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 fetalorigins 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.

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 represent 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.

AP) ¶These authors contributed equally to this work. ABSTRACTA 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, those of the devastation left by an earthquake-tsunami that struck the island in 1918, and those associated with the joint occurrence of these events. We use geographic variation to identify the effects of the quake and timing of birth variation to identify those of the flu. In addition, we use markers of nutritional status gathered in a nationally representative sample of individuals aged 75 and older in 2002. This unique data set enables to make two distinct contributions. First, unlike most fetal-origins research that singles out early nutritional status as a determinant of adult health, we test the hypothesis that the 1918 flu had deleterious effects on the nutritional status on adult survivors who at the time of the flu were in utero or infants. Second, and unlike most research on the effects of the flu, we focus on markers of nutritional status set when the adult survivors were children or adolescents. We find that estimates of effects of the pandemic are sizeable primarily among females and among those who, in addition to the flu, were exposed to the earthquake-tsunami. We argue that these findings constitute empirical evidence supporting the conjecture that effects of the 1918 flu alone and the combined effects of the flu and the earthquake are associated not just with damage experienced during the fetal period but also postnatally.

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 lowand 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.

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