Gene-environment interactions and the case of body mass index and obesity: How much do they matter?

We investigate the demographic and population health implications of gene-environment interactions (GxE) in the case of body mass index (BMI) and obesity. We seek to answer two questions: (a) what is the first-order impact of GxE effects on BMI and probability of obesity, e.g. the direct causal effect of G in different E's? and (b) how large is the impact of GxE effects on second-order health outcomes associated with BMI and obesity, such as type 2 diabetes (T2D) and disability? In contrast to most of the literature that focuses on estimating GxE effects, we study the implications of GxE effects for population health outcomes that are downstream of a causal chain that includes the target phenotype (in this case BMI) as the initial cause. To limit the scope of the paper, we focus on environments defined by birth cohorts. However, extensions to other environments (education, socioeconomic status (SES), early conditions, and physical settings) are straightforward.

Population-level impact of adverse early life conditions on adult healthy life expectancy in low- and middle-income countries.

Evidence from theories of Developmental Origins of Health and Disease (DOHaD) suggests that experiencing adverse early life conditions subsequently leads to detrimental adult health outcomes. The bulk of empirical DOHaD literature does not consider the nature and magnitude of the impact of adverse early life conditions at the population level. In particular, it ignores the distortion of age and cohort patterns of adult health and mortality and the increased load of chronic illness and disability that ensues. In this paper, we use a microsimulation model combined with empirical estimates of incidence and prevalence of obesity, type 2 diabetes, and associated disability in low- and middle-income countries to assess the magnitude of delayed effects on adult healthy life expectancy and on compression (or expansion) of morbidity at older ages. The main goal is to determine if, in what ways, and to what extent delayed effects due to early conditions can influence cohorts' chronic illness and disability profiles.

Modeling biological age and its link with the aging process.

Differences in health status at older ages are a result of genetic predispositions and physiological responses to exposure accumulation over the lifespan. These vary across individuals and lead to health status heterogeneity as people age. Chronological age (CA) is a standard indicator that reflects overall risks of morbidity and mortality. However, CA is only a crude proxy for individuals' latent physiological deterioration. An alternative to CA is biological age (BA), an indicator of accumulated age-related biological change reflected in markers of major physiological systems. We propose and validate two BA estimators that improve upon existing ones. These estimators (i) are based on a structural equation model (SEM) that represents the relation between BA and CA, (ii) circumvent the need to impose arbitrary assumptions about the relation between CA and BA, and (iii) provide tools to empirically test the validity of assumptions the researcher may wish to invoke. We use the US National Health and Nutrition Examination Survey 1988-1994 and compare results with three commonly used methods to compute BA (principal components-PCA, multiple regression-MLR, and Klemera-Doubal's method-KD). We show that SEM-based estimates of BA differ significantly from those generated by PCA and MLR and are comparable to, but have better predictive power than KD's. The proposed estimators are flexible, allow testing of assumptions about functional forms relating BA and CA, and admit a rich interpretation as indicators of accelerated aging.

Impacts of the 1918 flu on survivors' nutritional status: A double quasi-natural experiment.

Robust empirical evidence supports the idea that embryonic and, more generally, intrauterine disruptions induced by the 1918-flu pandemic had long-term consequences on adult health status and other conditions. In this paper we assess the 1918-flu long-term effects not just of in utero exposure but also during infancy and early childhood. A unique set of events that took place in Puerto Rico during 1918-1919 generated conditions of a "double quasi-natural experiment". We exploit these conditions to empirically identify effects of exposure to the 1918 flu pandemic and those of the devastation left by an earthquake-tsunami that struck the island in 1918. Because the earthquake-tsunami affected mostly the Western coast of the island whereas early (in utero and postnatal) exposure to the flu was restricted to those born in the interval 1917-1920, we use geographic variation to identify the effects of the quake and timing of birth variation to identify those of the flu. We benefit from availability of information on markers of nutritional status in a nationally representative sample of individuals aged 75 and older in 2002. We make two contributions. First, unlike most fetal-origins research that singles out early nutritional status as a determinant of adult health, we hypothesize that the 1918 flu damaged the nutritional status of adult survivors who, at the time of the flu, were in utero or infants. Second, we target markers of nutritional status largely set when the adult survivors were infants and young children. Estimates of effects of the pandemic are quite large mostly among females and those who were exposed to the earthquake-tsunami. Impacts of the flu in areas less affected by the earthquake are smaller but do vary by area flu severity. These findings constitute empirical evidence supporting the conjecture that effects of the 1918 flu and/or the earthquake are associated not just with disruption experienced during the fetal period but also postnatally.

Place and community inclusion: Locational patterns of supportive housing for people with intellectual disability and people with psychiatric disorders.

PURPOSE: This study examines the locational patterns of publicly-funded supportive housing for people with intellectual disability (people with ID) and people with psychiatric disorders (people with PD). METHODS: Administrative data provided housing locations of 4599 people with ID and people with PD in one urban county and one suburban county in the United States. Census tract data captured neighborhood characteristics. Descriptive statistics and spatial analysis were used to analyze the distribution of supportive housing sites. RESULTS: People with ID were more dispersed across a larger number of census tracts with smaller number of residents per tract than people with PD. While spatial dispersion in favor of people with ID was consistent across both counties, difference in dispersion was more pronounced in the urban county. People with PD were concentrated in neighborhoods with more socio-economic disadvantage, more residential instability, and a higher level of race/ethnic diversity than people with ID. CONCLUSION: This study suggests that spatial-analytic method can serve as a useful tool for assessing the extent to which integrated housing is achieved for people with ID and people with PD. Interpretation of findings should be given due consideration of the policy context and neighborhood characteristics of the study communities.