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

BackgroundCoronavirus 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 findingsIn 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.ConclusionsIn 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.

Andrew Stokes and co-workers report a county-level analysis of excess deaths owing to COVID-19 in the United States.     

Estimating incidence curves of several infections using symptom surveillance data

We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.     

Estimating the cumulative incidence of SARS-CoV-2 with imperfect serological tests: Exploiting cutoff-free approaches

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method—referred to as mixture-model approach—for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test’s ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.     

Outbreak dynamics of COVID-19 in China and the United States

Biomechanics and Modeling in Mechanobiology - On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective...     

Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

BackgroundWith the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines.Methods and findingsCounty-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R²); and (ii) effect estimates of each predictor.Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R². Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R² was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors.ConclusionsSignificant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

Sasikiran Kandula and Jeffrey Shaman study population health and COVID-19 mortality in the United States.     

Detection of avian influenza viruses from shorebirds: evaluation of surveillance and testing approaches

Although influenza A viruses have been isolated from numerous shorebird species (Family: Scolopacidae) worldwide, our understanding of natural history of these viruses in this diverse group is incomplete. Gaining this information can be complicated by sampling difficulties related to live capture, the need for large sample sizes related to a potentially low prevalence of infection, and the need to maintain flexibility in diagnostic approaches related to varied capabilities and resources. To provide information relevant to improving sampling and testing of shorebirds for influenza A viruses, we retrospectively evaluated a combined data set from Delaware Bay, USA, collected from 2000 to 2009. Our results indicate that prevalence trends and subtype diversity can be effectively determined by either direct sampling of birds or indirect sampling of feces; however, the extent of detected subtype diversity is a function of the number of viruses recovered during that year. Even in cases where a large number of viruses are identified, an underestimate of true subtype diversity is likely. Influenza A virus isolation from Ruddy Turnstones can be enhanced by testing both cloacal and tracheal samples, and matrix real-time PCR can be used as an effective screening tool. Serologic testing to target species of interest also has application to shorebird surveillance. Overall, all of the sampling and diagnostic approaches have utility as applied to shorebird surveillance, but all are associated with inherent biases that need to be considered when comparing results from independent studies.     

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.     

Estimating incidence of the French BSE infection using a joint analysis of both asymptomatic and clinical BSE surveillance data

Bovine Spongiform Encephalopathy (BSE) clinical surveillance data were the main source of information to perform back-calculation of BSE infection incidence. Since 2001, systematic BSE screening tests enhanced the clinical surveillance and allowed to detect some preclinical, i.e. asymptomatic, cases of BSE. We propose a method to incorporate additional information provided by screening tests. It was the first time that a back-calculation model was developed for a full BSE clinical surveillance. In the spirit, our approach resembles what it was done in the Acquired Immune Deficiency Syndrome (AIDS) epidemic to incorporate the Human Immunodeficiency Virus (HIV) diagnosis. Nevertheless, in the BSE epidemic, we had to consider different surveillance systems, their peculiarity, and the phenomenon of communicating vessels between these surveillance systems. In addition, both the preclinical sensitivity of tests and the status of BSE cases, asymptomatic or clinical, were not precisely known. We applied the model to the French BSE epidemic in order to obtain an updated estimate of the incidence of BSE infection. Our back-calculation model fitted very well the observed data of each surveillance system. We detected a lengthening of the incubation period and estimated that the number of infections was very small in the late 1990s and zero in July 2001.     

Healthy lifestyle,endoscopic screening,and colorectal cancer incidence and mortality in the United States: A nationwide cohort study

