To investigate how seasonal patterns affect causal inference in environmental epidemiology, particularly regarding the associations between temperature, nitrogen dioxide (NO₂), and mortality, focusing on the implications for model selection and interpretation.
Key Findings:
Estimated associations for temperature and NO₂ varied significantly across different seasonal adjustment specifications, indicating the sensitivity of results to modeling choices.
As stronger seasonal controls were introduced, the estimated effects of NO₂ attenuated, suggesting a redistribution of overlapping seasonal variation.
Temperature associations were less sensitive to NO₂ adjustment but still dependent on the specification of seasonal structure, highlighting the complexity of causal inference.
Interpretation:
Reproducible associations in environmental epidemiology may reflect shared analytical conventions rather than unique causal effects, underscoring the need for transparency in modeling choices and their implications for causal interpretation.
Limitations:
Collinearity limits identifiability and can lead to model-dependent estimates, complicating causal interpretation.
The study focuses on a specific geographic area (Stockholm County), which may limit the generalizability of findings to other contexts.
Conclusion:
Reproducibility should be viewed as a starting point for inference rather than definitive evidence of causal isolation, necessitating careful consideration of seasonal specification and transparency in environmental epidemiological studies.
