All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021)

AbstractOfficial COVID-19 mortality statistics are strongly influenced by the local diagnostic capacity, strength of the healthcare system, and the recording and reporting capacities on causes of death. This can result in significant undercounting of COVID-19 attributable deaths, making it challenging to understand the total mortality burden of the pandemic. Excess mortality, which is defined as the increase in observed death counts compared to a baseline expectation, provides an alternate measure of the mortality shock of the COVID-19 pandemic. Here, we use data from civil death registers for 54 municipalities across the state of Gujarat, India, to estimate the impact of the COVID-19 pandemic on all-cause mortality. Using a model fit to monthly data from January 2019 to February 2020, we estimate excess mortality over the course of the pandemic from March 2020 to April 2021. We estimated 16,000 [95% CI: 14,000, 18,000] excess deaths across these municipalities since March 2020. The sharpest increase in deaths was observed in April 2021, with an estimated 480% [95% CI: 390%, 580%] increase in mortality from expected counts for the same period. Females and the 40 to 60 age groups experienced a greater increase from baseline mortality compared to other demographic groups. Our excess mortality estimate for these 54 municipalities, representing approximately 5% of the state population, exceeds the official COVID-19 death count for the entire state of Gujarat.

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Premature mortality attributable to COVID-19: potential years of life lost in 17 countries around the world, January–August 2020

BMC Public Health ◽

10.1186/s12889-021-12377-1 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Maider Pagola Ugarte ◽

Souzana Achilleos ◽

Annalisa Quattrocchi ◽

John Gabel ◽

Ourania Kolokotroni ◽

...

Keyword(s):

Age Groups ◽

Premature Mortality ◽

The United States ◽

Cape Verde ◽

Years Of Life Lost ◽

Demographic Groups ◽

The World ◽

The Usa ◽

Research Consortium ◽

The Impact

Abstract Background Understanding the impact of the burden of COVID-19 is key to successfully navigating the COVID-19 pandemic. As part of a larger investigation on COVID-19 mortality impact, this study aims to estimate the Potential Years of Life Lost (PYLL) in 17 countries and territories across the world (Australia, Brazil, Cape Verde, Colombia, Cyprus, France, Georgia, Israel, Kazakhstan, Peru, Norway, England & Wales, Scotland, Slovenia, Sweden, Ukraine, and the United States [USA]). Methods Age- and sex-specific COVID-19 death numbers from primary national sources were collected by an international research consortium. The study period was established based on the availability of data from the inception of the pandemic to the end of August 2020. The PYLL for each country were computed using 80 years as the maximum life expectancy. Results As of August 2020, 442,677 (range: 18–185,083) deaths attributed to COVID-19 were recorded in 17 countries which translated to 4,210,654 (range: 112–1,554,225) PYLL. The average PYLL per death was 8.7 years, with substantial variation ranging from 2.7 years in Australia to 19.3 PYLL in Ukraine. North and South American countries as well as England & Wales, Scotland and Sweden experienced the highest PYLL per 100,000 population; whereas Australia, Slovenia and Georgia experienced the lowest. Overall, males experienced higher PYLL rate and higher PYLL per death than females. In most countries, most of the PYLL were observed for people aged over 60 or 65 years, irrespective of sex. Yet, Brazil, Cape Verde, Colombia, Israel, Peru, Scotland, Ukraine, and the USA concentrated most PYLL in younger age groups. Conclusions Our results highlight the role of PYLL as a tool to understand the impact of COVID-19 on demographic groups within and across countries, guiding preventive measures to protect these groups under the ongoing pandemic. Continuous monitoring of PYLL is therefore needed to better understand the burden of COVID-19 in terms of premature mortality.

