Factors Associated with Reported COVID-like Symptoms and Seroprevalence Data Matched with COVID-like Symptoms in Slums and Non-Slums of Two Major Cities in Bangladesh

Objectives: To examine the levels and socio-demographic differentials of: (a) reported COVID-like symptoms; and (b) seroprevalence data matched with COVID-like symptoms. Methods: Survey data of reported COVID-like symptoms and seroprevalence were assessed by Roche Elecsys((R)) Anti-SARS-CoV-2 immunoassay. Survey data of 10,050 individuals for COVID-like symptoms and seroprevalence data of 3205 individuals matched with COVID-like symptoms were analyzed using bivariate and multivariate logistic analysis. Results: The odds of COVID-like symptoms were significantly higher for Chattogram city, for non-slum, people having longer years of schooling, working class, income-affected households, while for households with higher income had lower odd. The odds of matched seroprevalence and COVID-like symptoms were higher for non-slum, people having longer years of schooling, and for working class. Out of the seropositive cases, 37.77% were symptomatic-seropositive, and 62.23% were asymptomatic, while out of seronegative cases, 68.96% had no COVID-like symptoms. Conclusions: Collecting community-based seroprevalence data is important to assess the extent of exposure and to initiate mitigation and awareness programs to reduce COVID-19 burden.

Automated summary

This study aimed to examine the levels and socio-demographic differentials of reported COVID-like symptoms and seroprevalence data matched with COVID-like symptoms. The findings revealed that the odds of COVID-like symptoms were significantly higher for Chattogram city, non-slum areas, individuals with longer years of schooling, working class, and income-affected households, while households with higher income had lower odds. The odds of matched seroprevalence and COVID-like symptoms were higher for non-slum areas, individuals with longer years of schooling, and the working class. Conclusion: Collecting community-based seroprevalence data is important for assessing exposure levels and initiating mitigation and awareness programs to reduce the burden of COVID-19.