American Journal of Epidemiology Advance Access originally published online on February 24, 2009
American Journal of Epidemiology 2009 169(7):798-801; doi:10.1093/aje/kwp025
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Invited Commentary: Crossing Curves—It's Time to Focus on Gestational Age-specific Mortality
Correspondence to Dr. Jennifer D. Parker, Office of Analysis and Epidemiology, National Center for Health Statistics, 3311 Toledo Road, Hyattsville, MD 20782 (e-mail: jdparker{at}cdc.gov).
Received for publication October 21, 2008. Accepted for publication January 7, 2009.
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.
birth weight; directed graph; gestational age; infant mortality
Abbreviations: DAG, directed acyclic graph
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Am. J. Epidemiol. 2009 169: 787-797.[Abstract] [Full Text]