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Are infant mortality rate declines exponential? The general pattern of 20th century infant mortality rate decline

机译:婴儿死亡率是否呈指数下降? 20世纪婴儿死亡率的总体下降趋势

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Background Time trends in infant mortality for the 20th century show a curvilinear pattern that most demographers have assumed to be approximately exponential. Virtually all cross-country comparisons and time series analyses of infant mortality have studied the logarithm of infant mortality to account for the curvilinear time trend. However, there is no evidence that the log transform is the best fit for infant mortality time trends. Methods We use maximum likelihood methods to determine the best transformation to fit time trends in infant mortality reduction in the 20th century and to assess the importance of the proper transformation in identifying the relationship between infant mortality and gross domestic product (GDP) per capita. We apply the Box Cox transform to infant mortality rate (IMR) time series from 18 countries to identify the best fitting value of lambda for each country and for the pooled sample. For each country, we test the value of λ against the null that λ = 0 (logarithmic model) and against the null that λ = 1 (linear model). We then demonstrate the importance of selecting the proper transformation by comparing regressions of ln(IMR) on same year GDP per capita against Box Cox transformed models. Results Based on chi-squared test statistics, infant mortality decline is best described as an exponential decline only for the United States. For the remaining 17 countries we study, IMR decline is neither best modelled as logarithmic nor as a linear process. Imposing a logarithmic transform on IMR can lead to bias in fitting the relationship between IMR and GDP per capita. Conclusion The assumption that IMR declines are exponential is enshrined in the Preston curve and in nearly all cross-country as well as time series analyses of IMR data since Preston's 1975 paper, but this assumption is seldom correct. Statistical analyses of IMR trends should assess the robustness of findings to transformations other than the log transform.
机译:背景技术20世纪婴儿死亡率的时间趋势显示出曲线模式,大多数人口统计学家都认为该曲线模式是指数级的。几乎所有婴儿死亡率的跨国比较和时间序列分析都研究了婴儿死亡率的对数以说明曲线时间趋势。但是,没有证据表明对数变换最适合婴儿死亡率时间趋势。方法我们使用最大似然方法来确定最佳的转换方法,以适应20世纪婴儿死亡率降低的时间趋势,并评估适当的转换方法对于确定婴儿死亡率与人均国内生产总值(GDP)之间关系的重要性。我们将Box Cox变换应用于来自18个国家/地区的婴儿死亡率(IMR)时间序列,以确定每个国家和汇总样本的最佳lambda拟合值。对于每个国家,我们针对λ= 0的零值(对数模型)和λ= 1的零值(线性模型)测试λ的值。然后,我们通过将ln(IMR)对当年人均GDP的回归与Box Cox转换模型进行比较,证明选择适当转换的重要性。结果根据卡方检验统计数据,婴儿死亡率下降最好仅描述为美国的指数下降。对于我们研究的其余17个国家,IMR下降既不能最好地建模为对数模型,也不能最好地模型化为线性过程。对IMR进行对数变换会导致在拟合IMR与人均GDP之间的关系时产生偏差。结论自1975年普雷斯顿发表论文以来,普雷斯顿曲线以及几乎所有的跨国和IMR数据的时间序列分析都包含了IMR呈指数下降的假设,但是这种假设很少是正确的。 IMR趋势的统计分析应评估发现对除对数变换外的其他变换的稳健性。

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