Source, timing and dynamics of ionic species mobility in the Svalbard annual snowpack.

Nearly all ice core archives from the Arctic and middle latitudes (such as the Alps), apart from some very high elevation sites in Greenland and the North Pacific, are strongly influenced by melting processes. The increases in the average Arctic temperature has enhanced surface snow melting even of higher elevation ice caps, especially on the Svalbard Archipelago. The increase of the frequency and altitude of winter "rain on snow" events as well as the increase of the length of the melting season have had a direct impact on the chemical composition of the seasonal and permanent snow layers due to different migration processes of water-soluble species, such as inorganic ions. This re-allocation along the snowpack of ionic species could significantly modify the original chemical signal present in the annual snow. This paper aims to give a picture of the evolution of the seasonal snow strata with a daily time resolution to better understand: a) the processes that can influence deposition b) the distribution of ions in annual snow c) the impact of the presence of liquid water on chemical re-distribution within the annual snow pack. Specifically, the chemical composition of the first 100 cm of seasonal snow on the Austre Brøggerbreen Glacier (Spitsbergen, Svalbard Islands, Norway) was monitored daily from the 27th of March to the 31st of May 2015. The experimental period covered almost the entire Arctic spring until the melting season. This unique dataset gives us a daily picture of the snow pack composition, and helps us to understand the behaviour of cations (K+, Ca2+, Na+, Mg2+) and anions (Br-, I-, SO42-, NO3-, Cl-, MSA) in the Svalbard snow pack. We demonstrate that biologically related depositions occur only at the end of the snow season and that rain and melting events have different impacts on the snowpack chemistry.

Pairwise likelihood estimation of latent autoregressive count models.

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.

An integer-valued time series model for multivariate surveillance.

In recent days, different types of surveillance data are becoming available for public health purposes. In most cases, several variables are monitored and events of different types are reported. As the amount of surveillance data increases, statistical methods that can effectively address multivariate surveillance scenarios are demanded. Even though research activity in this field is increasing rapidly in recent years, only a few approaches have simultaneously addressed the integer-valued property of the data and its correlation (both time correlation and cross-correlation) structure. In this article, we suggest a multivariate integer-valued autoregressive model that allows for both serial and cross-correlations between the series and can easily accommodate overdispersion and covariate information. Moreover, its structure implies a natural decomposition into an endemic and an epidemic component, a common distinction in dynamic models for infectious disease counts. Detection of disease outbreaks is achieved through the comparison of surveillance data with one-step-ahead predictions obtained after fitting the suggested model to a set of clean historical data. The performance of the suggested model is illustrated on a trivariate series of syndromic surveillance data collected during Athens 2004 Olympic Games.

Feedback mechanisms between snow and atmospheric mercury: Results and observations from field campaigns on the Antarctic plateau.

The Antarctic Plateau snowpack is an important environment for the mercury geochemical cycle. We have extensively characterized and compared the changes in surface snow and atmospheric mercury concentrations that occur at Dome C. Three summer sampling campaigns were conducted between 2013 and 2016. The three campaigns had different meteorological conditions that significantly affected mercury deposition processes and its abundance in surface snow. In the absence of snow deposition events, the surface mercury concentration remained stable with narrow oscillations, while an increase in precipitation results in a higher mercury variability. The Hg concentrations detected confirm that snowfall can act as a mercury atmospheric scavenger. A high temporal resolution sampling experiment showed that surface concentration changes are connected with the diurnal solar radiation cycle. Mercury in surface snow is highly dynamic and it could decrease by up to 90% within 4/6 h. A negative relationship between surface snow mercury and atmospheric concentrations has been detected suggesting a mutual dynamic exchange between these two environments. Mercury concentrations were also compared with the Br concentrations in surface and deeper snow, results suggest that Br could have an active role in Hg deposition, particularly when air masses are from coastal areas. This research presents new information on the presence of Hg in surface and deeper snow layers, improving our understanding of atmospheric Hg deposition to the snow surface and the possible role of re-emission on the atmospheric Hg concentration.

Detailed statistical assessment of the characteristics of the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) threshold rules.

BACKGROUND: The European Society for Medical Oncology (ESMO) has developed the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS), a tool to assess the magnitude of clinical benefit from new cancer therapies. Grading is guided by a dual rule comparing the relative benefit (RB) and the absolute benefit (AB) achieved by the therapy to prespecified threshold values. The ESMO-MCBS v1.0 dual rule evaluates the RB of an experimental treatment based on the lower limit of the 95%CI (LL95%CI) for the hazard ratio (HR) along with an AB threshold. This dual rule addresses two goals: inclusiveness: not unfairly penalising experimental treatments from trials designed with adequate power targeting clinically meaningful relative benefit; and discernment: penalising trials designed to detect a small inconsequential benefit. METHODS: Based on 50 000 simulations of plausible trial scenarios, the sensitivity and specificity of the LL95%CI rule and the ESMO-MCBS dual rule, the robustness of their characteristics for reasonable power and range of targeted and true HRs, are examined. The per cent acceptance of maximal preliminary grade is compared with other dual rules based on point estimate (PE) thresholds for RB. RESULTS: For particularly small or particularly large studies, the observed benefit needs to be relatively big for the ESMO-MCBS dual rule to be satisfied and the maximal grade awarded. Compared with approaches that evaluate RB using the PE thresholds, simulations demonstrate that the MCBS approach better exhibits the desired behaviour achieving the goals of both inclusiveness and discernment. CONCLUSIONS: RB assessment using the LL95%CI for HR rather than a PE threshold has two advantages: it diminishes the probability of excluding big benefit positive studies from achieving due credit and, when combined with the AB assessment, it increases the probability of downgrading a trial with a statistically significant but clinically insignificant observed benefit.

