Online Program

Examining the correlation between individual metrics and overall score in a health index

Monday, November 2, 2015 : 8:45 a.m. - 9:00 a.m.

Thomas Eckstein, MBA, Arundel Metrics, Incorporated, St. Paul, MN
Mariah Quick, School of Public Health, University of Minnesota, Minneapolis, MN
Using America’s Health Rankings as an example, this presentation will show how tightly the individual metrics used to create composite indices of health are correlated.

In 2014, 29 individual metrics were used to construct the rankings, including the prevalence of smoking, obesity and binge drinking, immunization rates, cancer, cardiovascular disease and infant mortality, violent crime, air pollution and the density of primary care physicians and dentists. The correlation between  individual metrics and the overall score  were examined. The overall score is the sum of the z-scores of all 29 metrics multiplied by the metric’s assigned weight. The states are ranked based on the overall score. The correlations range from more than r=0.8 for smoking, obesity and diabetes to r < 0.4 for public health funding, teen immunization, and disparity in health status by educational attainment.

Over the last ten years, the correlation of many individual metrics with the overall score has remained relatively constant. For example, the prevalence of smoking has remained correlated at 0.76 <r < 0.79  and high school graduation has remained between 0.55 < r < 0.67.

Modeling the components within the rankings shows that changes in the prevalence of smoking, infectious disease, children in poverty and obesity have the largest impact on a state improving in its comparative health. However, even large changes in any single metric will have minimal impact on the rank because of the high inter-correlation of all of the components and the small weight of each individual metric.

Learning Areas:

Advocacy for health and health education
Communication and informatics

Learning Objectives:
Describe the inter-correlations between individual metrics contained in a summary measure. Discuss how correlations reduce the ability to modify the summary measure by modifying individual metrics.

Keyword(s): Health Promotion and Education, Statistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am an MPH epidemiology student that has worked on the compilation and dissemination of the annual America's Health Rankings report for the last 2 editions
Any relevant financial relationships? Yes

Name of Organization Clinical/Research Area Type of relationship
United Health Foundation Public Health Information Independent Contractor (contracted research and clinical trials)

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.