The ‘joys’ of digital media in new parenting

Maja Sonne Damkjaer explores how new parents are using digital media in their transition to parenthood. As new parents engage with media such as pregnancy calendars and social media such as Facebook, Maja suggests that it is now necessary for parents to decide on a parental communication strategy as they navigate these digital contexts. Maja is a research assistant at the School of Communication and Culture, Aarhus University, Denmark, and her PhD project is part of the research programme, The Mediatization of Culture: The Challenge of New Media, affiliated with the Digital Footprints research group

The measurement of water scarcity: Defining a meaningful indicator

Metrics of water scarcity and stress have evolved over the last three decades from simple threshold indicators to holistic measures characterising human environments and freshwater sustainability. Metrics commonly estimate renewable freshwater resources using mean annual river runoff, which masks hydrological variability, and quantify subjectively socio-economic conditions characterising adaptive capacity. There is a marked absence of research evaluating whether these metrics of water scarcity are meaningful. We argue that measurement of water scarcity (1) be redefined physically in terms of the freshwater storage required to address imbalances in intra- and inter-annual fluxes of freshwater supply and demand; (2) abandons subjective quantifications of human environments and (3) be used to inform participatory decision-making processes that explore a wide range of options for addressing freshwater storage requirements beyond dams that include use of renewable groundwater, soil water and trading in virtual water. Further, we outline a conceptual framework redefining water scarcity in terms of freshwater storage

Beyond Human Right vs. Commodity: Time to Realistically Assess Water Scarcity

The water resources community remains stuck in a futile debate of whether water constitutes a human right or a commodity, which is resolved through the content of General Comment 15: water constitutes a human right, which puts conditions on economic approaches to water and its commodification. Instead, it is time to address the issue of globally adopted misrepresentative water scarcity metrics that misleadingly show increasing conditions of scarcity, which risks biasing the argument towards the commodification of water. Redefining these metrics to portray hydrologic realities, will more precisely inform the formulation of water policies and help advance solutions to global water problems

Occupational risk of exposure to methicillin-resistant Staphylococcus aureus (MRSA) and the quality of infection hygiene in nursing homes

Methicillin-resistant Staphylococcus aureus (MRSA) is an increasing health concern across the globe and is often prevalent at long-term care facilities, such as nursing homes. However, we know little of whether nursing home staff is exposed to MRSA via air and surfaces. We investigated whether staff members at nursing homes are colonised with and exposed to culturable MRSA, and assessed staff members' self-reported knowledge of MRSA and compliance with infection hygiene guidelines. Five nursing homes with MRSA positive residents were visited in Copenhagen, Denmark. Personal bioaerosol exposure samples and environmental samples from surfaces, sedimented dust and bioaerosols were examined for MRSA and methicillin-susceptible S. aureus (MSSA) to determine occupational exposure. Swabs were taken from staffs' nose, throat, and hands to determine whether they were colonised with MRSA. An online questionnaire about MRSA and infection control was distributed. No staff members were colonised with MRSA, but MRSA was detected in the rooms of the colonised residents in two out of the five nursing homes. MRSA was observed in air (n =4 out of 42, ranging from 2.9-7.9 CFU/m(3)), sedimented dust (n = 1 out of 58, 1.1 x 10(3) CFU/m(2)/d), and on surfaces (n = 9 out of 113, 0.04-70.8 CFU/m(2)). The questionnaire revealed that half of the staff members worry about spreading MRSA to others. Identified aspects for improvement were improved availability and use of protective equipment, not transferring cleaning supplies (e.g., vacuum cleaners) between residents' rooms and to reduce worry of MRSA, e.g., through education. (c) The Author(s) 2020

065 583 MAPPING INDOOR RADON-222 IN DENMARK: DESIGN AND TEST OF THE STATISTICAL MODEL USED IN THE SECOND NATION-WIDE SURVEY

