This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates ...This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates were investigated against fine particulate matter and socioeconomic status, for all counties in the United States in 2013. Multivariate multiple regressions as well as multivariate geostatistical predictions show that these are significant factors towards assessing the causal inferences between exposure to air pollution and socioeconomic status and the three mortality rates.展开更多
Introduction: In 2008, cardiovascular disease (CVD) accounted for one in three deaths in the United States. Epidemiological analyses suggest that two or more risk factors are the indicator of high risk and/or poor CVD...Introduction: In 2008, cardiovascular disease (CVD) accounted for one in three deaths in the United States. Epidemiological analyses suggest that two or more risk factors are the indicator of high risk and/or poor CVD outcomes. Knowledge of heart attack and stroke symptomology has been the focus of much research based on the assumption that accurate identification of an event is critical to reducing time to treatment. There is a paucity of research showing a clear association between knowledge of heart attack and stroke symptomology, risk factors, and mortality rates. In this study, we hypothesized that high stroke and heart attack symptomology knowledge scores would correspond to lower stroke or CVD mortality rankings as well as to a lower prevalence of two or more CVD risk factors. Methods: State was the unit of analysis used to examine data from two different sources and combined into a customized database. The first source was a multiyear Behavioral Risk Factor Surveillance Survey (BRFSS) heart attack and stroke symptom knowledge module database. CVD and stroke mortality data used came from the American Heart Association’s (AHA) 2012 Heart Disease and Stroke Statistics Update. Spearman’s Rho was the test statistic. Results: A moderate negative correlation was found between high heart attack and stroke symptom knowledge scores and the percentage of adults with two or more CVD or stroke risk factors. Likewise, a similar correlation resulted from the two variables, high heart attack and stroke symptoms knowledge score and CVD mortality rank. Conclusions: This study demonstrated a significant relationship between high heart attack and stroke symptom knowledge and lower CVD mortality rates and lower prevalence of two or more CVD risk factors at the state level. Our findings suggest that it is important to continue education efforts regarding heart attack and stroke symptom knowledge. Pharmacists are one group of health care providers who could enhance the needed public health education efforts.展开更多
Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may ...Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.展开更多
文摘This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates were investigated against fine particulate matter and socioeconomic status, for all counties in the United States in 2013. Multivariate multiple regressions as well as multivariate geostatistical predictions show that these are significant factors towards assessing the causal inferences between exposure to air pollution and socioeconomic status and the three mortality rates.
文摘Introduction: In 2008, cardiovascular disease (CVD) accounted for one in three deaths in the United States. Epidemiological analyses suggest that two or more risk factors are the indicator of high risk and/or poor CVD outcomes. Knowledge of heart attack and stroke symptomology has been the focus of much research based on the assumption that accurate identification of an event is critical to reducing time to treatment. There is a paucity of research showing a clear association between knowledge of heart attack and stroke symptomology, risk factors, and mortality rates. In this study, we hypothesized that high stroke and heart attack symptomology knowledge scores would correspond to lower stroke or CVD mortality rankings as well as to a lower prevalence of two or more CVD risk factors. Methods: State was the unit of analysis used to examine data from two different sources and combined into a customized database. The first source was a multiyear Behavioral Risk Factor Surveillance Survey (BRFSS) heart attack and stroke symptom knowledge module database. CVD and stroke mortality data used came from the American Heart Association’s (AHA) 2012 Heart Disease and Stroke Statistics Update. Spearman’s Rho was the test statistic. Results: A moderate negative correlation was found between high heart attack and stroke symptom knowledge scores and the percentage of adults with two or more CVD or stroke risk factors. Likewise, a similar correlation resulted from the two variables, high heart attack and stroke symptoms knowledge score and CVD mortality rank. Conclusions: This study demonstrated a significant relationship between high heart attack and stroke symptom knowledge and lower CVD mortality rates and lower prevalence of two or more CVD risk factors at the state level. Our findings suggest that it is important to continue education efforts regarding heart attack and stroke symptom knowledge. Pharmacists are one group of health care providers who could enhance the needed public health education efforts.
基金Researchers Supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia。
文摘Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.
