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Epidemiological studies of children's cancer risks associated with background gamma radiation exposure have used geographic exposure models to estimate exposure at their locations of residence. We measured personal exposure to background gamma radiation, and we investigated the extent to which it was associated with children's whereabouts. We collected data on whereabouts and exposure to background gamma radiation over a 5-day period among children aged 4-15 years in Switzerland. We used D-Shuttle dosimeters to measure children's exposure, and we asked parents to write their children's activities in diaries. We used Poisson mixed-effects and linear regression models to investigate the association of hourly and overall doses, respectively, with children's reported whereabouts. During the observed time, 149 participating children spent 66% indoors at home; 19% indoors away from home; and 15% outdoors. The mean personal exposure was 85.7 nSv/h (range 52.3 nSv/h-145 nSv/h). Exposure was 1.077 (95% CI 1.067, 1.087) times higher indoors than outdoors and varied by building material and (predicted) outdoor dose rates. Our study provides detailed information about children's patterns of exposure to background gamma radiation in Switzerland. Dwelling building materials and outdoor dose rates are important determinants of children's exposure. Future epidemiological studies may benefit from including information about building materials.

RationaleThe Predicting Asthma Risk in Children (PARC) tool uses questionnaire-based respiratory symptoms collected from preschool children to predict their risk of asthma 5 years later. The tool was originally developed and externally validated in population-based settings and has not yet been validated in a clinical setting.ObjectiveTo externally validate the PARC tool in children seen in paediatric pulmonology clinics.MethodsThe Swiss Paediatric Airway Cohort (SPAC) is a prospective study of children seen in respiratory outpatient clinics across Switzerland. This analysis included children seen at ages 1-6 years for cough or wheeze at baseline and who completed the follow-up questionnaire 2 years later. The outcome was defined as current wheeze plus use of asthma medication. In sensitivity analyses, we explored effects of varied inclusion criteria and outcomes. We assessed performance by describing sensitivity, specificity, negative and positive predictive value (NPV, PPV), area under the curve (AUC), scaled Brier’s score and Nagelkerke’s R2 scores and compared performance in SPAC to that achieved in the original population, the Leicester Respiratory Cohort (LRC).ResultsAmong the 346 children included, 125 (36%) reported the outcome after 2 years. At a PARC score cut-off of 4, sensitivity was higher (95% vs 79%) but specificity lower (14% vs 57%) in SPAC compared to LRC. NPV was comparable (0.84 vs. 0.87) as was PPV (0.37 vs.0.42). Discrimination was lower in SPAC (AUC of 0.71 vs 0.78), as were Nagelkerke’s R2 (0.18 vs 0.28) and scaled Brier’s scores (0.13 vs 0.22). When the outcome was changed to moderately severe asthma (>4 attacks plus use of asthma medication), there were improvements in AUC (0.74), sensitivity (0.97), specificity (0.22) and NPV (0.99), but some deterioration in PPV (0.13), R2 (0.15) and scaled Brier score (0.09).ConclusionWhile the PARC tool performs well in a population-based setting and has some clinical utility, in particular for ruling out the development of asthma, this study highlights the need for new prognostic prediction tools to be developed specifically for the clinical setting.FundingSNSF:320030_182628, SLA2019-03_641670

RATIONALE The lung clearance index (LCI) is increasingly being used in the clinical surveillance of patients with cystic fibrosis (CF). However, there are limited data on long-term variability and clinically relevant changes in LCI during routine clinical surveillance. OBJECTIVES To evaluate long-term variability of LCI and propose a threshold for a physiologically relevant change. METHODS Children with CF aged 4-18 years performed LCI measurements every three months as part of routine clinical surveillance during 2011-2020 in two centers. The variability of LCI during periods of clinical stability was assessed using mixed-effects models and was used to identify thresholds for clinically relevant changes. RESULTS Repeated LCI measurements of acceptable quality (N= 858) were available in 100 patients with CF, for 74 patients 399 visits at clinical stability were available. Variability of repeated LCI measurements over time expressed as coefficient of variation (CV%) was 7.4%. The upper limit of normal (ULN) for relative changes in LCI between visits was 19%. CONCLUSION We report the variability of LCI in children and adolescents with CF during routine clinical surveillance. According to our data, a change in LCI beyond 19% may be considered physiologically relevant. These findings will help guide clinical decisions according to LCI changes. This article is protected by copyright. All rights reserved.

