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

Impact of the COVID-19 pandemic on admissions of deceased to an institute of legal medicine in Germany

All over the world, the COVID-19 pandemic has impacted mortality beyond deaths that can be directly attributed to the viral disease. This study investigates the effects of the pandemic on admissions of deceased to a large institute of legal medicine and metropolitan morgue in Germany. Employing statistical models, the general time trend was separated from the effect of the pandemic in terms of place of death, autopsy category, age and sex. In addition, the pandemic's impact on one of the structurally most important public health determinants, poverty, on numbers of admissions in different place of death categories was analyzed. We find that the pandemic has caused a significant increase in admissions of those who died at residential addresses, which appears to be primarily driven by SARS-CoV-2 negative cases, and those who died in retirement and care facilities, with a significant overrepresentation of SARS-CoV-2 positive cases. A high degree of poverty in a neighborhood does not impact the likelihood to be admitted to the institute in those two categories before or during the pandemic. For dead bodies found in public spaces, however, a poverty variable causes a significant increase in the number of admissions during the pre-pandemic phase. Interestingly, this effect is reversed during the pandemic period. The number of admissions without an autopsy being ordered or requested increased significantly during the pandemic. Further, the COVID-19 pandemic caused a significant increase in admissions in the senile population. Our results indicate that the reluctance of treating physicians to conduct house calls to certify a death has persisted throughout the pandemic and has caused a surge of admissions of deceased to the institute of legal medicine without any criminological indications or subsequent rise in forensic autopsy orders.

Understanding excess mortality during COVID in Belgium: the influence of pre-existing health status and social factors

BACKGROUND

This study aims to investigate how pre-existing health status and social background contribute to excess mortality during the COVID-19 crisis in Belgium.

METHODS

The study population consists of almost 1.4 million adult members of Solidaris, the second largest health insurance fund in Belgium. Pre-existing health status was identified using health care reimbursement data, including medication use. Social characteristics included a proxy for low socio-economic status, nationality of origin, and living arrangement. Excess mortality during the COVID-19 crisis was measured by computing the relative difference between all-cause mortality in 2020 or 2021 and the average yearly mortality in 2015-2019. Directly Standardised Mortality Rates (DSMRs) were calculated to investigate absolute mortality inequalities. Mortality Rate Ratios (MRRs) were computed using Poisson regression analyses to investigate relative mortality inequalities.

RESULTS

DSMRs show that persons with no previous disease experienced significant excess mortality in 2020, like men with one, two or three diseases and women with various numbers of pre-existing diseases. Results by specific disease show heterogenous results. After adjusting for age, sex and social characteristics, persons with cancer experienced a significant mortality deficit of 17% in 2020 and of 9% in 2021. For persons with cancer and asthma or COPD, significant mortality deficits of 10% and  3% were observed in 2020 and 2021, respectively.

CONCLUSION

The study provides insights into the complex dynamics of mortality during the COVID-19 crisis, emphasising the need to consider individual-level information on pre-existing health and social background jointly.

Patterns and drivers of excess mortality during the COVID-19 pandemic in 13 Western European countries

BACKGROUND

Important differences in excess mortality between European countries during the COVID-19 pandemic have been reported. Understanding the drivers of these differences is essential to pandemic preparedness.

METHODS

We examined patterns in age- and sex-standardized cumulative excess mortality in 13 Western European countries during the first 30 months of the COVID-19 pandemic and the correlation of country-level characteristics of interest with excess mortality.

RESULTS

In a timeline analysis, we identified notable differences in seeding events, particularly in early 2020 and when the Alpha variant emerged, likely contributing to notable differences in excess mortality between countries (lowest in Denmark during that period). These differences were more limited from July 2021 onwards. Lower excess mortality was associated with implementing stringent non-pharmaceutical interventions (NPIs) when hospital admissions were still low in 2020 (correlation coefficient rho = 0.65, p = 0.03) and rapid rollout of vaccines in the elderly in early 2021 (rho =  - 0.76, p = 0.002). Countries which implemented NPIs while hospital admissions were low tended to experience lower gross domestic product (GDP) losses in 2020 (rho =  - 0.55, p = 0.08). Structural factors, such as high trust in the national government (rho =  - 0.77, p = 0.002) and low ratio of population at risk of poverty (rho = 0.55, p = 0.05), were also associated with lower excess mortality.

