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Evidence-Based Public Health: Moving Beyond Randomized Trials
Open accessAbstract
Randomized controlled trials (RCTs) are essential for evaluating the efficacy of clinical interventions, where the causal chain between the agent and the outcome is relatively short and simple and where results may be safely extrapolated to other settings. However, causal chains in public health interventions are complex, making RCT results subject to effect modification in different populations. Both the internal and external validity of RCT findings can be greatly enhanced by observational studies using adequacy or plausibility designs. For evaluating large-scale interventions, studies with plausibility designs are often the only feasible option and may provide valid evidence of impact. There is an urgent need to develop evaluation standards and protocols for use in circumstances where RCTs are not appropriate.
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“For example, in several fields such as public health, RCTs are not always appropriate nor sufficient to address complex and multifaceted problems [3].”
From Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence (2017) · cites this paper as [3]
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Metadata
- Authors
- Victora, C. G., Habicht, J.-P., & Bryce, J.
- Affiliations
- Universidade Federal de Pelotas (BR)
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 2004-03
- Volume
- 94
- Issue
- 3
- Pages
- 400-405
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC1448265
- OpenAlex
- W1984963575
- Cited by
- 1,056
- References
- 28
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 41 · 2023: 41 · 2022: 27 · 2021: 40 · 2020: 43 · 2019: 50 · 2018: 46 · 2017: 52 · 2016: 53 · 2015: 65 · 2014: 55 · 2013: 62 · 2012: 42
- FWCI
- 49.97
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DigitalGeorgetown (Georgetown University Library) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- evidence-based public health, randomized controlled trials, observational studies, causal inference, effect modification, generalizability, internal validity, large-scale interventions, evaluation standards, public health intervention evaluation
- MeSH terms
- Decision Making, Humans, Reproducibility of Results, Epidemiologic Research Design, Public Health Practice, Causality, Effect Modifier, Epidemiologic, Randomized Controlled Trials as Topic, Evidence-Based Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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