In this paper, Weibull unobserved heterogeneity (frailty) survival models are utilized to analyze the determinants of infant and child mortality in Kenya. The results of these models are compared to those of standard Weibull survival models. The study particularly examines the extent to which child survival risks continue to vary net of observed factors and the extent to which nonfrailty models are biased due to the violation of the statistical assumption of independence. The data came from the 1998 Kenya Demographic and Health Survey. The results of the standard Weibull survival models clearly show that biodemographic factors are more important in explaining infant mortality, while socioeconomic, sociocultural and hygienic factors are more important in explaining child mortality. Frailty effects are substantial and highly significant both in infancy and in childhood, but the conclusions remain the same as in the nonfrailty models. Copyright Springer Science+Business Media B.V. 2007Determinants of infant and child mortality, Mortality differentials, Unobserved heterogeneity, Frailty, Sub-Saharan Africa, Kenya,

The 'Health Belief Model' (HBM) identifies perception of HIV/AIDS risks, recognition of its seriousness, and knowledge about prevention as predictors of safer sexual activity. Using data from the Cape Area Panel Survey (CAPS) and hazard models, this study examines the impact of risk perception, considered the first step in HIV prevention, set within the context of the HBM and socio-economic, familial and school factors, on the timing of first sexual intercourse among youth aged 14-22 in Cape Town, South Africa. Of the HBM components, female youth who perceive their risk as 'very small' and males with higher knowledge, experience their sexual debut later than comparison groups, net of other influences. For both males and females socio-economic and familial factors also influence timing of sexual debut, confirming the need to consider the social embeddedness of this sexual behavior as well as the rational components of decision making when designing prevention programs.

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latent scores, national survey of giving, social cohesion, structural equation modeling, volunteering and participating,

Random-effect models have been useful in demonstrating how unobserved factors are related to infant or child death clustering. Another potential hypothesis is state dependence whereby the death of an older sibling affects the risk of death of a subsequent sibling. Probit regression models incorporating state dependence and unobserved heterogeneity are applied to the 1998 Demographic and Health Survey (DHS) data for Kenya. We find that mortality risks of adjacent siblings are dependent: a child whose preceding sibling died is 1.8 times more likely to die. After adjusting for unobserved heterogeneity, the death of the previous child accounts for 40% of child death clustering. Further, eliminating state dependence would reduce infant mortality among second-and higher-order births by 12.5%.