CONCLUSIONS

These results suggest the benefit of early implementation of NPIs and swift rollout of vaccines to the most vulnerable. Further analyses are required at a more granular level to better understand how these factors impacted excess mortality and help guide pandemic preparedness plans.

Mortality associated with the COVID-19 pandemic in the Swiss dialysis population beyond SARS-CoV-2 infection

Background

While SARS-CoV-2 infection has direct obvious consequences on patients undergoing dialysis, the COVID-19 pandemic also had an indirect impact on health systems. Therefore, we aimed to determine whether the COVID-19 era itself was associated with adverse consequences in the Swiss dialysis population as compared to the pre-COVID-19 era, while accounting for direct impact of SARS-CoV-2 infection.

Methods

We retrospectively included all patients recorded in the Swiss dialysis registry from January 2014 to December 2022. The pre-COVID-19 era and the COVID-19 era were defined based on the cut-off date of January 2020. Cox proportional hazard model was used with all-cause mortality as the primary outcome.

Results

The cohort consisted of 7837 patients from 97 dialysis centres. Median age was 68.6 years with 66.1% men. Crude mortality rates were 11.6% (11.0% to 12.2%) and 14.2% (13.4% to 14.9%) person-years for the pre-COVID-19 era and the COVID-19 era, respectively. In multivariable analysis, SARS-CoV-2 infection was associated with an increased risk of mortality (HR 4.26, 95% CI 3.65 to 4.97, P < .001) while the COVID-19 era itself was not (HR 0.98, 95% CI 0.88 to 1.08, P = .687).

Conclusions

The COVID-19 era was not associated with an excess of mortality in the Swiss dialysis population as compared to the pre-COVID-19 era when accounting for the direct effect of SARS-CoV-2 infection. This suggests that control measures established during the pandemic did not have a negative impact on dialysis patients at the national level. These results could inform health policy makers in the eventuality of future pandemics.

Gender/Sex Disparities in the COVID-19 Cascade From Testing to Mortality: An Intersectional Analysis of Swiss Surveillance Data

Objectives

This study investigates gender and sex disparities in COVID-19 epidemiology in the Canton of Vaud, Switzerland, focusing on the interplay with socioeconomic position (SEP) and age.

Methods

We analyzed COVID-19 surveillance data from March 2020 to June 2021, using an intersectional approach. Negative binomial regression models assessed disparities between women and men, across SEP quintiles and age groups, in testing, positivity, hospitalizations, ICU admissions, and mortality (Incidence Rate Ratios [IRR], with 95% Confidence Intervals [CI]).

Results

Women had higher testing and positivity rates than men, while men experienced more hospitalizations, ICU admissions, and deaths. The higher positivity in women under 50 was mitigated when accounting for their higher testing rates. Within SEP quintiles, gender/sex differences in testing and positivity were not significant. In the lowest quintile, women's mortality risk was 68% lower (Q1: IRR 0.32, CI 0.20-0.52), with decreasing disparities with increasing SEP quintiles (Q5: IRR 0.66, CI 0.41-1.06).

Conclusion

Our findings underscore the complex epidemiological patterns of COVID-19, shaped by the interactions of gender/sex, SEP, and age, highlighting the need for intersectional perspectives in both epidemiological research and public health strategy development.

New insights into three trajectories of omicron-related all-cause death reduced by COVID-19 booster vaccination

BACKGROUND

The trajectories of all-cause deaths linked to omicron infections are rarely studied, especially in relation to the efficacy of booster shots. For assessing three epidemiological death trajectories, including dying from COVID-19, dying with COVID-19, and non-COVID-19 death, we offer a new COVID-19-and-death competing risk model that deals with the primary pathway (e.g., dying from COVID-19) competing with two other pathways.

METHODS

We applied this model to track three trajectories: deaths directly from COVID-19, deaths with COVID-19 as a contributing factor, and indirect non-COVID-19 deaths. The study used data from a Taiwanese cohort, covering periods of Omicron subvariants BA.2, BA.5, and BA.2.75. It focused on the effectiveness of monovalent and bivalent booster vaccines against these death trajectories.

