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PROPORTION OF DISEASE CAUSED OR PREVENTED BY A GIVEN EXPOSURE, TRAIT OR INTERVENTION1
Abstract
The structures of two epidemiologic parameters are explored. One, the “etiologic fraction, ” relates to markers of increased risk, and it is the proportion of disease attributable to the marker and/or to factors associated with it. The other, the “prevented fraction, ” is the equivalent of this for a marker of reduced risk. It is shown that both parameters depend—in different ways—-on the frequency of the marker among cases of the disease, and on the “standardized morbidity ratio” for those with the marker. Point estimation of these parameters is often straight-forward, particularly in case-control studies.
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“ETT has been used in econometrics to evaluate the effectiveness of social programs on their participants (Heckman, 1992) and has long been the target of research in epidemiology, where it came to be known as 'the effect of exposure on the exposed,' or 'standardized morbidity' (Miettinen, 1974; Greenland and Robins, 1986).”
From Causal inference in statistics: An overview (2009) · cites this paper as Miettinen, 1974
In the citing paper
“Population-attributable fractions, adjusted for aforementioned model covariates, were calculated using Miettinen’s formula, aPRs, and weighted prevalence estimates of each health condition and risk behavior by each cumulative ACEs count level”
From Adverse childhood experiences and health conditions and risk behaviors among high school students—Youth Risk Behavior Survey, United States, 2023 (2024) · cites this paper as [11]
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“38,39 Given that modi fi able risk factors occur simultaneously within individuals, aggregating the PAF for each speci fi c risk factor may lead to an overestimation of their combined effects.”
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“First, preventive fractions (PF) were calculated from each pooled RR (PF = [RR − 1]/RR (Miettinen, 1974).”
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“where RR is the relative risk and P1 is the proportion of cases exposed (15).”
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“We further translated the PAF into the estimated number of attributable cases under the counterfactual assumption 46,47 that exposure to high-risk EBV strains could be eliminated.”
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“This formula is often referred to as “Miettinen’s formula” [27].”
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“The overall EF calculation on sickness absence based on contributions from all included risk factors is calculated by using the following formula [20], which is referred to as ‘sum-formula’: Age, gender and education adjusted RR are also calculated.”
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“The authors use the concept of population-attributable fraction (PAF) for risk factors (5).”
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“Assessing PAR or PAF can be performed in different ways, and we decided to use the approach proposed by Miettinen,[17] as this gives illustrative information fairly easy to interpret.”
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Metadata
- Authors
- MIETTINEN, O. S.
- Corresponding author
- MIETTINEN, OLLI S.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 1974-05
- Volume
- 99
- Issue
- 5
- Pages
- 325-332
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W1953501928
- Cited by
- 1,036
- References
- 0
- Citations by year
- 2026: 24 · 2025: 34 · 2024: 41 · 2023: 35 · 2022: 26 · 2021: 35 · 2020: 39 · 2019: 40 · 2018: 31 · 2017: 22 · 2016: 27 · 2015: 25 · 2014: 28 · 2013: 22 · 2012: 26
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Epidemiology › Historical and modern epidemiology studies
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- population attributable fraction, prevented fraction, attributable fraction, risk indicators, standardized morbidity ratio, case-control study, disease prevention
- MeSH terms
- Coffee, Coronary Disease, Drinking, Epidemiologic Methods, Humans, Hypercholesterolemia, Hypertension, Models, Theoretical, Morbidity, Myocardial Infarction, Population, Preventive Medicine, Smoking, Statistics as Topic
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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