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首页> 外文期刊>Risk analysis >Why Reduced-Form Regression Models of Health Effects Versus Exposures Should Not Replace QRA: Livestock Production and Infant Mortality as an Example
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Why Reduced-Form Regression Models of Health Effects Versus Exposures Should Not Replace QRA: Livestock Production and Infant Mortality as an Example

机译：为什么对健康影响与暴露的简化形式回归模型不能代替QRA：以牲畜生产和婴儿死亡率为例

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Do pollution emissions from livestock operations increase infant mortality rate (IMR)? A recent regression analysis of changes in IMR against changes in aggregate "animal units" (a weighted sum of cattle, pig, and poultry numbers) over time, for counties throughout the United States, suggested the provocative conclusion that they do: "[A] doubling of production leads to a 7.4% increase in infant mortality." Yet, we find that regressing IMR changes against changes in specific components of "animal units" (cattle, pigs, and broilers) at the state level reveals statistically significant negative associations between changes in livestock production (especially, cattle production) and changes in IMR. We conclude that statistical associations between livestock variables and IMR variables are very sensitive to modeling choices (e.g., selection of explanatory variables, and use of specific animal types vs. aggregate "animal units). Such associations, whether positive or negative, do not warrant causal interpretation. We suggest that standard methods of quantitative risk assessment (QRA), including emissions release (source) models, fate and transport modeling, exposure assessment, and dose-response modeling, really are important-and indeed, perhaps, essential-for drawing valid causal inferences about health effects of exposures to guide sound, well-informed public health risk management policy. Reduced-form regression models, which skip most or all of these steps, can only quantify statistical associations (which may be due to model specification, variable selection, residual confounding, or other noncausal factors). Sound risk management requires the extra work needed to identify and model valid causal relations.
机译：畜牧业产生的污染排放是否会增加婴儿死亡率（IMR）？最近，针对整个美国的县，IMR的变化相对于总“动物单位”（牛，猪和家禽数量的加权总和）变化的回归分析表明，它们得出了具有说服力的结论： ]生产量翻倍导致婴儿死亡率增加7.4％。”但是，我们发现，针对州一级“动物单位”（牛，猪和肉鸡）特定成分的变化，对IMR进行回归分析可发现，牲畜生产（尤其是牛的生产）变化与IMR的变化之间存在统计学上的显着负相关性。我们得出的结论是，牲畜变量和IMR变量之间的统计关联对模型选择非常敏感（例如，解释变量的选择，特定动物类型的使用与“动物总单位”的组合）。这种关联，无论是正数还是负数，都不能保证我们建议，定量风险评估（QRA）的标准方法，包括排放释放（源）模型，命运和运输模型，暴露评估和剂量反应模型，确实非常重要，甚至可能对于得出有关暴露对健康影响的有效因果推论，以指导合理，信息灵通的公共健康风险管理政策。简化形式的回归模型（跳过了大多数或所有这些步骤）只能量化统计关联（这可能是由于模型规格所致） ，变量选择，残余混杂或其他非因果关系）。健全的风险管理需要识别和确定风险的额外工作建立有效的因果关系模型。

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