries, during 2020.

METHODS

Total, sex- and age-specific weekly all-cause mortality for 2015-2020 was collected from national vital statistics databases. Excess mortality for 2020 was calculated by comparing weekly 2020 observed mortality against expected mortality, estimated from historical data (2015-2019) accounting for seasonality, long- and short-term trends. Crude and age-standardized rates were analysed for total and sex-specific mortality.

RESULTS

Austria, Brazil, Cyprus, England and Wales, France, Georgia, Israel, Italy, Northern Ireland, Peru, Scotland, Slovenia, Sweden, and the USA displayed substantial excess age-standardized mortality of varying duration during 2020, while Australia, Denmark, Estonia, Mauritius, Norway, and Ukraine did not. In sex-specific analyses, excess mortality was higher in males than females, except for Slovenia (higher in females) and Cyprus (similar in both sexes). Lastly, for most countries substantial excess mortality was only detectable (Austria, Cyprus, Israel, and Slovenia) or was higher (Brazil, England and Wales, France, Georgia, Italy, Northern Ireland, Sweden, Peru and the USA) in the oldest age group investigated. Peru demonstrated substantial excess mortality even in the <45 age group.

CONCLUSIONS

This study highlights that excess all-cause mortality during 2020 is context dependent, with specific countries, sex- and age-groups being most affected. As the pandemic continues, tracking excess mortality is important to accurately estimate the true toll of COVID-19, while at the same time investigating the effects of changing contexts, different variants, testing, quarantine, and vaccination strategies.

The contribution of population age-sex structure to the excess mortality estimates of 2020-2021 in Denmark, Finland, Iceland, Norway, and Sweden

The Nordic countries offer an ideal case study of the COVID-19 pandemic due to their comparability, high data quality, and variable mitigations. We investigated the age- and sex-specific mortality patterns during 2020-2021 for the five Nordic countries and analysed the total age- and sex-adjusted excess deaths, ratios of actual to expected death rates, and age-standardized excess death estimates. We assessed excess deaths using several time periods and sensitivity tests, and 42 sex and age groups. Declining pre-pandemic age-specific death rates reflected improving health demographics. These affect the expected death estimates and should be accounted for in excess mortality models. Denmark had the highest death rates both before and during the pandemic, whereas in 2020 Sweden had the largest mortality increase. The age-standardized mortality of Denmark, Iceland and Norway was lowest in 2020. 2021 was one of the lowest mortality years for all Nordic countries. The total excess deaths in 2020-2021 were dominated by 70-89-year-olds, were not identified in children, and were more pronounced among men than women. Sweden had more excess deaths in 2020 than in 2021, whereas Finland, Norway and Denmark had the opposite. Our study provides new details on Nordic sex- and age-specific mortality during the first two years of the pandemic and shows that several metrics are important to enable a full understanding and comparison of the pandemic mortality.

The Effect of COVID-19 Pandemic on Overall and Cause-Specific Mortality in Pavia, Northern Italy: Updated Estimates for the Year 2021

Excess mortality estimates are considered relevant indicators of direct and indirect pandemic effects on the population. Scant data have been published on cause-specific excess mortality. Using individual-level administrative data covering the Pavia province of Italian northern Lombardy region, we provided all-cause and cause-specific raw (RMR) and age-standardized (ASMR) mortality rates in 2021 and 2015-2019, the rate ratio, and 95% confidence intervals, overall and by sex. We obtained the excess deaths in 2021 as the difference between the number of observed and expected deaths from all causes and the two leading causes of death (all neoplasms and circulatory system diseases) by fitting over-dispersed quasi-Poisson regression models, accounting for temporal, seasonal and demographic changes. The total ASMR in 2021 was 972.4/100,000 (6836 certified deaths), with the highest ASMRs for circulatory system diseases (272.6/100,000) and all neoplasms (270.3/100,000), followed by COVID-19 (94.8/100,000 and 662 deaths). Compared to the expected, we estimated a total of 6.2% excess deaths in 2021 (7.2% in males and 5.4% in females), with no excess deaths from all neoplasms and a 6.2% reduction from circulatory system diseases. COVID-19 continued to affect total mortality in 2021, albeit to a lesser extent than in 2020, consistently with national patterns.

