A randomized controlled trial testing the feasibility, acceptability, and preliminary effects of a mental health Screening, Brief Intervention, and Referral to Treatment among refugees in Malaysia.

BACKGROUND: Among refugees residing in countries of first asylum, such as Malaysia, high rates of psychological distress call for creative intervention responses. AIMS: This study examines implementation of a Screening, Brief Intervention, and Referral to Treatment (SBIRT) model promoting emotional well-being and access to services. METHOD: The one-session intervention was implemented in community settings by refugee facilitators during 2017 to 2020. 140 Participants including Afghan (n = 43), Rohingya (n = 41), and Somali (n = 56) refugees were randomized to receive either the intervention at baseline, or to a waitlist control group. At 30 days post-intervention, all participants completed a post-assessment. Additionally, after completing the intervention, participants provided feedback on SBIRT content and process. RESULTS: Findings indicate the intervention was feasible to implement. Among the full sample, Refugee Health Screening-15 emotional distress scores reduced significantly among participants in the intervention group when compared to those in the waitlist control group. Examining findings by nationality, only Afghan and Rohingya participants in the intervention condition experienced significant reductions in distress scores compared to their counterparts in the control condition. Examining intervention effects on service access outcomes, only Somali participants in the intervention condition experienced significant increases in service access compared to the control condition. CONCLUSIONS: Findings indicate the potential value of this SBIRT intervention, warranting further research.

Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality.

In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NOx), and approximately 50% of PM2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.