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Intersecting Birth Weight-specific Mortality Curves: Solving the Riddle

机译:相交的出生体重特定死亡率曲线:解决难题

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摘要

Small babies from a population with higher infant mortality often have better survival than small babies from a lower-risk population. This phenomenon can in principle be explained entirely by the presence of unmeasured confounding factors that increase mortality and decrease birth weight. Using a previously developed model for birth weight-specific mortality, the authors demonstrate specifically how strong unmeasured confounders can cause mortality curves stratified by known risk factors to intersect. In this model, the addition of a simple exposure (one that reduces birth weight and independently increases mortality) will produce the familiar reversal of risk among small babies. Furthermore, the model explicitly shows how the mix of high- and low-risk babies within a given stratum of birth weight produces lower mortality for high-risk babies at low birth weights. If unmeasured confounders are, in fact, responsible for the intersection of weight-specific mortality curves, then they must also (by virtue of being confounders) contribute to the strength of the observed gradient of mortality by birth weight. It follows that the true gradient of mortality with birth weight would be weaker than what is observed, if indeed there is any true gradient at all.
机译:来自婴儿死亡率较高的人口中的小型婴儿通常比低风险人口的小型婴儿具有更好的生存率。原则上,可以通过存在无法测量的混杂因素来完全解释这种现象,这些混杂因素会增加死亡率并降低出生体重。使用先前开发的针对出生体重的死亡率模型,作者具体证明了强大的未经测量的混杂因素如何导致由已知风险因素分层的死亡率曲线相交。在此模型中,增加一次简单的暴露(一种降低出生体重并独立增加死亡率的暴露)将使小婴儿熟悉的风险逆转。此外,该模型明确显示了给定出生体重层次中高风险和低风险婴儿的混合如何对低出生体重的高风险婴儿产生较低的死亡率。如果实际上无法衡量的混杂因素是体重比死亡率曲线的交点,那么它们也必须(由于是混杂因素)也有助于观察到的出生体重死亡率梯度的强度。因此,如果确实存在任何真实的梯度,则死亡率与出生体重的真实梯度将比所观察到的弱。

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