Interpreting mutual adjustment for multiple indicators of socioeconomic position without committing mutual adjustment fallacies

• Main Authors: Michael J. Green (Author), Frank Popham (Author)
• Format: Book
• Published: BMC, 2019-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-018-6364-y; 1471-2458
• Keywords: Socioeconomic position; Education; Occupation; Income; Regression; DAGs; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 19, Iss 1, Pp 1-7 (2019)

Abstract Research into the effects of Socioeconomic Position (SEP) on health will sometimes compare effects from multiple, different measures of SEP in "mutually adjusted" regression models. Interpreting each effect estimate from such models equivalently as the "independent" effect of each measure may be misleading, a mutual adjustment (or Table 2) fallacy. We use directed acyclic graphs (DAGs) to explain how interpretation of such models rests on assumptions about the causal relationships between those various SEP measures. We use an example DAG whereby education leads to occupation and both determine income, and explain implications for the interpretation of mutually adjusted coefficients for these three SEP indicators. Under this DAG, the mutually adjusted coefficient for education will represent the direct effect of education, not mediated via occupation or income. The coefficient for occupation represents the direct effect of occupation, not mediated via income, or confounded by education. The coefficient for income represents the effect of income, after adjusting for confounding by education and occupation. Direct comparisons of mutually adjusted coefficients are not comparing like with like. A theoretical understanding of how SEP measures relate to each other can influence conclusions as to which measures of SEP are most important. Additionally, in some situations adjustment for confounding from more distal SEP measures (like education and occupation) may be sufficient to block unmeasured socioeconomic confounding, allowing for greater causal confidence in adjusted effect estimates for more proximal measures of SEP (like income).