scholarly journals Influenza-attributable deaths, Canada 1990–1999

2007 ◽  
Vol 135 (7) ◽  
pp. 1109-1116 ◽  
Author(s):  
D. L. SCHANZER ◽  
T. W. S. TAM ◽  
J. M. LANGLEY ◽  
B. T. WINCHESTER
Keyword(s):  
Excess Mortality ◽  
Influenza Season ◽  
Vital Statistics ◽  
Attributable Mortality ◽  
Mortality Data ◽  
Poisson Regression Model ◽  
Severe Influenza ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
The Impact

SUMMARYThe number of deaths attributable to influenza is believed to be considerably higher than the number certified by vital statistics registration as due to influenza. Weekly mortality data for Canada from the 1989/1990 to the 1998/1999 influenza seasons were analysed by cause of death, age group, and place of death to estimate the impact of influenza on mortality. A Poisson regression model was found to accurately predict all-cause, as well as cause-specific mortality, as a function of influenza-certified deaths, after controlling for seasonality, and trend. Influenza-attributable deaths were calculated as predicted less baseline-predicted deaths. In summary, throughout the 1990s there were on average just under 4000 deaths attributable to influenza annually (for an influenza-attributable mortality rate of 13/100 000 persons), varying from no detectable excess mortality for the 1990/1991 influenza season, to 6000–8000 influenza-attributable deaths for the more severe influenza seasons of 1997/1998 and 1998/1999. On average, 8% (95% CI 7–10) of influenza-attributable deaths were certified as influenza, although this percentage varied from 4% to 12% from year to year. Only 15% of the influenza-attributable deaths were certified as pneumonia, and for all respiratory causes, 40%. Deaths were distributed over most causes. The weekly pattern of influenza-certified deaths was a good predictor of excess all-cause mortality.

scholarly journals Risk factors for excess all-cause mortality during the first wave of the COVID-19 pandemic in England: A retrospective cohort study of primary care data

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260381
Author(s):  
Iain M. Carey ◽  
Derek G. Cook ◽  
Tess Harris ◽  
Stephen DeWilde ◽  
Umar A. R. Chaudhry ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Excess Mortality ◽  
Total Mortality ◽  
Individual Risk ◽  
Mortality Data ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Mortality Ratio ◽  
The Impact

Background The COVID-19 pandemic’s first wave in England during spring 2020 resulted in an approximate 50% increase in all-cause mortality. Previously, risk factors such as age and ethnicity, were identified by studying COVID-related deaths only, but these were under-recorded during this period. Objective To use a large electronic primary care database to estimate the impact of risk factors (RFs) on excess mortality in England during the first wave, compared with the impact on total mortality during 2015–19. Methods Medical history, ethnicity, area-based deprivation and vital status data were extracted for an average of 4.8 million patients aged 30–104 years, for each year between 18-March and 19-May over a 6-year period (2015–2020). We used Poisson regression to model total mortality adjusting for age and sex, with interactions between each RF and period (pandemic vs. 2015–19). Total mortality during the pandemic was partitioned into "usual" and "excess" components, assuming 2015–19 rates represented "usual" mortality. The association of each RF with the 2020 "excess" component was derived as the excess mortality ratio (EMR), and compared with the usual mortality ratio (UMR). Results RFs where excess mortality was greatest and notably higher than usual were age >80, non-white ethnicity (e.g., black vs. white EMR = 2.50, 95%CI 1.97–3.18; compared to UMR = 0.92, 95%CI 0.85–1.00), BMI>40, dementia, learning disability, severe mental illness, place of residence (London, care-home, most deprived). By contrast, EMRs were comparable to UMRs for sex. Although some co-morbidities such as cancer produced EMRs significantly below their UMRs, the EMRs were still >1. In contrast current smoking has an EMR below 1 (EMR = 0.80, 95%CI 0.65–0.98) compared to its UMR = 1.64. Conclusions Studying risk factors for excess mortality during the pandemic highlighted differences from studying cause-specific mortality. Our approach illustrates a novel methodology for evaluating a pandemic’s impact by individual risk factor without requiring cause-specific mortality data.