Long-term exposure to traffic-related air pollution and cardiovascular health in a Greek cohort study.

Our objective is to evaluate the association of exposure to traffic-related air pollution with the incidence of fatal and non-fatal ischemic heart disease (IHD), stroke and total cardiovascular disease (CVD) events in a Greek cohort. We used data from the European Prospective Investigation on Nutrition and Cancer (EPIC) for 2752 subjects followed from 1997 to 2011, whose residence was in 10 municipalities of the Greater Athens area. Air pollution exposure estimation was based on a spatio-temporal land use regression model linking geo-coded residential addresses to long-term average NO2 and PM10 concentrations. We conducted Cox proportional hazards regression analysis, adjusting for potential confounders. Hazard ratios (HR) above 1 (not all statistically significant) were associated with higher PM10 exposure for all outcomes. Weaker associations were found with NO2 exposure. Specifically, the estimated HR for a CVD event associated with 10 µg/m(3) increase in long-term exposure to PM10 was 1.50 (1.05-2.16, p-value: 0.027). The relationship was more evident for subjects ≤50 years old at recruitment. Associations of PM10 and NO2 exposure with IHD events were found only among women with HRs respectively of 2.24 (0.89-5.64, p-value: 0.086) and 1.54 (1.01-2.37, p-value: 0.046) associated with 10 µg/m(3) increase in the corresponding pollutant. In conclusion, the present study suggests that long-term exposure to traffic-related air pollution has an impact on CVD and IHD morbidity, particularly among women and younger subjects.

Spatio-temporal semiparametric models for NO2 and PM10 concentration levels in Athens, Greece.

BACKGROUND AND AIMS: Studies of air pollution effects on health are often based on ecological measurements. Our aim was to develop spatio-temporal models that estimate daily levels of NO2 and PM10 at every point in space, within the greater Athens area. METHODS: We applied a semiparametric approach using spatial and temporal covariates and a bivariate smooth thin plate function. We evaluated the predictions of our models against the exposure estimates that are typically used in health studies. For model validation we used a temporal and a spatial approach. RESULTS: The adjusted-R(2) of the developed exposure models was 0.53 and 0.75 for PM10 and NO2 respectively; the spatial terms in our models explained 41.5% and 64.5% and the temporal explained 52.85% and 32.0% of the variability in PM10 and NO2, respectively. There was no temporal or spatial left over autocorrelation in the residuals. We performed a leave-one-out cross validation and the adjusted-R(2) were 0.41 for PM10 and 0.71 for NO2. The developed model showed good validity when comparing predicted and observed measures for the 2010 data. Our models performed better compared to the "ecological" estimates and estimates based on the "nearest monitoring site". CONCLUSIONS: Our spatio-temporal model makes valid predictions, it introduces substantial geographical variability, it reduces the bias when compared with the "ecological" estimates and the estimates based on the "nearest monitoring site" and it can be used for a more personalized exposure assessment in health studies.

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: the ESCAPE project.

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Interobserver variability, and visual and quantitative parameters of (123)I-FP-CIT SPECT (DaTSCAN) studies.

OBJECTIVE: To evaluate the degree of interobserver agreement in the visual interpretation of (123)I-FP-CIT studies and to investigate for potential associations between visual and semi-quantitative parameters. METHODS: Eighty-nine (123)I-FP-CIT studies were blindly reviewed by 3 independent observers: a consultant, a resident doctor and a radiographer. They classified every study as either "normal" or "abnormal" and assigned visual (123)I-FP-CIT uptake scores (2: normal, 1: reduced and 0: no uptake) in basal ganglia nuclei (right and left putamina and caudate nuclei) on every scan. Striatal (123)I-FP-CIT binding ratios were calculated using crescent-ROI software. The interobserver agreement for the interpretation of studies and for visual score assignment was evaluated by means of κ statistics. We investigated for associations of binding ratios with visual scores and clinical parameters; patients' clinical diagnoses served as the reference standard. RESULTS: There was excellent interobserver agreement (κ 0.89-0.93) in classifying studies as "normal" or "abnormal" and fine agreement in assignment of visual scores (κ 0.71-0.80 for putamina and 0.50-0.79 for caudate nuclei). Nuclei with scores of 1 and 0 showed significantly reduced binding ratios (about 30 and 50%, respectively) compared with the nuclei scored as 2. ROC analysis indicated the optimal cutoff point of striatal binding ratio at 3.8 (sensitivity 98.5%, specificity 95%) for the detection of parkinsonian syndromes. Striatal binding ratios were negatively associated with age in normal subjects and disease duration in Parkinson's disease patients. CONCLUSION: Visual interpretation of (123)I-FP-CIT studies showed very good interobserver agreement. We found significant associations among visual, semi-quantitative and clinical parameters.

