Differences in mortality among families give us clues about the importance of unobserved health-related behaviors. For example, if lower mortality was due to types of personal behavior learned in childhood, it should carry over to mortality at older ages. In this paper we use records from a nineteenth-century Belgian community to look at differences at mortality differences among families in two ways. First, we construct a direct measure of exposure to disease in childhood by counting the number of children in each family that died before age 15. Second, we calculate the overall effect of inter-family differences by using a "random effect" model that estimates the variance of the "family effect". Both of these measures show a strong family effect in childhood, but this effect diminishes after age 15 and disappears after age 55. Moreover, in a period still dominated by infectious diseases, those who survived diseases in childhood acquired immunities that helped them in later life.
Causality is an important but complicated issue, not only within social sciences in general but also within economic and historical demography. Here we are dealing with two different, but related, problems of causality. The first is to what extent the impact of food prices on mortality is biased when selecting on years with mortality crises. The second concerns the problem of mixing factors that directly and indirectly have an impact on mortality. Dealing with the first problem, we compare the effects of food prices on child and adult mortality when selecting on mortality crises with a standard approach without selection. When dealing with the second problem we use the additive hazards model, in combination with dynamic path analysis, which allows for investigating the mediating effect of intermediate covariates in a causal framework. We use individual level data from the Scanian Economic Demographic Database for five rural parishes for the period 1766 to 1865. Data on food prices refers to the local area of these parishes. The statistical analyses are performed in the R statistical computing environment, especially with the aid of the package eha. The main findings are that selecting on mortality crises created a large bias in the direction of overestimating the impact of food prices and that that the direct effects of food prices are dominating.
With an emphasis on social science applications, Event History Analysis with R, Second Edition, presents an introduction to survival and event history analysis using real-life examples. Since publication of the first edition, focus in the field has gradually shifted towards the analysis of large and complex datasets. This has led to new ways of tabulating and analysing tabulated data with the same precision and power as that of an analysis of the full data set. Tabulation also makes it possible to share sensitive data with others without violating integrity.
The new edition extends on the content of the first by both improving on already given methods and introducing new methods. There are two new chapters, Explanatory Variables and Regression, and Register- Based Survival Data Models. The book has been restructured to improve the flow, and there are significant updates to the computing in the supporting R package.
With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.
Features:
A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.
This paper is focusing, first, on the very concept of aging, what does it mean for a population, and what does it mean to an individual. Second, on the individual level, is longevity clustered within certain families (family trees)?
The impact of the family’s socioeconomic status at marriage on later child births during thedemographic transition (1821–1950) is studied. It is found that the fertility decline starts inthe upper classes in the decades prior to 1900. The farmers are characterized by relativelyhigh fertility in high ages even in the end of the study period.A stopping behavior seems to dominate a spacing one, especially along cohorts. The effectof declining infant mortality over time is minor.
The impact of the family's socioeconomic status at marriage on later child births during the demographic transition (1821--1950) is studied. It is found that the fertility decline starts in the upper classes in the decades prior to 1900. The farmers are characterized by relatively high fertility in high ages even in the end of the study period. A stopping behavior seems to dominate a spacing one, especially along cohorts. The effect of declining infant mortality over time is minor. We show how efficient statistical modeling leads to easy and fast estimation of rather complicated data. The tools are statistical sufficiency and data reduction combined with models for fertility behavior, stopping and spacing.
Two research questions are answered: (i) Is human life unlimited?, and (ii) Are there any differences in survival at extreme age between women and men, or between different socioeconomic groups? Mortality above the age of 105 years is studied. The answer to the first question is Yes, and to the second question the answer is No.
A variation of the Bourgeois-Pichat biometric analysis of infant mortality is suggested. In the original model, cumulative mortality in the last eleven months of infancy is assumed to follow a uniform distribution given a log-cube transformation of age. Instead, we assume an exponential distribution. The difference is that while the denominator is constant in the Bourgeois-Pichat model, equal to the number of births, in our model, the denominator is the current population at risk. We argue that our assumption is more satisfactory from a theoretical point of view, since it focus on the conditional probability of dying. Our model gives different estimates of endogenous and exogenous mortality and, in addition, the model fit is slightly better, especially in cases with higher levels of infant mortality.
