The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Categorical versus continuous risk factors and the calculation of potential impact fractions
Abstract
Background: The potential impact fraction is a measure of effect that calculates the proportional change in disease risk after a change in the exposure of a related risk factor. Potential impact fractions are increasingly used to calculate attributable fractions when the lowest exposure is non-zero. Methods: Risk-factor exposure can be expressed as a categorical or a continuous variable. For a categorical risk factor, a change in risk-factor exposure can be expressed as a change in the proportion of the population in each category (‘proportions shift’). For a continuous risk factor, the change is expressed as a change in its parameters (‘distribution shift’). A third method (‘RR shift’) takes elements of both the categorical and the continuous approach. We compare the three calculation methods using hypothetical data on BMI and an intervention that affects the obese category. Results: The ‘proportion shift’ calculation produces non-linear artefacts and is best avoided. The ‘RR shift’ and ‘distribution shift’ calculation require the estimation of an RR function to describe excess risk, but perform much better. Conclusion: The ‘proportion shift’ calculation is best avoided. The ‘RR shift’ and ‘distribution shift’ calculation produce virtually the same results. For evaluating high-risk strategies, the ‘RR shift’ calculation is the simplest and therefore preferred. The ‘distribution shift’ is best suited for evaluating population strategies.
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Sick individuals and sick populations
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Sick individuals and sick populations
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Estimates of global and regional potential health gains from reducing multiple major risk factors
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On the Comparable Quantification of Health Risks: Lessons from the Global Burden of Disease Study
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Cited by
Cacciottolo, M., Wang, X., Driscoll, I., Woodward, N., Saffari, A., Reyes, J., Serre, M. L., Vizuete, W., Sioutas, C., Morgan, T. E., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.280 · Cited by 432
Lee, M., Whitsel, E., Avery, C., Hughes, T. M., Griswold, M. E., Sedaghat, S., Gottesman, R. F., Mosley, T. H., Heiss, G., & Lutsey, P. L. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.19672 · Cited by 179
Tarp, J., Støle, A. P., Blond, K., & Grøntved, A. (2019) · Diabetologia · doi:10.1007/s00125-019-4867-4 · Cited by 173
Saxena, A., Koon, A. D., Lagrada-Rombaua, L., Angeles-Agdeppa, I., Johns, B., & Capanzana, M. (2019) · Bulletin of the World Health Organization · doi:10.2471/blt.18.219980 · Cited by 72
Saxena, A., Stacey, N., Puech, P. D. R., Mudara, C., Hofman, K., & Verguet, S. (2019) · BMJ Global Health · doi:10.1136/bmjgh-2018-001317 · Cited by 58
Metadata
- Authors
- Barendregt, J. J., & Veerman, J. L.
- Venue
- Journal of Epidemiology and Community Health
- Publisher
- BMJ
- Published
- 2009-08-19
- Volume
- 64
- Issue
- 3
- Pages
- 209-212
- ISSN
- 0141-7681 (ISSN-L)
- 0143-005X
- 1470-2738
- OpenAlex
- W2059193733
- Cited by
- 223
- References
- 5
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- closed
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