Over the last few decades, many countries, especially islands in the Caribbean, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure is key for effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain incompletely mapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g., Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-2 and Sentinel-1) and derived classification schemes (e.g., forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of the OSM database, especially in countries with high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management.

Electroconvulsive therapy (ECT) in the presence of metal in the skull may cause concern among clinicians. The literature is sparse, with only a few case reports in this area. We present here a case where ECT was administered in a patient with metallic internal fixation for the fracture of mandible. The 37-year-old man presented with severe depression, suicide risk, and alcohol dependence. Administration of ECT was uneventful, with no complications during ECT or at follow-up; and there was successful resolution of symptoms. This case report demonstrates that ECT may be safe in the presence of metallic implants in mandible.

Over the last few decades, many countries, especially Caribbean island ones, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure are key for an effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain unmapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g. VIIRS, Sentinel-2 and Sentinel-1) and derived classification schemes (e.g. forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of OSM database, especially in countries at high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management actions.

The impacts of natural disasters are often disproportionally borne by poor or otherwise marginalized groups. However, while disaster risk modelling studies have made progress in quantifying the exposure of populations, limited advances have been made in determining the socioeconomic characteristics of these exposed populations. Here, we generate synthetic structural and socioeconomic microdata for around 9.5 million persons for six districts in Bangladesh as vector points using a combination of spatial microsimulation techniques and dasymetric modelling. We overlay the dataset with satellite-derived flood extents of Cyclone Fani, affecting the region in 2019, quantifying the number of exposed households, their socioeconomic characteristics, and the exposure bias of certain household variables. We demonstrate how combining various modelling techniques could provide novel insights into the exposure of poor and vulnerable groups, which could help inform the emergency response after extreme events as well targeting adaptation options to those most in need of them.