A framework for estimating and visualising excess mortality during the COVID-19 pandemic

COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.

Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors

BACKGROUND

Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy.

METHODS

Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster.

RESULTS

The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began.

CONCLUSIONS

The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021

INTRODUCTION

Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations.

METHODS

We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons.

RESULTS

We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE.

DISCUSSION

Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

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].

Is Covid-19 a dread risk? The death toll of the pandemic year 2020 in long-term and transnational perspective

"Dread risks" are threats that can have catastrophic consequences. To analyse this issue we use excess mortality and corresponding life years lost as simple measures of the severity of pandemic events. As such, they are more robust than figures from models and testing procedures that usually inform public responses. We analyse data from OECD countries that are already fully available for the whole of 2020. To assess the severity of the pandemic, we compare with historical demographic events since 1880. Results show that reports of high excess mortality during peak periods and local outbreaks should not be taken as representative. Six countries saw a somewhat more increased percentage of life years lost (over 7%), nine countries show mild figures (0-7%), while seven countries had life year gains of up to 7%. So, by historical standards, Covid-19 is worse than regular flu, but a far cry from the Spanish Flu, which has become the predominant frame of reference for the current pandemic. Even though the demographic impact is modest, psychological aspects of the pandemic can still lead to transformative futures, as the reactions of East Asian societies to SARS I in 2003 showed.

Sex-differences in excess death risk during the COVID-19 pandemic: an analysis of the first wave across Italian regions. What have we learned?

In this commentary, we bring together knowledge on sex-differences in excess death during the first wave of the COVID-19 pandemic in Italy, one of the most hit European countries. We zoom into Italian regions to account for the spatial gradient of the spread of the virus. Analyses of excess death by sex during the COVID-19 pandemic have been possible thanks to weekly mortality data released by national statistical offices, mainly in developed countries. The general finding is that males up to 75 years old have been suffering more excess death compared to females. However, the picture is less clear-cut at older ages. During previous epidemics, such as SARS, Swine Flu, and MERS, studies are limited and produce scattered, non-conclusive evidence. Knowledge of the sex-pattern of susceptibility to mortality from virulent respiratory diseases and its interplay with age could improve crisis management during future epidemics and pandemics. National statistical offices should provide weekly mortality data with spatial granularity, disaggregated by sex and age groups, to allow for such analyses.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Non-COVID outcomes associated with the coronavirus disease-2019 (COVID-19) pandemic effects study (COPES): A systematic review and meta-analysis

BACKGROUND

As the Coronavirus Disease-2019 (COVID-19) pandemic continues, healthcare providers struggle to manage both COVID-19 and non-COVID patients while still providing high-quality care. We conducted a systematic review/meta-analysis to describe the effects of the COVID-19 pandemic on patients with non-COVID illness and on healthcare systems compared to non-pandemic epochs.

METHODS

We searched Ovid MEDLINE/EMBASE/Cochrane Database of Systematic Reviews/CENTRAL/CINAHL (inception to December 31, 2020). All study types with COVID-pandemic time period (after December 31, 2019) with comparative non-pandemic time periods (prior to December 31, 2019). Data regarding study characteristics/case-mix/interventions/comparators/ outcomes (primary: mortality; secondary: morbidity/hospitalizations/disruptions-to-care. Paired reviewers conducted screening and abstraction, with conflicts resolved by discussion. Effect sizes for specific therapies were pooled using random-effects models. Risk of bias was assessed by Newcastle-Ottawa Scale, with evidence rating using GRADE methodology.

