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 )

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 )

scholarly journals Evaluation of County-Level Heterogeneity in Excess Mortality in Colorado from March to September 2020

2021 ◽  
Author(s):  
Jay Chandra ◽  
Marie Charpignon ◽  
Mathew C Samuel ◽  
Anushka Bhaskar ◽  
Saketh Sundar ◽  
...  
Keyword(s):  
Public Health ◽  
Mortality Rate ◽  
Excess Mortality ◽  
Ethnic Disparities ◽  
The United States ◽  
The State ◽  
Mortality Data ◽  
County Level ◽  
All Cause Mortality ◽  
Excess Deaths

Importance: Tracking the direct and indirect impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in the United States has been hindered by the lack of testing and by reporting delays. Evaluating excess mortality, or the number of deaths above what is expected in a given time period, provides critical insights into the true burden of the COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Stratifying mortality data by demographics such as age, sex, race, ethnicity, and geography helps quantify how subgroups of the population have been differentially affected. Similarly, stratifying mortality data by cause of death reveals the public health effects of the pandemic in terms of other acute and chronic diseases. Objective: To provide stratified estimates of excess mortality in Colorado from March to September 2020. Design, Setting, and Population: This study evaluated the number of excess deaths both directly due to SARS-CoV-2 infection and from all other causes between March and September 2020 at the county level in Colorado. Data were obtained from the Vital Statistics Program at the Colorado Department of Public Health and Environment. These estimates of excess mortality were derived by comparing population- adjusted mortality rates in 2020 with rates in the same months from 2015 to 2019. Results: We found evidence of excess mortality in Colorado between March and September 2020. Two peaks in excess deaths from all causes were recorded in the state, one mid-April and the other at the end of June. Since the first documented SARS-CoV-2 infection on March 5th, we estimated that the excess mortality rate in Colorado was two times higher than the officially reported COVID-19 mortality rate. State-level cumulative excess mortality from all causes reached 71 excess deaths per 100k residents (~4000 excess deaths in the state); in contrast, 35 deaths per 100k directly due to SARS-CoV-2 were recorded in the same period (~1980 deaths. Excess mortality occurred in 52 of 64 counties, accounting for 99% of the state's population. Most excess deaths recorded from March to September 2020 were associated with acute events (estimated at 44 excess deaths per 100k residents and at 9 after excluding deaths directly due to SARS-CoV-2) rather than with chronic conditions (~21 excess deaths per 100k). Among Coloradans aged 14-44, 1.4 times more deaths occurred in those months than during the same period in the five previous years. Hispanic White males died of COVID-19 at the highest rate during this time (~90 deaths from COVID-19 per 100k residents); however, Non-Hispanic Black/African American males were the most affected in terms of overall excess mortality (~204 excess deaths per 100k). Beyond inequalities in COVID-19 mortality per se, these findings signal considerable regional and racial-ethnic disparities in excess all-cause mortality that need to be addressed for a just recovery and in future public health crises.

scholarly journals Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the EuroMOMO network, March to April 2020

2020 ◽  
Vol 25 (26) ◽  
Author(s):  
Lasse S Vestergaard ◽  
Jens Nielsen ◽  
Lukas Richter ◽  
Daniela Schmid ◽  
Natalia Bustos ◽  
...  
Keyword(s):  
Public Health ◽  
Excess Mortality ◽  
European Countries ◽  
Public Health Action ◽  
Federal States ◽  
All Cause Mortality ◽  
Health Action ◽  
Excess Deaths

A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected  ≥ 65 year olds (91% of all excess deaths), but also 45–64 (8%) and 15–44 year olds (1%). No excess mortality was observed in 0–14 year olds.

scholarly journals Timeliness of U.S. mortality data releases during the COVID-19 pandemic: delays are associated with electronic death registration system and elevated weekly mortality

2021 ◽  
Author(s):  
Janet E Rosenbaum ◽  
Marco Stillo ◽  
Nathaniel Graves ◽  
Roberto Rivera
Keyword(s):  
Standard Deviation ◽  
Mortality Data ◽  
Death Registration ◽  
Registration System ◽  
Residual Standard Deviation ◽  
Health Authorities ◽  
All Cause Mortality ◽  
Public Health Authorities ◽  
Reporting Delays ◽  
Excess Deaths

All-cause mortality counts allow public health authorities to identify populations experiencing excess deaths from pandemics, natural disasters, and other emergencies. Further, delays in mortality reporting may contribute to misinformation because death counts take weeks to become accurate. We estimate the timeliness of all-cause mortality releases during the Covid-19 pandemic, and identify potential reasons for reporting delays, using 35 weeks of provisional mortality counts between April 3 and December 4, 2020 for 52 states/jurisdictions. On average, states' mortality counts are delayed by 5.6 weeks (standard deviation 1.74), with a range of 8.8 weeks between the fastest state and the slowest state. States that hadn't adopted the electronic death registration system were about 4 weeks slower, and 100 additional weekly deaths per million were associated with 0.4 weeks delays, but the residual standard deviation was 0.9 weeks, suggesting other sources of delay. Disaster planning should include improving the timeliness of mortality data.

