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2
Epidemiologic programs for computers and calculators. Use of Poisson regression models in estimating incidence rates and ratios.适用于计算机和计算器的流行病学程序。泊松回归模型在估计发病率和比率中的应用。
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在地区、性别和特定年龄层面上对公共卫生研究的人群分母进行估计。

Estimation of population denominators for public health studies at the tract, gender, and age-specific level.

作者信息

Aickin M, Dunn C N, Flood T J

机构信息

Arizona Department of Health Services, Division of Disease Prevention, Phoenix.

出版信息

Am J Public Health. 1991 Jul;81(7):918-20. doi: 10.2105/ajph.81.7.918.

DOI:10.2105/ajph.81.7.918
PMID:2053674
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1405184/
Abstract

In epidemiologic and public health studies of disease incidence in geographic subpopulations, attention is properly directed toward the ascertainment of accurate numerators. Population or person-years denominators are generally given less consideration, under the assumption that estimates produced by sources other than the state health department are sufficiently accurate. Here, we report our experience in estimating person-years denominators in the highly urbanized, rapidly expanding population of Maricopa County, Arizona. The usual sources of population estimates were found to be of little use for public health purposes, and so we report on a method for obtaining smoothed person-years figures that can accurately reflect population acceleration which varies from one time period to another. Our method is to regress the logarithm of census enumerations on quadratic or tertic polynomials in time. We describe how differential reliability of census figures can be incorporated into our procedure, and how the problem of missing census data can be handled by an iterated regression method. Our evidence suggests that the logarithmic regression model works well, even in the face of rapid and erratic population growth or decline.

摘要

在针对地理亚人群疾病发病率的流行病学和公共卫生研究中,注意力通常正确地集中在确定准确的分子上。人口或人年分母通常较少受到关注,因为人们假定州卫生部门以外的来源所产生的估计足够准确。在此,我们报告我们在估计亚利桑那州马里科帕县高度城市化、快速扩张人口的人年分母方面的经验。我们发现,通常的人口估计来源对公共卫生目的几乎没有用处,因此我们报告一种获取平滑人年数字的方法,该方法可以准确反映不同时间段内变化的人口加速情况。我们的方法是将人口普查计数的对数与时序二次或三次多项式进行回归。我们描述了如何将人口普查数据的差异可靠性纳入我们的程序,以及如何通过迭代回归方法处理人口普查数据缺失的问题。我们的证据表明,即使面对人口快速且不稳定的增长或下降,对数回归模型也能很好地发挥作用。