Life course models: improving interpretation by consideration of total effects

Area of Science:

• Epidemiology
• Public Health
• Biostatistics

Background:

• Life course epidemiology utilizes accumulation and critical/sensitive period models.
• These models traditionally focus on direct effects of exposures on disease etiology.
• Timing of exposure is crucial for understanding disease development.

Purpose of the Study:

• To demonstrate how considering total effects improves interpretation of life course models.
• To provide clearer insights into optimal intervention timing.
• To explore the influence of exposure stability on total effects.

Main Methods:

• Causal inference framework.
• Comparison of direct effects versus total effects in life course models.
• Analysis of exposure stability and its impact on effect modification.

Main Results:

• Considering total effects enhances the interpretability of life course models.
• Total effects vary with direct effects magnitude and exposure stability.
• Clearer identification of effective intervention timings is achieved.

Conclusions:

• Total effects provide a more comprehensive understanding of exposure timing in disease.
• Intervention effectiveness is better predicted by analyzing total, direct, and indirect effects.
• Causal assumptions are critical for determining optimal intervention timing.