Department of Economic and Legal Studies, University of Naples "Parthenope", Naples, Italy.
Department of Management and Quantitative Studies, University of Naples "Parthenope", Naples, Italy.
Soc Sci Med. 2021 Oct;287:114328. doi: 10.1016/j.socscimed.2021.114328. Epub 2021 Aug 21.
The region of Campania in Southern Italy features high levels of socio-economic deprivation and low levels of environmental quality. A vast strand of the scientific literature has tried to verify whether poor environmental quality and widespread socio-economic deprivation might explain the high cancer mortality rates (CMRs) observed, especially in the municipalities - infamously labelled as the 'Land of Fires' - that were hit most severely by the crisis. While some studies managed to identify links between these two confounding factors and cancer mortality, the evidence is overall mixed. Interesting information may be drawn from the observation of municipal data: in spite of previous claims, some municipalities featuring high environmental quality and low socio-economic deprivation also display high CMRs, while other Campanian municipalities facing disastrous environmental and socio-economic conditions are characterised by low CMRs. These figures, in contrast to common sentiment and previous studies, need to be investigated thoroughly in order to assess the exact role of the confounding factors. In this work, we aim to identify the municipalities where confounding factors act as driving forces in the determination of high CMRs through an original multi-step analysis based on frequentist and Bayesian analysis. Pinpointing these municipalities could allow policymakers to design targeted and effective policy measures aimed at reducing cancer mortality.
意大利南部坎帕尼亚地区的社会经济贫困程度较高，环境质量较低。大量科学文献试图验证环境质量差和广泛的社会经济贫困是否可以解释观察到的高癌症死亡率（CMR），特别是在受危机影响最严重的那些市镇 - 这些市镇被贴上了“火之地”的耻辱标签。虽然一些研究设法确定了这两个混杂因素与癌症死亡率之间的联系，但总体证据是混杂的。从观察市政数据中可以得出有趣的信息：尽管有先前的说法，但一些环境质量高、社会经济贫困程度低的市镇也显示出高 CMR，而其他面临灾难性环境和社会经济条件的坎帕尼亚市镇则以低 CMR 为特征。这些数字与普遍看法和先前的研究相反，需要进行彻底调查，以评估混杂因素的确切作用。在这项工作中，我们旨在通过基于频率论和贝叶斯分析的原始多步骤分析，确定混杂因素在确定高 CMR 中起驱动作用的市镇。确定这些市镇可以使政策制定者设计有针对性和有效的政策措施，以降低癌症死亡率。
