scholarly journals Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

2020 ◽  
Author(s):  
Christian Dudel ◽  
Tim Riffe ◽  
Enrique Acosta ◽  
Alyson A van Raalte ◽  
Cosmo Strozza ◽  
...  
Keyword(s):  
New York ◽  
South Korea ◽  
Age Structure ◽  
Case Fatality ◽  
Population Level ◽  
Decomposition Methods ◽  
The United States ◽  
Cross Country ◽  
The Difference ◽  
Over Time

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late April 2020, CFRs varied from 2.2% in South Korea to 13.0% in Italy. The age-structure of detected cases often explains more than two thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 13.0% between March 9 and April 22, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

scholarly journals Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

2020 ◽  
Author(s):  
Christian Dudel ◽  
Tim Riffe ◽  
Enrique Acosta ◽  
Alyson van Raalte ◽  
Cosmo Strozza ◽  
...  
Keyword(s):  
New York ◽  
South Korea ◽  
Age Structure ◽  
Case Fatality ◽  
Population Level ◽  
Decomposition Methods ◽  
The United States ◽  
Cross Country ◽  
The Difference ◽  
Over Time

AbstractThe population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late April 2020, CFRs varied from 2.2% in South Korea to 13.0% in Italy. The age-structure of detected cases often explains more than two thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 13.0% between March 9 and April 22, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

scholarly journals Changes in SARS CoV-2 Seroprevalence Over Time in Ten Sites in the United States, March – August, 2020

2021 ◽  
Author(s):  
Travis Lim ◽  
Mark Delorey ◽  
Nicolette Bestul ◽  
Michael Johannsen ◽  
Carrie Reed ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Population Level ◽  
The United States ◽  
South Florida ◽  
Population Projections ◽  
Cumulative Number ◽  
Geographic Scale ◽  
Convenience Sample ◽  
Over Time

Abstract Background Monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody prevalence can complement case reporting to inform more accurate estimates of SARS-CoV-2 infection burden, but few studies have undertaken repeated sampling over time on a broad geographic scale. Methods We performed serologic testing on a convenience sample of residual sera obtained from persons of all ages, at ten sites in the United States from March 23 through August 14, 2020, from routine clinical testing at commercial laboratories. We age-sex-standardized our seroprevalence rates using census population projections and adjusted for laboratory assay performance. Confidence intervals were generated with a two-stage bootstrap. We used Bayesian modeling to test whether seroprevalence changes over time were statistically significant. Results Seroprevalence remained below 10% at all sites except New York and Florida, where it reached 23.2% and 13.3%, respectively. Statistically significant increases in seroprevalence followed peaks in reported cases in New York, South Florida, Utah, Missouri and Louisiana. In the absence of such peaks, some significant decreases were observed over time in New York, Missouri, Utah, and Western Washington. The estimated cumulative number of infections with detectable antibody response continued to exceed reported cases in all sites. Conclusions Estimated seroprevalence was low in most sites, indicating that most people in the U.S. have not been infected with SARS-CoV-2 as of July 2020. The majority of infections are likely not reported. Decreases in seroprevalence may be related to changes in healthcare-seeking behavior, or evidence of waning of detectable anti-SARS CoV-2 antibody levels at the population level. Thus, seroprevalence estimates may underestimate the cumulative incidence of infection.

scholarly journals Black and White Differences in Life Expectancy in 4 US States, 1969-2013

2019 ◽  
Vol 134 (6) ◽  
pp. 634-642 ◽  
Author(s):  
Jay S. Kaufman ◽  
Corinne A. Riddell ◽  
Sam Harper
Keyword(s):  
New York ◽  
Life Expectancy ◽  
Racial Differences ◽  
State Level ◽  
Decomposition Methods ◽  
The United States ◽  
Future Research ◽  
Age Group ◽  
Life Expectancy At Birth ◽  
Racial Gap

