what are the mortality rates overall and in specific populations

Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. METHODS We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. FINDINGS Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates-a measure of relative inequality-increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7-87·2), and for men in Singapore, at 81·3 years (78·8-83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. INTERPRETATION Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either increased this advantage or rapidly decreased the deficit from expected levels, could provide insight into the means to accelerate progress in nations where progress has stalled. FUNDING Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.

Mortality among dentists in Taiwan, 1985–2009

Background/Purpose Controversy exists in the literature regarding whether dentists with multiple occupational exposures suffer from premature mortality. A cohort mortality study was conducted to evaluate the survival outcome and determine if potential exposure to harmful agents leads to premature mortality among dentists. Methods Using the Life Table Analysis System, we calculated standardized mortality ratios (SMRs) for a cohort of 11,700 dentists affiliated with the Taiwan Dental Association. These dentists were followed from 1985–2009. Reference rates were derived from cause-, gender-, and age-specific mortality rates of the general population of Taiwan and 18,664 Taiwanese internists, who were considered to be more socioeconomically proximal to dentists. A Cox proportional hazard model was also constructed to determine multiple risk factors associated with mortality. Results Compared with the general population, dentists in Taiwan consistently demonstrated reduced from all-cause mortality. However, compared with internists, significant and excess mortality were observed in dentists for overall mortality (SMR=1.13; 95% confidence interval [CI]=1.00–1.26), drowning (SMR=6.62; 95% CI=2.15–15.45), and heart diseases (SMR=1.66; 95% CI=1.22–2.21). After adjusting for other risk factors, the Cox model showed an increased hazard ratio of 1.17 (95% CI=1.01–1.37) for dentists. Conclusion Taiwanese dentists demonstrated significant elevated SMRs for overall causes, drowning, and heart diseases. Careful precaution should be taken to reduce these trends. Future studies are also needed for in-depth exploration of the mechanisms regarding how professional stress and exposure contribute to the increased risk of mortality in Taiwanese dentists.

Mortality from COVID-19 in 12 countries and 6 states of the United States

Importance: Reliable estimates of COVID-19 mortality are crucial to aid control strategies and to assess the effectiveness of interventions. Objective: Project COVID-19 mortality trends to October 1, 2020, in 12 countries or regions that constitute >90% of the global COVID-19 deaths reported as of April 12, 2020. Design, Setting, and Participants: The Global COVID-19 Assessment of Mortality (GCAM) is an open, transparent, and continuously updated (www.cghr.org/covid) statistical model that combines actual COVID-19 mortality counts with Bayesian inference to forecast COVID-19 deaths, the date of peak deaths, and the duration of excess mortality. The analyses covered a total of 700 million population above age 20 in 12 countries or regions: USA; Italy; Spain; France; UK; Iran; Belgium; a province of China (Hubei, which accounted for 90% of reported Chinese deaths); Germany; the Netherlands; Switzerland; and Canada; and six US states: New York, New Jersey, Michigan, Louisiana, California, and Washington. Results: Forecasted deaths across the 12 current high-burden countries sum 167,000 to 593,000 (median 253,000). The trajectory of US deaths (49,000-249,000 deaths; median 86,000)- over half of which are expected in states beyond the initial six states analysed in this study- will have the greatest impact on the eventual total. Mortality ranges are 25,000-109,000 (median 46,000) in the UK; 23,000-31,000 (median 26,000) in Italy; 21,000-37,000 (median 26,000) in France and 21,000-32,000 (median 25,000) in Spain. Estimates are most precise for Hubei, China, where the epidemic curve is complete, and least precise in California, where it is ongoing. New York has the highest cumulative median mortality rate per million (1135), about 12-fold that of Germany. Mortality trajectories are notably flatter in Germany, California, and Washington State, each of which took physical distancing and testing strategies seriously. Using past country-specific mortality as a guide, GCAM predicts surge capacity needs, reaching more than twice existing capacity in a number of places., In every setting, the results might be sensitive to undercounts of COVID-19 deaths, which are already apparent. Conclusion and Relevance: Mortality from COVID-19 will be substantial across many settings, even in the best case scenario. GCAM will provide continually updated and increasingly precise estimates as the pandemic progresses.

Employment insecurity, mental health and suicide.

With the economic crisis an increase in suicidality has been reported across Europe but especially in Greece. Τhese reports hit the mass media headlines and were also included in the debate among political parties. The literature suggests that during periods of deep economic crisis, there is an increase specifically in suicides but causality remains unclear. The prevailing picture both in the scientific literature and in the mass media is that the economic crisis acts as a more or less generic risk factor on the entire population putting at risk literally anybody. Two recent studies clearly dispute it by reporting that suicides had increased several months before unemployment increased. Additionally and specifically concerning Greece, where the economic crisis is deeper and more prolonged, the detailed inspection of age and gender specific rates are not in accord with a "male gender" by "unemployment" interaction. Taking into consideration the above and since the rise in suicides also affects prospering countries without high unemployment, including Germany and Norway, another possible explanation is that the changes in the socioeconomic environment and especially in the employment conditions have overstressed vulnerable populations (e.g. mental patients) leading to the increased suicide rates. The problem is that in the majority of the literature the economic crisis/austerity is considered to be a generic risk factor affecting the entire population and subsequently generic horizontal measures are proposed. Unfortunately patients at risk to commit suicide are not considered as such; instead they are rather considered as normal healthy people from the general population who respond with suicide to generic adverse events.

Long-term trends in seasonality of mortality in urban Madagascar: the role of the epidemiological transition

Background: Seasonal patterns of mortality have been identified in Sub-Saharan Africa but their changes over time are not well documented. Objective: Based on death notification data from Antananarivo, the capital city of Madagascar, this study assesses seasonal patterns of all-cause and cause-specific mortality by age groups and evaluates how these patterns changed over the period 1976–2015. Methods: Monthly numbers of deaths by cause were obtained from death registers maintained by the Municipal Hygiene Office in charge of verifying deaths before the issuance of burial permits. Generalized Additive Mixed regression models (GAMM) were used to test for seasonality in mortality and its changes over the last four decades, controlling for long-term trends in mortality. Results: Among children, risks of dying were the highest during the hot and rainy season, but seasonality in child mortality has significantly declined since the mid-1970s, as a result of declines in the burden of infectious diseases and nutritional deficiencies. In adults aged 60 and above, all-cause mortality rates are the highest in the dry and cold season, due to peaks in cardiovascular diseases, with little change over time. Overall, changes in the seasonality of all-cause mortality have been driven by shifts in the hierarchy of causes of death, while changes in the seasonality within broad categories of causes of death have been modest. Conclusion: Shifts in disease patterns brought about by the epidemiological transition, rather than changes in seasonal variation in cause-specific mortality, are the main drivers of trends in the seasonality of all-cause mortality.

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

Objectives: Mortality from Covid-19 is monitored in detail both within as well as between countries with different strategies against the virus. However, death counts and relative risks based on crude numbers can be misleading. Instead, age specific death rates should be used for comparability. Given the difficulty of ascertainment of Covid-19 specific deaths, excess all-cause mortality is currently more appropriate for comparisons. By estimating age- and sex-specific death rates we aim to get more accurate estimates of the excess mortality attributed to Covid-19, as well as the difference between men and women in Sweden. Design: We make use of Swedish register data about total weekly deaths, total population at risk, and estimate age- and sex-specific weekly death rates for 2020 and the 5 previous years. The data is provided by Statistics Sweden. Results: From the first week of April and onwards, the death rates at all ages above 60 are higher than those in previous years in Sweden. Persons above age 80 are dis-proportionally more affected, and men suffer higher levels of excess mortality than women at all ages with 75% higher death rates for males and 50% higher for females. Current excess mortality corresponds to a decline in remaining life expectancy of 3 years for men and 2 years for women. Conclusion: The Covid-19 pandemic has so far had a clear and consistent effect on total mortality in Sweden, with male death rates being comparably more affected. What consequences the pandemic will eventually have on mortality and life expectancy will depend on the progression of the pandemic, the extent that some of the deaths would have occurred in the absence of the pandemic, only somewhat later, the consequences for other health conditions, as well as the health care sector at large.

The overall mortality caused by COVID-19 in the European region is highly associated with demographic composition: A spatial regression-based approach

The demographic factors have a substantial impact on the overall casualties caused by the COVID-19. In this study, the spatial association between the key demographic variables and COVID-19 cases and deaths were analyzed using the spatial regression models. Total 13 (for COVID-19 case factor) and 8 (for COVID-19 death factor) key variables were considered for the modelling. Total five spatial regression models such as Geographically weighted regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), Spatial Error_Lag model (SEM_SLM), and Ordinary Least Square (OLS) were performed for the spatial modelling and mapping of model estimates. The local R2 values, which suggesting the influences of the selected demographic variables on overall casualties caused by COVID-19, was found highest in Italy and the UK. The moderate local R2 was observed for France, Belgium, Netherlands, Ireland, Denmark, Norway, Sweden, Poland, Slovakia, and Romania. The lowest local R2 value for COVID-19 cases was accounted for Latvia and Lithuania. Among the 13 variables, the highest local R2 was calculated for total population (R2 = 0.92), followed by death crude death rate (R2 = 0.9), long time illness (R2 = 0.84), population with age>80 (R2 = 0.59), employment (R2 = 0.46), life expectancy at 65 (R2 = 0.34), crude birth rate (R2 = 0.31), life expectancy (R2 = 0.31), Population with age 65-80 (R2 = 0.29), Population with age 15-24 (R2 = 0.27), Population with age 25-49 (R2 = 0.27), Population with age 0-14 (R2 = 0.23), and Population with age 50-65 (R2 = 0.23), respectively.

Explosion in mortality in the Amazonian epicenter of the COVID-19 epidemic 19

Manaus, the capital of the Brazilian State of Amazonas, is the current epicenter of the COVID-19 epidemic in Amazonia The sharp increase in deaths is a huge concern for health system administrators and society The study aimed to analyze excess overall mortality according to Epidemiological Week (EW) in order to identify changes potentially associated with the epidemic in Manaus Overall and cause-specific mortality data were obtained from the Central Database of the National Civil Registry and the Mortality Information System for 2018, 2019, and 2020 The study analyzed age bracket, sex, place of death, EW, calendar year, and causes of death Ratios were calculated between deaths in 2019/2018 and 2020/2019 to estimate excess deaths, with 5% confidence intervals No significant excess overall mortality was seen in the ratios for 2019/2018, independently of EW Meanwhile, the ratios for 2020/2019 increased from 1 0 (95%CI: 0 9-1 3) in EW 11 to 4 6 (95%CI: 3 9-5 3) in EW 17 Excess overall mortality was observed with increasing age, especially in individuals 60 years or older, who accounted for 69 1% (95%CI: 66 8-71 4) of the deaths The ratios for 2020/2019 for deaths at home or on public byways were 1 1 (95%CI: 0 7-1 8) in EW 12 and 7 8 (95%CI: 5 4-11 2) in EW 17 The explosion in overall mortality in Manaus and the high proportion of deaths at home or on public byways reveals the epidemic's severity in contexts of heavy social inequality and weak effectiveness of government policies, especially policies meant to deal with social inequalities and strengthen the Unified Health System

Potential Years of Life Lost Due to COVID-19 in the United States, Italy, and Germany: An Old Formula with Newer Ideas

Today, the world is facing the challenge of a major pandemic due to COVID-19, which has caused more than 6.1 million cases of infection and nearly 370,000 deaths so far. Most of the deaths from the disease are clustered in the older population, but the young and children are not spared. In this context, there is a critical need to revisit the formula for calculating potential years of life lost (PYLL). Data on age-specific deaths due to COVID-19 in three countries, including the United States (US), Italy, and Germany, were evaluated. New York State, as a significant outlier within the US, was also included. PYLLs in the US were five times as high as those of Italy. Compared with Germany, PYLLs in Italy were 4 times higher, and the rates in the US were 23, 25, and 18 times higher when using upper age limits of 70, 75, and 80, respectively. Standardized PYLLs in New York were 2 times as high as the rates in Italy, and 7 to 9 times as high as PYLLs in Germany. The revised formula of PYLL, using an upper limit of age 80, is recommended to accurately measure premature deaths due to a major disastrous disease such as COVID-19.

A scaling approach to estimate the COVID-19 infection fatality ratio from incomplete data

SARS-CoV-2 has disrupted the life of billions of people around the world since the first outbreak was officially declared in China at the beginning of 2020. Yet, important questions such as how deadly it is or its degree of spread within different countries remain unanswered. In this work, we exploit the `universal' growth of the mortality rate with age observed in different countries since the beginning of their respective outbreaks, combined with the results of the antibody prevalence tests in the population of Spain, to unveil both unknowns. We validate these results with an analogous antibody rate survey in the canton of Geneva, Switzerland. We also argue that the official number of deaths over 70 years old is importantly underestimated in most of the countries, and we use the comparison between the official records with the number of deaths mentioning COVID-19 in the death certificates to quantify by how much. Using this information, we estimate the fatality infection ratio (IFR) for the different age segments and the fraction of the population infected in different countries assuming a uniform exposure to the virus in all age segments. We also give estimations for the non-uniform IFR using the sero-epidemiological results of Spain, showing a very similar growth of the fatality ratio with age. Only for Spain, we estimate the probability (if infected) of being identified as a case, being hospitalized or admitted in the intensive care units as function of age. In general, we observe a nearly exponential growth of the fatality ratio with age, which anticipates large differences in total IFR in countries with different demographic distributions, with numbers that range from 1.82\% in Italy, to 0.62\% in China or even 0.14\% in middle Africa.

SARS-CoV-2 has disrupted the life of billions of people around the world since the first outbreak was officially declared in China at the beginning of 2020. Yet, important questions such as how deadly it is or its degree of spread within different countries remain unanswered. In this work, we exploit the 'universal' growth of the mortality rate with age observed in different countries since the beginning of their respective outbreaks, combined with the results of the antibody prevalence tests in the population of Spain, to unveil both unknowns. We validate these results with an analogous antibody rate survey in the canton of Geneva, Switzerland. We also argue that the official number of deaths over 70 years old is importantly underestimated in most of the countries, and we use the comparison between the official records with the number of deaths mentioning COVID-19 in the death certificates to quantify by how much. Using this information, we estimate the fatality infection ratio (IFR) for the different age segments and the fraction of the population infected in different countries assuming a uniform exposure to the virus in all age segments. We also give estimations for the non-uniform IFR using the sero-epidemiological results of Spain, showing a very similar growth of the fatality ratio with age. Only for Spain, we estimate the probability (if infected) of being identified as a case, being hospitalized or admitted in the intensive care units as function of age. In general, we observe a nearly exponential growth of the fatality ratio with age, which anticipates large differences in total IFR in countries with different demographic distributions, with numbers that range from 1.82% in Italy, to 0.62% in China or even 0.14% in middle Africa.