In Denmark, a new survey of indoor radon-222 has been carried out. One-year alpha track measurements (CR-39) have been done in 3019 single-family houses. There is from 3 to 23 house measurements in each of the 275 municipalities. Within each municipality, houses have been selected randomly. One important outcome of the survey is the prediction of the fraction of houses in each municipality with an annual average radon concentration above . To obtain the most accurate estimate and to assess the associated uncertainties, a statistical model has been developed. The purpose of this paper is to describe the design of this model, and to report results of model tests. The model is based on a transformation of the data to normality and on analytical (conditionally) unbiased estimators of the quantities of interest. Bayesian statistics is used to minimize the effect of small sample size. In each municipality, the correction is dependent on the fraction of area where sand and gravel is a dominating surface geology. The uncertainty analysis is done with a Monte Carlo technique. It is demonstrated that the weighted sum of all municipality model estimates of fractions above 200 Bqm -3 (3.9 % with 95 %-confidence interval = [3.4,4.5]) is consistent with the weighted sum of the observations for Denmark taken as a whole (4.6 % with 95 %-confidence interval = [3. 8,5.6]). The total number of single-family houses within each municipality is used as weight. Model estimates are also found to be consistent with observations at the level of individual counties. These typically include a few hundred house measurements. These tests indicate that the model is well suited for its purpose. Keywords: Houses; Radon-222; Survey; Statistical model INTRODUCTION Radon is believed to cause an increased risk of lung cancer and it is therefore of interest to identify houses with high levels of indoor radon. It is important to know how many houses that have "high" levels (e.g. annual levels above 200 or 400 Bqm -3 ) and it is important to know where these houses are located. Likewise, it is also of interest to know about the low-radon houses where there is no cause for alarm. This paper reports on a new Danish survey of indoor radon designed to tackle these problems. The survey is much larger than the first one from 1985/86 SURVEY DESIGN Denmark is divided into 15 counties. Each county consists of a number of smaller municipalities. In total there are 275 municipalities. One-year alpha track measurements (CR-39) were done in 3019 single-family houses from December 1995 to December 1996. Detectors were placed in living 065 Radon in the Living Environment, 19-23 April 1999, Athens, Greece 584 rooms. Within each municipality, houses were selected randomly by the Building and Dwelling Register (BBR). The median number of house measurements per municipality is 11. Nine municipalities have only 6 or less measurements, and nine municipalities have 18 or more measurements. The only geological information used directly in the model is the fraction of area (later referred to as k g ) in each municipality which is dominated by sand and gravel. These values are found by visual inspection of a map of the surface geology of Denmark MODEL Transformations We define the 'house concentration' c of a given house to be the average radon concentration of the living room and the bedroom: , is closer to normality. All of the statistical analyses are therefore conducted for transformed radon concentrations x . Distribution parameters It is assumed that within each municipality k , the transformed radon concentration x is normally distributed with a (true) mean k µ and a (true) standard deviation σ . We allow that k µ can be different from one municipality to another, but require that all municipalities have the same σ . The latter requirement is supported by an analysis of the homogeneity of variances with a modified version of the Levene test based on absolute deviations from the municipality medians of transformed radon concentrations The estimator σˆof σ is found as follows: First, we calculate the simple mean k x and standard deviation k s of the k N measurements in each municipality k : and . Finally, we pool the 275 k σˆ-values into a single weighted mean value: σˆ. The number of house measurements ( k N ) is used as weight. The value amounts to: 0.59418 = σ . The estimators k μ of k µ are found as follows: A simple estimate would be to let . However, as demonstrated by where k g is an estimate of the fraction of the total area of municipality k that has a surface geology dominated by sand and gravel. Based on all 275 municipalities, the regression coefficients amount to 4.54 0 = β (standard error 0.0296) and -0.69 1 = β (standard error 0.06). The R-squared value is 36 %. The variance 2 ε σ of the residuals k ε is 0.082. For each municipality, we calculate: and use the following weighted average as the model estimate of k µ : where the weights are: . Essentially, we estimate k µ to be equal to the observed value k x with some weigthed correction towards what on-the-averaged is found for municipalities with that type of surface geology. If there are few (or no) measurements in a municipality, then . If there are many measurements, then . Essentially, the influence of k θ equivalents about 4 extra measurements in each municipality. The main source of uncertainty in the survey is the small sample size. We apply equation (6) as a way to gently "stabilize" modelling results in all municipalities except those on the island Bornholm. and the bias term: and insert into: the Living Environment, 19-23 April 1999, Athens, Greece which is different from the observed value given by equation (7 f . RESULTS In the survey, house radon levels ( c ) in the range from 2 to 590 Bqm . The middle plot of DISCUSSION Improved estimates by modelling? The primary purpose of the statistical model is to provide estimates of the fraction of houses above 200 Bqm -3 at the level of individual municipalities. The idea is to make estimates that are better (i.e. more accurate and less variable) than estimates deduced from simple observations: in municipality k , and k N is the number of measurements. The main problem with such simple observations is that for the typical case of about 10 house measurements per municipality, the outcome will be in steps of 10 % (i.e. 0 %, 10 %, 20 % etc.). This can be illustrated with synthetic data. We draw 3019 synthetic (transformed radon concentrations) x from a normal distribution with mean 4.33 and standard deviation 0.5941. Subsequently we transform the data to ordinary radon concentrations ( c -values) using the inverse of ) log( b c x + = . The true value of 200 f in this case is 4.60 % (about the same as the national average). The data are grouped in municipalities and counties exactly as in the survey (this is important as the number of measurements determines the variability of parameter estimates). Also, we preserve the fraction of sand and gravel ( k g ) which is needed in equation (6). In this case, however, the regression (see equation 5) will only be by chance. The model is applied exactly as with the real data set. To evaluate the importance of the Bayesian correction, we will also consider simplified-model estimates where 0 ω in equation 6 is set to 0 (such that mean and standard deviation of the results are 4.9 % and 7.2 %, respectively. In one case, 200 f is found to be as high as 40 %. It is particularly problematic that about 60 % of the municipalities are without measured houses with concentrations above . It is little help that many of the remaining municipalities, have observed fractions above 10 %, such that on-the-average the correct result of about 4.6 % is observed. The curved labelled simplified model are the results of model estimates without the Bayesian correction ( 0 0 = ω ). Compared with the first curve, these estimates are much better in the sense that the results are less variable (mean 4.9 % and standard deviation 3.7 %). The final curve labelled full model present by far the best estimates (mean 4.3 % and standard deviation 1.7 %). However, because the data in each municipality come from the same distribution, the variance of the regression residuals ( ε in equation 5) is lower than in the real survey. This means that in this (synthetic) example, the Bayesian correction will correspond to about 9 extra measurements in each municipality (compared to 4 in the real situation). The confidence intervals of the simulations are not shown in the Model versus measurements The (weighted) national average of model predictions amounts to . The latter agreement (that concerns the tail of the distribution) suggests that the assumption of normality is not greatly violated. As shown in the top plot of To illustrate how the model treats counties with different types of geology it is of interest to study Model elements The model includes some special elements: Measurement uncertainty Considerable measurement uncertainty is associated with the c -estimates (typically about 20 %). Part of this comes from the conversion from living room to house concentrations. Such uncertainties tend to have little impact on averages of quantities that relates linearly to the measurements (e.g. arithmetic means) as such random errors on the average will tend to cancel each other. Unfortunately, estimation of the fraction of houses above 200 Bqm -3 is an non-linear function of the individual radon concentration results, and random errors therefore will bias the estimation. This has previously been demonstrated by CONCLUSION A statistical model has been developed. It predicts the fraction of single-family houses (in each municipality) with an annual radon level above 200 Bqm . The investigation suggests that these estimates are better (more accurate and less variable) than simple observations based on direct observation of houses with levels above 200 Bqm . Also, the model provides estimates of uncertainties associated with these predictions. The main source of uncertainty relates to the small sample size (typically only about 11 measurements in each municipality). Comparison between model predictions and measurements indicated that the model is well suited for mapping of indoor radon in Denmark. ACKNOWLEDGEMEN

