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<sup>®</sup> Ant...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/10/1444 |
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| author | Abdur Razzaque Tarique Mohammad Nurul Huda Razib Chowdhury Md. Ahsanul Haq Protim Sarker Evana Akhtar Md Arif Billah Mohammad Zahirul Islam Dewan Md. Emdadul Hoque Shehlina Ahmed Yasmin H. Ahmed Fahmida Tofail Rubhana Raqib |
| author_facet | Abdur Razzaque Tarique Mohammad Nurul Huda Razib Chowdhury Md. Ahsanul Haq Protim Sarker Evana Akhtar Md Arif Billah Mohammad Zahirul Islam Dewan Md. Emdadul Hoque Shehlina Ahmed Yasmin H. Ahmed Fahmida Tofail Rubhana Raqib |
| author_sort | Abdur Razzaque |
| collection | DOAJ |
| description | 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<sup>®</sup> 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. |
| first_indexed | 2024-03-11T03:41:47Z |
| format | Article |
| id | doaj.art-ad251a7c975940589393dce6ea59ff07 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T03:41:47Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-ad251a7c975940589393dce6ea59ff072023-11-18T01:32:29ZengMDPI AGHealthcare2227-90322023-05-011110144410.3390/healthcare11101444Factors Associated with Reported COVID-like Symptoms and Seroprevalence Data Matched with COVID-like Symptoms in Slums and Non-Slums of Two Major Cities in BangladeshAbdur Razzaque0Tarique Mohammad Nurul Huda1Razib Chowdhury2Md. Ahsanul Haq3Protim Sarker4Evana Akhtar5Md Arif Billah6Mohammad Zahirul Islam7Dewan Md. Emdadul Hoque8Shehlina Ahmed9Yasmin H. Ahmed10Fahmida Tofail11Rubhana Raqib12Health System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshDepartment of Public Health, College of Public Health and Health Informatics, Qassim University, Al Bukairiyah 52741, Saudi ArabiaHealth System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshInfectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshInfectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshInfectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshHealth System and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshEmbassy of Sweden in Bangladesh, Gulshan 2, Dhaka 1212, BangladeshPopulation Fund (UNFPA), Dhaka 1207, BangladeshForeign, Commonwealth & Development Office (FCDO), Dhaka 1212, BangladeshBangladesh Health Watch, James P Grant School of Public Health, BRAC University, Dhaka 1213, BangladeshNutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshInfectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, BangladeshObjectives: 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<sup>®</sup> 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.https://www.mdpi.com/2227-9032/11/10/1444reported COVID-19seroprevalenceslumnon-slumBangladesh |
| spellingShingle | Abdur Razzaque Tarique Mohammad Nurul Huda Razib Chowdhury Md. Ahsanul Haq Protim Sarker Evana Akhtar Md Arif Billah Mohammad Zahirul Islam Dewan Md. Emdadul Hoque Shehlina Ahmed Yasmin H. Ahmed Fahmida Tofail Rubhana Raqib 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 Healthcare reported COVID-19 seroprevalence slum non-slum Bangladesh |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | reported COVID-19 seroprevalence slum non-slum Bangladesh |
| url | https://www.mdpi.com/2227-9032/11/10/1444 |
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