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

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.

Download Full-text

Excess Mortality During the COVID-19 Pandemic in Philadelphia

American Journal of Public Health ◽

10.2105/ajph.2021.306285 ◽

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 )

Download Full-text

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

PLoS Medicine ◽

10.1371/journal.pmed.1003571 ◽

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.

Download Full-text

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

Eurosurveillance ◽

10.2807/1560-7917.es.2020.25.26.2001214 ◽

2020 ◽

Vol 25 (26) ◽

Cited By ~ 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.

Download Full-text

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

10.1101/2020.08.31.20184036 ◽

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.

Download Full-text

Abstract P296: Investigation Of Geographic Disparities Of Pre-diabetes And Diabetes And Associated County-level Risk Factors In Florida, 2016

Circulation ◽

10.1161/circ.141.suppl_1.p296 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Jennifer E Lord ◽

Shamarial L Roberson ◽

Agricola Odoi

Keyword(s):

Public Health ◽

Spatial Patterns ◽

The United States ◽

The State ◽

Scan Statistics ◽

Diabetes Prevalence ◽

County Level ◽

Poverty Level ◽

Geographic Disparities ◽

Strength Of Association

Background: Diabetes and its complications represent a significant public health burden in the United States, with evidence of geographic disparities. Identifying these disparities and their determinants is useful for guiding control programs. Therefore, this study investigated geographic disparities of pre-diabetes and diabetes prevalence in Florida in 2016, and identified predictors of the observed spatial patterns. Additionally, we investigated changes in geographic distribution of the two conditions between 2013 and 2016. Methods: The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Global ordinary least squares regression and local Poisson geographically weighted generalized linear models were used to investigate predictors of the identified spatial patterns. Counties with significant changes in prevalence of the two conditions between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using Simes method. Results: The state-wide diabetes prevalence was 11.2% in 2013, and 11.8% in 2016. Statistically significant ( p ≤0.05) increases in prevalence were identified in 73% (49/67) of the counties. Similarly, the state-wide prevalence of pre-diabetes was 7.1% in 2013 and 9.2% in 2016 with 76% (51/67) of the counties reporting statistically significant increases. Significant local hotspots were identified for both conditions. Predictors of county-level diabetes prevalence were: proportion of the obese population, number of physicians per 1000 persons, proportion of the population living below the poverty level, and proportion of the population with arthritis. Predictors of pre-diabetes prevalence included proportion of the population with arthritis and proportion of the population that identified as non-Hispanic black. There was evidence of geographical variability of all regression coefficients for both the pre-diabetes and diabetes models indicating that the strength of association of the relationships between the predictors and outcomes varied by geographic area. Conclusions: Geographic disparities of both conditions continue to exist in Florida. Moreover, there was a state-wide increase in the burden of both conditions between 2013 and 2016. The fact that the strength of association of the relationships between the predictors and outcomes varied across the counties implies that some predictors may be more important in some counties than others. These findings imply that local models provide useful information to guide public health decision-making and resource allocation. Identifying high-risk geographic areas and location-specific determinants of chronic disease prevalence should be used to inform targeted intervention programs.

Download Full-text

Mortality in Norway and Sweden during the COVID-19 pandemic

Scandinavian Journal of Public Health ◽

10.1177/14034948211047137 ◽

2021 ◽

pp. 140349482110471

Author(s):

Frederik E. Juul ◽

Henriette C. Jodal ◽

Ishita Barua ◽

Erle Refsum ◽

Ørjan Olsvik ◽

...

Keyword(s):

