目的: 分析社区社会经济地位与城市和农村成年居民自评健康是否存在独立关联,为采用社区层面干预措施,改善社会经济发展落后社区居民健康状况提供政策依据。方法: 基于中国家庭追踪调查(China Family Panel Studies, CFPS)2010年基线数据,对社区人均教育年限、人均收入和人均财富进行主成分分析,提取第一主成分得分,标准化后作为社区社会经济指数(socioeconomic index, SEI)。再合并2012年CFPS成人追访数据,利用多水平Logistic回归模型在控制个体人口和社会经济因素后,分别分析社区SEI对城市居民和农村居民自评健康是否具有独立关联。结果: 纳入最终分析样本分布在577个社区中,共包含31 321名成人居民,其中城市居民8 423人,农村居民22 898人。社区SEI取值范围为-2.41~3.16,其均值为0,方差为1。随着社区SEI增加,不同维度的贫困发生率下降,社区社会经济地位升高。控制个体因素和社区社会经济因素的多水平Logistic模型显示,随着社区SEI增加,自评不健康概率下降,社区社会经济地位对成人自评健康存在情境效应。但是只有社区社会经济地位对农村居民自评不健康的情境效应存在统计学意义(OR=0.84,95%CI:0.76~0.94),对城市居民自评不健康的情境效应不具有统计学意义(OR=0.94,95%CI:0.83~1.06)。结论: 控制个体社会经济因素后,社区经济地位和农村居民自评不健康之间存在独立关联,提高社会经济发展落后农村社区居民健康状况,不仅仅需要采取基于个体的健康干预,同时需要考虑采取基于社区的健康干预。

Objective: To examine whether community socioeconomic status is associated with self-rated health independent of individual socioeconomic status for urban and rural residents, and to provide policy implications for improving the health status of the socioeconomically underdeveloped communities in China. Methods: Based on the baseline data of China Family Panel Studies (CFPS) in 2010, principal component analysis was used to construct community socioeconomic index (SEI) based on average years of schooling, average income and average wealth at the community level. Community SEI was defined as the standardized first principal component score. In combination with the adult data from CFPS 2012 follow-up data, the multilevel Logistic regression model was used to analyze whether the community socioeconomic status had an independent contextual effect on the self-rated health of urban residents and rural residents after controlling individual-level socioeconomic status. Results: In the final analysis, 31 321 adult residents in 577 communities were included, of whom 8 423 were urban residents and 22 898 were rural residents. Community SEI ranged from -2.41 to 3.16, with a mean of 0 and a stan-dard deviation of 1. As the community SEI increased, the incidence of deprivations in different dimensions decreased, indicating the community socioeconomic status increased. The multilevel Logistic model controlling for both individual sociodemographic factors and community socioeconomic status showed that as the community SEI increased, the probability of poor self-rated health decreased, which indicated community SEI had a contextual effect on poor self-rated health. The contextual effect of community SEI on poor self-rated health was statistically significant for the rural residents (OR=0.84, 95%CI: 0.76-0.94) but not statistically significant for the urban adults (OR=0.94, 95%CI: 0.83-1.06). Conclusion: After controlling for individual socioeconomic status, community socioeconomic status was associa-ted with poor self-rated health for rural residents independent of individual socioeconomic status. Therefore, in order to improve the health status of the rural population, it needs not only individual-based health interventions, but also community-based health interventions.