Abstract
Self-rated health (SRH) is an independent predictor of mortality; studies have investigated correlates of SRH to explain this predictive capability. However, the interplay of a broad array of factors that influence health status may not be adequately captured with parametric multivariate regression. This study investigated associations between several health determinants and SRH using recursive partitioning methods. This non-parametric analytic approach aimed to reflect the social-ecological model of health, emphasizing relationships between multiple health determinants, including biological, behavioral, and from social/physical environments. The study sample of 3648 men and women was drawn from the year 15 (2000–2001) data collection of the CARDIA Study, USA, in order to study a young adult sample. Classification tree analysis identified 15 distinct, mutually exclusive, subgroups (eight with a larger proportion of individuals with higher SRH, and seven with a larger proportion of lower SRH), and multi-domain risk and protective factors associated with subgroup membership. Health determinant profiles were not uniform between subgroups, even for those with similar health status. The subgroup with the largest proportion of higher SRH was characterized by several protective factors, whilst that with the largest proportion of lower SRH, with several negative risk factors; certain factors were associated with both higher and lower SRH subgroups. In the full sample, physical activity, education and income were highest ranked by variable importance (random forests analysis) in association with SRH. This exploratory study demonstrates the utility of recursive partitioning methods in studying the joint impact of multiple health determinants. The findings indicate that factors do not affect SRH in the same way across the whole sample. Multiple factors from different domains, and with varying relative importance, are associated with SRH in different subgroups. This has implications for developing and prioritizing appropriate interventions to target conditions and factors that improve self-rated health status.
Highlights
Self-rated health (SRH) is recognized as a valid assessment of health status, and independent predictor of mortality (Idler & Benyamini, 1997)
The aim of this study is to demonstrate the use of recursive partitioning methods for investigating multi-domain correlates of SRH status
We show that these methods offer valuable insight, which is distinct to that gained by parametric regression models, on the joint impact of multiple factors, and the way in which varying combinations of health determinants influence SRH in different subgroups
Summary
Self-rated health (SRH) is recognized as a valid assessment of health status, and independent predictor of mortality (Idler & Benyamini, 1997). Higher education and income are positively associated with higher SRH status (Bobak, Pikhart, Hertzman, Rose, & Marmot, 1998; Franks et al, 2003; Molarius et al, 2007; Pleis et al, 2010; Shields & Shooshtari, 2001). Behavioral factors associated with poorer SRH include diet, physical inactivity, smoking, alcohol consumption, and higher body weight (Benyamini & Leventhal, 1999; Ferraro & Yu, 1995; Manderbacka, Lundberg, & Martikainen, 1999; Molarius et al, 2007). Psychosocial variables related to low SRH include lack of social support, sense of community belonging (Shields, 2008), low perceived control over life, indicators of happiness, and working conditions (Benyamini & Leventhal, 1999; Bobak et al, 1998; Molarius et al, 2007). A. Kaplan & Camacho, 1983; Syme & Berkman, 1976)
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