Background: In light of climate change, health risks are expected to be exacerbated by extreme temperatures. Many studies showed that high and low ambient temperatures are associated with increased short-term mortality risk, but little is known about these risks in Kuwait and the Gulf Region.
Objective: To examine the dose-response relationship between 24-h average ambient temperatures and daily mortality risk in Kuwait.
Methods: We gathered mortality and meteorological data from 2010 to 2016 in Kuwait. We did a time-series analysis using a negative binomial distribution, and studied the lag effects of temperature with distributed lag non-linear models.
Results: A total of 33,574 all-cause non-accidental deaths were analyzed. The relationship was shown to be non-linear. Overall relative risks of death comparing the 1st percentile (10.9 °C) and the 99th percentile (42.7 °C) to the optimum temperature were 1.67 (1.02-2.73), and 1.65 (1.09-2.48), respectively. Cold effects persisted for 9 days, while the effects of hot temperatures were the highest at lag 0 and only persisted for a week. Adjusting for PM and ozone did not change the temperature-mortality estimates.
Conclusion: Our findings show evidence that there is a statistically significant positive association between temperature extremes and mortality in Kuwait. The evidence has significant implications in assessing climate vulnerability and provides insight into environmental challenges in an inherently hot and arid region.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.envres.2019.01.029 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial Intelligence in Pancreatic Imaging: A Systematic Review.
United European Gastroenterol J
January 2025
"Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation.
View Article and Find Full Text PDFThe role of myocardial energy metabolism perturbations in diabetic cardiomyopathy: from the perspective of novel protein post-translational modifications.
Clin Epigenetics
January 2025
Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Diabetic cardiomyopathy (DbCM), a significant chronic complication of diabetes, manifests as myocardial hypertrophy, fibrosis, and other pathological alterations that substantially impact cardiac function and elevate the risk of cardiovascular diseases and patient mortality. Myocardial energy metabolism disturbances in DbCM, encompassing glucose, fatty acid, ketone body and lactate metabolism, are crucial factors that contribute to the progression of DbCM. In recent years, novel protein post-translational modifications (PTMs) such as lactylation, β-hydroxybutyrylation, and succinylation have been demonstrated to be intimately associated with the myocardial energy metabolism process, and in conjunction with acetylation, they participate in the regulation of protein activity and gene expression activity in cardiomyocytes.
View Article and Find Full Text PDFStress hyperglycemia ratio as an important predictive indicator for severe disturbance of consciousness and all-cause mortality in critically ill patients with cerebral infarction: a retrospective study using the MIMIC-IV database.
Eur J Med Res
January 2025
Department of Rehabilitation Medicine, The Affiliated Hospital of Yunnan University, No. 176 Qingnian Road, Wuhua District, Kunming, Yunnan, China.
Background: Stress hyperglycemia ratio (SHR) has been linked to prognosis of cerebrovascular diseases. Nevertheless, the association between SHR and severe disturbance of consciousness (DC) and mortality among patients with cerebral infarction remains explored. This study seeks to assess the predictive potential of SHR for severe DC and mortality among patients with cerebral infarction.
View Article and Find Full Text PDFRisk factors for disease progression and clinical outcomes in patients with COVID-19 in Taiwan: a nationwide population-based cohort study.
BMC Pulm Med
January 2025
Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei City, Taiwan.
Background: Since 2021, COVID-19 has had a substantial impact on global health and continues to contribute to serious health outcomes. In Taiwan, most research has focused on hospitalized patients or mortality cases, leaving important gaps in understanding the broader effects of the disease and identifying individuals at high risk. This study aims to investigate the risk factors for disease progression through a nationwide population-based cohort study on COVID-19 in Taiwan.
View Article and Find Full Text PDFDevelopment and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.
BMC Anesthesiol
January 2025
Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Background: Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.
Methods: Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!