Health Inequalities, General Trends in Mortality and Morbidity, and Associated Factors

All measures of health status are ultimately derived from observations of individuals. At the field level we have such measures as self-assessed health status, report of a specific disease, record of a particular death, or an individual’s test on a biomarker, such as blood pressure or serum cholesterol. The observations for individuals are combined and summarized to represent subnational geographic areas, demographic or socioeconomic groups within countries, or national populations. The summary measures, whether they are percentages, averages, or rates, apply to groups. A problem arises when the measures that are based on groups are assumed to represent individuals. The analysis becomes especially problematic when the units analyzed are geographic areas and inferences are being made about individuals from the analysis for these geographic areas.

Analysis of Austrian COVID-19 deaths by age and sex

We analyze the age and sex distribution of the reported COVID-19 deaths in Austria. In accordance with international studies, the Austrian data also suggests that the risk of death increases substantially with age. The observed age dependency of the proportions of registered COVID-19 deaths in relation to the population sizes in the age groups is approximately exponential, similar to the age dependency of the general age specific mortality rate. Furthermore, we compare the general age specific mortality rate in Austria with the estimates of the SARS-CoV‑2 infection fatality rate by Ferguson et al. (2020). The parallels to the general age specific mortality rates do not imply that COVID-19 does not pose an additional risk. On the contrary, it follows from the structure and magnitude of the infection fatality rate that it is substantial, especially for higher age groups. However, since in many cases persons with severe pre-existing conditions are affected, it is not yet possible to estimate what effects COVID-19 will have on life expectancy.

The Contribution of Age Structure to the Number of Deaths from Covid-19 in the UK by Geographical Units

This study investigates the contribution of population age structure to mortality from Covid-19 in the UK by geographical units. We project death rates at various spatial scales by applying data on age-specific fatality rates to the area's population by age and sex. Our analysis shows a significant variation in the projected death rates between the constituent countries of the UK, between its regions and within regions. First, Scotland and Wales have higher projected fatality levels from Covid-19 than England, whereas Northern Ireland has lower rate. Second, the infection fatality rates are projected to be substantially higher in small towns and rural areas than those in large urban areas. Third, our analysis shows that within urban regions there are also 'pockets' of high projected death rates. Overall, the areas with high and low fatality rates tend to cluster because of the high residential separation of different population age-groups in the UK. Our analysis also reveals that the Welsh-, Gaelic- and Cornish-speaking communities with relatively old populations are likely to experience heavy population losses if the virus spreads widely across the UK.

Determinants of COVID-19 incidence and mortality: A cross-country analysis

Objective: We undertook this study to explore the role of important determinants affecting global COVID-19 incidence and mortality taking multifactorial disease dynamics into consideration. Design: Secondary data as on March 28, 2020 were obtained for 97 countries. Association of COVID-19 cumulative incidence and mortality measures were assessed with ten indictors representing health system characteristics, climate, demography, promptness of international travel restriction and population movement using Generalized Linear Modelling. Main outcome measures: Country-specific COVID-19 cumulative incidence, cumulative cause-specific mortality and case fatality rate. Results: Significant inter-country variation in incidence and mortality rates were observed. Five variables were found to be associated with cumulative incidence: testing rate per 1000 population ({beta} = 0.119, p < 0.01), UHC index ({beta} = 0.043, p = 0.04), percentage elderly population ({beta} = 0.122, p < 0.01), percentage below-poverty line population ({beta} = -0.048, p < 0.01) and disability adjusted life years due to NCDs ({beta} = -0.013, p < 0.01). Case fatality rate was observed to be associated with testing rate per 1000 population ({beta} = -0.058, p = 0.03) and population density ({beta} = 0.002, p = 0.02), while the cumulative cause-specific mortality was associated with only percentage elderly population ({beta} = 0.096, p = 0.04) in the country. Conclusions: Health system response, population susceptibility and demography were the most important factors determining the progression. Policy response should focus towards increasing testing, primarily targeting high population density areas. Health system strengthening and reduction in population risk factors should be long term goals for a better response to such epidemics.

Age- and Sex-Specific Mortality Associated With the 1918–1919 Influenza Pandemic in Kentucky

Background. The reasons for the unusual age-specific mortality patterns of the 1918–1919 influenza pandemic remain unknown. Here we characterize pandemic-related mortality by single year of age in a unique statewide Kentucky data set and explore breakpoints in the age curves. Methods. Individual death certificates from Kentucky during 1911–1919 were abstracted by medically trained personnel. Pandemic-associated excess mortality rates were calculated by subtracting observed rates during pandemic months from rates in previous years, separately for each single year of age and by sex. Results. The age profile of excess mortality risk in fall 1918 was characterized by a maximum among infants, a minimum at ages 9–10 years, a maximum at ages 24–26 years, and a second minimum at ages 56–59 years. The excess mortality risk in young adults had been greatly attenuated by winter 1919. The age breakpoints of mortality risk did not differ between males and females. Conclusions. The observed mortality breakpoints in male and female cohorts born during 1859–1862, 1892–1894, and 1908–1909 did not coincide with known dates of historical pandemics. The atypical age mortality patterns of the 1918–1919 pandemic cannot be explained by military crowding, war-related factors, or prior immunity alone and likely result from a combination of unknown factors.

Age- and sex-specific total mortality impacts of the early weeks of the Covid-19 pandemic in England and Wales: Application of a Bayesian model ensemble to mortality statistics

Background: The Covid-19 pandemic affects mortality directly through infection as well as through changes in the social, environmental and healthcare determinants of health. The impacts on mortality are likely to vary, in both magnitude and timing, by age and sex. Our aim was to estimate the total mortality impacts of the pandemic, by sex, age group and week. Methods: We developed an ensemble of 16 Bayesian models that probabilistically estimate the weekly number of deaths that would be expected had the Covid-19 pandemic not occurred. The models account for seasonality of death rates, medium-long-term trends in death rates, the impact of temperature on death rates, association of death rates in each week on those in preceding week(s), and the impact of bank holidays. We used data from January 2010 through mid-February 2020 (i.e., week starting 15th February 2020) to estimate the parameters of each model, which was then used to predict the number of deaths for subsequent weeks as estimates of death rates if the pandemic had not occurred. We subtracted these estimates from the actual reported number of deaths to measure the total mortality impact of the pandemic. Results: In the week that began on 21st March, the same week that a national lockdown was put in place, there was a >92% probability that there were more deaths in men and women aged [≥]45 years than would occur in the absence of the pandemic; the probability was 100% from the subsequent week. Taken over the entire period from mid-February to 8th May 2020, there were an estimated [~] 49,200 (44,700-53,300) or 43% (37-48) more deaths than would be expected had the pandemic not taken place. 22,900 (19,300-26,100) of these deaths were in females (40% (32-48) higher than if there had not been a pandemic), and 26,300 (23,800-28,700) in males (46% (40-52) higher). The largest number of excess deaths occurred among women aged >85 years (12,400; 9,300-15,300), followed by men aged >85 years (9,600; 7,800-11,300) and 75-84 years (9,000; 7,500-10,300). The cause of death assigned to the majority (37,295) of these excess deaths was Covid-19. There was nonetheless a >99.99% probability that there has been an increase in deaths assigned to other causes in those aged [≥]45 years. However, by the 8th of May, the all-cause excess mortality had become virtually equal to deaths assigned to Covid-19, and non-Covid excess deaths had diminished to close to zero, or possibly become negative, in all age-sex groups. Interpretation: The death toll of Covid-19 pandemic, in middle and older ages, is substantially larger than the number of deaths reported as a result of confirmed infection, and was visible in vital statistics when the national lockdown was put in place. When all-cause mortality is considered, the mortality impact of the pandemic on men and women is more similar than when comparing deaths assigned to Covid-19 as underlying cause of death.

The potential effect of the African population age structure on COVID-19 mortality

Currently (mid May 2020), most active cases of COVID-19 are found in Europe and North America while it is still in the initial phases in Africa. As COVID-19 mortality occurs mainly in elderly and as Africa has a comparably young population, the death rates should be lower than on other continents. We calculated standardised mortality ratios (SMR) using age-specific case fatality rates for COVID-19 and the age structure of the population of Africa and of other continents. Compared to a European or Northern American population, the standardised mortality ratio was only 0.22 and 0.25, respectively, corresponding to reduction of deaths rates to a quarter. Compared to the Asian and Latin American & Caribbean population, the SMR was 0.43 and 0.44, respectively, corresponding to half the death rate for Africa. It is useful to quantify the isolated effect of the African age-structure on potential COVID-19 mortality for illustrative and communication purposes, keeping in mind the importance of public health measures that have been shown to be effective in reducing cases and deaths. The different aspect of age pyramids of a European and an African population are striking and the potential implications for the pandemic are often discussed but rarely quantified.

What does and does not correlate with COVID-19 death rates

We correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state's death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California's death rate is the lowest across all states.

Motor vehicle-related death rates--United States, 1999-2005.

In 2005, the most recent year for which data are available, 45,520 deaths in the United States were related to motor vehicles. A Healthy People 2010 objective calls for reducing the rate of deaths related to motor vehicles to 9.2 per 100,000 population from a baseline of 15.6 in 1998. To assess progress toward the Healthy People objective and to examine characteristics of motor vehicle--related death rates, CDC analyzed data from the National Vital Statistics System (NVSS) for the period 1999--2005. This report summarizes the results of that analysis, which determined that, during 1999--2005, although annual age-adjusted motor vehicle--related death rates overall were nearly unchanged (range: 15.2--15.7 per 100,000 population), substantial differences were observed by state, U.S. Census region, sex, race, and age group. Among states, the average annual death rate ranged from 7.9 per 100,000 population in Massachusetts to 31.9 in Mississippi. Among regions, the rate ranged from 9.8 per 100,000 population in the Northeast to 19.5 in the South. The rate for men (21.7 per 100,000 population) was more than double the rate for women (9.4); the rate for American Indians/Alaska Natives (27.2) was nearly twice the rate for whites (15.7) and blacks (15.2), and the rate for persons aged 15--24 years (26.8) was 74% higher than the average annual rate overall (15.4). Additional analysis and research to determine the causes of geographic and demographic variations in motor vehicle--related deaths might result in more effective targeted interventions among the states, regions, and populations at greatest risk.

Estimating Lower Bounds for COVID-19 Mortality from Northern Italian Towns

For COVID-19 the Infection Fatality Rate or IFR - a crucial variable in epidemiological modeling - is difficult to estimate because many cases are asymptomatic and the overall infection rate is generally not known. Circumstances in the Italian provinces of Milano, Bergamo, Brescia, and Lodi allow estimation of lower bounds for age- and sex-specific all-cause excess mortality (a proxy for IFR) since anecdotal reports indicate some towns were close to fully infected. Using data from ISTAT on mortality from January 1 through April 15 for 2020 and the three preceding years, I estimate excess mortality by sex and age categories (0-14, 15-54, 55-64, 65-74, and 75+ years) while controlling for town-specific mortality that proxies for town-specific infection rate. The 99th percentile from the tail of the town distribution gives a lower-bound estimate for COVID-19 mortality. The overall population-weighted mortality at the 99th percentile is 1.09 percent (95% CI 1.06-1.14). The age- and sex-specific rates vary considerably: for men age 65-74 the estimate is 2.10 percent (95% CI 1.94-2.28) which is 3.5-times higher than men 55-64 and 2.7-times higher than women 65-74.

We analyze the age and sex distribution of the reported COVID-19 deaths in Austria. In accordance with international studies, the Austrian data also suggests that the risk of death increases substantially with age. The observed age dependency of the proportions of registered COVID-19 deaths in relation to the population sizes in the age groups is approximately exponential, similar to the age dependency of the general age specific mortality rate. Furthermore, we compare the general age specific mortality rate in Austria with the estimates of the SARS-CoV2 infection fatality rate by Ferguson et al. (2020). The parallels to the general age specific mortality rates do not imply that COVID-19 does not pose an additional risk. On the contrary, it follows from the structure and magnitude of the infection fatality rate that it is substantial, especially for higher age groups. However, since in many cases persons with severe pre-existing conditions are affected, it is not yet possible to estimate what effects COVID-19 will have on life expectancy.

The Longevity-Frailty Hypothesis: Evidence from COVID-19 Death Rates in Europe

COVID-19 death rates vary strikingly across Europe. The death rate in Spain, for example, is greater than the death rate in Germany by more than a factor of ten. Few if any epidemiological indicators distinguish the countries of Europe by such a vast margin. Evidence on age-specific case-fatality rates (deaths over observed infections) and age-specific death rates (deaths over population) indicate that COVID-19 disproportionately afflicts the elderly and frail, suggesting that the share of elderly population (≥ 65 years of age) in a country ought to be a strong predictor of the COVID-19 death rate. However, the COVID-19 death rate and the share of elderly population are statistically uncorrelated (r = 0.163, p = 0.399). Share of population ≥ 65 years of age is confounded by mortality selection, as well as other demographic dynamics. By contrast, elderly longevity or life expectancy at 65 more effectively captures population survival and the accumulation of age-related frailty in society. We find a strong statistical relationship between the COVID-19 death rate (r = 0.839, p < .001) and elderly longevity, and a moderately strong relationship between the date of epidemic timing and elderly longevity (r = −0.634, p < .001). These relationships are robust to the inclusion of statistical controls for international tourism inflow and hospital bed capacity. While the countries of Europe vary meaningfully in healthcare system capacity and in the timing and intensity of non-pharmaceutical interventions, the striking variation in COVID-19 death rates across these countries is statistically and intuitively associated with elderly survival and consequent frailty.

COVID-19 Fatalities, Latitude, Sunlight, and Vitamin D

BACKGROUND: Since Vitamin D is known to be vital in regulating the immune system, and sunlight UV radiation exposure on the skin produces Vitamin D and UV intensity is highest nearest the equator, a study was done to examine the correlation between the latitude and COVID-19 fatality rates for countries. METHODS: Eighty-eight countries were selected based on their likelihood of providing reliable data. Using death rates/million for each country from the "worldometer" web site, a correlation analysis was done between death rates and a country's latitude. RESULTS: A highly significant, positive correlation was found between lower death rates and a country's proximity to the equator (Pearson râ¯=â¯.40 p<.0001, two-tailed t-test). The R squared of .16 means that 16% of the variation in death rates among nations is accounted for by the latitude of the country. Evidence is presented suggesting a direct correlation between sunlight exposure and reduced mortality. DISCUSSION: This study is the first to document a statistically significant correlation between a country's latitude and its COVID-19 mortality and is consistent with other research regarding latitude, Vitamin D deficiency, and COVID-19 fatalities. Limitations of this study are noted. CONCLUSION: Further research is needed to confirm the correlation between latitude and COVID-19 fatalities, and to determine the optimum amounts of safe sunlight exposure and/or vitamin D oral supplementation to reduce COVID-19 fatalities in populations that are at high risk for vitamin D deficiency.

The effect of public health measures on the 1918 influenza pandemic in U.S. cities.

