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

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.

scholarly journals Estimation of Years of Life Lost by Sweden's Relaxed COVID-19 Mitigation Strategy

2020 ◽  
Author(s):  
Martin Rypdal ◽  
Kristoffer Rypdal ◽  
Ola Løvsletten ◽  
Sigrunn Sørbye ◽  
Elinor Ytterstad ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Years Of Life Lost ◽  
Life Years ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Auto Regressive ◽  
Life Years Lost ◽  
Expected Mortality ◽  
Auto Regressive Model

Abstract Objective: To estimate the weekly excess all-cause mortality in Norway and Sweden, and to estimate the years of life lost (YLL) attributed to COVID-19 in Sweden and the significance of mortality displacement. Methods: We found expected mortality by taking the declining trend and the seasonality in mortality into account. 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. Results: We found that excess all-cause mortality over the epidemic year (July to July) 2019/20 was 517 (95%CI -12, 1074) in Norway and 4329 (3331, 5325) in Sweden. There were reported 255 COVID-19 related deaths in Norway, and 5741 in Sweden, that year. During the epidemic period March 11 – November 11, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. The estimated number of life-years lost attributed to the more relaxed Swedish strategy was 45850 (13915, 80276) without adjusting for mortality displacement and 43073 (12160, 85451) after adjusting for possible displacement.

scholarly journals Mortality in Norway and Sweden before and after the Covid-19 outbreak: a cohort study

2020 ◽  
Author(s):  
Frederik E Juul ◽  
Henriette C Jodal ◽  
Ishita Barua ◽  
Erle Refsum ◽  
Ørjan Olsvik ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Mortality Rates ◽  
List Type ◽  
School Closures ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Before And After ◽  
Rate Ratios ◽  
Expected Mortality

AbstractObjectivesNorway and Sweden are similar countries regarding ethnicity, socioeconomics and health care. To combat Covid-19, Norway implemented extensive measures such as school closures and lock-downs, while Sweden has been criticised for relaxed measures against Covid-19. We compared the effect of the different national strategies on all-cause and Covid-19 associated mortality.DesignRetrospective cohort.SettingThe countries Norway and Sweden.ParticipantsAll inhabitants.Main outcome measuresWe calculated weekly mortality rates (MR) with 95% confidence intervals (CI) per 100,000 individuals as well as mortality rate ratios (MRR) comparing the epidemic year (29th July, 2019 to 26th July, 2020) to the four preceding years (July 2015 to July 2019). We also compared Covid-19 associated deaths and mortality rates for the weeks of the epidemic in Norway and Sweden (16th March to 26th July, 2020).ResultsIn Norway, mortality rates were stable during the first three 12-month periods of 2015/16; 2016/17 and 2017/18 (MR 14.8 to 15.1 per 100,000), and slightly lower in the two most recent periods including during epidemic period (2018/19 and 2019/20; 14.5 per 100,000). In Sweden, all-cause mortality was stable during the first three 12-month periods of 2015/16; 2016/17 and 2017/18 (MR 17.2 to 17.5 per 100,000), but lower in the year 2018/19 immediately preceding the epidemic (16.2 per 100,000). Covid-19 associated mortality rates were 0.2 per 100,000 (95%CI 0.1 to 0.4) in Norway and 2.9 (95%CI 1.9 to 3.9) in Sweden. The increase in mortality was confined to individuals in 70 years or older.ConclusionsAll-cause mortality remained unaltered in Norway. In Sweden, the observed increase in all-cause mortality during Covid-19 was partly due to a lower than expected mortality preceding the epidemic and the observed excess mortality, was followed by a lower than expected mortality after the first Covid-19 wave. This may suggest mortality displacement.Strengths and limitations of this studyCompares two similar contries in all aspects but the handling of the Covid-19 epidemicEvaluates the mortality for several years before and during the epidemicProvides a possible explanation of the observed mortality changesDiscusses the socioeconomic effects of the different strategies in the two countriesDoes not evaluate cause-specific mortality

scholarly journals Estimation of excess all-cause mortality due to COVID-19 in Thailand

2022 ◽  
Author(s):  
Chaiwat Wilasang ◽  
Thanchanok Lincharoen ◽  
Charin Modchang ◽  
Sudarat Chadsuthi
Keyword(s):  
Correlation Coefficient ◽  
Mixed Models ◽  
New Record ◽  
Excess Mortality ◽  
Generalized Linear Mixed Models ◽  
Age Groups ◽  
Significant Rise ◽  
Excess Death ◽  
All Cause Mortality ◽  
Excess Deaths

