A study of lung cancer risk from residential radon exposure and its radioactive progeny was performed with 200 cases (58% male, 42% female) and 397 controls matched on age and sex, all from the same health maintenance organization. Emphasis was placed on accurate and extensive year-long dosimetry with etch-track detectors in conjunction with careful questioning about historic patterns of in-home mobility. Conditional logistic regression was used to model the outcome of cancer on radon exposure, while controlling for years of residency, smoking, education, income, and years of job exposure to known or potential carcinogens. Smoking was accounted for by nine categories: never smokers, four categories of current smokers, and four categories of former smokers. Radon exposure was divided into six categories (model 1) with break points at 25, 50, 75, 150, and 250 Bq m−3, the lowest being the reference. Surprisingly, the adjusted odds ratios (AORs) were, in order, 1.00, 0.53, 0.31, 0.47, 0.22, and 2.50 with the third category significantly below 1.0 (p < 0.05), and the second, fourth, and fifth categories approaching statistical significance (p < 0.1). An alternate analysis (model 2) using natural cubic splines allowed calculating AORs as a continuous function of radon exposure. That analysis produces AORs that are substantially less than 1.0 with borderline statistical significance (0.048 ≤ p ≤ 0.05) between approximately 85 and 123 Bq m−3. College-educated subjects in comparison to high-school dropouts have a significant reduction in cancer risk after controlling for smoking, years of residency, and job exposures with AOR = 0.30 (95% CI: 0.13, 0.69), p = 0.005 (model 1).
* Biostatistics Department, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; † Department of Physics, Worcester Polytechnic Institute, Worcester, MA 01609; ‡ St. Vincent Hospital and Fallon Clinic, Worcester Medical Center, Worcester, MA 01608.
For correspondence contact: Richard E. Thompson, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, or email at email@example.com.
(Manuscript accepted 29 August 2007)