Invited Commentary: Simple Models for a Complicated Reality

Boston University, Boston, Massachusetts, United States
American Journal of Epidemiology (Impact Factor: 5.23). 09/2006; 164(4):312-4; discussion 315-6. DOI: 10.1093/aje/kwj238
Source: PubMed
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  • American Journal of Epidemiology 07/2006; 164(4):315-316. DOI:10.1093/aje/kwj239 · 5.23 Impact Factor
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    ABSTRACT: We developed and evaluated a structural model of the determinants of neonatal mortality in Hungary that embodies the causal mechanisms by which its proximate and indirect determinants--socio-economic, behavioural, and biological--are related. The statistical model used distinguishes between endogenous and exogenous variables and allows the causal effect of each to be correctly estimated. Unobserved variables are integrated into the model, which was tested using Hungarian data for the periods 1984-88 and 1994-98. The principal findings are as follows: weight at birth and duration of gestation are the most important of the (direct) causal determinants of neonatal mortality. Mother's age has an indirect and detrimental effect: when mothers are older than 30 years of age, the risk of lower birth weight or multiple births and, in consequence, neonatal mortality is increased. Father's age has no direct or indirect causal effect on neonatal mortality.
    Population Studies 04/2008; 62(1):85-111. DOI:10.1080/00324720701804165 · 1.08 Impact Factor
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    ABSTRACT: For decades, epidemiologists have observed that, among lower birth weight infants, higher risk infants have lower mortality rates than do lower risk infants. However, among higher birth weight infants, the pattern reverses, leading to a riddle of crossing birth weight-specific mortality curves. The riddle has been considered from different perspectives, including relative z scores, directed acyclic graphs, and, most recently, simulated mathematical models of underlying causal factors that produce the observed curves; similarly paradoxical gestational age-specific mortality curves uncross when calculations include all fetuses-at-risk rather than just infants delivered at a particular gestational age. However, researchers have generally focused on birth weight rather than gestational age, likely because birth weight is accurately measured and, if one assumes that birth weight is an intermediate variable between the underlying causal factors and mortality, is easier to model. Within the framework of existing analytical approaches, adding the complexity of a direct relation between gestational age and mortality, and possibly more complex relations among the casual factors, may be difficult. Nevertheless, duration of pregnancy seems a better proxy for the true construct of interest, whether the baby is mature enough to survive, so shifting attention to understanding the riddle of gestational age-specific mortality is encouraged.
    American journal of epidemiology 03/2009; 169(7):798-801. DOI:10.1093/aje/kwp025 · 5.23 Impact Factor
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