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

The Differential Experience of COVID-19 on Asian American Subgroups: The Los Angeles Pandemic Surveillance Cohort Study

Data from Asian Americans (AsA) are commonly aggregated in research studies and reporting, obscuring the significant differences across AsA subgroups. We investigated the differential experience of AsA subgroups in COVID-19 testing, vaccination, engagement in risky and protective behaviors and mental health status against this infectious disease. We surveyed a representative sample of the Los Angeles County population (N = 5500) in April 2021 as part of the Los Angeles Pandemic Surveillance Cohort Study and focused on participants who self-identified as AsA (N = 756). There were significant differences across the AsA subgroups, with Koreans, Asian Indians, and Other Asians living in areas with higher COVID-19 mortality rates, and Asian Indians demonstrating the lowest proportion of COVID-19 vaccination. Vietnamese and Koreans had a higher proportion of becoming unemployed during the pandemic. Although the AsA sample on average demonstrated better outcomes than other racial and ethnic groups, the apparent advantages were heterogenous and due to specific subgroups of AsAs rather than AsAs as a whole. The observed differences in COVID-19 measures across AsA subgroups underscore the need to disaggregate AsA data to identify and reduce existing disparities.

SARS-CoV-2 infection is associated with intestinal permeability, systemic inflammation, and microbial dysbiosis in hospitalized patients

Coronavirus disease 2019 (COVID-19) and its associated severity have been linked to uncontrolled inflammation and may be associated with changes in the microbiome of mucosal sites including the gastrointestinal tract and oral cavity. These sites play an important role in host-microbe homeostasis, and disruption of epithelial barrier integrity during COVID-19 may potentially lead to exacerbated inflammation and immune dysfunction. Outcomes in COVID-19 are highly disparate, ranging from asymptomatic to fatal, and the impact of microbial dysbiosis on disease severity is unclear. Here, we obtained plasma, rectal swabs, oropharyngeal swabs, and nasal swabs from 86 patients hospitalized with COVID-19 and 12 healthy volunteers. We performed 16S rRNA sequencing to characterize the microbial communities in the mucosal swabs and measured concentrations of circulating cytokines, markers of gut barrier integrity, and fatty acids in the plasma samples. We compared these plasma concentrations and microbiomes between healthy volunteers and COVID-19 patients, some of whom had unfortunately died by the end of the study enrollment, and performed a correlation analysis between plasma variables and bacterial abundances. Rectal swabs of COVID-19 patients had reduced abundances of several commensal bacteria including Faecalibacterium prausnitzii and an increased abundance of the opportunistic pathogens Eggerthella lenta and Hungatella hathewayi. Furthermore, the oral pathogen Scardovia wiggsiae was more abundant in the oropharyngeal swabs of COVID-19 patients who died. The abundance of both H. hathewayi and S. wiggsiae correlated with circulating inflammatory markers including IL-6, highlighting the possible role of the microbiome in COVID-19 severity and providing potential therapeutic targets for managing COVID-19.IMPORTANCEOutcomes in coronavirus disease 2019 (COVID-19) are highly disparate and are associated with uncontrolled inflammation; however, the individual factors that lead to this uncontrolled inflammation are not fully understood. Here, we report that severe COVID-19 is associated with systemic inflammation, microbial translocation, and microbial dysbiosis. The rectal and oropharyngeal microbiomes of COVID-19 patients were characterized by a decreased abundance of commensal bacteria and an increased abundance of opportunistic pathogens, which positively correlated with markers of inflammation and microbial translocation. These microbial perturbations may, therefore, contribute to disease severity in COVID-19 and highlight the potential for microbiome-based interventions in improving COVID-19 outcomes.

Stability and Volatility in the Contextual Predictors of Working-Age Mortality in the United States

The contextual predictors of mortality in the United States are well documented, but the COVID-19 pandemic may have upended those associations. Informed by the social history of disease framework (SHDF), this study examined how the importance of county contexts on adult deaths from all causes, drug poisonings, and COVID-19-related causes fluctuated during the pandemic. Using 2018 to 2021 vital statistics data, for each quarter, we estimated associations between county-level deaths among adults ages 25 to 64 and prepandemic county-level contexts (economic conditions, racial-ethnic composition, population health profile, and physician supply). The pandemic significantly elevated the importance of county contexts-particularly median household income and counties' preexisting health profile-on all-cause and drug poisoning deaths. The elevated importance of household income may be long-lasting. Contextual inequalities in COVID-19-related deaths rose and then fell, as the SHDF predicts, but rose again along with socio-political disruptions. The findings support and extend the SHDF.

Unbiased care, unequal outcomes: a nursing telehealth intervention reveals systematic inequities in COVID-19 care delivery

BACKGROUND

The Covid Connected Care Center (C4), a low-barrier telephone nurse hotline, was developed at an academic medical center to increase access to healthcare information and services across the state of Oregon, including to those without a usual source of care. Other studies have demonstrated that telephone triage services can positively influence health behaviors, but it is not known how this effect is maintained across racial/ethnic groups. The objective of this study was to show that the C4 reached throughout the state of Oregon, was valuable to callers, and that recommendations given affected callers' subsequent health-related behaviors.

METHODS

This mixed-methods study, informed by the RE-AIM (Reach, Effectiveness, Addoption, Implementation and Maintenance) framework, assessed caller demographics and clinical care from March 30 2020 until September 8, 2021. Descriptive statistics, multivariable risk models and Zou's modified Poisson modeling were applied to electronic health record and call system data; An inductive approach was used for patient and staff experience surveys and semi-structured interviews. Approval was obtained from the OHSU Institutional Review Board (Study 00021413).

RESULTS

145,537 telephone calls and 92,100 text-based contacts (61% and 39%, respectively) were included. Callers tended to not have a usual source of primary care and utilized recommended services. Emergency department utilization was minimal (1.5%). Racial or ethnic disparities were not detected in the recommendations, but Black (RR 0.92, CI 0.86-0.98) and Multiracial (RR 0.90 CI 0.81-0.99) callers were less likely than non-Hispanic white callers to receive a COVID-19 test. Participants in the post-call survey (n = 50) would recommend this service to friends or family. Interviews with callers (n = 9) revealed this was because they valued assistance translating general recommendations into a personalized care plan. C4 staff interviewed (n = 9) valued the opportunity to serve the public. The C4 was a trusted resource to the public and reached the intended audiences. However, disparities in access to COVID-19 testing persisted.

CONCLUSIONS

Nursing triage hotlines can guide caller behavior and be an effective part of a robust public health information infrastructure.

