what are the mortality rates overall and in specific populations

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.

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

Cancer is the fourth most common cause of death (after unintentional injury, homicide, and suicide) among persons aged 1-19 years in the United States. Because recent childhood cancer mortality has not been well characterized in terms of temporal, demographic, and geographic trends, CDC analyzed cancer death rates among children (defined as aged 0-14 years) and adolescents (defined as aged 15-19 years) for the period 1990-2004 by sex, age group, race, ethnicity, U.S. Census region, and primary cancer site/leading diagnosis, using the most recent data available from the National Vital Statistics System (NVSS). This report describes the results of that analysis, which indicated that, overall, age-adjusted childhood cancer death rates decreased significantly during 1990-2004 among both sexes, both age groups, all races (except American Indians/Alaska Natives [AI/ANs]), Hispanics, non-Hispanics, and all U.S. Census regions. However, decreases in death rates varied among U.S. Census regions and between Hispanics and non-Hispanics. Eliminating racial/ethnic health disparities is one of the overarching goals of Healthy People 2010. Further research is needed to understand geographic and ethnic disparities in childhood cancer death rates. Moreover, cancer prevention and intervention measures should be designed to reach populations that are underserved and at high risk.

Trends in childhood cancer mortality--United States, 1990-2004.

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.

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.

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.

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

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.

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

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.

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

The objective of study was to evaluate the incidence and mortality rates of disasters and mass casualty incidents (MCIs) over the past 10 yr in the administrative system of Korea administrative system and to examine their relationship with population characteristics. This was a population-based cross-sectional study. We calculated the nationwide incidence, as well as the crude mortality and injury incidence rates, of disasters and MCIs. The data were collected from the administrative database of the National Emergency Management Agency (NEMA) and from provincial fire departments from January 2000 to December 2009. A total of 47,169 events were collected from the NEMA administrative database. Of these events, 115 and 3,079 cases were defined as disasters and MCIs that occurred in Korea, respectively. The incidence of technical disasters/MCIs was approximately 12.7 times greater than that of natural disasters/MCIs. Over the past 10 yr, the crude mortality rates for disasters and MCIs were 2.36 deaths per 100,000 persons and 6.78 deaths per 100,000 persons, respectively. The crude injury incidence rates for disasters and MCIs were 25.47 injuries per 100,000 persons and 152 injuries per 100,000 persons, respectively. The incidence and mortality of disasters/MCIs in Korea seem to be low compared to that of trend around the world.

Incidence and Mortality Rates of Disasters and Mass Casualty Incidents in Korea: A Population-Based Cross-Sectional Study, 2000-2009

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.

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

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.

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

BACKGROUND Since Macao's return of sovereignty to China in December 1999, the life style of Macao residents has changed. The aim of this study was to investigate changes of death patterns in Macao residents from 1986 to 2006 in order to identify the trends and patterns of major public health problems, which could provide the guidance for developing public health policies. METHODS A retrospective study was conducted for this investigation. Research data were collected from official websites and statistical yearbooks and classified by the International Classification of Diseases (ICD)-9. RESULTS It was observed that mortality from the three major causes of (1) infectious, maternal and childhood diseases, (2) chronic non-communicable diseases, and (3) injury and poisoning were 17.7, 298.2 and 26.0 per 100 000, respectively. The largest decrease in death rate over the 21-year study-period was from infectious, maternal and childhood diseases (62.5%). The highest mortality rate was ischemic heart diseases (37.0%). The largest increase in mortality rate was lung cancer (46.9%). CONCLUSIONS Mortality rate of Macao residents progressively decreased, but the constituent ratio of death from chronic non-communicable diseases was increasing. The mortality rate of lung cancer was clearly ascending, so emphasis should be put on tertiary prevention in future.

Changes in main causes of death in Macao residents from 1986 - 2006.

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.

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

Background: South Korea was among the first countries to report a case of the novel coronavirus (COVID-19) outside of China. As of 22 March, 2020, South Korea reported 8897 confirmed cases of and 104 deaths from COVID-19. Methods: We collected the number of laboratory-confirmed cases and deaths in South Korea from the World Health Organization (as of 21 March, 2020) and case distribution and fatality rates by age from the Korean Center for Disease Control and Prevention (as of 22 March, 2020). We estimated population-level mortality rates by fitting a negative binomial regression model with the number of deaths as the outcome and population by age as an offset. We then calculated the age-standardized death rate (ASDR) based on the current COVID-19 figures and for alternative scenarios of increased prevalence. Findings: The COVID-19 population-level mortality rate (per 100,000 person-years) increased with age: from 0.1 deaths among 30-39 year olds to 9.5 deaths among ≥80 year olds. The ASDR (per 100,000 person-years) was 0.8 deaths. The ASDR would increase to 52.0 deaths at a 1% prevalence (becoming the third leading cause of death) and 155.9 deaths at 3% prevalence (becoming the leading cause of death). Interpretation: Currently, the population-level mortality burden of COVID-19 in South Korea, as measured by the ASDR, was relatively low compared to other causes of death partly due to the low prevalence of COVID-19. If the prevalence increases from another outbreak, the mortality burden could increase substantially and surpass other leading causes.

Population-Level Mortality Rates from Novel Coronavirus (COVID-19) in South Korea

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.

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

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.

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

Background: Since partial oxygen pressure decreases as altitude increases, environmental hypoxia could worsen COVID-19 patient's hypoxemia. We compared COVID-19 mortality at different altitudes. Methods: Retrospective analysis of population-level data on COVID-19 deaths in the U.S. (1,016 counties) and Mexico (567 municipalities). Mixed-model Poisson regression analysis of the association between altitude and COVID-19 mortality using individual-level data from 40,168 Mexican subjects with COVID-19, adjusting for multiple covariates. Results: Between January 20 and April 13, 2020, mortality rates were higher in U.S. counties located at [≥]2,000 m elevation vs. those located below 1,500 m (12.3 vs. 3.2 per 100,000; P<0.001). In Mexico, between March 13 and May 13, 2020, mortality rates were higher in municipalities located at [≥]2,000 m vs. <1,500 m (5.3 vs. 3.9 per 100,000; P<0.001). Among Mexican subjects <65 years old, the risk of death was 36% higher in those living at [≥]2,000 m vs. <1,500 m (adjusted incidence rate ratio: 1.36; 95% CI, 1.05-1.78; P=0.022). Among men, the risk of death was 31% higher at [≥]2,000 m vs. <1,500 m (adjusted IRR: 1.31; 95% CI, 1.03-1.66; P=0.025). No association was found among women. Conclusion: Altitude is associated with COVID-19 mortality in men younger than 65 years.

Mortality Attributed to COVID-19 in High-Altitude Populations

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.

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

Summary Background This study was conducted in Kunming, the capital of Yunnan, a poor province in south-west China experiencing rapid economic growth. The study examined the short-term trend in premature mortality burden from common causes of death in a suburban region between 1998 and 2003. Methods Years of life lost (YLL) per 1000 population and mortality rate per 100,000 population were calculated from medical death certificates, and broken down by cause of death, sex and year without age weighting but with a discounting rate of 3%. Results Non-communicable diseases contributed over 80% of all causes of YLL, with a slightly increasing trend. The combined rate for communicable, maternal, prenatal and nutritional deficiencies declined from 4.7 to 2.4 per 1000 population. Remarkably, declining trends in YLL were also seen for chronic obstructive pulmonary disease, drug use and road traffic accidents, whereas increasing trends were seen for ischaemic heart disease (IHD) and liver cancer (males). The YLL rate for stroke, self-inflicted injuries, lung cancer and stomach cancer fluctuated over time. Conclusions The region should focus on further control of IHD and liver cancer.

Changing pattern of premature mortality burden over 6 years of rapid growth of the economy in suburban south-west China: 1998–2003

BACKGROUND AND AIMS: COVID-19 disease has been associated with disproportionate mortality amongst world population. We try to elucidate various reasons for lower mortality rate in the Indian subcontinent due to COVID-19 pandemic. METHOD: We carried out a comprehensive review of the literature using suitable keywords such as 'COVID-19', 'Pandemics', 'disease outbreaks' and 'India' on the search engines of PubMed, SCOPUS, Google Scholar and Research Gate in the month of May 2020 during the current COVID-19 pandemic and assessed mortality data. RESULTS: The mortality observed in Indian and south Asian subcontinent is lower than in the west. Multifactorial reasons indicated for this differential mortality due to COVID-19 have been described in the current literature. CONCLUSIONS: The effects of COVID-19 on the health of racial and ethnic minority groups are still emerging with disproportionate burden of illness and death amongst some black and ethnic minority groups. Overall the current COVID-19 related mortality appears to be lower in the health and resource challenged populous Indian subcontinent. Further scientific studies would be helpful to understand this disparity in mortality due to COVID-19 in the world population.