During the 1918 influenza pandemic, the U.S., unlike Europe, put considerable effort into public health interventions. There was also more geographic variation in the autumn wave of the pandemic in the U.S. compared with Europe, with some cities seeing only a single large peak in mortality and others seeing double-peaked epidemics. Here we examine whether differences in the public health measures adopted by different cities can explain the variation in epidemic patterns and overall mortality observed. We show that city-specific per-capita excess mortality in 1918 was significantly correlated with 1917 per-capita mortality, indicating some intrinsic variation in overall mortality, perhaps related to sociodemographic factors. In the subset of 23 cities for which we had partial data on the timing of interventions, an even stronger correlation was found between excess mortality and how early in the epidemic interventions were introduced. We then fitted an epidemic model to weekly mortality in 16 cities with nearly complete intervention-timing data and estimated the impact of interventions. The model reproduced the observed epidemic patterns well. In line with theoretical arguments, we found the time-limited interventions used reduced total mortality only moderately (perhaps 10-30%), and that the impact was often very limited because of interventions being introduced too late and lifted too early. San Francisco, St. Louis, Milwaukee, and Kansas City had the most effective interventions, reducing transmission rates by up to 30-50%. Our analysis also suggests that individuals reactively reduced their contact rates in response to high levels of mortality during the pandemic.

Deaths in SARS-Cov-2 Positive Patients in Italy: The Influence of Underlying Health Conditions on Lethality

This study aims to underline the clinical characteristics of patients who died after testing positive for SARS-CoV-2 infection in one region of Italian and to evaluate the influence of underlying health conditions on the fatal outcome. A matched case-control study was designed by analyzing the data regarding positive subjects observed up to April 21, 2020. The case fatality rate was 7.9%, with a higher proportion of deaths in men than women. The specific standardized mortality ratio was 0.15-0.13 for males and 0.2 for females, showing that mortality is much lower than expected. Cardiovascular diseases, chronic lung diseases and diabetes mellitus showed a significant association with the outcome. Although the case fatality rate in Sardinia in regard to age and gender patterns seems to be similar to that for Italy as a whole, its quantitative value was far lower than the national one and possible explanations might include the genetic characteristics of the Sardinian population or the immediate closure of its borders as soon as the epidemic started. Our results highlighted that lethality is strongly dependent on the presence of multiple concomitant serious diseases. It is important to have epidemiological strategies for effective guidance on public health actions in order to improve chances of survival.

This study aims to underline the clinical characteristics of patients who died after testing positive for SARS-CoV-2 infection in one region of Italian and to evaluate the influence of underlying health conditions on the fatal outcome. A matched case-control study was designed by analyzing the data regarding positive subjects observed up to April 21, 2020. The case fatality rate was 7.9%, with a higher proportion of deaths in men than women. The specific standardized mortality ratio was 0.15—0.13 for males and 0.2 for females, showing that mortality is much lower than expected. Cardiovascular diseases, chronic lung diseases and diabetes mellitus showed a significant association with the outcome. Although the case fatality rate in Sardinia in regard to age and gender patterns seems to be similar to that for Italy as a whole, its quantitative value was far lower than the national one and possible explanations might include the genetic characteristics of the Sardinian population or the immediate closure of its borders as soon as the epidemic started. Our results highlighted that lethality is strongly dependent on the presence of multiple concomitant serious diseases. It is important to have epidemiological strategies for effective guidance on public health actions in order to improve chances of survival.

Estimating the Size of High-risk Populations for COVID-19 Mortality across 442 US Cities

A variety of predisposing factors have been associated with serious illness and death from COVID-19. Understanding the distribution of risks associated with these factors by local communities can provide important opportunities for targeting interventions. We characterize the distribution of risk for COVID-19 mortality for populations at large across 442 US cities, by utilizing recently published estimates of risk associated with age, gender, ethnicity, social deprivation and 12 health conditions from a very large UK-based study, combined with the information available on prevalence and co-occurrence of these factors in the US through a variety of population-based public databases. We estimate that across all the cities, an underlying weighted risk-score can identify a total of approximately 12.65 million, 4.09 million and 1.34 million individuals who are at 2-, 5- and 10-fold higher risk, respectively, compared to the average risk for the US population. The percentage of population which exceed the respective risk thresholds varies across the cities in the range (1st-99th percentile), 3.6%-20.1%, 0.7%-8.0% and 0.1%-3.2%, respectively. The percentage of deaths within a city that are expected to occur above these risk-thresholds varies in the range of 20.1%-53.5%, 8.5%-38.2% and 2.9%-25.4%, respectively. Our analysis can provide guidance to national and local policy makers regarding resources needed to protect the most vulnerable populations in these communities, and how much utility such interventions may have in reducing the total population burden of death.

On estimating the number of deaths related to Covid-19

In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the modeling results to two related existing models based on a new criteria and several existing criteria for model selection. The results show the proposed model fits significantly better than the other two related models based on the U.S. Covid-19 death data. We observe that the errors of the fitted data and the predicted data points on the total number of deaths in the U.S. on the last available data point and the next coming day are less than 0.5% and 2.0%, respectively. The results show very encouraging predictability for the model. The new model predicts that the maximum total number of deaths will be approximately 62,100 across the United States due to the Covid-19 virus, and with a 95% confidence that the expected total death toll will be between 60,951 and 63,249 deaths based on the data until 22 April, 2020. If there is a significant change in the coming days due to various testing strategies, social-distancing policies, the reopening of community strategies, or a stay-home policy, the predicted death tolls will definitely change. Future work can be explored further to apply the proposed model to global Covid-19 death data and to other applications, including human population mortality, the spread of disease, and different topics such as movie reviews in recommender systems.

Racial/ethnic disparities in fatal unintentional drowning among persons aged ≤ 29 years - United States, 1999-2010.

In the United States, almost 4,000 persons die from drowning each year. Drowning is responsible for more deaths among children aged 1-4 years than any other cause except congenital anomalies. For persons aged ≤29 years, drowning is one of the top three causes of unintentional injury death (2). Previous research has identified racial/ethnic disparities in drowning rates. To describe these differences by age of decedent and drowning setting, CDC analyzed 12 years of combined mortality data from 1999-2010 for those aged ≤29 years. Among non-Hispanics, the overall drowning rate for American Indians/Alaska Natives (AI/AN) was twice the rate for whites, and the rate for blacks was 1.4 times the rate for whites. Disparities were greatest in swimming pools, with swimming pool drowning rates among blacks aged 5-19 years 5.5 times higher than those among whites in the same age group. This disparity was greatest at ages 11-12 years; at these ages, blacks drown in swimming pools at 10 times the rate of whites. Drowning prevention strategies include using barriers (e.g., fencing) and life jackets, actively supervising or lifeguarding, teaching basic swimming skills and performing bystander cardiopulmonary resuscitation (CPR). The practicality and effectiveness of these strategies varies by setting; however, basic swimming skills can be beneficial across all settings.

COVID-19 Deaths: Which Explanatory Variables Matter the Most?

As Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spreads around the World, many questions about the disease are being answered; however, many more remain poorly understood. Although the situation is rapidly evolving, with datasets being continually corrected or updated, it is crucial to understand what factors may be driving transmission through different populations. While studies are beginning to highlight specific parameters that may be playing a role, few have attempted to thoroughly estimate the relative importance of these disparate variables that likely include: climate, population demographics, and imposed state interventions. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted density (PWD), some "stay at home" metrics, monthly temperature and precipitation, race/ethnicity, and chronic low respiratory death rate, were all statistically significant. Of these, PWD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWD. Our results strongly support the idea that the loosening of "lock-down" orders should be tailored to the local PWD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present.

Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them

Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the "CFR" has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is thecase fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual's probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data such as undertesting and heterogeneous populations.

Inverse correlation between average monthly high temperatures and COVID-19-related death rates in different geographical areas

BACKGROUND: With the aim of providing a dynamic evaluation of the effects of basic environmental parameters on COVID-19-related death rate, we assessed the correlation between average monthly high temperatures and population density, with death/rate (monthly number of deaths/1 M people) for the months of March (start of the analysis and beginning of local epidemic in most of the Western World, except in Italy where it started in February) and April 2020 (continuation of the epidemic). Different geographical areas of the Northern Hemisphere in the United States and in Europe were selected in order to provide a wide range among the different parameters. The death rates were gathered from an available dataset. As a further control, we also included latitude, as a proxy for temperature. METHODS: Utilizing a publicly available dataset, we retrieved data for the months of March and April 2020 for 25 areas in Europe and in the US. We computed the monthly number of deaths/1 M people of confirmed COVID-19 cases and calculated the average monthly high temperatures and population density for all these areas. We determined the correlation between number of deaths/1 M people and the average monthly high temperatures, the latitude and the population density. RESULTS: We divided our analysis in two parts: analysis of the correlation among the different variables in the month of March and subsequent analysis in the month of April. The differences were then evaluated. In the month of March there was no statistical correlation between average monthly high temperatures of the considered geographical areas and number of deaths/1 M people. However, a statistically significant inverse correlation became significant in the month of April between average monthly high temperatures (p = 0.0043) and latitude (p = 0.0253) with number of deaths/1 M people. We also observed a statistically significant correlation between population density and number of deaths/1 M people both in the month of March (p = 0.0297) and in the month of April (p = 0.0116), when three areas extremely populated (NYC, Los Angeles and Washington DC) were included in the calculation. Once these three areas were removed, the correlation was not statistically significant (p = 0.1695 in the month of March, and p = 0.7076 in the month of April). CONCLUSIONS: The number of COVID-19-related deaths/1 M people was essentially the same during the month of March for all the geographical areas considered, indicating essentially that the infection was circulating quite uniformly except for Lombardy, Italy, where it started earlier. Lockdown measures were implemented between the end of March and beginning of April, except for Italy which started March 9th. We observed a strong, statistically significant inverse correlation between average monthly high temperatures with the number of deaths/1 M people. We confirmed the data by analyzing the correlation with the latitude, which can be considered a proxy for high temperature. Previous studies indicated a negative effect of high climate temperatures on Sars-COV-2 spreading. Our data indicate that social distancing measure are more successful in the presence of higher average monthly temperatures in reducing COVID-19-related death rate, and a high level of population density seems to negatively impact the effect of lockdown measures.

The confounded crude case-fatality rates for COVID-19 hide more than they reveal - a comparison of age-specific and age-adjusted rates between six countries

Background The reported crude case-fatality rates (CFRs) vary widely between countries. The serious limitations of using crude rates for comparisons are sometimes overlooked. In this paper we examined to what extent the age distribution of the cases is responsible for the differences in CFRs between countries. Methods Data on COVID-19 were extracted from the reports of individual countries. Overall and age-specific CFRs were available for six countries. The CFRs by country were adjusted for age using the direct method, using the combined age-specific number of cases of all six countries as the standard population. Findings The age distribution of the cases varied widely between countries. The crude CFRs varied between 1.6% and 11%. The differences in the age-specific CFRs were much smaller and the age-adjusted rates were much closer than the crude rates. The ratio of the crude CFR for the country with the highest to that with the lowest, was reduced substantially from 7.4 to 2.3 for the age-adjusted rates. Conclusions The age structure of the cases dramatically impacts on the differences in the crude CFRs between countries. Adjusting for age substantially reduces this variation. Other factors such as the differences in the definition of the denominators, the definition of a case and the standard of healthcare are likely to account for much of the residual variation. It is misleading to compare the crude COVID-19 CFRs between countries and should be avoided. Comparisons should be based on age-specific and age-adjusted rates. Key words: COVID-19, case-fatality rates, age-specific rates, age-adjusted rates, confounding

A Real-Time Statistical Model for Tracking and Forecasting COVID-19 Deaths, Prevalence and Incidence

Background: Pandemics do not occur frequently and when they do there is a paucity of predictive tools that could help drive government responses to mitigate worst outcomes. Here we provide a forecasting model that is based on measurable variables and that strives for simplicity over complexity to obtain stable convergent forecasts of death, prevalence, incidence, and safe days for social easing. Methods: We assume, based on prior pandemic data, that death rate rise and fall approximately follows a Gaussian distribution, which can be asymmetric, which we describe. By taking daily death data for foreign countries and U.S. states and fitting it to an appropriate Gaussian function provides an estimate of where in the cycle a particular population lies. From that time point one can integrate remaining time to obtain a final total death. By also using measured values for the time from infection to recovery or death and a mortality factor, the prevalence (active cases) and incidence (new cases) totals and rate curves can be constructed. It is also possible by setting a downward threshold on prevalence that an estimate of a minimum date to begin relaxing social restrictions may be considered. Results: To demonstrate the model we chose the most severe hot-bed countries and U.S. states as a test-bed to evolve and improve our model and to compare with other models. The model can readily be applied to other countries by inputting data from public data bases. We also compare our forecasts to the University of Washington (UW) IHME model and are reassuringly similar yet show less variability on a weekly basis. The sum of squares for error (SSE) for international and U.S. states, respectively, that we track are: 34% and 33% for our model vs. 49% and 59% for the IHME model. Conclusions: Our model appears closest to the UW IHME model; however, there are important differences and while both models forecast many of the same results of interest, each one offers unique benefits that the other does not. We believe that the model reported here excels for its simplicity, which makes the model easy to use.

Are black and Hispanic persons disproportionately affected by COVID-19 because of higher obesity rates?

BACKGROUND: On March 13, 2020, the World Health Organization declared COVID-19 a pandemic. Shortly after that, it was reported that mortality rates in New York City (NYC), the epicenter of the pandemic in the United States, were found to be significantly higher in black and Hispanic populations. OBJECTIVES: The aim of this article is to evaluate the mortality rates in NYC among the different ethnic groups and the different boroughs as they relate to the obesity rates to see whether this issue merits further evaluation. SETTING: NYC. METHODS: COVID-19 data were obtained from the official New York authorities in relation to total number of cases in the different boroughs of NYC. Age-adjusted COVID-19-related mortality rates of the different ethnic groups were also obtained. These data were cross-compared with historic community health data on obesity rates previously published and also obesity rates among the different ethnic groups in NYC. RESULTS: The 2 NYC boroughs that have the highest mortality rates are the Bronx (6%) and Brooklyn (5.4%). Both the Bronx and Brooklyn were also found to have the highest obesity rates at 32% and 27%, respectively. The 2 ethnic groups with the highest obesity rates (Hispanic and black) were also found to have the highest age-adjusted mortality rates per 100,000 compared with the other ethnic groups (22.8% and 19.8%, respectively). CONCLUSIONS: The Hispanic and black populations in NYC seem to be disproportionately affected by the COVID-19 pandemic because of the higher incidence of mortality rates. Obesity may have played a role in the high incidence of mortality in those ethnic groups.

Improved measurement of racial/ethnic disparities in COVID-19 mortality in the United States

Different estimation methods produce diverging accounts of racial/ethnic disparities in COVID-19 mortality in the United States. The Center for Disease Control's decision to present the racial/ethnic distribution of COVID-19 deaths at the state level alongside the weighted racial/ethnic distribution of the counties within each state reporting those death -- in effect, a geographic adjustment -- makes it seem that Whites have the highest death rates. Age adjustment procedures used by others, including the New York City Department of Health and Mental Hygiene, lead to the opposite conclusion that Blacks and Hispanics are dying from COVID-19 at higher rates than Whites. In this paper, we use indirect standardization methods to adjust per capita death rates for both age and geography simultaneously, avoiding the one-sided adjustment procedures currently in use. Using CDC data, we find age-and-place-adjusted COVID-19 death rates are 80% higher for Blacks and over 50% higher for Hispanics, relative to Whites, on a national level. State-specific estimates show wide variation in mortality disparities. Comparison with nonepidemic mortality reveals potential roles for preexisting health disparities and differential rates of infection and care.

