Institute for Leadership and Management in Health, Kingston Business School, Kingston University London, Kingston Hill, Kingston upon Thames KT2 7LB, United Kingdom.
Faculty of Economics, Economic Evaluation and HTA (EEHTA), CEIS, University of Rome "Tor Vergata", Via Columbia 2, 00133, Rome, RM, Italy.
J Public Health (Oxf). 2021 Jun 7;43(2):261-269. doi: 10.1093/pubmed/fdaa248.
Disparities in cross-regional coronavirus disease 2019 (Covid-19) mortality remain poorly understood. The association between pre-epidemic health and epidemic mortality can inform a policy response to future outbreaks.
We conducted an ecological study of the association between the cumulative deaths attributed to Covid-19 epidemic in the 20 Italian regions and nine determinants of population health derived from a systematic review of the literature. We used a multiple least square regression to predict the cross-regional variation in mortality observed from the onset of the epidemic to 23 September 2020.
Four independent variables best explained the cross-regional differences in the number of deaths attributed to Covid-19: the force of infection, population density, number of elderly living in assisted facilities and the standard rate of diabetes. The semi-partial correlation coefficients suggest that the force of infection and the number of elderly residents in nursing homes were the dominant predictors of the number of deaths attributed to Covid-19. Statistical controls and validation confirmed the generalizability of the predictive model.
Our findings indicate that a significant reduction of social contacts in main metropolitan areas and the timely isolation of elderly and diabetic residents could significantly reduce the death toll of the next wave of Covid-19 infection in Italy.
新冠肺炎（COVID-19）死亡的跨区域差异仍未得到充分了解。流行前健康状况与流行期间死亡率之间的关系可以为未来疫情的防控提供决策依据。
我们对意大利 20 个地区归因于 COVID-19 大流行的累积死亡人数与从文献系统综述中得出的 9 个人口健康决定因素之间的关联进行了生态研究。我们使用多元最小二乘回归来预测从疫情开始到 2020 年 9 月 23 日期间观察到的跨区域死亡率差异。
四个独立变量能够很好地解释归因于 COVID-19 的死亡人数的跨区域差异：感染力度、人口密度、居住在辅助设施中的老年人口数量以及糖尿病的标准发病率。偏部分相关系数表明，感染力度和养老院中老年人的数量是归因于 COVID-19 的死亡人数的主要预测因素。统计控制和验证证实了预测模型的通用性。
我们的研究结果表明，在主要大都市区显著减少社会接触，并及时隔离老年和糖尿病患者，可能会显著降低意大利下一波 COVID-19 感染的死亡人数。
