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使用边际结构模型估计无条件税收抵免对自评健康的累积影响。

Using Marginal Structural Modeling to Estimate the Cumulative Impact of an Unconditional Tax Credit on Self-Rated Health.

作者信息

Pega Frank, Blakely Tony, Glymour M Maria, Carter Kristie N, Kawachi Ichiro

出版信息

Am J Epidemiol. 2016 Feb 15;183(4):315-24. doi: 10.1093/aje/kwv211. Epub 2016 Jan 24.

Abstract

In previous studies, researchers estimated short-term relationships between financial credits and health outcomes using conventional regression analyses, but they did not account for time-varying confounders affected by prior treatment (CAPTs) or the credits' cumulative impacts over time. In this study, we examined the association between total number of years of receiving New Zealand's Family Tax Credit (FTC) and self-rated health (SRH) in 6,900 working-age parents using 7 waves of New Zealand longitudinal data (2002-2009). We conducted conventional linear regression analyses, both unadjusted and adjusted for time-invariant and time-varying confounders measured at baseline, and fitted marginal structural models (MSMs) that more fully adjusted for confounders, including CAPTs. Of all participants, 5.1%-6.8% received the FTC for 1-3 years and 1.8%-3.6% for 4-7 years. In unadjusted and adjusted conventional regression analyses, each additional year of receiving the FTC was associated with 0.033 (95% confidence interval (CI): -0.047, -0.019) and 0.026 (95% CI: -0.041, -0.010) units worse SRH (on a 5-unit scale). In the MSMs, the average causal treatment effect also reflected a small decrease in SRH (unstabilized weights: β = -0.039 unit, 95% CI: -0.058, -0.020; stabilized weights: β = -0.031 unit, 95% CI: -0.050, -0.007). Cumulatively receiving the FTC marginally reduced SRH. Conventional regression analyses and MSMs produced similar estimates, suggesting little bias from CAPTs.

摘要

在以往的研究中,研究人员使用传统回归分析估计金融信贷与健康结果之间的短期关系,但他们没有考虑受先前治疗影响的随时间变化的混杂因素(CAPTs)或信贷随时间的累积影响。在本研究中,我们使用7波新西兰纵向数据(2002 - 2009年),研究了6900名工作年龄父母获得新西兰家庭税收抵免(FTC)的总年数与自评健康状况(SRH)之间的关联。我们进行了传统的线性回归分析,包括未调整的以及针对基线时测量的时间不变和随时间变化的混杂因素进行调整的分析,并拟合了边际结构模型(MSM),该模型能更全面地调整混杂因素,包括CAPTs。在所有参与者中,5.1% - 6.8%的人获得FTC 1 - 3年,1.8% - 3.6%的人获得4 - 7年。在未调整和调整后的传统回归分析中,每多接受一年FTC,SRH就会降低0.033(95%置信区间(CI):-0.047,-0.019)和0.026(95%CI:-0.041,-0.010)个单位(在5分制量表上)。在MSM中,平均因果治疗效应也反映出自评健康状况略有下降(未稳定权重:β = -0.039单位,95%CI:-0.058,-0.020;稳定权重:β = -0.031单位;95%CI:-0.050,-0.007)。累积获得FTC会轻微降低自评健康状况。传统回归分析和MSM得出了相似的估计结果,表明CAPTs造成的偏差很小。

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