Cause-specific excess mortality in Denmark, Finland, Norway, and Sweden during the COVID-19 pandemic 2020-2022: a study using nationwide population data

While there is substantial evidence on excess mortality in the first two years of the COVID-19 pandemic, no study has conducted a cause-specific analysis of excess mortality for the whole period 2020-2022 across multiple countries. We examined cause-specific excess mortality during 2020-2022 in Denmark, Finland, Norway, and Sweden-four countries with similar demographics and welfare provisions, which implemented different pandemic response policies. To this end, we utilized nationwide register-based information on annual cause-specific deaths stratified by age and sex, and applied linear regression models to predict mortality in 2020-2022 based on the reference period 2010-2019. Excess deaths were obtained by contrasting actual and expected deaths. Additional analyses employed standardization to a common population, as well as population adjustments to account for previous deaths. Our results showed that, besides deaths due to COVID-19 (a total of 32,491 during 2020-2022), all countries experienced excess deaths due to cardiovascular diseases (in total 11,610 excess deaths), and under-mortality due to respiratory diseases other than COVID-19 (in total 9878) and dementia (in total 8721). The excess mortality due to cardiovascular diseases was particularly pronounced in Finland and Norway in 2022, and the under-mortality due to dementia was particularly pronounced in Sweden in 2021-2022. In conclusion, while COVID-19 deaths emerge as the most apparent consequence of the pandemic, our findings suggest that mortality has also been influenced by substitutions between different causes of death and over time, as well as indirect consequences of COVID-19 infection and pandemic responses-albeit to different extents in the different countries.

Racial and Regional Disparities Surrounding In-Hospital Mortality among Patients with 2019 Novel Coronavirus Disease (COVID-19): Evidence from NIS Sample in 2020

OBJECTIVE

This study explores differences in COVID-19 in-hospital mortality rates by patient and geographic factors to identify at-risk populations and analyze how strained health disparities were exacerbated during the pandemic.

METHODS

The latest 2020 United States National Inpatient Sample (NIS) data was used to obtain a population-based estimate for patients with COVID-19. We conducted a cross-sectional retrospective data analysis, and sampling weights were used for all statistical analyses to represent nationwide in-hospital mortality of patients with COVID-19. We used multivariate logistic regression models to identify predictors for how patients with COVID-19 are associated with in-hospital death.

RESULTS

Of 200,531 patients, 88.9% did not have an in-hospital death (n=178,369), and 11.1% had in-hospital death (n=22,162). Patients older than 70 were 10 times more likely to have an in-hospital death than patients younger than 40 (p<0.001). Male patients were 37% more likely to have an in-hospital death than female patients (p<0.001). Hispanic patients were 25% more likely to have in-hospital deaths than White patients (p<0.001). In the sub-analysis, Hispanic patients in the 50-60, 60-70, and 70 age groups were 32%, 34%, and 24%, respectively, more likely to have in-hospital death than White patients (p<0.001). Patients with hypertension and diabetes were 69% and 29%, respectively, more likely to have in-hospital death than patients without hypertension and diabetes.

CONCLUSION

Health disparities in the COVID-19 pandemic occurred across races and regions and must be addressed to prevent future deaths. Age and comorbidities like diabetes have a well-established link to increased disease severity, and we have linked both to higher mortality risk. Low-income patients had a significantly increased risk of in-hospital death starting at over 40 years old.

Gender-Based Socio-Economic Inequalities in the Pre-Vaccination Era of the COVID-19 Pandemic in Istanbul: A Neighborhood-Level Analysis of Excess Mortality

Worldwide excess mortality (EM) data have the potential to provide a better estimation of the impact of the pandemic. This study aims to investigate and map the inequalities in EM in Istanbul during the pre-vaccination era of the COVID-19 pandemic in 2020 and its association with selected demographic and socio-economic variables at the neighborhood level according to gender. This ecological study was conducted with the EM data of Istanbul. The EM data were obtained from the Istanbul Metropolitan Municipality (IMM) and analyzed according to socio-demographic indicators (gender, age), neighborhood-level indicators (population density, educational attainment) and neighborhood vulnerability (socio-economic and transportation) for the 808 neighborhoods, then presented separately according to gender to examine gender-specific factors. Socio-economic and transportation vulnerability indexes are provided the IMM. The excess mortality rate per 1000 (EMR) in 2020 has been calculated by using the number of deaths in the years 2018-2019. We have mapped EMRs of each neighborhood and used linear regression analysis in three datasets to examine gender specific factors. EMRs in Istanbul showed two peaks one in April and one in November. Male EMRs were higher compared to females in Istanbul during the pre-vaccination era of the pandemic. Higher EMRs were observed in neighborhoods with a higher share of 50+ year old age groups and higher neighborhood socio-economic vulnerability scores. Neighborhood socio-economic vulnerability was significantly associated with EMRs in males but not in females. Unequal distribution of EM between neighborhoods underlines the need for gender-specific pandemic measures to alleviate the burden of the COVID-19 pandemic, especially in socio-economically vulnerable settings. Increased use of area-based indicators with a gender perspective can enhance pandemic measures.

COVID-19 Death Determination Methods, Minnesota, USA, 2020-20221

Accurate and timely mortality surveillance is crucial for elucidating risk factors, particularly for emerging diseases. We compared use of COVID-19 keywords on death certificates alone to identify COVID-19 deaths in Minnesota, USA, during 2020-2022, with use of a standardized mortality definition incorporating additional clinical data. For analyses, we used likelihood ratio χ2 and median 1-way tests. Death certificates alone identified 96% of COVID-19 deaths confirmed by the standardized definition and an additional 3% of deaths that had been classified as non-COVID-19 deaths by the standardized definition. Agreement between methods was >90% for most groups except children, although agreement among adults varied by demographics and location at death. Overall median time from death to filing of death certificate was 3 days; decedent characteristics and whether autopsy was performed varied. Death certificates are an efficient and timely source of COVID-19 mortality data when paired with SARS-CoV-2 testing data.

Underreporting of diabetes mellitus as the cause of death in Bauru, State of São Paulo, Brazil over 40 years: a documental study

Objective

To evaluate, characterize and search for trends in the underreporting of diabetes mellitus (DM) as the cause of death in Bauru, São Paulo, Brazil, over 40 years.

Subjects and methods

This was a documental study. Clinical and mortality data were collected from individuals known to have type 1 (DM1) and type 2 diabetes mellitus (DM2), residing in Bauru, State of São Paulo, followed at a local endocrinology clinic from 1982 to 2021, who deceased during this period.

Results

A significant underreporting of DM as the cause of death (64.41%) was found, mostly associated with male gender (OR = 1.59 [95% CI: 1.18; 2.15]; p < 0.01), DM2 (OR = 2.64 [95% CI: 1.32; 5.26]; p < 0.01), dying in the first decade of the study (OR = 4.07 [95% CI: 1.54; 10.71]; p < 0.001) and shorter DM duration (OR = 1.02 [95% CI: 1.01; 1.04]; p < 0.01). Age, type of treatment, body mass index, marital status and ethnicity, did not show a significant association with DM underreporting. There was a decreasing trend in DM1 underreporting (Decade Percentual Change = -7.10 [95% CI: -11.35; -3.40]), but a stationary trend for DM and DM2. The main primary cause of death was cardiovascular-related complications.

Conclusion

The underreporting of DM as the cause of death was very frequently found, and was associated with male gender, decade of death, shorter DM duration and DM2. If our data could be applied to the whole country, DM would possibly emerge as a more prominent cause of death in Brazil. Future studies in other cities and geographic regions are warranted to confirm our findings.

Excess deaths associated with COVID-19 by rurality and demographic factors in the United States

PURPOSE

To estimate percent excess deaths during the COVID-19 pandemic by rural-urban residence in the United States and to describe rural-urban disparities by age, sex, and race/ethnicity.

