Excess Mortality During the COVID-19 Pandemic in Guatemala

2021 ◽  
pp. e1-e8
Author(s):  
Kevin Martinez-Folgar ◽  
Diego Alburez-Gutierrez ◽  
Alejandra Paniagua-Avila ◽  
Manuel Ramirez-Zea ◽  
Usama Bilal
Keyword(s):  
Excess Mortality ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Mortality Rates ◽  
Place Of Death ◽  
Mortality Data ◽  
Registration System ◽  
Health Crisis ◽  
Vital Registration System ◽  
Excess Deaths

Objectives. To describe excess mortality during the COVID-19 pandemic in Guatemala during 2020 by week, age, sex, and place of death. Methods. We used mortality data from 2015 to 2020, gathered through the vital registration system of Guatemala. We calculated weekly mortality rates, overall and stratified by age, sex, and place of death. We fitted a generalized additive model to calculate excess deaths, adjusting for seasonality and secular trends and compared excess deaths to the official COVID-19 mortality count. Results. We found an initial decline of 26% in mortality rates during the first weeks of the pandemic in 2020, compared with 2015 to 2019. These declines were sustained through October 2020 for the population younger than 20 years and for deaths in public spaces and returned to normal from July onward in the population aged 20 to 39 years. We found a peak of 73% excess mortality in mid-July, especially in the population aged 40 years or older. We estimated a total of 8036 excess deaths (95% confidence interval = 7935, 8137) in 2020, 46% higher than the official COVID-19 mortality count. Conclusions. The extent of this health crisis is underestimated when COVID-19 confirmed death counts are used. (Am J Public Health. Published online ahead of print September 23, 2021: e1–e8. https://doi.org/10.2105/AJPH.2021.306452 )

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 Covid-19 and excess mortality rates not comparable across countries

2021 ◽  
Author(s):  
Gabrielle E Kelly ◽  
Stefano Petti ◽  
Norman Noah
Keyword(s):  
Excess Mortality ◽  
Spatial Clustering ◽  
Mortality Rates ◽  
Mortality Data ◽  
Published Data ◽  
Death Rates ◽  
Government Response ◽  
Health Measures ◽  
Specific Mortality ◽  
Excess Deaths

Abstract: Evidence that more people in some countries and fewer in others are dying because of the pandemic, than is reflected by reported Covid-19 mortality rates, is derived from mortality data. Worldwide, mortality data is used to estimate the full extent of the effects of the Covid-19 pandemic, both direct and indirect; the possible short fall in the number of cases reported to the WHO; and to suggest explanations for differences between countries. Excess mortality data is largely varying across countries and is not directly proportional to Covid-19 mortality. Using publicly available databases, deaths attributed to Covid-19 in 2020 and all deaths for the years 2015-2020 were tabulated for 36 countries together with economic, health, demographic, and government response stringency index variables. Residual death rates in 2020 were calculated as excess deaths minus death rates due to Covid-19 where excess deaths were observed deaths in 2020 minus the average for 2015-2019. For about half the countries, residual deaths were negative and for half, positive. The absolute rates in some countries were double those in others. In a regression analysis, the stringency index (p=0.026) was positively associated with residual mortality. There was no evidence of spatial clustering of residual mortality. The results show that published data on mortality from Covid-19 cannot be directly comparable across countries, likely due to differences in Covid-19 death reporting. In addition, the unprecedented public health measures implemented to control the pandemic may have produced either increased or reduced excess deaths due to other diseases. Further data on cause-specific mortality is required to determine the extent to which residual mortality represents non-Covid-19 deaths and to explain differences between countries.

scholarly journals Excess mortality in England and Wales during the first wave of the COVID-19 pandemic

2020 ◽  
Author(s):  
Evangelos Kontopantelis ◽  
Mamas A Mamas ◽  
John Deanfield ◽  
Miqdad Asaria ◽  
Tim Doran
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Mortality Rates ◽  
Mortality Data ◽  
List Type ◽  
Age Group ◽  
The West ◽  
England And Wales ◽  
West Midlands ◽  
Excess Deaths

AbstractBackgroundDeaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19 associated deaths.MethodsWeekly mortality data for 1 Jan 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: i) all-cause deaths; and ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (07-13 March to 25 April to 8 May).FindingsBetween 7 March and 8 May 2020, there were 47,243 (95%CI: 46,671 to 47,815) excess deaths in England and Wales, of which 9,948 (95%CI: 9,376 to 10,520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100,000 (95%CI: 49 to 50) in the South West to 102 per 100,000 (95%CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100,000 (95%CI: −1 to 0) in Wales (i.e. mortality rates were no higher than expected) to 26 per 100,000 (95%CI: 25 to 26) in the West Midlands.InterpretationThe COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response.FundingNoneSummary boxWhat is already known on the subjectThe number of deaths due to COVID-19 have been quantified by the Office of National StatisticsThese have also been reported across age groups and regionsWhat this study addsWe report the number of excess deaths, using weekly mortality data from 1/1/2010We also quantify the number of excess deaths, excluding COVID-19 associated deaths, which can be attributed to COVID-19 directly (but not coded as such) or indirectly (due to other urgent but unmet health need)Highest excess mortality, excluding COVID-19 deaths, was observed in the West Midlands, followed by London and the North WestAlthough males had larger excess mortality rates than females across all age groups, female excess mortality rates excluding COVID-19 were higher in the 85+ age group, indicating a large undocumented impact of the virus on older females (direct and/or indirect)The three provided appendices will be updated weekly on the BMJ-JECH website, to provide up-to-date information of excess mortality by region, sex and age group

NEONATAL MORTALITY IN THE EMPOWERED ACTION GROUP STATES OF INDIA: TRENDS AND DETERMINANTS

2008 ◽  
Vol 40 (2) ◽  
pp. 183-201 ◽  
Author(s):  
PERIANAYAGAM AROKIASAMY ◽  
ABHISHEK GAUTAM
Keyword(s):  
Health Care ◽  
Infant Mortality ◽  
Neonatal Mortality ◽  
Universal Coverage ◽  
Mortality Rates ◽  
Mortality Data ◽  
Registration System ◽  
Action Group ◽  
Eag States ◽  
The Impact

SummaryIn India, the eight socioeconomically backward states of Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttaranchal and Uttar Pradesh, referred to as the Empowered Action Group (EAG) states, lag behind in the demographic transition and have the highest infant mortality rates in the country. Neonatal mortality constitutes about 60% of the total infant mortality in India and is highest in the EAG states. This study assesses the levels and trends in neonatal mortality in the EAG states and examines the impact of bio-demographic compared with health care determinants on neonatal mortality. Data from India’s Sample Registration System (SRS) and National Family and Health Survey (NFHS-2, 1998–99) are used. Cox proportional hazard models are applied to estimate adjusted neonatal mortality rates by health care, bio-demographic and socioeconomic determinants. Variations in neonatal mortality by these determinants suggest that universal coverage of all pregnant women with full antenatal care, providing assistance at delivery and postnatal care including emergency care are critical inputs for achieving a reduction in neonatal mortality. Health interventions are also required that focus on curtailing the high risk of neonatal deaths arising from the mothers’ younger age at childbirth, low birth weight of children and higher order births with short birth intervals.

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 Influenza Vaccine Match with Circulating Strains and Indicators of Influenza Strain Impact - Canada 1980 to 1992

1998 ◽  
Vol 9 (3) ◽  
pp. 143-148 ◽  
Author(s):  
Edward Ellis ◽  
John M Weber ◽  
Wilf Cuff ◽  
Susan G Mackenzie
Keyword(s):  
Influenza Vaccine ◽  
Ecological Study ◽  
Influenza Season ◽  
National Laboratory ◽  
Mortality Rates ◽  
Mortality Data ◽  
Influenza Strain ◽  
Previous Season ◽  
Vaccine Antigens ◽  
Excess Deaths