BackgroundHealthy lifestyle and screening represent 2 major approaches to colorectal cancer (CRC) prevention. It remains unknown whether the CRC-preventive benefit of healthy lifestyle differs by endoscopic screening status, and how the combination of healthy lifestyle with endoscopic screening can improve CRC prevention.Methods and findingsWe assessed lifestyle and endoscopic screening biennially among 75,873 women (Nurses’ Health Study, 1988 to 2014) and 42,875 men (Health Professionals Follow-up Study, 1988 to 2014). We defined a healthy lifestyle score based on body mass index, smoking, physical activity, alcohol consumption, and diet. We calculated hazard ratios (HRs) and population-attributable risks (PARs) for CRC incidence and mortality in relation to healthy lifestyle score according to endoscopic screening. Participants’ mean age (standard deviation) at baseline was 54 (8) years. During a median of 26 years (2,827,088 person-years) follow-up, we documented 2,836 incident CRC cases and 1,013 CRC deaths. We found a similar association between healthy lifestyle score and lower CRC incidence among individuals with and without endoscopic screening, with the multivariable HR per one-unit increment of 0.85 (95% CI, 0.80 to 0.90) and 0.85 (95% CI, 0.81 to 0.88), respectively (P-interaction = 0.99). The fraction of CRC cases that might be prevented (PAR) by endoscopic screening alone was 32% (95% CI, 31% to 33%) and increased to 61% (95% CI, 42% to 75%) when combined with healthy lifestyle (score = 5). The corresponding PAR (95% CI) increased from 15% (13% to 16%) to 51% (17% to 74%) for proximal colon cancer and from 47% (45% to 50%) to 75% (61% to 84%) for distal CRC. Results were similar for CRC mortality. A limitation of our study is that our study participants are all health professionals and predominantly whites, which may not be representative of the general population.ConclusionsOur study suggests that healthy lifestyle is associated with lower CRC incidence and mortality independent of endoscopic screening. An integration of healthy lifestyle with endoscopic screening may substantially enhance prevention for CRC, particularly for proximal colon cancer, compared to endoscopic screening alone.

Kai Wang and colleagues study contributions of healthy lifestyles and endoscopic screening to colorectal cancer outcomes.     

Vaccine-escape and fast-growing mutations in the United Kingdom,the United States,Singapore, Spain,India, and other COVID-19-devastated countries

Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa, and Brazil have received much attention for their increased infectivity, potentially high virulence, and possible threats to existing vaccines and antibody therapies. The question remains if there are other more infectious variants transmitted around the world. We carry out a large-scale study of 506,768 SARS-CoV-2 genome isolates from patients to identify many other rapidly growing mutations on the spike (S) protein receptor-binding domain (RBD). We reveal that essentially all 100 most observed mutations strengthen the binding between the RBD and the host angiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward more infectious variants. In particular, we discover new fast-growing RBD mutations N439K, S477N, S477R, and N501T that also enhance the RBD and ACE2 binding. We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants, L452R and E484Q found in India variants, can potentially disrupt the binding between the RBD and many known antibodies. Among these RBD mutations, L452R is also now known as part of the California variant B.1.427. Finally, we hypothesize that RBD mutations that can simultaneously make SARS-CoV-2 more infectious and disrupt the existing antibodies, called vaccine escape mutations, will pose an imminent threat to the current crop of vaccines. A list of most likely vaccine escape mutations is given, including S494P, Q493L, K417N, F490S, F486L, R403K, E484K, L452R, K417T, F490L, E484Q, and A475S. Mutation T478K appears to make the Mexico variant B.1.1.222 the most infectious one. Our comprehensive genetic analysis and protein-protein binding study show that the genetic evolution of SARS-CoV-2 on the RBD, which may be regulated by host gene editing, viral proofreading, random genetic drift, and natural selection, gives rise to more infectious variants that will potentially compromise existing vaccines and antibody therapies.     

Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends

Background

Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.

Methods and Findings

Influenza activity data from 2003–04 through 2007–08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson''s correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson''s correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003–04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).

Conclusions

This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003–04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior.     