METHODS

Using US mortality data, we used overdispersed Poisson regression models to estimate monthly expected death counts by rurality of residence, age group, sex, and race/ethnicity, and compared expected death counts with observed deaths. We then summarized excess deaths over 6 6-month time periods.

FINDINGS

There were 16.9% (95% confidence interval [CI]: 16.8, 17.0) more deaths than expected between March 2020 and February 2023. The percent excess varied by rurality (large central metro: 18.2% [18.1, 18.4], large fringe metro: 15.6% [15.5, 15.8], medium metro: 18.1% [18.0, 18.3], small metro: 15.5% [15.3, 15.7], micropolitan rural: 16.3% [16.1, 16.5], and noncore rural: 15.8% [15.6, 16.1]). The percent excess deaths were 20.2% (20.1, 20.3) for males and 13.6% (13.5, 13.7) for females, and highest for Hispanic persons (49% [49.0, 49.6]), followed by non-Hispanic Black persons (28% [27.5, 27.9]) and non-Hispanic White persons (12% [11.6, 11.8]). The 6-month time periods with the highest percent excess deaths for large central metro areas were March 2020-August 2020 and September 2020-February 2021; for all other areas, these time periods were September 2020-February 2021 and September 2021-February 2022.

CONCLUSION

Percent excess deaths varied by rurality, age group, sex, race/ethnicity, and time period. Monitoring excess deaths by rurality may be useful in assessing the impact of the pandemic over time, as rural-urban patterns appear to differ.

Urban-rural disparities in COVID-19 hospitalisations and mortality: A population-based study on national surveillance data from Germany and Italy

PURPOSE

Recent literature has highlighted the overlapping contribution of demographic characteristics and spatial factors to urban-rural disparities in SARS-CoV-2 transmission and outcomes. Yet the interplay between individual characteristics, hospitalisation, and spatial factors for urban-rural disparities in COVID-19 mortality have received limited attention.

METHODS

To fill this gap, we use national surveillance data collected by the European Centre for Disease Prevention and Control and we fit a generalized linear model to estimate the association between COVID-19 mortality and the individuals' age, sex, hospitalisation status, population density, share of the population over the age of 60, and pandemic wave across urban, intermediate and rural territories.

FINDINGS

We find that in what type of territory individuals live (urban-intermediate-rural) accounts for a significant difference in their probability of dying given SARS-COV-2 infection. Hospitalisation has a large and positive effect on the probability of dying given SARS-CoV-2 infection, but with a gradient across urban, intermediate and rural territories. For those living in rural areas, the risk of dying is lower than in urban areas but only if hospitalisation was not needed; while for those who were hospitalised in rural areas the risk of dying was higher than in urban areas.

CONCLUSIONS

Together with individuals' demographic characteristics (notably age), hospitalisation has the largest effect on urban-rural disparities in COVID-19 mortality net of other individual and regional characteristics, including population density and the share of the population over 60.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Projecting the long-term effects of the COVID-19 pandemic on U.S. population structure

The immediate, direct effects of the COVID-19 pandemic on the United States population are substantial. Millions of people were affected by the pandemic: many died, others did not give birth, and still others could not migrate. Research that has examined these individual phenomena is important, but fragmented. The disruption of mortality, fertility, and migration jointly affected U.S. population counts and, consequently, future population structure. We use data from the United Nations World Population Prospects and the cohort component projection method to isolate the effect of the pandemic on U.S. population estimates until 2060. If the pandemic had not occurred, we project that the population of the U.S. would have 2.1 million (0.63%) more people in 2025, and 1.7 million (0.44%) more people in 2060. Pandemic-induced migration changes are projected to have a larger long-term effect on future population size than mortality, despite comparable short-term effects.

Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

Demographic and Geographic Characterization of Excess Mortality During the COVID-19 Pandemic in Baltimore City, Maryland, March 2020 to March 2021

Estimates of excess mortality can provide insight into direct and indirect impacts of the coronavirus disease 2019 (COVID-19) pandemic beyond deaths specifically attributed to COVID-19. We analyzed death certificate data from Baltimore City, Maryland, from March 1, 2020, to March 31, 2021, and found that 1,725 individuals (95% confidence interval: 1,495, 1,954) died in excess of what was expected from all-cause mortality trends in 2016-2019; 1,050 (61%) excess deaths were attributed to COVID-19. Observed mortality was 23%-32% higher than expected among individuals aged 50 years and older. Non-White residents of Baltimore City also experienced 2 to 3 times higher rates of excess mortality than White residents (e.g., 37.4 vs. 10.7 excess deaths per 10,000 population among Black residents vs. White residents). There was little to no observed excess mortality among residents of hospice, long-term care, and nursing home facilities, despite accounting for nearly 30% (312/1,050) of recorded COVID-19 deaths. There was significant geographic variation in excess mortality within the city, largely following racial population distributions. These results demonstrate the substantial and unequal impact of the COVID-19 pandemic on Baltimore City residents and the importance of building robust, timely surveillance systems to track disparities and inform targeted strategies to remediate the impact of future epidemics.

Racial and Ethnic Disparities in Excess All-Cause Mortality in the First Year of the COVID-19 Pandemic

Research on the COVID-19 pandemic in the United States has consistently found disproportionately high mortality among ethnoracial minorities, but reports differ with respect to the magnitude of mortality disparities and reach different conclusions regarding which groups were most impacted. We suggest that these variations stem from differences in the temporal scope of the mortality data used and difficulties inherent in measuring race and ethnicity. To circumvent these issues, we link Social Security Administration death records for 2010 through 2021 to decennial census and American Community Survey race and ethnicity responses. We use these linked data to estimate excess all-cause mortality for age-, sex-, race-, and ethnicity-specific subgroups and examine ethnoracial variation in excess mortality across states and over the course of the pandemic's first year. Results show that non-Hispanic American Indians and Alaska Natives experienced the highest excess mortality of any ethnoracial group in the first year of the pandemic, followed by Hispanics and non-Hispanic Blacks. Spatiotemporal and age-specific ethnoracial disparities suggest that the socioeconomic determinants driving health disparities prior to the pandemic were amplified and expressed in new ways in the pandemic's first year to disproportionately concentrate excess mortality among racial and ethnic minorities.

Excess Mortality in the United States, 2020-21: County-level Estimates for Population Groups and Associations with Social Vulnerability

To assess the excess mortality burden of Covid-19 in the United States, we estimated sex, age and race stratified all-cause excess deaths in each county of the US during 2020 and 2021. Using spatial Bayesian models trained on all recorded deaths between 2003-2019, we estimated 463,187 (95% uncertainty interval (UI): 426,139 - 497,526) excess deaths during 2020, and 544,105 (95% UI: 492,202 - 592,959) excess deaths during 2021 nationally, with considerable geographical heterogeneity. Excess mortality rate (EMR) nearly doubled for each 10-year increase in age and was consistently higher among men than women. EMR in the Black population was 1.5 times that of the White population nationally and as high as 3.8 times in some states. Among the 25-54 year population excess mortality was highest in the American Indian/Alaskan Native (AI/AN) population among the four racial groups studied, and in a few states was as high as 6 times that of the White population. Strong association of EMR with county-level social vulnerability was estimated, including positive associations with prevalence of disability (standardized effect: 40.6 excess deaths per 100,000), older population (37.6), poverty (23.6), and unemployment (18.5), whereas population density (-50), higher education (-38.6), and income (-35.4) were protective. Together, these estimates provide a more reliable and comprehensive understanding of the mortality burden of the pandemic in the US thus far. They suggest that Covid-19 amplified social and racial disparities. Short-term measures to protect more vulnerable groups in future Covid-19 waves and systemic corrective steps to address long-term societal inequities are necessary.