Beyond Deaths per Capita: Three CoViD-19 Mortality Indicators for Temporal and International Comparisons

CoViD-19 deaths to population size ratios fail to account for well-documented age and sex differences in CoViD-19 mortality. To assess trends across populations for which CoViD-19 deaths might not be available by age and sex, an indirect age-and-sex adjustment can still be performed. The corresponding Comparative CoViD-19 Mortality Ratio (CCMR) only requires population age and sex compositions. To compare CoViD-19 and overall mortality levels, the Crude Death Rate (CDR) and life expectancy at birth for recent calendar years are the most widely available overall mortality indicators. Readily comparable to an annual CDR, a Crude CoViD-19 Death Rate (CCDR) can be calculated for periods of any duration. CoViD-19-induced declines in projected life expectancy at birth for 2020 can also be calculated from existing life tables. We calculate the CCMR and CCDR for the period from their first CoViD-19 death to the present using US age and sex data and current estimates of CoViD-19 deaths in 166 Countries whose population composition is available from the UN, 28 Provinces in China, the 50 United States and DC. Across these 245 populations, 14 States and 11 Countries have CCMR values above 1—the US value by construction. Most affected to date, the period CCDR in New York exceeds its CDR for the most recent year available (7.83 per thousand in 2017). We also calculate CCMR and CCDR values corresponding to projections for the 50 States and DC, and for 49 countries, for which we can additionally calculate reductions in 2020 life expectancy at birth using UN life tables. This suggests life-expectancy reductions between .5 and 1 year for 7 European Countries, 3 South-American Countries and the US. The .55 reduction in the U.S. amounts to nearly twice the largest single-year decline induced by HIV/AIDS (−.3 between 1992 and 1993) or the total decline induced by opioid overdoses (also −.3 between 2014 and 2017), and would bring US life expectancy at birth down to its lowest level since 2008. As current CoViD-19 death counts likely underestimate the total increase in deaths and current projections do not account for possible new infection waves later this year, the impact on 2020 life expectancies at birth should be expected to exceed these figures.

Relative health performance in BRICS over the past 20 years: the winners and losers.

OBJECTIVE To determine whether the health performance of Brazil, the Russian Federation, India, China and South Africa--the countries known as BRICS--has kept in step with their economic development. METHODS Reductions in age- and sex-specific mortality seen in each BRICS country between 1990 and 2011 were measured. These results were compared with those of the best-performing countries in the world and the best-performing countries with similar income levels. We estimated each country's progress in reducing mortality and compared changes in that country's mortality rates against other countries with similar mean incomes to examine changes in avoidable mortality. FINDINGS The relative health performance of the five study countries differed markedly over the study period. Brazil demonstrated fairly even improvement in relative health performance across the different age and sex subgroups that we assessed. India's improvement was more modest and more varied across the subgroups. South Africa and the Russian Federation exhibited large declines in health performance as well as large sex-specific inequalities in health. Although China's levels of avoidable mortality decreased in absolute terms, the level of improvement appeared low in the context of China's economic growth. CONCLUSION When evaluating a country's health performance in terms of avoidable mortality, it is useful to compare that performance against the performance of other countries. Such comparison allows any country-specific improvements to be distinguished from general global improvements.

Why does COVID-19 case fatality rate vary among countries?

Background While the epidemic of SARS-CoV-2 is spreading worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate varies temporally (as the epidemic progresses) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries. Methods We used data on the temporal trajectory of case fatality rate provided by the European Center for Disease Prevention and Control, and country-specific data on different metrics describing the incidence of known comorbidity factors associated with an increased risk of COVID-19 mortality at the individual level (Institute for Health Metrics and Evaluation). We also compiled data on demography, economy and political regimes for each country. Findings We first showed that temporal trajectories of case fatality rate greatly vary among countries. We found no evidence for association between comorbidities and case fatality rate at the country level. Case fatality rate was negatively associated with number of hospital beds x1,000 inhabitants. We also report evidence suggesting an association between case fatality rate and the political regime, with democracies suffering from the highest mortality burden, compared to autocratic regimes. However, most of the among-country variance in case fatality rate remained unexplained. Interpretation Overall, these results emphasize the role of socio-economic and political factors as possible drivers of COVID-19 case fatality rate at the country level. Funding None.

Age-specific and sex-specific morbidity and mortality from avian influenza A(H7N9)

Abstract We used data on age and sex for 136 laboratory confirmed human A(H7N9) cases reported as of 11 August 2013 to compare age-specific and sex-specific patterns of morbidity and mortality from the avian influenza A(H7N9) virus with those of the avian influenza A(H5N1) virus. Human A(H7N9) cases exhibit high degrees of age and sex bias: mortality is heavily biased toward males >50 years, no deaths have been reported among individuals <25 years old, and relatively few cases documented among children or adolescents. The proportion of fatal cases (PFC) for human A(H7N9) cases as of 11 August 2013 was 32%, compared to a cumulative PFC for A(H5N1) of 83% in Indonesia and 36% in Egypt. Approximately 75% of cases of all A(H7N9) cases occurred among individuals >45 years old. Morbidity and mortality from A(H7N9) are lowest among individuals between 10 and 29 years, the age group which exhibits the highest cumulative morbidity and case fatality rates from A(H5N1). Although individuals <20 years old comprise nearly 50% of all human A(H5N1) cases, only 7% of all reported A(H7N9) cases and no deaths have been reported among individuals in this age group. Only 4% of A(H7N9) cases occurred among children<5 years old, and only one case from the 10 to 20 year age group. Age- and sex-related differences in morbidity and mortality from emerging zoonotic diseases can provide insights into ecological, economic, and cultural factors that may contribute to the emergence and proliferation of novel zoonotic diseases in human populations.

Explaining national differences in the mortality of Covid-19: individual patient simulation model to investigate the effects of testing policy and other factors on apparent mortality.

There has been extensive speculation on the apparent differences in mortality between countries reporting on the confirmed cases and deaths due to Covid-19. A number of explanations have been suggested, but there is no clear evidence about how apparent fatality rates may be expected to vary with the different testing regimes, admission policies and other variables. An individual patient simulation model was developed to address this question. Parameters and sensitivity analysis based upon recent international data sources for Covid-19 and results were averaged over 100 iterations for a simulated cohort of over 500,000 patients. Different testing regimes for Covid-19 were considered; testing admitted patients only, various rates of community testing of symptomatic cases and active contact-tracing and screening. In the base case analysis, apparent mortality ranged from 10.5% under a policy of testing only admitted patients to 0.4% with intensive contact tracing and community testing. These findings were sensitive to assumptions regarding admission rates and the rate of spread, with more selective admission policies and suppression of spread increasing the apparent mortality and the potential for apparent mortality rates to exceed 18% under some circumstances. Under all scenarios the proportion of patients tested in the community had the greatest impact on apparent mortality. Whilst differences in mortality due to health service and demographic factors cannot be excluded, the current international differences in reported mortality are all consistent with differences in practice regarding screening, community testing and admission policies.

Age-Specific Excess Mortality Patterns During the 1918–1920 Influenza Pandemic in Madrid, Spain

Although much progress has been made to uncover age-specific mortality patterns of the 1918 influenza pandemic in populations around the world, more studies in different populations are needed to make sense of the heterogeneous death impact of this pandemic. We assessed the absolute and relative magnitudes of 3 pandemic waves in the city of Madrid, Spain, between 1918 and 1920, on the basis of age-specific all-cause and respiratory excess death rates. Excess death rates were estimated using a Serfling model with a parametric bootstrapping approach to calibrate baseline death levels with quantified uncertainty. Excess all-cause and pneumonia and influenza mortality rates were estimated for different pandemic waves and age groups. The youngest and oldest persons experienced the highest excess mortality rates, and young adults faced the highest standardized mortality risk. Waves differed in strength; the peak standardized mortality risk occurred during the herald wave in spring 1918, but the highest excess rates occurred during the fall and winter of 1918/1919. Little evidence was found to support a “W”-shaped, age-specific excess mortality curve. Acquired immunity may have tempered a protracted fall wave, but recrudescent waves following the initial 2 outbreaks heightened the total pandemic mortality impact.

COVID-19 Fatalities, Latitude, Sunlight, and Vitamin D

BACKGROUND: Since Vitamin D is known to be vital in regulating the immune system, and sunlight UV radiation exposure on the skin produces Vitamin D and UV intensity is highest nearest the equator, a study was done to examine the correlation between the latitude and COVID-19 fatality rates for countries. METHODS: Eighty-eight countries were selected based on their likelihood of providing reliable data. Using death rates/million for each country from the “worldometer” web site, a correlation analysis was done between death rates and a country's latitude. RESULTS: A highly significant, positive correlation was found between lower death rates and a country's proximity to the equator (Pearson r = .40 p<.0001, two-tailed t-test). The R squared of .16 means that 16% of the variation in death rates among nations is accounted for by the latitude of the country. Evidence is presented suggesting a direct correlation between sunlight exposure and reduced mortality. DISCUSSION: This study is the first to document a statistically significant correlation between a country's latitude and its COVID-19 mortality and is consistent with other research regarding latitude, Vitamin D deficiency, and COVID-19 fatalities. Limitations of this study are noted. CONCLUSION: Further research is needed to confirm the correlation between latitude and COVID-19 fatalities, and to determine the optimum amounts of safe sunlight exposure and/or vitamin D oral supplementation to reduce COVID-19 fatalities in populations that are at high risk for vitamin D deficiency.

The male excess in case-fatality rates for COVID-19. A meta-analytic study of the age-related differences and consistency over six countries

Background Early in the COVID-19 pandemic, it was noted that males seemed to be more affected than females. We examined the magnitude and consistency of the sex differences in age-specific case-fatality rates (CFRs) in six countries. Methods Data on the cases and deaths from COVID-19, by sex and age group, were extracted from the published reports from Denmark, England, Israel, Italy, Spain, and the United States . Age-specific CFRs were computed for males and females separately. The ratio of the male to female CFRs were computed and meta-analytic methods were used to obtained pooled estimates of the male to female ratio of the CFRs over the six countries, for seven age-groups. Findings The CFRs were consistently higher in males at all ages. The differences were greater in the younger age groups. The pooled M:F CFR ratios were 2.53, 2.92, 2.57, 1.83, 1.57, 1.58 and 1.48 for ages 0-39, 40-49, 50-59, 60-69, 70-79, 80-89 and 90+. There was remarkable consistency between countries in the magnitude of the M:F CFRs, in each age group. In meta-regression, age group explained almost all the heterogeneity in the CFR ratios. Conclusions The sex differences in the CFRs are intriguing and are compatible with the male dominance in the incidence rates of many infectious diseases. For COVID-19, factors such as sex differences in the prevalence of underlying diseases may play a part in the CFR differences. However, the greater severity of the disease in males, particularly at younger ages, may be part of the disease mechanism and should be explored further.

Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates (8 March - 9 May 2020)

Objective. Scrutiny of COVID-19 mortality in Belgium over the period 8 March-9 May 2020 (Weeks 11-19), using number of deaths per million, infection fatality rates, and the relation between COVID-19 mortality and excess death rates. Data. Publicly available COVID-19 mortality (2020); overall mortality (2009-2020) data in Belgium and demographic data on the Belgian population; data on the nursing home population; results of repeated sero-prevalence surveys in March-April 2020. Statistical methods. Reweighing, missing-data handling, rate estimation, visualization. Results. Belgium has virtually no discrepancy between COVID-19 reported mortality (confirmed and possible cases) and excess mortality. There is a sharp excess death peak over the study period; the total number of excess deaths makes April 2020 the deadliest month of April since WWII, with excess deaths far larger than in early 2017 or 2018, even though influenza-induced January 1951 and February 1960 number of excess deaths were similar in magnitude. Using various sero-prevalence estimates, infection fatality rates (IFRs; fraction of deaths among infected cases) are estimated at 0.38-0.73% for males and 0.20-0.39% for females in the non-nursing home population (non-NHP), and at 0.79-1.52% for males and 0.88-1.31% for females in the entire population. Estimates for the NHP range from 38 to 73% for males and over 22 to 37% for females. The IFRs rise from nearly 0% under 45 years, to 4.3% and 13.2% for males in the non-NHP and the general population, respectively, and to 1.5% and 11.1% for females in the non-NHP and general population, respectively. The IFR and number of deaths per million is strongly influenced by extensive reporting and the fact that 66.0% of the deaths concerned NH residents. At 764 (our re-estimation of the figure 735, presented by "Our World in Data"), the number of COVID-19 deaths per million led the international ranking on May 9, 2020, but drops to 262 in the non-NHP. The NHP is very specific: age-related increased risk; highly prevalent comorbidities that, while non-fatal in themselves, exacerbate COVID-19; larger collective households that share inadvertent vectors such as caregivers and favor clustered outbreaks; initial lack of protective equipment, etc. High-quality health care countries have a relatively older but also more frail population [1], which is likely to contribute to this result.

Are African American and Hispanics Disproportionately Affected by COVID-19 Because of Higher Obesity Rates?

INTRODUCTION: On March 13, 2020 the WHO declared COVID-19 a pandemic. Shortly after that, it was reported that mortality rates in New York City (NYC), the epicenter of the pandemic in the United States, were found to be significantly higher in African American and Hispanics. The aim of this manuscript is to evaluate the mortality rates in NYC among the different ethnic groups and the different boroughs as it relates to the obesity rates to see whether this issue merits further evaluation. METHODS: COVID-19 data was obtained from the official New York (NY) authorities in relation to total number of cases in the different boroughs of NYC. Age adjusted COVID-19 related mortality rates of the different ethnic groups were also obtained. This data was cross compared to historic community health data on obesity rates previously published and also obesity rates among the different ethnic groups in NYC. RESULTS: The two NYC boroughs that have the highest mortality rates are The Bronx (6%) and Brooklyn (5.4%). Both The Bronx and Brooklyn were also found to have the highest obesity rates 32% and 27% respectively. The two ethnic groups with the highest obesity rates (Hispanics and African Americans) were also found to have the highest age adjusted mortality rates per 100,000 compared to the other ethnic groups (22.8% and 19.8% respectively). CONCLUSION: Hispanics and African Americans in NYC seem to be disproportionately affected by the COVID-19 pandemic because of the higher incidence of mortality rates. Obesity may have played a role in the high incidence of mortality in those ethnic groups.

COVID-19 Propagation and Mortality in a Two-Part Population

There has recently emerged a striking consistency to the mortality from SARS-CoV-2, as a fraction of population, across many nations. We have constructed a model for the spread of the virus that reproduces this phenomenon via inclusion of two (or more) categories of susceptibility to the virus. In the simplest case, the population is given a smaller fraction of 10-20% with higher susceptibility and the balance of 80-90% with lower susceptibility. Susceptibility is taken to include the level of immunity to the virus combined with the societal circumstances of certain smaller groups within a population. This is programmed numerically by considering a realistic random rate of contacts, together with an assumed constant viral genome. The remaining major variable is the societal response of nations to the outbreak, with earlier or later application of various degrees of lockdown, tracing and sanitation. China, South Korea and other nations, including Germany, have stopped or greatly slowed the spread of the disease before it could run its course through a whole population. Using this model the extent of progress toward herd immunity is discussed, with an in-principle estimate of the remaining toll to be experienced.

