There are 3 item(s) tagged with the keyword "Cardiovascular Disease".
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- America is Losing Ground on Dire Diabetes Complications: Let's Turn It Around
Epidemiologic and statistical experts from the diabetes division of the Center for Disease Control and Prevention (CDC) wrote an important article on the surge of diabetes complications between 2010 and 2015, largely due to lifestyle modifiable factors in the May 21, 2019 issue of JAMA. The article is a dire warning about young (age 18-44) and middle-aged (age 45-64) adults who disproportionately represented the resurgence in diabetes complications.
While work needs to continue to better understand the demographics and behaviors of subsets in these age groups, the message is that we have to do more to prevent diabetes and reach diabetic individuals who are on a collision course with serious maladies.
APH is dedicated to reversing this untoward trend – and has done so for many clients – but there is more work to do.
- Reducing Stroke and Heart Disease Risks Through Updated Dietary Best Practices
Following our first May blog, and in honor of National Stroke Awareness Month, our clinical team asked us to compile some information here about dietary best practices to reduce the risk of heart disease and stroke.
- Three of the Costliest Health Conditions Ravaging Your Health Plan, and Steps for Prevention
High-cost claimants are only a part of the equation. There is a secondary subset of the insured population who are at-risk of entering the 5-7% of highest cost claimants that predictive modeling can uncover and help address. Not all predictive modeling engines have the capability to identify this subset of the employee population, but with Advanced Plan for Health’s Poindexter’s predictive modeling capabilities, determining the likelihood of an occurrence of coronary events, neurological events, orthopedic events and chronic kidney disease is made easy. Once interventions are made to improve the health of at-risk patients, the analytics system can retrospectively report actual cost versus the original predictive model to measure the positive health and financial effects the interventions created. Without the predictive-modelling enabled ability to identify those at-risk and intervene with targeted prevention strategies, health plan optimization will always be an uphill battle.
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