scholarly journals Excess mortality associated with the 2009 A(H1N1)v influenza pandemic in Antananarivo, Madagascar

2012 ◽  
Vol 141 (4) ◽  
pp. 745-750 ◽  
Author(s):  
S. RAJATONIRINA ◽  
B. RAKOTOSOLOFO ◽  
F. RAKOTOMANANA ◽  
L. RANDRIANASOLO ◽  
M. RATSITOHARINA ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Excess Mortality ◽  
H1n1 Virus ◽  
Influenza Pandemic ◽  
Mortality Data ◽  
Age Group ◽  
Influenza Epidemic ◽  
Poisson Regression Analysis ◽  
Mortality Burden ◽  
The Impact

SUMMARYIt is difficult to assess the mortality burden of influenza epidemics in tropical countries. Until recently, the burden of influenza was believed to be negligible in Africa. We assessed the impact of the 2009 influenza epidemic on mortality in Madagascar by conducting Poisson regression analysis on mortality data from the deaths registry, after the first wave of the 2009 A(H1N1) virus pandemic. There were 20% more human deaths than expected in Antananarivo, Madagascar in November 2009, with excess mortality in the ⩾50 years age group (relative risk 1·41). Furthermore, the number of deaths from pulmonary disease was significantly higher than the number of deaths from other causes during this pandemic period. These results suggest that the A(H1N1) 2009 virus pandemic may have been accompanied by an increase in mortality.

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.

DEATH AND THE MODERN EMPIRE: THE 1918–19 INFLUENZA EPIDEMIC IN INDIA

2019 ◽  
Vol 29 ◽  
pp. 181-200
Author(s):  
David Arnold
Keyword(s):  
Influenza Pandemic ◽  
Influenza Epidemic ◽  
Local Conditions ◽  
Military Recruitment ◽  
Visual Evidence ◽  
Private Correspondence ◽  
Political Unrest ◽  
Food Shortages ◽  
The Impact ◽  
The Great War

ABSTRACTIn India the 1918–19 influenza pandemic cost at least twelve million lives, more than in any other country; it caused widespread suffering and disrupted the economy and infrastructure. Yet, despite this, and in contrast to the growing literature on recovering the ‘forgotten’ pandemic in other countries, remarkably little was recorded about the epidemic in India at the time or has appeared in the subsequent historiography. An absence of visual evidence is indicative of a more general paucity of contemporary material and first-hand testimony. In seeking to explain this absence, it is argued that, while India was exposed to influenza as a global event and to the effects of its involvement in the Great War, the influenza episode needs to be more fully understood in terms of local conditions. The impact of the disease was overshadowed by the prior encounter with bubonic plague, by military recruitment and the war, and by food shortages and price rises that pushed India to the brink of famine. Subsumed within a dominant narrative of political unrest and economic discontent, the epidemic found scant expression in official documentation, public debate and/or even private correspondence.

Quantitative analysis of the impact of craft worker availability on construction project safety performance

2016 ◽  
Vol 16 (3) ◽  
pp. 307-322 ◽  
Author(s):  
Hossein Karimi ◽  
Timothy R.B. Taylor ◽  
Paul M. Goodrum ◽  
Cidambi Srinivasan
Keyword(s):  
Regression Analysis ◽  
Poisson Regression ◽  
Construction Projects ◽  
Construction Project ◽  
Safety Performance ◽  
Content Type ◽  
Poisson Regression Analysis ◽  
Incident Cases ◽  
The Impact ◽  
Craft Worker

Purpose This paper aims to quantify the impact of craft worker shortage on construction project safety performance. Design/methodology/approach A database of 50 North American construction projects completed between 2001 and 2014 was compiled by taking information from a research project survey and the Construction Industry Institute Benchmarking and Metrics Database. The t-test and Mann-Whitney test were used to determine whether there was a significant difference in construction project safety performance on projects with craft worker recruiting difficulty. Poisson regression analysis was then used to examine the relationship between craft worker recruiting difficulty and Occupational Safety and Health Administration Total Number of Recordable Incident Cases per 200,000 Actual Direct Work Hours (TRIR) on construction projects. Findings The result showed that the TRIR distribution of a group of projects that reported craft worker recruiting difficulty tended to be higher than the TRIR distribution of a group of projects with no craft worker recruiting difficulty (p-value = 0.004). Moreover, the average TRIR of the projects that reported craft worker recruiting difficulty was more than two times the average TRIR of projects that experienced no craft recruiting difficulty (p-value = 0.035). Furthermore, the Poisson regression analysis demonstrated that there was a positive exponential relationship between craft worker recruiting difficulty and TRIR in construction projects (p-value = 0.004). Research limitations/implications The projects used to construct the database are heavily weighted towards industrial construction. Practical implications There have been significant long-term gains in construction safety within the USA. However, if recent craft shortages continue, the quantitative analyses presented herein indicate a strong possibility that more safety incidents will occur unless the shortages are reversed. Innovative construction means and methods should be developed and adopted to work in a safe manner with a less qualified workforce. Originality/value The Poisson regression model is the first model that quantifiably links project craft worker availability to construction project safety performance.

