Amaratunga Dhammika, Cabrera Javier, Ghosh Debopriya, Katehakis Michael N, Wang Jin, Wang Wenting
Princeton Data Analytics, Princeton, USA.
Rutgers, The State University of New Jersey, New Brunswick, USA.
Ann Oper Res. 2022;317(1):5-18. doi: 10.1007/s10479-021-03941-4. Epub 2021 Feb 10.
Socio-economic factors could impact how epidemics spread. In this study, we investigated the possible effect of several local socio-economic factors on the case count and time course of confirmed Covid-19 cases and Covid-19-related deaths across the twenty one counties of New Jersey. Socio-economic and geographic factors considered included population, percentage of elders in the population, percentage of low-income households, access to food and health facilities and distance to New York. We found that the counties could be clustered into three groups based on (a) the case totals, (b) the total number of deaths, (c) the time course of the cases and (d) the time course of the deaths. The four groupings were very similar to one another and could all be largely explained by the county population, the percentage of low-income population, and the distance of the county from New York. As for food and health factors, the numbers of local restaurants and pharmacies significantly influenced the total number of cases and deaths as well as the epidemic's evolution. In particular, the number of cases and deaths showed a decrease with the number of McDonald's within the county in contrast to other fast-food or non-fast food restaurants. Overall, our study found that the evolution of the epidemic was influenced by certain socio-economic factors, which could be helpful for the formulation of public health policies.
社会经济因素可能会影响流行病的传播方式。在本研究中,我们调查了新泽西州21个县的若干当地社会经济因素对新冠确诊病例数和新冠相关死亡病例数及时间进程可能产生的影响。所考虑的社会经济和地理因素包括人口、老年人口比例、低收入家庭比例、获得食品和医疗设施的情况以及与纽约的距离。我们发现,根据(a)病例总数、(b)死亡总数、(c)病例的时间进程和(d)死亡的时间进程,这些县可以分为三组。这四种分组彼此非常相似,并且在很大程度上都可以由县人口、低收入人口比例以及该县与纽约的距离来解释。至于食品和医疗因素,当地餐馆和药店的数量对病例总数和死亡总数以及疫情的演变有显著影响。特别是,与其他快餐店或非快餐店相比,病例数和死亡数随着该县麦当劳餐厅数量的增加而减少。总体而言,我们的研究发现疫情的演变受到某些社会经济因素的影响,这可能有助于制定公共卫生政策。