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

Abstract

Background and aimOutbreak 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. MethodsA 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. ResultsAmong the three parameters, the present study revealed that 20–40 cohort had the highest incidence of infection rate (51.80%, n = 2895) among the different age groups. Among the infected individuals, 56.8% (n = 3177) were male and 43.2% (n = 2412) were female, denoting males being the most susceptible to this disease. Urban residents (52.7%, n = 2948) were more vulnerable to SARS-CoV-2 infection than those residing in rural areas (47.3%, n = 2641). The prevalence of COVID-19 positive cases among the four districts was recorded highest in the Noakhali district with 36.8% (n = 2057), followed by the Feni, Lakshmipur and Chandpur districts with 25.9% (n = 1448), 20.8% (n = 1163) and 16.5% (n = 921), respectively. ConclusionsThis 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.