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， 2002年，即报告的最近一年，下降速度有所减缓，甚至出现逆转。此外，种族和族裔 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， 理论工具，如婴儿死亡率的直接决定因素模型，是可以利用的，但 但这些模式的全面运作所需的统计工具却不具备。理想情况下，这些工具应该能够直接 和间接（通过最接近的决定因素）的影响，允许非线性效应，适当 parameterized的最接近的决定因素（其中最重要的是出生体重和妊娠 年龄），并解释了出生队列中的“隐藏”异质性。本建议的目的是继续 协变量密度定义的logistic回归混合（和其他GLM）的统计发展 方法，可以操作的直接决定因素模型。我们以前工作的目标是 是为了验证统计模型，检查一些问题的结构，近 决定因素的组成部分，并展示了研究外生协变量的模型的实用性。我们 我将在这里扩展这些结果，以测试关于出生体重的“因果”滚动的特定假设， 关于婴儿死亡率，即直接决定因素的“结构”如何与外生因素相互作用， 协变量和婴儿死亡率。出生体重的“因果”关系受到质疑，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检验 这些假设。我们将在近似的背景下检查一些潜在的协变量， 决定因素模型的样本种族不同的出生队列。我们将开发并提供 用于进行协变量密度定义的GLM混合的程序。

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.