We examine how key early family circumstances affect mortality risks decades later. Early life conditions are measured by parental mortality, parental fertility (e.g., offspring sibship size, parental age at offspring birth), religious upbringing, and parental socioeconomic status. Prior to these early life conditions are familial and genetic factors that affect life-span. Accordingly, we consider the role of parental and familial longevity on adult mortality risks. We analyze the large Utah Population Database which contains a vast amount of genealogical and other vital/health data that contain full life histories of individuals and hundreds of their relatives. To control for unobserved heterogeneity, we analyze sib-pair data for 12,000 sib-pairs using frailty models. We found modest effects of key childhood conditions (birth order, sibship size, parental religiosity, parental SES, and parental death in childhood). Our measures of familial aggregation of longevity were large and suggest an alternative view of early life conditions.

These data suggest that melanomas occurring in the context of an underlying inherited susceptibility do not have a significantly different biologic behavior.

Multigenerational pedigrees provide an opportunity for assessing the effects of unobserved environmental and genetic effects on longevity (i.e., frailty). This article applies Cox proportional hazards models to data from three-generation pedigrees in the Utah Population Database using two different frailty specification schemes that account for common environments (shared frailty) and genetic effects (correlated frailty). In a model that includes measures of familial history of longevity and both frailty effects, we find that the variance component due to genetic factors is comparable to the one attributable to shared environments: Standard deviations of the correlated and the shared frailty distributions are 0.143 and 0.186, respectively. Through simulations, we also show a greater reduction in the bias of parameter estimates for fixed covariates through the use of the correlated frailty model.