Orignally posted 5/18/2010
Health outcomes, however defined and measured, are produced by determinants or factors. They often are sorted into the five categories presented on the right in the following model – health care, individual behavior, social environment, physical environment, and genetics.
Health care determinants generally include access, cost, quantity, and quality of health care services. Individual behavior determinants include choices about lifestyle or habits (either spontaneously or through response to incentives) such as diet, exercise, and substance abuse.Social environment determinants include elements of the social environment such as education, income, occupation, class, social support. Physical environment determinants include elements of the natural and built environment such as air and water quality, lead exposure, and the design of neighborhoods. Genetic determinants include the genetic composition of individuals or populations.
The subcomponents of these determinants or factors can be measured in many different ways. The County Health Rankings includes many such measures in each category that are available at the county level. A series of articles commissioned by the MATCH project, to be published in the online journal Preventing Chronic Disease starting in June 2010, outline current thinking regarding conceptualizing and measuring each of these categories.
In the model above, each category is depicted as the same size, implying that they each contribute equally to health outcomes. Although useful for illustration, in reality those determinants will carry different weights (and hence would be different sizes). Differences exist depending on the population studied, and because cross-sectoral analysis is complicated by interactions between determinants and the latency over time of their effects. In the MATCH County Health Rankings, health care is weighted 20%, behaviors 30%, the social environment 40%, and the physical environment 10%. An explanation of the process used to assign these particular weights is available. However, determining the correct weights for each category and the policies and programs underpinning them remains a major challenge for population health research.It is important, too, to realize the presence of “reverse causality,” which is why there is a small arrow in the above model going from outcomes to determinants/factors. This reflects the fact that outcomes such as morbidity can produce a change in a determinant or risk factor. For example, childhood illness can be responsible for lower educational attainment. In this case, the definitions of outcomes and determinants are reversed; morbidity would be the determinant or factor and educational attainment the outcome. Separating out the different directions of causality is an important and difficult research challenge.