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Background: During the coronavirus disease 2019 (COVID-19) pandemic, all European countries were hit, but mortality rates were heterogenous. The aim of the current paper was to identify factors responsible for this heterogeneity., Methods: Data concerning 40 countries were gathered, concerning demographics, vulnerability factors and characteristics of the national response. These variables were tested against the rate of deaths per million in each country. The statistical analysis included Person correlation coefficient and Forward Stepwise Linear Regression Analysis (FSLRA)., Results: The FSLRA results suggested that 'days since first national death for the implementation of ban of all public events' was the only variable significantly contributing to the final model, explaining 44% of observed variability., Discussion: The current study suggests that the crucial factor for the different death rates because of COVID-19 outbreak was the fast implementation of public events ban. This does not necessarily mean that the other measures were useless, especially since most countries implemented all of them as a 'package'. However, it does imply that this is a possibility and focused research is needed to clarify it, and is in accord with a model of spreading where only a few superspreaders infect large numbers through prolonged exposure., (© The Author(s) 2020. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Embryonic development is of great importance because it determines congenital anomalies and influences their severity. However, little is known about the actual probabilities of success or failure and about the nature of early embryonic defects. Here, we propose that the analysis of embryonic mortality as a function of post-fertilization time provides a simple way to identify major defects. By reviewing the literature, we show that even small initial defects, e.g., spatial cellular asymmetries or irregularities in the timing of development, carry with them lethal effects in subsequent stages of embryogenesis. Although initially motivated by human study, in this contribution, we review the few embryonic mortality data available for farm animals and highlight zebrafish as a particularly suited organism for such a kind of study because embryogenesis can be followed from its very beginning and observed easily thanks to eggshell transparency. In line with the few other farm animals for which data are available, we provide empirical evidence that embryonic mortality in zebrafish has a prominent peak shortly after fertilization. Indeed, we show how subsequent mortality rates decay according to a power law, supporting the role of the early embryonic mortality peak as a screening process rapidly removing defective embryos.

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

Drowning risk factors may be identified by comparing drowning incidence rates for comparable at-risk populations but precise methods are lacking. To address this knowledge gap, an ecological study extrapolated crude time-duration exposure to water for a specified at-risk sample of surf bathers to estimate the bather population for all wave-dominated beaches in Victoria, Australia, over a four-year summer season period. An incidence rate was calculated using surf bather drowning deaths frequencies matched for time and location. For the sample, 47,341 hours of surf bathing were estimated from 177,528 bathing episodes. Generalising these results to Victoria, the crude drowning deaths incidence rate in the summer season was 0.41 per 1,000,000 person-hours of surf bathing (95% CI 0.37-0.45). Further application of the method, particularly in open water settings, may be used to identify candidate drowning risk factors to advance drowning prevention strategies.

We conducted a systematic review and meta-analysis to investigate the associations between menarcheal age and all-cause and cardiovascular death. Medline, Embase, Scopus, and Web of Knowledge were searched for articles published prior to March 2013 reporting on the associations between menarcheal age and death from all causes or from cardiovascular disease (total cardiovascular disease, ischemic heart disease (IHD), and stroke) in adult women. Nine articles were eligible for inclusion; these reported 5 estimates each for death from all causes and total cardiovascular death, 6 estimates for IHD, and 7 estimates for death from stroke. Our meta-analysis showed that each 1-year increase in age at menarche was associated with a 3% lower relative risk of death from all causes (pooled hazard ratio = 0.97, 95% confidence interval: 0.96, 0.98) with low heterogeneity (I(2) = 32.2%). Meta-analysis of 2 cohorts showed a higher risk of death from all causes for women who experienced early menarche (at <12 years of age) versus "not early" menarche (at ≥ 12 years of age) (pooled hazard ratio = 1.23, 95% confidence interval: 1.10, 1.38; I(2) = 0%). An inverse association between age at menarche and death from IHD was observed only among nonsmoking populations or populations with low prevalence of smoking. We found no evidence of association between age at menarche and death from all cardiovascular diseases or stroke. Early menarche was consistently associated with higher risk of death from all causes. Further studies are needed to clarify the role of menarcheal age on cardiovascular outcomes and to investigate the potential modifying role of smoking., (© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Unobserved differences in individual's susceptibility to death are an important aspect in the analysis of contemporary mortality patterns. However, observed mortality rates at adult ages, which are usually well described by a Gompertz curve, are often perceived inconsistent with frailty models of mortality. The authors therefore propose a modified DeMoivre hazard function that is suitable for the application of frailty models to adult and old ages. The proposed hazard increases faster than exponential, and when combined with unobserved frailty it can capture a broad range of patterns encountered in the analysis of adult mortality. The authors' application to Bulgaria during 1992-93 suggests that the stronger selection process in the male population, caused by an overall higher level of mortality, may constitute a primary mechanism leading to the convergence of male and female mortality at higher ages. Hence, the convergence between male and female mortality is not necessarily caused by differential process of aging across sexes, but is merely a consequence of the different levels of mortality at adult ages.

