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Point and interval estimates of partial population attributable risks in cohort studies: examples and software.
D Spiegelman1, E Hertzmark, H C Wand
1Department of Epidemiology, School of Public Health, Harvard University, 677 Huntington Avenue, Boston, MA 02115, USA. stdls@channing.harvard.edu
Cancer Causes & Control : CCC
|March 28, 2007
Summary
This study introduces methods for calculating partial population attributable risk (PAR) percent, estimating disease prevention by removing specific exposures. These methods are crucial for public health research on multifactorial diseases.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Background:
- Population attributable risk (PAR) percent is widely used in public health.
- PAR quantifies disease proportion preventable by eliminating an exposure.
- Estimating PAR is essential for understanding disease burden and prevention strategies.
Purpose of the Study:
- To present methods for point and interval estimation of partial population attributable risks (PARs).
- To quantify the impact of modifiable determinants on disease burden in cohort studies.
- To apply these methods to multifactorial diseases where other risk factors remain unchanged.
Main Methods:
- Development of statistical methods for partial PAR estimation.
- Application of methods to a cohort study on bladder cancer incidence.
- Utilizing SAS macro for practical implementation and accessibility.
Main Results:
- Methods provide point and interval estimates for partial PARs.
- Demonstrated application in a real-world cohort study.
- Availability of a user-friendly SAS macro for broader use.
Conclusions:
- Partial PAR methods are essential for multifactorial diseases.
- These methods allow estimation of disease prevention by targeting specific exposures.
- The presented methods and tools aid public health research and intervention planning.
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