Psychotropic drug-prescribing correlates of disorganized speech in Asians with schizophrenia: The REAP-AP study.

BACKGROUND: Although disorganized speech is seen as one of the nuclear features of schizophrenia, there have been few reports of disorganized speech-associated psychotropic drug-prescribing patterns in large samples of schizophrenia patients. OBJECTIVE: We aimed to examine the prevalence of disorganized speech and its correlates in terms of psychotropic drug prescribing, using the data from the Research on Asian Psychotropic Patterns for Antipsychotics (REAP-AP) study. METHOD: A total of 3744 patients with the ICD-10 diagnosis of schizophrenia were enrolled from 71 survey centers in 15 Asian countries/areas. An essential criterion of disorganized speech was that it was "severe enough to impair substantially effective communication" as defined in the DSM-5. A binary logistic model was fitted to identify the psychotropic drug-prescribing correlates of disorganized speech. RESULTS: After adjusting for the potential effects of confounding variables, the binary logistic regression model showed that the presence of disorganized speech was directly associated with adjunctive use of mood stabilizers (P < 0.001) and cumulative diazepam equivalent dose (P < 0.0001), and inversely associated with adjunctive use of anti-Parkinson drugs (P < 0.0001). CONCLUSION: The association between disorganized speech and adjunctive use of mood stabilizers could perhaps be understood in the context of a relationship with impulsiveness/aggressiveness, or in terms of deconstructing the Kraepelinian dualism.

A cross-sectional study of avian influenza A virus in Myanmar live bird markets: Detection of a newly introduced H9N2?

BACKGROUND: Zoonotic influenza surveillance in Myanmar is sparse, despite the risks of introduction of such viruses from neighboring countries that could impact the poultry industry and lead to spillover to humans. METHODS: In July and August 2019, our multi-institutional partnership conducted a One Health-oriented, cross-sectional surveillance (weekly for 3 weeks) for influenza A and influenza D viruses at the three largest live bird markets in Yangon, Myanmar. RESULTS: The 27 bioaerosols, 90 bird cage swabs, 90 bird oropharyngeals, and 90 human nasopharyngeal samples yielded molecular influenza A detections in 8 bioaerosols (30.0%), 16 bird cages (17.8%), 15 bird oropharyngeals (16.7%), and 1 human nasopharyngeal (1.1%) samples. No influenza D was detected. Seven of the influenza A virus detections were found to be subtype A/H9N2, and one human nasopharyngeal sample was found to be subtype A/H1pdm. Among all IAV-positive samples, three of the A/H9N2-positive samples yielded live viruses from egg culture and their whole genome sequences revealing they belonged to the G9/Y280 lineage of A/H9N2 viruses. Phylogenetic analyses showed that these A/H9N2 sequences clustered separately from A/H9N2 viruses that were previously detected in Myanmar, supporting the notion that A/H9N2 viruses similar to those seen in wider Southeast Asia may have been introduced to Myanmar on multiple occasions. CONCLUSIONS: These findings call for increased surveillance efforts in Myanmar to monitor for the introduction of novel influenza viruses in poultry, as well as possible reassortment and zoonotic virus transmission.

Empowering health workers and leveraging digital technology to improve access to mental health and epilepsy care: A longitudinal quasi-experimental study in Hlaing Thar Yar Township.

Background: Despite an estimated 8% prevalence of mental disorders in Myanmar, the treatment gap is high, up to 90%. This project aimed to assess the effects of a series of activities implemented by the Myanmar Medical Association over a 2-year period in Hlaing Thar Yar Township involving community health workers (CHWs) and general practitioners (GPs) on the identification, diagnosis and management of people with psychotic disorders, depression and epilepsy. Methods: Seventy-six CHWs were trained to raise awareness, identify people with mental disorders and refer them to GPs. Fifty GPs were upskilled to diagnose and manage patients. Prevalence, treatment gap and general population's Knowledge-Attitudes-Practices (KAP) were evaluated through door-to-door surveys, whilst CHWs' and GPs' KAP were measured pre-, and post-training as well as post-intervention. Patient identification, diagnosis and management were analysed through data collected by CHWs and GPs via smartphones and tablets. Findings: At baseline, the average treatment gap was 79·7%. During the 2 year-intervention, 1,378 suspected cases were referred by CHWs to GPs and 1,186 (86%) of them saw a GP. Among the 1,088 patients (92%) diagnosed, the concordance between GPs' diagnosis and CHWs' screening was 75·6%. For CHWs, knowledge improved post-training (16·9 vs. 15·3; p = 0·0010), whilst attitudes and practices improved post-intervention (17·1 vs. 15·7; p = 0·010 and 19·4 vs. 11·2; p < 0·0001 respectively). GPs' global KAP score improved post-training (14·6 vs. 12·8; p = 0·0010), and remained stable post-intervention. General population's KAP score improved between baseline and end-line (8·3 vs. 12·7; p < 0·0001). Interpretation: This project suggests that a 2-year intervention including the training of frontline health workers and raising awareness among the population can have positive outcomes and lead to a greater number of people with mental disorders being diagnosed and managed. Funding: This project was implemented as part of a partnership involving the Myanmar Medical Association, the Myanmar Mental Health Society, the World Association of Social Psychiatry, the Université Numérique Francophone Mondiale and Sanofi Global Health. It was funded by Sanofi Global Health, within the framework of the Fight Against STigma (FAST) Program.

