Originally posted 4/11/2012
Listening to the national conference call for the release of the 2012 County Heath Rankings last week, I was struck by the number of questions regarding the socioeconomic factors in the model. It made me recall the early morning call from a small town reporter when we released the first Wisconsin County Rankings in 2003, saying “do you mean that county income levels might be as important as the number of uninsured or the smoking rates?”
This concept is better understood today than it was in 2003, but it is still hard to communicate. We accept the health-compromising effects of smoking and lack of health care access, but how education and income ‘get under the skin” to produce disease and death is less obvious. Yet a growing body of literature supports this finding, and is the reason why we give social factors a weighting of 40% in the County Health Rankings model.
This week we’re calling attention to a study I co-authored with one of our PhD candidates, Erika Cheng. The article, Disparities in premature mortality between high and low income U.S. counties, appears in the April 2012 issue of Preventing Chronic Disease.
The figure below shows the relationship between median household income and age-adjusted, all-cause mortality rates for all counties in the country.
As expected, people tend to live longer in high income counties (HIC), with the diagonal line indicating the overall relationship. However, this relationship is not as strong in HIC; an increase of $9,000 in median household income was associated with an 18% better average mortality rate among low income counties (LIC) but only 12% better in higher income counties.
Equally interesting is the very large variation in mortality rates among LIC. Some of the LIC mortality rates are comparable to those of HICs (in the 200/100,000 range), while other LIC rates are triple or quadruple this level.
Also, a more nuanced picture emerges when controlling for other factors that impact mortality. Several factors were associated with longer lifespans, including percentages of adults with a 4-year college degree and percent Hispanic in the county. Other factors were associated with shorter lifespans, including preventable hospital stays, percent black in the county, percent children living below federal poverty guidelines, and percent adult smokers.
Importantly, when these other factors were controlled for, statistically significant linkages were found between median household income and mortality in the HICs, but not the LICs. This result highlights the main point of this study – that there is an interplay, apparent at the county level, between patterns of health factors and income. For example, two variables — the percent of children living below the federal poverty guidelines and the percent of single-parent households — are more closely linked to mortality in LICs than in HICs. We suggest that perhaps county-level income may buffer the effects of some variables associated with poor health. This buffer may operate through coping resources; county-level income may reflect overall availability of material and social resources that enable affluent single parents’ access to child care, neighborhood support, social networks, or higher-quality health care. Of course, we caution that the nature of the study and the data only allow for associations, not causal relationships.
From a policy perspective, these findings remind us that “one size fits all” does not apply to improving population health in communities. Our data reveal complicated relationships among the many factors that determine how long (not to mention how well) we live. While finding feasible and effective solutions can be difficult, we believe there’s a lot that can be done — including helping communities move on down the road toward better health and continuing to think about and work toward operationalizing the concept of locally customized policy approaches.