Latent variation in pathogen strain-specific effects under multiple-versions-of-treatment theory

Evidence-informed infectious disease policy requires estimates of the health effects of infections. However, pathogenic variation, whereby the risk of adverse outcomes depends on the strain-specific characteristics of the pathogen, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies of infectious diseases when there is such heterogeneity in strain-specific effects and when information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for non-compound exposures. This discussion, considering potential heterogeneity in strain-specific effects, will facilitate interpretation of epidemiologic studies and also highlights the value of pathogen subtype data.

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Publication Details

Journal
Epidemiology
Published
2026-04-22
DOI
https://doi.org/10.1097/ede.0000000000001975
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
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article

Latent variation in pathogen strain-specific effects under multiple-versions-of-treatment theory

Bronner P. Gonçalves
Epidemiology
Advanced Causal Inference Techniques
article

Latent variation in pathogen strain-specific effects under multiple-versions-of-treatment theory

Bronner P. Gonçalves
article en

Abstract

Evidence-informed infectious disease policy requires estimates of the health effects of infections. However, pathogenic variation, whereby the risk of adverse outcomes depends on the strain-specific characteristics of the pathogen, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies of infectious diseases when there is such heterogeneity in strain-specific effects and when information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for non-compound exposures. This discussion, considering potential heterogeneity in strain-specific effects, will facilitate interpretation of epidemiologic studies and also highlights the value of pathogen subtype data.

Epidemiology
University of Surrey (GB)
Good health and well-being
Openalex Percentile: Top 5%
Advanced Causal Inference Techniques
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