Department of Geography, Autonomous University of Barcelona, Barcelona, Spain
Center for Demographic Studies, Bellaterra, Spain.
J Epidemiol Community Health. 2022 Jan;76(1):1-7. doi: 10.1136/jech-2020-216325. Epub 2021 Jun 22.
Intraurban sociodemographic risk factors for COVID-19 have yet to be fully understood. We investigated the relationship between COVID-19 incidence and sociodemographic factors in Barcelona at a fine-grained geography.
This cross-sectional ecological study is based on 10 550 confirmed cases of COVID-19 registered during the first wave in the municipality of Barcelona (population 1.64 million). We considered 16 variables on the demographic structure, urban density, household conditions, socioeconomic status, mobility and health characteristics for 76 geographical units of analysis (neighbourhoods), using a lasso analysis to identify the most relevant variables. We then fitted a multivariate Quasi-Poisson model that explained the COVID-19 incidence by neighbourhood in relation to these variables.
Neighbourhoods with: (1) greater population density, (2) an aged population structure, (3) a high presence of nursing homes, (4) high proportions of individuals who left their residential area during lockdown and/or (5) working in health-related occupations were more likely to register a higher number of cases of COVID-19. Conversely, COVID-19 incidence was negatively associated with (6) percentage of residents with post-secondary education and (7) population born in countries with a high Human Development Index.
Like other historical pandemics, the incidence of COVID-19 is associated with neighbourhood sociodemographic factors with a greater burden faced by already deprived areas. Because urban social and health injustices already existed in those geographical units with higher COVID-19 incidence in Barcelona, the current pandemic is likely to reinforce both health and social inequalities, and urban environmental injustice all together.
城市内部的 COVID-19 社会人口学危险因素尚未得到充分了解。我们研究了巴塞罗那精细地理尺度上 COVID-19 发病率与社会人口学因素之间的关系。
本横断面生态研究基于巴塞罗那市首次疫情期间登记的 10550 例 COVID-19 确诊病例(人口 164 万)。我们考虑了 16 个变量,包括人口结构、城市密度、家庭状况、社会经济地位、流动性和健康特征,用于 76 个分析地理单元(邻里),使用套索分析来确定最相关的变量。然后,我们拟合了一个多变量拟泊松模型,根据这些变量解释邻里 COVID-19 发病率。
人口密度较大、人口结构老龄化、养老院比例高、封锁期间离开居住区域的比例较高、从事与健康相关职业的人群比例较高的邻里更有可能报告更多的 COVID-19 病例。相反,COVID-19 发病率与(6)接受过高等教育的居民比例和(7)出生于人类发展指数较高国家的居民比例呈负相关。
与其他历史大流行一样,COVID-19 的发病率与邻里社会人口学因素有关,已经贫困地区的负担更大。由于巴塞罗那 COVID-19 发病率较高的地理单元已经存在城市社会和健康不平等,当前的大流行很可能会加剧健康和社会不平等以及城市环境不平等。