Mortality Rate ◽

Excess Mortality ◽

Mortality Rates ◽

Mitigation Measures ◽

Real World Data ◽

School Closures ◽

All Cause Mortality ◽

Rate Ratios ◽

Excess Deaths ◽

Related Mortality

Background: Norway and Sweden are similar countries in terms of socioeconomics and health care. Norway implemented extensive COVID-19 measures, such as school closures and lockdowns, whereas Sweden did not. Aims: To compare mortality in Norway and Sweden, two similar countries with very different mitigation measures against COVID-19. Methods: Using real-world data from national registries, we compared all-cause and COVID-19-related mortality rates with 95% confidence intervals (CI) per 100,000 person-weeks and mortality rate ratios (MRR) comparing the five preceding years (2015–2019) with the pandemic year (2020) in Norway and Sweden. Results: In Norway, all-cause mortality was stable from 2015 to 2019 (mortality rate 14.6–15.1 per 100,000 person-weeks; mean mortality rate 14.9) and was lower in 2020 than from 2015 to 2019 (mortality rate 14.4; MRR 0.97; 95% CI 0.96–0.98). In Sweden, all-cause mortality was stable from 2015 to 2018 (mortality rate 17.0–17.8; mean mortality rate 17.1) and similar to that in 2020 (mortality rate 17.6), but lower in 2019 (mortality rate 16.2). Compared with the years 2015–2019, all-cause mortality in the pandemic year was 3% higher due to the lower rate in 2019 (MRR 1.03; 95% CI 1.02–1.04). Excess mortality was confined to people aged ⩾70 years in Sweden compared with previous years. The COVID-19-associated mortality rates per 100,000 person-weeks during the first wave of the pandemic were 0.3 in Norway and 2.9 in Sweden. Conclusions: All-cause mortality in 2020 decreased in Norway and increased in Sweden compared with previous years. The observed excess deaths in Sweden during the pandemic may, in part, be explained by mortality displacement due to the low all-cause mortality in the previous year.

Download Full-text

Identifying County-Level All-Cause Mortality Rate Trajectories and Their Spatial Distribution Across the United States

Preventing Chronic Disease ◽

10.5888/pcd16.180486 ◽

2019 ◽

Vol 16 ◽

Cited By ~ 1

Author(s):

Peter Baltrus ◽

Khusdeep Malhotra ◽

George Rust ◽

Robert Levine ◽

Chaohua Li ◽

...

Keyword(s):

United States ◽

Spatial Distribution ◽

Mortality Rate ◽

The United States ◽

County Level ◽

All Cause Mortality

Download Full-text

All-cause mortality supports the COVID-19 mortality in Belgium and comparison with major fatal events of the last century

Archives of Public Health ◽

10.1186/s13690-020-00496-x ◽

2020 ◽

Vol 78 (1) ◽

Author(s):

Natalia Bustos Sierra ◽

Nathalie Bossuyt ◽

Toon Braeye ◽

Mathias Leroy ◽

Isabelle Moyersoen ◽

...

Keyword(s):

Mortality Rate ◽

Nursing Homes ◽

Excess Mortality ◽

Influenza Pandemic ◽

Mortality Rates ◽

Mortality Data ◽

Term Care ◽

All Cause Mortality ◽

Hospital Deaths ◽

Mortality Surveillance

Abstract Background The COVID-19 mortality rate in Belgium has been ranked among the highest in the world. To assess the appropriateness of the country’s COVID-19 mortality surveillance, that includes long-term care facilities deaths and deaths in possible cases, the number of COVID-19 deaths was compared with the number of deaths from all-cause mortality. Mortality during the COVID-19 pandemic was also compared with historical mortality rates from the last century including those of the Spanish influenza pandemic. Methods Excess mortality predictions and COVID-19 mortality data were analysed for the period March 10th to June 21st 2020. The number of COVID-19 deaths and the COVID-19 mortality rate per million were calculated for hospitals, nursing homes and other places of death, according to diagnostic status (confirmed/possible infection). To evaluate historical mortality, monthly mortality rates were calculated from January 1900 to June 2020. Results Nine thousand five hundred ninety-one COVID-19 deaths and 39,076 deaths from all-causes were recorded, with a correlation of 94% (Spearman’s rho, p < 0,01). During the period with statistically significant excess mortality (March 20th to April 28th; total excess mortality 64.7%), 7917 excess deaths were observed among the 20,159 deaths from all-causes. In the same period, 7576 COVID-19 deaths were notified, indicating that 96% of the excess mortality were likely attributable to COVID-19. The inclusion of deaths in nursing homes doubled the COVID-19 mortality rate, while adding deaths in possible cases increased it by 27%. Deaths in laboratory-confirmed cases accounted for 69% of total COVID-19-related deaths and 43% of in-hospital deaths. Although the number of deaths was historically high, the monthly mortality rate was lower in April 2020 compared to the major fatal events of the last century. Conclusions Trends in all-cause mortality during the first wave of the epidemic was a key indicator to validate the Belgium’s high COVID-19 mortality figures. A COVID-19 mortality surveillance limited to deaths from hospitalised and selected laboratory-confirmed cases would have underestimated the magnitude of the epidemic. Excess mortality, daily and monthly number of deaths in Belgium were historically high classifying undeniably the first wave of the COVID-19 epidemic as a fatal event.

Download Full-text

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

10.1101/2021.09.30.21264376 ◽

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.

Download Full-text

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18083913 ◽

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.

Download Full-text