Impacts of Early Interventions on the Age-Specific Incidence of COVID-19 in New York, Los Angeles, Daegu and Nairobi

Background COVID-19 has caused an unprecedented public health crisis and economic shock to the global economy. While many countries were affected, regions with an older population and weaker public health interventions tended to suffer more morbidity and mortality. Here we model and quantify the age-specific incidence of COVID-19 in four pandemic cities under different interventions. Methods We developed an age-specific and multiple-stage susceptible-exposed-infected-recovered-hospitalized-quarantined-dead (SEIR-HQD) dynamical systems model expanded from the more basic SEIR model by incorporating location- and age-specific contact matrices to estimate the outcomes of COVID-19. Utilizing latest estimates of epidemiological parameters and demographic data, we model the potential effects of various interventions in four representative cities with different population structures - New York, Los Angeles, Daegu and Nairobi. We compared the effects of different interventions in the age-structure populations specific to each city. These policy options are then applied to determine the potential for effective containment. We model these dynamic policy scenarios to assess the risks of less-stringent social distancing, as has been proposed by those arguing to enhance economic activity over public health and safety. Finally, we explored the health impacts of different policy action timelines to understand the benefits of early interventions. Findings We find the spread of COVID-19 to be dramatically different in the regions modeled, with the primary drivers the variation of population age structures, and the dynamics of interactions of the younger demographics, whose higher interaction rates can lead to increasing transmission rates across these communities. A city with younger citizens may also have fewer hospitalized cases and deaths. Our modeling quantifies the value of early interventions, which avoided an additional 5%, 16%, 37% and 43% of the infections in Daegu, Nairobi, New York and Los Angeles, respectively, compared to what has been observed in the four cities. The finding is clear: in the absence of pharmaceutical options, delaying strict social policy interventions has resulted in substantial public health cost. This modeling can, and will, be applied to other cities and regions, and conducted in conjunction with other health insults, such as exposure to air pollution. Critically, we find that school closures, working from home, and reduction in other mobility were most beneficial for younger population (0-19 years old), middle-age (20-59 years old) population and older population (60 years and older), respectively across each city. Specifically, school closure avoided 25%, 18%, 16% and 12% of the infections for the population under 20 years old in Daegu, Los Angeles, New York and Nairobi, respectively. A 50% and 80% population working from home policy avoids 8% and 15% of the infections. Reduction in mobility was more effective than the working from home strategy. Any single social distancing policy if enacted alone can delay the spread of COVID-19 but was unable to totally suppress the infection. Coordinated policy action can be highly effective. Increasing the quarantine rate to 10% of infectious cases was more effective than strict social distancing alone in this study, although together they can suppress 80% of the epidemic. A combination of moderate social distancing and quarantine strategies was able to avoid 99% of the infections. Interpretation Moderate social distancing together with high quarantine rates was effective in each of the four cities. COVID-19 caused more deaths and hospitalization in cities with an ageing population than those with a younger population. However, in the cities with a younger population, there is a clear need to implement a social distancing strategy that is even more strict due to the higher transmission rates among younger people. Cities with more older people should prepare more hospital beds and healthcare facilities to save people who are in critical conditions. Cities with ageing population should take targeted action for the elderly to avoid the severe impacts on the vulnerable populations. Increasing quarantine rate is an effective strategy to avoid the substantial infection while also does not influence the economy fiercely. We recommend countries or regions experiencing, or likely to experience the rapid spread of COVID-19, to implement combination of multiple strategies in the early stage of the breakout which can avoid over 90% of infected cases.

A demographic scaling model for estimating the total number of COVID-19 infections

Background: The total number of COVID-19 infections is critical information for decision makers when assessing the progress of the pandemic, its implications, and policy options. Despite efforts to carefully monitor the COVID-19 pandemic, the reported number of confirmed cases is likely to underestimate the actual number of infections. We aim to estimate the total number of COVID-19 infections in a straightforward manner using a demographic scaling approach based on life tables. Methods: We use data on total number of COVID-19 attributable deaths, population counts, and life tables as well as information on infection fatality rates as reported in Verity et al. (2020) for Hubei, China. We develop a scaling approach based on life tables and remaining life expectancy to map infection fatality rates between two countries to account for differences in their age structure, health status, and the health care system. The scaled infection fatality rates can be used in combination with COVID-19 attributable deaths to calculate estimates of the total number of infected. We also introduce easy to apply formulas to quantify the bias that would be required in death counts and infection fatality rates in order to reproduce a certain estimate of infections. Findings: Across the 10 countries with most COVID-19 deaths as of April 17, 2020, our estimates suggest that the total number of infected is approximately 4 times the number of confirmed cases. The uncertainty, however, is high, as the lower bound of the 95% prediction interval suggests on average twice as many infections than confirmed cases, and the upper bound 10 times as many. Country-specific variation is high. For Italy, our estimates suggest that the total number of infected is approximately 1 million, or almost 6 times the number of confirmed cases. For the U.S., our estimate of 1.4 million is close to being twice as large as the number of confirmed cases, and the upper bound of 3 million is more than 4 times the number of confirmed cases. For Germany, where testing has been comparatively extensive, we estimate that the total number of infected is only 1.2 times (upper bound: 3 times) than the number of confirmed cases. Comparing our results with findings from local seroprevalence studies and applying our bias formulas shows that some of their infection estimates would only be possible if just a small fraction of COVID-19 related deaths were recorded, indicating that these seroprevalence estimates might not be representative for the total population. Interpretation: As many countries lack population based seroprevalence studies, straightforward demographic adjustment can be used to deliver useful estimates of the total number of infected cases. Our results imply that the total number COVID-19 cases may be approximately 4 times (95%: 2 to 10 times) that of the confirmed cases. Although these estimates are uncertain and vary across countries, they indicate that the COVID-19 pandemic is much more broadly spread than what confirmed cases would suggest, and the number of asymptomatic cases or cases with mild symptoms may be high. In cases in which estimates from local seroprevalence studies or from simulation models exist, our approach can provide a simple benchmark to assess the quality of those estimates.

A Method to Identify the Missing COVID-19 Cases in the U.S. and Results for mid-April 2020

I use the COVID-19 death rate in South Korea and a method relating the ratio of death rates in a U.S. state to its share of cumulative positive tests to estimate the total cases of COVID-19 in the U.S. and to estimate the extent of infection and the unidentified share of the infected population in each of the lower-48 states and in New York City in mid-April, 2020. I identify a logarithmic relationship between the cumulative death rate in a state and its cumulative positive share of tests. Using this relationship, I find that 4.3-5.4 million people, 1.4-1.7% of the U.S. population, were infected, with rates of infection that ranged from 0.1% in more rural states to 8-10% in New York state and 11-13% in New York City. Only 16-20% of these infected individuals were identified later through testing.

Global Mortality Impact of the 1957–1959 Influenza Pandemic

Background. Quantitative estimates of the global burden of the 1957 influenza pandemic are lacking. Here we fill this gap by modeling historical mortality statistics. Methods. We used annual rates of age- and cause-specific deaths to estimate pandemic-related mortality in excess of background levels in 39 countries in Europe, the Asia-Pacific region, and the Americas. We modeled the relationship between excess mortality and development indicators to extrapolate the global burden of the pandemic. Results. The pandemic-associated excess respiratory mortality rate was 1.9/10 000 population (95% confidence interval [CI], 1.2–2.6 cases/10 000 population) on average during 1957–1959. Excess mortality rates varied 70-fold across countries; Europe and Latin America experienced the lowest and highest rates, respectively. Excess mortality was delayed by 1–2 years in 18 countries (46%). Increases in the mortality rate relative to baseline were greatest in school-aged children and young adults, with no evidence that elderly population was spared from excess mortality. Development indicators were moderate predictors of excess mortality, explaining 35%–77% of the variance. Overall, we attribute 1.1 million excess deaths (95% CI, .7 million–1.5 million excess deaths) globally to the 1957–1959 pandemic. Conclusions. The global mortality rate of the 1957–1959 influenza pandemic was moderate relative to that of the 1918 pandemic but was approximately 10-fold greater than that of the 2009 pandemic. The impact of the pandemic on mortality was delayed in several countries, pointing to a window of opportunity for vaccination in a future pandemic.

Serology-informed estimates of SARS-COV-2 infection fatality risk in Geneva, Switzerland

The infection fatality risk (IFR) is the average number of deaths per infection by a pathogen and is key to characterizing the severity of infection across the population and for specific demographic groups. To date, there are few empirical estimates of IFR published due to challenges in measuring infection rates. Outside of closed, closely surveilled populations where infection rates can be monitored through viral surveillance, we must rely on indirect measures of infection, like specific antibodies. Representative seroprevalence studies provide an important avenue for estimating the number of infections in a community, and when combined with death counts can lead to robust estimates of the IFR. We estimated overall and age-specific IFR for the canton of Geneva, Switzerland using age-stratified daily case and death incidence reports combined with five weekly population-based seroprevalence estimates. From February 24th to June 2nd there were 5'039 confirmed cases and 286 reported deaths within Geneva (population of 506'765). We inferred age-stratified (5-9, 10-19, 20-49, 50-65 and 65+) IFRs by linking the observed number of deaths to the estimated number of infected individuals from each serosurvey. We account for the delays between infection and seroconversion as well as between infection and death. Inference is drawn in a Bayesian framework that incorporates uncertainty in seroprevalence estimates (supplement). Of the 286 reported deaths caused by SARS-CoV-2, the youngest person to die was 31 years old. Infected individuals younger than 50 years experienced statistically similar IFRs (range 0.00032-0.0016%), which increases to 0.14% (95% CrI 0.096-0.19) for those 50-64 years old to 5.6% (95% CrI 4.3-7.4) for those 65 years and older (supplement). After accounting for demography and age-specific seroprevalence, we estimate a population-wide IFR of 0.64% (95% CrI 0.38-0.98). Our results are subject to two notable limitations. Among the 65+ age group that died of COVID-19 within Geneva, 50% were reported among residents of assisted care facilities, where around 0.8% of the Geneva population resides. While the serosurvey protocol did not explicitly exclude these individuals, they are likely to have been under-represented. This would lead to an overestimation of the IFR in the 65+ age group if seroprevalence in this institutionalized population was higher than in the general population (supplement). Further, our IFR estimates are based on current evidence regarding post-infection antibody kinetics, which may differ between severe and mild infections. If mild infections have significantly lower and short-lived antibody responses, our estimates of IFR may be biased upwards. Estimates of IFR are key for understanding the true pandemic burden and for weighing different risk reduction strategies. The IFR is not solely determined by host and pathogen biology, but also by the capacity of health systems to treat severe cases. Despite having among the highest per capita incidence in Switzerland, Geneva's health system accommodated the influx of cases needing intensive care (peak of 80/110 ICU-beds including surge capacity) while maintaining care quality standards. As such, our IFR estimates can be seen as a best-case scenario with respect to health system capacity. Our results reveal that population-wide estimates of IFR mask great heterogeneity by age and point towards the importance of age-targeted interventions to reduce exposures among those at highest risk of death.

The Rise of the Current Mortality Pattern of the United States, 1890–1930

This article examines how the epidemiologic transition and the reduction of the urban mortality penalty gave rise to the current mortality regime of the United States and demonstrates how the 1918 influenza pandemic signaled its advent. This article approaches those issues through the analysis of urban-rural mortality differentials from 1890 to 1930. Until 1910, infectious diseases dwarfed degenerative diseases in leading causes of death, and generally, the more urban the location was, the higher infectious disease and overall death rates were—a direct relationship. But by 1930, degenerative diseases had eclipsed infectious diseases, and infectious disease mortality had ceased to differ between cities and rural areas. The 1918 influenza pandemic broke out toward the end of these changes, and the larger the city was, the lower influenza and overall death rates were in that year—an inverse relationship. Such gradations characterized a new mortality regime emerging in the late 1910s and foreshadowed urban-rural mortality differentials in 1930 among persons aged 45 years or older, the group whose high rates of degenerative disease death would symbolize that regime. Thus, intertwined changes in the late 19th and early 20th centuries—a shift in leading causes of death from infectious diseases to degenerative diseases and a concomitant shift from a direct relationship to an inverse relationship between urban environment and mortality—produced the current mortality regime of the United States.

Association of BCG vaccination policy with prevalence and mortality of COVID-19

There is some evidence that tuberculosis vaccine bacillus Calmette-Guérin (BCG) has non-specific beneficial effects against non-related infections. Here, we examined the possible association between BCG vaccination with prevalence and mortality by COVID-19 by using publicly available data of COVID-19 in 199 countries/regions and the BCG World Atlas. By using linear regression modeling, we found that the number of total cases and deaths per one million population were significantly associated with the country's policy concerning BCG vaccine administration. The amount of variance in cases and deaths explained by BCG vaccination policy ranged between 12.5% and 38%. Importantly, this effect remained significant after controlling for the country's life expectancy and the average temperature in February and March 2020, which themselves are significantly correlated with the cases and deaths indices, respectively. By contrast, the ratio between deaths and cases was weakly affected. This latter outcome suggested that BCG vaccination may have hindered the overall spread of the virus or progression of the disease rather than reducing mortality rates (i.e., deaths/cases ratio). Finally, by roughly dividing countries into three categories showing high, middle, or low growth rate of the cases, we found a highly significant difference between the slope categories among the BCG groups, suggesting that the time since the onset of the spread of the virus was not a major confounding factor. While this study potentially suffers from a number of unknown confounding factors, these associations support the idea that BCG vaccination may provide protection against SARS-CoV-2, which, together with its proven safety, encourages consideration of further detailed epidemiological studies, large-scale clinical trials on the efficacy of this vaccine on COVID-19, and/or re-introduction of BCG vaccination practice in the countries which are currently devoid of the practice.

An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak

Background: The coronavirus 2019 (COVID-19) pandemic has been spread-ing globally for months, yet the infection fatality ratio of the disease is still uncertain. This is partly because of inconsistencies in testing and death reporting standards across countries. Our purpose is to provide accurate estimates which do not rely on testing and death count data directly but only use population level statistics. Methods: We collected demographic and death records data from the Italian Institute of Statistics. We focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years. We also assessed the sensitivity of our estimates to alternative assumptions on the proportion of population infected. Findings: We estimate an overall infection fatality rate of 1.29% (95% credible interval [CrI] 0.89 - 2.01), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0-.19) and a substantially higher 4.25% (CrI 3.01-6.39) for people above 60 years of age. In our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information. For instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60. Interpretation: Our empirical estimates based on population level data show a sharp difference in fatality rates between young and old people and firmly rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.