Risk assessment of diesel exhaust and lung cancer: combining human and animal studies after adjustment for biases in epidemiological studies.

BACKGROUND: Risk assessment requires dose-response data for the evaluation of the relationship between exposure to an environmental stressor and the probability of developing an adverse health effect. Information from human studies is usually limited and additional results from animal studies are often needed for the assessment of risks in humans. Combination of risk estimates requires an assessment and correction of the important biases in the two types of studies. In this paper we aim to illustrate a quantitative approach to combining data from human and animal studies after adjusting for bias in human studies. For our purpose we use the example of the association between exposure to diesel exhaust and occurrence of lung cancer. METHODS: Firstly, we identify and adjust for the main sources of systematic error in selected human studies of the association between occupational exposure to diesel exhaust and occurrence of lung cancer. Evidence from selected animal studies is also accounted for by extrapolating to average ambient, occupational exposure concentrations of diesel exhaust. In a second stage, the bias adjusted effect estimates are combined in a common effect measure through meta-analysis. RESULTS: The random-effects pooled estimate (RR) for exposure to diesel exhaust vs. non-exposure was found 1.37 (95% C.I.: 1.08-1.65) in animal studies and 1.59 (95% C.I.: 1.09-2.10) in human studies, whilst the overall was found equal to 1.49 (95% C.I.: 1.21-1.78) with a greater contribution from human studies. Without bias adjustment in human studies, the pooled effect estimate was 1.59 (95% C.I.: 1.28-1.89). CONCLUSIONS: Adjustment for the main sources of uncertainty produced lower risk estimates showing that ignoring bias leads to risk estimates potentially biased upwards.

Medical students' perceptions on factors influencing a surgical career: the fate of general surgery in Greece.

BACKGROUND: A decline of medical students' interest in a general surgery career is occurring in the Western medical world. We sought data on the mentality of Greek students toward specialty selection, and we determined whether trends indicated a decline in interest for general surgery in Greece. METHODS: A structured questionnaire was distributed to 3 groups of medical students: to pre-4th-year (group 1) surgical clerkship, post-4th-year (group 2) surgical clerkship, and post-6th-year internship students in surgery (group 3). The questions covered a wide spectrum of data including career choices, influential factors, and satisfaction rates on educational and training issues. RESULTS: From a total of 500 distributed questionnaires 363 were returned. Most students (63.1%) indicated preference toward nonsurgical (medical) specialties. Surgical specialties within the 3 groups gathered 19.5% (group 1), 26.5% (group 2) and 31.2% (group 3) preference rates. Among surgical specialties, general surgery was chosen by 29.4% in group 1, 10.0% in group 2, and 17.9% in group 3. The most common criterion for specialty selection was "quality of life" (68.6%) among group 1 students and "patient contact" for group 2 and group 3 students (77.3% and 65.3%, respectively). Among the 96 students who chose surgical specialties, the most common criterion for specialty selection was "scientific challenge" (100%) in group 1 and "patient contact" in groups 2 and 3 (62.5% and 69.2%, respectively). The 3 more frequently chosen factors that influenced the "picture" of surgery positively were attending live surgery cases in the operating room (37.6%), clinical experience (29.6%), and patient care (14.4%), followed by assisting in the operating room (8.8%). CONCLUSION: Our survey suggests a limited interest of Greek medical students for surgical specialties and general surgery in particular. As the medical curriculum is restructured, our data underscore the need for actions by surgical educators and medical school authorities so as to enhance the interest of medical students in general surgery in Greece.

Adolescents' wellbeing and functioning: relationships with parents' subjective general physical and mental health.

BACKGROUND: This study aimed at examining the relationship between parental subjective health status and adolescents' health-related quality of life (HRQoL) as well as the role of gender, socioeconomic status, presence of chronic health care needs and social support on the above interaction. METHODS: Questionnaires were administered to a Greek nation-wide random sample of adolescents (N = 1,194) aged 11-18 years and their parents (N = 973) in 2003. Adolescents' and parents' status was assessed, together with reports of socio-economic status and level of social support. Various statistical tests were used to determine the extent to which these variables were related to each other. RESULTS AND DISCUSSION: Parental subjective mental health status was significantly correlated with adolescents' better physical and psychological wellbeing, moods and emotions, parent-child relationships, school environment and financial resources. Parental subjective physical health status was strongly associated with more positive adolescents' self-perception. Adolescents' male gender, younger age, absence of chronic health care needs, high social support, and higher family income were positively associated with better HRQoL. CONCLUSIONS: This study reinforces the importance of parental subjective health status, along with other variables, as a significant factor for the adolescents' HRQoL.