scholarly journals Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis

2020 ◽  
Author(s):  
Andrew C Stokes ◽  
Dielle J Lundberg ◽  
Katherine Hempstead ◽  
Irma T Elo ◽  
Samuel H Preston
Keyword(s):  
Causes Of Death ◽  
Excess Mortality ◽  
Structural Characteristics ◽  
County Level ◽  
Structural Racism ◽  
All Cause Mortality ◽  
Black Residents ◽  
The Impact ◽  
Level Analysis ◽  
Excess Deaths

Covid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. In this study, we take advantage of spatial variation in Covid-19 mortality across US counties to estimate its relationship with all-cause mortality. We then examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by demographic and structural characteristics. We estimate that 26.3% [95% CI, 20.1% to 32.5%] of excess deaths between February 1 and September 23, 2020 were ascribed to causes of death other than Covid-19 itself. Excess deaths not assigned to Covid-19 were even higher than predicted by our model in counties with high income inequality, low homeownership, and high percentages of Black residents, showing a pattern related to socioeconomic disadvantage and structural racism. The standard deviation of mortality across counties increased by 9.5% as a result of excess deaths directly assigned to Covid-19 and an additional 5.3% as a result of excess deaths not assigned to Covid-19. Our work suggests that inequities in excess deaths attributable to Covid-19 may be even greater than revealed by data reporting deaths assigned to Covid-19 alone.

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 COVID-19 and excess mortality in the United States: A county-level analysis

PLoS Medicine ◽  
2021 ◽  
Vol 18 (5) ◽  
pp. e1003571
Author(s):  
Andrew C. Stokes ◽  
Dielle J. Lundberg ◽  
Irma T. Elo ◽  
Katherine Hempstead ◽  
Jacob Bor ◽  
...  
Keyword(s):  
United States ◽  
Formal Education ◽  
Excess Mortality ◽  
Time Lag ◽  
The United States ◽  
County Level ◽  
Level Variation ◽  
Cross Sectional ◽  
All Cause Mortality ◽  
Excess Deaths

Background Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. Methods and findings In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. Conclusions In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

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 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.

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 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 EXCESS MORTALITY FROM COVID-19. WEEKLY EXCESS DEATH RATES BY AGE AND SEX FOR SWEDEN AND ITS MOST AFFECTED REGION

2020 ◽  
Author(s):  
Karin Modig ◽  
Anders Ahlbom ◽  
Marcus Ebeling
Keyword(s):  
Causes Of Death ◽  
Excess Mortality ◽  
National Level ◽  
Mortality Rates ◽  
Regional Level ◽  
Death Rates ◽  
Excess Death ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Age And Sex

Abstract Background Sweden has one of the highest numbers of COVID-19 deaths per inhabitant globally. However, absolute death counts can be misleading. Estimating age- and sex-specific mortality rates is necessary in order to account for the underlying population structure. Furthermore, given the difficulty of assigning causes of death, excess all-cause mortality should be estimated to assess the overall burden of the pandemic. Methods By estimating weekly age- and sex-specific death rates during 2020 and during the preceding five years, our aim is to get more accurate estimates of the excess mortality attributed to COVID-19 in Sweden, and in the most affected region Stockholm. Results Eight weeks after Sweden’s first confirmed case, the death rates at all ages above 60 were higher than for previous years. Persons above age 80 were disproportionally more affected, and men suffered greater excess mortality than women in ages up to 75 years. At older ages, the excess mortality was similar for men and women, with up to 1.5 times higher death rates for Sweden and up to 3 times higher for Stockholm. Life expectancy at age 50 declined by less than 1 year for Sweden and 1.5 years for Stockholm compared to 2019. Conclusions The excess mortality has been high in older ages during the pandemic, but it remains to be answered if this is because of age itself being a prognostic factor or a proxy for comorbidity. Only monitoring deaths at a national level may hide the effect of the pandemic on the regional level.

Crop Yield Estimation Using Multi-source Satellite Image Series and Deep Learning

2020 ◽  
Author(s):  
Gohar Ghazaryan ◽  
Sergii Skakun ◽  
Simon König ◽  
Ehsan Eyshi Rezaei ◽  
Stefan Siebert ◽  
...  
Keyword(s):  
Time Series ◽  
Agricultural Production ◽  
Remotely Sensed ◽  
Growth Conditions ◽  
Crop Growth ◽  
County Level ◽  
Yield Estimation ◽  
Field Level ◽  
The Impact ◽  
Level Analysis

&lt;p&gt;Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and extreme events. Remotely sensed time-series can be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Thus, our primary goal was to study the impact of applying different remotely sensed time series on yield estimation in U.S. at the county and field scale. Furthermore, the impact of crop growth conditions on yield variability was assessed. For county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Whereas field-level analysis was carried out using NASA&amp;#8217;s Harmonized Landsat Sentinel-2 (HLS) product. 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R&lt;sup&gt;2 &lt;/sup&gt;exceeding 0.8 when data from mid growing season were used. The results highlight the potential of yield estimation at different management scales.&lt;/p&gt;

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.

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