Introduction: The timely provision of critical care to hospitalised patients at risk for cardiopulmonary arrest is contingent upon identification and referral by frontline providers. Current approaches require improvement. In a single-centre study, we developed the Bedside Paediatric Early Warning System (Bedside PEWS) score to identify patients at risk. The objective of this study was to validate the Bedside PEWS score in a large patient population at multiple hospitals. Methods: We performed an international, multicentre, case-control study of children admitted to hospital inpatient units with no limitations on care. Case patients had experienced a clinical deterioration event involving either an immediate call to a resuscitation team or urgent admission to a paediatric intensive care unit. Control patients had no events. The scores ranged from 0 to 26 and were assessed in the 24 hours prior to the clinical deterioration event. Score performance was assessed using the area under the receiver operating characteristic (AUCROC) curve by comparison with the retrospective rating of nurses and the temporal progression of scores in case patients. Results: A total of 2,074 patients were evaluated at 4 participating hospitals. The median (interquartile range) maximum Bedside PEWS scores for the 12 hours ending 1 hour before the clinical deterioration event were 8 (5 to 12) in case patients and 2 (1 to 4) in control patients (P < 0.0001). The AUCROC curve (95% confidence interval) was 0.87 (0.85 to 0.89). In case patients, mean scores were 5.3 at 20 to 24 hours and 8.4 at 0 to 4 hours before the event (P < 0.0001). The AUCROC curve (95% CI) of the retrospective nurse ratings was 0.83 (0.81 to 0.86). This was significantly lower than that of the Bedside PEWS score (P < 0.0001). Conclusions: The Bedside PEWS score identified children at risk for cardiopulmonary arrest. Scores were elevated and continued to increase in the 24 hours before the clinical deterioration event. Prospective clinical evaluation is needed to determine whether this score will improve the quality of care and patient outcomes.

The Applied Research Group for Kids (TARGet Kids!) is an ongoing open longitudinal cohort study enrolling healthy children (from birth to 5 years of age) and following them into adolescence. The aim of the TARGet Kids! cohort is to link early life exposures to health problems including obesity, micronutrient deficiencies and developmental problems. The overarching goal is to improve the health of Canadians by optimizing growth and developmental trajectories through preventive interventions in early childhood. TARGet Kids!, the only child health research network embedded in primary care practices in Canada, leverages the unique relationship between children and families and their trusted primary care practitioners, with whom they have at least seven health supervision visits in the first 5 years of life. Children are enrolled during regularly scheduled well-child visits. To date, we have enrolled 5062 children. In addition to demographic information, we collect physical measurements (e.g. height, weight), lifestyle factors (nutrition, screen time and physical activity), child behaviour and developmental screening and a blood sample (providing measures of cardiometabolic, iron and vitamin D status, and trace metals). All data are collected at each well-child visit: twice a year until age 2 and every year until age 10. Information can be found at: http://www.targetkids.ca/contact-us/.

It is unknown if young children’s parent-reported physical activity and sedentary time are correlated with direct measures. The study objectives were to compare parent-reported physical and sedentary activity versus directly measured accelerometer data in early childhood. From 2013 to 2014, 117 healthy children less than 6 years of age were recruited to wear Actical accelerometers for 7 days. Accelerometer data and questionnaires were available on 87 children (74 %). Average daily physical activity was defined as the sum of activity ≥100 counts per minute, and sedentary time as the sum of activity <100 counts per minute during waking hours. Parents reported daily physical activity (unstructured free play in and out of school, and organized activities) and selected sedentary behaviors (screen time, stroller time, time in motor vehicle). Spearman correlation coefficients and Bland–Altman plots were used to assess the validity of parent-reported measures compared to accelerometer data. Total physical activity was significantly greater when measured by accelerometer than parent-report; the median difference was 131 min/day (p < 0.001). Parent-reported child physical activity was weak to moderately correlated with directly measured total physical activity (r = 0.39, 95 % CI 0.19, 0.56). The correlations between types of physical activity (unstructured free play in and outside of school/daycare, and organized structured activity) and accelerometer were r = 0.30 (95 % CI 0.09, 0.49); r = 0.42 (95 % CI 0.23, 0.58); r = 0.26 (95 % CI 0.05, 0.46), respectively. There was no correlation between parent-reported and accelerometer-measured total sedentary time in children (r = 0.10, 95 % CI −0.12, 0.33). When the results were stratified by age group (<18, 18–47, and 48–70 months of age) no statistically significant correlations were observed and some inverse associations were observed. The correlation between parent-report of young children’s physical activity and accelerometer-measured activity was weak to moderate depending on type of activity and age group. Parent-report of children’s sedentary time was not correlated with accelerometer-measured sedentary time. Additional validation studies are needed to determine if parent-reported measures of physical activity and sedentary time are valid among children less than 6 years of age and across these young age groups.

