ArticleJournal of epidemiology2026
Effect Modification and Its Impact on Preventable and Attributable Fractions in the Potential Outcomes Framework.
Article in Journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPolicy decisions should be guided by measures that capture the impact of exposures on outcomes and that explicitly account for present-day exposure distribution. Both the preventable and attributable fractions have been used for this purpose; however, exposure effects can vary across subpopulations, and when this occurs, appropriate interpretation of these measures should be facilitated by a discussion of the contributions of different subpopulations.
methodsWe analyze preventable and attributable fractions in the presence of effect modification. In particular, we use potential outcomes to formally define these quantities and to clarify the weighting of different strata in the total population measures.
resultsOur derivations show that stratum-specific preventable and attributable fractions are weighted in proportion to the relative frequencies of effect modifiers among individuals with the outcome of interest. We also demonstrate that these weights are valid for the related quantities, preventable and attributable proportions. Finally, we present an example that illustrates how effect modification affects interpretation of these measures.
conclusionIn sum, when effect modification is present, investigators should consider reporting these measures by the relevant population strata, and information that would allow quantification of their implicit weights in the total population estimate. Our study provides a formal justification for this approach.
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