Lifetime excess absolute risk for lung cancer due to exposure to radon: results of the pooled uranium miners cohort study PUMA.

The Pooled Uranium Miners Analysis (PUMA) study is the largest uranium miners cohort with 119,709 miners, 4.3 million person-years at risk and 7754 lung cancer deaths. Excess relative rate (ERR) estimates for lung cancer mortality per unit of cumulative exposure to radon progeny in working level months (WLM) based on the PUMA study have been reported. The ERR/WLM was modified by attained age, time since exposure or age at exposure, and exposure rate. This pattern was found for the full PUMA cohort and the 1960 + sub-cohort, i.e., miners hired in 1960 or later with chronic low radon exposures and exposure rates. The aim of the present paper is to calculate the lifetime excess absolute risk (LEAR) of lung cancer mortality per WLM using the PUMA risk models, as well as risk models derived in previously published smaller uranium miner studies, some of which are included in PUMA. The same methods were applied for all risk models, i.e., relative risk projection up to <95 years of age, an exposure scenario of 2 WLM per year from age 18-64 years, and baseline mortality rates representing a mixed Euro-American-Asian population. Depending upon the choice of model, the estimated LEAR per WLM are 5.38 × 10-4 or 5.57 × 10-4 in the full PUMA cohort and 7.50 × 10-4 or 7.66 × 10-4 in the PUMA 1960 + sub-cohort, respectively. The LEAR per WLM estimates derived from risk models reported for previously published uranium miners studies range from 2.5 × 10-4 to 9.2 × 10-4. PUMA strengthens knowledge on the radon-related lung cancer LEAR, a useful way to translate models for policy purposes.

Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset.

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.