Differential mortality in COVID-19 patients from India and western countries

BACKGROUND AND AIMS: COVID-19 disease has been associated with disproportionate mortality amongst world population. We try to elucidate various reasons for lower mortality rate in the Indian subcontinent due to COVID-19 pandemic. METHOD: We carried out a comprehensive review of the literature using suitable keywords such as ‘COVID-19’, ‘Pandemics’, ‘disease outbreaks’ and ‘India’ on the search engines of PubMed, SCOPUS, Google Scholar and Research Gate in the month of May 2020 during the current COVID-19 pandemic and assessed mortality data. RESULTS: The mortality observed in Indian and south Asian subcontinent is lower than in the west. Multifactorial reasons indicated for this differential mortality due to COVID-19 have been described in the current literature. CONCLUSIONS: The effects of COVID-19 on the health of racial and ethnic minority groups are still emerging with disproportionate burden of illness and death amongst some black and ethnic minority groups. Overall the current COVID-19 related mortality appears to be lower in the health and resource challenged populous Indian subcontinent. Further scientific studies would be helpful to understand this disparity in mortality due to COVID-19 in the world population.

BACKGROUND 258 million people reside outside their country of birth; however, to date no global systematic reviews or meta-analyses of mortality data for these international migrants have been done. We aimed to review and synthesise available mortality data on international migrants. METHODS In this systematic review and meta-analysis, we searched MEDLINE, Embase, the Cochrane Library, and Google Scholar databases for observational studies, systematic reviews, and randomised controlled trials published between Jan 1, 2001, and March 31, 2017, without language restrictions. We included studies reporting mortality outcomes for international migrants of any age residing outside their country of birth. Studies that recruited participants exclusively from intensive care or high dependency hospital units, with an existing health condition or status, or a particular health exposure were excluded. We also excluded studies limited to maternal or perinatal outcomes. We screened studies using systematic review software and extracted data from published reports. The main outcomes were all-cause and International Classification of Diseases, tenth revision (ICD-10) cause-specific standardised mortality ratios (SMRs) and absolute mortality rates. We calculated summary estimates using random-effects models. This study is registered with PROSPERO, number CRD42017073608. FINDINGS Of the 12 480 articles identified by our search, 96 studies were eligible for inclusion. The studies were geographically diverse and included data from all global regions and for 92 countries. 5464 mortality estimates for more than 15·2 million migrants were included, of which 5327 (97%) were from high-income countries, 115 (2%) were from middle-income countries, and 22 (<1%) were from low-income countries. Few studies included mortality estimates for refugees (110 estimates), asylum seekers (144 estimates), or labour migrants (six estimates). The summary estimate of all-cause SMR for international migrants was lower than one when compared with the general population in destination countries (0·70 [95% CI 0·65-0·76]; I2=99·8%). All-cause SMR was lower in both male migrants (0·72 [0·63-0·81]; I2=99·8%) and female migrants (0·75 [0·67-0·84]; I2=99·8%) compared with the general population. A mortality advantage was evident for refugees (SMR 0·50 [0·46-0·54]; I2=89·8%), but not for asylum seekers (1·05 [0·89-1·24]; I2=54·4%), although limited data was available on these groups. SMRs for all causes of death were lower in migrants compared with the general populations in the destination country across all 13 ICD-10 categories analysed, with the exception of infectious diseases and external causes. Heterogeneity was high across the majority of analyses. Point estimates of all-cause age-standardised mortality in migrants ranged from 420 to 874 per 100 000 population. INTERPRETATION Our study showed that international migrants have a mortality advantage compared with general populations, and that this advantage persisted across the majority of ICD-10 disease categories. The mortality advantage identified will be representative of international migrants in high-income countries who are studying, working, or have joined family members in these countries. However, our results might not reflect the health outcomes of more marginalised groups in low-income and middle-income countries because little data were available for these groups, highlighting an important gap in existing research. Our results present an opportunity to reframe the public discourse on international migration and health in high-income countries. FUNDING Wellcome Trust, National Institute for Health Research, Medical Research Council, Alliance for Health Policy and Systems Research, Department for International Development, Fogarty International Center, Grand Challenges Canada, International Development Research Centre Canada, Inter-American Institute for Global Change Research, National Cancer Institute, National Heart, Lung and Blood Institute, National Institute of Mental Health, Swiss National Science Foundation, World Diabetes Foundation, UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, and European Society for Clinical Microbiology and Infectious Diseases (ESCMID) Study Group Research Funding for the ESCMID Study Group for Infections in Travellers and Migrants.

Global patterns of mortality in international migrants: a systematic review and meta-analysis.

Using data from the WHO's Situation Report on the COVID-19 pandemic from 21 January 2020 to 30 March 2020 along with other health, demographic, and macroeconomic indicators from the WHO's Application Programming Interface and the World Bank's Development Indicators, this paper explores the death rates of infected persons and their possible associated factors. Through the panel analysis, we found consistent results that healthcare system conditions, particularly the number of hospital beds and medical staff, have played extremely important roles in reducing death rates of COVID-19 infected persons. In addition, both the mortality rates due to different non-communicable diseases (NCDs) and rate of people aged 65 and over were significantly related to the death rates. We also found that controlling international and domestic travelling by air along with increasingly popular anti-COVID-19 actions (i.e., quarantine and social distancing) would help reduce the death rates in all countries. We conducted tests for robustness and found that the Driscoll and Kraay (1998) method was the most suitable estimator with a finite sample, which helped confirm the robustness of our estimations. Based on the findings, we suggest that preparedness of healthcare systems for aged populations need more attentions from the public and politicians, regardless of income level, when facing COVID-19-like pandemics.

COVID-19: A relook at healthcare systems and aged populations

Background: Current reporting of Covid-19 mortality data by race and ethnicity across the United States could bias our understanding of population-mortality disparities. Moreover, stark differences in age distribution by race and ethnicity groups are seldom accounted for in analyses. Methods: To address these gaps, we conducted a cross-sectional study using publicly-reported Covid-19 mortality data to assess the quality of race and ethnicity data (Black, Latinx, white), and estimated age-adjusted disparities using a random effects meta-analytic approach. Results: We found only 28 states, and NYC, reported race and ethnicity-stratified Covid-19 mortality along with large variation in the percent of missing race and ethnicity data by state. Aggregated relative risk of death estimates for Black compared to the white population was 3.57 (95% CI: 2.84-4.48). Similarly, Latinx population displayed 1.88 (95% CI: 1.61-2.19) times higher risk of death than white patients. Discussion: In states providing race and ethnicity data, we identified significant population-level Covid-19 mortality disparities. We demonstrated the importance of adjusting for age differences across population groups to prevent underestimating disparities in younger population groups. The availability of high-quality and comprehensive race and ethnicity data is necessary to address factors contributing to inequity in Covid-19 mortality.

Racial and Ethnic Disparities in Population Level Covid-19 Mortality

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.

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

In this article, we analyse the factors that determine the fatality rates across 29 economies spread across both the developing and developed world. Recent emerging literature and expert opinions in popular media have indicated various factors that may explain cross-country difference in fatality rates. These factors range from access to public health infrastructure, BCG vaccination policies, demographic structure, restrictive policy interventions and the weather. In addition, articles are examining different kinds of fatality rates that can be explained. Progressing beyond fragmented databases and anecdotal evidence, we have developed a database for such factors, have explored various econometric models to test the explanatory power of these factors in explaining several kinds of fatality rates. Based on available data, our study reveals that factors such as public health system, population age structure, poverty level and BCG vaccination are powerful contributory factors in determining fatality rates. Interactions between factors such as poverty level and BCG vaccination provide interesting insights into the complex interplay of factors. Our analysis suggests that poor citizens’ access to the public healthcare system are worse in many countries irrespective of whether they are developed or developing countries.

Why Do COVID-19 Fatality Rates Differ Across Countries? An Explorative Cross-country Study Based on Select Indicators

Purpose: To estimate influenza-associated excess mortality rates (EMRs) in Chongqing from 2012 to 2018.Methods: We obtained weekly mortality data for all-cause and four underlying causes of death (circulatory and respiratory disease (CRD), pneumonia and influenza (P&I), chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IDH)), and influenza surveillance data, from 2012 to 2018. A negative-binomial regression model was used to estimate influenza-associated EMRs in two age groups (<65 years and ≥65 years).Results: It was estimated that an annual average of 10025 influenza-associated deaths occurred in Chongqing, corresponding to 5.2% of all deaths. The average EMR for all-cause death associated with influenza was 33.5 (95% confidence interval (CI): 31.5-35.6) per 100 000 persons, and in separate cause-specific models we attributed 24.7 (95% CI: 23.3-26.0), 0.8 (95% CI: 0.7-0.8), 8.5 (95% CI: 8.1-9.0) and 5.0 (95% CI: 4.7-5.3) per 100 000 persons EMRs to CRD, P&I, COPD and IDH, respectively. The estimated EMR for influenza B virus was 20.6 (95% CI: 20.3-21.0), which was significantly higher than the rates of 5.3 (95% CI: 4.5-6.1) and 7.5 (95% CI: 6.7-8.3) for A(H3N2) and A(H1N1) pdm09 virus, respectively. The estimated EMR was 152.3 (95% CI: 136.1-168.4) for people aged ≥65 years, which was significantly higher than the rate for those aged <65 years (6.8, 95% CI: 6.3-7.2).Conclusions: Influenza was associated with substantial EMRs in Chongqing, especially among elderly people. Influenza B virus caused a relatively higher excess mortality impact compared with A(H1N1)pdm09 and A(H3N2). It is advisable to optimize future seasonal influenza vaccine reimbursement policy in Chongqing to curb disease burden.

