Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study.
Covid-19: Regional policies and local infection risk: Evidence from Italy with a modelling study.
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DOI：
10.1016/j.lanepe.2021.100169
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Pancrazi R
中科院分区：
文献类型：
--
作者：
Guaitoli G;Pancrazi R
背景：政策制定者试图通过国家和地方的非药物干预措施来缓解新冠肺炎的传播。此外，有证据表明，由于不同的地方特点，一些地区比其他地区更容易受到传染风险的影响。我们研究意大利于2020年11月4日推出的区域政策是否有效地应对了这种异质性带来的局部感染风险。方法：意大利由19个地区(2个自治省)组成，分为107个省。我们收集了35个与人口、地理、经济活动和流动性相关的省份特定的前寒潮变量。首先，我们检验它们的区域内差异是否可以解释意大利第二波新冠肺炎的发生。使用套索算法，我们分离出具有高解释能力的变量。然后，我们检验在区域层面的政策出台后，它们的解释力是否消失。结果：在区域政策出台前，7个前期感染特征的区域内差异具有统计学意义(F检验p值)，并解释了19%的省级水平的新冠肺炎发病率差异，此外还有区域特有的因素。在区域政策出台后，它的解释力下降到7%，但仍然显著(p值)，即使在政策更严格的地区(p值)。解读：即使在同一地区，由于当地特点，意大利各省接触新冠肺炎的风险也不同。地区政策并没有消除这些差异，但可能已经抑制了它们。我们的证据可能与需要设计非药物干预的政策制定者相关。它还为试图估计其因果影响的研究人员提供了方法论建议。资金来源：没有。
Background: Policy-makers have attempted to mitigate the spread of covid-19 with national and local non-pharmaceutical interventions. Moreover, evidence suggests that some areas are more exposed than others to contagion risk due to heterogeneous local characteristics. We study whether Italy’s regional policies, introduced on 4th November 2020, have effectively tackled the local infection risk arising from such heterogeneity. Methods: Italy consists of 19 regions (and 2 autonomous provinces), further divided into 107 provinces. We collect 35 province-specific pre-covid variables related to demographics, geography, economic activity, and mobility. First, we test whether their within-region variation explains the covid-19 incidence during the Italian second wave. Using a LASSO algorithm, we isolate variables with high explanatory power. Then, we test if their explanatory power disappears after the introduction of the regional-level policies. Findings: The within-region variation of seven pre-covid characteristics is statistically significant (F-test p-value ) and explains 19% of the province-level variation of covid-19 incidence, on top of region-specific factors, before regional policies were introduced. Its explanatory power declines to 7% after the introduction of regional policies, but is still significant (p-value ), even in regions placed under stricter policies (p-value ). Interpretation: Even within the same region, Italy’s provinces differ in exposure to covid-19 infection risk due to local characteristics. Regional policies did not eliminate these differences, but may have dampened them. Our evidence can be relevant for policy-makers who need to design non-pharmaceutical interventions. It also provides a methodological suggestion for researchers who attempt to estimate their causal effects. Funding: None.
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