RESULTS

Of 11,581 citations, 167 studies met eligibility. Our meta-analysis showed an increased mortality of 16% during the COVID pandemic for non-COVID illness compared with 11% mortality during the pre-pandemic period (RR 1.38, 95% CI: 1.28-1.50; absolute risk difference: 5% [95% CI: 4-6%], p<0.00001, very low certainty evidence). Twenty-eight studies (17%) reported significant changes in morbidity (where 93% reported increases), while 30 studies (18%) reported no significant change (very low certainty). Thirty-nine studies (23%) reported significant changes in hospitalizations (97% reporting decreases), while 111 studies (66%) reported no significant change (very low certainty). Sixty-two studies (37%) reported significant disruptions in standards-to-care (73% reporting increases), while 62 studies (37%) reported no significant change (very low certainty).

CONCLUSIONS

There was a significant increase in mortality during the COVID pandemic compared to pre-pandemic times for non-COVID illnesses. When significant changes were reported, there was increased morbidity, decreased hospitalizations and increased disruptions in standards-of-care.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020201256 (Sept 2, 2020).

Environmental risk factors of airborne viral transmission: Humidity, Influenza and SARS-CoV-2 in the Netherlands

OBJECTIVE

The relationship between specific humidity and influenza/SARS-CoV-2 in the Netherlands is evaluated over time and at regional level.

DESIGN

Parametric and non-parametric correlation coefficients are calculated to quantify the relationship between humidity and influenza, using five years of weekly data. Bayesian spatio-temporal models-with a Poisson and a Gaussian likelihood-are estimated to find the relationship between regional humidity and the daily cases of SARS-CoV-2 in the municipalities and provinces of the Netherlands.

RESULTS

An inverse (negative) relationship is observed between specific humidity and the incidence of influenza between 2015 and 2019. The space-time analysis indicates that an increase of specific humidity of one gram of water vapor per kilogram of air (1 g/kg) is related to a reduction of approximately 5% in the risk of COVID-19 infections.

CONCLUSIONS

The increase in humidity during the outbreak of the SARS-CoV-2 in the Netherlands may have helped to reduce the risk of regional COVID-19 infections. Policies that lead to an increase in household specific humidity to over 6g/Kg will help reduce the spread of respiratory viruses such as influenza and SARS-CoV-2.

Impact of the COVID-19 Pandemic on Total and Cause-Specific Mortality in Pavia, Northern Italy

The COVID-19 pandemic had an unfavorable impact on overall mortality in Italy, with the strongest consequences in northern Italy. Scant data are available on cause-specific mortality. This study aims at investigating the impact of the pandemic on the overall and cause-specific mortality in one province in northern Italy, Pavia. We linked individual-level administrative data (i.e., death certification and population data) from the Health Protection Agency (HPA) in Pavia province between 2015 and 2020. We computed age-standardized mortality rates (Italian population 2011) by cause, sex, and calendar year, and computed the rate ratio and the corresponding 95% confidence intervals to compare rates in 2020 versus 2015–2019. The 2020 excess total mortality in Pavia was 24% in men and 25% in women, reaching rates of 1272.6/100,000 and 1304.4/100,000, respectively. Significant excesses were found for infectious and parasitic diseases, excluding COVID-19 (about +30% in both sexes); respiratory system diseases (44% in men; 30% in women); and dementia and Alzheimer’s disease among men (24%). Reductions were reported for neoplasms (−14% in men); cerebrovascular diseases (−25% in men); and ischemic diseases (−13% in women), but also for transport accidents in men. COVID-19 was the third cause of death in both sexes with rates of 274.9/100,000 men (859 total deaths) and 213.9/100,000 women (758 total deaths). Excess mortality in Pavia was higher than Italy but lower than Lombardy. Increases in mortality from causes related to infectious and respiratory diseases can likely be explained by underdiagnosed deaths from COVID-19.

Indirect impact of the COVID-19 pandemic and its containment measures on social inequalities in hospital utilisation in Italy

BACKGROUND

The pandemic may undermine the equity of access to and utilisation of health services for conditions other than COVID-19. The objective of the study is to evaluate the indirect impact of COVID-19 and lockdown measures on sociodemographic inequalities in healthcare utilisation in seven Italian areas.