Background: Thailand has recently experienced the most prominent COVID-19 outbreak, resulting in a new record for COVID-19 cases and deaths. To assess the influence of the COVID-19 outbreak on mortality, we aimed to estimate excess mortality in Thailand. Methods: We estimated the baseline number of deaths in the absence of COVID-19 using generalized linear mixed models (GLMMs). The models were adjusted for seasonality and demographics. We evaluated the excess mortality from April to October 2021 in Thailand. Results: We found that the estimated cumulative excess death from April to October 2021 was 14.3% (95% CI: 8.6%-18.8%) higher than the baseline. The results also showed that the excess deaths in males were higher than in females by approximately 26.3%. The excess deaths directly caused by the COVID-19 infections accounted for approximately 75.0% of the all-cause excess deaths. Furthermore, the cumulative COVID-19 cases were found to be correlated with the cumulative excess deaths with a correlation coefficient of 0.9912 (95% CI, 0.9392-0.9987). Conclusions: The recent COVID-19 outbreak in Thailand significantly impacts mortality and affects people for specific ages and sex. During the outbreak in 2021, there was a significant rise in excess fatalities, especially in the older age groups. The increase in mortality was higher in men than in women.

scholarly journals Excess Deaths associated with the COVID-19 Pandemic in Ukraine in 2020

2021 ◽  
Author(s):  
Neil K. Mehta ◽  
Ihor Honchar ◽  
Olena Doroshenko ◽  
Igor Brovchenko ◽  
Khrystyna Pak ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
European Countries ◽  
List Type ◽  
Younger Age ◽  
Influenza Activity ◽  
Expected Mortality ◽  
The Impact ◽  
Excess Deaths

AbstractCOVID-19 related mortality has been understudied in Ukraine. As part of a World Bank project, we estimated excess mortality in Ukraine during 2020. Data on all deaths registered in government-controlled Ukraine from 2016-2020 (N=2,946,505) were utilized. We predicted deaths in 2020 by five-year age groups, sex, and month and calculated the number of deaths that deviated from expected levels (excess deaths). We compared excess deaths with the number of recorded COVID-19 deaths on death certificates and with published estimates for 30 European countries. We estimated 38,095 excess deaths in 2020 (6% of all deaths). Death rates were above expected levels in February and from June-December and lower in January and March-May. From June-December, we estimated 52,124 excess deaths with a peak in November (16,891 deaths). COVID-19 recorded deaths were approximately one-third of excess deaths in June-December (18,959 vs. 52,124). Higher than expected mortality was detected for all age groups 40-44 years and above and for those ages 0-4, 15-19, and 20-24. Ukraine’s excess mortality was about average compared to 30 other European countries. Excess deaths may be attributed directly to SARS-COV2 infection or indirectly to death causes associated with social and economic upheavals resulting in from the pandemic. Lower than expected mortality during the early part of 2020 is consistent with low influenza activity and reductions in deaths from restricted movement. Further studies are required to examine the causes of death that have contributed to positive excess mortality, particularly among younger aged groups.Key MessagesUkraine has experienced sizeable changes in its recent demography and the impact of the COVID-19 pandemic on the country’s aggregate mortality patterns is understudiedBased on recent death trends, we found that Ukraine experienced lower than expected mortality during the early part of 2020 and consistently higher than expected mortality from June-December with peak levels occurring in NovemberPositive excess mortality was observed for all age groups beginning at ages 40-44 as well as some younger age groups.

Excess Mortality During the COVID-19 Pandemic in Philadelphia

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 )

scholarly journals Dynamically Engaged Smiling Predicts Cooperation Above and Beyond Average Smiling Levels

2017 ◽  
Author(s):  
Alexander Francois Danvers ◽  
Michelle N. Shiota
Keyword(s):  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Honest Signal ◽  
Same Sex ◽  
Dyadic Data ◽  
Regressive Model ◽  
Auto Regressive ◽  
Auto Regressive Model

Smiling has been conceptualized as a signal of cooperative intent, yet smiles are easy to fake. We suggest that contextually appropriate, dynamically engaged smiling imposes an attentional cost, thereby making engaged smiling a plausible “honest signal” of cooperative intent. To test this hypothesis, we analyzed data from 123 pairs of same-sex strangers having “getting-to-know-you” conversations who subsequently played a one-shot prisoner’s dilemma together. We calculated the strength of engagement in smiling using a cross-lagged auto-regressive model for dyadic data. We found that when an individual’s partner (the signaler) tended to smile in a more responsive way, that individual (the receiver) was more likely to cooperate. Conversely, when a signaler tended to smile in a less responsive way, the receiver was less likely to cooperate. These effects were present over-and-above the effects of average levels of smiling and self-reported liking, which also predicted likelihood of cooperation. However, dynamically engaged smiling did not predict cooperation on the part of the signaler, suggesting that receivers weight the importance of engagement more highly than they should, or even that engaged smiling might be a manipulative display. These results illustrate how conversational dynamics can influence evolutionary signaling.

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