Abstract
The lack of mass testing for COVID-19 diagnosis creates the need to determine the magnitude of the disease based on its clinical symptoms. The study aimed to analyze the profile of COVID-19 symptoms and related aspects in Brazil. The author analyzed the sample of participants from the Brazilian National Household Sample Survey (PNAD-COVID19) conducted in May 2020. Latent class analysis (LCA) was performed with sociodemographic covariables and 11 symptoms reported by 346,181 individuals. Rao-Scott test and standardized residual analysis were used to measure the association with the pattern of health services use. Spatial scan analysis was performed to identify areas at risk of COVID-19 cases. LCA showed six classes of symptoms based on the pattern of answers by participants: (1) all the symptoms; (2) high prevalence of symptoms; (3) predominance of fever; (4) predominance of cough/sore throat; (5) mild symptoms with predominance of headache; and (6) absence of symptoms. Female sex, brown skin color, the North and Northeast regions of Brazil, and all three older age brackets showed stronger association with the class with all the symptoms (class 1). Most use of health services was also by this group of individuals, but with different profiles of use. Spatial analysis showed juxtaposition of this class with areas at greater risk of COVID-19. These finding underline the importance of investigating symptoms for the epidemiological identification of possible cases in a scenario with low population testing rates.
Highlights
The lack of mass testing for COVID-19 diagnosis creates the need to determine the magnitude of the disease based on its clinical symptoms
The study aimed to analyze the profile of COVID-19 symptoms
The author analyzed the sample of participants from the Brazilian National Household Sample Survey
Summary
A ausência de testagens em massa para o diagnóstico da COVID-19 gera a necessidade de conhecer a dimensão da doença por meio da sua sintomatologia clínica. Para conhecer o perfil dos sintomas relacionados à COVID-19, considerada a variável dependente neste estudo, foi realizada análise de classes latentes (ACL) com covariáveis. Devido ao elevado número de indivíduos que não procuraram os serviços de saúde, especialmente pela ausência de sintomas, foi a preferência por realizar apenas uma análise bivariada com tabelas de contingência, pois a inclusão dessas variáveis em um modelo múltiplo de regressão logística multinomial violaria tanto a parcimônia do modelo quanto a ausência de colinearidade entre essas variáveis independentes. Com relação à distribuição espacial dos quintis do número de casos confirmados por 100 mil habitantes e das áreas circulares com maior risco relativo da doença, observamos uma sobreposição de grande parte das regiões/áreas com maiores proporções de indivíduos que foram classificados com presença de todos os sintomas (classe 1).
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