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

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ariel Karlinsky ◽  
Dmitry Kobak
Keyword(s):  
New Zealand ◽  
Excess Mortality ◽  
Mortality Data ◽  
Objective Indicator ◽  
Death Toll ◽  
The World ◽  
All Cause Mortality ◽  
Expected Mortality ◽  
The Impact ◽  
Annual Mortality

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.

scholarly journals Impact of obesity on life expectancy among different European countries: secondary analysis of population-level data over the 1975–2012 period

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028086 ◽  
Author(s):  
Nikoletta Vidra ◽  
Sergi Trias-Llimós ◽  
Fanny Janssen
Keyword(s):  
Life Expectancy ◽  
Secondary Analysis ◽  
Population Level ◽  
European Countries ◽  
Attributable Mortality ◽  
Mortality Data ◽  
Weighted Sum Method ◽  
All Cause Mortality ◽  
The Usa ◽  
The Impact

ObjectiveThis study assesses the impact of obesity on life expectancy for 26 European national populations and the USA over the 1975–2012 period.DesignSecondary analysis of population-level obesity and mortality data.SettingEuropean countries, namely Austria, Belarus, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, the Russian Federation, Slovakia, Spain, Sweden, Switzerland, Ukraine and the UK; and the USA.ParticipantsNational populations aged 18–100 years, by sex.MeasurementsUsing data by age and sex, we calculated obesity-attributable mortality by multiplying all-cause mortality (Human Mortality Database) with obesity-attributable mortality fractions (OAMFs). OAMFs were obtained by applying the weighted sum method to obesity prevalence data (non-communicable diseases (NCD) Risk Factor Collaboration) and European relative risks (Dynamic Modeling for Health Impact Assessment (DYNAMO- HIA)). We estimated potential gains in life expectancy (PGLE) at birth by eliminating obesity-attributable mortality from all-cause mortality using associated single-decrement life tables.ResultsIn the 26 European countries in 2012, PGLE due to obesity ranged from 0.86 to 1.67 years among men, and from 0.66 to 1.54 years among women. In all countries, PGLE increased over time, with an average annual increase of 2.68% among men and 1.33% among women. Among women in Denmark, Switzerland, and Central and Eastern European countries, the increase in PGLE levelled off after 1995. Without obesity, the average increase in life expectancy between 1975 and 2012 would have been 0.78 years higher among men and 0.30 years higher among women.ConclusionsObesity was proven to have an impact on both life expectancy levels and trends in Europe. The differences found in this impact between countries and the sexes can be linked to contextual factors, as well as to differences in people’s ability and capacity to adopt healthier lifestyles.

scholarly journals Early estimates of the excess mortality associated with the 2008-9 influenza season in Portugal

2009 ◽  
Vol 14 (18) ◽  
Author(s):  
P J Nogueira ◽  
B Nunes ◽  
A Machado ◽  
E Rodrigues ◽  
V Gómez ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Total Population ◽  
Influenza Season ◽  
Statistical Modelling ◽  
Mortality Data ◽  
Age Group ◽  
Gender And Age ◽  
Influenza Activity ◽  
The Impact ◽  
Excess Deaths

The aim of this study was to estimate the excess mortality associated with the influenza activity registered in Portugal between week 49 of 2008 and week 5 of 2009. For this purpose available mortality data from the Portuguese Daily Mortality Monitoring (VDM) System were used. Several estimates of excess deaths associated with the recent recorded influenza activity were determined through statistical modelling (cyclic regression) for the total population and disaggregated by gender and age group. The results show that the impact of the 2008-9 influenza season was 1,961 excess deaths, with approximately 82% of these occurring in the age group of 75 years and older.

scholarly journals Impact of Different Estimation Methods on Obesity-Attributable Mortality Levels and Trends: The Case of The Netherlands