METHODS

In this multicentre retrospective study, we evaluated whether COVID-19 modified the association between educational level or deprivation and indicators of hospital utilisation and quality of care. We also assessed variations in gradients by sex and age class. We estimated age-standardised rates and prevalence and their relative per cent changes comparing pandemic (2020) and pre-pandemic (2018-2019) periods, and the Relative Index of Inequalities (RIIs) fitting multivariable Poisson models with an interaction between socioeconomic position and period.

RESULTS

Compared with 2018-2019, hospital utilisation and, to a lesser extent, timeliness of procedures indicators fell during the first months of 2020. Larger declines were registered among women, the elderly and the low educated resulting in a shrinkage (or widening if RII <1) of the educational gradients for most of the indicators. Timeliness of procedures indicators did not show any educational gradient neither before nor during the pandemic. Inequalities by deprivation were nuanced and did not substantially change in 2020.

CONCLUSIONS

The socially patterned reduction of hospital utilisation may lead to a potential exacerbation of health inequalities among groups who were already vulnerable before the pandemic. The healthcare service can contribute to contrast health disparities worsened by COVID-19 through more efficient communication and locally appropriate interventions.

COVID-19 Medical and Pharmacological Management in the European Countries Compared to Italy: An Overview

(1) Background: Italy accounts for more than 150,000 deaths due to the COVID-19 pandemic, leading the top rank in SARS-CoV-2-caused deceases in Europe. A survey on the different ways by which the COVID-19 pandemic emergency was managed in the foreign European countries compared to Italy is the purpose of this paper. (2) Methods: A literature search and various mathematical algorithms to approach a rank scoring scale were used to describe in detail the different approaches used by European countries to manage the COVID-19 pandemic emergency. (3) Results: The study showed that Italy stands at the bottom ranking for COVID-19 management due to its high mortality rate. Possible causes of the observed huge numbers of hospitalization and deaths were (a) the demographic composition of the European country; (b) its decentralized healthcare system organization; (c) the role of correct pharmacology in the early stages before hospitalization. Post-mortem examinations were of paramount importance to elucidate the etiopathogenesis of COVID-19 and to tailor a suitable and proper therapy in the early symptomatic stages of COVID-19, preventing hospitalization. (4) Conclusions: Factors such as the significant impact on elderly people, the public health organization prevalently state-owned and represented mainly by hospitals, and criticism of the home therapy approach toward SARS-CoV-2-infected people, may have concurred in increasing the number of COVID-19 deaths in Italy.

Extent of COVID-19 Healthcare Services of Isolation Center of Private Hospital across Khartoum State, Sudan

Introduction

Spatial presentation is considered a useful tool for analyzing and mapping the frequencies of incidences of different pathogens. Khartoum State accounted for 78% of the overall cases of COVID-19 in Sudan. The aim of this study was to present the spatial extent of healthcare services of a private isolation center during the pandemic at the locality level.

Materials and Methods

A spatial descriptive study was conducted using ArcGIS to present the locations of all COVID-19 patients who attended Imperial Hospital isolation center on November–December 2020 in Khartoum, Sudan.

Results

Patients diagnosed with COVID-19 during the study period were 188; they had attended Imperial Hospital from 9 states. Patients from Khartoum State were 167 patients. Of those 167 patients, 75 were from the Khartoum locality; it is the locality in which Imperial Hospital is located, followed by Khartoum Bahri (34 patients), Omdurman (19 patients), and South Khartoum (14 patients), while 10 patients each were from the Sharg En Nile and Karary localities.

Conclusion

Patients from 8 different states of Sudan had travelled to reach Khartoum State to get health services. At the state level, Khartoum State was the most benefited state from the healthcare services of Imperial Hospital. At a locality level, Khartoum locality was the most benefited one.

Estimating COVID-19-induced excess mortality in Lombardy, Italy

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.

Regional excess mortality during the 2020 COVID-19 pandemic in five European countries

The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in the first stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.

Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea

This study aims to provide an improved understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

Excess total mortality in 2021 in Italy was about one third of that observed in 2020

Background:

Italy was severely hit by the Covid-19 pandemic with an excess of around 90,000 total deaths in 2020. Comparable data in 2021 are needed for monitoring the effects of the interventions adopted to control its spread and reduce the burden. This study estimates the excess mortality in Italy in the first eight months of 2021, with a focus on the working age population.

