Estimation of R0 for the spread of SARS-CoV-2 in Germany from Excess Mortality

Abstract Background For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of PCR testing capacity. Methods We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). Results The uncorrected R0 value for the spread of SARS-CoV-2 based on PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95%CI 1.96-2.10) for Covid-19-related deaths. However, because the number of PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Discussion Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.

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Estimation of R0 for the spread of SARS-CoV-2 in Germany from Excess Mortality

10.1101/2021.11.14.21266295 ◽

2021 ◽

Author(s):

Juan Pablo Prada Salcedo ◽

Luca Estelle Maag ◽

Laura Siegmund ◽

Elena Bencurova ◽

Liang Chunguang ◽

...

Keyword(s):

Excess Mortality ◽

Seasonal Pattern ◽

Robert Koch Institute ◽

Serial Interval ◽

Excess Deaths

For SARS-CoV-2, R0 calculations report usually 2-3, biased by PCR testing increases. Covid-19-induced excess mortality is less biased. We used data from Robert Koch Institute on Covid incidence, deaths, and PCR tests and excess mortality to determine early, policy-free R0 estimates with a serial interval of 4.7 days. The PCR-based R0 value was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95%CI 1.96-2.10) for Covid-19-related deaths. As the number of PCR tests increased, R0 values were corrected accordingly, yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths, excess deaths were 1.34 (95% CI 1.32-1.37). R0 is much lower than previously thought. This fits the observed seasonal pattern of infection across Europe in 2020-2021, including emergence of more contagious escape variants such as delta.

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Modelling the Long-Term Effects of Covid-19 Cancer Services Disruption on Patient Outcome in Scotland

10.1101/2021.01.17.21249993 ◽

2021 ◽

Author(s):

Jonine D Figueroa ◽

Ewan Gray ◽

Yasuko Maeda ◽

Peter S Hall ◽

Melanie Mackean ◽

...

Keyword(s):

Public Health ◽

Excess Mortality ◽

Service Planning ◽

Long Term Effects ◽

Referral Patterns ◽

Incidence Data ◽

Cancer Services ◽

Shift Model ◽

Excess Deaths

AbstractBackgroundModelling the long-term effects of disruption of cancer services and minimising any excess cancer mortality due to the Covid-19 pandemic is of great importance. Here we adapted a stage-shift model to inform service planning decisions within NHS Scotland for the ‘‘Detect Cancer Early’ tumours, breast, colorectal and lung cancer which represent 46% of all cancers diagnosed in Scotland.Methods & DataLung, colorectal and breast cancer incidence data for years 2017-18 were obtained from Public Health Scotland Cancer Quality Performance Indicators (QPI), to define a baseline scenario. The most current stage-specific 5-year survival data came from 2009-2014 national cancer registry and South East Scotland Cancer Network (SCAN) QPI audit datasets. The Degeling et al., inverse stage-shift model was adapted to estimate changes in stage at diagnosis, excess mortality and life-years lost from delays to diagnosis and treatment due to Covid-19-related health services disruption. Three and 6-month periods of disruption were simulated to demonstrate the model predictions.ResultsApproximately, 1-9% reductions in stage I/II presentations leading up to 2-10% increases in stage III/IV presentations are estimated across the three cancer types. A 6-month period of service disruption is predicted to lead to excess deaths at 5 years of 32.5 (31.1, 33.9) per 1000 cases for lung cancer, 16.5 (7.9, 24.3) for colorectal cancer and 31.6 (28.5, 34.4) for breast cancer.ConclusionsDisruption of cancer diagnostic services can lead to significant excess deaths in following years. Increasing diagnostic and capacity for cancer services to deal with the backlog of care are needed. Real time monitoring of incidence and referral patterns over the disruption and post-disruption period to reduce excess deaths including more rapid incidence data by stage and other key tumour/clinical characteristics at presentation for key cancer cases (on a quarterly basis). Real time monitoring in cancer care and referral patterns should help inform what type of interventions are needed to reduce excess mortality and whether different population subgroups require public health messaging campaigns. Specific mitigation measures can be the subject of additional modelling analysis to assess the benefits and inform service planning decision making.

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Mortality in Norway and Sweden during the COVID-19 pandemic

Scandinavian Journal of Public Health ◽

10.1177/14034948211047137 ◽

2021 ◽

pp. 140349482110471

Author(s):

Frederik E. Juul ◽

Henriette C. Jodal ◽

Ishita Barua ◽

Erle Refsum ◽

Ørjan Olsvik ◽

...

