Biodemography

Some biologists have observed a close connection between game theory and evolution theory. Indeed, game theory allow biologists and economists to predict the outcome of some evolutionary processes. According to the Red Queen Hypothesis (Van Valen 1973), every species is a priori equally likely to get extinguished, regardless of the time during which it has existed. Each species is part of a zero-sum game against other species. This hypothesis refers to a kind of evolutionary arms race, where players constantly evolve together. We will consider its relevance for demographic development. Just as rat races among financial engineers can lead to a financial crisis (Aoki 2010) and competition without ceiling among lawyers can produce the bankruptcy of a law firm (Landers et al 1996), biotechnological competition can produce environmental crises. We will address these issues with the models of the theory of noncooperative games (arms races, rat races, etc).

Short inter-pregnancy intervals are associated with poorer birth outcomes and child survival. Adverse effects of short intervals for maternal outcomes have also been reported. Stresses arising from depleted nutrition or strains associated with raising two children close in age might be hypothesised to also have longer term effects on parental health. Such possible longer term implications have rarely been investigated but two UK studies suggest negative effects of short birth intervals for mortality and disability in later life. We investigate associations between inter-birth intervals and mortality risks in late middle age using high quality register data for the whole Norwegian population. Associations with use of prescription medication are also investigated. We focus on parents of two-four children and control for age, year of observation, education, marital status, age at first birth, parity and, in some models, change in co-parent since the previous birth. Results show negative effects of birth intervals of less than 18 months on mortality risks, and medication use, for mothers and fathers later in the life course and raised risks for mothers of twins. Results suggests the operation of biosocial pathways which, together with possible confounding via various selective influences, need further investigation.

Far from being an asocial process, reproductive decision-making in humans is affected by family and other social networks. However, the direction and strength of kin effects is inconsistent across studies. Explaining this variation requires a better understanding of what various fertility outcomes mean functionally and of each individual’s strategic interests. Fertility metrics such as age at first birth, interbirth intervals, and total number of births are not as clearly related to a woman’s fitness as say, child survival rates. Furthermore, these outcomes are functionally distinct, and decisions about them can be made independently of one another. Additionally complicating matters, woman and her kin do not always have the same strategic considerations. In this paper, we develop a framework for comparing various accounts of why kin affect fertility. We test these possibilities by compiling and analyzing a database of kin effects on fertility from 130 articles from pre- and post-demographic transition settings. Focusing on how parents and in-laws might affect different fertility outcomes, we find that parents are more likely to have anti-natal effects on a woman’s total fertility, and age at first birth, while in-laws are more consistently pro-natal. However, both parents and in-laws tend to shorten inter-birth intervals.

Based on evolutionary theory, Trivers and Willard (TW) predicted that high-status parents display male-biased and low-status parents female-biased parental investment and sex ratios. In previous research we demonstrated that the TW effect does not show in parental gender preferences as expressed in differential fertility stopping (Kolk & Schnettler 2012) and likely also not in parental investment (Schnettler 2010). Research on the TW effect in sex ratios at birth remains mixed, however. Part of the reason for mixed results is that generally, the expected effect is small in human populations. Here we look at how sensitive the TW effect is to different status indicators and what role female employment status plays in mediating the TW effect. We use logistic regression on micro level data to examine the effect of socioeconomic status on the sex ratio at birth. We use individual data on births and individuals from Swedish administrative register data (N≈ 2 000 000 births). A wide arrange of socioeconomic traits are analyzed including income, wealth, occupational class and educational level. Early analysis reveals an ambiguous pattern that is contrary to the Trivers-Williard effect.

Many industrialized societies experienced massive changes in both the postponement of age at first birth (tempo) and a drop in the total number of offspring (quantum) (Kohler, Billari & Ortega 2002; Mills et al. 2011). We carry out the first genome-wide-association-search (GWAS) meta-analysis to identify genes associated with age at first birth (AFB) and number of children ever born (NEB). GWAS represents a considerable methodological advance from previous approaches such as candidate-gene studies (Ku, Pawitan & Chia 2009). Previous research has demonstrated a genetic component to biological fecundity (Stolk et al, 2009) with twin studies estimating heritability of fertility behavior at approximately 40% (Kohler et al. 1999). Although research suggests an underlying genetic architecture of fertility behavior, the identification of genes is yet to be determined. Our meta-analysis combines GWAS findings of approximately 100,000 individuals from 52 cohorts, with preliminary results of the discovery stage completed by the IUSSP Conference.