Excess mortality in US Veterans during the COVID-19 pandemic: an individual-level cohort study

BACKGROUND

Most analyses of excess mortality during the COVID-19 pandemic have employed aggregate data. Individual-level data from the largest integrated healthcare system in the US may enhance understanding of excess mortality.

METHODS

We performed an observational cohort study following patients receiving care from the Department of Veterans Affairs (VA) between 1 March 2018 and 28 February 2022. We estimated excess mortality on an absolute scale (i.e. excess mortality rates, number of excess deaths) and a relative scale by measuring the hazard ratio (HR) for mortality comparing pandemic and pre-pandemic periods, overall and within demographic and clinical subgroups. Comorbidity burden and frailty were measured using the Charlson Comorbidity Index and Veterans Aging Cohort Study Index, respectively.

RESULTS

Of 5 905 747 patients, the median age was 65.8 years and 91% were men. Overall, the excess mortality rate was 10.0 deaths/1000 person-years (PY), with a total of 103 164 excess deaths and pandemic HR of 1.25 (95% CI 1.25-1.26). Excess mortality rates were highest among the most frail patients (52.0/1000 PY) and those with the highest comorbidity burden (16.3/1000 PY). However, the largest relative mortality increases were observed among the least frail (HR 1.31, 95% CI 1.30-1.32) and those with the lowest comorbidity burden (HR 1.44, 95% CI 1.43-1.46).

CONCLUSIONS

Individual-level data offered crucial clinical and operational insights into US excess mortality patterns during the COVID-19 pandemic. Notable differences emerged among clinical risk groups, emphasizing the need for reporting excess mortality in both absolute and relative terms to inform resource allocation in future outbreaks.

Associations between county-level public health expenditures and community health planning activities with COVID-19 incidence and mortality

The COVID-19 pandemic has revealed consequences of past defunding of the U.S. public health system, but the extent to which public health infrastructure is associated with COVID-19 burden is unknown. We aimed to determine whether previous county-level public health expenditures and community health planning activities are associated with COVID-19 cases and deaths. We examined 3050 of 3143 U.S. counties and county equivalents from March 1, 2020 to February 28, 2022. Multivariable-adjusted linear regression and generalized additive models were used to estimate associations between county-level public health expenditures and completion of community health planning activities by a county health department with outcomes of county-level COVID-19 cases and deaths per 100,000 population. After adjusting for county-level covariates, counties in the highest tertile of public health expenditures per capita had on average 542 fewer COVID-19 cases per 100,000 population (95% CI, -1004 to -81) and 21 fewer deaths per 100,000 population (95% CI, -32 to -10) than counties in the lowest tertile. For analyses of community health planning activities, adjusted estimates of association remained negative for COVID-19 deaths, but confidence intervals included negative and positive values. In conclusion, higher levels of local public health expenditures and community health planning activities were associated with fewer county-level COVID-19 deaths, and to a lesser extent, cases. Future public health funding should be aligned with evidence for the value of county health departments programs and explore further which types of spending are most cost effective.

COVID-19 Boosters: If The US Had Matched Israel's Speed And Take-Up, An Estimated 29,000 US Lives Would Have Been Saved

Israel was the first country to launch COVID-19 boosters, in late July 2021, with strong public health messaging. The booster campaign reversed rising infection rates from the Delta variant and reduced hospitalizations and deaths. The US booster rollout was slower, and public health messaging was mixed. We used the Israeli experience to ask the counterfactual question: How many lives could the US have saved if it had authorized boosters sooner? We estimated that through June 30, 2022, if the US had moved at Israel's speed and booster take-up percentages, it would have saved 29,000 lives. US regulatory caution, in the middle of a pandemic, thus had a large, avoidable cost. Yet the US booster rollout still avoided 42,000 deaths. Moving more slowly to approve boosters, as some advocated, would have cost many additional lives.

The effect of the COVID-19 pandemic on the trends and characteristics of natural and unnatural deaths in an urban Sri Lankan cohort viewed through retrospective analysis of forensic death investigations from 2019 to 2022

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a severe impact on global health. Apart from the disease itself, the strict restrictions and lockdowns enforced to minimize its spread have also substantially disrupted personal and public health.

METHODS

An analysis of forensic autopsy investigations was conducted between 2019 and 2022 on a selected urban population in Colombo, Sri Lanka, assessing the effects of the COVID-19 pandemic on mortality within these communities.

RESULTS

During the COVID-19 restrictions, there was a 2.5-fold increase in the total number of deaths, with a significantly higher percentage of female deaths than before. The majority of these deaths were due to cardiovascular causes, while COVID-19-related deaths ranked third overall. The highest proportion of COVID-19 deaths occurred among unvaccinated females. The monthly frequency of deaths from traffic accidents, poisoning, and asphyxiation decreased, while deaths from blunt trauma, sharp trauma, burns, and immersion increased. There was also a rise in blunt homicides and a greater number of femicides during the COVID-19 restrictions than in the pre-pandemic period. A significantly higher percentage of males who received the COVID-19 vaccine died from cardiovascular causes compared to those in the unvaccinated group.

CONCLUSION

The significant changes in mortality demographics and causes of death within this community during the COVID-19 restrictions underscore the disruption in healthcare, healthseeking behavior, and social interactions during this period. The vulnerability of individuals residing in highly urbanized areas with lower socioeconomic status, particularly women, is brought into sharp focus.

Evaluation of concordance in estimation of excess mortality due to COVID-19 pandemic

BACKGROUND

The World Health Organization (WHO) kept track of COVID-19 data at country level daily during the pandemic that included the number of tests, infected cases and fatalities. This daily record was susceptible to change depending on the time and place and impacted by underreporting. In addition to reporting cases of excess COVID-19-related deaths, the WHO also provided estimates of excess mortality based on mathematical models.

OBJECTIVE

To evaluate the WHO reported and model-based estimate of excess deaths to determine the degree of agreement and universality.

METHODOLOGY

Epidemiological data gathered from nine different countries between April 2020 and December 2021 are used in this study. These countries are India, Indonesia, Italy, Russia, United Kingdom, Mexico, the United States, Brazil and Peru and each of them recorded more than 1.5 million deaths from COVID-19 during these months. Statistical tools including correlation, linear regression, intraclass correlation and Bland-Altman plots are used to assess the degree of agreement between reported and model-based estimates of excess deaths.

RESULTS

The WHO-derived mathematical model for estimating excess deaths due to COVID-19 was found to be appropriate for only four of the nine chosen countries, namely Italy, United Kingdom, the United States and Brazil. The other countries showed proportional biases and significantly high regression coefficients.

CONCLUSION

The study revealed that, for some of the chosen nations, the mathematical model proposed by the WHO is practical and capable of estimating the number of excess deaths brought on by COVID-19. However, the derived approach cannot be applied globally.

