Nayak Shilpa, Hubbard Alan, Sidney Stephen, Syme S Leonard
Department of Public Health and Policy, The Whelan Building, Quadrangle, The University of Liverpool, Liverpool L69 3GB, UK.
School of Public Health, The University of California, Berkeley, CA 94720, USA.
SSM Popul Health. 2017 Dec 15;4:178-188. doi: 10.1016/j.ssmph.2017.12.002. eCollection 2018 Apr.
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.
自评健康状况(SRH)是死亡率的独立预测指标;已有研究探讨了SRH的相关因素以解释其预测能力。然而,参数多元回归可能无法充分捕捉影响健康状况的一系列广泛因素之间的相互作用。本研究使用递归划分方法调查了几种健康决定因素与SRH之间的关联。这种非参数分析方法旨在反映健康的社会生态模型,强调多种健康决定因素之间的关系,包括生物、行为以及社会/物理环境方面的因素。研究样本包括3648名男性和女性,取自美国CARDIA研究第15年(2000 - 2001年)的数据收集,以研究年轻成年人样本。分类树分析确定了15个不同的、相互排斥的亚组(8个亚组中SRH较高的个体比例较大,7个亚组中SRH较低的个体比例较大),以及与亚组成员身份相关的多领域风险和保护因素。即使对于健康状况相似的亚组,健康决定因素概况也不一致。SRH较高个体比例最大的亚组具有几个保护因素,而SRH较低个体比例最大的亚组则有几个负面风险因素;某些因素与SRH较高和较低的亚组均有关联。在整个样本中,身体活动、教育程度和收入在与SRH的关联中按变量重要性(随机森林分析)排名最高。这项探索性研究证明了递归划分方法在研究多种健康决定因素的联合影响方面的实用性。研究结果表明,各因素在整个样本中对SRH的影响方式不同。来自不同领域、相对重要性各异的多种因素在不同亚组中与SRH相关。这对于制定和确定适当干预措施的优先级以针对改善自评健康状况的条件和因素具有启示意义。