RESULTS

The highest mortality was observed during the BA.2 phase, which decreased in the BA.5 period and increased again in the BA.2.75 period. Analyzing each trajectory, we noted similar trends in deaths directly from and with COVID-19, while non-COVID-19 deaths remained stable across subvariants. Booster vaccines reduced all-cause mortality by 58% (52%-62%) for BA.2, 70% (65%-75%) for BA.5%, and 75% (70%-80%) for BA.2.75, compared to incomplete vaccination. The reduction in deaths directly from COVID-19 was 66% (61%-72%) for BA.2, 78% (72%-84%) for BA.5%, and 85% (76%-93%) for BA.2.75. For deaths with COVID-19, the figures were 46% (36%-55%), 76% (68%-84%), and 90% (86%-95%). Additionally, the booster shots decreased non-COVID-19 deaths by 64% (63%-66%) for BA.2, 38% (36%-40%) for BA.5, and 19% (17%-21%) for BA.2.75.

CONCLUSION

Our competing risk analysis is effective for monitoring all-cause death trajectories amidst various Omicron infections. It provides insights into the impact of booster vaccines, especially bivalent ones, and highlights the consequences of inadequate healthcare for vulnerable groups.

A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients

The scarcity of data is likely to have a negative effect on machine learning (ML). Yet, in the health sciences, data is diverse and can be costly to acquire. Therefore, it is critical to develop methods that can reach similar accuracy with minimal clinical features. This study explores a methodology that aims to build a model using minimal clinical parameters to reach comparable performance to a model trained with a more extensive list of parameters. To develop this methodology, a dataset of over 1,000 COVID-19-positive patients was used. A machine learning model was built with over 90% accuracy when combining 24 clinical parameters using Random Forest (RF) and logistic regression. Furthermore, to obtain minimal clinical parameters to predict the mortality of COVID-19 patients, the features were weighted using both Shapley values and RF feature importance to get the most important factors. The six most highly weighted features that could produce the highest performance metrics were combined for the final model. The accuracy of the final model, which used a combination of six features, is 90% with the random forest classifier and 91% with the logistic regression model. This performance is close to that of a model using 24 combined features (92%), suggesting that highly weighted minimal clinical parameters can be used to reach similar performance. The six clinical parameters identified here are acute kidney injury, glucose level, age, troponin, oxygen level, and acute hepatic injury. Among those parameters, acute kidney injury was the highest-weighted feature. Together, a methodology was developed using significantly minimal clinical parameters to reach performance metrics similar to a model trained with a large dataset, highlighting a novel approach to address the problems of clinical data collection for machine learning.

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.

Improving consistency in estimating future health burdens from environmental risk factors: Case study for ambient air pollution

Future changes in exposure to risk factors should impact mortality rates and population. However, studies commonly use mortality rates and population projections developed exogenously to the health impact assessment model used to quantify future health burdens attributable to environmental risks that are therefore invariant to projected exposure levels. This impacts the robustness of many future health burden estimates for environmental risk factors. This work describes an alternative methodology that more consistently represents the interaction between risk factor exposure, population and mortality rates, using ambient particulate air pollution (PM2.5) as a case study. A demographic model is described that estimates future population based on projected births, mortality and migration. Mortality rates are disaggregated between the fraction due to PM2.5 exposure and other factors for a historic year, and projected independently. Accounting for feedbacks between future risk factor exposure and population and mortality rates can greatly affect estimated future attributable health burdens. The demographic model estimates much larger PM2.5-attributable health burdens with constant 2019 PM2.5 (∼10.8 million deaths in 2050) compared to a model using exogenous population and mortality rate projections (∼7.3 million), largely due to differences in mortality rate projection methods. Demographic model-projected PM2.5-attributable mortality can accumulate substantially over time. For example, ∼71 million more people are estimated to be alive in 2050 when WHO guidelines (5 µg m-3) are achieved compared to constant 2019 PM2.5 concentrations. Accounting for feedbacks is more important in applications with relatively high future PM2.5 concentrations, and relatively large changes in non-PM2.5 mortality rates.