Objective: To evaluate the misclassification resulting from the use of body mass index (BMI) cut-points defined by rounded percentiles instead of Z-scores in early childhood. Using data from theTARGet Kidsprimary care network we conducted a cross-sectional study among 5836 children < 6 years of age. The World Health Organization growth standards were used to calculate BMI-for-age Z-scores. BMI Z-score cut-points of < − 2.0, > 1.0, > 2.0, > 3.0 are recommended to define wasted, at risk of overweight, overweight and obese. However, rounded percentiles of the 3rd, 85th, 97th, and 99.9th are commonly used. Misclassification was calculated comparing the frequency distributions for BMI categories defined by rounded percentiles and Z-score cut-points.Results: Using rounded percentiles, the proportion of children who were wasted, at risk of overweight, overweight, and obese was 4.2, 12.5, 4.3 and 0.8%, whereas the distribution using Z-scores was: 3.6, 13.8, 3.4 and 1.0%, respectively. Overall, 117 (2%) children were misclassified when using percentiles instead of Z-scores; however, 13% (33/245) of children who were wasted and 14% (8/57) of children who were obese were misclassified. Misclassification of child growth results from the use of cut-points defined by rounded percentiles instead of Z-scores and limits comparability between studies.Trial registrationClinicaltrials.gov NCT01869530 June 5, 2013

