Poster session on Biodemography

A substantial proportion of child deaths in sub-Saharan Africa are caused by diseases which could be prevented through greater levels of health-seeking behaviour. Socioeconomic and structural barriers in parental use and engagement with modern health technologies, such as bed net usage, vaccination and seeking medical treatment are well-established by population health scientists. However, understanding of how broader socio-demographic and child-level characteristics influence health-seeking behaviour remains limited. In this study, using data from the Demographic and Health Surveys, we explore the determinants of such behaviours from the perspective of human behavioural ecology (HBE), a framework notable for emphasizing the inherent resource allocation trade-offs parents face when investing in children, and for considering human behaviour as adapted to maximize lineage survival rather than individual health. Preliminary analyses confirm that wealth and education increase odds of health-seeking behaviour, but also demonstrate substantial differences in health-seeking behaviour by maternal age, marital status and child sex and birth order. Analyses to presented at IUSSP will also consider how these main effects interact as predicted by HBE theory.

Global advances in medicine and health practices, along with epidemiological changes, have improved health across the world. However, these changes appear to be mediated by environmental, cultural factors and behavioral gaps remain across countries. Given these factors, it is reasonable to assume that the United States and South Korea likely have differential underlying biological statuses; however, little research has investigated possible cross-national differences. Drawing from nationally representative cross-sectional panel data (2007-2010 of NHANES and Korean NHANES), we compared two countries who aged 50+ along 8 indicators of biological risk: Systolic blood pressure (SBP), diastolic blood pressure (DBP), total, HDL, LDL cholesterol, triglycerides, Body Mass Index (BMI), and glycated hemoglobin(N=5,761 for US; N=11,315 for South Korea). Findings indicate that while Americans were more likely to be obese, Koreans were more likely to have higher DBP and glycosylated hemoglobin levels. Americans, rates of obesity increased through age 70; however, among Koreans, no obesity differences were found across age-groups. DBP was higher among younger Koreans, particularly among men. Significant gender differences across all biomarkers were found among Koreans while no differences in fasting triglycerides were found among Americans.

Using GWAS data and about 1,000 married couples from the Framingham Heart study, this analysis investigates genomic assortative mating in human marriages. The study calculates (1) a SNP-specific correlation for each of the 320,195 SNPs, averaging 1,023 married couples and (2) a married couple-specific correlation for each couple, averaging 287,295 SNPs. Of the 320,195 SNP correlations, eight have a p-value 5x10-8 or smaller. These SNPs are all positively correlated for married couples, with a range of 0.16-0.27. The SNPs implicated in genes associated with BMI, height, risk behavior, or the human leukocyte antigen system are further examined. For the half of all SNPs having a more positive assortment, married couples average a statistically significant correlation that is 0.001 higher than randomly paired individuals. Our simulation shows that this difference in correlation can be explained by married couples assorting on about 200 SNPs.

Health and disease are critical concepts in bioethics with far-reaching social and political implications. In view of the aforesaid background it is proposed to study the differentials in Basal Metabolic Rate (BMR), Body Mass Index (BMI) and Diabetes and to study the effect of socio-economic and demographic indicators on BMR, BMI and Diabetes in selected states of India. The present study relies on National Family Health Survey-III conducted in 2005-06 for understanding the gender differentials in BMR, BMI and Diabetes (main dependent variables). The Independent variables will be various socio-demographic indicators like age, sex, highest educational level, place of residence, religion, caste/tribe, wealth index, occupation, alcohol and tobacco use. BMR, BMI and Diabetes are correlated with each other and differ by gender with different socio-economic and demographic characteristics. The means BMR is higher in males besides higher prevalence of diabetes than females. Females have highest percentage of overweight and underweight. Logistic regression result showed high correlation between those indicators with diabetes.

Overweight and obesity are major contributors of insulin resistance and their prevalence and determinants are known to vary in different populations.The objective of the study was to assess the proportion and determinants of overweight and obesity among diabetic women of reproductive age attending a tertiary hospital in Bangladesh.This cross-sectional analytic study was conducted among 888 diabetic women of reproductive age using a semi-structured questionnaire and purposive sampling technique.The overall prevalence of overweight was47%(95%CI48-45) and that of obesity was23%(95%CI21-24).On Pearson’s correlation analysis, BMI(r=0.135,p=0.001) and WC(r=0.162,p=0.001) were significantly associated with age and similarly BMI(r=0.151,p=0.001),WC(r=0.087,p=0.009)and WHR(r=0.094, p=0.005) were correlated with income.On binary logistic regression, BMI was significantly associated with age(p<0.05),income(p<0.05) and management of diabetes by OHA(p<0.05). On the other hand, WC was significantly associated with age(p<0.001),income(p<0.05) and management of diabetes by OHA(p<0.05).WHR was significantly associated with duration of diabetes(p<0.05).A high prevalence of both overweight and obesity exists in diabetic women of reproductive age and associated with increasing age, income, duration of diabetes, and use of oral hypoglycemic agents.

The Gompertz distribution is often fitted to lifespan data, however testing whether the fit satisfies theoretical criteria was neglected. Here five goodness-of-fit measures, the Anderson-Darling statistic, the Kullback-Leibler discrimination information, the correlation coefficient test, testing for the mean of the sample hazard and a nested test against the generalized extreme value distributions are discussed. Along with an application to laboratory rat data, critical values calculated by the empirical distribution
of the test statistics are also presented.

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).

This research analyzes the evolution of the life expectancy in Tarragona, Catalonia and Spain between 1950 and 2011. Tarragona showed the biggest increase until 1960 but from 1960 until 1995 the tendency becomes increasingly closer to each other geographically. To understand this inequality between different geographical regions we have used the conceptual framework of the Theory of Epidemiologic Transition. Information from the Instituto Nacional de Estadística de España and Institut Nacional d’Estadistica de Catalunya and methodology of indirect standardization were analyzed. Furthermore, a joint grouping of the leading causes of death has been created based on the International Classification of Diseases. During 1960-1990, Tarragona had a high incidence of deaths from respiratory diseases and cancers, greater than in the rest of Spain. The indirect standardization is a good way to describe in greater detail the changes in the structure of the population and its impact on health inequality. Some causes of respiratory diseases and cancers are due to environmental abuse, particularly through the nuclear and petrochemical industries. These causes may be responsible for some of the effects, and measures to improve the environment during the decade from 1980-1990 may be causing their decline.