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

2018 ◽  
Vol 146 (16) ◽  
pp. 2059-2065 ◽  
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.

scholarly journals Real-time estimation of the influenza-associated excess mortality in Hong Kong

2019 ◽  
Vol 147 ◽  
Author(s):  
Jessica Y. Wong ◽  
Edward Goldstein ◽  
Vicky J. Fang ◽  
Benjamin J. Cowling ◽  
Peng Wu
Keyword(s):  
Hong Kong ◽  
Real Time ◽  
Excess Mortality ◽  
Time Estimation ◽  
Surveillance Data ◽  
Control Measures ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Real Time Estimation ◽  
The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

scholarly journals Effects of pollution, low temperature and influenza syndrome on the excess mortality risk in winter 2016–2017

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rossella Murtas ◽  
Antonio Giampiero Russo
Keyword(s):  
Risk Factors ◽  
Air Pollution ◽  
Environmental Protection Agency ◽  
Low Temperatures ◽  
Excess Mortality ◽  
Environmental Data ◽  
Combined Effect ◽  
Mortality Data ◽  
Influenza Epidemic ◽  
Respiratory Conditions

Abstract Background In the winter of 2016–2017, the number of deaths recorded in the north-west Europe was significantly higher than that in previous years. This spike in mortality was attributed principally to an influenza epidemic, but the contribution of air pollution and cold temperature has not been investigated. Information on the combined effect of low temperatures, influenza epidemic, and air pollution on mortality is inadequate. The objective of this study was to estimate the excess mortality in the winter of 2016–2017 in the metropolitan area of Milan, and to evaluate the independent short-term effect of 3 risk factors: low temperatures, the influenza epidemic, and air pollution. Methods We used a case-crossover, time-stratified study design. Mortality data were collected on all people aged > 65 years who died of natural causes, due to respiratory diseases or cardiovascular diseases, between December 1, 2016 and February 15, 2017. Environmental data were extracted from the Regional Environmental Protection Agency. The National Surveillance Network provided data on influenza epidemic. Results Among the 7590 natural deaths in people aged > 65 years, 965 (13%) were caused by respiratory conditions, and 2688 (35%) were caused by cardiovascular conditions. There were statistically significant associations between the minimum recorded temperature and deaths due to natural causes (OR = 0.966, 95% CI: 0.944–0.989), and cardiovascular conditions (OR = 0.961, 95% CI: 0.925–0.999). There were also statistically significant association between the influenza epidemic and deaths due to natural causes (OR = 1.198, 95% CI: 1.156–1.241), cardiovascular conditions (OR = 1.153, 95% CI: 1.088–1.223), and respiratory conditions (OR = 1.303, 95% CI: 1.166–1.456). High levels of PM10 (60 and 70 μg/m3) were associated with a statistically significant increase in natural and cause-specific mortality. There were statistically significant interactions between PM10 and influenza for cardiovascular-related mortality, and between influenza and temperature for deaths due to natural causes. Conclusions Excess of mortality in Milan during winter 2016–2017 was associated with influenza epidemic and concomitant environmental exposures, specifically, the combined effect of air pollution and low temperatures. Policies mitigating the effects of environmental risk factors should be implemented to prevent future excess mortality.

Excess mortality monitoring in England and Wales during the influenza A(H1N1) 2009 pandemic

2011 ◽  
Vol 139 (9) ◽  
pp. 1431-1439 ◽  
Author(s):  
P. HARDELID ◽  
N. ANDREWS ◽  
R. PEBODY
Keyword(s):  
Pandemic Influenza ◽  
Influenza A ◽  
Excess Mortality ◽  
Cold Weather ◽  
Mortality Data ◽  
Age Group ◽  
England And Wales ◽  
Health Events ◽  
Influenza A H1n1 ◽  
All Cause Mortality

SUMMARYWe present the results from a novel surveillance system for detecting excess all-cause mortality by age group in England and Wales developed during the pandemic influenza A(H1N1) 2009 period from April 2009 to March 2010. A Poisson regression model was fitted to age-specific mortality data from 1999 to 2008 and used to predict the expected number of weekly deaths in the absence of extreme health events. The system included adjustment for reporting delays. During the pandemic, excess all-cause mortality was seen in the 5–14 years age group, where mortality was flagged as being in excess for 1 week after the second peak in pandemic influenza activity; and in age groups >45 years during a period of very cold weather. This new system has utility for rapidly estimating excess mortality for other acute public health events such as extreme heat or cold weather.

