Bayesian Prediction of Mean Indoor Radon Concentrations for Minnesota Counties

Past efforts to identify areas with higher than average indoor radon concentrations by examining the statistical relationship between local mean concentrations and physical parameters such as the soil radium concentration have been hampered by the variation in local means caused by the small number of homes monitored in most areas. In this paper, indoor radon data from a survey in Minnesota are analyzed to minimize the effect of finite sample size within counties, to determine the true county-to-county variation of indoor radon concentrations in the state, and to find the extent to which this variation is explained by the variation in surficial radium concentration among counties. The analysis uses hierarchical modeling, in which some parameters of interest (such as county geometric mean (GM) radon concentrations) are assumed to be drawn from a single population, for which the distributional parameters are estimated from the data. Extensions of this technique, known as a random effects regression and mixed effects regression, are used to determine the relationship between predictive variables and indoor radon concentrations; the results are used to refine the predictions of each county's radon levels, resulting in a great decrease in uncertainty. The true county-to-county variation of GM radon levels is found to be substantially less than the county-to-county variation of the observed GMs, much of which is due to the small sample size in each county. The variation in the logarithm of surficial radium content is shown to explain approximately 80% of the variation of the logarithm of GM radon concentration among counties. The influences of housing and measurement factors, such as whether the monitored home has a basement and whether the measurement was made in a basement, are also discussed. The statistical method can be used to predict mean radon concentrations, or applied to other geographically distributed environmental parameters

Predicting New Hampshire Indoor Radon Concentrations from geologic information and other covariates

Generalized geologic province information and data on house construction were used to predict indoor radon concentrations in New Hampshire (NH). A mixed-effects regression model was used to predict the geometric mean (GM) short-term radon concentrations in 259 NH towns. Bayesian methods were used to avoid over-fitting and to minimize the effects of small sample variation within towns. Data from a random survey of short-term radon measurements, individual residence building characteristics, along with geologic unit information, and average surface radium concentration by town, were variables used in the model. Predicted town GM short-term indoor radon concentrations for detached houses with usable basements range from 34 Bq/m{sup 3} (1 pCi/l) to 558 Bq/m{sup 3} (15 pCi/l), with uncertainties of about 30%. A geologic province consisting of glacial deposits and marine sediments, was associated with significantly elevated radon levels, after adjustment for radium concentration, and building type. Validation and interpretation of results are discussed

Skeletal alkaline phosphatase as a serum marker of bone metastases in the follow-up of patients with breast cancer

Immunoradiometric determination of the bone isoenzyme of alkaline phosphatase with a method provided by Hybritech Inc., San Diego CA (USA) was carried out in 145 female patients, 97 of whom with radically operated breast cancer and 48 with benign mammary cysts, in order to evaluate the correlation of serum levels with the metabolic process of bone rearrangement in patients with bone metastases. This study shows that skeletal ALP, having high specificity (86.48%) and sensitivity (78.6%) for early progression (the average anticipation time compared to scintigraphic detection was 101 days) could represent a valid marker for bone metastases in association with mucinous markers in the follow-up of patients operated for breast cancer. In addition, dynamic serum determination of skeletal ALP could be a valid help in monitoring the efficacy of therapy in patients with bone progression

Reduction of PSA values by combination pharmacological therapy in patients with chronic prostatitis: implications for prostate cancer detection

We identified from our clinical database a total of 471 patients affected by cat. II chronic bacterial prostatitis (CBP), cat. III (IIIa and IIIb) chronic pelvic pain syndrome (CP/CPPS), or cat. IV asymptomatic inflammatory prostatitis (AIP), according to NIH criteria. 132 intent-to-treat patients, showing levels of PSA 654 ng/mL, were subjected to a 6-week course of combination pharmacological therapy with 500 mg/day ciprofloxacin, 500 mg/day azithromycin (3 days/week), 10 mg/day alfuzosin and 320 mg b.i.d. Serenoa repens extract. At the end of treatment, 111 per-protocol patients belonging to all categories of prostatitis showed a total 32.5% reduction of PSA levels. In the same group, 66 patients (59.4%) showed "normalization" of PSA values under the 4 ng/mL limit. Patients affected by cat. IIIb CP/CPPS showed the highest PSA reduction and normalization rates (40% and 68.4%, respectively). Follow-up data show that, after a marked, significant reduction at completion of therapy, PSA levels, urine peak flow rates and NIH-CPSI symptom scores remained constant or decreased throughout a period of 18 months in patients showing normalization of PSA values. Prostatic biopsy was proposed to 45 patients showing persistently high PSA values ( 654 ng/mL) at the end of treatment. Fourteen patients rejected biopsy; of the remaining 31, 10 were diagnosed with prostate cancer. Four months after a first biopsy, a second biopsy was proposed to the 21 patients with a negative first diagnosis and persistently elevated PSA levels. Three patients rejected the procedure; of the remaining 18, four were diagnosed with prostatic carcinoma. In summary, combination pharmacological therapy decreased the number of patients undergoing prostatic biopsy from 111 to 45. Normalization of PSA values in 59.4% of patients - not subjected to biopsy - increased the prostate cancer detection rate from 12.6% (14/111) to 31.1% (14/45). The reduction of PSA after a 6-week course of therapy was calculated in patients affected by cat. II, IIIa, IIIb and IV prostatitis after stratification with respect to the concomitant presence or absence of benign prostatic hyperplasia (BPH). PSA was reduced by 41 % in cat. II CBP patients without BPH, compared to a 12.7% reduction in patients affected by BPH. Cat. IIIa CP/CPPS patients without BPH showed a 58.3% reduction of PSA levels, compared to a 20.7% reduction observed in CPPS/BPH patients. These data show that the presence of BPH may prevent the reduction of PSA induced by combination pharmacological therapy, and suggest that care has to be taken in the adoption of PSA as a marker of therapeutic efficacy in the presence of confounding factors like BPH. PSA should in our opinion be used as a significant component of a strategy integrating multiple diagnostic approaches