Background: There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the "Eight Americas") defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.
Methods And Findings: We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age-sex-disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South--5-7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%-80%; the corresponding reduction for probabilities of dying from cancers would be 29%-50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.
Conclusions: Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US. Please see later in the article for the Editors' Summary.
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http://dx.doi.org/10.1371/journal.pmed.1000248 | DOI Listing |
J Med Internet Res
January 2025
Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, US.
Background: Most cancer survivors have multiple cardiovascular risk factors, increasing their risk of poor cardiovascular and cancer outcomes. The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association's Life's Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors.
View Article and Find Full Text PDFPLoS One
January 2025
Instituto de Cardiologia do Rio Grande do Sul/Fundação Universitária de Cardiologia (IC/FUC), Serviço de Nutrição e Dietética, Porto Alegre, Rio Grande do Sul, Brazil.
Background: Obesity is a risk factor for cardiovascular diseases and associated with reduced life expectancy metabolic bariatric surgery (MBS) is the treatment indicated when patients are unable to lose weight through lifestyle changes and medication alone. However, more evidence is necessary to show non-inferiority of e-health compared to in-person monitoring with regard to important parameters for the success of surgical treatment of obesity such as anthropometric changes.
Methods And Analyses: This review study will include cohort studies involving individuals with obesity and e-health or in-person patient monitoring before and after MBS.
Stat Med
February 2025
School of Mathematical Science, Queensland University of Technology, Brisbane, Australia.
To date, there have not been any population-based cancer studies quantifying geographical patterns of the loss in life expectancy (LLE) and crude probability of death due to cancer ( ). These absolute measures of survival are complementary to the more typically used relative measures of excess mortality and relative survival, and, together, they provide a fuller understanding of geographical disparities in survival outcomes for cancer patients. We propose using a spatially flexible parametric relative survival model in the Bayesian framework, which allows for the inclusion of spatial effects in hazard-level model components.
View Article and Find Full Text PDFJAMA Health Forum
January 2025
Shorenstein Asia-Pacific Research Center, Stanford University, Stanford, California.
Importance: Health care spending in South Korea (hereafter Korea) nearly doubled from 2010 to 2019. However, little is known about the drivers and effectiveness of these spending increases in terms of changes in disability-adjusted life-years (DALYs).
Objectives: To evaluate the factors contributing to changes in health care spending and DALYs and estimate the value of health care spending from 2010 to 2019 in Korea.
Sports (Basel)
January 2025
Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Rey Juan Carlos University, 28922 Madrid, Spain.
Background: Nowadays, not only is a high, long life expectancy desired, but also longevity with quality. Quality of life in adulthood is a multidimensional construct related to the perception of one's own health, psychological and socio-emotional factors, functionality for daily activities, and body composition.
Objective: This study evaluates the effects of physical activity level (PAL), strength, balance, and body composition on perceived health in healthy adults.
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