The Determinants of Infant Mortality: Statistical Models

从历史上看，美国婴儿死亡的总体风险已经下降。然而，这一比率 根据最近一年的报告，下降趋势在2002年有所减缓，甚至逆转。此外，种族和民族 差距扩大了。因此，婴儿死亡率仍然是一个严重的公共卫生问题。 此外，种族和族裔差异的原因仍然难以捉摸。我们认为这是有效的 理论工具，例如婴儿死亡率的近似决定因素模型，是可用的，但 完全使这些模型发挥作用的统计工具并非如此。理想情况下，这些工具应该支持直接 和间接效应(通过最接近的决定因素)，适当地允许非线性效应 将最接近的决定因素(其中最重要的是出生体重和妊娠)设置为参数 年龄)，并解释了出生队列中“隐藏的”异质性。这项提议的目标是继续 发展协变量密度定义的Logistic回归混合(和其他GLM)a统计 能够使最接近的决定因素模型具有可操作性的方法。我们以前工作的目标是 是为了验证统计模型，检查与近端结构有关的一些问题 并论证了该模型在外生协变量研究中的实用性。我们 我将在这里扩展这些结果，以测试关于出生体重与 关于婴儿死亡率，即最近决定因素的“结构”如何与外源因素相互作用 协变量和婴儿死亡率。出生体重的“因果”问题受到了质疑，这是一个极端的问题 设计干预措施的重要性。我们开发的方法能够进行统计测试 这些假设。我们将在近端的上下文中检验一些潜在的协变量 决定因素以不同种族出生队列的样本为模型。我们将开发并提供 用于引导共变量密度定义的GLM混合物的程序。

Historically, the overall risk of infant mortality in the United States has declined. However, the rate of decline has slowed and even reversed in 2002 the most recent year reported. Further, racial and ethnic disparities have increased. Consequently, infant mortality remains a serious public health problem. Moreover, the cause of the racial and ethnic differentials has remained elusive. It is our view that effective theoretical tools, e.g. the proximate determinants model of infant mortality, are available, but that the statistical tools to fully operationalize these models are not. Ideally, these tools should accommodate direct and indirect (through the proximate determinants) effects, allow for non-linear effects, appropriately parameterize the proximate determinants (the most important of which are birth weight and gestational age), and account for "hidden" heterogeneity in the birth cohort. The objective of this proposal is to continue development of Covariate Density Defined mixtures of logistic regression (and other GLMs) a statistical methodology that can operationalize the proximate determinants model. The objective of our previous work was to validate the statistical model, examine a number of issues concerning the structure of the proximate determinants component, and demonstrate the utility of the model for studying exogenous covariates. We will extend these results here to test specific assumptions concerning the "causal" roll of birthweight with respect to infant mortality, i.e. how the "structure" of the proximate determinants interacts with exogenous covariates and infant mortality. The "causal" roll of birth weight has been questioned, an issue of extreme importance for designing interventions. The method we have developed is capable of statistically testing these assumptions. We will examine a number of potential covariates in the context of the proximate determinants model on a sample of ethnically diverse birth cohorts. We will develop and make available programs for conducting Covariate Density Defined mixtures of GLMs.