A modeling study of functional magnetic resonance imaging to individualize target definition of seminal vesicles for external beam radiotherapy

Background Pre-treatment magnetic resonance imaging (MRI) can give patient-specific evaluation of 25 suspected pathologically-involved volumes in the seminal vesicles (SV) in prostate cancer patients. By 26 targeting this suspicious volume we hypothesize that radiotherapy is more efficient without introducing more 27 toxicity. In this study we evaluate the concept of using MRI-defined target volumes in terms of tumor 28 control probability (TCP) and rectal normal tissue complication probability (NTCP). Materials and methods Twenty-one high-risk prostate cancer patients were included. Pre-treatment CT 30 images, T2 weighted (T2w) MRI and two multi-parametric MRI were acquired. Overlap between a 31 suspicious volume in the SV observed on T2w images and a suspicious volume observed on either multi-32 parametric MRI was assumed to reflect a true malignant region (named “MRI positive”). In addition the 33 entire SV on the CT-scan was delineated. Three treatment plans of 2Gyx39 fractions were generated per 34 patient: one covering the MRI positive volume in SV and prostate with margin of 11 mm to the MRI positive 35 in the SV and two plans covering prostate and SV using 11mm and 7mm SV margin, respectively. All plans 36 prescribed the same PTV mean dose. Rectal NTCP grade≥2 was evaluated with the Lyman-Kutcher-Burman 37 model and TCP was estimated by a logistic model using the combined MRI positive volume in SV and 38 prostate as region-of-interest. Results 14/21 patients were classified as MRI positive, 6 of which had suspicious volumes in all three MRI 40 modalities. On average TCP for the plan covering prostate and the MRI positive volume was 3% higher (up 41 to 11%) than the two other plans which was statistically significant. The increased TCP was obtained without 42 increasing rectal NTCP grade≥2. Conclusion Using functional MRI for individualized target delineation in the seminal vesicles may improve 44 the treatment outcome in radiotherapy of prostate cancer without increasing the rectal toxicity.</p

Hospital length of stay and surgery among European children with rare structural congenital anomalies – A population-based data linkage study

Little is known about morbidity for children with rare structural congenital anomalies. This European, population-based data-linkage cohort study analysed data on hospitalisations and surgical procedures for 5948 children born 1995-2014 with 18 rare structural congenital anomalies from nine EUROCAT registries in five countries. In the first year of life, the median length of stay (LOS) ranged from 3.5 days (anotia) to 53.8 days (atresia of bile ducts). Generally, children with gastrointestinal anomalies, bladder anomalies and Prune-Belly had the longest LOS. At ages 1-4, the median LOS per year was ≤3 days for most anomalies. The proportion of children having surgery before age 5 years ranged from 40% to 100%. The median number of surgical procedures for those under 5 years was two or more for 14 of the 18 anomalies and the highest for children with Prune-Belly at 7.4 (95% CI 2.5-12.3). The median age at first surgery for children with atresia of bile ducts was 8.4 weeks (95% CI 7.6-9.2) which is older than international recommendations. Results from the subset of registries with data up to 10 years of age showed that the need for hospitalisations and surgery continued. The burden of disease in early childhood is high for children with rare structural congenital anomalies