The Age Pattern of the Male- to- Female Ratio in Mortality from COVID-19 Mirrors that of Cardiovascular Disease but not Cancer in the General Population

Background: Males are at a higher risk of dying from COVID-19. Older age and cardiovascular disease are also associated with COVID-19 mortality. We compared the male-to-female (sex) ratios in mortality by age for COVID-19 with cardiovascular mortality and cancer mortality in the general population. Methods: We obtained data from official government sources in the US and five European countries: Italy, Spain, France, Germany, and the Netherlands. We analyzed COVID-19 deaths by sex and age in these countries and similarly analyzed their deaths from cardiovascular disease (coronary heart disease or stroke) and cancer, the two leading age-related causes of death in middle-to-high income countries. Findings: In both the US and European countries, the sex ratio of deaths from COVID-19 exceeded one throughout adult life. The sex ratio increased up to a peak in midlife, and then declined markedly in later life. This pattern was also observed for the sex ratio of deaths from cardiovascular disease, but not cancer, in the general populations of the US and European countries. Interpretation: The sex ratios of deaths from COVID-19 and from cardiovascular disease exhibit similar patterns across the adult life course. The underlying mechanisms are poorly understood, but could stem partially from sex-related biological differences that underlie the similar pattern for cardiovascular disease. These include, we propose, comparatively longer telomeres in females, ovarian hormones, and X chromosome mosaicism.

Understanding how widely COVID-19 has spread is critical for examining the pandemic's progression. Despite efforts to carefully monitor the pandemic, the number of confirmed cases may underestimate the total number of infections. We introduce a demographic scaling model to estimate COVID-19 infections using an broadly applicable approach that is based on minimal data requirements: COVID-19 related deaths, infection fatality rates (IFRs), and life tables. As many countries lack reliable estimates of age-specific IFRs, we scale IFRs between countries using remaining life expectancy as a marker to account for differences in age structures, health conditions, and medical services. Across 10 countries with most COVID-19 deaths as of May 13, 2020, the number of infections is estimated to be four [95% prediction interval: 2-11] times higher than the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 1.4 million (six times the number of confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany, where testing has been comparatively extensive. Our prevalence estimates, however, are markedly lower than most others based on local seroprevalence studies. We introduce formulas for quantifying the bias that is required in our data on deaths in order to reproduce estimates published elsewhere. This bias analysis shows that either COVID-19 deaths are severely underestimated, by a factor of two or more; or alternatively, the seroprevalence based results are overestimates and not representative for the total population.

Misrepresentation of health risks by mass media

BACKGROUND: Mass media are a leading source of health information for general public. We wished to examine the relationship between the intensity of media coverage for selected health topics and their actual risk to public health. METHODS: Mass media reports in the United States on emerging and chronic health hazards (severe acute respiratory syndrome (SARS), bioterrorism, West Nile Fever, AIDS, smoking and physical inactivity) were counted for the year 2003, using LexisNexis database. The number of media reports for each health risk was correlated with the corresponding death rate as reported by the Centers for Disease Control and Prevention. RESULTS: The number of media reports inversely correlated with the actual number of deaths for the health risks evaluated. SARS and bioterrorism killed less than a dozen people in 2003, but together generated over 100 000 media reports, far more than those covering smoking and physical inactivity, which killed nearly a million Americans. CONCLUSIONS: Emerging health hazards are over-reported in mass media by comparison to common threats to public health. Since premature mortality in industrialized societies is most often due to well-known risks such as smoking and physical inactivity, their under-representation on public agendas may cause suboptimal prioritization of public health resources.

Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high risk population subgroups aids policymakers and health officials in combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that overcomes the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates --- key quantities for guiding public policy related to the control and prevention of COVID-19 --- for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race and ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are African American male, Multi-race male, Asian male, African American female, and American Indian or Alaska Native male, indicating that African Americans are an especially vulnerable California subpopulation.

How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates

In this work, we assess the global impact of COVID-19 showing how demographic factors, testing policies and herd immunity are key for saving lives. We extend a standard epidemiological SEIR model in order to: (a) identify the role of demographics (population size and population age distribution) on COVID-19 fatality rates; (b) quantify the maximum number of lives that can be saved according to different testing strategies, different levels of herd immunity, and specific population characteristics; and (d) infer from the observed case fatality rates (CFR) what the true fatality rate might be. Different from previous SEIR model extensions, we implement a Bayesian Melding method in our calibration strategy which enables us to account for data limitation on the total number of deaths. We derive a distribution of the set of parameters that best replicate the observed evolution of deaths by using information from both the model and the data.

Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)

To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this risk. First, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed. Second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed. Third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests. In the ongoing COVID-19 epidemic, health authorities must cope with the uncertainty in the risk of death from COVID-19, and high-risk individuals should be identified using approaches that can address the abovementioned three problems. Although COVID-19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care.

Climatic factors influence COVID-19 outbreak as revealed by worldwide mortality

The COVID-19 outbreak is triggering a global crisis that is challenging governments, health systems and the scientific community worldwide. A central question in the COVID-19 pandemic is whether climatic factors modulate its progression. This information is key to epidemiologists and healthcare decision-makers for improving their management plans. Previous attempts to assess the impact of climatic parameters have yielded ambiguous results, either because they were using geographically or temporally restricted data, or because they were comparing infection rates across countries, which were measured differently depending on local screening strategies. In March 2020, the spread of COVID-19 dramatically increased the number of countries recording deaths, providing an opportunity to use mortality rate, which is measured more homogeneously across countries than infection rates, as a descriptor of the COVID-19 outbreak over a large latitudinal range. Here, using data recorded in 208 territories from 88 countries, we show that mortality rate is negatively influenced by warmer air temperature and positively affected by higher relative humidity. Each additional Celsius degree decreases mortality rate by ~4%, while a 1% increase in relative humidity raises mortality rate by ~2%. Temperature is positively correlated with UV-index, for which one unit of increase results in a ~15% decrease in the mortality rate. We also show that other factors contribute to the dynamics of the COVID-19 outbreak, such as the proportion of vulnerable age classes in the population, access to a non-overwhelmed health system, as well as governmental travel restrictions for controlling the spread of the disease. All of them are critical factors impacting the mortality rate of COVID-19. The influence of climatic factors is a warning to all southern hemisphere countries where winter is coming soon. Northern hemisphere countries should also be warned that climatic factors alone will likely not be sufficient to contain the disease.

The Impact of Population Growth on the Epidemiology and Evolution of Infectious Diseases

It is generally expected that in developing countries the epidemiological transition, with improved health and lower mortality rates, will eventually lead to a demographic transition with lower fertility rates. The reductions in mortality characterising the epidemiological transition are often associated with controlling the infectious diseases within populations, which leaves the chronic diseases associated with old age, cancer and heart disease dominating the causes of death. However, if the demographic transition does not occur quickly, populations can grow rapidly, creating an increased potential for spread of infectious disease. These infectious diseases could, in turn, increase death rates amongst young people and reverse the epidemiological transition. The relationship between population growth, size and infection depends upon the changes in contact pattern associated with there being more people. If facilities can keep pace with growth, then the increase in contact rates can be kept to a minimum, and the potential reversal in the epidemic transition prevented. This makes development a crucial adjunct to population growth if the global community is not to be increasingly exposed to pandemics of infectious disease. Here we review the epidemiological and demographic theory which relates population growth and infectious disease.

Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data.

Background: International and UK data suggest that Black, Asian and Minority Ethnic (BAME) groups are at increased risk of infection and death from COVID-19. We aimed to explore the risk of death in minority ethnic groups in England using data reported by NHS England. Methods: We used NHS data on patients with a positive COVID-19 test who died in hospitals in England published on 28th April, with deaths by ethnicity available from 1st March 2020 up to 5pm on 21 April 2020. We undertook indirect standardisation of these data (using the whole population of England as the reference) to produce ethnic specific standardised mortality ratios (SMRs) adjusted for age and geographical region. Results: The largest total number of deaths in minority ethnic groups were Indian (492 deaths) and Black Caribbean (460 deaths) groups. Adjusting for region we found a lower risk of death for White Irish (SMR 0.52; 95%CIs 0.45-0.60) and White British ethnic groups (0.88; 95%CIs 0.86-0.0.89), but increased risk of death for Black African (3.24; 95%CIs 2.90-3.62), Black Caribbean (2.21; 95%CIs 2.02-2.41), Pakistani (3.29; 95%CIs 2.96-3.64), Bangladeshi (2.41; 95%CIs 1.98-2.91) and Indian (1.70; 95%CIs 1.56-1.85) minority ethnic groups. Conclusion: Our analysis adds to the evidence that BAME people are at increased risk of death from COVID-19 even after adjusting for geographical region, but was limited by the lack of data on deaths outside of NHS settings and ethnicity denominator data being based on the 2011 census. Despite these limitations, we believe there is an urgent need to take action to reduce the risk of death for BAME groups and better understand why some ethnic groups experience greater risk. Actions that are likely to reduce these inequities include ensuring adequate income protection, reducing occupational risks, reducing barriers in accessing healthcare and providing culturally and linguistically appropriate public health communications.

Is investing in religious institutions a viable pathway to reduce mortality in the population?

There is established and consistent findings from epidemiologic studies, among individuals, that religion— broadly assessed through frequency of attending worship services—is associated with lower all-cause and cause-specific mortality attributed to suicide, alcohol, cardiovascular disease and cancer. Religious norms, social support, character, virtue, compassion, love, generosity, and religious community are among some mechanisms purported to explain lower mortality, on aggregate. The religious ecology or characteristics of religion within an area or geographic level (e.g., county, ZIP-code, country), has been linked with overall and cause-specific mortality, but directions of findings are mixed. Mechanisms to explain the links between the religious ecology and mortality included social integration, civic engagement, and social control. The manuscript (SSM-D-19-03928R2) adds a fresh and timely perspective by investigating another mechanism: investment in local healthcare spending. The study found some support of an indirect association from county-level religious denominational composition, through investments in health spending, on Black and White all-cause mortality rates. Should society or government invest finances in religious institutions to indirectly improve population health? This work adds evidence to debate that question. Future work on the topic will need to address several conceptual and methodological challenges. Conceptually, is investigating the market share of religious denominations (i.e., % Catholics vs % Protestants) relevant today given diversity in population and declining trends of worship attendance? Is mortality the most relevant for moving policy or should the focus be on well-being? Methodologically, are there alternate observable measures religious investments/spending in the local economy? Mechanisms, challenges, and opportunities for social epidemiology research on this topic are discussed.

How lethal is the novel coronavirus, and how many undetected cases there are? The importance of being tested.

There is big concern for estimating the lethality and the extent of undetected infections associated with the novel coronavirus SARS-CoV2 outbreak. While detailed epidemiological models are certainly needed, I suggest here an orthogonal approach based on a minimum number of parameters robustly fitted from the cumulative data easily accessible for all countries at the John Hopkins University database that became the worldwide reference for the pandemics. I show that, after few days from the beginning of the outbreak, the apparent death rate can be extrapolated to infinite time through regularized regression such as rescaled ridge regression. The variation from country to country of these extrapolated death rates appears to depend almost only (r^2=0.91) on the ratio between performed tests and detected cases even when the instantaneous apparent lethality rates are as different as 9% in Italy and 0.4% in Germany. Extrapolating to the limit of infinite number of tests, I obtain a death rate of 0.012+/- 0.012, in agreement with other estimates. The inverse relationship between the extrapolated death rate and the intensity tests allows estimating that more than 50% of cases were undetected in most countries, with more than 90% undetected cases in countries severely hit by the epidemics such as Italy. Finally, I propose to adopt the ratio between the cumulative number of recovered and deceased persons as an indicator that can anticipate the halting of the epidemics.

Unintentional poisoning deaths--United States, 1999-2004.

In 2004, poisoning was second only to motor-vehicle crashes as a cause of death from unintentional injury in the United States . Nearly all poisoning deaths in the United States are attributed to drugs, and most drug poisonings result from the abuse of prescription and illegal drugs. Previous reports have indicated a substantial increase in unintentional poisoning mortality during the 1980s and 1990s. To further examine this trend, CDC analyzed the most current data from the National Vital Statistics System. This report summarizes the results of that analysis, which determined that poisoning mortality rates in the United States increased each year from 1999 to 2004, rising 62.5% during the 5-year period. The largest increases were among females (103.0%), whites (75.8%), persons living in the southern United States (113.6%), and persons aged 15-24 years (113.3%). Larger rate increases occurred in states with mostly rural populations. Rates for drug poisoning deaths increased 68.3%, and mortality rates for poisonings by other substances increased 1.3%. The largest increases were in the "other and unspecified," psychotherapeutic, and narcotic drug categories. The results suggest that more aggressive regulatory, educational, and treatment measures are necessary to address the increase in fatal drug overdoses.

Mortality of the COVID-19 outbreak in Sweden in relation to previous severe disease outbreaks

Influenza viruses have caused disease outbreaks in human societies for a long time. Influenza often has rapid onset and relatively short duration, both in the individual and in the population. The case fatality rate varies for different strains of the virus, as do the effects on total mortality. Outbreaks related to coronavirus infections have recently become a global concern but much less is known about the dynamics of these outbreaks and their effects on mortality. In this work, disease outbreaks in Sweden, in the time period of 1860-2020, are characterized and compared to the currently ongoing COVID-19 outbreak. The focus is on outbreaks with a sharp increase in all-cause mortality. Outbreak onset is defined as the time point when deaths counts starts to increase consistently for a period of 10 days. The duration of the outbreak is defined as the time period in which mortality rates are elevated. Excess mortality is estimated by standard methods. In total there were 15 outbreaks detected in the time period, the first 14 were likely caused by influenza virus infections, the last by SARS-CoV-2. The mortality dynamics of the SARS-CoV-2 outbreak is shown to be similar to outbreaks due to influenza virus, and in terms of the number of excess deaths, might become the worst outbreak since the 'Spanish flu' of 1918-1919.

Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

BACKGROUND Measurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI). METHODS We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate. FINDINGS The highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9-78·6) for females and 72·0 years (68·8-75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0-49·5]) and for males was in Lesotho (41·5 years [39·0-44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97-6·48) for both sexes combined. Global HALE increased by 6·04 years (5·74-6·27) for males and 6·49 years (6·08-6·77) for females, whereas HALE at age 65 years increased by 1·78 years (1·61-1·93) for males and 1·96 years (1·69-2·13) for females. Total global DALYs remained largely unchanged from 1990 to 2016 (-2·3% [-5·9 to 0·9]), with decreases in communicable, maternal, neonatal, and nutritional (CMNN) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). The exemplars, calculated as the five lowest ratios of observed to expected age-standardised DALY rates in 2016, were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. The leading three causes of DALYs globally were ischaemic heart disease, cerebrovascular disease, and lower respiratory infections, comprising 16·1% of all DALYs. Total DALYs and age-standardised DALY rates due to most CMNN causes decreased from 1990 to 2016. Conversely, the total DALY burden rose for most NCDs; however, age-standardised DALY rates due to NCDs declined globally. INTERPRETATION At a global level, DALYs and HALE continue to show improvements. At the same time, we observe that many populations are facing growing functional health loss. Rising SDI was associated with increases in cumulative years of life lived with disability and decreases in CMNN DALYs offset by increased NCD DALYs. Relative compression of morbidity highlights the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning. The analysis of DALYs and HALE and their relationship to SDI represents a robust framework with which to benchmark location-specific health performance. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform health policies, health system improvement initiatives, targeted prevention efforts, and development assistance for health, including financial and research investments for all countries, regardless of their level of sociodemographic development. The presence of countries that substantially outperform others suggests the need for increased scrutiny for proven examples of best practices, which can help to extend gains, whereas the presence of underperforming countries suggests the need for devotion of extra attention to health systems that need more robust support. FUNDING Bill & Melinda Gates Foundation.

