ArticleGlobal epidemiology2023
What is an exposure-response curve?
Article in Global epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of associations between inhaled formaldehyde and lymphohematopoietic cancer through the integration of epidemiological and toxicological evidence with biological plausibility.Toxicological sciences : an official journal of the Society of Toxicology · 2024Pooled it
- Tobacco retailer density and rurality across four US states: California, Connecticut, North Carolina, and Ohio.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025Article
- AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times.Global epidemiology · 2025Article
- Calculating preventable risk fractions for exposure-reducing interventions.Global epidemiology · 2025Article
- Associations between organophosphorus pesticides exposure and age-related macular degeneration risk in U.S. adults: analysis from interpretable machine learning approaches.International journal of ophthalmology · 2025Article
- Clarifying causality and information flows between time series: Particulate air pollution, temperature, and elderly mortality.Global epidemiology · 2024Article
- Heat and Cardiovascular Mortality: An Epidemiological Perspective.Circulation research · 2024Article
- Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exposure-response curves are among the most widely used tools of quantitative health risk assessment. However, we propose that exactly what they mean is usually left ambiguous, making it impossible to answer such fundamental questions as whether and by how much reducing exposure by a stated amount would change average population risks and distributions of individual risks. Recent concepts and computational methods from causal artificial intelligence (CAI) and machine learning (ML) can be applied to clarify what an exposure-response curve means; what other variables are held fixed (and at what levels) in estimating it; and how much inter-individual variability there is around population average exposure-response curves. These advances in conceptual clarity and practical computational methods not only enable epidemiologists and risk analysis practitioners to better quantify population and individual exposure-response curves but also challenge them to specify exactly what exposure-response relationships they seek to quantify and communicate to risk managers and how to use the resulting information to improve risk management decisions.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.