Keyword(s):

Mortality Rate ◽

Excess Mortality ◽

Mortality Rates ◽

Mitigation Measures ◽

Real World Data ◽

School Closures ◽

All Cause Mortality ◽

Rate Ratios ◽

Excess Deaths ◽

Related Mortality

Background: Norway and Sweden are similar countries in terms of socioeconomics and health care. Norway implemented extensive COVID-19 measures, such as school closures and lockdowns, whereas Sweden did not. Aims: To compare mortality in Norway and Sweden, two similar countries with very different mitigation measures against COVID-19. Methods: Using real-world data from national registries, we compared all-cause and COVID-19-related mortality rates with 95% confidence intervals (CI) per 100,000 person-weeks and mortality rate ratios (MRR) comparing the five preceding years (2015–2019) with the pandemic year (2020) in Norway and Sweden. Results: In Norway, all-cause mortality was stable from 2015 to 2019 (mortality rate 14.6–15.1 per 100,000 person-weeks; mean mortality rate 14.9) and was lower in 2020 than from 2015 to 2019 (mortality rate 14.4; MRR 0.97; 95% CI 0.96–0.98). In Sweden, all-cause mortality was stable from 2015 to 2018 (mortality rate 17.0–17.8; mean mortality rate 17.1) and similar to that in 2020 (mortality rate 17.6), but lower in 2019 (mortality rate 16.2). Compared with the years 2015–2019, all-cause mortality in the pandemic year was 3% higher due to the lower rate in 2019 (MRR 1.03; 95% CI 1.02–1.04). Excess mortality was confined to people aged ⩾70 years in Sweden compared with previous years. The COVID-19-associated mortality rates per 100,000 person-weeks during the first wave of the pandemic were 0.3 in Norway and 2.9 in Sweden. Conclusions: All-cause mortality in 2020 decreased in Norway and increased in Sweden compared with previous years. The observed excess deaths in Sweden during the pandemic may, in part, be explained by mortality displacement due to the low all-cause mortality in the previous year.

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Estimation of Excess Mortality and Years of Life Lost to COVID-19 in Norway and Sweden between March and November 2020

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18083913 ◽

2021 ◽

Vol 18 (8) ◽

pp. 3913

Author(s):

Martin Rypdal ◽

Kristoffer Rypdal ◽

Ola Løvsletten ◽

Sigrunn Holbek Sørbye ◽

Elinor Ytterstad ◽

...

Keyword(s):

Excess Mortality ◽

Age Groups ◽

Years Of Life Lost ◽

Regressive Model ◽

Epidemic Period ◽

All Cause Mortality ◽

Auto Regressive ◽

Expected Mortality ◽

Auto Regressive Model ◽

Excess Deaths

We estimate the weekly excess all-cause mortality in Norway and Sweden, the years of life lost (YLL) attributed to COVID-19 in Sweden, and the significance of mortality displacement. We computed the expected mortality by taking into account the declining trend and the seasonality in mortality in the two countries over the past 20 years. From the excess mortality in Sweden in 2019/20, we estimated the YLL attributed to COVID-19 using the life expectancy in different age groups. We adjusted this estimate for possible displacement using an auto-regressive model for the year-to-year variations in excess mortality. We found that excess all-cause mortality over the epidemic year, July 2019 to July 2020, was 517 (95%CI = (12, 1074)) in Norway and 4329 [3331, 5325] in Sweden. There were 255 COVID-19 related deaths reported in Norway, and 5741 in Sweden, that year. During the epidemic period of 11 March–11 November, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. We estimated that the number of YLL attributed to COVID-19 in Sweden was 45,850 [13,915, 80,276] without adjusting for mortality displacement and 43,073 (12,160, 85,451) after adjusting for the displacement accounted for by the auto-regressive model. In conclusion, we find good agreement between officially recorded COVID-19 related deaths and all-cause excess deaths in both countries during the first epidemic wave and no significant mortality displacement that can explain those deaths.

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Excess Mortality During the COVID-19 Pandemic in Philadelphia

American Journal of Public Health ◽

10.2105/ajph.2021.306285 ◽

2021 ◽

pp. e1-e6

Author(s):

Megan Todd ◽

Meagan Pharis ◽

Sam P. Gulino ◽

Jessica M. Robbins ◽

Cheryl Bettigole

Keyword(s):

Public Health ◽

Excess Mortality ◽

Poisson Model ◽

People Of Color ◽

Mortality Data ◽

Registration System ◽

Vital Registration System ◽

All Cause Mortality ◽

Race Ethnicity ◽

Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certiﬁcate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; ofﬁcial counts signiﬁcantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

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Magnitude, change over time, demographic characteristics and geographic distribution of excess deaths among nursing home residents during the first wave of COVID-19 in France: a nationwide cohort study

10.1101/2021.01.09.20248472 ◽

2021 ◽

Author(s):

Florence Canouï-Poitrine ◽

Antoine Rachas ◽

Martine Thomas ◽

Laure Carcaillon-Bentata ◽

Roméo Fontaine ◽

...

