SARS-CoV-2 molecular identification and clinical data analysis of associated risk factors from a COVID-19 testing laboratory of a coastal region in Bangladesh

17, November 2020 | Bangladesh

Authors:

Abstract

Background and aim: Outbreak of COVID-19 seems to have exacerbated across the globe, including Bangladesh. Scientific literature on the clinical data record of COVID-19 patients in Bangladesh is inadequate. Our study analyzes the clinical data of COVID-19 positive patients based on molecular identification and risk factor correlated with three variables (age, sex, residence) and COVID-19 prevalence in the four districts of Chattogram Division (Noakhali, Feni, Lakshmipur and Chandpur) with an aim to understand the trajectory of this pandemic in Chattogram, Southern Bangladesh. Methods: A cross-sectional study is conducted in the context of RT-PCR-based COVID-19 positive 5,589 individuals diagnosed with SARS-CoV-2 infection from the COVID-19 testing laboratory, Abdul Malek Ukil Medical College, Noakhali-3800, Bangladesh. For molecular confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), standard diagnostic protocols through real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) were conducted. Different patient demographics were analyzed using SPSS version 22 for exploring the relationship of three factors – age, sex, and residence with a cumulative number of COVID-19 positive cases and prevalence of COVID-19 in four districts in Chattogram division. The data was recorded between May to July, 2020. Results: Among the three parameters, the present study revealed that 20–40 cohort had the highest incidence of infection rate (51.80%, n 1⁄4 2895) among the different age groups. Among the infected individuals, 56.8% (n 1⁄4 3177) were male and 43.2% (n 1⁄4 2412) were female, denoting males being the most susceptible to this disease. Urban residents (52.7%, n 1⁄4 2948) were more vulnerable to SARS-CoV-2 infection than those residing in rural areas (47.3%, n 1⁄4 2641). The prevalence of COVID-19 positive cases among the four districts was recorded highest in the Noakhali district with 36.8% (n 1⁄4 2057), followed by the Feni, Lakshmipur and Chandpur districts with 25.9% (n 1⁄4 1448), 20.8% (n 1⁄4 1163) and 16.5% (n 1⁄4 921), respectively. Conclusions: This study presents a statistical correlation of certain factors linked to Bangladesh with confirmed COVID-19 patients, which will enable health practitioners and policy makers to take proactive steps to control and mitigate disease transmission.