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 Mortality surveillance in the Netherlands: winter 2015/2016 of moderate severity

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Marit De Lange ◽  
Anne Teirlinck ◽  
Frederika Dijkstra ◽  
Lenny Stoeldraijer ◽  
Carel Harmsen ◽  
...  
Keyword(s):  
Infectious Disease ◽  
The Netherlands ◽  
Winter Season ◽  
Mortality Data ◽  
Historical Period ◽  
Time Lags ◽  
Influenza Epidemic ◽  
Time Data ◽  
Moderate Severity ◽  
The Impact

ObjectiveWeekly numbers of deaths are monitored to increase the capacityto deal with both expected and unusual (disease) events such aspandemic influenza, other infections and non-infectious incidents.The monitoring information can potentially be used to detect, trackand estimate the impact of an outbreak or incident on all-causemortality.IntroductionThe mortality monitoring system (initiated in 2009 during theinfluenza A(H1N1) pandemic) is a collaboration between the Centrefor Infectious Disease Control (CIb) and Statistics Netherlands.The system monitors nation-wide reported number of deaths(population size 2014: 16.8 million) from all causes, as cause ofdeath information is not available real-time. Data is received fromStatistics Netherlands by weekly emails.MethodsOnce a week the number of reported deaths is checked for excessabove expected levels at 2 different time-lags: within 1 and 2 weeksafter date of death (covering a median 43% and 96% of all deathsrespectively). A weekly email bulletin reporting the findings is sentto the Infectious Disease Early Warning Unit (at CIb) and a summaryof results is posted on the RIVM website (National Institute for PublicHealth and the Environment). Any known concurrent and possiblyrelated events are also reported. When excess deaths coincide withhot temperatures, the bulletin is sent to the Heat Plan Team (also atRIVM). Data are also sent to EuroMOMO which monitors excessmortality at a European level. For the Dutch system baselines andprediction limits are calculated using a 5 year historical period(updated each July). A serfling-like algorithm based on regressionanalysis is used to produce baselines which includes cyclical seasonaltrends (models based on historical data in which weeks with extremeunderreporting have been removed. Also periods with high excessmortality in winter and summer were removed so as not to influencethe baseline with previous outbreaks).ResultsIncreased mortality occurred during the entire influenza epidemicand up to three weeks thereafter (weeks 1-14 of 2016), except for adrop in week 7 (figure1). Excess mortality was primarily observedin persons 75 or older. Additionally, in several weeks mortality wasincreased in 65-74 year olds, (weeknr 4-6; peaking in week 4 with564 deaths, when 468 baseline deaths were predicted). Also, inweek 4, mortality in the 25-34 year-old age group was significantlyincreased (25 deaths, while 14 were expected as baseline). Cumulativeexcess mortality was estimated at 3,900 deaths occurring duringthe 11 weeks of the 2015/2016 influenza epidemic and at 6,085during the total winter season (44 weeks running from week 40 up toweek 20).ConclusionsIn terms of number of deaths during the winter season (weeks40-20) and during the influenza epidemic (weeks 1-11), the 2015/2016season in the Netherlands was of moderate severity compared with theprevious five years (and was of similar magnitude as the 2011/2012winter). Notable was the short three-week time span with a higherpeak in mortality in 65-74 year olds than has been observed in recentyears. Although the influenza epidemic reached its peak in week7, the mortality data showed a dip in week 7. The reason for thetemporary decrease is unknown but there was a partial overlap witha public holiday.

scholarly journals Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time series analysis

2020 ◽  
Author(s):  
Matteo Scortichini ◽  
Rochelle Schneider dos Santos ◽  
Francesca De' Donato ◽  
Manuela De Sario ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
Time Series ◽  
Excess Mortality ◽  
Temporal Distribution ◽  
Interrupted Time Series ◽  
Baseline Risk ◽  
Mortality Data ◽  
Interrupted Time Series Analysis ◽  
Two Stage ◽  
Smooth Functions ◽  
The Impact

Background: Italy was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group, and period of the outbreak. Methods: The analysis was performed using a two-stage interrupted time series design using daily mortality data for the period January 2015 - May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis. Results: In the period 15 February - 15 May 2020, we estimated an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in Italy, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced up to 800% increase during the peak in late March. There were differences by sex, age, and area both in the overall impact and in its temporal distribution. Conclusions: This study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.

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