Motor vehicle exhaust is a major contributor to air pollution, and exposure to benzene or other carcinogenic components may increase cancer risks. We aimed to investigate the association between traffic-related air pollution and risk of childhood cancer in a nationwide cohort study in Switzerland. We identified incident cases from the Swiss Childhood Cancer Registry diagnosed < 16 years of age between 1990 and 2015 and linked them probabilistically with the census-based Swiss National Cohort study. We developed land use regression models to estimate annual mean ambient levels of nitrogen dioxide (NO2) and benzene outside 1.4 million children's homes. We used risk-set sampling to facilitate the analysis of time-varying exposure and fitted conditional logistic regression models adjusting for neighborhood socio-economic position, level of urbanization, and background ionizing radiation. We included 2,960 cancer cases in the analyses. The adjusted hazard ratios (HR) and 95% confidence intervals for exposure to NO2 per 10 mug/m(3) were 1.00 (95%-CI 0.88-1.13) for acute lymphoblastic leukemia (ALL) and 1.31 (95%-CI 1.00-1.71) for acute myeloid leukemia (AML). Using exposure lagged by 1 to 5 years instead of current exposure attenuated the effect for AML. The adjusted HR for exposure to benzene per 1 mug/m(3) was 1.03 (95%-CI 0.86-1.23) for ALL and 1.29 (95%-CI 0.86-1.95) for AML. We also observed increased HRs for other diagnostic groups, notably non-Hodgkin lymphoma. Our study adds to the existing evidence that exposure to traffic-related air pollution is associated with an increased risk of childhood leukemia, particularly AML.

BACKGROUND Improvement of paediatric healthcare is hampered by inefficient processes for generating new evidence. Clinical research often requires extra encounters with patients, is costly, takes place in an artificial situation with a biased selection of patients, and entails long delays until new evidence is implemented into health care. Electronic health records (EHR) contain detailed information on real patients and cover the entirety of patients. However, the use of EHR for research is limited because they are not standardised between hospitals. This leads to disproportionate amounts of work for extracting data of interest and frequently data are incomplete and of poor quality. AIMS SwissPedData aims to lay the foundation for a paediatric learning health system in Switzerland by facilitating EHR-based research. In this project, we aimed to assess the way routine clinical data are currently recorded in large paediatric clinics in Switzerland and to develop a national EHR-based set of common data elements (CDEs) that covers all processes of routine paediatric care in hospitals. METHODS A taskforce of paediatricians from large Swiss children's hospitals reviewed the current status of routine data documentation in paediatric clinical care and the extent of digitalisation. We then used a modified Delphi method to reach a broad consensus on a national EHR-based set of CDEs. RESULTS All Swiss children's hospitals use EHR to document some or all aspects of care. One hundred and nineteen paediatricians, representing eight hospitals and all paediatric subspecialties, participated in an extended Delphi process to create SwissPedData. The group agreed on a national set of CDEs that comprises a main module with general paediatric data and sub-modules relevant to paediatric subspecialties. The data dictionary includes 336 CDEs: 76 in the main module on general paediatrics and between 11 and 59 CDEs per subspecialty module. Among these, 266 were classified as mandatory, 52 as recommended and 18 as optional. CONCLUSION SwissPedData is a set of CDEs for information to be collected in EHR of Swiss children's hospitals. It covers all care processes including clinical and paraclinical assessment, diagnosis, treatment, disposition and care site. All participating hospitals agreed to implement SwissPedData in their clinical routine and clinic information systems. This will pave the way for a national paediatric learning health system in Switzerland that enables fast and efficient answers to urgent clinical questions by facilitating high-quality nationwide retrospective and prospective observational studies and recruitment of patients for nested prospective studies and clinical trials.