Carmignani Fabrizio, Shankar Sriram, Tan Eng Joo, Tang Kam Ki
Department of Accounting, Finance, and Economics, Griffith University, 170 Kessels Road, Brisbane, QLD, 4111, Australia,
Eur J Health Econ. 2014 Jun;15(5):515-31. doi: 10.1007/s10198-013-0492-1. Epub 2013 Jun 14.
The literature is full of lively discussion on the determinants of population health outcomes. However, different papers focus on small and different sets of variables according to their research agenda. Because many of these variables are measures of different aspects of development and are thus correlated, the results for one variable can be sensitive to the inclusion/exclusion of others.
We tested for the robustness of potential predictors of population health using the extreme bounds analysis. Population health was measured by life expectancy at birth and infant mortality rate.
We found that only about half a dozen variables are robust predictors for life expectancy and infant mortality rate. Among them, adolescent fertility rate, improved water sources, and gender equality are the most robust. All institutional variables and environment variables are systematically non-robust predictors of population health.
The results highlight the importance of robustness tests in identifying predictors or potential determinants of population health, and cast doubts on the findings of previous studies that fail to do so.
文献中充斥着关于人口健康结果决定因素的热烈讨论。然而，不同的论文根据其研究议程关注的是少量且不同的变量集。由于这些变量中的许多都是发展不同方面的衡量指标，因此相互关联，一个变量的结果可能会对其他变量的纳入/排除敏感。
我们使用极端边界分析测试了人口健康潜在预测因素的稳健性。人口健康通过出生时预期寿命和婴儿死亡率来衡量。
我们发现，只有大约六个变量是预期寿命和婴儿死亡率的稳健预测因素。其中，青少年生育率、改善的水源和性别平等最为稳健。所有制度变量和环境变量都是人口健康的系统性非稳健预测因素。
结果突出了稳健性检验在识别人口健康预测因素或潜在决定因素方面的重要性，并对以往未进行此类检验的研究结果提出质疑。