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

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

Airborne transmission risks of tuberculosis and COVID-19 in schools in South Africa, Switzerland, and Tanzania: Modeling of environmental data

The COVID-19 pandemic renewed interest in airborne transmission of respiratory infections, particularly in congregate indoor settings, such as schools. We modeled transmission risks of tuberculosis (caused by Mycobacterium tuberculosis, Mtb) and COVID-19 (caused by SARS-CoV-2) in South African, Swiss and Tanzanian secondary schools. We estimated the risks of infection with the Wells-Riley equation, expressed as the median with 2.5% and 97.5% quantiles (credible interval [CrI]), based on the ventilation rate and the duration of exposure to infectious doses (so-called quanta). We computed the air change rate (ventilation) using carbon dioxide (CO2) as a tracer gas and modeled the quanta generation rate based on reported estimates from the literature. The share of infectious students in the classroom is determined by country-specific estimates of pulmonary TB. For SARS-CoV-2, the number of infectious students was estimated based on excess mortality to mitigate the bias from country-specific reporting and testing. Average CO2 concentration (parts per million [ppm]) was 1,610 ppm in South Africa, 1,757 ppm in Switzerland, and 648 ppm in Tanzania. The annual risk of infection for Mtb was 22.1% (interquartile range [IQR] 2.7%-89.5%) in South Africa, 0.7% (IQR 0.1%-6.4%) in Switzerland, and 0.5% (IQR 0.0%-3.9%) in Tanzania. For SARS-CoV-2, the monthly risk of infection was 6.8% (IQR 0.8%-43.8%) in South Africa, 1.2% (IQR 0.1%-8.8%) in Switzerland, and 0.9% (IQR 0.1%-6.6%) in Tanzania. The differences in transmission risks primarily reflect a higher incidence of SARS-CoV-2 and particularly prevalence of TB in South Africa, but also higher air change rates due to better natural ventilation of the classrooms in Tanzania. Global comparisons of the modeled risk of infectious disease transmission in classrooms can provide high-level information for policy-making regarding appropriate infection control strategies.

Life-expectancy changes from 2019 to 22: A case study of Japan using provisional death count

Many countries struggled with suppressing the incidence of COVID-19 caused by SARS-CoV-2 Omicron variant (B.1.1.529). As the epidemic size of COVID-19 in 2022 became bigger than earlier years in Japan, the present study aimed to estimate life expectancy at birth at the end of 2022, using provisional death datasets in Aichi and Fukui prefectures. We collected monthly death count from 2019 to the end of 2022, computing the period life table. While the life expectancy at birth in Aichi, 2019 was 84.6 years, it was very slightly extended to 84.7 years in 2020 and 2021, followed by a shortening for nearly 0.4 years in 2022. In Fukui, monotonous extension pattern was seen, i.e., 85.5 years in 2019, 85.6 in 2020, followed by 85.8 and 86.2 years in 2021 and 2022, respectively. Although decades-long trend of extending life expectancy at birth was partly discontinued from 2020 due to the pandemic at the national level, we have shown that the pandemic impact was still small in Japan by the end of 2022. First Omicron wave occurred shortly after primary series vaccination, and even real time booster program was underway during that wave. Different demographic consequences between Aichi and Fukui are explained by differential epidemic sizes prior to vaccination.

Unmasking the COVID-19 pandemic prevention gains: excess mortality reversal in 2022

OBJECTIVES

The purpose of this study was to assess the long-term effectiveness of COVID-19 pandemic prevention measures in saving lives after European governments began to lift restrictions.

STUDY DESIGN

Excess mortality interrupted time series.

METHODS

Country-level weekly data on deaths were fitted to the Poisson mixed linear model to estimate excess deaths. Based on this estimate, the percentage of excess deaths above the baseline during the pandemic (week 11 in 2020 to week 15 in 2022) (when public health interventions were in place) and during the post-pandemic period (week 16 in 2022 to week 52 in 2022) were calculated. These results were fitted to the linear regression model to determine any potential relationship between mortality during these two periods.

RESULTS

The model used in this study had high predictive value (adjusted R2 = 59.4%). Mortality during the endemic (post-pandemic) period alone increased by 7.2% (95% confidence interval [CI]: 5.7, 8.6) above baseline, while each percentage increase in mortality during the pandemic corresponded to a 0.357% reduction (95% CI: 0.243, 0.471) in mortality during the post-pandemic period.

CONCLUSIONS

The most successful countries in terms of protective measures also experienced the highest mortality rates after restrictions were lifted. The model used in this study clearly shows a measure of bidirectional mortality displacement that is sufficiently clear to mask any impact of long COVID on overall mortality. Results from this study also seriously impact previous cost-benefit analyses of pandemic prevention measures, since, according to the current model, 12.2% (95% CI: 8.3, 16.1) of the gains achieved in pandemic containment were lost after restrictions were lifted.