Temporal estimates of case-fatality rate for COVID-19 outbreaks in Canada and the United States

BACKGROUND:Estimates of the case-fatality rate (CFR) associated with coronavirus disease 2019 (COVID-19) vary widely in different population settings. We sought to estimate and compare the COVID-19 CFR in Canada and the United States while adjusting for 2 potential biases in crude CFR.METHODS:We used the daily incidence of confirmed COVID-19 cases and deaths in Canada and the US from Jan. 31 to Apr. 22, 2020. We applied a statistical method to minimize bias in the crude CFR by accounting for the survival interval as the lag time between disease onset and death, while considering reporting rates of COVID-19 cases less than 50% (95% confidence interval 10%–50%).RESULTS:Using data for confirmed cases in Canada, we estimated the crude CFR to be 4.9% on Apr. 22, 2020, and the adjusted CFR to be 5.5% (credible interval [CrI] 4.9%–6.4%). After we accounted for various reporting rates less than 50%, the adjusted CFR was estimated at 1.6% (CrI 0.7%–3.1%). The US crude CFR was estimated to be 5.4% on Apr. 20, 2020, with an adjusted CFR of 6.1% (CrI 5.4%–6.9%). With reporting rates of less than 50%, the adjusted CFR for the US was 1.78 (CrI 0.8%–3.6%).INTERPRETATION:Our estimates suggest that, if the reporting rate is less than 50%, the adjusted CFR of COVID-19 in Canada is likely to be less than 2%. The CFR estimates for the US were higher than those for Canada, but the adjusted CFR still remained below 2%. Quantification of case reporting can provide a more accurate measure of the virulence and disease burden of severe acute respiratory syndrome coronavirus 2.

The risk of death associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is fundamental to the disease burden imposed by the coronavirus disease 2019 (COVID-19) pandemic. Quantification of this risk can provide critical information on the health and socioeconomic impact of the pandemic and identify population subgroups at highest risk for severe outcomes. The risk of death from a diagnosed infection, often referred to as the case-fatality rate (CFR), is the proportion of people who die from a disease among all those diagnosed with the disease over a certain period.Estimates of the COVID-19 CFR vary in different populations and at different stages of the outbreak, ranging from 0.4% in China¹ to 31.4% in the northwest region of Italy.² From individual-level data for patients in Hubei Province, Mainland China,³ an adjusted CFR of 3.6% (95% confidence interval [CI] 3.6%–3.8%) was estimated. For the outbreak on the Diamond Princess cruise ship, the age-adjusted CFR was estimated at 2.6% (95% CI 0.9%–6.7%) in all age groups but was substantially higher (13.0%, 95% CI 5.2%–26.0%) among those aged 70 years or older.⁴For ongoing outbreaks and especially during the exponential growth phase, the delay between onset of disease and knowledge of the final outcome may result in biased estimates of the CFR.⁵ Furthermore, underestimation of the number of COVID-19 cases will inflate the CFR. Limited ability to test or recognize mildly or moderately symptomatic people in both the United States and Canada has likely led to substantial underestimation of the rate of infection in affected communities.^6,7Given the importance of the CFR in public health planning, we sought to estimate the CFR for ongoing COVID-19 outbreaks in the US and Canada while accounting for preferential ascertainment of severe cases (leading to underestimation) and the lag time between disease onset and death.     

Estimating the size of the HIV epidemic by using mortality data

Evidence that more people are dying as a result of HIV infection than is reflected by the number of deaths among reported cases meeting the WHO definition of AIDS is derived from mortality data. Ninety-five causes of death likely to be associated with HIV infection were selected. Standardized mortality ratios due to these causes increased for single men aged 15-54 years from 100 in 1984 to 118 in 1987. The age, sex, marital status, temporal and geographic distribution of these excess deaths suggest that they are HIV-associated. It is estimated that 58% of excess deaths due to HIV-related causes were among cases reported to the CDSC AIDS Surveillance Programme in 1987. Some of these deaths may have been among HIV-positive people who did not meet the WHO definition at the time of death. There is a need for surveillance to be extended to include HIV-positive people who die before meeting the WHO definition if the full extent of the HIV epidemic is to be identified.     

Obesity and early mortality in the United States

Objective:

Although obesity is a serious public health problem, there are few reliable measures of its health hazards in the United States. The objective of this study was to estimate how much earlier mortality is likely to occur for Americans who are obese (body mass index [BMI], ≥ 30).