Excess Death Rates for Republican and Democratic Registered Voters in Florida and Ohio During the COVID-19 Pandemic

Importance

There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination; further data on these rates may be useful.

Objective

To assess political party affiliation and mortality rates for individuals during the initial 22 months of the COVID-19 pandemic.

Design, Setting, and Participants

A cross-sectional comparison of excess mortality between registered Republican and Democratic voters between March 2020 and December 2021 adjusted for age and state of voter registration was conducted. Voter and mortality data from Florida and Ohio in 2017 linked to mortality records for January 1, 2018, to December 31, 2021, were used in data analysis.

Exposures

Political party affiliation.

Main Outcomes and Measures

Excess weekly deaths during the COVID-19 pandemic adjusted for age, county, party affiliation, and seasonality.

Results

Between January 1, 2018, and December 31, 2021, there were 538 159 individuals in Ohio and Florida who died at age 25 years or older in the study sample. The median age at death was 78 years (IQR, 71-89 years). Overall, the excess death rate for Republican voters was 2.8 percentage points, or 15%, higher than the excess death rate for Democratic voters (95% prediction interval [PI], 1.6-3.7 percentage points). After May 1, 2021, when vaccines were available to all adults, the excess death rate gap between Republican and Democratic voters widened from -0.9 percentage point (95% PI, -2.5 to 0.3 percentage points) to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis; the excess death rate among Republican voters was 43% higher than the excess death rate among Democratic voters. The gap in excess death rates between Republican and Democratic voters was larger in counties with lower vaccination rates and was primarily noted in voters residing in Ohio.

Conclusions and Relevance

In this cross-sectional study, an association was observed between political party affiliation and excess deaths in Ohio and Florida after COVID-19 vaccines were available to all adults. These findings suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been factors in the severity and trajectory of the pandemic in the US.

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

A Saliva-Based Serological and Behavioral Analysis of SARS-CoV-2 Antibody Prevalence in Howard County, Maryland

The objective of the study was to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the Howard County, Maryland, general population and demographic subpopulations attributable to natural infection or coronavirus disease 2019 (COVID-19) vaccination and to identify self-reported social behaviors that may affect the likelihood of recent or past SARS-CoV-2 infection. A cross-sectional, saliva-based serological study of 2,880 residents of Howard County, Maryland, was carried out from July through September 2021. Natural SARS-CoV-2 infection prevalence was estimated by inferring infections among individuals according to anti-nucleocapsid immunoglobin G levels and calculating averages weighted by sample proportions of various demographics. Antibody levels between BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) recipients were compared. Antibody decay rate was calculated by fitting exponential decay curves to cross-sectional indirect immunoassay data. Regression analysis was carried out to identify demographic factors, social behaviors, and attitudes that may be linked to an increased likelihood of natural infection. The estimated overall prevalence of natural infection in Howard County, Maryland, was 11.9% (95% confidence interval, 9.2% to 15.1%), compared with 7% reported COVID-19 cases. Antibody prevalence indicating natural infection was highest among Hispanic and non-Hispanic Black participants and lowest among non-Hispanic White and non-Hispanic Asian participants. Participants from census tracts with lower average household income also had higher natural infection rates. After accounting for multiple comparisons and correlations between participants, none of the behavior or attitude factors had significant effects on natural infection. At the same time, recipients of the mRNA-1273 vaccine had higher antibody levels than those of BNT162b2 vaccine recipients. Older study participants had overall lower antibody levels compared with younger study participants. The true prevalence of SARS-CoV-2 infection is higher than the number of reported COVID-19 cases in Howard County, Maryland. A disproportionate impact of infection-induced SARS-CoV-2 positivity was observed across different ethnic/racial subpopulations and incomes, and differences in antibody levels across different demographics were identified. Taken together, this information may inform public health policy to protect vulnerable populations. IMPORTANCE We employed a highly innovative noninvasive multiplex oral fluid SARS-CoV-2 IgG assay to ascertain our seroprevalence estimates. This laboratory-developed test has been applied in NCI's SeroNet consortium, possesses high sensitivity and specificity according to FDA Emergency Use Authorization guidelines, correlates strongly with SARS-CoV-2 neutralizing antibody responses, and is Clinical Laboratory Improvement Amendments-approved by the Johns Hopkins Hospital Department of Pathology. It represents a broadly scalable public health tool to improve understanding of recent and past SARS-CoV-2 exposure and infection without drawing any blood. To our knowledge, this is the first application of a high-performance salivary SARS-CoV-2 IgG assay to estimate population-level seroprevalence, including identifying COVID-19 disparities. We also are the first to report differences in SARS-CoV-2 IgG responses by COVID-19 vaccine manufacturers (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]). Our findings demonstrate remarkable consistency with those of blood-based SARS-CoV-2 IgG assays in terms of differences in the magnitude of SARS-CoV-2 IgG responses between COVID-19 vaccines.

Comparison of vaccination and booster rates and their impact on excess mortality during the COVID-19 pandemic in European countries

Aim

To evaluate the effect of vaccination/booster administration dynamics on the reduction of excess mortality during COVID-19 infection waves in European countries.

Methods

We selected twenty-nine countries from the OurWorldInData project database according to their population size of more than one million and the availability of information on dominant SARS-CoV-2 variants during COVID-19 infection waves. After selection, we categorized countries according to their "faster" or "slower" vaccination rates. The first category included countries that reached 60% of vaccinated residents by October 2021 and 70% by January 2022. The second or "slower" category included all other countries. In the first or "faster" category, two groups, "boosters faster'' and "boosters slower" were created. Pearson correlation analysis, linear regression, and chi-square test for categorical data were used to identify the association between vaccination rate and excess mortality. We chose time intervals corresponding to the dominance of viral variants: Wuhan, Alpha, Delta, and Omicron BA.1/2.

Results and discussion

The "faster" countries, as opposed to the "slower" ones, did better in protecting their residents from mortality during all periods of the SARS-CoV-2 pandemic and even before vaccination. Perhaps higher GDP per capita contributed to their better performance throughout the pandemic. During mass vaccination, when the Delta variant prevailed, the contrast in mortality rates between the "faster" and "slower" categories was strongest. The average excess mortality in the "slower" countries was nearly 5 times higher than in the "faster" countries, and the odds ratio (OR) was 4.9 (95% CI 4.4 to 5.4). Slower booster rates were associated with significantly higher mortality during periods dominated by Omicron BA.1 and BA.2, with an OR of 2.6 (CI 95%. 2.1 to 3.3). Among the European countries we analyzed, Denmark, Norway, and Ireland did best, with a pandemic mortality rate of 0.1% of the population or less. By comparison, Bulgaria, Serbia, and Russia had a much higher mortality rate of up to 1% of the population.

Conclusion

Thus, slow vaccination and booster administration was a major factor contributing to an order of magnitude higher excess mortality in "slower" European countries compared to more rapidly immunized countries.

Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city

Globally, excess deaths during 2020-21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India-an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% (95% confidence interval: 27-33%). Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0-13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting.