Methods:

Excess mortality was estimated as difference between the number of registered deaths and the expected deaths. Expected deaths in March-December 2020 and January-August 2021 were estimated separately by sex, through an over-dispersed Poisson regression model using mortality and population data for the period 2011-2019 (before the Covid-19 outbreak). The models included terms for calendar year, age group, a smooth function of week of the year and the natural logarithm of the population as offset term.

Results:

In the first eight months of 2021, we estimated 34,599 excess deaths (+7.9% of the expected deaths), of these 3667 were among individuals of working age (25-64 years). In this age group, mortality was 8.2% higher than expected with higher excesses among men (2972 deaths, +10.7%) than women (695 deaths, +4.1%).

Conclusions:

The excess deaths in the first eight months of 2021 account for about one third of that registered in 2020. Current data indicate that around 5000 excess deaths are expected by the end of the year, leading to a total excess for 2021 of around 40 thousand deaths. Despite the absence of influenza in January-March 2021, a relevant excess was also observed among the working age population.

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected-partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

Investigating regional excess mortality during 2020 COVID-19 pandemic in selected Latin American countries

In this paper, we measure the effect of the 2020 COVID-19 pandemic wave at the national and subnational levels in selected Latin American countries that were most affected: Brazil, Chile, Ecuador, Guatemala, Mexico, and Peru. We used publicly available monthly mortality data to measure the impacts of the pandemic using excess mortality for each country and its regions. We compare the mortality, at national and regional levels, in 2020 to the mortality levels of recent trends and provide estimates of the impact of mortality on life expectancy at birth. Our findings indicate that from April 2020 on, mortality exceeded its usual monthly levels in multiple areas of each country. In Mexico and Peru, excess mortality was spreading through many areas by the end of the second half of 2020. To a lesser extent, we observed a similar pattern in Brazil, Chile, and Ecuador. We also found that as the pandemic progressed, excess mortality became more visible in areas with poorer socioeconomic and sanitary conditions. This excess mortality has reduced life expectancy across these countries by 2-10 years. Despite the lack of reliable information on COVID-19 mortality, excess mortality is a useful indicator for measuring the effects of the coronavirus pandemic, especially in the context of Latin American countries, where there is still a lack of good information on causes of death in their vital registration systems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s41118-021-00139-1.

The impact of COVID-19 pandemic on AMI and stroke mortality in Lombardy: Evidence from the epicenter of the pandemic

BACKGROUND

The first Covid-19 epidemic outbreak has enormously impacted the delivery of clinical healthcare and hospital management practices in most of the hospitals around the world. In this context, it is important to assess whether the clinical management of non-Covid patients has not been compromised. Among non-Covid cases, patients with Acute Myocardial Infarction (AMI) and stroke need non-deferrable emergency care and are the natural candidates to be studied. Preliminary evidence suggests that the time from onset of symptoms to emergency department (ED) presentation has significantly increased in Covid-19 times as well as the 30-day mortality and in-hospital mortality.

METHODS

We check, in a causal inference framework, the causal effect of the hospital's stress generated by Covid-19 pandemic on in-hospital mortality rates (primary end-point of the study) of AMI and stroke over several time-windows of 15-days around the implementation date of the State of Emergency restrictions for COVID-19 (March, 9th 2020) using two quasi-experimental approaches, regression-discontinuity design (RDD) and difference-in-regression-discontinuity (DRD) designs. Data are drawn from Spedali Civili of Brescia, one of the most hit provinces in Italy by Covid-19 during March and May 2020.

FINDINGS

Despite the potential adverse effects on expected mortality due to a longer time to hospitalization and staff extra-burden generated by the first wave of Covid-19, the AMI and stroke mortality rates are overall not statistically different during the first wave of Covid-19 than before the first peak. The obtained results provided by RDD models are robust also when we account for seasonality and unobserved factors with DRD models.