First Report of Lumpy Skin Disease in Myanmar and Molecular Analysis of the Field Virus Isolates.

Lumpy skin disease virus (LSDV) causes lumpy skin disease in cattle and buffaloes, which is associated with significant animal production and economic losses. Since the 2000s, LSDV has spread from Africa to several countries in the Middle East; Europe; and Asia; including, more recently, several south-east Asian countries. In November 2020, Myanmar reported its first LSD outbreak. This study reports on the first incursion of LSD in Myanmar and the molecular analysis of the LSDV detected. Staff from the Livestock Breeding and Veterinary Department (LBVD) of the Ministry of Agriculture, Livestock, and Irrigation collected samples from cattle with suspected LSD infection. The Food and Agriculture Organization (FAO) of the United Nations' Emergency Centre for Transboundary Animal Diseases (ECTAD) and the Joint International Atomic Energy Agency (IAEA)/FAO program's Animal Health and Production laboratory provided LSDV diagnostic support to two regional veterinary diagnostic laboratories in Myanmar. Samples from 13 cattle tested positive by real-time PCR. Selected samples underwent sequence analysis in IAEA laboratories. The results show that the Myanmar LSDV sequences clustered with LSDV isolates from Bangladesh and India, LSDV Kenya, and LSDV NI-2490. Further characterization showed that the Myanmar LSDV is 100% identical to isolates from Bangladesh and India, implying a common source of introduction. These findings inform diagnosis and development of control strategies.

How context affects transdisciplinary research: insights from Asia, Africa and Latin America.

Transdisciplinary research (TDR) has been developed to generate knowledge that effectively fosters the capabilities of various societal actors to realize sustainability transformations. The development of TDR theories, principles, and methods has been largely governed by researchers from the global North and has reflected their contextual conditions. To enable more context-sensitive TDR framing, we sought to identify which contextual characteristics affect the design and implementation of TDR in six case studies in Asia, Latin America, and Africa, and what this means for TDR as a scientific approach. To this end, we distinguished four TDR process elements and identified several associated context dimensions that appeared to influence them. Our analysis showed that contextual characteristics prevalent in many Southern research sites-such as highly volatile socio-political situations and relatively weak support infrastructure-can make TDR a challenging endeavour. However, we also observed a high degree of variation in the contextual characteristics of our sites in the global South, including regarding group deliberation, research freedom, and dominant perceptions of the appropriate relationship between science, society, and policy. We argue that TDR in these contexts requires pragmatic adaptations as well as more fundamental reflection on underlying epistemological concepts around what it means to conduct "good science", as certain contextual characteristics may influence core epistemological values of TDR. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01201-3.

Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia.

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.

Ventriculo-peritoneal shunt infection in a patient on hemodialysis.

Sepsis is an important and serious complication in hemodialysis (HD) patients. Here we report on a case of spina bifida with ventriculo-peritoneal (VP) shunt infection who was on HD and underwent at least 5 months of investigations before a source of the infection was found and eventually treated successfully. We believe this to be the first reported case of VP shunt-associated sepsis in a patient on HD.

Assessing ecosystem service provision under climate change to support conservation and development planning in Myanmar.

Inclusion of ecosystem services (ES) information into national-scale development and climate adaptation planning has yet to become common practice, despite demand from decision makers. Identifying where ES originate and to whom the benefits flow-under current and future climate conditions-is especially critical in rapidly developing countries, where the risk of ES loss is high. Here, using Myanmar as a case study, we assess where and how ecosystems provide key benefits to the country's people and infrastructure. We model the supply of and demand for sediment retention, dry-season baseflows, flood risk reduction and coastal storm protection from multiple beneficiaries. We find that locations currently providing the greatest amount of services are likely to remain important under the range of climate conditions considered, demonstrating their importance in planning for climate resilience. Overlap between priority areas for ES provision and biodiversity conservation is higher than expected by chance overall, but the areas important for multiple ES are underrepresented in currently designated protected areas and Key Biodiversity Areas. Our results are contributing to development planning in Myanmar, and our approach could be extended to other contexts where there is demand for national-scale natural capital information to shape development plans and policies.

Emerging Zoonotic Influenza A Virus Detection in Myanmar: Surveillance Practices and Findings.

We describe 2-season, risk-based, virological surveillance for zoonotic avian influenza in Myanmar and report the first detection of influenza A subtypes H5N6 and H9N2 in Myanmar. The study focused mainly on the live bird markets in border townships, where illegal poultry importation from China usually takes place. The objective was to enhance early warning for low pathogenic avian influenza A(H7N9) incursion. The study followed the guidelines of the Food and Agriculture Organization (FAO) of the United Nations for influenza A(H7N9) surveillance in uninfected countries. The sampling strategy was risk-based at all sampling levels. Sample collection and laboratory analysis were carried out with the government of the Union of the Republic of Myanmar. Laboratory testing was according to a previously published FAO laboratory protocol and algorithm designed to detect a range of influenza A subtypes. Challenges to implementation are outlined. The study provided evidence that the H7N9 subtype had not entered Myanmar but detected other subtypes, including H5N6 and H9N2. Although there were logistical difficulties associated with nation-related issues, the results highlight the importance and feasibility of this risk-based active surveillance, which should be urgently established in other countries, especially those located at the east-southeast influenza epicenter.