April 16th, 2012 11:27 am ET -
Amanda K. Hall, MHSE, MS; Michael Stellefson, PhD; Jay M. Bernhardt, PhD, MPH
Suggested citation for this article: Hall AK, Stellefson M, Bernhardt JM. Healthy Aging 2.0: the potential of new media and technology. Prev Chronic Dis 2012;9:110241. DOI: http://dx.doi.org/10.5888/pcd9.110241.
The emergence of e-patients (consumers who use the Internet and electronic communication tools to research and communicate about medical conditions) has spawned the era of “Healthy Aging 2.0” to support chronic disease management. Approximately 125 million Americans are living with 1 or more chronic diseases, and this number is expected to grow to 157 million by 2020 (1). Approximately 84% of adults who are aged 65 or older have 1 or more chronic conditions (1). Healthy Aging 2.0 proposes that 21st century information and communications technology offers public health practitioners the unique opportunity to empower, engage, and educate these older adults in chronic disease management.
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April 16th, 2012 11:02 am ET -
Heather Schacht Reisinger, PhD; Stephen C. Hunt, MD, MPH; A. Lucile Burgo-Black, MD; Madhulika A. Agarwal, MD, MPH
Suggested citation for this article: Reisinger HS, Hunt SC, Burgo-Black AL, Agarwal MA. A population approach to mitigating the long-term health effects of combat deployments. Prev Chronic Dis 2012;9:110116. DOI: http://dx.doi.org/10.5888/pcd9.110116.
A major focus of the mission of the US Department of Veterans Affairs (VA) is to respond to the needs of military personnel returning from war. Given the broad spectrum of the potential effects of combat deployment on the health and well being of service members, VA is increasingly oriented toward comprehensive postcombat support, health promotion, disease prevention, and proactive approaches to caring for combat veterans. This article briefly summarizes the health care needs of service members returning from Afghanistan and Iraq, describes VA’s approaches to addressing their needs, and outlines VA’s evolving vision for how to apply principles of population health management to ensure prompt and effective response to the postdeployment needs of veterans returning from future conflicts. At the heart of postcombat care will be population-based approaches oriented to health recovery using interdisciplinary, team-based platforms.
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April 11th, 2012 2:55 pm ET -
Erika R. Cheng, MPA; David A. Kindig, MD, PhD
Suggested citation for this article: Cheng ER, Kindig DA. Disparities in premature mortality between high- and low-income US counties. Prev Chronic Dis 2012;9:110120. DOI:
Several well-established determinants of health are associated with premature mortality. Using data from the 2010 County Health Rankings, we describe the association of selected determinants of health with premature mortality among counties with broadly differing levels of income.
County-level data on 3,139 US counties from the 2010 County Health Rankings were linked to county mortality data from the Centers for Disease Control and Prevention Compressed Mortality database. We divided counties into 3 groups, defined by sample median household income levels: low-income (≤25th percentile, $29,631), mid-income (25th-75th percentile, $29,631-$39,401), and high-income (≥75th percentile, ≥$39,401). We analyzed group differences in geographic, sociodemographic, racial/ethnic, health care, social, and behavioral factors. Stratified multivariable linear regression explored the associations of these health determinants with premature mortality for high- and low-income groups.
The association between income and premature mortality was stronger among low-income counties than high-income counties. We found differences in the pattern of risk factors between high- and low-income groups. Significant geographic, sociodemographic, racial/ethnic, health care, social, and behavioral disparities exist among income groups.
Geographic location and the percentages of adult smokers and adults with a college education were associated with premature mortality rates in US counties. These relationships varied in magnitude and significance across income groups. Our findings suggest that population health policies aimed at reducing mortality disparities require an understanding of the socioeconomic context within which modifiable variables exist.
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