Keyword(s):

Nursing Home ◽

General Population ◽

Excess Mortality ◽

Nursing Home Residents ◽

Primary Objective ◽

List Type ◽

Key Points ◽

Standardized Mortality Ratios ◽

Using Data ◽

Excess Deaths

AbstractImportanceNursing home (NH) residents are particularly vulnerable to SARS-CoV-2 infections and coronavirus disease 2019 (COVID-19) lethality. However, excess deaths in this population have rarely been documented.ObjectivesThe primary objective was to assess the number of excess deaths among NH residents during the first wave of the COVID-19 pandemic in France. The secondary objectives were to determine the number of excess deaths as a proportion of the total excess deaths in the general population and determine whether a harvesting effect was present.DesignWe studied a cohort of 494,753 adults (as of March 1st, 2020) aged 60 and over in 6,515 NHs in mainland France. This cohort was exposed to the first wave of the COVID-19 pandemic (from March 1st to May 31st, 2020) and was compared with the corresponding, reference cohorts from 2014 to 2019 (using data from the French National Health Data System).Main outcome and measuresThe main outcome was all-cause death. Weekly excess deaths and standardized mortality ratios (SMRs) were estimated.ResultThere were 13,505 excess deaths among NH residents. Mortality increased by 43% (SMR: 1.43). The mortality excess was higher among males than among females (SMR: 1.51 and 1.38, respectively) and decreased with age (SMRs in females: 1.61 in the 60-74 age group, 1.58 for 75-84, 1.41 for 85-94, and 1.31 for 95 or over; Males: SMRs: 1.59 for 60-74, 1.69 for 75-84, 1.47 for 85-94, and 1.41 for 95 or over). We did not observe a harvesting effect (up until August 30th, 2020). By extrapolating to all NH residents nationally (N=570,003), the latter accounted for 51% of the total excess deaths in the general population (N=15,114 out of 29,563).ConclusionNH residents accounted for about half of the total excess deaths in France during the first wave of the COVID-19 pandemic. The excess death rate was higher among males than females and among younger residents than among older residents. We did not observe a harvesting effect. A real-time mortality surveillance system and the identification of individual and environmental risk factors might help to design the future model of care for older dependent adults.Key pointsDuring the first wave of the COVID-19 pandemic in France, the mortality among nursing home residents increased by 43%.Nursing home residents accounted for 51% of the total excess deaths in France.The excess mortality was higher among younger residents than among older residents.The excess mortality was higher among males than among females.We did not observe a harvesting effect during the study period (ending on August 30th, 2020, i.e., three months after the end of the first wave).

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Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

Epidemiology and Infection ◽

10.1017/s0950268818002315 ◽

2018 ◽

Vol 146 (16) ◽

pp. 2059-2065 ◽

Cited By ~ 10

Author(s):

A. R. R. Freitas ◽

P. M. Alarcón-Elbal ◽

M. R. Donalisio

Keyword(s):

Strong Correlation ◽

Excess Mortality ◽

Pearson Correlation ◽

Age Groups ◽

Epidemiological Data ◽

Mortality Data ◽

Surveillance Systems ◽

Mortality And Morbidity ◽

The Impact ◽

Excess Deaths

AbstractIn some chikungunya epidemics, deaths are not completely captured by traditional surveillance systems, which record case and death reports. We evaluated excess deaths associated with the 2014 chikungunya virus (CHIKV) epidemic in Guadeloupe and Martinique, Antilles. Population (784 097 inhabitants) and mortality data, estimated by sex and age, were accessed from the Institut National de la Statistique et des Études Économiques in France. Epidemiological data, cases, hospitalisations and deaths on CHIKV were obtained from the official epidemiological reports of the Cellule de Institut de Veille Sanitaire in France. Excess deaths were calculated as the difference between the expected and observed deaths for all age groups for each month in 2014 and 2015, considering the upper limit of 99% confidence interval. The Pearson correlation coefficient showed a strong correlation between monthly excess deaths and reported cases of chikungunya (R= 0.81,p< 0.005) and with a 1-month lag (R= 0.87,p< 0.001); and a strong correlation was also observed between monthly rates of hospitalisation for CHIKV and excess deaths with a delay of 1 month (R= 0.87,p< 0.0005). The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. There were excess deaths in almost all age groups, and excess mortality rate was higher among the elderly but was similar between male and female individuals. The overall mortality estimated in the current study (639 deaths) was about four times greater than that obtained through death declarations (160 deaths). Although the aetiological diagnosis of all deaths associated with CHIKV infection is not always possible, already well-known statistical tools can contribute to the evaluation of the impact of CHIKV on mortality and morbidity in the different age groups.