OBJECTIVES: To determine the similarity between influenza vaccine antigens and viruses associated with laboratory-confirmed infections by virus type/subtype, strain and influenza season; to correlate pneumonia and influenza hospitalization and mortality rates with the number of laboratory-confirmed influenza infections in an influenza season; and to develop predictive indicators of the likely incidence of current strains in the following season.DESIGN: Ecological study using national laboratory, pneumonia and influenza hospitalization and mortality data.SETTING: Canada, influenza seasons from 1980 to 1992.POPULATION STUDIED: Individuals with laboratory-confirmed influenza infections, pneumonia and influenza hospitalizations or deaths.INTERVENTION: Influenza immunization.MAIN RESULTS: Similarity of circulating strains and vaccine antigens was 99% for A(H1N1), 65% for A(H3N2) and 65% for B strains. During outbreaks, pneumonia and influenza hospitalization, and mortality rates increased 19% or less and 21% or less for A(H1N1), respectively; 28% or less and 51% or less for A(H3N2), and 19% or less and 16% or less for B strains. There were usually fewer than 25 laboratory-confirmed A(H1N1) infections with a particular strain in a season if there had been more than 25 infections with similar strains the previous season. For A(H3N2), the figure was 100, and for B it was 150.CONCLUSIONS: Matches were excellent for A(H1N1) and good for A(H3N2) plus B strains. Hospitalization and mortality rates increased substantially during outbreaks, eg, estimated 1609 excess deaths during a widespread A(H3N2) outbreak. This study identifies relationships that provide some ability to predict the incidence of a particular influenza strain in a coming season based on the incidence of strains similar to it in the previous season.

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

2011 ◽  
Vol 140 (9) ◽  
pp. 1542-1550 ◽  
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.

scholarly journals High excess mortality during the COVID-19 outbreak in Stockholm Region areas with young and socially vulnerable populations

2020 ◽  
Author(s):  
Amaia Calderón-Larrañaga ◽  
Davide L Vetrano ◽  
Debora Rizzuto ◽  
Tom Bellander ◽  
Laura Fratiglioni ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Excess Mortality ◽  
Age Distribution ◽  
Fold Increase ◽  
Mortality Rates ◽  
Individual Risk ◽  
Mortality Data ◽  
Gainful Employment ◽  
Viral Spread ◽  
The Individual

AbstractBackgroundWe aimed to describe the distribution of excess mortality (EM) during the first weeks of the COVID-19 outbreak in the Stockholm Region, Sweden, according to individual age and sex, and the sociodemographic contextMethodsWeekly all-cause mortality data were obtained from Statistics Sweden for the period 01/01/2015 to 17/05/2020. EM during the first 20 weeks of 2020 was estimated by comparing observed mortality rates with expected mortality rates during the five previous years (N=2,379,792). EM variation by socioeconomic status (tertiles of income, education, Swedish-born, gainful employment) and age distribution (share of 70+ year-old persons) was explored based on Demographic Statistics Area (DeSO) data.FindingsAn EM was first detected during the week of March 23-29 2020. During the peaking week of the epidemic (6-12 April 2020), an EM of 160% was observed: 211% in 80+ year-old women; 179% in 80+ year-old men. During the same week, the highest EM was observed for DeSOs with lowest income (171%), lowest education (162%), lowest share of Swedish-born (178%), and lowest share of gainfully employed (174%). There was a 1.2 to 1.7-fold increase in EM between those areas with a higher vs. lower proportion of young people.InterpretationLiving in areas with lower socioeconomic status and younger populations is linked to COVID-19 EM. These conditions might have facilitated the viral spread. Our findings add to the well-known biological vulnerability linked to increasing age, the relevance of the sociodemographic context when estimating the individual risk to COVID-19.FundingNone.

scholarly journals EXCESS DEATHS FROM ALL CAUSES AND BY COVID-19 IN BRAZIL IN 2020

2021 ◽  
Author(s):  
Alcione Miranda dos Santos ◽  
Bruno Feres de Souza ◽  
Carolina Abreu de Carvalho ◽  
Marcos Adriano Garcia Campos ◽  
Bruno Luciano Carneiro Alves de Oliveira ◽  
...  
Keyword(s):  
Racial Differences ◽  
Respiratory Diseases ◽  
Excess Mortality ◽  
Negative Binomial ◽  
Mortality Rates ◽  
Risk Of Death ◽  
External Causes ◽  
Log Linear ◽  
Restrictive Measures ◽  
Excess Deaths