Impact of the COVID-19 pandemic on total, sex- and age-specific all-cause mortality in 20 countries worldwide during 2020: results from the C-MOR project

BACKGROUND

To understand the impact of the COVID-19 pandemic on mortality, this study investigates overall, sex- and age-specific excess all-cause mortality in 20 countries, during 2020.

METHODS

Total, sex- and age-specific weekly all-cause mortality for 2015-2020 was collected from national vital statistics databases. Excess mortality for 2020 was calculated by comparing weekly 2020 observed mortality against expected mortality, estimated from historical data (2015-2019) accounting for seasonality, long- and short-term trends. Crude and age-standardized rates were analysed for total and sex-specific mortality.

RESULTS

Austria, Brazil, Cyprus, England and Wales, France, Georgia, Israel, Italy, Northern Ireland, Peru, Scotland, Slovenia, Sweden, and the USA displayed substantial excess age-standardized mortality of varying duration during 2020, while Australia, Denmark, Estonia, Mauritius, Norway, and Ukraine did not. In sex-specific analyses, excess mortality was higher in males than females, except for Slovenia (higher in females) and Cyprus (similar in both sexes). Lastly, for most countries substantial excess mortality was only detectable (Austria, Cyprus, Israel, and Slovenia) or was higher (Brazil, England and Wales, France, Georgia, Italy, Northern Ireland, Sweden, Peru and the USA) in the oldest age group investigated. Peru demonstrated substantial excess mortality even in the <45 age group.

CONCLUSIONS

This study highlights that excess all-cause mortality during 2020 is context dependent, with specific countries, sex- and age-groups being most affected. As the pandemic continues, tracking excess mortality is important to accurately estimate the true toll of COVID-19, while at the same time investigating the effects of changing contexts, different variants, testing, quarantine, and vaccination strategies.

Disability-Adjusted Life-Years for Drug Overdose Crisis and COVID-19 Are Comparable During the Two Years of Pandemic in the United States

OBJECTIVES

The drug overdose crisis with shifting patterns from primarily opioid to polysubstance uses and COVID-19 infections are 2 concurrent public health crises in the United States, affecting the population of sizes in different magnitudes (approximately < 10 million for substance use disorder [SUD] and drug overdoses vs 80 million for COVID-19 within 2 years of the pandemic). Our objective is to compare the relative scale of disease burden for the 2 crises within a common framework, which could help inform policy makers with resource allocation and prioritization strategies.

METHODS

We calculated disability-adjusted life-years (DALYs) for SUD (including opioids and stimulants) and COVID-19 infections, respectively. We collected estimates for SUD prevalence, overdose deaths, COVID-19 cases and deaths, disability weights, and life expectancy from multiple publicly available sources. We then compared age distributions of estimated DALYs.

RESULTS

We estimated a total burden of 13.83 million DALYs for SUD and drug overdoses and 15.03 million DALYs for COVID-19 in 2 years since March 2020. COVID-19 burden was dominated by the fatal burden (> 95% of total DALYs), whereas SUD burden was attributed to both fatal (53%) and nonfatal burdens (47%). The highest disease burden was among individuals aged 30 to 39 years for SUD (27%) and 50 to 64 years for COVID-19 (31%).

CONCLUSIONS

Despite the smaller size of the affected population, SUD and drug overdoses resulted in comparable disease burden with the COVID-19 pandemic. Additional resources supporting evidence-based interventions in prevention and treatment may be warranted to ameliorate SUD and drug overdoses during both the pandemic and postpandemic recovery.

Missing Americans: Early death in the United States-1933-2021

We assessed how many US deaths would have been averted each year, 1933-2021, if US age-specific mortality rates had equaled the average of 21 other wealthy nations. We refer to these excess US deaths as "missing Americans." The United States had lower mortality rates than peer countries in the 1930s-1950s and similar mortality in the 1960s and 1970s. Beginning in the 1980s, however, the United States began experiencing a steady increase in the number of missing Americans, reaching 622,534 in 2019 alone. Excess US deaths surged during the COVID-19 pandemic, reaching 1,009,467 in 2020 and 1,090,103 in 2021. Excess US mortality was particularly pronounced for persons under 65 years. In 2020 and 2021, half of all US deaths under 65 years and 90% of the increase in under-65 mortality from 2019 to 2021 would have been avoided if the United States had the mortality rates of its peers. In 2021, there were 26.4 million years of life lost due to excess US mortality relative to peer nations, and 49% of all missing Americans died before age 65. Black and Native Americans made up a disproportionate share of excess US deaths, although the majority of missing Americans were White.

Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared

During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.

Absolute and relative excess mortality across demographic and clinical subgroups during the COVID-19 pandemic: an individual-level cohort study from a nationwide healthcare system of US Veterans

Background

Most analyses of excess mortality during the COVID-19 pandemic have employed aggregate data. Individual-level data from the largest integrated healthcare system in the US may enhance understanding of excess mortality.

Methods

We performed an observational cohort study following patients receiving care from the Department of Veterans Affairs (VA) between 1 March 2018 and 28 February 2022. We estimated excess mortality on an absolute scale (i.e., excess mortality rates, number of excess deaths), and a relative scale by measuring the hazard ratio (HR) for mortality comparing pandemic and pre-pandemic periods, overall, and within demographic and clinical subgroups. Comorbidity burden and frailty were measured using the Charlson Comorbidity Index and Veterans Aging Cohort Study Index, respectively.

Results

Of 5,905,747 patients, median age was 65.8 years and 91% were men. Overall, the excess mortality rate was 10.0 deaths/1000 person-years (PY), with a total of 103,164 excess deaths and pandemic HR of 1.25 (95% CI 1.25-1.26). Excess mortality rates were highest among the most frail patients (52.0/1000 PY) and those with the highest comorbidity burden (16.3/1000 PY). However, the largest relative mortality increases were observed among the least frail (HR 1.31, 95% CI 1.30-1.32) and those with the lowest comorbidity burden (HR 1.44, 95% CI 1.43-1.46).

Conclusions

Individual-level data offered crucial clinical and operational insights into US excess mortality patterns during the COVID-19 pandemic. Notable differences emerged among clinical risk groups, emphasising the need for reporting excess mortality in both absolute and relative terms to inform resource allocation in future outbreaks.

COVID-19 Mortality by Race and Ethnicity in US Metropolitan and Nonmetropolitan Areas, March 2020 to February 2022

Importance

Prior research has established that Hispanic and non-Hispanic Black residents in the US experienced substantially higher COVID-19 mortality rates in 2020 than non-Hispanic White residents owing to structural racism. In 2021, these disparities decreased.

Objective

To assess to what extent national decreases in racial and ethnic disparities in COVID-19 mortality between the initial pandemic wave and subsequent Omicron wave reflect reductions in mortality vs other factors, such as the pandemic's changing geography.

Design, Setting, and Participants

This cross-sectional study was conducted using data from the US Centers for Disease Control and Prevention for COVID-19 deaths from March 1, 2020, through February 28, 2022, among adults aged 25 years and older residing in the US. Deaths were examined by race and ethnicity across metropolitan and nonmetropolitan areas, and the national decrease in racial and ethnic disparities between initial and Omicron waves was decomposed. Data were analyzed from June 2021 through March 2023.

Exposures

Metropolitan vs nonmetropolitan areas and race and ethnicity.