scholarly journals Estimates of pandemic excess mortality in India based on civil registration data

2021 ◽  
Author(s):  
Murad Banaji ◽  
Aashish Gupta
Keyword(s):  
Excess Mortality ◽  
Strong Association ◽  
Mortality Data ◽  
Indian States ◽  
Middle Income ◽  
Civil Registration ◽  
Registration Data ◽  
Seroprevalence Data ◽  
All Cause Mortality ◽  
Excess Deaths

Background: The COVID-19 pandemic has had large impacts on population health. These impacts are less well understood in low-and middle-income countries, where mortality surveillance before the pandemic was patchy. Although limited all-cause mortality data are available in India, interpreting this data remains a challenge. Objective: We use existing data on all-cause mortality from civil registration systems of twelve Indian states comprising around 60% of the national population to understand the scale and timing of excess deaths in India during the COVID-19 pandemic. Methods: We characterize the available data, discuss the various reasons why these data are incomplete, and estimate the extent of coverage in the data. Comparing the pandemic period to 2019, we estimate excess mortality in twelve Indian states, and extrapolate our estimates to the rest of India. We explore sensitivity of the estimates to various assumptions, and present optimistic and pessimistic scenarios along with our central estimates. Results: For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020-May 2021 relative to expectations from 2019. This level of increase in mortality, if it applies nationally, would imply 2.8-2.9 million excess deaths. More limited data from June 2021 increases national estimates of excess deaths during April 2020-June 2021 to 3.8 million. With more optimistic or pessimistic assumptions, excess deaths during this period could credibly lie between 2.8 million and 5.2 million. We find that the scale of estimated excess deaths is broadly consistent with expectations based on seroprevalence data and international data on COVID-19 fatality rates. Moreover, there is a strong association between the timing of excess deaths, and of recorded COVID-19 deaths. Contribution: We show that the surveillance of pandemic mortality in India has been extremely poor, with around 8-10 times as many excess deaths as officially recorded COVID-19 deaths. Our findings highlight the utility of all-cause mortality data, as well as the significant challenges in interpreting such data from LMICs. These data reveal that India is among the countries most severely impacted by the pandemic. It is likely that in absolute terms India has seen the highest number of pandemic excess deaths of any country in the world.

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

2021 ◽  
Vol 18 (8) ◽  
pp. 3913
Author(s):  
Martin Rypdal ◽  
Kristoffer Rypdal ◽  
Ola Løvsletten ◽  
Sigrunn Holbek Sørbye ◽  
Elinor Ytterstad ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Years Of Life Lost ◽  
Regressive Model ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Auto Regressive ◽  
Expected Mortality ◽  
Auto Regressive Model ◽  
Excess Deaths

We estimate the weekly excess all-cause mortality in Norway and Sweden, the years of life lost (YLL) attributed to COVID-19 in Sweden, and the significance of mortality displacement. We computed the expected mortality by taking into account the declining trend and the seasonality in mortality in the two countries over the past 20 years. From the excess mortality in Sweden in 2019/20, we estimated the YLL attributed to COVID-19 using the life expectancy in different age groups. We adjusted this estimate for possible displacement using an auto-regressive model for the year-to-year variations in excess mortality. We found that excess all-cause mortality over the epidemic year, July 2019 to July 2020, was 517 (95%CI = (12, 1074)) in Norway and 4329 [3331, 5325] in Sweden. There were 255 COVID-19 related deaths reported in Norway, and 5741 in Sweden, that year. During the epidemic period of 11 March–11 November, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. We estimated that the number of YLL attributed to COVID-19 in Sweden was 45,850 [13,915, 80,276] without adjusting for mortality displacement and 43,073 (12,160, 85,451) after adjusting for the displacement accounted for by the auto-regressive model. In conclusion, we find good agreement between officially recorded COVID-19 related deaths and all-cause excess deaths in both countries during the first epidemic wave and no significant mortality displacement that can explain those deaths.

scholarly journals 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.

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 Excess mortality among the elderly in 12 European countries, February and March 2012

2012 ◽  
Vol 17 (14) ◽  
Author(s):  
A Mazick ◽  
B Gergonne ◽  
J Nielsen ◽  
F Wuillaume ◽  
M J Virtanen ◽  
...  
Keyword(s):  
Preliminary Data ◽  
Influenza A ◽  
Excess Mortality ◽  
The Elderly ◽  
European Countries ◽  
H3n2 Virus ◽  
All Cause Mortality ◽  
Cold Snap ◽  
The Impact ◽  
Excess Deaths

In February and March 2012, excess deaths among the elderly have been observed in 12 European countries that carry out weekly monitoring of all-cause mortality. These preliminary data indicate that the impact of influenza in Europe differs from the recent pandemic and post-pandemic seasons. The current excess mortality among the elderly may be related to the return of influenza A(H3N2) virus, potentially with added effects of a cold snap.

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.

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