SUMMARYObjectiveTo estimate the 2020 all-cause and COVID-19 excess mortality according to sex, age, race/color, and state, and to compare mortality rates by selected causes with that of the five previous years in Brazil.MethodsData from the Mortality Information System were used. Expected deaths for 2020 were estimated from 2015 to 2019 data using a negative binomial log-linear model.ResultsExcess deaths in Brazil in 2020 amounted to 13.7%, and the ratio of excess deaths to COVID-19 deaths was 0.90. Reductions in deaths from cardiovascular diseases (CVD), respiratory diseases, and external causes, and an increase in ill-defined causes were all noted. Excess deaths were also found to be heterogeneous, being higher in the Northern, Center-Western, and Northeastern states. In some states, the number of COVID-19 deaths was lower than that of excess deaths, whereas the opposite occurred in others. Moreover, excess deaths were higher in men, in those aged 20 to 59, and in black, yellow, or indigenous individuals. Meanwhile, excess mortality was lower in women, individuals aged 80 years or older, and in whites. Additionally, deaths among those aged 0 to 19 were 7.2% lower than expected, with reduction in mortality from respiratory diseases and external causes. There was also a drop in mortality due to external causes in men and in those aged 20 to 39 years. Furthermore, reductions in deaths from CVD and neoplasms were noted in some states and groups.ConclusionThere is evidence of underreporting of COVID-19 deaths and of the possible impact of restrictive measures in the reduction of deaths from external causes and respiratory diseases. The impacts of COVID-19 on mortality were heterogeneous among the states and groups, revealing that regional, demographic, socioeconomic, and racial differences expose individuals in distinct ways to the risk of death from both COVID-19 and other causes.

scholarly journals Tracking Excess Deaths (TRACKED) – an interactive online tool to monitor excess deaths associated with the COVID-19 pandemic in the United Kingdom

2020 ◽  
Vol 5 ◽  
pp. 168 ◽  
Author(s):  
Michael T. C. Poon ◽  
Paul M. Brennan ◽  
Kai Jin ◽  
Jonine D. Figueroa ◽  
Cathie L. M. Sudlow
Keyword(s):  
United Kingdom ◽  
Northern Ireland ◽  
Cause Of Death ◽  
Excess Mortality ◽  
Dynamic Assessment ◽  
Mortality Data ◽  
Mortality Trends ◽  
Online Tool ◽  
The United Kingdom ◽  
Excess Deaths

Background: We aimed to describe trends of excess mortality in the United Kingdom (UK) stratified by nation and cause of death, and to develop an online tool for reporting the most up to date data on excess mortality Methods: Population statistics agencies in the UK including the Office for National Statistics (ONS), National Records of Scotland (NRS), and Northern Ireland Statistics and Research Agency (NISRA) publish weekly mortality data. We used mortality data up to 22nd May in the ONS and the NISRA and 24th May in the NRS. The main outcome measures were crude mortality for non-COVID deaths (where there is no mention of COVID-19 on the death certificate) calculated, and excess mortality defined as difference between observed mortality and expected average of mortality from previous 5 years. Results: There were 56,961 excess deaths, of which 8,986 were non-COVID excess deaths. England had the highest number of excess deaths per 100,000 population (85) and Northern Ireland the lowest (34). Non-COVID mortality increased from 23rd March and returned to the 5-year average on 10th May. In Scotland, where underlying cause mortality data besides COVID-related deaths was available, the percentage excess over the 8-week period when COVID-related mortality peaked was: dementia 49%, other causes 21%, circulatory diseases 10%, and cancer 5%. We developed an online tool (TRACKing Excess Deaths - TRACKED) to allow dynamic exploration and visualisation of the latest mortality trends. Conclusions: Continuous monitoring of excess mortality trends and further integration of age- and gender-stratified and underlying cause of death data beyond COVID-19 will allow dynamic assessment of the impacts of indirect and direct mortality of the COVID-19 pandemic.

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