Literature DB >> 26803908

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

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

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.
© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  New Zealand; cohort studies; confounding factors; health status; income; parents; public policy

Mesh:

Year:  2016        PMID: 26803908     DOI: 10.1093/aje/kwv211

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  8 in total

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