2018 ◽  
Vol 15 (10) ◽  
pp. 2146 ◽  
Author(s):  
Nikoletta Vidra ◽  
Maarten Bijlsma ◽  
Fanny Janssen
Keyword(s):  
The Netherlands ◽  
Estimation Methods ◽  
Attributable Mortality ◽  
Mortality Data ◽  
Weighted Sum ◽  
Weighted Sum Method ◽  
Relative Risks ◽  
Specific Mortality ◽  
Comparative Risk ◽  
The Impact

The available methodologies to estimate the obesity-attributable mortality fraction (OAMF) affect the levels found and hamper the construction of time series. Our aim was to assess the impact of using different techniques to estimate the levels and the trends in obesity-attributable mortality for The Netherlands between 1981 to 2013. Using Body Mass Index (BMI), all-cause and cause-specific mortality data, and worldwide and European relative risks (RRs), we estimated OAMFs using three all-cause approaches (partially adjusted, weighted sum, and the two combined) and one cause-of-death approach (Comparative Risk Assessment; CRA). We adjusted the CRA approach to purely capture obesity (BMI ≥ 30 kg/m2). The different approaches led to a range of estimates. The weighted sum method using worldwide RRs generated the lowest (0.9%) while the adjusted CRA approach using 2013 RRs generated the highest estimate (1.5%). Using European-specific RRs instead of worldwide RRs resulted in higher estimates. Most of the approaches revealed an increasing OAMF over the period 1981 to 2013 especially from 1993 onwards except for the adjusted CRA approach among women. Estimates of OAMF levels and trends differed depending on the method applied. Given the limited available data, we recommend using the weighted-sum method to compare obesity-attributable mortality across European countries over time.

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

2021 ◽  
Author(s):  
Ariel Karlinsky ◽  
Dmitry Kobak
Keyword(s):  
Excess Mortality ◽  
Mortality Data ◽  
Objective Indicator ◽  
Death Toll ◽  
The World ◽  
All Cause Mortality ◽  
Order Of Magnitude ◽  
The Impact ◽  
Entire Dataset ◽  
Annual Mortality

AbstractComparing 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 recent average, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no central, 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 77 countries, 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 the worst-affected countries the annual mortality increased by over 50%, while in several other countries it decreased by over 5%, presumably due to lockdown measures decreasing the non-COVID mortality. Moreover, we found that while some countries have been reporting the COVID-19 deaths very accurately, many countries have been underreporting their COVID-19 deaths by an order of magnitude or more. Averaging across the entire dataset suggests that the world’s COVID-19 death toll may be at least 1.6 times higher than the reported number of confirmed deaths.

scholarly journals Excess all-cause and influenza-attributable mortality in Europe, December 2016 to February 2017

2017 ◽  
Vol 22 (14) ◽  
Author(s):  
Lasse S Vestergaard ◽  
Jens Nielsen ◽  
Tyra G Krause ◽  
Laura Espenhain ◽  
Katrien Tersago ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Influenza A ◽  
Excess Mortality ◽  
Influenza Season ◽  
European Countries ◽  
Cold Weather ◽  
Attributable Mortality ◽  
Influenza Virus A ◽  
All Cause Mortality

Since December 2016, excess all-cause mortality was observed in many European countries, especially among people aged ≥ 65 years. We estimated all-cause and influenza-attributable mortality in 19 European countries/regions. Excess mortality was primarily explained by circulation of influenza virus A(H3N2). Cold weather snaps contributed in some countries. The pattern was similar to the last major influenza A(H3N2) season in 2014/15 in Europe, although starting earlier in line with the early influenza season start.

