the-lancet-public-health

name	the-lancet-public-health
description	Use when targeting The Lancet Public Health or deciding whether a population-health study fits this venue. Encodes the journal's fit, the population-level intervention, epidemiology, and policy evidence bar, reporting-guideline and registration requirements, Lancet specialty house style, official-submission re-check, and desk-reject heuristics. Venue-fit aid only, not clinical advice.

The Lancet Public Health (the-lancet-public-health)

Journal positioning

The Lancet Public Health is a Lancet specialty journal for population and public-health research — population-level interventions, health policy and systems, epidemiology and disease burden, and global-health and health-equity research with population-level relevance. It favors rigorous studies whose unit of interest and consequence is the population, not the individual patient: large-scale epidemiology, policy and natural experiments, modelling with clear public-health decision value, and interventions addressing inequalities or the social determinants of health. The defining misfit is an individual-level clinical study (a drug or device effect in patients) with no population, policy, or equity dimension — that belongs in a clinical journal. This skill is a fit / venue-selection / re-framing aid; it is not clinical or regulatory advice and does not replace the journal's current instructions for authors. Before submitting, re-check the live The Lancet Public Health author instructions.

When to trigger

• The author names The Lancet Public Health for a population-health, epidemiology, or policy study and wants a fit/framing check.
• A study must be re-framed around a population-level intervention, burden, or policy question with equity relevance.
• The author is choosing between The Lancet Public Health, a clinical specialty journal, and general medicine.
• The author needs the journal's reporting-guideline, registration, and desk-reject expectations for population/policy evidence.

Scope & topic fit

• Population-level interventions and policy/natural experiments (taxation, regulation, screening programmes, vaccination, public-health service delivery).
• Epidemiology, disease-burden, surveillance, and risk-factor studies at population scale, including global and comparative analyses.
• Health-systems, health-services, and health-economics research with population-level decision relevance.
• Health-equity, social-determinants, and inequalities research with population-level framing and policy implication.
• Modelling and forecasting studies (transmission, burden, intervention impact) with transparent assumptions and public-health decision value.
• Systematic reviews and meta-analyses answering a focused population-health or policy question.

Method & evidence bar

• Studies must have a clear population-level question and an appropriate population denominator; individual-level clinical endpoints alone do not establish public-health relevance.
• The applicable reporting guideline and completed checklist are expected: STROBE for observational studies, CONSORT (incl. cluster-CONSORT) for trials, PRISMA for reviews, GATHER for global-health estimates, and modelling-reporting standards where relevant.
• Trials and pre-specified evaluations require prospective registration; protocols and analysis plans are expected, with cluster/stepped-wedge design detail where used.
• Observational and natural-experiment claims must address confounding, secular trends, ecological bias, and missing data; causal language must match the design.
• Modelling studies must state assumptions, perform sensitivity/uncertainty analysis, and report data sources transparently; code/data sharing strengthens the submission.
• Effect estimates need uncertainty intervals and, where relevant, equity-stratified or absolute population-impact measures.

Structure & house style

• Lancet specialty format with a structured summary and a Research in context / evidence-before-this-study panel; re-check current article types and limits on the live guide.
• The introduction frames the population-health or policy gap; the discussion states the policy or public-health consequence and limitations plainly.
• A STROBE/CONSORT/PRISMA flow diagram and (for estimates) GATHER reporting are expected where applicable; tables/figures follow Lancet statistical-reporting standards.
• The role of the funding source statement and a data-sharing statement are expected; appendices carry protocol/model specification, full methods, and additional analyses.

Official-submission checklist

• Before giving submission-ready advice, read ../../resources/source-basis.md and ../../resources/official-source-map.md; start from the ICMJE/EQUATOR and Lancet anchors, then cite the current The Lancet Public Health page you checked.
• Search the live site for "The Lancet Public Health information for authors" and follow the current version.
• Re-check article types, structured-summary and Research in context format, and word/reference/figure limits.
• Confirm registration where applicable, the reporting checklist (STROBE/CONSORT/PRISMA/GATHER), protocol/model specification, role-of-funding-source, and data/code-sharing statement.
• Re-check IRB/ethics and consent or data-governance approvals, ICMJE authorship and conflict-of-interest disclosure, funding, and AI-use disclosure.
• If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

• The study has a genuine population-level question, denominator, and policy/equity consequence.
• The correct reporting checklist (STROBE/CONSORT/PRISMA/GATHER) is completed and attached.
• Trials/evaluations are registered where applicable; protocol or model specification is provided.
• Confounding, secular trends, ecological bias, and missing data are addressed; causal language matches the design.
• Modelling assumptions, uncertainty, and data sources are transparent; code/data sharing is planned.
• Ethics/data-governance approvals, ICMJE disclosures, role-of-funding-source, and a data-sharing statement are prepared.

Common desk-reject triggers

• Individual-level clinical studies with no population, policy, or equity dimension.
• Small or local descriptive surveys with no generalizable population-health implication.
• Modelling with opaque assumptions, no sensitivity analysis, or undocumented data sources.
• Ecological analyses with overstated individual-level causal claims.
• Missing reporting checklist, registration (where applicable), or data-governance approvals.
• Narrow scope without international or policy relevance, better suited to a regional or clinical venue.

Re-routing decision

• Individual-level clinical trial or patient-outcome focus → the relevant clinical specialty journal or general medicine (jama / NEJM / The Lancet in the natural-science bundle).
• Population mental-health and psychiatric epidemiology dominant → the-lancet-psychiatry.
• Diabetes/obesity population research with a clinical-metabolic core → the-lancet-diabetes-and-endocrinology.
• Respiratory population/clinical research with a respiratory endpoint → the-lancet-respiratory-medicine.
• Cancer epidemiology with a clinical-oncology endpoint → annals-of-oncology / jama-oncology.

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] The Lancet Public Health
[Specialty tags] <2–3 closest population-health/policy/epidemiology topics>
[Study design / reporting guideline] <observational-STROBE / cluster-RCT-CONSORT / review-PRISMA / estimates-GATHER / modelling>
[Method/evidence] <population-level question, denominator, confounding/assumptions — does it clear the policy-relevance bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / registration / checklist / role-of-funding / ethics-data-governance / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>