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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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Do alternative weighting approaches for an Index of Multiple Deprivation change the association with mortality? A sensitivity analysis from Germany

BMJ Open ◽

10.1136/bmjopen-2018-028553 ◽

2019 ◽

Vol 9 (8) ◽

pp. e028553 ◽

Cited By ~ 1

Author(s):

Florian Schederecker ◽

Christoph Kurz ◽

Jon Fairburn ◽

Werner Maier

Keyword(s):

Age Groups ◽

Total Mortality ◽

Premature Mortality ◽

Area Deprivation ◽

Multiple Deprivation ◽

The Stability ◽

Weighting Procedures ◽

The Impact ◽

The Relationship ◽

Index Of Multiple Deprivation

ObjectivesThis study aimed to assess the impact of using different weighting procedures for the German Index of Multiple Deprivation (GIMD) investigating their link to mortality rates.Design and settingIn addition to the original (normative) weighting of the GIMD domains, four alternative weighting approaches were applied: equal weighting, linear regression, maximization algorithm and factor analysis. Correlation analyses to quantify the association between the differently weighted GIMD versions and mortality based on district-level official data from Germany in 2010 were applied (n=412 districts).Outcome measuresTotal mortality (all age groups) and premature mortality (<65 years).ResultsAll correlations of the GIMD versions with both total and premature mortality were highly significant (p<0.001). The comparison of these associations using Williams’s t-test for paired correlations showed significant differences, which proved to be small in respect to absolute values of Spearman’s rho (total mortality: between 0.535 and 0.615; premature mortality: between 0.699 and 0.832).ConclusionsThe association between area deprivation and mortality proved to be stable, regardless of different weighting of the GIMD domains. The theory-based weighting of the GIMD should be maintained, due to the stability of the GIMD scores and the relationship to mortality.

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Estimating the mortality caused by great cormorant predation on fish stocks: pikeperch in the Archipelago Sea, northern Baltic Sea, as an example

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2015-0033 ◽

2016 ◽

Vol 73 (1) ◽

pp. 84-93 ◽

Cited By ~ 7

Author(s):

Outi Heikinheimo ◽

Pekka Rusanen ◽

Katja Korhonen

Keyword(s):

Age Groups ◽

Total Mortality ◽

Sensitivity Analyses ◽

Fish Stock ◽

Great Cormorant ◽

Phalacrocorax Carbo ◽

Fish Stocks ◽

Archipelago Sea ◽

Phalacrocorax Carbo Sinensis ◽

The Impact

Estimates of the mortality rates caused by cormorants are needed to assess the impact on fish stock dynamics and fisheries. In this study, we calculated the annual instantaneous mortality caused by great cormorants (Phalacrocorax carbo sinensis) on young pikeperch (Sander lucioperca), using data from Archipelago Sea, southwestern coast of Finland. The pikeperch are vulnerable to cormorant predation mainly at the ages 2–4. The annual instantaneous mortality caused by cormorants was between 0.04 and 0.13, and the estimated effect on the pikeperch stock size at recruitment to the fishery ranged from 4% to 23%, respectively. The average annual cormorant-induced mortality accounted for 5%–34% of the total mortality in these age groups. The sensitivity analyses proved that the rates of mortality from other sources largely affect the estimated mortality from cormorant predation. In cases with strong fluctuations in the abundance of the prey fish stocks, ignoring the size and density dependence of the natural mortality may lead to overestimation of the importance of cormorants as competitors of fisheries.

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Excess Mortality Associated With COVID-19 by Demographic Group: Evidence From Florida and Ohio

Public Health Reports ◽

10.1177/00333549211041550 ◽

2021 ◽

pp. 003335492110415

Author(s):

Troy Quast ◽

Ross Andel

Keyword(s):

