Emergency Department Visits for Heat-Related Emergency Conditions in the United States from 2008-2020

Association between summertime emergency department visits and maximum daily heat index in rural and non-rural areas of Virginia (2015-2022)

There is growing evidence that high ambient temperatures are associated with a range of adverse health outcomes. Further evidence suggests differences in rural versus non-rural populations' vulnerability to heat-related adverse health outcomes. The current project aims to 1) refine estimated associations between maximum daily heat index (HI) and emergency department (ED) visits in regions of Virginia, and 2) compare associations between maximum daily HI and ED visits in rural versus non-rural areas of Virginia and within those areas, for persons 65 years of age and older versus those younger than 65 years. Our study utilized 16,873,213 healthcare visits from Virginia facilities reporting to the Virginia Department of Health syndromic surveillance system between May and September 2015-2022. Federal Office of Rural Health Policy defined rural areas were assigned to patient home ZIP code. The estimated daily maximum HI at which ED visits begin to rise varies between 25 °C and 33 °C across climate zones and regions of Virginia. Across all regions, estimated ED visits attributable to days with maximum HI above 25.7 °C were higher in rural areas (3.7%, 95% CI: 3.5%, 3.9%) versus in non-rural areas (3.1%, 95% CIs: 3.0%, 3.2%). Patients aged 0-64 years had a higher estimated heat attributable fraction of ED visits (4.2%, 95% CI: 4.0%, 4.3%) than patients 65 years and older (3.1%, 95% CI: 2.9%, 3.4%). Rural patients older than 65 have a higher estimated fraction of heat attributable ED visits (2.7%, 95% CI: 2.2%, 3.1%) compared to non-rural patients 65 years and older (1.5%, 95% CI: 1.3%, 1.8%). State-level syndromic surveillance data can be used to optimize heat warning messaging based on expected changes in healthcare visits given a set of meteorological variables, and can be further refined based on climate, rurality and age.

Mental Health, Climate Change, and Bodily Autonomy: An Analysis of Adolescent Health Policy in the Post-Pandemic Climate

The COVID-19 pandemic exacerbated the vulnerability of adolescents and young adults (AYAs) who face economic disadvantage, depend on social safety net resources, have politically targeted identities, are geopolitically displaced, and/or are racially or ethnically marginalized. A rapid change in social safety net policies has impacts that reverberate throughout interrelated domains of AYA health, especially for vulnerable AYAs. The authors analyze policy-related changes in mental health, climate change, and bodily autonomy to offer a paradigm for an equitable path forward.

Vulnerability assessment of heat waves within a risk framework using artificial intelligence

Current efforts to adapt to climate change are not sufficient to reduce projected impacts. Vulnerability assessments are essential to allocate resources where they are needed most. However, current assessments that use principal component analysis suffer from multiple shortcomings and are hard to translate into concrete actions. To address these issues, this article proposes a novel data-driven vulnerability assessment within a risk framework. The framework is based on the definitions from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, but some definitions, such as sensitivity and adaptive capacity, are clarified. Heat waves that occurred between 2001 and 2018 in Quebec (Canada) are used to validate the framework. The studied impact is the daily mortality rates per cooling degree-days (CDD) region. A vulnerability map is produced to identify the distributions of summer mortality rates in aggregate dissemination areas within each CDD region. Socioeconomic and environmental variables are used to calculate impact and vulnerability. We compared abilities of AutoGluon (an AutoML framework), Gaussian process, and deep Gaussian process to model the impact and vulnerability. We offer advice on how to avoid common pitfalls with artificial intelligence and machine-learning algorithms. Gaussian process is a promising approach for supporting the proposed framework. SHAP values provide an explanation for the model results and are consistent with current knowledge of vulnerability. Recommendations are made to implement the proposed framework quantitatively or qualitatively.