Introduction: Alcohol consumption is associated with great morbidity-mortality rate. The aim of this study is to analyze the mortality that can be attributed to alcohol consumption in Spain and in its different Autonomous Communities during 2004.
Method: The records of deaths by cause of death were used, grouped by age, gender and 60 diagnostic categories. The number of deaths attributable to alcohol consumption according to gender and age group in 2004 for Spain and the different Autonomous Communities was calculated by means of the alcohol attributable fractions proposed by the Centers for Disease Control and Prevention for calculating the mortality rates in the U.S.A. in 2001. The raw and adjusted mortality rates attributable to alcohol per 100,000 inhabitants were calculated with respect to the European population standard.
Results: Alcohol-related mortality in Spain was 2.3%; 3.2% for men and 1.2% for women. The Community of Murcia, with a mortality rate of 2.9%, together with those of Andalusia, the Canary Islands, the Basque Country and Asturias, had the highest rates of mortality attributable to alcohol in 2004. The highest adjusted mortality rates attributable to alcohol were found in Asturias, Murcia, Galicia and the Basque Country.
Conclusions: In the different Autonomous Communities of Spain, alcohol consumption is an important cause of death. This information is of use for identifying priorities and to evaluate intervention programs. Chronic causes, in general, have a greater weight in mortality rates than acute causes, disease of the digestive system having the highest rate of contribution.
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BMC Genomics
January 2025
College of Animal Science and Technology, Ningxia University, Yinchuan, 750021, China.
Background: Trimethylamine N-oxide (TMAO) is a metabolite produced by gut microbiota, and its potential impact on lipid metabolism in mammals has garnered widespread attention in the scientific community. Bovine fatty liver disease, a metabolic disorder that severely affects the health and productivity of dairy cows, poses a significant economic burden on the global dairy industry. However, the specific role and pathogenesis of TMAO in bovine fatty liver disease remain unclear, limiting our understanding and treatment of the condition.
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January 2025
Center for Pan-third Pole Environment, Lanzhou University, Lanzhou, China.
In polar and alpine regions, global warming and landform changes are draining lakes, transforming them into permafrost with altered microbial communities and element cycling. In this study, we investigated bacterial and archaeal (prokaryotic) community changes in the newly exposed sediment of Zonag Lake (Tibetan Plateau), focusing on prokaryotic diversity, community structure, and genes involved in carbon fixation and nitrogen cycling across lateral (up to 800 m) and vertical (up to 80 cm) horizons. The results showed that prokaryotic richness decreased across the lateral horizons, coinciding with reductions in carbon concentrations.
View Article and Find Full Text PDFPLoS One
January 2025
School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia.
Introduction: One of the key strategies to achieve the sustainable development goal by reducing maternal deaths below 70 per 100,000 is improving knowledge of obstetric danger signs (ODS). However, mothers' knowledge of ODS is low in general and very low in rural settings, regardless of local and national efforts in Ethiopia. Further, there is significant variation of ODS knowledge among women from region to region and urban/rural settings.
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December 2024
Universidad Loyola Andalucía, Seville, Spain.
Afr J Reprod Health
November 2024
Department of Social Research, Loyola Institute of Social Research, Tamil Nadu, India
We are writing to address the growing interest in the role of artificial intelligence (AI) within healthcare, particularly in the field of reproductive health. As technology continues to evolve, AI offers an unprecedented opportunity to transform how we diagnose, treat, and improve access to reproductive services, especially in underserved communities. AI-driven tools, supported by machine learning and big data analytics, are already demonstrating their potential in enhancing outcomes in reproductive health.
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