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https://www.arca.fiocruz.br/handle/icict/13978
WITHIN- AND BETWEEN-GROUP REGRESSION FOR IMPROVING THE ROBUSTNESS OF CAUSAL CLAIMS IN CROSS-SECTIONAL ANALYSIS
Cross-sectional studies
Multilevel modelling
Ecological fallacy
Ecological inference
Affilliation
University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine, Ludolf-Krehl-Strasse. Mannheim, Germany / Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine, Ludolf-Krehl-Strasse. Mannheim, Germany
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil
University of Heidelberg. Mannheim Institute of Public Health, Social and Preventive Medicine, Ludolf-Krehl-Strasse. Mannheim, Germany
Abstract
Background: A 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.
Methods: We 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.
Results: For 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 betweenand
within-group effects, which corroborates the causal claim for the association between PFOA blood levels and CRP.
Conclusion: Between- 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
Keywords
Causal claimsCross-sectional studies
Multilevel modelling
Ecological fallacy
Ecological inference
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