Mortality burden from seasonal influenza in Chongqing, China, 2012-2018

BACKGROUND Inclusion health focuses on people in extremely poor health due to poverty, marginalisation, and multimorbidity. We aimed to review morbidity and mortality data on four overlapping populations who experience considerable social exclusion: homeless populations, individuals with substance use disorders, sex workers, and imprisoned individuals. METHODS For this systematic review and meta-analysis, we searched MEDLINE, Embase, and the Cochrane Library for studies published between Jan 1, 2005, and Oct 1, 2015. We included only systematic reviews, meta-analyses, interventional studies, and observational studies that had morbidity and mortality outcomes, were published in English, from high-income countries, and were done in populations with a history of homelessness, imprisonment, sex work, or substance use disorder (excluding cannabis and alcohol use). Studies with only perinatal outcomes and studies of individuals with a specific health condition or those recruited from intensive care or high dependency hospital units were excluded. We screened studies using systematic review software and extracted data from published reports. Primary outcomes were measures of morbidity (prevalence or incidence) and mortality (standardised mortality ratios [SMRs] and mortality rates). Summary estimates were calculated using a random effects model. FINDINGS Our search identified 7946 articles, of which 337 studies were included for analysis. All-cause standardised mortality ratios were significantly increased in 91 (99%) of 92 extracted datapoints and were 11·86 (95% CI 10·42-13·30; I2=94·1%) in female individuals and 7·88 (7·03-8·74; I2=99·1%) in men. Summary SMR estimates for the International Classification of Diseases disease categories with two or more included datapoints were highest for deaths due to injury, poisoning, and other external causes, in both men (7·89; 95% CI 6·40-9·37; I2=98·1%) and women (18·72; 13·73-23·71; I2=91·5%). Disease prevalence was consistently raised across the following categories: infections (eg, highest reported was 90% for hepatitis C, 67 [65%] of 103 individuals for hepatitis B, and 133 [51%] of 263 individuals for latent tuberculosis infection), mental health (eg, highest reported was 9 [4%] of 227 individuals for schizophrenia), cardiovascular conditions (eg, highest reported was 32 [13%] of 247 individuals for coronary heart disease), and respiratory conditions (eg, highest reported was 9 [26%] of 35 individuals for asthma). INTERPRETATION Our study shows that homeless populations, individuals with substance use disorders, sex workers, and imprisoned individuals experience extreme health inequities across a wide range of health conditions, with the relative effect of exclusion being greater in female individuals than male individuals. The high heterogeneity between studies should be explored further using improved data collection in population subgroups. The extreme health inequity identified demands intensive cross-sectoral policy and service action to prevent exclusion and improve health outcomes in individuals who are already marginalised. FUNDING Wellcome Trust, National Institute for Health Research, NHS England, NHS Research Scotland Scottish Senior Clinical Fellowship, Medical Research Council, Chief Scientist Office, and the Central and North West London NHS Trust.

Morbidity and mortality in homeless individuals, prisoners, sex workers, and individuals with substance use disorders in high-income countries: a systematic review and meta-analysis.

Since the beginning of the SARS-CoV-2/COVID-19 epidemic in China, elderly and multimorbid subjects showed a higher mortality rate. However, other factors could influence the mortality and the spread of contagion such as the population density. An archival research based on the Italian data stratified by region was performed in order to quantify the association between the population density, ageing index, number of positive cases, number of deaths, case-fatality rate, and medical equipment (gloves, masks, and ventilators). Results showed a significant positive linear relationship between the population density and cases, deaths, and case-fatality rate. No correlation with the ageing index was shown. Furthermore, we found a significant positive correlation between the number of medical supplies and population density, cases, and deaths. However, the medical supplies did not show any correlation with the case-fatality rate. Taken together, these findings suggest that the population density and the lack of medical equipment are key factors explaining morbidity and mortality of COVID-19 in Italy.

SARS-CoV-2 in Italy: Population Density correlates with Morbidity and Mortality.

The relationship between log cumulative number of patients (X) and that of deaths (Y) in an epidemic follows the equation logY = klogX - klogN(0), where k is a constant determining the slope and N(0) is the value of X when Y = 1. Diseases with k = 1 are Ebola hemorrhagic fever, avian influenza H5N1, cholera, and hand, foot, and mouth disease; those with k > 1 are the influenza H1N1 2009 pandemic in countries other than Mexico and the SARS epidemic in some countries; and those with k < 1 include the influenza H1N1 2009 pandemic in Mexico. Epidemics with k > 1 can be simulated by postulating two subpopulations (normal population [NP] and vulnerable population [VP]), where the epidemic proceeds at higher speed and at higher mortality in VP than in NP. Epidemics with k < 1 can be simulated by postulating coexisting high virulence virus (HVV) and low virulence virus (LVV), with the former being propagated at slower speed and with a higher mortality rate than the latter. An epidemic with k > 1 was simulated using parameters that are fractions of subpopulations NP or VP from the total population (f) and NP- or VP-specific patient multiplication (M) and mortality (D) rates. An epidemic with k < 1 was simulated using parameters that are fractions of HVV- or LVV-infected human populations (f), and HVV- or LVV-specific M and D.

On case-fatality rate: review and hypothesis.

Clarification of Mortality Rate and Data in Abstract, Results, and Table 2

Clarification of Mortality Rate and Data in Abstract, Results, and Table 2.

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.

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

SARS-CoV-2 in Italy: Population Density correlates with Morbidity and Mortality

Mortality statistics due to COVID-19 worldwide are compared, by adjusting for the size of the population and the stage of the pandemic. Data from the European Centre for Disease Control and Prevention, and Our World in Data websites were used. Analyses are based on number of deaths per one million inhabitants. In order to account for the stage of the pandemic, the baseline date was defined as the day in which the 10th death was reported. The analyses included 78 countries and territories which reported 10 or more deaths by April 9. On day 10, India had 0.06 deaths per million, Belgium had 30.46 and San Marino 618.78. On day 20, India had 0.27 deaths per million, China had 0.71 and Spain 139.62. On day 30, four Asian countries had the lowest mortality figures, whereas eight European countries had the highest ones. In Italy and Spain, mortality on day 40 was greater than 250 per million, whereas in China and South Korea, mortality was below 4 per million. Mortality on day 10 was moderately correlated with life expectancy, but not with population density. Asian countries presented much lower mortality figures as compared to European ones. Life expectancy was found to be correlated with mortality.

Worldwide differences in COVID-19-related mortality.

Although testing is widely regarded as critical to fighting the Covid-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics--population testing number and testing coverage--to population mortality outcomes and identify a benchmark for testing adequacy with respect to population mortality and capture of potential disease burden. This ecological study aggregated publicly available data through April 12 on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. All OECD countries and Taiwan were included in this population-based study as a proxy for countries with highly developed economic and healthcare infrastructure. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate, and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. Testing coverage, but not population testing number, was highly correlated with population mortality (rs= -0.79, P=5.975e-09 vs rs =- 0.3, P=0.05) and case fatality rate (rs= -0.67, P=9.067e-06 vs rs = -0.21, P=0.20). A testing coverage threshold of 15-45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality, whereas above 45, increased testing did not yield significant incremental mortality benefit. Testing coverage was better than population testing number in explaining country performance and can be used as an early and sensitive indicator of testing adequacy and disease burden. This may be particularly useful as countries consider re-opening their economies.

Correlation of population mortality of COVID-19 and testing coverage: a comparison among 36 OECD countries and Taiwan

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.

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

We aimed to clarify if the infection and death rate by COVID-19 differ among gender in the top 50 countries with the highest death rates. Also, we investigated if secondary variables such as HDI, number of hospital beds, average age, temperature, percentage of elderly, smoker and obesity are contributing to the variability observed among countries. Meta-analyses and meta-regressions approaches were applied to official public data reported by the Word Health Organization and governments until May, 2020. A random effect model was used for the meta-analysis and heterogeneity was calculated by I2 statistic. There was not significative difference between men and women to be infected by COVID-19 (P = 0.42), though a significative difference was observed for death rate (P < 0.0001). High heterogeneity was observed among countries. For both infection and death rates this variability was mainly explained by the HDI (42.3% and 54.2%), average age (40.9% and 40.3%) and temperature (30.1% and 39.3%). Man are dying more than women around the word by COVID-19. Countries with highest HDI present less difference between sexes. These results reinforce that public politics promoting social isolation, health care and general well-being of the population are key factors in combating COVID-19.

Are men dying more than women by COVID-19?

Worldwide differences in COVID-19-related mortality

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.

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

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.