Main Outcomes and Measures

Age-standardized death rates.

Results

There were death certificates for 977 018 US adults aged 25 years and older (mean [SD] age, 73.6 [14.6] years; 435 943 female [44.6%]; 156 948 Hispanic [16.1%], 140 513 non-Hispanic Black [14.4%], and 629 578 non-Hispanic White [64.4%]) that included a mention of COVID-19. The proportion of COVID-19 deaths among adults residing in nonmetropolitan areas increased from 5944 of 110 526 deaths (5.4%) during the initial wave to a peak of 40 360 of 172 515 deaths (23.4%) during the Delta wave; the proportion was 45 183 of 210 554 deaths (21.5%) during the Omicron wave. The national disparity in age-standardized COVID-19 death rates per 100 000 person-years for non-Hispanic Black compared with non-Hispanic White adults decreased from 339 to 45 deaths from the initial to Omicron wave, or by 293 deaths. After standardizing for age and racial and ethnic differences by metropolitan vs nonmetropolitan residence, increases in death rates among non-Hispanic White adults explained 120 deaths/100 000 person-years of the decrease (40.7%); 58 deaths/100 000 person-years in the decrease (19.6%) were explained by shifts in mortality to nonmetropolitan areas, where a disproportionate share of non-Hispanic White adults reside. The remaining 116 deaths/100 000 person-years in the decrease (39.6%) were explained by decreases in death rates in non-Hispanic Black adults.

Conclusions and Relevance

This study found that most of the national decrease in racial and ethnic disparities in COVID-19 mortality between the initial and Omicron waves was explained by increased mortality among non-Hispanic White adults and changes in the geographic spread of the pandemic. These findings suggest that despite media reports of a decline in disparities, there is a continued need to prioritize racial health equity in the pandemic response.

Increases in 'deaths of despair' during the COVID-19 pandemic in the United States and the United Kingdom

OBJECTIVES

The COVID-19 pandemic significantly impacted mental health, health-related behaviours such as drinking and illicit drug use and the accessibility of health and social care services. How these pandemic shocks affected 'despair'-related mortality in different countries is less clear. This study uses public data to compare deaths from alcohol, drugs and suicide in the United States and the United Kingdom to identify similarities or differences in the impact of the pandemic on important non-COVID causes of death across countries and to consider the public health implications of these trends.

STUDY DESIGN AND METHODS

Data were taken from publicly available mortality figures for England and Wales, Northern Ireland, Scotland and the United States of America, 2001-2021, and analysed descriptively through age-standardised and age-specific mortality rates from suicide, alcohol and drug use.

RESULTS

Alcohol-specific deaths increased in all countries between 2019 and 2021, most notably in the United States and, to a lesser extent, England and Wales. Suicide rates did not increase markedly during the pandemic in any of the included nations. Drug-related mortality rates rose dramatically over the same period in the United States but not in other nations.

CONCLUSIONS

Mortality from 'deaths of despair' during the pandemic has displayed divergent trends between causes and countries. Concerns about increases in deaths by suicide appear to have been unfounded, whereas deaths due to alcohol have risen across the United Kingdom and in the United States and across almost all age groups. Scotland and the United States had similarly high levels of drug-related deaths pre-pandemic, but the differing trends during the pandemic highlight the different underlying causes of these drug death epidemics and the importance of tailoring policy responses to these specific contexts.

A systematic review of excess all-cause mortality estimation studies in India during COVID-19 pandemic

Background

Mortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context.

Methods

The PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.

Results

Significant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.

Conclusion

The review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.

Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data

Mechanistic models fit to streaming surveillance data are critical to understanding the transmission dynamics of an outbreak as it unfolds in real-time. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We model the transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparametrically. We compare our base model against modified versions which do not use diagnostics test counts or seroprevalence data to demonstrate the utility of including these often unused data streams. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March 2020 and February 2021 and find that 32-72% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest that the abrupt end of the winter surge in January 2021 was due to both behavioral changes and a high level of accumulated natural immunity.

Associations between mortality from COVID-19 and other causes: A state-level analysis

BACKGROUND

During the COVID-19 pandemic, the high death toll from COVID-19 was accompanied by a rise in mortality from other causes of death. The objective of this study was to identify the relationship between mortality from COVID-19 and changes in mortality from specific causes of death by exploiting spatial variation in these relationships across US states.

METHODS

We use cause-specific mortality data from CDC Wonder and population estimates from the US Census Bureau to examine relationships at the state level between mortality from COVID-19 and changes in mortality from other causes of death. We calculate age-standardized death rates (ASDR) for three age groups, nine underlying causes of death, and all 50 states and the District of Columbia between the first full year of the pandemic (March 2020-February 2021) and the year prior (March 2019-February 2020). We then estimate the relationship between changes in cause-specific ASDR and COVID-19 ASDR using linear regression analysis weighted by the size of the state's population.

RESULTS

We estimate that causes of death other than COVID-19 represent 19.6% of the total mortality burden associated with COVID-19 during the first year of the COVID-19 pandemic. At ages 25+, circulatory disease accounted for 51.3% of this burden while dementia (16.4%), other respiratory diseases (12.4%), influenza/pneumonia (8.7%) and diabetes (8.6%) also contribute. In contrast, there was an inverse association across states between COVID-19 death rates and changes in death rates from cancer. We found no state-level association between COVID-19 mortality and rising mortality from external causes.

CONCLUSIONS

States with unusually high death rates from COVID-19 experienced an even larger mortality burden than implied by those rates alone. Circulatory disease served as the most important route through which COVID-19 mortality affected death rates from other causes of death. Dementia and other respiratory diseases made the second and third largest contributions. In contrast, mortality from neoplasms tended to decline in states with the highest death rates from COVID-19. Such information may help to inform state-level responses aimed at easing the full mortality burden of the COVID-19 pandemic.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

COVID-19 survey among NYC non-Hispanic Black and Hispanic/Latino adults with diabetes

OBJECTIVES

To better understand the knowledge, practice, importance, awareness, usefulness, and confidence of non-Hispanic Black and English- and Spanish-speaking Hispanic/Latino adults with diabetes.

DESIGN

A descriptive cross-sectional survey study design was used and descriptive statistics was conducted.

SAMPLE

Non-Hispanic Black and Hispanic/Latino adults with diabetes were recruited from three New York City public hospitals.

MEASUREMENTS

A one-time survey was delivered via email, text message or over the phone.

RESULTS

Of the 96 participants, 47.9% were Hispanic/Latino and 52.1% were non-Hispanic Black individuals; 43.8% of the surveys were completed in Spanish and 56.3% in English; 41.7% were female and 58.3% male; 77.1% preferred to complete the survey via the telephone, 14.6% through email, and 8.3% via text message. Chi-square findings showed, 90.6% knew mask wearing prevented COVID-19; 96.9% knew that covering the nose and mouth during mask wearing is needed, 93.8% wore a mask, and 92.8% felt it important or very important to wear a mask to prevent the spread of COVID-19. For social distancing, 88.5% knew it prevented the spread of COVID-19, 93.8% practiced it, and 95.8% felt it important or very important.

