Genetic Influences on Incidence and Case-Fatality of Infectious Disease

BACKGROUND: Family, twin and adoption studies suggest that genetic susceptibility contributes to familial aggregation of infectious diseases or to death from infections. We estimated genetic and shared environmental influences separately on the risk of acquiring an infection (incidence) and on dying from it (case fatality). METHODS: Genetic influences were estimated by the association between rates of hospitalization for infections and between case-fatality rates of adoptees and their biological full- and half- siblings. Familial environmental influences were investigated in adoptees and their adoptive siblings. Among 14,425 non-familial adoptions, granted in Denmark during the period 1924-47, we selected 1,603 adoptees, who had been hospitalized for infections and/or died with infection between 1977 and 1993. Their siblings were considered predisposed to infection, and compared with non-predisposed siblings of randomly selected 1,348 adoptees alive in 1993 and not hospitalized for infections in the observation period. The risk ratios presented were based on a Cox regression model. RESULTS: Among 9971 identified siblings, 2829 had been hospitalised for infections. The risk of infectious disease was increased among predisposed compared with non-predisposed in both biological (1.18; 95% confidence limits 1.03-1.36) and adoptive siblings (1.23; 0.98-1.53). The risk of a fatal outcome of the infections was strongly increased (9.36; 2.94-29.8) in biological full siblings, but such associations were not observed for the biological half siblings or for the adoptive siblings. CONCLUSION: Risk of getting infections appears to be weakly influenced by both genetically determined susceptibility to infection and by family environment, whereas there appears to be a strong non-additive genetic influence on risk of fatal outcome

The Role of Frailty Models and Accelerated Failure Time Models in Describing Heterogeneity Due to Omitted Covariates

INTRODUCTION Statistical modelling of heterogeneity may be based on strati#cation according to factors, regression on covariates, or by assuming a probability distribution of the interindividual variation. In survival analysis Vaupel et al. coined the phrase #frailty" in connection with a particular version of such a stochastic model, in which individual i was assumed to have death intensity Z i ##a# at age a, where the random variable Z i #the #frailty"# is assumed to have a gamma distribution. The assumptions that the randomness is ageindependent and that it acts multiplicatively on an underlying intensity ##a# are in principle arbitrary but have been taken as the basis for much subsequent work on random heterogeneity in survival analysis. Useful surveys are by Andersen et al. , Chapter IX, Nielsen et al. , Klein et al. , Aalen Schumacher et al. and Hougaard . The frailty models are likely to be particularly useful for modelling multivariate survival times, wheth

Testing for Center Effects in Multicenter Survival Studies: A Monte Carlo Comparison of Fixed and Random Effects Tests

The problem of testing for a center effect following a proportional hazards regression is considered. Two approaches..