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

OBJECTIVE: To provide estimates of the relative rate of COVID-19 death in people <65 years old versus older individuals in the general population, the absolute risk of COVID-19 death at the population level during the first epidemic wave, and the proportion of COVID-19 deaths in non-elderly people without underlying diseases in epicenters of the pandemic. ELIGIBLE DATA: Cross-sectional survey of countries and US states with at least 800 COVID-19 deaths as of April 24, 2020 and with information on the number of deaths in people with age <65. Data were available for 14 countries (Belgium, Canada, France, Germany, India, Ireland, Italy, Mexico, Netherlands, Portugal, Spain, Sweden, Switzerland, UK) and 13 US states (California, Connecticut, Florida, Georgia, Illinois, Indiana, Louisiana, Maryland, Massachusetts, Michigan, New Jersey, New York, Pennsylvania). We also examined available data on COVID-19 deaths in people with age <65 and no underlying diseases. MAIN OUTCOME MEASURES: Proportion of COVID-19 deaths in people <65 years old; relative mortality rate of COVID-19 death in people <65 versus ≥65 years old; absolute risk of COVID-19 death in people <65 and in those ≥80 years old in the general population as of June 17, 2020; absolute COVID-19 mortality rate expressed as equivalent of mortality rate from driving a motor vehicle. RESULTS: Individuals with age <65 account for 4.5–11.2% of all COVID-19 deaths in European countries and Canada, 8.3–22.7% in the US locations, and were the majority in India and Mexico. People <65 years old had 30- to 100-fold lower risk of COVID-19 death than those ≥65 years old in 11 European countries and Canada, 16- to 52-fold lower risk in US locations, and less than 10-fold in India and Mexico. The absolute risk of COVID-19 death as of June 17, 2020 for people <65 years old in high-income countries ranged from 10 (Germany) to 349 per million (New Jersey) and it was 5 per million in India and 96 per million in Mexico. The absolute risk of COVID-19 death for people ≥80 years old ranged from 0.6 (Florida) to 17.5 per thousand (Connecticut). The COVID-19 mortality rate in people <65 years old during the period of fatalities from the epidemic was equivalent to the mortality rate from driving between 4 and 82 miles per day for 13 countries and 5 states, and was higher (equivalent to the mortality rate from driving 106–483 miles per day) for 8 other states and the UK. People <65 years old without underlying predisposing conditions accounted for only 0.7–3.6% of all COVID-19 deaths in France, Italy, Netherlands, Sweden, Georgia, and New York City and 17.7% in Mexico. CONCLUSIONS: People <65 years old have very small risks of COVID-19 death even in pandemic epicenters and deaths for people <65 years without underlying predisposing conditions are remarkably uncommon. Strategies focusing specifically on protecting high-risk elderly individuals should be considered in managing the pandemic.

Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters

Background. Inequalities and burden of comorbidities of the Coronavirus disease 2019 (COVID-19) vary importantly inside the countries. We aimed to analyze the Municipality-level factors associated with a high COVID-19 mortality rate of in Mexico. Methods. We retrieved information from 142,643 cumulative confirmed symptomatic cases and 18,886 deaths of COVID-19 as of June 20th, 2020 from the publicly available database of the Ministry of Health of Mexico. Public official data of the most recent census and surveys of the country were used to adjust a negative binomial regression model with the quintiles (Q) of the distribution of sociodemographic and health outcomes among 2,457 Municipality-level. Expected Mortality Rates (EMR), Incidence Rate Ratios (IRR) and 95% Confidence Intervals are reported. Results. Factors associated with high MR of COVID-19, relative to Quintile 1 (Q1), were; diabetes prevalence (Q4, IRR=2.60), obesity prevalence (Q5, IRR=1.93), diabetes mortality rate (Q5, IRR=1.58), proportion of indigenous population (Q2, IRR=1.68), proportion of economically active population (Q5, IRR=1.50), density of economic units that operate essential activities (Q4, IRR=1.54) and population density (Q5, IRR=2.12). We identified 1,351 Municipality-level without confirmed COVID-19 deaths, of which, 202 had nevertheless high (Q4, Mean EMR= 8.0 deaths per 100,000) and 82 very high expected COVID-19 mortality (Q5, Mean EMR= 13.8 deaths per 100,000). Conclusion. This study identified 1,351 Municipality-level of Mexico that, in spite of not having confirmed COVID-19 deaths yet, share characteristics that could eventually lead to a high mortality scenario later in the epidemic and warn against premature easing of mobility restrictions. Local information should be used to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19. Keywords: COVID-19; risk factors; social determinants; health determinants; Municipality-level; counties.

Municipality- level predictors of COVID-19 mortality in Mexico: a cautionary tale

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.

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

It is well known that statistics using cumulative data are insensitive to changes. World Health Organization (WHO) estimates of fatality rates are of the above type, which may not be able to reflect the latest changes in fatality due to treatment or government policy in a timely fashion. Here, the authors propose an estimate of a real-time fatality rate based on a chain multinomial model with a kernel function. It is more accurate than the WHO estimate in describing fatality, especially earlier in the course of an epidemic. The estimator provides useful information for public health policy makers for understanding the severity of the disease or evaluating the effects of treatments or policies within a shorter time period, which is critical in disease control during an outbreak. Simulation results showed that the performance of the proposed estimator is superior to that of the WHO estimator in terms of its sensitivity to changes and its timeliness in reflecting the severity of the disease.

A Chain Multinomial Model for Estimating the Real-Time Fatality Rate of a Disease, with an Application to Severe Acute Respiratory Syndrome

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.

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

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.

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

Mortality burden from seasonal influenza in Chongqing, China, 2012-2018.

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (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 the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.

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

Background: Initial reports suggest that ethnic minorities may be experiencing more severe clinical outcomes of coronavirus disease 2019 (COVID19) infections. We therefore assessed the association between ethnic composition, income deprivation and COVID19 mortality rates in England. Methods: We performed a cross-sectional ecological analysis across upper tier local authorities in England. We assessed the association between the proportion of the population from Black, Asian and Minority Ethnic (BAME) backgrounds, income deprivation and COVID19 mortality rates using negative binomial regression models, whilst adjusting for population density, proportion of the population aged 50-79 and 80+ years, and the duration of the epidemic in each area. Findings: Local authorities with a greater proportion of residents from ethnic minority backgrounds had statistically significantly higher COVID19 mortality rates, as did local authorities with a greater proportion of residents experiencing deprivation relating to low income. After adjusting for income deprivation and other covariates, each percentage point increase in the proportion of the population from BAME backgrounds was associated with a 1% increase in the COVID19 mortality rate [IRR=1.01, 95%CI 1.01 to 1.02]. Each percentage point increase in the proportion of the population experiencing income deprivation was associated with a 2% increase in the COVID19 mortality rate [IRR=1.02, 95%CI 1.01 to 1.04]. Interpretation: This study provides evidence that both income deprivation and ethnicity are associated with greater COVID19 mortality. To reduce these inequalities governments need to target effective control measures at these disadvantaged communities, ensuring investment of resources reflects their greater need and vulnerability to the pandemic. Funding: National Institute of Health Research; Medical Research Council

Inequalities in COVID19 mortality related to ethnicity and socioeconomic deprivation

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.

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

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.

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

The Covid-19 pandemic brings major new challenges to health services resulting from the lack of a vaccine and from the enormous resources it can consume over a prolonged period. The available control measures are currently limited to quarantining, contact tracking-and-tracing and social distancing. Disease transmission to health care workers is common and deaths among clinical and nursing staff have been reported in the UK (where serious concerns about the availability of personal protective equipment - PPE - have been raised) and elsewhere; particularly in Lombardy, where General Practitioners (Medici di Base) have died in disproportionate numbers.

Mortality rates from COVID-19 in Spain and Italy, Lessons for the UK!

In 2001, heart disease accounted for approximately 29.0% of deaths among U.S. residents; 16.8% of those deaths occurred among persons aged <65 years. Although mortality rates from heart disease have decreased, the decline has not been uniform for all populations. One of the two overall national health objectives for 2010 is to eliminate health disparities among different segments of the U.S. population. To better understand these disparities, CDC analyzed death certificate data for premature deaths from heart disease occurring in 2001. This report summarizes the results of that analysis, which indicated that the proportion of premature heart disease deaths varied by state and was higher among blacks, American Indians/Alaska Natives (AI/ANs), Asians/Pacific Islanders (A/PIs), and Hispanics. Reducing premature death from heart disease and eliminating disparities will require preventing, detecting, treating, and controlling risk factors for heart disease in young and middle-aged adults.

Disparities in premature deaths from heart disease--50 States and the District of Columbia, 2001.

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.

The current COVID‐19 outbreak has raised many questions, amongst them the higher mortality rates in men and the low overall mortality rates in Germany compared to other European countries. Here the authors explore some of the reasons behind both these phenomena and outline what we can learn from them for the future.