Excess Mortality ◽

Prediction Interval ◽

National Level ◽

Age Groups ◽

Black Population ◽

Infection Rates ◽

Black Adults ◽

Demographic Groups ◽

Younger Age ◽

Excess Deaths

Objective COVID-19 mortality varies across demographic groups at the national level, but little is known about potential differences in COVID-19 mortality across states. The objective of this study was to estimate the number of all-cause excess deaths associated with COVID-19 in Florida and Ohio overall and by sex, age, and race. Methods We calculated the number of weekly and cumulative excess deaths among adults aged ≥20 from March 15 through December 5, 2020, in Florida and Ohio as the observed number of deaths less the expected number of deaths, adjusted for population, secular trends, and seasonality. We based our estimates on death certificate data from the previous 10 years. Results The results were based on ratios of observed-to-expected deaths. The ratios were 1.17 (95% prediction interval, 1.14-1.21) in Florida and 1.15 (95% prediction interval, 1.11-1.19) in Ohio. Although the largest number of excess deaths occurred in the oldest age groups, in both states the ratios of observed-to-expected deaths were highest among adults aged 20-49 (1.21; 95% prediction interval, 1.11-1.32). The ratio of observed-to-expected deaths for the Black population was especially elevated in Florida. Conclusions Although excess deaths were largely concentrated among older cohorts, the high ratios of observed-to-expected deaths among younger age groups indicate widespread effects of COVID-19. The high levels of observed-to-expected deaths among Black adults may reflect in part disparities in infection rates, preexisting conditions, and access to care. The finding of high excess deaths among Black adults deserves further attention.

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Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

10.1101/2020.03.03.20030593 ◽

2020 ◽

Cited By ~ 68

Author(s):

Chaolong Wang ◽

Li Liu ◽

Xingjie Hao ◽

Huan Guo ◽

Qi Wang ◽

...

Keyword(s):

Attack Rate ◽

Healthcare Workers ◽

Age Groups ◽

Credible Interval ◽

Reproductive Number ◽

Demographic Groups ◽

Individual Level ◽

Epidemiological Characteristics ◽

The Impact ◽

Pharmaceutical Interventions

ABSTRACTBACKGROUNDWe described the epidemiological features of the coronavirus disease 2019 (Covid-19) outbreak, and evaluated the impact of non-pharmaceutical interventions on the epidemic in Wuhan, China.METHODSIndividual-level data on 25,961 laboratory-confirmed Covid-19 cases reported through February 18, 2020 were extracted from the municipal Notifiable Disease Report System. Based on key events and interventions, we divided the epidemic into four periods: before January 11, January 11-22, January 23 - February 1, and February 2-18. We compared epidemiological characteristics across periods and different demographic groups. We developed a susceptible-exposed-infectious-recovered model to study the epidemic and evaluate the impact of interventions.RESULTSThe median age of the cases was 57 years and 50.3% were women. The attack rate peaked in the third period and substantially declined afterwards across geographic regions, sex and age groups, except for children (age <20) whose attack rate continued to increase. Healthcare workers and elderly people had higher attack rates and severity risk increased with age. The effective reproductive number dropped from 3.86 (95% credible interval 3.74 to 3.97) before interventions to 0.32 (0.28 to 0.37) post interventions. The interventions were estimated to prevent 94.5% (93.7 to 95.2%) infections till February 18. We found that at least 59% of infected cases were unascertained in Wuhan, potentially including asymptomatic and mild-symptomatic cases.CONCLUSIONSConsiderable countermeasures have effectively controlled the Covid-19 outbreak in Wuhan. Special efforts are needed to protect vulnerable populations, including healthcare workers, elderly and children. Estimation of unascertained cases has important implications on continuing surveillance and interventions.

