ArticleEnvironmental health perspectives2025
Partial Effects in Environmental Mixtures: Evidence and Guidance on Methods and Implications.
Article in Environmental health perspectives, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Article
- Associations between joint air pollution exposure, mental health, and physical health and dementia incidence in an aging U.S. cohort.The journals of gerontology. Series B, Psychological sciences and social sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe effects of a mixture of exposures on health outcomes are of interest to public health but pose methodological hurdles. These exposures may impact the outcome in opposing ways, which we call the positive and negative partial effects of a mixture. There has been growing interest in estimating these partial effects and their ability to inform public health interventions.
objectivesMethods like quantile g-computation (QGC) and weighted quantile sums regression (WQSr) were originally developed for estimating an overall mixture effect. These approaches, however, have not been comprehensively evaluated in their ability to estimate partial effects. We study the bias-variance tradeoffs of these approaches in estimating partial effects.
methodsWe compare QGC with sample-splitting (QGCSS) and WQSr with sample-splitting (WQSSS) and new methods including
resultsOur simulation results showed that the estimation of negative and positive partial effects grows in root mean squared error and average bias as correlation among exposures increases, sample sizes shrink, the negative effect is spread over more exposures, or the imbalance between the negative and positive effects increases. Our results are demonstrated in two examples of mixtures in relation to oxidative stress biomarkers and telomere length. DISCUSSION: Outside of having
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.