Male mortality and the German response: lessons from COVID‐19

Abstract Background: The SARS-CoV-2 virus has spread all over the world infecting more than 3,585,936 people from over 210 countries and caused more than 245,803 deaths worldwide. We report the first epidemiological, socio-demographic, and clinical findings for the first 9,468 confirmed COVID-19 cases in Ecuador. Methods: We conducted a descriptive cross-sectional analysis of 9,468 COVID-19 confirmed cases in Ecuador from 27 February to 18 April 2020. The overall incidence, mortality, and case fatality rate was computed according to the entire population at risk living in a canton or a province. Disability adjusted life years, attack and crude mortality rates as well as relative risk and odds ratios were computed as an outcome. Results: Since the first case reported in Ecuador on 27 Feb 2020, at least 9,468 positive COVID-19 cases of which 474 deaths were officially registered over a 54-day period. Men accounted for 55.40% (n = 5, 247) of the overall cases with an incidence rate of 60.5 per 100,000 while women accounted for 44.60 % (n = 4, 221) representing 47.2 per 100,000. The mortality rate per canton showed that cantons with a lower attack rate had higher mortality rates. Coastal cantons have a lower attack rate than the highlands and living above >2,500 m seems to be linked with a lower risk of dying (RR: 0.63 [CI 95% 0.50 - 0.79]). Fatigue was reported in 53.2% of the patients, followed by headache (43%), dry cough (41.7%), ageusia (37.1%) and anosmia (36.1%). Conclusion: This study is the first of its kind in Ecuador. The results of this analysis show that men are at higher risk of dying from COVID-19 than women, which increases as with age and the presence of comorbidities. Areas with better testing capabilities reported lower CFR% and mortality, additionally cantons located above 2,500 m have lower attack and mortality rates although the risk of dying is greater among highlanders. Keywords: COVID-19; SARS-CoV-2; Ecuador; Epidemiology; Latin America

Epidemiological, socio-demographic and clinical features of the early phase of the COVID-19 epidemic in Ecuador

Background: Health disparities were often overlooked during the emerging epidemic. Objectives: This study examined geographic differences in the rates of health care use and deaths among elderly patients. Methods: Based on individual patient records, multivariate Poisson and logistic models were used to calculate adjusted incidences of COVID-19 and probabilities of emergency department (ED) visits, hospitalizations and deaths. Results: Of 8,203 elderly patients, 11% died. Elderly people living in small metropolitan areas were half as likely to be diagnosed with COVID-19. Elderly female patients living in small metropolitan areas had much lower rates of ED visits (23% vs. 34%; Odds Ratio (OR): 0.58; 95%confidence interval (CI): 0.41-0.81; p=0.002) and hospitalizations (22% vs. 31%; OR: 0.62; 95%CI: 0.44 - 0.87; p=0.006) than those living in large metropolitan areas. Furthermore, those living in non-metropolitan areas were more likely to be hospitalized than those living in large metropolitan areas (44% vs. 33%; OR: 1.46; 95%CI: 1.07-1.99; p=0.016), especially among elderly men (51% vs. 35%; OR:1.86; 95%CI: 1.18-2.93; p=0.008). Finally, there was a significant linear trend in hospitalization rates among elderly male patients (p for trend = 0.01). Conclusions: Profound health disparities exist in the time of emerging epidemic.

Did elderly people living in small towns or rural areas suffer heavier disease burden during the COVID-19 epidemic?

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.

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

Background The role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S. Methods Using Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity. Results Overall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country. Conclusion The existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity.

Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19 Related Mortality in the U.S.

BACKGROUND: New York City emerged as an epicenter of the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: To describe the clinical characteristics and risk factors associated with mortality in a large patient population in the USA. DESIGN: Retrospective cohort study. PARTICIPANTS: 6493 patients who had laboratory-confirmed COVID-19 with clinical outcomes between March 13 and April 17, 2020, who were seen in one of the 8 hospitals and/or over 400 ambulatory practices in the New York City metropolitan area MAIN MEASURES: Clinical characteristics and risk factors associated with in-hospital mortality. KEY RESULTS: A total of 858 of 6493 (13.2%) patients in our total cohort died: 52/2785 (1.9%) ambulatory patients and 806/3708 (21.7%) hospitalized patients. Cox proportional hazard regression modeling showed an increased risk of in-hospital mortality associated with age older than 50 years (hazard ratio [HR] 2.34, CI 1.47-3.71), systolic blood pressure less than 90 mmHg (HR 1.38, CI 1.06-1.80), a respiratory rate greater than 24 per min (HR 1.43, CI 1.13-1.83), peripheral oxygen saturation less than 92% (HR 2.12, CI 1.56-2.88), estimated glomerular filtration rate less than 60 mL/min/1.73m2 (HR 1.80, CI 1.60-2.02), IL-6 greater than 100 pg/mL (HR 1.50, CI 1.12-2.03), D-dimer greater than 2 mcg/mL (HR 1.19, CI 1.02-1.39), and troponin greater than 0.03 ng/mL (HR 1.40, CI 1.23-1.62). Decreased risk of in-hospital mortality was associated with female sex (HR 0.84, CI 0.77-0.90), African American race (HR 0.78 CI 0.65-0.95), and hydroxychloroquine use (HR 0.53, CI 0.41-0.67). CONCLUSIONS: Among patients with COVID-19, older age, male sex, hypotension, tachypnea, hypoxia, impaired renal function, elevated D-dimer, and elevated troponin were associated with increased in-hospital mortality and hydroxychloroquine use was associated with decreased in-hospital mortality.

Risk Factors for Mortality in Patients with COVID-19 in New York City

Using the Irish experience of the Spanish flu, we demonstrate that pandemic mortality statistics are sensitive to the demographic composition of a country. We build a new demographic database for Ireland's 32 counties with vital statistics on births, ageing, migration and deaths. We then show how age-at-death statistics in 1918 and 1919 should be reinterpreted in light of these data. Our new estimates suggest the very young were most impacted by the flu. New studies of the economic impact of Influenza-18 must better control for demographic factors if they are to yield useful policy-relevant results. Covid-19 mortality statistics must go through a similar procedure so policymakers can better target their public health interventions.

Death, Demography and the Denominator:New Influenza-18 Mortality Estimates for Ireland

Different ways of calculating mortality ratios during epidemics have yielded very different results, particularly during the current 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 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 confirmed infected cases to date. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality ratios and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality ratios during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data.

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

Internationally, health authorities and governments are warning older people that they are at a higher risk of more serious and possible fatal illness associated with COVID-19. Mortality data from Oxford COVID-19 Evidence Service (25/3/20) indicates a risk of mortality of 3.6% for people in their 60s, which increases to 8.0% and 14.8% for people in their 70s and over 80s. Therefore, the global recommendation for older populations includes social isolation, which involves staying at home and avoiding contact with other people, possibly for an extended period of time, currently estimated to be between three and four months. Older populations in this current context, refers to people over 70 years, and 50 years in some particularly vulnerable Indigenous populations.

Older people and COVID-19: Isolation, risk and ageism.

Pandemics tend to have higher occurrence (morbidity) in younger individuals but higher mortality for the elderly. The higher rate of mortality of COVID-19 in elderly individuals has been discussed in many reports. However, this pandemic is a double-edged sword as this comment shows higher morbidity rates in elderly as well. This is shown by comparing the age distribution of cases in China and South Korea to the relative populations. In every case, the relative number of elderly contracting the virus is far higher than the proportion of elderly in the population. This is unlike past pandemics and shows that aging populations are at an even higher risk than the perceived age dependent rates may imply.

On Determining the Age Distribution of COVID-19 Pandemic

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.

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

Objective: To model how known COVID-19 comorbidities will affect mortality rates and the age distribution of mortality in a large lower middle income country (India), as compared with a high income country (England), and to identify which health conditions drive any differences. Design: Modelling study. Setting: England and India. Participants: 1,375,548 respondents aged 18 to 99 to the District Level Household Survey-4 and Annual Health Survey in India. Additional information on health condition prevalence on individuals aged 18 to 99 was obtained from the Health Survey for England and the Global Burden of Diseases, Risk Factors, and Injuries Studies (GBD). Main outcome measures: The primary outcome was the proportional increase in age-specific mortality in each country due to the prevalence of each COVID-19 mortality risk factor (diabetes, hypertension, obesity, chronic heart disease, respiratory illness, kidney disease, liver disease, and cancer, among others). The combined change in overall mortality and the share of deaths under 60 from the combination of risk factors was estimated in each country. Results: Relative to England, Indians have higher rates of diabetes (10.6% vs. 8.5%), chronic respiratory disease (4.8% vs. 2.5%), and kidney disease (9.7% vs. 5.6%), and lower rates of obesity (4.4% vs. 27.9%), chronic heart disease (4.4% vs. 5.9%), and cancer (0.3% vs. 2.8%). Population COVID-19 mortality in India relative to England is most increased by diabetes (+5.4%) and chronic respiratory disease (+2.3%), and most reduced by obesity (-9.7%), cancer (-3.2%), and chronic heart disease (-1.9%). Overall, comorbidities lower mortality in India relative to England by 9.7%. Accounting for demographics and population health explains a third of the difference in share of deaths under age 60 between the two countries. Conclusions: Known COVID-19 health risk factors are not expected to have a large effect on aggregate mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality. Keywords: COVID-19, India, low- and middle-income countries, comorbidity

The COVID-19 mortality effects of underlying health conditions in India: a modelling study

We used a regression model to examine the impact of influenza on death rates in tropical Singapore for the period 1996–2003. Influenza A (H3N2) was the predominant circulating influenza virus subtype, with consistently significant and robust effect on mortality rates. Influenza was associated with an annual death rate from all causes, from underlying pneumonia and influenza, and from underlying circulatory and respiratory conditions of 14.8 (95% confidence interval 9.8–19.8), 2.9 (1.0–5.0), and 11.9 (8.3–15.7) per 100,000 person-years, respectively. These results are comparable with observations in the United States and subtropical Hong Kong. An estimated 6.5% of underlying pneumonia and influenza deaths were attributable to influenza. The proportion of influenza-associated deaths was 11.3 times higher in persons age >65 years than in the general population. Our findings support the need for influenza surveillance and annual influenza vaccination for at-risk populations in tropical countries.