Objectives: Racial differences in mortality in the United States have narrowed and vary by time and place. The objectives of our study were to (1) examine the gap in life expectancy between white and black persons (hereinafter, racial gap in life expectancy) in 4 states (California, Georgia, Illinois, and New York) and (2) estimate trends in the contribution of major causes of death (CODs) to the racial gap in life expectancy by age group. Methods: We extracted data on the number of deaths and population sizes for 1969-2013 by state, sex, race, age group, and 6 major CODs. We used a Bayesian time-series model to smooth and impute mortality rates and decomposition methods to estimate trends in sex- and age-specific contributions of CODs to the racial gap in life expectancy. Results: The racial gap in life expectancy at birth decreased in all 4 states, especially among men in New York (from 8.8 to 1.1 years) and women in Georgia (from 8.0 to 1.7 years). Although few deaths occurred among persons aged 1-39, racial differences in mortality at these ages (mostly from injuries and infant mortality) contributed to the racial gap in life expectancy, especially among men in California (1.0 year of the 4.3-year difference in 2013) and Illinois (1.9 years of the 6.7-year difference in 2013). Cardiovascular deaths contributed most to the racial gap in life expectancy for adults aged 40-64, but contributions decreased among women aged 40-64, especially in Georgia (from 2.8 to 0.5 years). The contribution of cancer deaths to inequality increased in California and Illinois, whereas New York had the greatest reductions in inequality attributable to cancer deaths (from 0.6 to 0.2 years among men and from 0.2 to 0 years among women). Conclusions: Future research should identify policy innovations and economic changes at the state level to better understand New York’s success, which may help other states emulate its performance.

Calculating Age-Standardized Death Rates Among People With HIV Comparable Across Jurisdictions and Over Time

2021 ◽  
Vol 111 (1) ◽  
pp. 121-126
Author(s):  
Qiang Xia ◽  
Ying Sun ◽  
Chitra Ramaswamy ◽  
Lucia V. Torian ◽  
Wenhui Li
Keyword(s):  
United States ◽  
New York ◽  
New York City ◽  
York City ◽  
Death Rate ◽  
Age Distribution ◽  
The United States ◽  
Local Health ◽  
Death Rates ◽  
Over Time

The Centers for Disease Control and Prevention (CDC) and local health jurisdictions have been using HIV surveillance data to monitor mortality among people with HIV in the United States with age-standardized death rates, but the principles of age standardization have not been consistently followed, making age standardization lose its purpose—comparison over time, across jurisdictions, or by other characteristics. We review the current practices of age standardization in calculating death rates among people with HIV in the United States, discuss the principles of age standardization including those specific to the HIV population whose age distribution differs markedly from that of the US 2000 standard population, make recommendations, and report age-standardized death rates among people with HIV in New York City. When we restricted the analysis population to adults aged between 18 and 84 years in New York City, the age-standardized death rate among people with HIV decreased from 20.8 per 1000 (95% confidence interval [CI] = 19.2, 22.3) in 2013 to 17.1 per 1000 (95% CI = 15.8, 18.3) in 2017, and the age-standardized death rate among people without HIV decreased from 5.8 per 1000 in 2013 to 5.5 per 1000 in 2017.

scholarly journals Trends in incidence and epidemiologic characteristics of cerebral venous thrombosis in the United States

Neurology ◽  
2020 ◽  
Vol 95 (16) ◽  
pp. e2200-e2213 ◽  
Author(s):  
Fadar Oliver Otite ◽  
Smit Patel ◽  
Richa Sharma ◽  
Pushti Khandwala ◽  
Devashish Desai ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Venous Thrombosis ◽  
Cerebral Venous Thrombosis ◽  
Census Data ◽  
Age Groups ◽  
The United States ◽  
Joinpoint Regression ◽  
Over Time ◽  
Age And Sex

