Response capacity to the COVID-19 pandemic in Latin America and the Caribbean
Response capacity to the COVID-19 pandemic in Latin America and the Caribbean
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DOI：
10.26633/rpsp.2020.109
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发表时间：
2020-01-01
影响因子：
2.6
通讯作者：
Debora Acosta, Laura
中科院分区：
文献类型：
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作者：
Debora Acosta, Laura
目标。分析2019冠状病毒病大流行在拉丁美洲和加勒比国家头90天的演变及其与公共卫生措施、人口、健康和社会特征相关变量的关系。使用Joinpoint回归程序4.8.0.1，采用Joinpoint回归分析方法分析COVID-19日新增病例趋势和粗死亡率（CMR）。数据来自Our World in Data注册表。在每个国家为应对COVID-19大流行而采取的公共卫生措施（通过牛津大学的严格指数衡量）以及卫生、人口和社会条件与大流行演变的结果之间进行了多重对应分析。采用SPSS统计软件。Joinpoint回归分析显示，巴西的病例数增幅最大（11.3%），墨西哥的CMR增幅最大（16.2%）。多重对应分析表明，CMR与人口总数、严格指数、城市化水平、日生活费不足1美元的人口比例、糖尿病患病率和医院床位数相关。该区域各国在COVID-19发病率方面呈现异质性演变。这种异质性既与所采取的公共卫生措施有关，也与人口规模、贫困水平和现有卫生系统有关。
Objective. To analyze the evolution of the COVID-19 pandemic in Latin American and Caribbean countries in its first 90 days and its association with variables related to public health measures, and demographic, health and social characteristics.Methods. The trend in new daily cases and the crude mortality rate (CMR) from COVID-19 were analyzed through the Joinpoint regression analysis methodology, using the Joinpoint Regression Program 4.8.0.1. Data was obtained from the Our World in Data registry. A multiple correspondence analysis was performed between the public health measures adopted in each country to face the COVID-19 pandemic (measured through the stringency index, Oxford University) and sanitary, demographic and social conditions, and the results of the evolution of the pandemic. SPSS software was used.Results. The Joinpoint regression analysis showed that the highest increase in the number of cases was observed in Brazil (11.3%) and the highest increase in CMR in Mexico (16.2%). The multiple correspondence analysis showed that CMR was associated with the total population, the stringency index, the level of urbanization, the proportion of the population living on less than one dollar a day, the prevalence of diabetes and the number of hospital beds.Conclusions. The countries of the region show a heterogeneous evolution in the incidence of COVID-19. This heterogeneity is associated with both the public health measures adopted, as well as with the population size, poverty levels and pre-existing health systems.