Robust and Timely Estimation of Excess Mortality: Statistical Analysis of Cremation Data During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Gemma Postill ◽  
Regan Murray ◽  
Andrew S Wilton ◽  
Richard A Wells ◽  
Renee Sirbu ◽  
...  
Keyword(s):  
Real Time ◽  
Excess Mortality ◽  
Age Groups ◽  
Time Estimation ◽  
Vital Statistics ◽  
Accurate Estimation ◽  
Mortality Data ◽  
Mortality Estimation ◽  
Statistics Canada ◽  
The Impact

BACKGROUND Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating timely mortality data that encompasses the majority of deaths within the province. OBJECTIVE Our objectives were to (1) validate the ability of cremation data in permitting real-time estimation of excess all-cause mortality, interim of vital statistics data, and (2) describe the patterns of excess mortality. METHODS Cremation records from January 2020 until April 2021 were compared to the historical records from 2017-2019, grouped according to week, age, sex, and COVID-19 status. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years to determine whether there was excess mortality and if so, which age groups had the greatest number of excess deaths during the COVID Pandemic, and whether deaths attributed to COVID-19 account for the entirety of the excess mortality. RESULTS Between 2017-2019, cremations were performed for 67.4% (95% CI: 67.3–67.5%) of deaths; the proportion of cremated deaths remained stable throughout 2020, establishing that the COVID-19 pandemic did not significantly alter cremation practices, even within age and sex categories. During the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI: 14.6–19.3%) in mortality. The accuracy of this excess mortality estimation was later confirmed by vital statistics data. CONCLUSIONS The stability in the percent of Ontarians cremated and the completion of cremation data several months before vital statistics data, enables accurate estimation of all-causes mortality in near real-time with cremation data. These findings demonstrate the utility of cremation data to provide timely mortality information during public health emergencies.

scholarly journals Association of mushroom consumption with all-cause and cause-specific mortality among American adults: prospective cohort study findings from NHANES III

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Djibril M. Ba ◽  
Xiang Gao ◽  
Joshua Muscat ◽  
Laila Al-Shaar ◽  
Vernon Chinchilli ◽  
...  
Keyword(s):  
Lower Risk ◽  
Proportional Hazards ◽  
Total Mortality ◽  
Cox Proportional Hazards ◽  
Dietary Factors ◽  
Mortality Data ◽  
Nhanes Iii ◽  
Dietary Recall ◽  
All Cause Mortality ◽  
Specific Mortality

Abstract Background Whether mushroom consumption, which is rich in several bioactive compounds, including the crucial antioxidants ergothioneine and glutathione, is inversely associated with low all-cause and cause-specific mortality remains uncertain. This study aimed to prospectively investigate the association between mushroom consumption and all-cause and cause-specific mortality risk. Methods Longitudinal analyses of participants from the Third National Health and Nutrition Examination Survey (NHANES III) extant data (1988–1994). Mushroom intake was assessed by a single 24-h dietary recall using the US Department of Agriculture food codes for recipe foods. All-cause and cause-specific mortality were assessed in all participants linked to the National Death Index mortality data (1988–2015). We used Cox proportional hazards regression models to calculate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) for all-cause and cause-specific mortality. Results Among 15,546 participants included in the current analysis, the mean (SE) age was  44.3 (0.5) years. During a mean (SD) follow-up duration of 19.5 (7.4) years , a total of 5826 deaths were documented. Participants who reported consuming mushrooms had lower risk of all-cause mortality compared with those without mushroom intake (adjusted hazard ratio (HR) = 0.84; 95% CI: 0.73–0.98) after adjusting for demographic, major lifestyle factors, overall diet quality, and other dietary factors including total energy. When cause-specific mortality was examined, we did not observe any statistically significant associations with mushroom consumption. Consuming 1-serving of mushrooms per day instead of 1-serving of processed or red meats was associated with lower risk of all-cause mortality (adjusted HR = 0.65; 95% CI: 0.50–0.84). We also observed a dose-response relationship between higher mushroom consumption and lower risk of all-cause mortality (P-trend = 0.03). Conclusion Mushroom consumption was associated with a lower risk of total mortality in this nationally representative sample of US adults.

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 )

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