The frequency of natural disasters appears to be mounting at an alarming rate, and the degree to which people are surviving such traumatic events also is increasing. Post-disaster survival often triggers increases in population and individual disability-related outcomes in the form of impairments, activity limitations, and participation restrictions, all of which have an important impact on the individual, his or her family, and their community.

Abstract

Purpose: The purpose of this paper is to determine the prevalence of Alcohol Use Disorders (AUDs) among Myanmar male migrant workers (> 15 years) living in Mae Sot, Thailand, and their patterns of drinking.

Design/methodology/approach: A cross-sectional survey was administered to 512 participants to measure AUDs and drinking patterns. ANOVA and χ2 analyses were performed to assess demographic differences between abstainers, harmful and hazardous drinkers (HHDs) (those showing signs of AUDs) and non-harmful drinkers.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard.

Abstract

BACKGROUND: Since Nepali cross-border migrants can freely enter, work and stay in India, they are largely undocumented. The majority is involved in semi-skilled or unskilled jobs with limited labour rights and social security, a fact which predisposes them to psychological distress. We aimed to assess the prevalence of and factors associated with psychological morbidity among Nepali migrants upon their return from India.