Objective. To evaluate the influence of average family income in a geographic area on the effectiveness of helmet legislation on observed helmet use by children (5–14 years). Methods. The study was conducted in East York, a health district of Metropolitan Toronto, in collaboration with the East York Health Unit. In 1996, the total population was 107 822, 11 340 of which were children 5 to 14 years. Census data were used to group the 21 census tracts in East York into 7 geographically distinct areas. The boundaries of these areas are natural barriers to travel, such as expressways, ravines, railway tracks, and hydroelectric power lines. The areas were also ranked according to average family income (based on Statistics Canada data). For analytical purposes, areas were defined as low-, mid-, and high-income areas. Census data profiles of the areas have been previously described. For each consecutive year from 1990 to 1997 inclusive, direct observations of children riding bicycles in East York during the months of April through October were made. In 1995, observations were completed before the introduction of the law on October 1, 1995. Only children who were between 5 and 14 years of age and riding a 2-wheeled bicycle were included in the study. In total, 111 sites across all 7 areas were selected for observation. Observational sites included school yards of all elementary and middle schools (kindergarten to grade 8) and all parks in East York. In addition, 5 major intersections and 5 residential streets from each area were randomly selected. Observers were trained and used a standardized data collection form. A pilot study showed that the data collected by observers were reliable and valid. Observers remained at each site for 1 hour and collected data on helmet use and sex. Ethical approval for the study was obtained from the Hospital for Sick Children Research Ethics Board, the East York Board of Education, and the Metropolitan Separate School Board. The proportion of children who were wearing a bicycle helmet was estimated by year (1990–1997, inclusive), sex (male, female), location (school, park, major intersection, residential street), and income area (low, mid, high). For estimating the effect of legislation on helmet use, data from the year immediately after legislation (1996) were compared with data from the year preceding legislation (1995). The relative risk (RR) of helmet use (after vs before legislation) was calculated along with a 95% confidence interval (CI). Logistic regression analysis was used to adjust for potential confounding variables (sex and location). Results. During the 8-year study period, 9768 observations were made (range: 914-1879 observations per year). The proportion of child cyclists who wore a bicycle helmet increased steadily during the first 4 years of the study period, from 4% in 1990 (34 of 914), to 16% in 1991 (303 of 1879), to 25% in 1992 (383 of 1563), and to 45% in 1993 (438 of 984). During 1994 (460 of 1083) and 1995 (568 of 1227), helmet use remained relatively stable at approximately 44%. Helmet use rose markedly in 1996 (the first year after helmet legislation was introduced) to 68% (818 of 1202) and remained stable at 66% (609 of 916) in 1997. Throughout the study period, girls were consistently more likely to wear helmets than were boys. In total, 47% (1420 of 3047) of girls wore helmets, compared with 33% (2193 of 6721) of boys (RR: 1.43; 95% CI: 1.36–1.50). In addition, children who were riding to school were more likely to use helmets, compared with children who were riding on residential streets, major intersections, and parks. Overall, 48% (1497 of 3129) of children who were riding to school wore bicycle helmets, compared with 32% (2116 of 6639) of children who were riding at other locations (RR: 1.50; 95% CI: 1.43–1.58). Children in the high-income areas were consistently more likely to wear helmets, compared with children in the mid- and low-income areas. Helmet legislation was associated with a significant increase in helmet use by children in East York. In 1995, 46% (568 of 1227) of children wore bicycle helmets, compared with 68% (818 of 1202) of children in 1996 (RR: 1.47; 95% CI: 1.37–1.58). The effect of legislation, however, varied by income area. In low-income areas, helmet use increased by 28% after legislation, from 33% (213 of 646) in 1995 to 61% (442 of 721) in 1996 (RR: 1.86; 95% CI: 1.64–2.11). In mid-income areas, helmet use increased by 29% after legislation, from 50% (150 of 300) in 1995 to 79% (185 of 234) in 1996 (RR: 1.58; 95% CI: 1.39–1.80). In high-income areas, helmet use increased by only 4%, from 73% (205 of 281) in 1995 to 77% (191 of 247) in 1996 (RR: 1.06; 95% CI: 0.96–1.17). This finding of a significant increase in helmet use after legislation in low- and mid-income areas but not in high-income areas remained even after logistic regression analysis adjusted for sex and location. Conclusions. This study showed that bicycle helmet use by children increased significantly after helmet legislation. In this urban area with socioeconomic diversity and in the context of prelegislation promotion and educational activities, the legislative effect was most powerful among children who resided in low-income areas.

Policy has been developed to promote the conduct of high-quality pediatric randomized controlled trials (RCTs). Whether these strategies have influenced publication trends in high-impact journals is unknown. We aim to evaluate characteristics, citation patterns, and publication trends of pediatric RCTs published in general medical journals (GMJs) compared with adult RCTs over a 13-year period. Studies were identified using Medline, and impact metrics were collected from Web of Science and Scopus. All RCTs published from 2005–2018 in 7 GMJs with the highest impact factors were identified for analysis. A random sample of matched pediatric and adult RCTs were assessed for publication characteristics, academic and non-academic citation. Citations were counted from publication until June 2019. Among 4146 RCTs, 2794 (67.3%) enrolled adults, 591 (14.2%) enrolled children, and 761 RCTs (18.3%) enrolled adult and pediatric patients. Adult RCTs published in GMJs grew by 5.1 publications per year (95% CI: 3.3–6.9), while the number of pediatric RCTs did not show significant change (−0.4 RCTs/year, 95% CI: −1.4–0.6). Adult RCTs were cited more than pediatric RCTs (median(IQR): 29.9 (68.5–462.8) citations/year vs. 13.2 (6.8–24.9) citations/year; p < 0.001); however, social media attention was similar (median(IQR) Altmetric Attention Score: 37 (13.75–133.8) vs. 26 (6.2–107.5); p = 0.25). Despite policies which may facilitate conduct of pediatric RCTs, the publishing gap in high-impact GMJs is widening.