Design and Methods:

Data from the National Health and Nutrition Examination Survey (NHANES) I (1971–1975), NHANES II (1976–1980), and NHANES III (1988–1994) for 37,632 participants who experienced 8,791 deaths during 15 years of follow‐up were prospectively analyzed. The relative risk of death from all causes and its advancement period, adjusted for covariates, were calculated. Stratification was used to investigate the effects of pre‐existing illness, smoking, and older age on the advancement period.

Results:

Compared to the participants of reference weight (BMI, 23 to <25 kg/m2), mortality was likely to occur 9.44 years (95% confidence interval [CI]: 0.72, 18.16) earlier for those who were obese (BMI, ≥ 30). For overweight (25 to <30 kg/m2), grade 1 obesity (BMI, 30 to <35) and grades 2–3 obesity (BMI, ≥ 35.0), the mortality was likely to occur earlier by 4.40 (?3.90, 12.70), 6.69 (?2.06, 15.43), and 14.16 (3.35, 24.97) years, respectively. These estimates apply to healthy nonsmoker young‐ and middle‐aged (21–55 years) adults, who constituted an estimated 32.8% of Americans with age of >21 years between 1988 and 1994. Without stratifying simultaneously for preexisting illness, smoking, and age, values of the advancement period for obesity were markedly smaller than those observed for healthy nonsmoker young and middle‐aged adults.

Conclusions:

For healthy nonsmokers young‐ and middle‐aged adults who constitute about one‐third of American adults, being obese is likely to hasten mortality by 9.44 years.

     

Aboriginal mortality in Canada, the United States and New Zealand

Indigenous populations in New World nations share the common experience of culture contact with outsiders and a prolonged history of prejudice and discrimination. This historical reality continues to have profound effects on their well-being, as demonstrated by their relative disadvantages in socioeconomic status on the one hand, and in their delayed demographic and epidemiological transitions on the other. In this study one aspect of aboriginals' epidemiological situation is examined: their mortality experience between the early 1980s and early 1990s. The groups studied are the Canadian Indians, the American Indians and the New Zealand Maori (data for Australian Aboriginals could not be obtained). Cause-specific death rates of these three minority groups are compared with those of their respective non-indigenous populations using multivariate log-linear competing risks models. The empirical results are consistent with the proposition that the contemporary mortality conditions of these three minorities reflect, in varying degrees, problems associated with poverty, marginalization and social disorganization. Of the three minority groups, the Canadian Indians appear to suffer more from these types of conditions, and the Maori the least.     

Cardiac electrophysiology consultative experience at the epicenter of the COVID-19 pandemic in the United States

BackgroundThe COVID-19 pandemic has greatly altered the practice of cardiac electrophysiology around the world for the foreseeable future. Professional organizations have provided guidance for practitioners, but real-world examples of the consults and responsibilities cardiac electrophysiologists face during a surge of COVID-19 patients is lacking.MethodsIn this observational case series we report on 29 consecutive inpatient electrophysiology consultations at a major academic medical center in New York City, the epicenter of the pandemic in the United States, during a 2 week period from March 30-April 12, 2020, when 80% of hospital beds were occupied by COVID-19 patients, and the New York City metropolitan area accounted for 10% of COVID-19 cases worldwide.ResultsReasons for consultation included: Atrial tachyarrhythmia (31%), cardiac implantable electronic device management (28%), bradycardia (14%), QTc prolongation (10%), ventricular arrhythmia (7%), post-transcatheter aortic valve replacement conduction abnormality (3.5%), ventricular pre-excitation (3.5%), and paroxysmal supraventricular tachycardia (3.5%). Twenty-four patients (86%) were positive for COVID-19 by nasopharyngeal swab. All elective procedures were canceled, and only one urgent device implantation was performed. Thirteen patients (45%) required in-person evaluation and the remainder were managed remotely.ConclusionOur experience shows that the application of a massive alteration in workflow and personnel forced by the pandemic allowed our team to efficiently address the intersection of COVID-19 with a range of electrophysiology issues. This experience will prove useful as guidance for emerging hot spots or areas affected by future waves of the pandemic.