ObjectiveTo test the hypothesis that race-, age-, and sex-specific incidence of cerebral venous thrombosis (CVT) has increased in the United States over the last decade.MethodsIn this retrospective cohort study, validated ICD codes were used to identify all new cases of CVT (n = 5,567) in the State Inpatients Databases (SIDs) of New York and Florida (2006–2016). A new CVT case was defined as first hospitalization for CVT in the SID without prior CVT hospitalization. CVT counts were combined with annual Census data to compute incidence. Joinpoint regression was used to evaluate trends in incidence over time.ResultsFrom 2006 to 2016, annual age- and sex-standardized incidence of CVT in cases per 1 million population ranged from 13.9 to 20.2, but incidence varied significantly by sex (women 20.3–26.9, men 6.8–16.8) and by age/sex (women 18–44 years of age 24.0–32.6, men 18–44 years of age 5.3–12.8). Incidence also differed by race (Blacks: 18.6–27.2; Whites: 14.3–18.5; Asians: 5.1–13.8). On joinpoint regression, incidence increased across 2006 to 2016, but most of this increase was driven by an increase in all age groups of men (combined annualized percentage change [APC] 9.2%, p < 0.001), women 45 to 64 years of age (APC 7.8%, p < 0.001), and women ≥65 years of age (APC 7.4%, p < 0.001). Incidence in women 18 to 44 years of age remained unchanged over time.ConclusionCVT incidence is disproportionately higher in Blacks compared to other races. New CVT hospitalizations increased significantly over the last decade mainly in men and older women. Further studies are needed to determine whether this increase represents a true increase from changing risk factors or an artifactual increase from improved detection.

scholarly journals The role of age distribution, time lag between reporting and death and healthcare system capacity on case fatality estimates of COVID-19

2020 ◽  
Author(s):  
Patrizio Vanella ◽  
Christian Wiessner ◽  
Anja Holz ◽  
Gerard Krause ◽  
Annika Moehl ◽  
...  
Keyword(s):  
Time Lag ◽  
Age Distribution ◽  
Case Fatality ◽  
System Capacity ◽  
Time Lags ◽  
Cross Country ◽  
The Difference ◽  
Case Fatality Risk ◽  
The Impact

European countries report large differences in coronavirus disease (COVID-19) case fatality risk (CFR). CFR estimates depend on demographic characteristics of the cases, time lags between reporting of infections and deaths and infrastructural characteristics, such as healthcare and surveillance capacities. We discuss the impact of these factors on the CFR estimates for Germany, Italy, France, and Spain for the COVID-19 pandemic from early March to mid-April, 2020. We found that, first, a large proportion of the difference in CFRs can be attributed to different age structures of the cases. Second, lags of 5-10 days between day of case report and death should be used, since these provide the most constant estimates. Third, for France, Italy, and Spain, intensive care beds occupied by COVID-19 patients were positively associated with fatality risks of hospitalized cases. Our results highlight that cross-country comparisons of crude CFR estimates can be misleading and should be avoided.

scholarly journals Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data

2020 ◽  
Author(s):  
Katie Labgold ◽  
Sarah Hamid ◽  
Sarita Shah ◽  
Neel R. Gandhi ◽  
Allison Chamberlain ◽  
...  
Keyword(s):  
Ethnic Disparities ◽  
Case Fatality ◽  
The United States ◽  
Infection Rates ◽  
Bias Adjustment ◽  
Social Inequities ◽  
Increased Risk ◽  
Race Ethnicity ◽  
The Difference ◽  
Racial Ethnic

AbstractBlack, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.

scholarly journals Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages

2020 ◽  
Author(s):  
Anthony Medford ◽  
Sergi Trias-Llimós
Keyword(s):  
Old Age ◽  
Age Structure ◽  
Age Distribution ◽  
Case Fatality ◽  
European Countries ◽  
Population Age Structure ◽  
Country Comparison ◽  
Cross Country ◽  
Age Structures

AbstractTo date any attention paid to the age shape of COVID-19 deaths has been mostly in relation to attempts to understand the differences in case fatality rates between countries. The aim of this paper is to explore differences in age distribution of deaths from COVID-19 among European countries which have old age structures. We do this by way of a cross-country comparison and put forward some reasons for potential differences.

scholarly journals SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

2020 ◽  
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Hospital Beds ◽  
The Impact ◽  
Over Time ◽  
The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

scholarly journals An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245845
Author(s):  
Miguel Sánchez-Romero ◽  
Vanessa di Lego ◽  
Alexia Prskawetz ◽  
Bernardo L. Queiroz
Keyword(s):  
New York ◽  
New Jersey ◽  
Large Scale ◽  
Total Population ◽  
Case Fatality ◽  
The United States ◽  
Complementary Approach ◽  
Large Scale Testing ◽  
Asymptomatic Individuals ◽  
Testing Policies

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

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