Inequalities in COVID-19 severe morbidity and mortality by country of birth in Sweden

Migrants have been more affected by the COVID-19 pandemic. Whether this has varied over the course of the pandemic remains unknown. We examined how inequalities in intensive care unit (ICU) admission and death related to COVID-19 by country of birth have evolved over the course of the pandemic, while considering the contribution of social conditions and vaccination uptake. A population-based cohort study was conducted including adults living in Sweden between March 1, 2020 and June 1, 2022 (n = 7,870,441). Poisson regressions found that migrants from Africa, Middle East, Asia and European countries without EU28/EEA, UK and Switzerland had higher risk of COVID-19 mortality and ICU admission than Swedish-born. High risks of COVID-19 ICU admission was also found in migrants from South America. Inequalities were generally reduced through subsequent waves of the pandemic. In many migrant groups socioeconomic status and living conditions contributed to the disparities while vaccination campaigns were decisive when such became available.

The footprint of human-induced climate change on heat-related deaths in the summer of 2022 in Switzerland

Human-induced climate change is leading to an increase in the intensity and frequency of extreme weather events, which are severely affecting the health of the population. The exceptional heat during the summer of 2022 in Europe is an example, with record-breaking temperatures only below the infamous 2003 summer. High ambient temperatures are associated with many health outcomes, including premature mortality. However, there is limited quantitative evidence on the contribution of anthropogenic activities to the substantial heat-related mortality observed in recent times. Here we combined methods in climate epidemiology and attribution to quantify the heat-related mortality burden attributed to human-induced climate change in Switzerland during the summer of 2022. We first estimated heat-mortality association in each canton and age/sex population between 1990 and 2017 in a two-stage time-series analysis. We then calculated the mortality attributed to heat in the summer of 2022 using observed mortality, and compared it with the hypothetical heat-related burden that would have occurred in absence of human-induced climate change. This counterfactual scenario was derived by regressing the Swiss average temperature against global mean temperature in both observations and CMIP6 models. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151-1068] due to heat between June and August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133-644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in the 2022 summer in Switzerland.

No Excess of Mortality from Lung Cancer during the COVID-19 Pandemic in an Area at Environmental Risk: Results of an Explorative Analysis

BACKGROUND

The COVID-19 pandemic and the restrictive measures associated with it placed enormous pressure on health facilities and may have caused delays in the treatment of other diseases, leading to increases in mortality compared to the expected rates. Areas with high levels of air pollution already have a high risk of death from cancer, so we aimed to evaluate the possible indirect effects of the pandemic on mortality from lung cancer compared to the pre-pandemic period in the province of Taranto, a polluted site of national interest for environmental risk in the south of Italy.

METHODS

We carried out a retrospective observational study on lung cancer data (ICD-10: C34) from the Registry of Mortality (ReMo) for municipalities in Taranto Province over the period of 1 January 2011 to 31 December 2021. Seasonal exponential smoothing, Holt-Winters additive, Holt-Winters multiplicative, and auto-regressive integrated moving average (ARIMA) models were used to forecast the number of deaths during the pandemic period. Data were standardized by sex and age via an indirect method and shown as monthly mortality rates (MRs), standardized mortality ratios (SMRs), and adjusted mortality rates (AMRs).

RESULTS

In Taranto Province, 3108 deaths from lung cancer were recorded between 2011 and 2021. In the province of Taranto, almost all of the adjusted monthly mortality rates during the pandemic were within the confidence interval of the predicted rates, with the exception of significant excesses in March (+1.82, 95% CI 0.11-3.08) and August 2020 (+2.09, 95% CI 0.20-3.44). In the municipality of Taranto, the only significant excess rate was in August 2020 (+3.51, 95% CI 0.33-6.69). However, in total, in 2020 and 2021, the excess deaths from lung cancer were not significant both for the province of Taranto (+30 (95% CI -77; +106) for 2020 and +28 (95% CI -130; +133) for 2021) and for the municipality of Taranto alone (+14 (95% CI -47; +74) for 2020 and -2 (95% CI -86; +76) for 2021).

CONCLUSIONS

This study shows that there was no excess mortality from lung cancer as a result of the COVID-19 pandemic in the province of Taranto. The strategies applied by the local oncological services during the pandemic were probably effective in minimizing the possible interruption of cancer treatment. Strategies for accessing care in future health emergencies should take into account the results of continuous monitoring of disease trends.