INTERPRETATION

The non-statistically significant impact on mortality rates for AMI and stroke patients provides evidence of the hospital ability to manage -with the implementation of a dual track organization- the simultaneous delivery of high-quality cares to both Covid and non-Covid patients.

COVID-19 and the excess of mortality in Italy from January to April 2020: what are the risks for oldest old?

In February 2020, Italy has been the first country in Europe fighting against COVID-19. In March 2020, Italian government declared national lockdown. Until May 4^th, people stayed in home confinement and only the so-called essential works and activities were continued. Like in other countries, both for the disease severity and for the risk of death, the higher the age of people the higher the risk. In the first months of 2020, Italy saw a very high number of deaths related to COVID-19, with a huge age effect. There is an agreement on the view that there had been also an excess of mortality and on the role of mortality as a correct way to reflect the dynamics of the virus’s spread. In this paper we briefly discuss the trends of mortality during the first 4 months of 2020 according to the data by the Italian National Institute of Statistics.

Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach

BACKGROUND

In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths.

METHODS

We estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths.

FINDINGS

We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021).

INTERPRETATION

Estimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic.

FUNDING

None.

Excess all-cause mortality and COVID-19-related mortality: a temporal analysis in 22 countries, from January until August 2020

Background

This study aimed to investigate overall and sex-specific excess all-cause mortality since the inception of the COVID-19 pandemic until August 2020 among 22 countries.

Methods

Countries reported weekly or monthly all-cause mortality from January 2015 until the end of June or August 2020. Weekly or monthly COVID-19 deaths were reported for 2020. Excess mortality for 2020 was calculated by comparing weekly or monthly 2020 mortality (observed deaths) against a baseline mortality obtained from 2015–2019 data for the same week or month using two methods: (i) difference in observed mortality rates between 2020 and the 2015–2019 average and (ii) difference between observed and expected 2020 deaths.

Results

Brazil, France, Italy, Spain, Sweden, the UK (England, Wales, Northern Ireland and Scotland) and the USA demonstrated excess all-cause mortality, whereas Australia, Denmark and Georgia experienced a decrease in all-cause mortality. Israel, Ukraine and Ireland demonstrated sex-specific changes in all-cause mortality.

Conclusions

All-cause mortality up to August 2020 was higher than in previous years in some, but not all, participating countries. Geographical location and seasonality of each country, as well as the prompt application of high-stringency control measures, may explain the observed variability in mortality changes.

Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 103 countries and territories, openly available as the regularly updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality (Peru, Ecuador, Bolivia, Mexico) or above 400 excess deaths per 100,000 population (Peru, Bulgaria, North Macedonia, Serbia). At the same time, in several other countries (e.g. Australia and New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), by up to two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

Assessing the Country-Level Excess All-Cause Mortality and the Impacts of Air Pollution and Human Activity during the COVID-19 Epidemic

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO2 and PM2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO2, PM2.5, park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO2 and PM2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.

Local mortality estimates during the COVID-19 pandemic in Italy

Estimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The "official" approach adopted by public institutions to estimate the "excess mortality" during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in "ordinary" years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00148-021-00857-y.

The World Mortality Dataset: Tracking excess mortality across countries during the COVID-19 pandemic

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

Small-scale spatial analysis shows the specular distribution of excess mortality between the first and second wave of the COVID-19 pandemic in Italy

OBJECTIVES

The objective of the study is to compare excess mortality (EM) patterns and spatial correlation between the first and second wave of the pandemic in Lombardy, the Italian region that paid an extremely high COVID-19-related mortality toll in March and April 2020.

STUDY DESIGN

We conducted a longitudinal study using municipality-level mortality data.

METHODS

We investigated the patterns and spatial correlation of EM of men aged ≥75 years during the first two pandemic waves (March-April 2020 vs November 2020) of COVID-19, using the mortality data released by the Italian National Institute of Statistics. EM was estimated at the municipality level to accurately detect the critical areas within the region.