Compression of mortality is measured here in four ways: 1) by standard deviation of the age at death above the mode; 2) by standard deviation of the age at death in the highest quartile; 3) by the inter-quartile range; and 4) by the shortest age interval in which a given proportion of deaths take place. The two first-mentioned are directed at old ages, while the other two measure compression over the entire age range. The fourth alternative is recommended as the most suitable for general use and offers several variations, called the C-family of compression indicators. Applied to historical and modern populations, all four measures show convincingly that the secular transition from high to low mortality has been accompanied by general and massive compression of mortality. In recent decades, however, this development has come close to stagnation even when life expectancy continues to increase. This has happened at a level where compression is still so incomplete that the shortest age interval in which 90% of deaths occur, is 35 years. It seems unrealistic to expect human mortality ever to be compressed into so narrow an age interval that the survival curve would even approximately rectangular. It is considered useful to monitor changes in the compression of mortality because the indicators describe relevant aspects of the length of life and may acquire new significance as indicators of population heterogeneity.

Background: Global and regional estimates show that non-communicable diseases in old age are rising in importance relative to other causes of ill health as populations age, and as progress continues against communicable diseases among infants and children. However, these estimates, which cover population groups at all income levels, do not accurately reflect conditions that prevail among the poor. We estimated the burden of disease among the 20% of the global population living in countries with the lowest per capita incomes, compared with the 20% of the world's people living in the richest countries., Methods: Estimates for the global poorest and richest 20% were prepared for 1990 for deaths and disability-adjusted life years (DALYs), by a procedure used in a prominent recent study of the global disease burden. Projected mortality rates in the year 2020 were established for the world's poorest and richest 20% under various assumptions about the future rate of decline in communicable and non-communicable diseases., Findings: In 1990, communicable diseases caused 59% of death and disability among the world's poorest 20%. Among the world's richest 20%, on the other hand, non-communicable diseases caused 85% of death and disability. A raised baseline rate of communicable disease decline between 1990 and 2020 would increase life-expectancy among the world's poorest 20% around ten times as much as it would the richest 20% (4.1 vs 0.4 years). However, the poorest 20% would gain only around a quarter to a third as much as the richest 20% from a similar increase in non-communicable diseases (1.4 vs 5.3 years). As a result, a faster decline in communicable diseases would decease the poor-rich gap in 2020, but under an accelerated rate of overall decline in non-communicable diseases, the poor-rich gap would widen., Interpretation: Our estimates are crude, but despite their limitations, they give a more accurate picture of changes in attributable mortality among the world's poor than do the global averages in current use.

Background: In Spain, studies on social inequalities in mortality based on individuals are few due to the poor quality of information on occupation in death certificates. This study looks at the differences in mortality according to educational level, using individual information obtained through the linkage between the Death Register and the Municipal Census, in the cities of Madrid and Barcelona, Spain., Methods: The study populations were residents of Madrid and Barcelona aged >24 years, who died in 1993 and 1994. Indicators obtained for each city and educational level were: age- and sex-specific mortality rates, and life expectancy at 25 years. Poisson regression models were fitted to obtain the relative risk (RR) of death for each educational level with respect to the reference level (higher education completed), adjusted for age., Results: The mortality rate was lower among individuals with higher educational levels, while life expectancy at 25 years was higher. In both cities men and women with no education showed the highest mortality in all age groups, with very high RR in the youngest age group (RR for men aged 25-34 years = 7.08 in Madrid and 6.02 in Barcelona, whereas in women these RR were 6.33 and 5.63 respectively). In Barcelona the greater part of the overall mortality difference for the group aged 25-34 years was due to AIDS (acquired deficiency syndrome, 33.4% in men and 59.3% in women)., Conclusion: The present study has found higher mortality (mainly from AIDS) among individuals with no academic qualifications thus drawing attention to the need to implement policies aimed at reducing these inequalities.

Background: Change in life expectancy may be more readily appreciated by a lay person as a measure of risk than the standardized mortality ratio (SMR)., Methods: The linear increase in the logarithm of the age-specific mortality rates with age (the Gompertz function) is used to deduce formulae connecting SMR with change in life expectancy. Their validity is checked by a comparison between the 1992 and 1952 mortality data for England and Wales, and between smokers and non-smokers in the American Cancer Society's second Cancer Prevention Study., Results: It is shown that the Gompertz function is a good fit to mortality data for England and Wales from age 30 years upwards. Changes in life expectancy at ages 15, 25, 45 and 65 are presented for values of SMR from 0.5 to 3. A very simple formula connecting the two is valid at ages 15 and 25, and provides a reasonable approximation at age 45., Conclusions: The Gompertz relationship can be used to calculate the change in life expectancy corresponding to a particular SMR over a greater range than have previous methods, and, although subject to some uncertainties, can provide a quick method of judging the change in life expectancy that is associated with a given SMR value.