COVID-19: intensive care units, mechanical ventilators, and latent mortality profiles associated with case-fatality in Brazil.

In response to the accelerated increase in the number of COVID-19 cases, countries must increase their supply of beds in intensive care units (ICUs). Respiratory diseases, neoplasms, cardiopathies and hypertension, and diabetes are associated with higher COVID-19 case-fatality. The study aimed to identify the regions of Brazil with higher specific mortality rates from these comorbidities and the regions with the greatest shortage of ICU beds and mechanical ventilators. A cross-sectional ecological study was performed in which the units of analysis were the country's Health Regions. Data were obtained from Brazilian Health Informatics Department - DATASUS (National Registry of Healthcare Establishments - 2019, Mortality Information Systems - 2017, and Population Projections - 2017). We calculated the disease group-specific mortality rates for hypertension, neoplasms, diabetes, cardiac diseases, respiratory diseases and the rates of total ICU beds, private ICU beds, ICU beds in the Brazilian Unified National Health System (SUS), and ventilators in the SUS, per 100,000 inhabitants. The mortality profile was determined by latent profiles analysis, and the cluster analysis of ICU beds and ventilators used the spatial scan method. Kernel maps were constructed for the data's visualization. Level of significance was set at 5%. Four latent mortality profiles were observed. The Health Regions with the highest mean mortality rates were located in regions with shortages of ICU beds and ventilators, especially in parts of the Northeast, Southeast, and South of Brazil. The spatial localization of regions with both the highest mortality and shortages of ICU beds/ventilators requires attention by policymakers and public planners to deal efficiently and fairly with the COVID-19 epidemic in Brazil.

Strict Lower Bound on the COVID-19 Fatality Rate in Overwhelmed Healthcare Systems

The Infection Fatality Rate (IFR) for COVID-19 is a poorly known, yet crucial, aspect of the disease. Counting only current deaths in a region and assuming everyone in that region is infected provides an absolute lower bound on the IFR. Using this estimator for New York City, Lombardy and Madrid yields strong bounds on the average IFR in overwhelmed health systems. Their combined 35,152 deaths implies IFR > 0.14% averaged over 25.1 million people. This is the best-case scenario and conclusively demonstrates that COVID-19 is more deadly than influenza. The actual value of the average COVID-19 IFR is likely to be higher than this bound.

Age effects in monetary valuation of reduced mortality risks: the relevance of age-specific hazard rates

This paper highlights the relevance of age-specific hazard rates in explaining the age variation in “value of statistical life” (VSL) figures. The analysis—which refers to a stated preference framework—contributes to the ongoing discussion of whether benefits resulting from reduced mortality risk should be valued differently depending on the age of the beneficiaries. By focussing on a life-threatening environmental phenomenon I show that the consideration of the individual’s age-specific hazard rate is important. If a particular risk affects all individuals regardless of their age so that their hazard rate is age-independent, VSL is rather constant for people at different age; if hazard rate varies with age, VSL estimates are sensitive to age. The results provide an explanation for the mixed outcomes in empirical studies and illustrate in which cases an adjustment to age may or may not be justified. Efficient provision of live-saving measures requires that such differences to be taken into account.

Analysis of COVID-19 under-reporting in Brazil.

OBJECTIVE To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. METHODS We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil's expected case-fatality ratio was also adjusted by the population's age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). RESULTS The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). CONCLUSION The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model

An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R(0) of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.

A Note on UK Covid19 death rates by religion: which groups are most at risk?

There has been great concern in the UK that people from the BAME (Black And Minority Ethnic) community have a far higher risk of dying from Covid19 than those of other ethnicities. However, the overall fatalities data from the Government's ONS (Office of National Statistics) most recent report on deaths by religion shows that Jews (very few of whom are classified as BAME) have a much higher risk than those of religions (Hindu, Sikh, Muslim) with predominantly BAME people. This apparently contradictory result is, according to the ONS statistical analysis, implicitly explained by age as the report claims that, when 'adjusted for age' Muslims have the highest fatality risk. However, the report fails to provide the raw data to support this. There are many factors other than just age that must be incorporated into any analysis of the observed data before making definitive conclusions about risk based on religion/ethnicity. We propose the need for a causal model for this. If we discount unknown genetic factors, then religion and ethnicity have NO impact at all on a person's Covid19 death risk once we know their age, underlying medical conditions, work/living conditions, and extent of social distancing.

Why estimating population-based case fatality rates during epidemics may be misleading

Different ways of calculating mortality ratios during epidemics can yield widely different results, particularly during the COVID-19 pandemic. We formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality ratios. The key parameters that affect the dynamics of the different mortality estimates are the incubation period and the length of time individuals were infected before confirmation of infection. We stress that none of these ratios are accurately represented by the often misinterpreted case fatality ratio (CFR), the number of deaths to date divided by the total number of infected cases to date. Using available data on the recent SARS-CoV-2 outbreaks and simple assumptions, we estimate and compare the different dynamic mortality ratios and highlight their differences. Informed by our modeling, we propose a more systematic method to determine mortality ratios during epidemic outbreaks and discuss sensitivity to confounding effects and errors in the data.

Relationship of suicide rates to economic variables in Europe: 2000-2011.

BACKGROUND It is unclear whether there is a direct link between economic crises and changes in suicide rates. AIMS The Lopez-Ibor Foundation launched an initiative to study the possible impact of the economic crisis on European suicide rates. METHOD Data was gathered and analysed from 29 European countries and included the number of deaths by suicide in men and women, the unemployment rate, the gross domestic product (GDP) per capita, the annual economic growth rate and inflation. RESULTS There was a strong correlation between suicide rates and all economic indices except GPD per capita in men but only a correlation with unemployment in women. However, the increase in suicide rates occurred several months before the economic crisis emerged. CONCLUSIONS Overall, this study confirms a general relationship between the economic environment and suicide rates; however, it does not support there being a clear causal relationship between the current economic crisis and an increase in the suicide rate.

A statistical forecast of LOW mortality and morbidity due to COVID-19, in ARGENTINA and other Southern Hemisphere countries.

A set of open source programs in Python is devised to fit a parametric integrated Gaussian equation to cumulative deaths due to COVID-19 in Southern Hemisphere countries. The programs were successfully tested using data from advanced outbreak trajectories (Italy and Spain). The procedure was applied to data reported by Argentina. The projected total death toll will be 182 (277-182) with a peak of deaths (6(+/-2)) the 14 of April. The outbreak begins the 9th of March and end completely the 20th of May. However, already on 1st of May, 2 s (95.45%) of the deaths have occurred. The death toll arises from a number of infected individuals between 36412 and 2275. Then, they were to use to process data from several Southern Hemisphere countries: Argentina, Brazil, Mexico, Peru, Colombia, Ecuador, Cuba, Chile, Panama, Australia, Bolivia, Honduras, New Zealand, Paraguay, Guatemala, Venezuela, Uruguay, El Salvador, Jamaica, Haiti, Costa Rica and Nicaragua. The trend is to show low number of total deaths compared with other disease outbreaks. A total projected number of deaths between 15148 and 9939 deaths for a total population of ca. 664 M inhabitants. The projected death toll is much lower (5-10 times) than those forecasted by the Imperial College Group (ICG) even considering the best scenario of total suppression of virus transmission. Using actual mortality rates it is possible to back calculate which number of infected individuals would produce such mortality. The calculated number of infected individuals (worst case scenario) is below 2.5 million. This is significantly lower than that calculated by ICG (> 45 millions). In most countries the outbreak will end in May or early June. The dynamics of the outbreaks seems to do not saturate the health services (hospital beds) but only Peru, Ecuador and Panama should have not enough ICU beds for grave COVID-19 patients

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6â¯days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.

Estimating the lifetime risk of a diagnosis of the HIV infection in 33 states, 2004-2005.

PURPOSE We estimated lifetime risk and age-conditional risk of being diagnosed with HIV in 33 states with name-based HIV reporting. METHODS We used vital statistics data on general and HIV-specific mortality, census data, and HIV surveillance data to calculate cross-sectional, period-specific (2004-2005), and age-specific probabilities of an HIV diagnosis. The probabilities were applied to a hypothetical cohort of 10 million live births, and estimates were derived for the lifetime risk, from birth, of being diagnosed with HIV. RESULTS The estimated lifetime risk of being diagnosed with HIV was 1.87% for males (95% confidence limit: 1.86 to 1.89) or 1 in 53 males and 0.71% for females (95% confidence limit: 0.70-0.72) or 1 in 141 females. Blacks and Hispanics experienced higher estimated lifetime risk of HIV than whites: 6.23% or 1 in 16 for blacks, 2.88% or 1 in 35 for Hispanics, 0.96% or 1 in 104 for white males; 3.29% or 1 in 30 for blacks, 0.88% or 1 in 114 for Hispanics, and 0.17% or 1 in 588 for white females. The highest risk of HIV diagnosis was observed among people in their 30s. CONCLUSIONS These estimates may help to communicate the risk of HIV infection to affected communities, increase public awareness, and promote early detection and prevention efforts for HIV.

Patterns of mortality during pandemic: An example of Spanish flu pandemic of 1918

Now the attention of the whole world is focused on the developing pandemic of the coronavirus infection COVID-19. This article discusses mortality patterns of the deadliest epidemic in the last 120 years – the Spanish flu pandemic of 1918. Statistical sources from Italy and the USA, published shortly after the pandemic, were analyzed. The analysis was carried out for mortality from all causes, since in this case inaccuracies associated with establishing the causes of death are minimized. Despite the fact that the first cases of the Spanish flu appeared in the United States as early as March 1918, this first wave of epidemic practically did not affect the total mortality rate. The main peak of mortality in 1918 occurred in October 1918 both in the USA and Italy, with a gradual decrease in mortality over several months. Analysis of age-specific mortality demonstrates a significant increase in mortality at middle ages (20–50 years) in 1918 compared with 1917. Analysis of mortality trends using the method of latent variables shows a significant increase in the background mortality factor in 1918, which turned out to be higher for Italy than the mortality losses during the Second World War. The Spanish flu pandemic differs from the current coronavirus pandemic, because of significant increase in mortality of middle-aged people, while the COVID-19 pandemic causes a more marked increase in mortality among the elderly. With this, the COVID-19 pandemic is more like the recent flu epidemics than the earlier Spanish flu pandemic.

Disease and healthcare burden of COVID-19 in the United States

As of 24 April 2020, the SARS-CoV-2 epidemic has resulted in over 830,000 confirmed infections in the United States1. The incidence of COVID-19, the disease associated with this new coronavirus, continues to rise. The epidemic threatens to overwhelm healthcare systems, and identifying those regions where the disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency medical care and public health resources. Globally, the risk of severe outcomes associated with COVID-19 has consistently been observed to increase with age2,3. We used age-specific mortality patterns in tandem with demographic data to map projections of the cumulative case burden of COVID-19 and the subsequent burden on healthcare resources. The analysis was performed at the county level across the United States, assuming a scenario in which 20% of the population of each county acquires infection. We identified counties that will probably be consistently, heavily affected relative to the rest of the country across a range of assumptions about transmission patterns, such as the basic reproductive rate, contact patterns and the efficacy of quarantine. We observed a general pattern that per capita disease burden and relative healthcare system demand may be highest away from major population centers. These findings highlight the importance of ensuring equitable and adequate allocation of medical care and public health resources to communities outside of major urban areas.

Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis

We perform a counterfactual time series analysis using two different Data Science methods applied to 2020 mortality data reported from towns in Italy, with data from the previous five years as control. We find an excess mortality that is correlated in time with the COVID-19 reported death rate time series. Our analysis shows good agreement with reported COVID-19 mortality for age<70 years, but an excess in total mortality increasing with age above 70 years, suggesting there is a large population of predominantly old people missing from the official fatality statistics. We estimate that the number of COVID-19 deaths in Italy is 52,000 ± 2000 as of April 18 2020, more than a factor of 2 higher than the official number. The Population Fatality Rate (PFR) has reached 0.22% in the most affected region of Lombardia and 0.57% in the most affected province of Bergamo,which constitutes a lower bound to the Infection Fatality Rate (IFR). We estimate PFR as a function of age, finding a steep age dependence: in Lombardia (Bergamo province) 0.6% (1.7%) of the total population in age group 70-79 died, 1.6% (4.6%) in age group 80-89, and 3.41% (10.2%) in the age group above 90. We combine this with the Test Positivity Rate to estimate the lower bound of 0.84% on the IFR for Lombardia. We observe IFR to trace the Yearly Mortality Rate (YMR) above 60 years, which can be used to estimate the IFR for other regions in the world. We predict an IFR lower bound of 0.5% for NYC and 26% of total COVID-19 mortality arising from the population below 65 years, in agreement with the existing data and several times higher than Lombardia. Combining PFR with the Princess Diamond cruise ship IFR for ages above 70 we estimate the infection rates(IR) of regions in Italy, which peak in Lombardia at 23% (12%-41%, 95% c.l.), and for provinces in Bergamo at 67% (33%-100%, 95% c.l.). This suggests that Bergamo may have reached herd immunity, and that the number of infected people greatly exceeds the number of positive tests, by a factor of 35 in Lombardia.

COVID-19 Infection Fatality Rate Associated with Incidence-A Population-Level Analysis of 19 Spanish Autonomous Communities

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69-1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

The misleading illusion of COVID-19 confirmed case data: alternative estimates and a monitoring tool

Confirmed Case Data have been widely cited during the current COVID-19 pandemic as an estimate of the spread of the SARS-CoV-2 virus. However, their central role in media, official reports and decision-making may be undeserved and misleading. Previously published Infection Fatality Rates were weighted by age structure in the 50 countries with more reported deaths to obtain country-specific rates. For each country, the number of infections up to the Infection Date (23 days ago = Incubation Period + Onset to Death period) and the present percentage of immune population were estimated using Infection Fatality Rate, the number of reported deaths (which is less prone to undersampling), and projecting back to Infection Date. We then estimated a Detection Index for each country as the percentage of estimated infections that confirmed cases represent. Assuming that detection remains constant after Infection Date, we estimated the number of deaths and the estimated percentage of the population of each country expected to be immune up to 23 days into the future. Estimated Infection Fatality Rates are higher in Europe. In most countries, confirmed cases currently represent less than 30% of estimated infections on Infection Date, and this value decreases with time. Countries with flat curves throughout the pandemic show the lowest immunity percentages and these values seem unlikely to change in the near future, suggesting that they remain vulnerable to new outbreaks. Estimates for some countries with low Infection Fatality Rates suggest a still steep increase in the number of casualties in the next three weeks. Countries that did not control initial outbreaks seem to have reached higher immunity percentages, although mostly still under 5%. We provide the code to monitor the trajectories of these estimates in 178 countries throughout the COVID-19 pandemic.

