Although many researchers agree that multiple determinants impact health, there is no consensus regarding the magnitude of the relative contributions of individual health factors to health outcomes. This study presents a method to empirically estimate the relative contributions of health behaviors, clinical care, social and economic factors, and the physical environment to health outcomes using nationally representative county-level data and statistical approaches that account for potential sources of bias. The analyses for this study were conducted in 2014. Data were from the 2010-2013 County Health Rankings & Roadmaps. Data covered 2,996 of 3,141 U.S. counties. Ordinary least squares modeling was used as a baseline model. Multilevel latent growth curve modeling was used to estimate the relative contributions of health factors to health outcomes while accounting for measurement errors and state-specific characteristics. Almost half of the variance of health outcomes was due to state-level variation rather than county-level variation. When adjusted for measurement errors and state-level variation using multilevel latent growth curve modeling, the relative contribution of clinical care decreased and that of social and economic factors increased compared with the baseline model. This study presents how potential sources of bias affected the estimates of the relative contributions of a set of modifiable health factors to health outcomes at the county level. Further verification of these approaches with other data sources could lead to a better understanding of the impact of specific health determinants to health outcomes, and will provide useful information on policy interventions.
Copyright © 2015 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.