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Cited by 1,214Statistical Methods in EpidemiologyGlobal Cancer Incidence and ScreeningAdvanced Causal Inference Techniques

Estimating the population attributable risk for multiple risk factors using case-control data.

BRUZZI, PAOLO, GREEN, SYLVAN B., BYAR, DAVID P., BRINTON, LOUISE A., SCHAIRER, CATHERINE·1985·American Journal of Epidemiology
DOI 10.1093/oxfordjournals.aje.a114174
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Abstract

A straightforward and unified approach is presented for the calculation of the population attributable risk per cent (etiologic fraction) in the general multivariate setting, with emphasis on using data from case-control studies. The summary attributable risk for multiple factors can be estimated, with or without adjustment for other (confounding) risk factors. The relation of this approach to procedures in the literature is discussed. Given values of the relative risks for various combinations of factors, all that is required is the distribution of these factors among the cases only. The required information can often be estimated solely from case-control data, and in some situations relative risk estimates from one population can be applied to calculation of attributable risk for another population. The authors emphasize the benefits to be obtained from logistic regression models, so that risks need not be estimated separately in a large number of strata, some of which may contain inadequate numbers of individuals. This approach allows incorporation of important interactions between factors, but does not require that all possible interactions be included. The approach is illustrated with data on four risk factors from a pair-matched case-control study of participants in a multicenter breast cancer screening project.

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Crossref · 10.1093/oxfordjournals.aje.a114174

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  • If there is correlation between exposure and disease-specific mortality due to correlations with other risks or unobserved factors, the equations may under- or over-estimate the true population-attributable fraction when used with adjusted relative risks.

    “However, if there is also a correlation between exposure and disease-specific mortality, due to correlations of exposure with other risks or other unobserved factors, the above equations may result in under- (when there is positive correlation) or over-estimation (negative correlation) of the true PAF when used with adjusted RRs [32]–[36].”

    From The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors (2009) · cites this paper as [34]

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Authors
BRUZZI, P., GREEN, S. B., BYAR, D. P., BRINTON, L. A., & SCHAIRER, C.
Affiliations
National Institutes of Health (US)
Corresponding author
BYAR, DAVID P.
ORCID
Venue
American Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
1985-11
Volume
122
Issue
5
Pages
904-914
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
DOI
10.1093/oxfordjournals.aje.a114174
OpenAlex
W2256786579
Cited by
1,214
References
16
Citations by year
2026: 12 · 2025: 10 · 2024: 25 · 2023: 20 · 2022: 20 · 2021: 44 · 2020: 37 · 2019: 29 · 2018: 31 · 2017: 25 · 2016: 36 · 2015: 25 · 2014: 41 · 2013: 49 · 2012: 36
FWCI
8.74
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 › Oncology › Global Cancer Incidence and Screening
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
population attributable risk, population attributable fraction, multiple risk factors, case-control study, logistic regression, confounding adjustment, relative risk, breast cancer screening
MeSH terms
Biopsy, Breast, Breast Neoplasms, Epidemiologic Methods, Female, Humans, Maternal Age, Menarche, Models, Biological, Risk, Statistics as Topic

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