AI Peer Review for Public Health & Epidemiology
Confounding, selection bias, and causal claims from observational data are where epidemiology papers get rejected. Catch them first.
Epidemiology reviewers are trained to find bias. They look for unmeasured confounding, selection effects baked into how the sample was assembled, exposure misclassification, and causal language that an observational design cannot support.
ManuscriptMind reviews your public health manuscript against these standards: a clear causal question, defensible control of confounding, honest treatment of missing data, and conclusions calibrated to the strength of the evidence.
What reviewers flag in Public Health & Epidemiology papers
Unmeasured and residual confounding
Adjusting for a convenient set of covariates without a stated causal model, leaving plausible confounders unaddressed and the estimate open to challenge.
Selection bias
Sampling, loss to follow-up, or eligibility criteria that distort the association under study. Reviewers want the selection mechanism examined, not assumed away.
Ecological fallacy
Inferring individual-level relationships from group-level data. A classic and frequently flagged error in population studies.
Causal language from observational data
Concluding that an exposure causes an outcome from a design that can only establish association, without the framing or sensitivity analyses to support it.
Exposure and outcome misclassification
Measurement error in self-reported exposures or administrative outcomes that biases the estimate in a predictable direction.
Statistical pitfalls specific to Public Health & Epidemiology
- Adjusting for a collider or mediator and inducing bias rather than removing it
- The Table 2 fallacy, interpreting every adjusted coefficient as a causal effect
- Multiple testing across many exposure-outcome pairs without correction
- Missing data handled by complete-case analysis without examining the mechanism
Reporting guidelines we check against
What ManuscriptMind checks in your Public Health & Epidemiology manuscript
- Whether the causal question and target estimate are clearly stated
- Whether confounding control reflects a defensible causal structure
- Selection mechanisms and loss to follow-up
- Whether causal language matches an observational design
- Handling of missing data and measurement error
Review your Public Health & Epidemiology manuscript before you submit
Upload your paper and get structured, severity-classified feedback in minutes. Methodology, statistics, and literature issues flagged with specific fixes. Free during beta.
Frequently asked questions
Does it understand epidemiological bias?
Yes. ManuscriptMind specifically evaluates confounding, selection bias, the ecological fallacy, and whether your causal language is supported by the study design.
Will it flag the Table 2 fallacy?
It looks for the common pattern of interpreting every adjusted coefficient in a model as if it were an unbiased causal effect, and prompts you to clarify which estimate is the target.
Can it review studies using administrative or routine data?
Yes. For routinely collected data it focuses on the issues RECORD emphasizes: data provenance, coding validity, and the limits of secondary data.
Is my data kept confidential?
ManuscriptMind never trains on your manuscripts and deletes data on request.