Influenza-associated Deaths in Tropical Singapore

Morbidity and mortality.

Introduction Non-specific effects of vaccines have gained increasing interest during the Covid-19 pandemic. In particular, population use of BCG vaccine has been associated with improved outcomes. This study sought to determine the association of population use of BCG, adult pneumococcal and adult seasonal influenza vaccination with Covid-19 mortality when adjusted for a number of confounding variables. Methods: Using publicly available data, mortality adjusted for the timeframe of crisis, population size and population characteristics was calculated. The primary analysis was the relationship between each of the day 15 and day 30 standardised mortality rates and BCG, adult pneumococcal and influenza vaccination scores using unadjusted measures and with adjustment for population structure and case fatality rates. Secondary analyses were measures of case increases and mortality increases from day 15 to day 30 for each of the relative vaccination scores. Finally, we also analysed the peak Z score reflecting increases in total mortality from historical averages reported by EuroMOMO (Euromomo.eu), Results: Following adjustment for the effects of population size, median age, population density, the proportion of population living in an urban setting, life-expectancy, the elderly dependency ratio (or proportion over 65 years), net migration, days from day 1 to lockdown and case-fatality rate, only BCG vaccination score remained significantly associated with Covid-19 mortality at day 30. In the best fit model, BCG vaccination score was associated with a 64% reduction in log(10) mortality per 10 million population (OR 0.362 reduction [95% CI 0.188 to 0.698]), following adjustment for population size, median age, density, urbanization, elderly dependency ratio, days to lockdown, yearly migration and case fatality rate. Conclusion BCG vaccine was associated with reduced mortality rates in level 4 countries while adult pneumococcal and adult seasonal influenza vaccines were not when adjusted for a number of confounding variables. A number of trials are ongoing to determine if BCG is protective against severe Covid-19 infection.

Association of Bacille Calmette-Guerin (BCG), Adult Pneumococcal and Adult Seasonal Influenza Vaccines with Covid-19 Adjusted Mortality Rates in Level 4 European countries

The purpose of this paper is to review the major sources of data on mortality, morbidity and health in Europe and in other developed regions in order to examine their potential for analysing mortality and morbidity levels and trends. The review is primarily focused on routinely collected information covering a whole country. No attempt is made to draw up an inventory of sources by country; the paper deals instead with the pros and cons of each source for mortality and morbidity studies in demography. While each source considered separately can already yield useful, though partial, results, record linkage among data sources can significantly improve the analysis. Record linkage can also lead to the detection of possible causal associations that could eventually be confirmed. More generally, Big Data can reveal changing mortality and morbidity trends and patterns that could lead to preventive measures being taken rather than more costly curative ones.

Mortality, morbidity and health in developed societies: a review of data sources

BACKGROUND: The 16 Southern Africa Development Community (SADC) countries remain the epicentre of the HIV/AIDS epidemic with the largest number of people living with HIV/AIDS. Anti-retroviral treatment (ART) has improved survival and prevention of mother-to-child transmission (PMTCT) of HIV, but the disease remains a serious cause of mortality. We conducted a descriptive epidemiological analysis of HIV/AIDS burden for the 16 SADC countries using secondary data from the Global Burden of Diseases, Injuries and Risk Factor (GBD) Study. METHODS: The GBD study is a systematic, scientific effort by the Institute for Health Metrics and Evaluation (IHME) to quantify the comparative magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and geographies for specific points in time. We analyzed the following outcomes: mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to HIV/AIDS for SADC. Input data for GBD was extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service utilisation, disease notifications, and other sources. Country- and cause-specific HIV/AIDS-related death rates were calculated using the Cause of Death Ensemble model (CODEm) and spatiotemporal Gaussian process regression (ST-GPR). Deaths were multiplied by standard life expectancy at each age-group to calculate YLLs. Cause-specific mortality was estimated using a Bayesian meta-regression modelling tool, DisMod-MR. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases to calculate YLDs. Crude and age-adjusted rates per 100,000 population and changes between 1990 and 2017 were determined for each country. RESULTS: In 2017, HIV/AIDS caused 336,175 deaths overall in SADC countries, and more than 20 million DALYs. This corresponds to a 3-fold increase from 113,631 deaths (6,915,170 DALYs) in 1990. The five leading countries with the proportion of deaths attributable to HIV/AIDS in 2017 were Botswana at the top with 28.7% (95% UI; 23.7–35.2), followed by South Africa 28.5% (25.8–31.6), Lesotho, 25.1% (21.2–30.4), eSwatini 24.8% (21.3–28.6), and Mozambique 24.2% (20.6–29.3). The five countries had relative attributable deaths that were at least 14 times greater than the global burden of 1.7% (1.6–1.8). Similar patterns were observed with YLDs, YLLs, and DALYs. Comoros, Seychelles and Mauritius were on the lower end, with attributable proportions less than 1%, below the global proportion. CONCLUSIONS: Great progress in reducing HIV/AIDS burden has been achieved since the peak but more needs to be done. The post-2005 decline is attributed to PMTCT of HIV, resources provided through the US President’s Emergency Plan For AIDS Relief (PEPFAR), and behavioural change. The five countries with the highest burden of HIV/AIDS as measured by proportion of death attributed to HIV/AIDS and age-standardized mortaility rate were Botswana, South Africa, Lesotho, eSwatini, and Mozambique. SADC countries should cooperate, work with donors, and embrace the UN Fast-Track approach, which calls for frontloading investment from domestic or other sources to prevent and treat HIV/AIDS. Robust tracking, testing, and early treatment are required, as well as refinement of individual treatment strategies for transient individuals in the region.

Burden and changes in HIV/AIDS morbidity and mortality in Southern Africa Development Community Countries, 1990–2017

Age-adjusted mortality rates for coronary heart disease (CHD) have declined steadily in the United States since the 1960s. Multiple factors likely have contributed to this decline in CHD deaths, including greater control of risk factors, resulting in declining incidence of CHD, and improved treatment. Greater control of risk factors and declining incidence can reduce CHD prevalence, whereas improved treatment that results in lower mortality rates and more persons living with CHD can increase prevalence. To estimate state-specific CHD prevalence and recent trends by age, sex, race/ethnicity, and education, CDC analyzed data from Behavioral Risk Factor Surveillance System (BRFSS) surveys for the period 2006-2010. This report summarizes the results of that analysis, which determined that, although self-reported CHD prevalence declined overall, substantial differences in prevalence existed by age, sex, race/ethnicity, education, and state of residence. These data can enable state and national health agencies to monitor CHD prevalence as a measure of progress toward meeting the Healthy People 2020 objective to reduce the U.S. rate of CHD deaths 20% from the 2007 baseline.

Prevalence of coronary heart disease--United States, 2006-2010.

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.

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.

The true infection rate of COVID-19 and the infection fatality rate in the whole population have been estimated for each country. The estimate well coincided with local surveys. The fact that several tens of times, or more, of identified cases are already infected might require reconsideration of the strategy.

Estimation of the true infection rate and infection fatality rate of COVID-19 in the whole population of each country

Covid-19 deaths and positive cases show a remarkable heterogeneity across countries which cannot be easily explained on the basis of similarities or differences in the quality of healthcare, access to healthcare, testing facilities, or preventive measures such as lockdowns. Here we show that there is a distinct correlation between the mortality level and the infection level across countries, which can explain the mortality levels for a wide spectrum of countries. This implies that the number of deaths per 100 infected individuals is approximately the same across diverse countries and can be estimated from the slope of the mortality level-infection level plot. The correlation presented here can potentially be combined with estimates of infection spread to forecast future mortality levels and therefore future needs in terms of healthcare and other resources. Tracking of an individual locations temporal path on this plot can potentially serve as a visual assessment of the nature of the epidemic. Methods presented here are not specific to the current epidemic. This is a preliminary report and uses data from a single source at a single time-point to demonstrate the capability of such an analysis.

Correlating Covid-19 mortality and infection levels

Objective To estimate the infection fatality rate of coronavirus disease 2019 (COVID-19) from data of seroprevalence studies. Methods Population studies with sample size of at least 500 and published as peer-reviewed papers or preprints as of May 12, 2020 were retrieved from PubMed, preprint servers, and communications with experts. Studies on blood donors were included, but studies on healthcare workers were excluded. The studies were assessed for design features and seroprevalence estimates. Infection fatality rate was estimated from each study dividing the number of COVID-19 deaths at a relevant time point by the number of estimated people infected in each relevant region. Correction was also attempted accounting for the types of antibodies assessed. Results Twelve studies were identified with usable data to enter into calculations. Seroprevalence estimates ranged from 0.113% to 25.9% and adjusted seroprevalence estimates ranged from 0.309% to 33%. Infection fatality rates ranged from 0.03% to 0.50% and corrected values ranged from 0.02% to 0.40%. Conclusions The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients as well as multiple other factors. Estimates of infection fatality rates inferred from seroprevalence studies tend to be much lower than original speculations made in the early days of the pandemic.

The infection fatality rate of COVID-19 inferred from seroprevalence data

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.