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Subnational mortality estimates for India in 2019: a baseline for evaluating excess deaths due to the COVID-19 pandemic

BMJ Global Health ◽

10.1136/bmjgh-2021-007399 ◽

2021 ◽

Vol 6 (11) ◽

pp. e007399

Author(s):

Chalapati Rao ◽

Amrit Jose John ◽

Ajit Kumar Yadav ◽

Mansha Siraj

Keyword(s):

Excess Mortality ◽

National Level ◽

Age Groups ◽

Uttar Pradesh ◽

The Elderly ◽

Registration System ◽

Infant Deaths ◽

Civil Registration ◽

The Impact ◽

Mortality Estimates

BackgroundEstimates of excess mortality are required to assess and compare the impact of the COVID-19 pandemic across populations. For India, reliable baseline prepandemic mortality patterns at national and subnational level are necessary for such assessments. However, available data from the Civil Registration System (CRS) is affected by incompleteness of death recording that varies by sex, age and location.MethodsUnder-reporting of CRS 2019 deaths was assessed for three age groups (< 5 years, 15–59 years and ≥60 years) at subnational level, through comparison with age-specific death rates from alternate sources. Age-specific corrections for under-reporting were applied to derive adjusted death counts by sex for each location. These were used to compute life expectancy (LE) at birth by sex in 2019, which were compared with subnational LEs from the Global Burden of Disease (GBD) 2019 Study.ResultsA total of 9.92 million deaths (95% UI 9.70 to 10.02) were estimated across India in 2019, about 2.28 million more than CRS reports. Adjustments to under-five and elderly mortality accounted for 30% and 56% of additional deaths, respectively. Adjustments in Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh accounted for 75% of all additional deaths. Adjusted LEs were below corresponding GBD estimates by ≥2 years for males at national level and in 20 states, and by ≥1 year for females in 12 states.ConclusionsThese results represent the first-ever subnational mortality estimates for India derived from CRS reported deaths, and serve as a baseline for assessing excess mortality from the COVID-19 pandemic. Adjusted life expectancies indicate higher mortality patterns in India than previously perceived. Under-reporting of infant deaths and those among women and the elderly is evident in many locations. Further CRS strengthening is required to improve the empirical basis for local mortality measurement across the country.

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Impact of different heat wave definitions on daily mortality in Bandafassi, Senegal

PLoS ONE ◽

10.1371/journal.pone.0249199 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0249199

Author(s):

Mbaye Faye ◽

Abdoulaye Dème ◽

Abdou Kâ Diongue ◽

Ibrahima Diouf

Keyword(s):

Heat Wave ◽

Heat Waves ◽

Age Groups ◽

Additive Model ◽

Total Mortality ◽

Warning System ◽

Decision Makers ◽

Maximum Temperature ◽

Climate Services ◽

The Impact

Objective The aim of this study is to find the most suitable heat wave definition among 15 different ones and to evaluate its impact on total, age-, and gender-specific mortality for Bandafassi, Senegal. Methods Daily weather station data were obtained from Kedougou situated at 17 km from Bandafassi from 1973 to 2012. Poisson generalized additive model (GAM) and distributed lag non-linear model (DLNM) are used to investigate the effect of heat wave on mortality and to evaluate the nonlinear association of heat wave definitions at different lag days, respectively. Results Heat wave definitions, based on three or more consecutive days with both daily minimum and maximum temperatures greater than the 90th percentile, provided the best model fit. A statistically significant increase in the relative risk (RRs 1.4 (95% Confidence Interval (CI): 1.2–1.6), 1.7 (95% CI: 1.5–1.9), 1.21 (95% CI: 1.08–1.3), 1.2 (95% CI: 1.04–1.5), 1.5 (95% CI: 1.3–1.8), 1.4 (95% CI: 1.2–1.5), 1.5 (95% CI: 1.07–1.6), and 1.5 (95% CI: 1.3–1.8)) of total mortality was observed for eight definitions. By using the definition based on the 90th percentile of minimum and maximum temperature with a 3-day duration, we also found that females and people aged ≥ 55 years old were at higher risks than males and other different age groups to heat wave related mortality. Conclusion The impact of heat waves was associated with total-, age-, gender-mortality. These results are expected to be useful for decision makers who conceive of public health policies in Senegal and elsewhere. Climate parameters, including temperatures and humidity, could be used to forecast heat wave risks as an early warning system in the area where we conduct this research. More broadly, our findings should be highly beneficial to climate services, researchers, clinicians, end-users and decision-makers.