CONCLUSION

In having a better understanding of the knowledge and practices of COVID-19 among non-Hispanic Black and Hispanic populations with diabetes, the development of culturally and linguistically tailored community-based mitigation strategies can be developed that are aimed at improving the preparedness of these groups for the next emerging infectious disease, such as COVID-19.

Differences Between Reported COVID-19 Deaths and Estimated Excess Deaths in Counties Across the United States, March 2020 to February 2022

Accurate and timely tracking of COVID-19 deaths is essential to a well-functioning public health surveillance system. The extent to which official COVID-19 death tallies have captured the true toll of the pandemic in the United States is unknown. In the current study, we develop a Bayesian hierarchical model to estimate monthly excess mortality in each county over the first two years of the pandemic and compare these estimates to the number of deaths officially attributed to Covid-19 on death certificates. Overall, we estimated that 268,176 excess deaths were not reported as Covid-19 deaths during the first two years of the Covid-19 pandemic, which represented 23.7% of all excess deaths that occurred. Differences between excess deaths and reported COVID-19 deaths were substantial in both the first and second year of the pandemic. Excess deaths were less likely to be reported as COVID-19 deaths in the Mountain division, in the South, and in nonmetro counties. The number of excess deaths exceeded COVID-19 deaths in all Census divisions except for the New England and Middle Atlantic divisions where there were more COVID-19 deaths than excess deaths in large metro areas and medium or small metro areas. Increases in excess deaths not assigned to COVID-19 followed similar patterns over time to increases in reported COVID-19 deaths and typically preceded or occurred concurrently with increases in reported COVID-19 deaths. Estimates from this study can be used to inform targeting of resources to areas in which the true toll of the COVID-19 pandemic has been underestimated.

Geographic disparities in COVID-19 testing and outcomes in Florida

BACKGROUND

Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS.

RESULTS

Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.

CONCLUSIONS

Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

Cerebro-/Cardiovascular Collateral Damage During the COVID-19 Pandemic: Fact or Fiction?

Numerous observational studies have identified a decline in cerebro-/cardiovascular (CV) admissions during the initial phase of the COVID-19 pandemic. Recent studies and meta-analyses indicated that the overall decrease was smaller than that found in initial studies during the first months of 2020. Two years later we still do not have clear evidence about the potential causes and impacts of the reduction of CV hospitalizations during the COVID-19 pandemic. It has becoming increasingly evident that collateral damage (i.e., incidental damage to the public and patients) from the COVID-19 outbreak is the main underlying cause that at least somewhat reflects the effects of imposed measures such as social distancing and self-isolation. However, a smaller true decline in CV events in the community due to a lack of triggers associated with such acute syndromes cannot be excluded. There is currently indirect epidemiological evidence about the immediate impact that the collateral damage had on excess mortality, but possible late consequences including a rebound increase in CV events are yet to be observed. In the present narrative review, we present the reporting milestones in the literature of the rates of CV admissions and collateral damage during the last 2 years, and discuss all possible factors contributing to the decline in CV hospitalizations during the COVID-19 pandemic. Healthcare systems need to be prepared so that they can cope with the increased hospitalization rates for CV events in the near future.

Impact of the COVID-19 pandemic on Ukrainian mortality, 2020-2021

The mortality impact of the COVID-19 pandemic in Ukraine has remained incomplete. We estimated excess deaths associated with the pandemic in Ukraine during 2020 and 2021. Excess deaths may be attributed directly to SARS-CoV-2 infection or indirectly to deaths associated with social and economic upheavals resulting from the pandemic. Data on all deaths registered in government-controlled Ukraine from 2016-2021 (N = 3,657,475) were utilized. Using a model-based approach, we predicted monthly excess deaths in 2020 and 2021. We estimated 47,578 excess deaths in 2020 as a whole (7.71% of all recorded deaths). This figure reflects both positive (higher than expected) excess deaths from June-December and negative (lower than expected) deaths in January and March-May. From June-December 2020, we estimated 59,363 excess deaths (15.75% of all recorded deaths in those months). In 2021, we estimated 150,049 excess deaths (21.01% of all recorded deaths). Positive excess deaths were detected across age groups even groups younger than 40 years. The number of excess deaths exceeded that of deaths with COVID-19 coded on the death certificate by more than two-fold in 2020, but that difference narrowed in 2021. We furthermore provide provisional estimates of the effect of low vaccine coverage on excess deaths in 2021 drawing from European cross-national evidence and provisional estimates of the hypothetical evolution of the pandemic in 2022 to serve as a rough basis for future studies analyzing the joint impacts of the COVID-19 pandemic and the Russian invasion on Ukrainian demography.

Emerging Health Disparities during the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has underscored social and racial discrimination in global health, showing that health equity is still a goal to be achieved. Understanding the impact of COVID-19 on public health potential is vital to present a fair opportunity for people of different backgrounds to be as healthy as possible. As such, this communication discusses the emerging health disparities in light of the COVID-19 pandemic and analyzes their implications. Original research, effective health communication, and promotion strategies ought to be leveraged to step closer toward national and international health equity.

Impact of social and cultural factors on incidence, transmission and control of Coronavirus disease in Iran: a qualitative study

INTRODUCTION

COVID-19 pandemic has had mixed reactions from nations, people and governments about ways to cope with, prevent and control the disease. The current study identifies social, cultural and policy factors affecting the incidence and control of Coronavirus disease in Iran.

METHODS

A qualitative study consists of content analysis as well as the views of 20 experienced and knowledgeable subjects specialized in social and cultural health management. The data were gathered using three semi-structured interviews and then continued by 17 semi-structured interviews. Data analysis was done using Graneheim approach. After each interview, the recorded audio files transcript and reviewed. Then codes extracted and divided to categories and sub-categories.

RESULTS

There are distinct social and cultural factors in coping with Coronavirus disease. These consisted of three categories of governance, individual and community related factors. A total of 17 subcategories and 215 primary codes that were extracted from the text of interviews as variables of the study and in relation to the research question. Ten subdomains of governance including vaccination, political issues, knowledge, support services, administrative services, transportation, health and treatment, culturalization, legislation and, managerial and financial policies impacted the spread and mitigation of the pandemic at various levels.

CONCLUSION

The management of pandemics requires a comprehensive capacity for identifying and determining social and cultural criteria. A healthy partnership between governments and the community may be required to remove unnecessary obstacles that hinder public health attempt to alleviate the risk. The obtained criteria and indicators from this study may be utilized by policy makers in an attempt to strengthen protocols for mitigating pandemics. Further studies may be warranted to confirm these findings.

Monthly excess mortality across counties in the United States during the Covid-19 pandemic, March 2020 to February 2022

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the overall impact of the Covid-19 pandemic that is not biased by differences in testing or cause-of-death assignment. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating and mitigating factors, and inform response efforts, including allocating resources to affected communities. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,159,580 excess deaths occurred during the first two years of the pandemic (first: 620,872; second: 538,708). Overall, excess mortality decreased in large metropolitan counties, but increased in nonmetro counties, between the first and second years of the pandemic. Despite the initial concentration of mortality in large metropolitan Northeast counties, beginning in February 2021, nonmetro South counties had the highest cumulative relative excess mortality. These results highlight the need for investments in rural health as the pandemic's disproportionate impact on rural areas continues to grow.