Radiocarbon analysis in an Alpine ice core: Record of anthropogenic and biogenic contributions to carbonaceous Radiocarbon analysis in an Alpine ice core: Record of anthropogenic and biogenic contributions to carbonaceous aerosols in the past (1650-1940)

Long-term concentration records of carbonaceous particles (CP) are of increasing interest in climate research due to their not yet completely understood effects on climate. Nevertheless, only poor data on their concentrations and sources before the 20th century are available. We present a first long-term record of organic carbon (OC) and elemental carbon (EC) concentrations – the two main fractions of CP – along with the corresponding fraction of modern carbon (fM) derived from radiocarbon (14C) analysis in ice. This allows a distinction and quantification of natural (biogenic) and anthropogenic (fossil) sources in the past. CP were extracted from an ice archive, with resulting carbon quantities in the microgram range. Analysis of 14C by accelerator mass spectrometry (AMS) was therefore highly demanding. We analysed 33 samples of 0.4 to 1 kg ice from a 150.5 m long ice core retrieved at Fiescherhorn glacier in December 2002 (46°33'3.2" N, 08°04'0.4" E; 3900 m a.s.l.). Samples were taken from bedrock up to the firn/ice transition, covering the time period 1650–1940 and thus the transition from the pre-industrial to the industrial era. Before ~1850, OC was approaching a purely biogenic origin with a mean concentration of 24 μg kg−1 and a standard deviation of 7 μg kg−1. In 1940, OC concentration was about a factor of 3 higher than this biogenic background, almost half of it originating from anthropogenic sources, i.e. from combustion of fossil fuels. The biogenic EC concentration was nearly constant over the examined time period with 6 μg kg−1 and a standard deviation of 1 μg kg−1. In 1940, the additional anthropogenic input of atmospheric EC was about 50 μg kg−1.

Continuous or repeated prolonged cisplatin infusions in children: a prospective study on ototoxicity, platinum concentrations, and standard serum parameters

BACKGROUND: To overcome the ototoxicity of cisplatin, single bolus infusions were replaced by repeated prolonged infusions of lower doses or by continuous infusions at still lower infusion rates. However, considering ototoxicity little is, in fact, known about the tolerance of repeated prolonged or continuous infusion in children. PROCEDURE: Auditory function was monitored along with plasma concentrations of free and total platinum (Pt), and with standard serum parameters (sodium, potassium, calcium, magnesium, phosphate, chloride, and creatinine) in 24 children receiving cisplatin by continuous infusion for the treatment of neuroblastoma and osteosarcoma or by repeated 1 or 6 hr infusions for the treatment of germ cell tumors. RESULTS: Hearing deteriorated in 10/15 osteosarcoma patients, 2/3 neuroblastoma patients, and 1/6 patients with germ cell tumors. Ototoxicity occurred after cumulative doses between 120 and 360 mg/m(2) cisplatin. In osteosarcoma patients, ototoxicity was associated with a comparatively higher mean plasma concentration of free Pt. However, Pt plasma concentrations did not discriminate between patients with or without ototoxicity. In patients experiencing ototoxicity serum creatinine increased by 45% compared to pre-treatment levels (mean). Serum creatinine increased by 26% in patients without ototoxicity (P < 0.05, Mann-Whitney Rank sum test). Despite standardized hydration, discrete but significant changes of potassium, sodium, magnesium, and phosphate were observed during and/or after cisplatin infusion, which, however, did not discriminate between patients with and without ototoxicity. CONCLUSIONS: While continuous cisplatin infusions are less nephrotoxic than repeated prolonged infusions, we observed considerable ototoxicity in patients treated with continuous cisplatin infusions, which necessitates further evaluations on the tolerance of continuous cisplatin infusions in children.

Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.

MULTIPLE MODEL EVALUATION ABSENT THE GOLD STANDARD VIA MODEL COMBINATION

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome — aka the gold standard — and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and for these estimates to be calibrated and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the dataset, operating characteristics of the commonly used genotyping procedures and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

A pain assessment scale for population-based studies: Development and validation of the Pain Module of the Standard Evaluation Questionnaire

The objectives of this study were to develop and validate a tool for assessing pain in population-based observational studies and to develop three subscales for back/neck, upper extremity and lower extremity pain. Based on a literature review, items were extracted from validated questionnaires and reviewed by an expert panel. The initial questionnaire consisted of a pain manikin and 34 items relating to (i) intensity of pain in different body regions (7 items), (ii) pain during activities of daily living (18 items) and (iii) various pain modalities (9 items). Psychometric validation of the initial questionnaire was performed in a random sample of the German-speaking Swiss population. Analyses included tests for reliability, correlation analysis, principal components factor analysis, tests for internal consistency and validity. Overall, 16,634 of 23,763 eligible individuals participated (70%). Test-retest reliability coefficients ranged from 0.32 to 0.97, but only three coefficients were below 0.60. Subscales were constructed combining four items for each of the subscales. Item-total coefficients ranged from 0.76 to 0.86 and Cronbach's alpha were 0.75 or higher for all subscales. Correlation coefficients between subscales and three validated instruments (WOMAC, SPADI and Oswestry) ranged from 0.62 to 0.79. The final Pain Standard Evaluation Questionnaire (SEQ Pain) included 28 items and the pain manikin and accounted for the multidimensionality of pain by assessing pain location and intensity, pain during activity, triggers and time of onset of pain and frequency of pain medication. It was found to be reliable and valid for the assessment of pain in population-based observational studies.