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

10.1101/228445 ◽

2017 ◽

Author(s):

A. R. R. Freitas ◽

P. M. Alarcon-Elbal ◽

M. R. Donalisio

Keyword(s):

Excess Mortality ◽

Case Reports ◽

Age Groups ◽

Epidemiological Data ◽

Mortality Data ◽

Population Total ◽

Mortality And Morbidity ◽

Pearson Coefficient ◽

The Difference ◽

The Impact

AbstractIn some chikugunya epidemics, deaths are not fully captured by the traditional surveillance system, based on case reports and death reports. This is a time series study to evaluate the excess of mortality associated with epidemic of chikungunya virus (CHIKV) in Guadeloupe and Martinique, Antilles, 2014. The population (total 784,097 inhabitants) and mortality data estimated by sex and age were accessed at the Institut National de la Statistique et des Etudes Economiques - France. Age adjusted mortality rates were calculated also in Reunion, Indian Ocean for comparison. Epidemiological data on CHIKV (cases, hospitalizations, and deaths) were obtained in the official epidemiological reports of the Cellule de Institut de Veille Sanitaire - France. The excess of deaths for each month in 2014 and 2015 was the difference between the expected and observed deaths for all age groups, considering the 99% confidence interval threshold. Pearson coefficient of correlation between monthly excess of deaths and reported cases of chikungunya show a strong correlation (R = 0.81, p <0.005), also with a 1-month lag (R = 0.87, p <0.001), and between monthly rates of hospitalization for CHIKV and the excess of 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. The overall mortality estimated by this method (639 deaths) was about 4 times greater than that obtained through death declarations (160 deaths). Excess mortality increased with age. Although etiological diagnosis of all deaths associated with CHIKV infection is not possible, already well-known statistical tools can contribute to an evaluation of the impact of this virus on the mortality and morbidity in the different age groups.

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Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis

Journal of Epidemiology & Community Health ◽

10.1136/jech-2020-215505 ◽

2021 ◽

pp. jech-2020-215505

Author(s):

Jose Manuel Aburto ◽

Ridhi Kashyap ◽

Jonas Schöley ◽

Colin Angus ◽

John Ermisch ◽

...

Keyword(s):

Life Expectancy ◽

Excess Mortality ◽

Prediction Interval ◽

Age Groups ◽

Population Level ◽

England And Wales ◽

Life Expectancy At Birth ◽

Using Data ◽

The Impact ◽

Excess Deaths

BackgroundDeaths directly linked to COVID-19 infection may be misclassified, and the pandemic may have indirectly affected other causes of death. To overcome these measurement challenges, we estimate the impact of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality from week 10 of 2020, when the first COVID-19 death was registered, to week 47 ending 20 November 2020 in England and Wales through an analysis of excess mortality.MethodsWe estimated age and sex-specific excess mortality risk and deaths above a baseline adjusted for seasonality with a systematic comparison of four different models using data from the Office for National Statistics. We additionally provide estimates of life expectancy at birth and lifespan inequality defined as the SD in age at death.ResultsThere have been 57 419 (95% prediction interval: 54 197, 60 752) excess deaths in the first 47 weeks of 2020, 55% of which occurred in men. Excess deaths increased sharply with age and men experienced elevated risks of death in all age groups. Life expectancy at birth dropped 0.9 and 1.2 years for women and men relative to the 2019 levels, respectively. Lifespan inequality also fell over the same period by 5 months for both sexes.ConclusionQuantifying excess deaths and their impact on life expectancy at birth provide a more comprehensive picture of the burden of COVID-19 on mortality. Whether mortality will return to—or even fall below—the baseline level remains to be seen as the pandemic continues to unfold and diverse interventions are put in place.

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