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Although Denmark and Sweden have close cultural and historical ties, lifespans for Danes have generally been lower than those of Swedes. Recent improvements in Danish mortality after a period of stagnation have led to the suspicion that there may be positive trends at the very high ages at death within that population and that these trends could be quite different from those observed in Sweden. Although the mean ages at death for Danish and Swedish centenarians have been relatively constant at about 102 years for the cohorts born 1870-1904, the oldest-old in Denmark have been getting older, but no evidence has suggested any increase in lifespan for Swedes. Using quantile regression, we show that Danish centenarian lifespans in the 90th percentile have been lengthening, with those in 94th percentile (6 % longest-lived individuals) having a trend that is statistically significant at the 5 % level. We demonstrate that the increase observed is not due to the increasing sizes of birth cohorts and thus must be due to improving survival among this select top tier. We postulate that this super-select group in Denmark is best able to take advantage of the factors driving mortality reduction, whereas the majority of centenarians are not. [ABSTRACT FROM AUTHOR]

The digital traces that we leave online are increasingly fruitful sources of data for social scientists, including those interested in demographic research. The collection and use of digital data also presents numerous statistical, computational, and ethical challenges, motivating the development of new research approaches to address these burgeoning issues. In this article, we argue that researchers with formal training in demography-those who have a history of developing innovative approaches to using challenging data-are well positioned to contribute to this area of work. We discuss the benefits and challenges of using digital trace data for social and demographic research, and we review examples of current demographic literature that creatively use digital trace data to study processes related to fertility, mortality, and migration. Focusing on Facebook data for advertisers-a novel "digital census" that has largely been untapped by demographers-we provide illustrative and empirical examples of how demographic researchers can manage issues such as bias and representation when using digital trace data. We conclude by offering our perspective on the road ahead regarding demography and its role in the data revolution. [ABSTRACT FROM AUTHOR]

This study illuminates the association between cigarette smoking and adult mortality in the contemporary United States. Recent studies have estimated smoking-attributable mortality using indirect approaches or with sample data that are not nationally representative and that lack key confounders. We use the 1990-2011 National Health Interview Survey Linked Mortality Files to estimate relative risks of all-cause and cause-specific mortality for current and former smokers compared with never smokers. We examine causes of death established as attributable to smoking as well as additional causes that appear to be linked to smoking but have not yet been declared by the U.S. Surgeon General to be caused by smoking. Mortality risk is substantially elevated among smokers for established causes and moderately elevated for additional causes. We also decompose the mortality disadvantage among smokers by cause of death and estimate the number of smoking-attributable deaths for the U.S. adult population ages 35+, net of sociodemographic and behavioral confounders. The elevated risks translate to 481,887 excess deaths per year among current and former smokers compared with never smokers, 14 % to 15 % of which are due to the additional causes. The additional causes of death contribute to the health burden of smoking and should be considered in future studies of smoking-attributable mortality. This study demonstrates that smoking-attributable mortality must remain a top population health priority in the United States and makes several contributions to further underscore the human costs of this tragedy that has ravaged American society for more than a century. [ABSTRACT FROM AUTHOR]

In this article, we discuss a study by Masters et al. (2014), published in Demography. Masters and associates estimated age, period, and cohort (APC) effects on U.S. mortality rates between 1959 and 2009 using the intrinsic estimator (IE). We first argue that before applying the IE, a grounded theoretical justification is needed for its fundamental constraint on minimum variance of the estimates. We next demonstrate IE's high sensitivity to the type of dummy parameterization used to obtain the estimates. Finally, we discuss challenges in the interpretation of APC models. Our comments are not restricted to the article in question but pertain generally to any research that uses the IE. [ABSTRACT FROM AUTHOR]

Estimates of worldwide mortality from the influenza pandemic of 1918-1919 vary widely, from 15 million to 100 million. In terms of loss of life, India was the focal point of this profound demographic event. In this article, we calculate mortality from the influenza pandemic in India using panel data models and data from the Census of India. The new estimates suggest that for the districts included in the sample, mortality was at most 13.88 million, compared with 17.21 million when calculated using the assumptions of Davis (). We conclude that Davis' influential estimate of mortality from influenza in British India is overstated by at least 24%. Future analyses of the effects of the pandemic on demographic change in India and worldwide will need to account for this significant downward revision. [ABSTRACT FROM AUTHOR]

Mortality patterns and trajectories in closely related populations are likely to be similar in some respects, and differences are unlikely to increase in the long run. It should therefore be possible to improve the mortality forecasts for individual countries by taking into account the patterns in a larger group. Using the Human Mortality Database, we apply the Lee-Carter model to a group of populations, allowing each its own age pattern and level of mortality but imposing shared rates of change by age. Our forecasts also allow divergent patterns to continue for a while before tapering off We forecast greater longevity gains for the United States and lesser ones for Japan relative to separate forecasts. [ABSTRACT FROM AUTHOR]

Using microdata from the Brazilian demographic censuses of 1960, 1970, 1980, and 1991, aggregated into 518 consistently defined spatial units called microregions, we estimated fertility and mortality and constructed indicators of development and living conditions in the rural and urban areas of the microregions in each census. We then estimated cross-sectional and fixed-effects models to answer questions about the degree to which changes in these indicators are associated with changes in fertility and whether the relationship between fertility and development shifts through time. We found strong and consistent relationships between the decline in fertility and measurable changes in social and economic circumstances. [ABSTRACT FROM AUTHOR]

Rectangularization of human survival curves is associated with decreasing variability in the distribution of ages at death. This variability, as measured by the interquartile range of life table ages at death, has decreased from about 65 years to 15 years since 1751 in Sweden. Most of this decline occurred between the 1870s and the 1950s. Since then, variability in age at death has been nearly constant in Sweden, Japan, and the United States, defying predictions of a continuing rectangularization. The United States is characterized by a relatively high degree of variability, compared with both Sweden and Japan. We suggest that the historical compression of mortality may have had significant psychological and behavioral impacts. [ABSTRACT FROM AUTHOR]

This article focuses on the fact that studies of fruit fly actuarial models are relevant in relation to fundamental aspects of mortality, relevant to human populations. Answers to certain basic actuarial questions are important to demography and human biology for several reasons. First, they provide biological perspective for interpreting human actuarial data such as the slowing of mortality at advanced ages. Second, they serve as a stimulus for new approaches to studying aspects of human mortality such as the gender gap or the existence life span limits. Studies of fly mortality provide important insights into the nature of many fundamental actuarial processes important to demography, whether differential rates of aging underlie the gender differences in Iongevity or whether Gompertz mortality rates are manifestations of universal senescence laws. Biology may have little to offer statistical or descriptive areas of demography such as new concepts in life table analysis or advances in census techniques, demographers have much to pin by acquiring a basic understanding and awareness of developments in many different areas. Three areas of biology of importance to demography are evolutionary biology and ecology, experimental demography and molecular biology.