Mapping the Burden of COVID-19 in the United States

As of April 5th 2020, SARS-CoV-2 has resulted in over 273,000 confirmed infections in the United States of America. Incidence continues to rise. As the epidemic threatens to overwhelm health care systems, identifying regions where the expected disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency resources. Across all global regions affected by the pandemic, an elevated risk of severe outcomes has consistently been observed in older age groups. Using age-specific mortality patterns in tandem with demographic data, we map a projection of the cumulative burden of COVID-19 and the associated cumulative burden on the healthcare system at the county-scale in the United States for a scenario in which 20% of the population of each county acquires infection. We identify regions that may be particularly impacted relative to the rest of the country, and observe a general trend that per capita disease burden and relative healthcare system demand may be highest away from major population centers.

COVID-19 Infection Fatality Rate Associated with Incidence—A Population-Level Analysis of 19 Spanish Autonomous Communities

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69–1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

Demographic science aids in understanding the spread and fatality rates of COVID-19

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the COVID-19 pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider both the age composition of local and national contexts as well as the social connectedness of older and younger generations. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted nowcasting.

A simulation of a COVID-19 epidemic based on a deterministic SEIR model

An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 25, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the number of initially exposed individuals and infected people, the incubation and infection periods and the fatality rate. First, we perform an analysis of the results of the model, by varying the parameters and initial conditions (in order the epidemic to start, there should be at least one exposed or one infected human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (April 19, 2020) and constraint the parameters on the basis of values reported in the literature. The peak occurs at day 37 (April 1) approximately, when there is a rapid decrease, with a reproduction ratio R0 = 3 initially, 1.38 at day 22 and 0.64 after day 35, indicating different degrees of lockdown. The predicted death toll is almost 14000 casualties, with 2.4 million infected individuals at the end of the epidemic. The incubation period providing a better fit of the dead individuals is 4.25 days and the infection period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57 %, and 2.36 % if twice the reported number of casualties is assumed. However, these rates depend on the initially exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47 %. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infection periods, we observe that a higher incubation period (13 versus 4.25 days) gives the same IFR (0.6 versus 0.57 %), but nine times more exposed individuals in the first case. Therefore, a precise determination of the fatality rate is subject to the knowledge of the characteristics of the epidemic. We plan to perform again these calculations and publish a short note when the epidemic is over and the complete and precise data is available. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, the importance to quantify the process to verify the effectiveness of the isolation.

Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data

Background: International and UK data suggest that Black, Asian and Minority Ethnic (BAME) groups are at increased risk of infection and death from COVID-19. We aimed to explore the risk of death in minority ethnic groups in England using data reported by NHS England. Methods: We used NHS data on patients with a positive COVID-19 test who died in hospitals in England published on 28th April, with deaths by ethnicity available from 1st March 2020 up to 5pm on 21 April 2020. We undertook indirect standardisation of these data (using the whole population of England as the reference) to produce ethnic specific standardised mortality ratios (SMRs) adjusted for age and geographical region. Results: The largest total number of deaths in minority ethnic groups were Indian (492 deaths) and Black Caribbean (460 deaths) groups. Adjusting for region we found a lower risk of death for White Irish (SMR 0.52; 95%CIs 0.45-0.60) and White British ethnic groups (0.88; 95%CIs 0.86-0.0.89), but increased risk of death for Black African (3.24; 95%CIs 2.90-3.62), Black Caribbean (2.21; 95%CIs 2.02-2.41), Pakistani (3.29; 95%CIs 2.96-3.64), Bangladeshi (2.41; 95%CIs 1.98-2.91) and Indian (1.70; 95%CIs 1.56-1.85) minority ethnic groups. Conclusion: Our analysis adds to the evidence that BAME people are at increased risk of death from COVID-19 even after adjusting for geographical region. We believe there is an urgent need to take action to reduce the risk of death for BAME groups and better understand why some ethnic groups experience greater risk. Actions that are likely to reduce these inequities include ensuring adequate income protection (so that low paid and zero-hours contract workers can afford to follow social distancing recommendations), reducing occupational risks (such as ensuring adequate personal protective equipment), reducing barriers in accessing healthcare and providing culturally and linguistically appropriate public health communications.

Spousal bereavement, mortality and risk of negative health outcomes among older adults: a population-based study

Objective: to examine whether spousal bereavement increases the risk of death and negative health outcomes and among older people. Design: cohort study and self-controlled cohort crossover study Setting: routinely collected administrative and healthcare data with individual-level linkage between several national registries in Sweden. Participants: older persons (>65 years) living in the community whose spouse died in 2013-2014, individually matched with controls. Main outcome measures: death from any cause (primary outcome), acute cardiovascular events, pneumonia, hip fracture, and intentional self-harm (secondary outcomes). In the cohort study, incidence rate ratios were estimated with conditional fixed-effect Poisson regression models adjusted for potential confounders. In the self-controlled cohort crossover study, relative incidence ratios were estimated over the 12 months before and after spousal loss with unadjusted conditional fixed-effect Poisson regression including a bereavement-by-time interaction. Results: 42 918 bereaved older spouses were included and matched to an equal number of married controls (mean age 78.9 [SD 7.2] years, 68% women). During the first year of follow-up, the risk of death from any cause was 1.66 (95% confidence interval 1.53 to 1.80) times higher for bereaved cases than for married controls, and bereaved cases survived on average 4.2 days shorter than married controls. Bereaved cases also experienced an increased risk of acute cardiovascular events (incidence rate ratio 1.34, 1.24 to 1.44), hip fracture (1.48, 1.30 to 1.68), pneumonia (1.14, 1.04 to 1.25), and self-harm (3.49, 2.11 to 5.76). These associations were strongly time-dependent, increasing sharply immediately after spousal loss and weakening as time elapsed. In the self-controlled cohort crossover study, the relative incidence ratios increased for all four secondary outcomes, starting already during the 6-month period preceding spousal loss. Conclusion: Among older persons, the association between spousal bereavement and the risk of negative health outcomes and mortality is most likely causal. Our finding that the risk of adverse health consequences increases already during the 6 months prior to spousal loss indicates that palliative care services have an important role to play in providing timely bereavement care to spouses and other family caregivers.

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

The population-level case fatality rate (CFR) associated with COVID-19 varies substantially, both across countries 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, France, Germany, Italy, South Korea, Spain, and the United States. We calculate the CFR for each country at the latest data point and for Italy also 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. CFRs vary from 0.7% in Germany and 1.6% in South Korea to 8.6% in Spain and 10.6% in Italy. The age-structure of detected cases can explain a substantial proportion of cross-country variation in the CFR. For example, 57% of Spain's difference with respect to South Korea is explained by the observed cases being older. In Italy, the CFR increased from 4.2% to 10.6% between March 9 and March 29, 2020, and more than 95% of the change was due to increasing age-specific case fatality rates. The importance of the age-structure of infected cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case fatality rates indicate 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.

Disease and healthcare burden of COVID-19 in the United States.

Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic

Mortality estimates of the 1918 influenza pandemic vary considerably, and recent estimates have suggested that there were 50 million to 100 million deaths worldwide. We investigated the global mortality burden using an indirect estimation approach and 2 publicly available data sets: the Human Mortality Database (13 countries) and data extracted from the records of the Statistical Abstract for British India. The all-cause Human Mortality Database was used to estimate mortality annually for 1916–1921 for detailed age groups. Three different calculation methods were applied to the data (low, medium, and high scenarios), and we used a multilevel regression model to control for distorting factors (e.g., war and the underlying time trend in mortality). Total pandemic mortality was an estimated 15 million deaths worldwide in 1918 (n = 2.5 million in 1919) after including the rates for British India and controlling for wars and the underlying mortality trend. According to our validity analysis, simulations of total number of deaths being greater than 25 million are not realistic based on the underlying mortality rates included in Human Mortality Database and in British India. Our results suggest the global death impact of the 1918 pandemic was important (n = 17.4 million) but not as severe as most frequently cited estimates.

Estimates of the reproduction numbers of Spanish influenza using morbidity data.

BACKGROUND There have been several studies of the transmissibility of the 1918 (Spanish) influenza virus, which has attributed to >20 million deaths. Many of the analyses to date have involved fitting predictions from a transmission model to the observed epidemic curves from different settings. METHODS Using morbidity data from cities in Europe and America and from confined settings during the 1918 influenza pandemic, we contrast the use of several different methods based on the growth rate and final size of the epidemic, which do not rely on transmission models, to estimate the effective and basic reproduction numbers. RESULTS The effective reproduction number (the average number of secondary infectious cases produced by a typical infectious case in a given population) for the 1918 influenza virus was in the range 1.2-3.0 and 2.1-7.5 for community-based and confined settings, respectively. CONCLUSIONS Assuming further that 30 and 50% of individuals were immune to Spanish influenza after the wave in April 1918 and the first subsequent wave, respectively, these findings imply that, in a totally susceptible population, an infectious case could have led to 2.4-4.3 and 2.6-10.6 cases in community-based and confined settings, respectively. These findings for community-based populations confirm the relatively low transmissibility of the 1918 (Spanish) influenza virus, which has been found by other studies using alternative data sources and methods.

Excess mortality during COVID-19 in five European countries and a critique of mortality analysis data

INTRODUCTION The COVID-19 pandemic is an ongoing event disrupting lives, health systems, and economies worldwide. Clear data about the pandemic's impact is lacking, namely regarding mortality. This work aims to study the impact of COVID-19 through the analysis of all-cause mortality data made available by different European countries, and to critique their mortality surveillance data. METHODS European countries that had publicly available data about the number of deaths per day/week were selected (England and Wales, France, Italy, Netherlands and Portugal). Two different methods were selected to estimate the excess mortality due to COVID19: (DEV) deviation from the expected value from homologue periods, and (RSTS) remainder after seasonal time series decomposition. We estimate total, age- and gender-specific excess mortality. Furthermore, we compare different policy responses to COVID-19. RESULTS Excess mortality was found in all 5 countries, ranging from 10.6% in Portugal (DEV) to 98.5% in Italy (DEV). Furthermore, excess mortality is higher than COVID-attributed deaths in all 5 countries. DISCUSSION The impact of COVID-19 on mortality appears to be larger than officially attributed deaths, in varying degrees in different countries. Comparisons between countries would be useful, but large disparities in mortality surveillance data could not be overcome. Unreliable data, and even a lack of cause-specific mortality data undermine the understanding of the impact of policy choices on both direct and indirect deaths during COVID-19. European countries should invest more on mortality surveillance systems to improve the publicly available data.

COVID-19 death rates by age and sex and the resulting mortality vulnerability of countries and regions in the world

The growing number of series on COVID-19 deaths classified by age and sex, released by national health authorities, has allowed us to compute age and sex patterns of its mortality, based on 183,619 deaths from Western Europe and the USA. We highlight the specific age schedule of COVID-19 mortality and its pronounced excess male mortality and we then apply these COVID-19 death rates to world populations, in 2020. Our results underscore that considerable variations exist between world regions, as concerns the potential impact of COVID-19 mortality, because of their demographic structures. When compared to younger countries in Sub-Saharan Africa, the vulnerability to COVID-19 mortality is shown to be 17 times higher in several industrialized countries of East Asia and Europe. There is a high correlation (r2= .44) between demographic vulnerability to COVID-19 mortality and current COVID-19 death rates.

Impacto de la COVID-19 en la mortalidad de la comunidad autónoma de Castilla y León./ [Impact of COVID-19 on mortality in the autonomous community of Castilla y León (Spain)]

OBJECTIVE: To estimate the increase in mortality associated with the SARS-CoV-2 coronavirus pandemic in the autonomous community of Castilla y León (Spain). METHOD: Ecological study based on population and death data for the months of March 2016 to 2020 in Castilla y León. The general and provincial standardized rates, the relative risks of the year 2020 with respect to previous years and the risks adjusted by sex, periods and province, using Poisson regression, were calculated. Trend analysis was performed using joinpoint linear regression. RESULTS: An increase in mortality was observed in March 2020 with respect to previous years, with an increase of 39% for men (relative risk [RR]: 1.39; 95% confidence interval [95%CI]: 1.32-1.47) and 28% for women (RR: 1.28; 95%CI: 1.21-1.35). The model predicts excess mortality of 775 deaths. In the trend analysis there is a significant turning point in 2019 in men, globally and for almost all provinces. The increase in mortality is general, although heterogeneous by sex, age group and province. CONCLUSIONS: Although the observed increase in mortality cannot be totally attributed to the disease, it is the best estimate we have of the real impact on deaths directly or indirectly related to it. The number of declared deaths only reaches two thirds of the increase in mortality observed.

Global Burden of Infectious Diseases

Systematic efforts to quantify and monitor the burden of specific health conditions in populations, at the national level, started in the mid-1950s for malaria, poliomyelitis, and influenza in the United States. Comprehensive surveillance of morbidity and mortality for dozens of conditions has since been well established in the United States and in other industrialized countries. However, despite the clear need for epidemiological data to inform health policies, reliable and comprehensive health statistics are not available in many developing countries. International efforts to assess and monitor the burden of certain diseases have been limited in the past to a small number of infectious diseases in the context of global eradication programs – smallpox, poliomyelitis, guinea worm, and, more recently, HIV/AIDS, severe acute respiratory syndrome (SARS), and avian flu influenza A (H5N1). The Global Burden of Disease Study (GBD), published in 1996, filled an important gap in our knowledge of population health status. It created a common metric, the disability-adjusted life year (DALY), to estimate morbidity and mortality for eight regions that collectively span the world’s population, generating comparable information on incidence and prevalence in global health. However, patterns of disease, disability, and risk factors have since changed significantly and new data on their distribution are available. Furthermore, the unprecedented money and attention now pouring into international health has made an accurate assessment of global health patterns a matter of utmost urgency. The new Global Burden of Diseases, Injuries, and Risk Factors (GBD 2005) project, which began in 2007, represents the first major effort at a systematic revision of estimates in health for every region in the world comprehensively, and will ensure that that the global health community bases its research and policies on complete, valid, and reliable information. Burden of disease estimates provided in this article are for 2001 – the year for which the most recent estimates of the global burden of disease and risk factors are currently available. Causes of deaths were categorized into three main groups: group I (infectious diseases and maternal, perinatal, and nutritional conditions), group II (noncommunicable diseases), and group III (injuries). Accordingly, estimates of the global burden of infectious diseases are provided in the context of the overall burden from other conditions, diseases, and injuries. The relative importance of the burden of infectious diseases was forecasted to change by 2020. As the epidemiological transition progresses worldwide, a decline in the burden of infectious diseases is expected as the burden of noncommunicable diseases and injuries gradually increases. The pace of the epidemiological transition, however, varies greatly among regions so that the projected decreases in the burden of infectious diseases are expected to vary between regions. Trends in the global burden due to specific infectious diseases projected to 2020 also vary among specific conditions. The global burden of HIV/AIDS, for instance, is expected to greatly increase, whereas the global burden due to respiratory infections and diarrheal diseases is expected to decrease. Contrary to expectations, the global burden of malaria has increased in recent years.