Within- and between-group regression for improving the robustness of causal claims in cross-sectional analysis

Environmental Health

Methodology

Carlos A. Teles¹  Mauricio L. Barreto¹  Joachim E. Fischer²  Bernd Genser³ 

[1] Instituto de Saúde Coletiva, Federal University of Bahia, Salvador, Brazil;Centro de Pesquisa Gonçalo Muniz, Fundação Oswaldo Cruz (FIOCRUZ), Salvador, Bahia, Brazil;Mannheim Institute of Public Health, Social and Preventive Medicine, University of Heidelberg, Ludolf-Krehl-Strasse 7-11, 68167, Mannheim, Germany;Mannheim Institute of Public Health, Social and Preventive Medicine, University of Heidelberg, Ludolf-Krehl-Strasse 7-11, 68167, Mannheim, Germany;Instituto de Saúde Coletiva, Federal University of Bahia, Salvador, Brazil;

关键词: Causal claims;    Cross-sectional studies;    Multilevel modelling;    Ecological fallacy;    Ecological inference;   

DOI  :  10.1186/s12940-015-0047-2

 received in 2015-02-21, accepted in 2015-06-19,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundA major objective of environmental epidemiology is to elucidate exposure-health outcome associations. To increase the variance of observed exposure concentrations, researchers recruit individuals from different geographic areas. The common analytical approach uses multilevel analysis to estimate individual-level associations adjusted for individual and area covariates. However, in cross-sectional data this approach does not differentiate between residual confounding at the individual level and at the area level. An approach allowing researchers to distinguish between within-group effects and between-group effects would improve the robustness of causal claims.MethodsWe applied an extended multilevel approach to a large cross-sectional study aimed to elucidate the hypothesized link between drinking water pollution from perfluoroctanoic acid (PFOA) and plasma levels of C-reactive protein (CRP) or lymphocyte counts. Using within- and between-group regression of the individual PFOA serum concentrations, we partitioned the total effect into a within- and between-group effect by including the aggregated group average of the individual exposure concentrations as an additional predictor variable.ResultsFor both biomarkers, we observed a strong overall association with PFOA blood levels. However, for lymphocyte counts the extended multilevel approach revealed the absence of a between-group effect, suggesting that most of the observed total effect was due to individual level confounding. In contrast, for CRP we found consistent between- and within-group effects, which corroborates the causal claim for the association between PFOA blood levels and CRP.ConclusionBetween- and within-group regression modelling augments cross-sectional analysis of epidemiological data by supporting the unmasking of non-causal associations arising from hidden confounding at different levels. In the application example presented in this paper, the approach suggested individual confounding as a probable explanation for the first observed association and strengthened the robustness of the causal claim for the second one.

【 授权许可】

Unknown   
© Genser et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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