Attainment of universal health coverage is a global health priority. The Myanmar Government has committed to attainment of universal health coverage by 2030, but progress so far has not been assessed. We aimed to estimate national and subnational health service coverage and financial risk protection. We used nationally representative data from the Myanmar Demographic and Health Survey (2016) and the Integrated Household Living Condition Assessment (2010) to examine 26 health service indicators and explored the incidence of catastrophic health payment and impoverishment caused by out-of-pocket payments. We used logistic regression models of inequalities in, and risk factors for, indicators of universal health coverage. Nationally, the coverage of health service indicators ranged from 18·4% (95% CI 14·9-21·9) to 96·2% (95·9-96·5). Coverage of most health services indicators was below the universal health coverage target of 80%. 14·6% (95% CI 13·9-15·3) of households that used health services faced catastrophic health-care payments. 2·0% (95% CI 1·7-2·3) of non-poor households became poor because of out-of-pocket payments for health. Health service coverage and financial risk protection varied substantially by region. Although the richest quintiles had better access to health services than the poorest quintiles, they also had a higher incidence of financial catastrophe as a result of payments for health care. Of the indicators included in the study, coverage of adequate sanitation, no indoor use of solid fuels, at least four antenatal care visits, postnatal care for mothers, skilled birth attendance, and institutional delivery were the most inequitable by wealth quintile. Attainment of universal health coverage in Myanmar in the immediate future will be very challenging as a result of the low health service coverage, high financial risk, and inequalities in access to care. Health service coverage and financial risk protection for vulnerable, disadvantaged populations should be prioritised. Japanese Ministry of Health, Labour and Welfare, Ministry of Education, Culture, Sports, Science and Technology of Japan.

Background Leptospirosis is a disease transmitted from animals to humans through water, soil, or food contaminated with the urine of infected animals, caused by pathogenic Leptospira species. Antibiotics are commonly prescribed for the management of leptospirosis. Despite the widespread use of antibiotic treatment for leptospirosis, there seems to be insufficient evidence to determine its effectiveness or to recommend antibiotic use as a standard practice. This updated systematic review evaluated the available evidence regarding the use of antibiotics in treating leptospirosis, building upon a previously published Cochrane review. Objectives To evaluate the benefits and harms of antibiotics versus placebo, no intervention, or another antibiotic for the treatment of people with leptospirosis. Search methods We identified randomised clinical trials following standard Cochrane procedures. The date of the last search was 27 March 2023. Selection criteria We searched for randomised clinical trials of various designs that examined the use of antibiotics for treating leptospirosis. We did not impose any restrictions based on the age, sex, occupation, or comorbidities of the participants involved in the trials. Our search encompassed trials that evaluated antibiotics, regardless of the method of administration, dosage, and schedule, and compared them with placebo or no intervention, or compared different antibiotics. We included trials regardless of the outcomes reported. Data collection and analysis During the preparation of this review, we adhered to the Cochrane methodology and used Review Manager. The primary outcomes were all‐cause mortality and serious adverse events (nosocomial infection). Our secondary outcomes were quality of life, proportion of people with adverse events considered non‐serious, and days of hospitalisation. To assess the risk of bias of the included trials, we used the RoB 2 tool, and for evaluating the certainty of evidence we used GRADEpro GDT software. We presented dichotomous outcomes as risk ratios (RR) and continuous outcomes as mean differences (MD), both accompanied by their corresponding 95% confidence intervals (CI). We used the random‐effects model for all our main analyses and the fixed‐effect model for sensitivity analyses. For our primary outcome analyses, we included trial data from the longest follow‐up period. Main results We identified nine randomised clinical trials comprising 1019 participants. Seven trials compared two intervention groups and two trials compared three intervention groups. Amongst the trials comparing antibiotics versus placebos, four trials assessed penicillin and one trial assessed doxycycline. In the trials comparing different antibiotics, one trial evaluated doxycycline versus azithromycin, one trial assessed penicillin versus doxycycline versus cefotaxime, and one trial evaluated ceftriaxone versus penicillin. One trial assessed penicillin with chloramphenicol and no intervention. Apart from two trials that recruited military personnel stationed in endemic areas or military personnel returning from training courses in endemic areas, the remaining trials recruited people from the general population presenting to the hospital with fever in an endemic area. The participants' ages in the included trials was 13 to 92 years. The treatment duration was seven days for penicillin, doxycycline, and cephalosporins; five days for chloramphenicol; and three days for azithromycin. The follow‐up durations varied across trials, with three trials not specifying their follow‐up periods. Three trials were excluded from quantitative synthesis; one reported zero events for a prespecified outcome, and two did not provide data for any prespecified outcomes. Antibiotics versus placebo or no intervention The evidence is very uncertain about the effect of penicillin versus placebo on all‐cause mortality (RR 1.57, 95% CI 0.65 to 3.79; I² = 8%; 3 trials, 367 participants; very low‐certainty evidence). The evidence is very uncertain about the effect of penicillin or chloramphenicol versus placebo on adverse events considered non‐serious (RR 1.05, 95% CI 0.35 to 3.17; I² = 0%; 2 trials, 162 participants; very low‐certainty evidence). None of the included trials assessed serious adverse events. Antibiotics versus another antibiotic The evidence is very uncertain about the effect of penicillin versus cephalosporin on all‐cause mortality (RR 1.38, 95% CI 0.47 to 4.04; I² = 0%; 2 trials, 348 participants; very low‐certainty evidence), or versus doxycycline (RR 0.93, 95% CI 0.13 to 6.46; 1 trial, 168 participants; very low‐certainty evidence). The evidence is very uncertain about the effect of cefotaxime versus doxycycline on all‐cause mortality (RR 0.18, 95% CI 0.01 to 3.78; 1 trial, 169 participants; very low‐certainty evidence). The evidence is very uncertain about the effect of penicillin versus doxycycline on serious adverse events (nosocomial infection) (RR 0.62, 95% CI 0.11 to 3.62; 1 trial, 168 participants; very low‐certainty evidence) or versus cefotaxime (RR 1.01, 95% CI 0.15 to 7.02; 1 trial, 175 participants; very low‐certainty evidence). The evidence is very uncertain about the effect of doxycycline versus cefotaxime on serious adverse events (nosocomial infection) (RR 1.01, 95% CI 0.15 to 7.02; 1 trial, 175 participants; very low‐certainty evidence). The evidence is very uncertain about the effect of penicillin versus cefotaxime (RR 3.03, 95% CI 0.13 to 73.47; 1 trial, 175 participants; very low‐certainty evidence), versus doxycycline (RR 2.80, 95% CI 0.12 to 67.66; 1 trial, 175 participants; very low‐certainty evidence), or versus chloramphenicol on adverse events considered non‐serious (RR 0.74, 95% CI 0.15 to 3.67; 1 trial, 52 participants; very low‐certainty evidence). Funding Six of the nine trials included statements disclosing their funding/supporting sources and three trials did not mention funding source. Four of the six trials mentioning sources received funds from public or governmental sources or from international charitable sources, and the remaining two, in addition to public or governmental sources, received support in the form of trial drug supply directly from pharmaceutical companies. Authors' conclusions As the certainty of evidence is very low, we do not know if antibiotics provide little to no effect on all‐cause mortality, serious adverse events, or adverse events considered non‐serious. There is a lack of definitive rigorous data from randomised trials to support the use of antibiotics for treating leptospirosis infection, and the absence of trials reporting data on clinically relevant outcomes further adds to this limitation.