"Three scenarios of future mortality trends are presented for all countries of the European Economic Area. The high scenario assumes a considerable decline in mortality rates. Consequently life expectancy at birth will continue to increase at about the same rate as during the last decades.... In the low scenario only a small decline in death rates is assumed. This scenario assumes that there will be no convergence between countries. Moreover the current differences between male and female mortality are assumed to persist.", (excerpt)

Background: To avoid the usual problems of multi-population correlation studies of air pollution and mortality, and for reasons of convenience, daily time-series mortality studies within single populations have recently become popular in air pollution epidemiology. Such studies describe how the short-term distribution of deaths relates to short-term fluctuations in air pollution levels. The regression-based risk coefficients from these acute-effects studies have been widely used to estimate the excess annual mortality within a population with a specified average level of air pollution. Such calculations are inappropriate. Since daily time-series data provide no simple direct information about the degree of life-shortening associated with the excess daily deaths (many of which are thought to be due to exacerbation of well-advanced disease, especially cardiovascular disease), such data cannot contribute to the estimation of the effects of air pollution upon chronic disease incidence and long-term death rates. Yet it is that category of effect that is of most public health importance., Conclusion: Such effects are best estimated from long-term cohort studies that incorporate good knowledge of local (or personal) exposure to air pollutants and of potential confounders. Time-series studies, properly evaluated, can identify the existence of acute toxic effects of transient peak levels of air pollution; they are thus useful for monitoring acute toxicity and for identifying the most noxious pollutants. However, to quantify the long-term health impacts of air pollution we cannot use acute-effects data.

"This study uses data on perceived and actual mortality risks to test several alternative Bayesian models of the factors influencing risk beliefs. The analysis...indicates that while the hazard rate for the individual age group is an influential factor, the overall population death rate and the discounted expected number of life years lost due to the cause of death are also influential in affecting risk perceptions.... The predictive power of a linear perception model increases with the level of the risk and is least accurate for very small risks.", (excerpt)

This paper examines the quality of age reporting on death certificates of elderly African-Americans by major causes of death. We utilize a sample of death certificates linked to early census records and to Social Security Administration records to establish a "true" age at death. We then examine the patterns, predictors, and consequences of age misreporting for major causes of death. We find a pattern of greater age misreporting among females, identify educational background as a key predictor of accurate age reporting, and show that mortality crossovers are eliminated for most causes of death when more accurate age data are used.

Background: Many European countries have in recent decades reported growing socioeconomic differentials in mortality. While these trends have usually paralleled high unemployment and increasing income disparities, Sweden had low unemployment and narrowing income differences. This study describes trends, 1961-1990, in total and cardiovascular mortality among men, 45-69 years of age, in major occupational classes in Sweden., Methods: From census data four cohorts were created from those enumerated in 1960, 1970, 1980 and 1985. Through record linkage with the Swedish cause of death registry the mortality in each cohort was followed for 5-10 years. Age-standardized mortality trends 1961-1990 were calculated for occupational groups, categorized according to sector of the economy., Results: The increase in mortality among middle-aged men in Sweden 1965-1980 was mainly a result of increasing cardiovascular mortality among industrial workers and farmers. In the 1980s the trend for these groups changed into a last decrease in mortality similar to that for non-manual occupations for the whole period. Consequently the rate ratio for industrial workers in comparison with men having a professional/managerial type of occupation increased from 0.98 to 1.43. The slowest decrease is now found among unqualified occupations in services and transportation., Conclusions: While Sweden, during the period studied, had narrowing income differentials and low unemployment this result points to the importance of working conditions in understanding trends and distribution of male adult mortality.

This the second of two feature articles at the end of this issue. "In Hong Kong, heart diseases have been the second leading cause of death, after cancer, since the 1960s.... The purpose of this article is to examine the trends of mortality and morbidity of heart diseases in Hong Kong from 1971 to 1994. Age and sex differentials of deaths due to heart diseases are also analysed.... In general, the number of deaths due to heart diseases increased with age for either sex and males were more vulnerable to heart diseases than females. The age-specific death rates of either sex were quite stable over time and no increasing trend was observed. Compared with the crude death rate due to heart diseases, the standardized death rate showed a marked downward trend. These suggest that the increase in deaths due to heart diseases was mainly due to the ageing population.", (excerpt)

The author analyzes trends in premature mortality in Poland from 1960 to 1995. "Over the last 35 years the percentage of premature deaths in Poland decreased systematically: from 55.5% to 33.2%.... The intensity of deaths among males over 30 [has increased rapidly]; during the same period the intensity of deaths among females was relatively stable, with a slight downward tendency.", (excerpt)