Psychological impairment and extreme weather event (EWE) exposure, 1980-2020: A global pooled analysis integrating mental health and well-being metrics.

Extreme Weather Events (EWEs) impose a substantial health and socio-economic burden on exposed populations. Projected impacts on public health, based on increasing EWE frequencies since the 1950s, alongside evidence of human-mediated climatic change represents a growing concern. To date, the impacts of EWEs on mental health remain ambiguous, largely due to the inherent complexities in linking extreme weather phenomena with psychological status. This exploratory investigation provides a new empirical and global perspective on the psychological toll of EWEs by exclusively focusing on psychological morbidity among individuals exposed to such events. Morbidity data collated from a range of existing psychological and well-being measures have been integrated to develop a single ("holistic") metric, namely, psychological impairment. Morbidity, and impairment, were subsequently pooled for key disorders-, specifically PTSD, anxiety and depression. A "composite" (any impairment) post-exposure pooled-prevalence rate of 23% was estimated, with values of 24% calculated for depression and ⁓17% for both PTSD and anxiety. Notably, calculated pooled odds ratios (pOR = 1.9) indicate a high likelihood of any negative psychological outcome (+90%) following EWE exposure. Pooled analyses of reported risk factors (p < 0.05) highlight the pronounced impacts of EWEs among individuals with higher levels of event exposure or experienced stressors (14.5%) and socio-demographic traits traditionally linked to vulnerable sub-populations, including female gender (10%), previous history (i.e., pre-event) of psychological impairment (5.5%), lower socio-economic status (5.5%), and a lower education level (5.2%). Inherent limitations associated with collating mental health data from populations exposed to EWEs, and key knowledge gaps in the field are highlighted. Study findings provide a robust evidence base for developing and implementing public health intervention strategies aimed at ameliorating the psychological impacts of extreme weather among exposed populations.

Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data.

A critical question in environmental epidemiology is whether air pollution exposures of large populations can be refined using individual mobile-device-based mobility patterns. Cellular network data has become an essential tool for understanding the movements of human populations. As such, through inferring the daily home and work locations of 407,435 mobile phone users whose positions are determined, we assess exposure to PM2.5. Spatiotemporal PM2.5 concentrations are predicted using an Aerosol Optical Depth- and Land Use Regression-combined model. Air pollution exposures of subjects are assigned considering modeled PM2.5 levels at both their home and work locations. These exposures are then compared to residence-only exposure metric, which does not consider daily mobility. In our study, we demonstrate that individual air pollution exposures can be quantified using mobile device data, for populations of unprecedented size. In examining mean annual PM2.5 exposures determined, bias for the residence-based exposures was 0.91, relative to the exposure metric considering the work location. Thus, we find that ignoring daily mobility potentially contributes to misclassification in health effect estimates. Our framework for understanding population exposure to environmental pollution could play a key role in prospective environmental epidemiological studies.