Post-Modern Epidemiology: When Methods Meet Matter.

Area of Science:

• Epidemiology
• Causal Inference
• Public Health

Background:

• In the late 20th century, epidemiology in high-income nations pivoted from addressing noncommunicable diseases to refining statistical and causal inference methodologies.
• This shift coincided with limited progress in applied epidemiology and a failure to account for ineffective interventions.

Observation:

• Significant advancements in understanding noncommunicable disease etiology often originated outside traditional epidemiology.
• Several major conditions were found to have infectious origins, challenging prior assumptions.

Findings:

• The discipline's focus on methodological development, while advanced, did not yield commensurate progress in disease etiology.
• Progress in understanding disease causes has been significantly boosted by integrating biological insights and data science advancements.

Implications:

• Retaining fundamental principles of population-based disease risk is crucial.
• The integration of advanced methodologies, biological understanding, and data science heralds a new era of 'post-Modern Epidemiology'.