Previous research has shown that the disease load experienced during the birth year, measured as the infant mortality rate, had a significant influence on old-age mortality in nineteenth-century rural Sweden. We know that children born in years with very high rates of infant mortality, due to outbreaks of smallpox or whooping cough, and who still survived to adulthood and married, faced a life length several years shorter than others. We do not know, however, whether this is a direct effect, caused by permanent physical damage leading to fatal outcomes later in life, or an indirect effect, via its influence on accumulation of wealth and obtained socio-economic status. The Scanian Demographic Database, with information on five rural parishes in southern Sweden between 1813 and 1894, contains the data needed to distinguish between the two mechanisms. First, the effects of conditions in childhood on obtained socio-economic status as an adult are analyzed, then the effects of both early-life conditions and socio-economic status at various stages of life on old-age mortality. By including random effects, we take into account possible dependencies in the data due to kinship and marriage. We find that a high disease load during the first year of life had a strong negative impact on a person's ability to acquire wealth, never before shown for a historical setting. This means that it is indeed possible that the effects of disease load in the first year of life indirectly affect mortality in old age through obtained socio-economic status. We find, however, no effects of obtained socio-economic status on old-age mortality. While the result is interesting per se, constituting a debatable issue, it means that the argument that early-life conditions indirectly affect old-age mortality is not supported. Instead, we find support for the conclusion that the effect of the disease load in early-life is direct or, in other words, that physiological damage from severe infections at the start of life leads to higher mortality at older ages. Taking random effects at family level into account did not alter this conclusion.
This essay explores the role played by the inheritance on human longevity. We estimate a model of overall mortality among married persons aged 50 years and above taking genetic as well as socioeconomic factors into account. We consider whether these factors have temporal or long-lasting effects on health. The demographic and economic individual level data come from the Scanian Demographic Database. These data cover five rural parishes in the southernmost part of Sweden for the period 1813-1894. To these, local grain prices, as an indicator of food costs, and the local infant mortality rate, as an indicator of the disease load, have been added. We find that age of death of the mother and the father have persistent impacts on their adult children's overall mortality regardless of sex, even after controlling for socioeconomic an environmental factors throughout the life course. In addition, we find strong birth cohort effects and effects of the disease load in the first year of life on male offspring. We are, however, unable to find any effects of socioeconomic status, neither at the time of birth or achieved later in life, a result consistent with earlier findings.
Recent regional studies on adult mortality and socio-economic status inSweden are merged and also completed with analyses from country-widecensuses in strategic time periods, with the purpose to find out whetherthe locally drawn conclusions about a changing social gradient in mortalitystill holds.The answer is firmly positive: While the upper classes have definiteadvantage in modern time (after, say, the 1960s), the reverse situationholds during the nineteenth and early twentieth century for men. Women, onthe other hand, seem to follow the expected pattern of a positive socialgradient through the last 200 years.
The statistical analysis of mixed effects models for binary and count data is investigated. In the statistical computing environment R, there are a few packages that estimate models of this kind. The packagelme4 is a de facto standard for mixed effects models. The packageglmmML allows non-normal distributions in the specification of random intercepts. It also allows for the estimation of a fixed effects model, assuming that all cluster intercepts are distinct fixed parameters; moreover, a bootstrapping technique is implemented to replace asymptotic analysis. The random intercepts model is fitted using a maximum likelihood estimator with adaptive Gauss–Hermite and Laplace quadrature approximations of the likelihood function. The fixed effects model is fitted through a profiling approach, which is necessary when the number of clusters is large. In a simulation study, the two approaches are compared. The fixed effects model has severe bias when the mixed effects variance is positive and the number of clusters is large.
The maximum likelihood and maximum partial likelihood approaches to the proportional hazards model are unified. The purpose is to give a general approach to the analysis of the proportional hazards model, whether the baseline distribution is absolutely continuous, discrete, or a mixture. The advantage is that heavily tied data will be analyzed with a discrete time model, while data with no ties is analyzed with ordinary Cox regression. Data sets in between are treated by a compromise between the discrete time model and Efron's approach to tied data in survival analysis, and the transitions between modes are automatic. A simulation study is conducted comparing the proposed approach to standard methods of handling ties. A recent suggestion, that revives Breslow's approach to tied data, is finally discussed.