COVID-19 is an emerging disease threatening the lives of patients and healthcare workers (HCWs) alike. In this article, we present initial results of COVID-19 screening performed among the hospital staff of an infectious diseases referral hospital in Manila, the Philippines. Of 324 HCWs tested, eight were positive; only one was exposed to COVID-19 patients, whereas seven others belonged to two different departments. Routine screening of hospital staff is invaluable for the safety of the HCWs and the patients in hospitals and should be performed on a regular basis. In monitoring HCWs, we protect one of our most valuable assets against COVID-19.

The COVID-19 global pandemic is entering its second year. In this short report we present additional results as a supplement to our previous paper on COVID-19 and common respiratory virus screening for healthcare workers (HCWs) in a tertiary infectious disease referral hospital in Manila, Philippines. We sought to understand what etiologic agents could explain the upper/lower respiratory tract infection-like (URTI/LRTI-like) symptoms exhibited by 88% of the 324 HCWs tested. Among the patients who had URTI/LRTI-like symptoms, only seven (2%) were positive for COVID-19, while 38 (13%) of the symptomatic participants were identified positive for another viral etiologic agent. Rhinovirus was the most common infection, with 21 (9%) of the symptomatic participants positive for rhinovirus. Based on these results, testing symptomatic HCWs for common respiratory illnesses in addition to COVID-19 should be considered during this time of global pandemic.

Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7–12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4–6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10–20 to 30–36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (R_eff: 1.2–1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4–6 years old and 1.45 (95% CI 1.33, 1.59) 7–12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.

Numerous studies have documented the evidence of virus–virus interactions at the population, host, and cellular levels. However, the impact of these interactions on the within-host diversity of influenza viral populations remains unexplored. Our study identified 13 respiratory viral pathogens from the nasopharyngeal swab samples (NPSs) of influenza-like-illness (ILI) patients during the 2012/13 influenza season using multiplex RT-PCR. Subsequent next-generation sequencing (NGS) of RT-PCR-confirmed influenza A infections revealed all samples as subtype A/H3N2. Out of the 2305 samples tested, 538 (23.3%) were positive for the influenza A virus (IAV), while rhinovirus (RV) and adenoviruses (Adv) were detected in 264 (11.5%) and 44 (1.9%) samples, respectively. Among these, the co-detection of more than one virus was observed in ninety-six samples, and five samples showed co-detections involving more than two viruses. The most frequent viral co-detection was IAV–RV, identified in 48 out of the 96 co-detection cases. Of the total samples, 150 were processed for whole-genome sequencing (WGS), and 132 met the criteria for intra-host single-nucleotide variant (iSNV) calling. Across the genome, 397 unique iSNVs were identified, with most samples containing fewer than five iSNVs at frequencies below 10%. Seven samples had no detectable iSNVs. Notably, the majority of iSNVs (86%) were unique and rarely shared across samples. We conducted a negative binomial regression analysis to examine factors associated with the number of iSNVs detected within hosts. Two age groups—elderly individuals (>64 years old) and school-aged children (6–18 years old)—were significantly associated with higher iSNV counts, with incidence rate ratios (IRR) of 1.80 (95% confidence interval [CI]: 1.09–3.06) and 1.38 (95% CI: 1.01–1.90), respectively. Our findings suggest a minor or negligible contribution of these viral co-detections to the evolution of influenza viruses. However, the data available in this study may not be exhaustive, warranting further, more in-depth investigations to conclusively determine the impact of virus–virus interactions on influenza virus genetic diversity.