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Excess Mortality in Istanbul during Extreme Heat Waves between 2013 and 2017

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16224348 ◽

2019 ◽

Vol 16 (22) ◽

pp. 4348 ◽

Cited By ~ 2

Author(s):

Günay Can ◽

Ümit Şahin ◽

Uğurcan Sayılı ◽

Marjolaine Dubé ◽

Beril Kara ◽

...

Keyword(s):

Heat Wave ◽

Excess Mortality ◽

Intervention Studies ◽

Heat Waves ◽

Epidemiological Studies ◽

Extreme Heat ◽

Mortality And Morbidity ◽

Climate Simulations ◽

Morbidity Risks ◽

Excess Deaths

Heat waves are one of the most common direct impacts of anthropogenic climate change and excess mortality their most apparent impact. While Turkey has experienced an increase in heat wave episodes between 1971 and 2016, no epidemiological studies have examined their potential impacts on public health so far. In this study excess mortality in Istanbul attributable to extreme heat wave episodes between 2013 and 2017 is presented. Total excess deaths were calculated using mortality rates across different categories, including age, sex, and cause of death. The analysis shows that three extreme heat waves in the summer months of 2015, 2016, and 2017, which covered 14 days in total, significantly increased the mortality rate and caused 419 excess deaths in 23 days of exposure. As climate simulations show that Turkey is one of the most vulnerable countries in the Europe region to the increased intensity of heat waves until the end of the 21st century, further studies about increased mortality and morbidity risks due to heat waves in Istanbul and other cities, as well as intervention studies, are necessary.

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Excess mortality from mental, neurological and substance use disorders in the Global Burden of Disease Study 2010

Epidemiology and Psychiatric Sciences ◽

10.1017/s2045796014000687 ◽

2014 ◽

Vol 24 (2) ◽

pp. 121-140 ◽

Cited By ~ 41

Author(s):

F. J. Charlson ◽

A. J. Baxter ◽

T. Dua ◽

L. Degenhardt ◽

H. A. Whiteford ◽

...

Keyword(s):

Substance Use ◽

Natural History ◽

Life Expectancy ◽

Substance Use Disorders ◽

Burden Of Disease ◽

Excess Mortality ◽

Global Burden Of Disease ◽

Global Burden ◽

Causal Pathways ◽

Excess Deaths

Aims.Mortality-associated burden of disease estimates from the Global Burden of Disease 2010 (GBD 2010) may erroneously lead to the interpretation that premature death in people with mental, neurological and substance use disorders (MNSDs) is inconsequential when evidence shows that people with MNSDs experience a significant reduction in life expectancy. We explore differences between cause-specific and excess mortality of MNSDs estimated by GBD 2010.Methods.GBD 2010 cause-specific death estimates were produced using the International Classification of Diseases death-coding system. Excess mortality (all-cause) was estimated using natural history models. Additional mortality attributed to MNSDs as underlying causes but not captured through GBD 2010 methodology is quantified in the comparative risk assessments.Results.In GBD 2010, MNSDs were estimated to be directly responsible for 840 000 deaths compared with more than 13 million excess deaths using natural history models.Conclusions.Numbers of excess deaths and attributable deaths clearly demonstrate the high degree of mortality associated with these disorders. There is substantial evidence pointing to potential causal pathways for this premature mortality with evidence-based interventions available to address this mortality. The life expectancy gap between persons with MNSDs and the general population is high and should be a focus for health systems reform.

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Excess mortality associated with the 2009 pandemic of influenza A(H1N1) in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268811002238 ◽

2011 ◽

Vol 140 (9) ◽

pp. 1542-1550 ◽

Cited By ~ 24

Author(s):

L. YANG ◽

K. P. CHAN ◽

B. J. COWLING ◽

S. S. CHIU ◽

K. H. CHAN ◽

...

Keyword(s):

Hong Kong ◽

Influenza A ◽

Excess Mortality ◽

Seasonal Influenza ◽

Mortality Rates ◽

Timely Manner ◽

Mortality Burden ◽

Influenza A H1n1 ◽

Excess Deaths ◽

Modelling Approach

SUMMARYReliable estimates of the burden of 2009 pandemic influenza A(pH1N1) cannot be easily obtained because only a small fraction of infections were confirmed by laboratory tests in a timely manner. In this study we developed a Poisson prediction modelling approach to estimate the excess mortality associated with pH1N1 in 2009 and seasonal influenza in 1998–2008 in the subtropical city Hong Kong. The results suggested that there were 127 all-cause excess deaths associated with pH1N1, including 115 with cardiovascular and respiratory disease, and 22 with pneumonia and influenza. The excess mortality rates associated with pH1N1 were highest in the population aged ⩾65 years. The mortality burden of influenza during the whole of 2009 was comparable to those in the preceding ten inter-pandemic years. The estimates of excess deaths were more than twofold higher than the reported fatal cases with laboratory-confirmed pH1N1 infection.

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