Introduction: Violence against children not only endangers a child's physical and mental health, but it also has long-term implications. At the same time, the results raise disease burden and medical expenditures. Objectives: This scoping review aims to describe the prevalence of certain sorts of violence among adolescents in 10 years from 2012 to 2022 in low and middle-income countries. Method: Searching strategy was combining terms about (1) Types of violence, (2) adolescents, and (3) prevalence in PubMed/MEDLINE, and Cochrane. Original English-language research papers on the prevalence of violence among adolescents were chosen. Data from selected studies was extracted and classified into themes and topics. The PRISMA principles were used to perform a scoping evaluation of several medical databases. Results: This research includes ten studies in all. In ten years, there were six forms of violence against adolescents: bullying, physical fights, physical attacks, dating violence, intimate partner violence, and child abuse. Males were always more vulnerable than females to these six types. Bullying and physical fighting including engaging in fights and being attacked are more common among children aged 10 to 14. Children aged 15 to 19 are more likely to experience dating violence. Bullying, in particular, has been ongoing for ten years and shows no signs of decline, although it is becoming more prevalent in numerous forms, including cyberbullying. Conclusion: Over 10 years, adolescents in LMICs experienced the most bullying, followed by physical fights. Organizations and governments need to implement more targeted initiatives to prevent an increase in violence against adolescents.

In healthcare situations, time-to-event (TTE) data are common outcomes. A parametric approach is often employed to handle TTE data because it is possible to easily visualize different scenarios via simulation. Not all pharmacometricians are familiar with the use of non-linear mixed effects models (NONMEMs) to deal with TTE data. Therefore, this tutorial simply explains how to analyze TTE data using NONMEM. We show how to write the code and evaluate the model. We also provide an example of a hands-on model for training.

INTRODUCTION: Extensively drug-resistant tuberculosis (XDR-TB) represents an emerging public health problem worldwide. According to the World Health Organization, an estimated 9.7% of multidrug-resistant TB (MDR-TB) cases are defined as XDR-TB globally. The objective of this study was to determine the prevalence of drug resistance to second-line TB drugs among MDR-TB cases detected in the Fourth National Anti-Tuberculosis Drug Resistance Survey in Viet Nam. METHODS: Eighty clusters of TB cases were selected using a probability-proportion-to-size approach. To identify MDR-TB cases, drug susceptibility testing (DST) was performed for the four major first-line TB drugs. DST of second-line drugs (ofloxacin, amikacin, kanamycin, capreomycin) was performed on isolates from MDR-TB cases to identify pre-XDR and XDR cases. RESULTS: A total of 1629 smear-positive TB cases were eligible for culture and DST. Of those, DST results for first-line drugs were available for 1312 cases, and 91 (6.9%) had MDR-TB. Second-line DST results were available for 84 of these cases. Of those, 15 cases (17.9%) had ofloxacin resistance and 6.0% were resistant to kanamycin and capreomycin. Five MDR-TB cases (6.0%) met the criteria of XDR-TB. CONCLUSION: This survey provides the first estimates of the proportion of XDR-TB among MDR-TB cases in Viet Nam and provides important information for local policies regarding second-line DST. Local policies and programmes that are geared towards TB prevention, early diagnosis and treatment with effective regimens are of high importance.