Maternal access to food during pregnancy affects birth weight and other characteristics of offspring. It has been suggested that fluctuations in food availability during infancy, ranging from plentiful to starvation, may influence cerebro-cardiovascular risk factors for the offspring during adult life. This study was designed to test the correlation between food availability changes during life before birth and adult sudden death from disease. This was a follow-up study of ancient cohorts in the parish of Skelleftea, Sweden, comprising 7,572 individuals born between 1805 and 1849 and still alive at age 40. Food availability variations in the parish during their prenatal life were ascertained from historical sources, the main outcome measures being overall mortality and mortality from sudden death in the age range 40-70 years. The risk of sudden death was almost doubled for those whose mothers were struck by a poor harvest during the early stages of pregnancy, but who experienced a good harvest toward the end. Yet almost the same over-risk was evident for the converse case: plentiful food supply in early pregnancy followed by a poor harvest toward the end. A stable maternal access to food during pregnancy is important for the offspring's risk of sudden death from cerebro- and cardiovascular disease as an adult.
Focusing on two regions in northern Sweden 1801–2013, we challenge common notions of the assumed advantage in survival of belonging to a high social class. The issue is analysed according to gender and age group (adults and elderly) and in relation to the development of economic inequality. The results show that high social class is not always favourable for survival. Men in the elite category, particularly in working age, had higher mortality compared to others during a large part of the studied period; a male mortality class reversal appears at a surprisingly late date, while the social gradient among women conforms to the expected pattern. We suggest that health-related behaviour is decisive not only in later but earlier phases of the mortality transition as well. The results implicate that the association between social class and health is more complex than is assumed in many of the dominant theories in demography and epidemiology.
Focusing on two regions in northern Sweden 1801–2013, we challenge common notions of the assumed advantage in survival of belonging to a high social class. The issue is analysed according to gender and age group (adults and elderly) and in relation to the developmentof economic inequality. The results show that high social class is not always favourable for survival. Men in the elite category had higher mortality compared to others during a large part of the studied period; a male mortality class reversal appears at a surprisingly late date, while the social gradient among women conforms to the expected pattern. Wesuggest that health-related behaviour is decisive not only in later but earlier phases of the mortality transition as well. The results implicate that the association between social class and health is more complex than is assumed in many of the dominant theories in demography and epidemiology.
We investigate the development of social inequality in Swedish mortality over the life course in the elderly and adult population during the mortality transition. The study focuses on two main questions, the first relate to the long-term change in social differences in mortality. The second question is whether socio-economic position have less impact on the elderly population compared to population in working age and if the age pattern of social inequalities has changed from the 19th century to the present. Furthermore, in this study we consider possible gender-specific patterns in this process. The development of mortality in different social classes is analysed according to both total mortality and major cause-of-death categories. For the later periods, we also compare the results from the class-based analysis with other measures of social position, in this case income and education. Focus is on mortality in the Skellefteå and Umeå regions in northern Sweden 1851-2013. The study is based on the historical population data from the Demographic Data Base, Umeå University and modern population register data from Statistics Sweden.
BACKGROUND
Social position is one of the major determinants of health. Less is known about its effect in historical contexts. Previous studies have shown surprisingly small effects of social class in working age populations. Not much is known about social differences in health among the elderly in history.
OBJECTIVE
The present paper analyses social differences in health among the elderly (60+) in the Sundsvall region in northern Sweden during the 19th century. We investigate whether social mortality differences are particularly apparent in old age when unpropertied groups lost their most important asset for survival: their capacity to work.
METHODS
The data, representing 9,535 fatal events, are analysed using a Cox regression model, assuming proportional hazards.
RESULTS
Social class had no significant effect for women during the pre-industrial period, while only those with unknown social position had higher mortality among men. During the industrial period female mortality was lowest in the skilled working class and highest in the upper class. Social position was not significant for men in the full model. Urban mortality was 30% higher for women and 59% higher for men during the pre-industrial period compared to the peripheral parishes.
CONCLUSIONS
The results lead us to question the accepted 'fact' of social health differences as a historical constant. Higher social position did not lead to better survival, and social differences in mortality did not increase in old age, despite the fact that the elderly were a highly vulnerable group. Instead, the spatial aspects of mortality were important, particularly during the pre-industrial period.
This article considers the interfamily clustering of infant mortality (defined as mortal- ity during the first year of life). We developed and evaluated statistical tools to detect clustering and a measure to quantify excess clustering for nineteenth-century data from Skellefteå, Sweden. The detection was performed using the standard methods of gener- alized linear models and logistic regression. The index of clustering was constructed by comparing the observed numbers of families with specific numbers of deaths to the cor- responding observed numbers, after correcting for explanatory variables. To the best of our knowledge, no clustering index of this kind has ever been created.