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Background Lower respiratory infections constitute a major disease burden worldwide. Treatment is usually empiric and targeted towards typical bacterial pathogens. Understanding the prevalence of pathogens not covered by empirical treatment is important to improve diagnostic and treatment algorithms. Methods A prospective observational study in peri-urban communities of Yangon, Myanmar was conducted between July 2018 and April 2019. Sputum specimens of 299 adults presenting with fever and productive cough were tested for Mycobacterium tuberculosis (microscopy and GeneXpert MTB/RIF [Mycobacterium tuberculosis/resistance to rifampicin]) and Burkholderia pseudomallei (Active Melioidosis Detect Lateral Flow Assay and culture). Nasopharyngeal swabs underwent respiratory virus (influenza A, B, respiratory syncytial virus) polymerase chain reaction testing. Results Among 299 patients, 32% (95% confidence interval [CI] 26 to 37) were diagnosed with tuberculosis (TB), including 9 rifampicin-resistant cases. TB patients presented with a longer duration of fever (median 14 d) and productive cough (median 30 d) than non-TB patients (median fever duration 6 d, cough 7 d). One case of melioidosis pneumonia was detected by rapid test and confirmed by culture. Respiratory viruses were detected in 16% (95% CI 12 to 21) of patients. Conclusions TB was very common in this population, suggesting that microscopy and GeneXpert MTB/RIF on all sputum samples should be routinely included in diagnostic algorithms for fever and cough. Melioidosis was uncommon in this population.

Highlights • Diagnosis of rickettsial infections is difficult in low-resource settings; this leads to delays in receiving appropriate treatment. • Before this study, the distribution of rickettsioses in Myanmar was not known. • This serosurvey shows that rickettsioses are widespread in Myanmar. • Particularly high prevalence of scrub typhus was found in central and northern regions., Background Little research has been published on the prevalence of rickettsial infections in Myanmar. This study determined the seroprevalence of immunoglobulin G (IgG) antibodies to rickettsial species in different regions of Myanmar. Methods Seven hundred leftover blood samples from patients of all ages in primary care clinics and hospitals in seven regions of Myanmar were collected. Samples were screened for scrub typhus group (STG), typhus group (TG) and spotted fever group (SFG) IgG antibodies using enzyme-linked immunosorbent assays (ELISA). Immunofluorescence assays were performed for the same rickettsial groups to confirm seropositivity if ELISA optical density ≥0.5. Results Overall IgG seroprevalence was 19% [95% confidence interval (CI) 16–22%] for STG, 5% (95% CI 3–7%) for TG and 3% (95% CI: 2–5%) for SFG. The seroprevalence of STG was particularly high in northern and central Myanmar (59% and 19–33%, respectively). Increasing age was associated with higher odds of STG and TG seropositivity [per 10-year increase, adjusted odds ratio estimate 1.68 (p < 0.01) and 1.24 (p = 0.03), respectively]. Conclusion Rickettsial infections are widespread in Myanmar, with particularly high seroprevalence of STG IgG antibodies in central and northern regions. Healthcare workers should consider rickettsial infections as common causes of fever in Myanmar.

BACKGROUND Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. OBJECTIVE This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. METHODS An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. RESULTS We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. CONCLUSIONS The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.

Background Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. Objective This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. Methods An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. Results We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. Conclusions The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.