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Local estimates of population attributable risk.
1Department of Clinical Epidemiology and Biostatistics, McMaster University, HSC-2C16, Hamilton, Ontario L8N 3Z5, Canada. walter@mcmaster.ca
Journal of Clinical Epidemiology
|May 1, 2009
Summary
Estimating population attributable risk (PAR) locally is improved by combining data sources. Incorporating external data enhances precision, especially when local exposure prevalence varies.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Population Attributable Risk (PAR) estimates are crucial for public health interventions.
- Calculating PAR often involves combining data from various sources, such as local studies or applying national findings to specific regions.
- Challenges arise when PAR components originate from different datasets, impacting estimate precision.
Purpose of the Study:
- To develop a framework for estimating local PAR values.
- To investigate the properties of PAR estimates when components are sourced independently.
- To assess methods for improving the precision of PAR estimates in localized populations.
Main Methods:
- A novel framework for estimating local PAR was developed.
- The framework was validated using both synthetic datasets and empirical data from an international case-control study.
- A general expression for the variance of local PAR estimates was formulated, considering relative risk and exposure prevalence.
Main Results:
- The variance of local PAR estimates depends on the relative risk variance, exposure prevalence variance, and their covariance.
- Synthetic scenarios demonstrated the impact of varying stratum sizes, case-control ratios, and exposure prevalence.
- Analysis of heart disease data illustrated the practical application and variability of local PAR estimates.
Conclusions:
- Local PAR estimates benefit significantly from the integration of external data sources.
- Relying solely on local data can limit the precision of PAR estimates.
- Uncertainty in local exposure prevalence is a key driver of variation in PAR estimates.
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