Background: Antimicrobial resistance (AMR) is a pressing global health concern, leading to increased treatment costs, prolonged hospital stays, and higher mortality rates. This study analyzes the prevalence and trends of AMR in pathogenic bacteria isolated from various clinical specimens from Chattogram Medical College Hospital (CMCH) in Chittagong, Bangladesh. The objective is to track AMR over an extended period and provide comparative analytics for local and global surveillance efforts. Methods: Retrospective data from June 2017 to November 2019 were collected from a tertiary care hospital, en-compassing both inpatients and outpatients. Bacterial identification and antibiotic susceptibility testing followed standard methods. WHONET and Quick Analysis of AMR Patterns and Trends (QAAPT) software were utilized for data management and analysis. Results: The analysis included 6,896 records, with an average bacterial growth positivity rate of 39%. The most common specimen type was urine, accounting for 48.9% of all specimens. Among the bacterial isolates, variations in AMR prevalence were observed, particularly with E. coli displaying high resistance to commonly used antibiotics. Soft tissue and blood fluid samples exhibited a high positivity rate for bacterial growth. The study underscores the urgent need for AMR surveillance and evidence-based treatment guidelines tailored to local antibiotic susceptibility patterns. Conclusion: This study highlights the significance of monitoring AMR trends in Chittagong, Bangladesh. By understanding and addressing AMR patterns, policymakers, and stakeholders can develop informed national policies and strategies to combat AMR effectively. Sharing these findings with relevant parties is crucial for creating awareness and promoting evidence-based practices. The study emphasizes the importance of ongoing surveillance efforts and the development of targeted interventions to mitigate the impact of AMR and improve patient outcomes in the region.

Objective: To delineate the survival rate of the patients with coronavirus disease 2019 (COVID-19) who did the diagnostic tests lately after the development of symptoms. The aim is to determine the socio-demographic risk factors associated with the delay of the detection of COVID-19 patients. Methods: For this cross-sectional study, 300 patients were selected who were diagnosed as COVID-19 patients in the Molecular Biology Laboratory of Chittagong Medical College, Chattogram, Bangladesh. Data were collected from May to July 2020. Clinical characteristics were obtained from over phone interviews and laboratory diagnosis by Real-time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR). Cox proportional hazard model is applied to estimate risk factors affecting the delay of detection of COVID-19 patients.Result: Female mortality rate was 44.9% higher compared to males, graduates died 32% more than undergraduates, unmarried peoples’ death rate were 56% more than married and those who were in traveling irregularly and in contact with symptomatic patients, were 86% more died than non-travelers.Conclusion: Early diagnosis of COVID-19 can save a huge amount of lives and special attention should be emphasized on the significant explanatory variable.