Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11026972PMC
http://dx.doi.org/10.1016/j.idm.2024.03.001DOI Listing

Publication Analysis

Top Keywords

optimal decision-making
8
high temperature-related
8
disease burden
8
global warming
8
respiratory diseases
8
gnn model
8
optimal intervention
8
intervention strategies
8
burden
6
diseases
6

Similar Publications

Is every pancreatic cancer patient a palliative care patient?

Ann Palliat Med

September 2025

Brown University Health Cancer Institute, Providence, RI, USA; Division of Geriatrics and Palliative Medicine, Department of Medicine, Alpert Medical School of Brown University, Providence, RI, US.

ancreatic cancer is an aggressive disease and often presents at an advanced stage with no curative options. The disease is often characterized by rapid progression, limited or short-lived responsiveness to standard therapies, and a profound impact on patients' quality of life. Despite advances in targeted therapies and immunotherapy, curative outcomes remain elusive for the majority of patients with advanced or high-grade disease with a 5-year survival rate of less than 10%.

View Article and Find Full Text PDF

Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.

View Article and Find Full Text PDF

Background: An estimated 44,680 people died in motor-vehicle crashes in the United States in 2024. A disproportionate share of these deaths involved young people. In 2023 alone, these crashes cost the U.

View Article and Find Full Text PDF

Fetal magnetic resonance imaging (MRI) is a safe method of in-utero evaluation of fetal anomalies and a valuable adjunct to prenatal ultrasound. The utilization of rapid sequences reduces the impact of fetal motion and allows for high contrast resolution of fetal structures. A thorough understanding of fetal anatomy and a systematic approach to MRI interpretation are essential for accurate diagnosis of fetal head and neck anomalies.

View Article and Find Full Text PDF

Atrial fibrillation (AF) is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies. This review explores the integration of large language models (LLMs) and machine learning into AF care, with a focus on clinical utility, privacy preservation, and ethical deployment. Federated and transfer learning methods have enabled high-performance predictive modeling across distributed datasets without compromising data security.

View Article and Find Full Text PDF