Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil.
Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, State of Rio de Janeiro, Brazil.
PLoS One. 2021 Mar 1;16(3):e0247794. doi: 10.1371/journal.pone.0247794. eCollection 2021.
Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country.
This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).
The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.
Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.
2019 年 12 月在中国武汉市发现的 COVID-19 疫情在全球范围内蔓延，其影响对不同人群的影响不同，巴西等社会和经济不平等程度较高的国家因此受到关注，以便了解与该疾病相关的脆弱因素。鉴于这种情况，在没有 COVID-19 疫苗或安全有效的抗病毒治疗方法的情况下，非药物措施对于预防和控制疾病至关重要。然而，对于生活在社会脆弱性增加地区的数百万人来说，其中许多措施是不可行的。本研究旨在分析巴西各城市（县）COVID-19 发病率的空间分布，并调查其与社会人口学决定因素的关联，以更好地了解该国的社会背景和疫情传播情况。
这是一项使用多种来源数据的分析性生态研究。研究期间为 2020 年 2 月 25 日至 9 月 26 日。数据分析采用全局回归模型：普通最小二乘法（OLS）、空间自回归模型（SAR）和条件自回归模型（CAR）以及称为多尺度地理加权回归（MGWR）的局部回归模型。
市一级的基尼指数越高，疾病的发病率就越高。同样，每千名居民中护士比例较高的城市，COVID-19 的发病率也较高。与此同时，比例死亡率与疾病的发病率呈负相关。
社会不平等加剧了城市 COVID-19 的风险。城市的社会发展状况较好与较低的疾病风险相关。更多地获得卫生服务改善了疾病的诊断和通报，并与城市中更多的病例相关。尽管普遍易感染 COVID-19，但社会脆弱性增加的人群面临更大的患病风险。