RESULTS

The areas that were mostly hit during the first wave of COVID-19 were generally spared by the second wave: EM of men aged ≥75 years in the municipality of Bergamo plummeted from +472% in March and April to -13% in November, and in Cremona the variation was from +344% to -19%. Conversely, in November 2020 EM was higher in some areas that had been protected in the first wave of the pandemic. Spatial correlation widely corroborates these findings, as large sections of the hot spots of EM detected in the first wave of the pandemic changed into cold spots in the second wave, and vice versa.

CONCLUSIONS

Our results reveal the specular distribution of EM between the first and second wave of the pandemic, which may entail the consequences of social distancing measures and individual behaviors, local management strategies, 'harvesting' of the frailer population and, possibly, acquired immune protection. In conclusion, our findings support the need for continuous monitoring and analysis of mortality data using detailed spatial resolution.

Non-COVID-19 patients in times of pandemic: Emergency department visits, hospitalizations and cause-specific mortality in Northern Italy

The COVID-19 pandemic forced healthcare services organization to adjust to mutating healthcare needs. Not exhaustive data are available on the consequences of this on non-COVID-19 patients. The aim of this study was to assess the impact of the pandemic on non-COVID-19 patients living in a one-million inhabitants' area in Northern Italy (Bologna Metropolitan Area-BMA), analyzing time trends of Emergency Department (ED) visits, hospitalizations and mortality. We conducted a retrospective observational study using data extracted from BMA healthcare informative systems. Weekly trends of ED visits, hospitalizations, in- and out-of-hospital, all-cause and cause-specific mortality between December 1st, 2019 to May 31st, 2020, were compared with those of the same period of the previous year. Non-COVID-19 ED visits and hospitalizations showed a stable trend until the first Italian case of COVID-19 has been recorded, on February 19th, 2020, when they dropped simultaneously. The reduction of ED visits was observed in all age groups and across all severity and diagnosis groups. In the lockdown period a significant increase was found in overall out-of-hospital mortality (43.2%) and cause-specific out-of-hospital mortality related to neoplasms (76.7%), endocrine, nutritional and metabolic (79.5%) as well as cardiovascular (32.7%) diseases. The pandemic caused a sudden drop of ED visits and hospitalizations of non-COVID-19 patients during the lockdown period, and a concurrent increase in out-of-hospital mortality mainly driven by deaths for neoplasms, cardiovascular and endocrine diseases. As recurrencies of the COVID-19 pandemic are underway, the scenario described in this study might be useful to understand both the population reaction and the healthcare system response at the early phases of the pandemic in terms of reduced demand of care and systems capability in intercepting it.

Prediction of COVID-19 mortality among hospitalized patients in Sudan.. [DOI: 10.1101/2021.03.09.21253179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

COVID-19 was primarily reported in China. The mortality rate across countries had ranged from 1% up to more than 10% and it is underestimated in some countries. Advanced age is the most frequently reported factor associated to mortality. Other factors were the presence of comorbidities such as diabetes mellitus, hypertension and obesity. Several models for mortality prediction had been developed to assist in improving the prognosis. The aim of our study was to assess the factors related to mortality among COVID-19 patients and develop a prediction model based on these factors.

Methods

A retrospective cohort study assessed the factors related to the mortality among COVID-19 patients who attended Imperial Hospital isolation centre on November-December, 2020, Khartoum, Sudan. Statistical tests performed were chi-square test, odds ratio and regression to develop the prediction model. Tests were considered statistically significant whenp< 0.05.

Results

105 patients were studied. 29% of the patients were deceased, while, 71% were discharged alive. A statistically significant association was found between the age and severity with regards to mortality rate (p=0.034, 0.018 respectively). The model equation for mortality prediction: Mortality = −14.724+ (1.387* Age) + (−0.323* Gender) + (1.814* Admission) + (0.193* Ischemic Heart Disease) + (−0.369* Fever) + (1.595* Cough) + (1.953* Complications) + (0.149* Duration of hospitalization) + (0.999* Enoxaparin dose).

Conclusions

Age, admission ward, cough and enoxaparin dose were statistically significant predictors for COVID-19 mortality (p= 0.014, 0.011, 0.015, 0.006 respectively).

Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R₀ varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number R_t in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R₀ at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, R_t in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.

Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.