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

As reporting of COVID‐19 at the US state level has become more granular, many states have reported a higher proportion of deaths among African‐Americans. In our study, we assessed state level data on race, population density, age, obesity rates, insurance data, GDP, per capita healthcare resources (hospital‐beds/ventilators per‐capita), median household income and high‐school graduation rates. We report a higher death rate among states with a greater proportion of African‐American residents despite adjusting for case rates and state‐level factors. To the best of our knowledge this is the first study looking at state level data (from across the US) and mortality with COVID‐19. This article is protected by copyright. All rights reserved.

Differences in race and other state‐level characteristics and associations with mortality from COVID‐19 infection

Homicide disproportionately affects persons aged 10-24 years in the United States and consistently ranks in the top three leading causes of death in this age group, resulting in approximately 4,800 deaths and an estimated $9 billion in lost productivity and medical costs in 2010. To investigate trends in homicide among persons aged 10-24 years for the period 1981-2010, CDC analyzed National Vital Statistics System data on deaths caused by homicide of persons in this age group and examined trends by sex, age, race/ethnicity, and mechanism of injury. This report describes the results of that analysis, which indicated that homicide rates varied substantially during the study period, with a sharp rise from 1985 to 1993 followed by a decline that has slowed since 1999. During the period 2000-2010, rates declined for all groups, although the decline was significantly slower for males compared with females and for blacks compared with Hispanics and persons of other racial/ethnic groups. By mechanism of injury, the decline for firearm homicides from 2000 to 2010 was significantly slower than for nonfirearm homicides. The homicide rate among persons aged 10-24 years in 2010 was 7.5 per 100,000, the lowest in the 30-year study period. Primary prevention strategies remain critical, particularly among groups at increased risk for homicide.

Homicide rates among persons aged 10-24 years - United States, 1981-2010.

ABSTRACT Objective: Analyze a set of indicators to understand the variability of the evolution and impact of the COVID-19 epidemic in a set of selected countries. Method: Ecological study of a group of countries with more than 200 reported cases. Demographic variables, health expenditure variables, and variables about characteristics of health services were included as explanatory variables. and incidence, mortality and fatality rates have been analyzed as response variables. In addition, a relative fatality index has been created. Data are from international organizations. Spearman's correlation coefficient was used to estimate the magnitude of the associations. Results: Number of tests and of medical professionals are associated with a higher incidence rate. Mortality and case fatality rate are not associated with demographic, health expenditure, or health services variables. Conclusion: Differences suggest a general underestimation of the magnitude of the epidemic. Improvement of case identification and effectiveness of epidemiological surveillance systems is necessary.

Letalidad del COVID-19: ausencia de patrón epidemiológico

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 among dentists in Taiwan, 1985–2009

BACKGROUND: Extraordinary infection control measures limited access to medical care in the Greater Toronto Area during the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak. The objective of this study was to determine if the period of these infection control measures was associated with changes in overall population mortality due to causes other than SARS. METHODS: Observational study of death registry data, using Poisson regression and interrupted time-series analysis to examine all-cause mortality rates (excluding deaths due to SARS) before, during, and after the SARS outbreak. The population of Ontario was grouped into the Greater Toronto Area (N = 2.9 million) and the rest of Ontario (N = 9.3 million) based upon the level of restrictions on delivery of clinical services during the SARS outbreak. RESULTS: There was no significant change in mortality in the Greater Toronto Area before, during, and after the period of the SARS outbreak in 2003 compared to the corresponding time periods in 2002 and 2001. The rate ratio for all-cause mortality during the SARS outbreak was 0.99 [95% Confidence Interval (CI) 0.93–1.06] compared to 2002 and 0.96 [95% CI 0.90–1.03] compared to 2001. An interrupted time series analysis found no significant change in mortality rates in the Greater Toronto Area associated with the period of the SARS outbreak. CONCLUSION: Limitations on access to medical services during the 2003 SARS outbreak in Toronto had no observable impact on short-term population mortality. Effects on morbidity and long-term mortality were not assessed. Efforts to contain future infectious disease outbreaks due to influenza or other agents must consider effects on access to essential health care services.

Population mortality during the outbreak of Severe Acute Respiratory Syndrome in Toronto

Covid-19: death rate is 0.66% and increases with age, study estimates.

We assessed late mortality in 854 individuals who had survived 2 or more years after autologous hematopoietic cell transplantation (HCT) for hematologic malignancies. Median age at HCT was 36.5 years, and median length of follow-up was 7.6 years. Overall survival was 68.8% +/- 1.8% at 10 years, and the cohort was at a 13-fold increased risk for late death (standardized mortality ratio [SMR] = 13.0) when compared with the general population. Mortality rates approached those of the general population after 10 years among patients at standard risk for relapse at HCT (SMR = 1.1) and in patients undergoing transplantation for acute myeloid leukemia (AML; SMR = 0.9). Relapse of primary disease (56%) and subsequent malignancies (25%) were leading causes of late death. Relapse-related mortality was increased among patients with Hodgkin disease (HD; relative risk [RR] = 3.6), non-Hodgkin lymphoma (NHL; RR = 2.1), and acute lymphoblastic leukemia (ALL; RR = 6.5). Total body irradiation (RR = 0.6) provided a protective effect. Nonrelapse-related mortality was increased after carmustine (RR = 2.3) and with use of peripheral blood stem cells (RR = 2.4). Survivors were more likely to report difficulty in holding jobs (RR = 9.4) and in obtaining health (RR = 7.7) or life insurance (RR = 8.4) when compared with siblings. Although mortality rates approach that of the general population after 10 years in certain subgroups, long-term survivors of autologous HCT continue to face challenges affecting their health and well-being.

Late mortality in survivors of autologous hematopoietic-cell transplantation: report from the Bone Marrow Transplant Survivor Study.

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.

Global Mortality Impact of the 1957–1959 Influenza Pandemic

Objective: Severity of the coronavirus disease 2019 (covid-19) has been assessed in terms of absolute mortality in SARS-CoV-2 positive cohorts. An assessment of mortality relative to mortality in the general population is presented. Design: Retrospective population-based study. Setting: Individual information on symptomatic confirmed SARS-CoV-2 patients and subsequent deaths from any cause were compared to the all-cause mortality in the Swiss population of 2018. Starting February 23, 2020, mortality in covid-19 patients was monitored for 80 days and compared to the population mortality observed in the same time-of-year starting February 23, 2018. Participants: 5 160 595 inhabitants of Switzerland aged 35 to 95 without covid-19 (general population in spring 2018) and 20 769 persons tested positively for covid-19 (spring 2020). Measurements: Sex- and age-specific mortality rates were estimated using Cox proportional hazards models. Absolute probabilities of death were predicted and risk was assessed in terms of relative mortality by taking the ratio between the sex- and age-specific absolute mortality in covid19 patients and the corresponding mortality in the 2018 general population. Results: A confirmed SARS-CoV-2 infection substantially increased the probability of death across all patient groups, ranging from nine (6 to 15) times the population mortality in 35-year old infected females to a 53-fold increase (46 to 59) for 95 year old infected males. The highest relative risks were observed among males and older patients. The magnitude of these effects was smaller compared to increases observed in absolute mortality risk. Male covid-19 patients exceeded the population hazard for males (hazard ratio 1.20, 1.00 to 1.44). Each additional year of age increased the population hazard in covid-19 patients (hazard ratio 1.04, 1.03 to 1.05). Limitations: Information about the distribution of relevant comorbidities was not available on population level and the associated risk was not quantified. Conclusions: Health care professionals, decision makers, and societies are provided with an additional population-adjusted assessment of covid-19 mortality risk. In combination with absolute measures of risk, the relative risks presented here help to develop a more comprehensive understanding of the actual impact of covid-19.

Relative Coronavirus Disease 2019 Mortality: A Swiss Population-based Study

Introduction With the pandemic of COVID-19, the number of confirmed cases and related deaths are increasing in the US. We aimed to understand the potential impact of health and demographic factors on the infection and mortality rates of COVID-19 at the population level. Methods We collected total number of confirmed cases and deaths related to COVID-19 at the county level in the US from January 21, 2020 to April 23, 2020. We extracted health and demographic measures for each US county. Multivariable linear mixed effects models were used to investigate potential correlations of health and demographic characteristics with the infection and mortality rates of COVID-19 in US counties. Results Our models showed that several health and demographic factors were positively correlated with the infection rate of COVID-19, such as low education level and percentage of Black. In contrast, several factors, including percentage of smokers and percentage of food insecure, were negatively correlated with the infection rate of COVID-19. While the number of days since first confirmed case and the infection rate of COVID-19 were negatively correlated with the mortality rate of COVID-19, percentage of elders (65 and above) and percentage of rural were positively correlated with the mortality rate of COVID-19. Conclusions At the population level, health and demographic factors could impact the infection and mortality rates of COVID-19 in US counties.

