Department of Nursing, Ceará State University, Fortaleza, Brazil.
J Clin Nurs. 2020 Nov;29(21-22):4343-4348. doi: 10.1111/jocn.15472. Epub 2020 Sep 15.
To estimate the prevalence and associated factors of COVID-19 in people with flu-like syndrome in Ceará, Brazil.
COVID-19 is an infectious disease that has led to a worldwide public health emergency. More than 30,000 cases were confirmed in Brazil, especially in the States of São Paulo, Rio de Janeiro and Ceará. The capital of the Ceará State is the one with the highest incidence of COVID-19 in Brazil. Estimating the prevalence of the disease and its associated factors is important to offer adequate health care.
A cross-sectional study with secondary data of people notified with flu-like syndrome and COVID-19 test results.
19,967 cases of flu-like syndrome were analysed according to the result of the COVID-19 test. Predictive variables were as follows: age range, sex, women in puerperium, presence or absence of cardiovascular diseases, diabetes, haematological illness, immunodeficiencies, neurological diseases, obesity, renal diseases and Down syndrome. Robust Poisson regression models estimated the prevalence ratios of COVID-19. The research was reported via STROBE guidelines for cross-sectional studies.
The prevalence of COVID-19 in the population was 10.37%. In the final model, the following variables were associated with COVID-19: aged people, male sex, cardiovascular diseases and diabetes.
Among the flu-like syndrome cases, COVID-19 prevalence was high. In the Ceará State, clinical factors such as aged people, male sex, cardiovascular diseases and diabetes can enhance the prevalence of COVID-19 by up to 2.57 times.
The identification of factors that are associated with the enhanced prevalence of COVID-19 facilitates early diagnosis, and adequate and prompt treatment. This knowledge may avoid an unfavourable prognosis of the disease.
评估巴西塞阿拉州流感样综合征患者中 COVID-19 的患病率及其相关因素。
COVID-19 是一种传染病,已导致全球突发公共卫生事件。巴西已确诊超过 30,000 例病例,尤其是在圣保罗州、里约热内卢州和塞阿拉州。塞阿拉州首府是巴西 COVID-19 发病率最高的州。估计疾病的患病率及其相关因素对于提供适当的医疗保健至关重要。
一项具有二次数据的横断面研究,涉及报告有流感样综合征和 COVID-19 检测结果的人群。
根据 COVID-19 检测结果,对 19,967 例流感样综合征病例进行了分析。预测变量如下:年龄范围、性别、产褥期女性、是否存在心血管疾病、糖尿病、血液疾病、免疫缺陷、神经疾病、肥胖、肾脏疾病和唐氏综合征。稳健泊松回归模型估计了 COVID-19 的患病率比。研究按照横断面研究的 STROBE 指南进行报告。
人群中 COVID-19 的患病率为 10.37%。在最终模型中,与 COVID-19 相关的变量包括:老年人、男性、心血管疾病和糖尿病。
在流感样综合征病例中,COVID-19 的患病率较高。在塞阿拉州,临床因素如老年人、男性、心血管疾病和糖尿病可使 COVID-19 的患病率增加 2.57 倍。
识别与 COVID-19 患病率增加相关的因素有助于早期诊断,并提供适当和及时的治疗。这些知识可以避免疾病的不良预后。