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Approaches to harmonize mortality data sets in three diverse radiation worker cohorts
Jianqi Zhang1, Daniel O Stram1, Sarah S Cohen2
1Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States of America.
Summary
Investigating low-dose radiation effects requires combining diverse worker and veteran data. This study illustrates methods for harmonizing health data, focusing on heart disease mortality across different cohorts.
Area of Science:
- Environmental Health
- Epidemiology
- Radiation Biology
Background:
- Established link between ionizing radiation and cancer.
- Uncertainties remain regarding low-dose, low-dose-rate radiation effects.
- The Million Person Study (MPS) investigates chronic low dose-rate exposure effects.
Purpose of the Study:
- Evaluate non-cancer mortality in three diverse cohorts (Rocketdyne, Mound, Atomic Veterans).
- Illustrate methods for combining and harmonizing data from disparate populations.
- Assess statistical approaches for analyzing health outcomes in radiation-exposed groups.
Main Methods:
- Dose reconstructions for Rocketdyne, Mound, and Atomic Veterans cohorts.
- Analysis of heart disease mortality (underlying and contributing causes).
- Evaluation of five data combination methods: pooled analysis, stratified analysis, and meta-analysis (fixed/random effects).
Main Results:
- Radiation dose estimates varied significantly by socio-economic status across cohorts.
- Birth cohort was a significant factor in heart disease mortality, with later cohorts showing lower rates.
- Different combination methods yielded varying results, highlighting the importance of methodological choice.
Conclusions:
- Combining diverse cohort data for radiation effect studies presents challenges in harmonization.
- Statistical methods for combining data must account for confounders like socio-economic status and birth cohort.
- The presented methods offer insights for future analyses within the Million Person Study.
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