Health and Demographic Impact on COVID-19 Infection and Mortality in US Counties

BACKGROUND The various epidemiological indicators used to communicate the impact of COVID-19 have different strengths and limitations. METHODS We conducted a selective literature review to identify the indicators used and to derive appropriate definitions. We calculated crude and age-adjusted indicators for selected countries. RESULTS The proportion of deaths (case fatality proportion [CFP]; number of deaths/ total number of cases) is commonly used to estimate the severity of a disease. If the CFP is used for purposes of comparison, the existence of heterogeneity in the detection and registration of cases and deaths has to be taken into account. In the early phase of an epidemic, when case numbers rise rapidly, the CFP suffers from bias. For these reasons, variants have been proposed: the "confirmed CFP" (number of deaths/total number of confirmed cases), and the "delay-adjusted CFP," which considers the delay between infection with the disease and death from the disease. The indicator mortality (number of deaths/total population) has at first sight the advantage of being based on a defined denominator, the total population. During the outbreak of a disease, however, the cumulative deaths rise while the total population remains stable. The phase of the epidemic therefore has to be considered when using this indicator. In this context, R0 and R(t) are important indicators. R0 estimates the maximum rate of spread of a disease in a population, while R(t) describes the dynamics of the epidemic at a given time. Age-adjusted analysis of the CFP shows that the differences between countries decrease but do not dis - appear completely. If the test strategies depend on age or symptom severity, however, the bias cannot be entirely eliminated. CONCLUSION Various indicators of the impact of the COVID-19 epidemic at population level are used in daily communication. Considering the relevance of the pandemic and the importance of relevant communications, however, the strengths and the limitations of each parameter must be considered carefully.

Epidemiological Measures in the Context of the COVID-19 Pandemic.

BACKGROUND: The coronavius disease 2019 (COVID-9) caused by the severe acute respiratory syndrome coronavirus 2 reached Spain by 31 January 2020, in April 2020, the Comunidad de Madrid suffered one of the world's highest crude mortality rate ratios. This study aimed to detect risk factors for mortality in patients with COVID-19. METHODS: Our cohort were all consecutive adult patients with laboratory-confirmed COVID-19 at a secondary hospital in Madrid, March 3-16, 2020. Clinical and laboratory data came from electronic clinical records and were compared between survivors and non-survivors, with outcomes followed up until April 4. Univariable and multivariable logistic regression methods allowed us to explore risk factors associated with in-hospital death. FINDINGS: The cohort comprised 562 patients with COVID-19. Clinical records were available for evaluation for 392 patients attended at the emergency department of our hospital, of whom 199 were discharged, 85 remained hospitalized and 108 died during hospitalization. Among 311 of the hospitalized patients, 34.7% died. Of the 392 patients with records, the median age was 71.5 years (50.6-80.7); 52.6% were men. 252 (64.3%) patients had a comorbidity, hypertension being the most common: 175 (44.6%), followed by other cardiovascular disease: 102 (26.0%) and diabetes: 97 (24.7%). Multivariable regression showed increasing odds of in-hospital death associated with age over 65 (odds ratio 8.32, 95% CI 3.01-22.96; p<0.001), coronary heart disease (2.76, 1.44-5.30; 0.002), and both lower lymphocyte count (0.34, 0.17-0.68; 0.002) and higher LDH (1.25, 1.05-1.50; 0.012) per 1-unit increase and per 100 units respectively. INTERPRETATION: COVID-19 was associated in our hospital at the peak of the pandemic with a crude mortality ratio of 19.2% and a mortality ratio of 34.7% in admitted patients, considerably above most of the ratios described in the Chinese series. These results leave open the question as to which factors, epidemiological or intrinsically viral, apart from age and comorbidities, can explain this difference in excess mortality. FUNDING: None.

Prevalence and risk factors for mortality related to COVID-19 in a severely affected area of Madrid, Spain

ABSTRACT Objectives Initial data on Covid-19 infection has pointed out a special vulnerability of elderly people. Design we performed a meta-analysis with available national reports at May 7th 2020 from China, Italy, Spain, United Kingdom and New York State. Analyses were performed by a random effects model and sensitivity analyses were performed for the identification of potential sources of heterogeneity. Setting and Participants: covid-19 positive patients reported in literature and national reports. Measures all-cause mortality by age. Results A total of 611,1583 subjects were analyzed and 141,745 (23.2%) had age ≥80. The percentage of octogenarians was different in the 5 registries being the lowest in China (3.2%) and the highest and the highest in UK and New York State. The overall mortality rate was 12.10% and it varied widely between countries being the lowest in China (3.1%) and the highest in UK (20.8%) and New York State (20.99%). Mortality was <1.1% in patients with age <50 and it increased exponentially after that age in the 5 national registries. As expected, the highest mortality rate was observed in patients ≥80 years old. All age groups had significantly higher mortality compared to the inmediatelly younger age group. The largest increase in mortality risk was observed in patients with age 60-69 compared to 50-59 (OR: 3.13 95% CI 2.61-3.76). Conclusions and implications this metanalysis with more than half-million of Covid-19 patients from different countries highlights the determinant effect of age on mortality with the relevant thresholds on age >50 and, especially, >60. Elderly patients should be priorized in the implementation of preventive measures.

The effect of age on mortality in patients with Covid-19: a metanalysis with 611,583 subjects

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.

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.

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

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.

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

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.

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

Mortality rate and gender differences in COVID-19 patients dying in Italy: A comparison with other countries.

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.

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

Objective: Overall mortality is a relevant indicator of the population burden during an epidemic. It informs on both undiagnosed cases and on the effects of health system disruption. Methods: We aimed at evaluating the extent of the total death excess during the COVID-19 epidemic in Italy. Data from 4433 municipalities providing mortality reports until April 15th, 2020 were included for a total of 34.5 million residents from all Italian regions. Data were analyzed by region, sex and age, and compared to expected from 2015–2019. Results: In both genders, overall mortality was stable until February 2020 and abruptly increased from March 1st onwards. Within the municipalities studied, 77,339 deaths were observed in the period between March 1st to April 15th, 2020, in contrast to the 50,822.6 expected. The rate ratio was 1.11 before age 60 and 1.55 afterwards. Both sexes were affected. The excess was greater in the regions most affected by COVID-19 but always exceeded the deaths attributed to COVID-19. The extrapolation to the total Italian population suggests an excess of 45,033 deaths in the study period, while the number of COVID–19 deaths was 21,046. Conclusion: Our paper shows a large death excess during the COVID-19 epidemic in Italy; greater than the number attributed to it. Possible causes included both the undetected cases and the disruption of the Health Service organization. Timely monitoring of overall mortality based on unbiased nationwide data is an essential tool for epidemic control.

How Large Was the Mortality Increase Directly and Indirectly Caused by the COVID-19 Epidemic? An Analysis on All-Causes Mortality Data in Italy

Epidemiological studies suggest that age distribution of a population has a non-trivial effect on how morbidity rates, mortality rates and case fatality rates (CFR) vary when there is an epidemic or pandemic. We look at the empirical evidence from a large cohort of countries to see the sensitivity of Covid-19 data to their respective median ages. The insights that emerge could be used to control for age structure effects while investigating other factors like cross-protection, co-morbidities, etc.

Covid-19 and Population Age Structure

This study aims to ascertain the long-term epidemic trends of malaria and evaluates the probability of achieving the eradication goal by 2020 in China. Data on malaria incidence and deaths were extracted from the China Information System for Disease Control and Prevention. The epidemic trends by sex, age and spatial distribution and predictions of malaria were estimated by using Joinpoint and Poisson regressions. From 1950 to 2016, 227 668 374 malaria cases were reported in China, with an annualised average incidence of 337.02 (336.98–337.07, 95% confidence interval (CI)) per 100 000 population. The incidence decreased with an average annual per cent change (AAPC) of −11.4% (−16.6 to −6.0). There were 36 085 malaria deaths, with an annualised average mortality of 0.534 (0.529–0.540) per 1 000 000 population. The mortality decreased with an AAPC of −8.7% (−13.7 to −3.4). The predicted number of malaria cases and deaths for 2020 is 2 562 and 10, respectively, and zero for indigenous cases. The disease burden of malaria dramatically decreased in China. Though, the goal of malaria elimination is realistic by 2020 in China, routine clinical and entomological surveillance should be continually conducted, especially for the cross-border areas and imported malaria cases.

Malaria in China: a longitudinal population-based surveillance study

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.

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

Objective: Overall mortality is a relevant indicator of the population burden during an epidemic. It informs on both undiagnosed cases and on the effects of health system disruption. Methods: We aimed at evaluating the extent of the total death excess during the COVID-19 epidemic in Italy. Data from 4433 municipalities providing mortality reports until April 15th, 2020 were included for a total of 34.5 million residents from all Italian regions. Data were analyzed by region, sex and age, and compared to expected from 2015-2019. Results: In both genders, overall mortality was stable until February 2020 and abruptly increased from March 1st onwards. Within the municipalities studied, 77,339 deaths were observed in the period between March 1st to April 15th, 2020, in contrast to the 50,822.6 expected. The rate ratio was 1.11 before age 60 and 1.55 afterwards. Both sexes were affected. The excess was greater in the regions most affected by COVID-19 but always exceeded the deaths attributed to COVID-19. The extrapolation to the total Italian population suggests an excess of 45,033 deaths in the study period, while the number of COVID-19 deaths was 21,046. Conclusion: Our paper shows a large death excess during the COVID-19 epidemic in Italy; greater than the number attributed to it. Possible causes included both the undetected cases and the disruption of the Health Service organization. Timely monitoring of overall mortality based on unbiased nationwide data is an essential tool for epidemic control.