Studying interplays between Socio-Demographic structures, food and nutrition has been a very essential part of demographic research. This study attempt to provide an understanding of the food consumption and nutritional patterns in Iran with respect to Socio-Demographic variables. The analyses are based on primary data from Household Earning and Income survey conducted by SCI in 2010. National Nutrition and Technology Research Institute in Iran decelerated that Food consumption pattern in Iran indicate that % 20 of people faced with the energy deficient, about 40 to 50 percent were deficient in calcium and vitamins A and B2 while about %40 of Iranian Households get over %120 required energy. During a decade (1985-1995) two types of development in urban and rural household food diet had been created. The first were changing in quantity of nitration diet and second was displacement between food baskets.
Generally speaking, results are indicate that food and nutrition diet patterns are different by household Socio-Demographic characteristics such as family and structure, type of residence, household with retiree person, household's income, head of household's education, employment status.

It is a common assumption that men desire more children than women do in high fertility countries. Evolutionary demographers have tried to explain females’ relatively lower desired fertility by invoking the larger costs faced by women, a woman’s desire to invest more in each child, and a woman’s certainty that her children are genetically related to her. There are known trade-offs between the quantity and quality of children, but no clear reason why women and men should experience these differently. Furthermore, in the simplest scenario, in the case of strict (i.e. life-long) monogamy, since a man’s lifetime reproductive success is tied to that of his reproductive partner, both individuals should have the same optimal family size. In this paper we develop a theoretical framework for examining the circumstances under which we expect conflicts of interest between partnered men and women in their fitness-maximizing optimal number of offspring. Using evolutionary simulations and dynamic programming to model behaviour, we examine the effects of paternity uncertainty, reproductive senescence, and the ability to switch or acquire more partners on the extent to which women and men will have different optima regarding the numbers of offspring and the pace of reproduction.

Allostatic load has been conceptualised as a biological burden induced by chronic stress which predicts health outcomes. Few studies have investigated whether this is the case. We investigate reciprocal associations between allostatic load, limiting long-term illness and self-rated health using cross-lagged longitudinal modelling. The sample included men and women aged 52+ who participated in Wave 2 (2004) and Wave 4 (2006) of English Longitudinal Study of Ageing (n = 4,688 in wave 4). Allostatic load was measured with nine biomarkers using a multisystem summary approach. Self-rated health was measured using a global 5-point indicator; the measure of limiting tong-term illness was dichotomous. Autoregressive cross-lagged models between allostatic load, limiting long-term illness and self-rated health in waves 2 and 4 were tested. Models were adjusted for age, gender and education in wave 2, and for time-varying variables of being married, wealth, physical activity, smoking, and social support. Allostatic load predicted limiting long-term illness. The association between allostatic load and self-rated health was reciprocal, and the strength of the estimates suggested that the path from poor self-rated health to low allostatic load score was stronger than the path from low allostatic load to poor self-rated health.

This research considers whether the availability and investments of parents and partners influences women’s birth progressions, and whether interactions between these investments and socioeconomic position (SEP) are observed. There is ample evidence that parents and partners influence women’s fertility throughout the world. Parents may influence women’s fertility through proximity, childcare provision, financial transfers, and emotional support. Investments of partners in household tasks are positively linked to fertility desires. SEP is associated with fertility, but also availability and investments of parents and partners. Our analysis of Generations and Gender Survey data confirm different availabilities of parents and partner investments by SEP: wealth positively relates to partner investments and the proportions of women with living parents. Results from discrete-time event history models indicate that education level and partner investments negatively relate to birth progressions, but that these effects differ by education: partner investments have a less negative relationship to the probability of birth for highly educated women than women with lower completed education. Further analyses will be used to determine if the effects of family and SEP may vary depending on women’s parity progressions.

Many biodemographic studies incorporate biomarkers of physiological dysregulation and/or allostatic load. However, it is not clear how well these measures represent a global process of dysregulation, nor whether the various biomarkers incorporated into summary measures have been appropriately weighted or considered. Here we introduce a measure based on statistical distance – how common or rare a combination of variables is, according to its multivariate distribution – as an alternative approach to measuring physiological dysregulation. The measure is based on the principle that the more “strange” an individual’s biomarker profile is relative to a baseline population (used to define the distribution, ideally composed of young, healthy individuals), the more dysregulation is occurring. We use three examples to show strengths and limitations of the method. The biomarkers included must be chosen carefully (based on statistical or biological criteria) and the interpretation should be made in light of the variables included. Suites of appropriately chosen variables can help illuminate underlying physiology as well as serve as useful proxies for both mortality risk and biological age. Individual trajectories of statistical distance and in multivariate space are extensions that may help in understanding mortality and disease risk.