Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system.

BACKGROUND: Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. METHODS: Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. RESULTS: The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. CONCLUSIONS: Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.

Mental Health and Emotional Disorders During the COVID-19 Pandemics: Prevalence and Extent in PICU Staff.

OBJECTIVES: To assess the prevalence of burnout, anxiety and depression symptoms, and posttraumatic stress disorder (PTSD) in PICU workers in Brazil during the first peak of the COVID-19 pandemic. To compare the results of subgroups stratified by age, gender, professional category, health system, and previous mental health disorders. DESIGN: Multicenter, cross-sectional study using an electronic survey. SETTING: Twenty-nine public and private Brazilian PICUs. SUBJECTS: Multidisciplinary PICU workers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Self-reported questionnaires were used to measure burnout (Maslach Burnout Inventory), anxiety and depression (Hospital Anxiety and Depression Scale), and PTSD (Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5]) in 1,084 respondents. Subjects were mainly young (37.1 ± 8.4 yr old) and females (85%), with a median workload of 50 hours per week. The prevalence of anxiety and depression was 33% and 19%, respectively, whereas PTSD was 13%. The overall median burnout scores were high in the emotional exhaustion and personal accomplishment dimensions (16 [interquartile range (IQR), 8-24] and 40 [IQR, 33-44], respectively) whereas low in the depersonalization one (2 [IQR, 0-5]), suggesting a profile of overextended professionals, with a burnout prevalence of 24%. Professionals reporting prior mental health disorders had higher prevalence of burnout (30% vs 22%; p = 0.02), anxiety (51% vs 29%; p < 0.001), and depression symptoms (32.5% vs 15%; p < 0.001), with superior PCL-5 scores for PTSD ( p < 0.001). Public hospital workers presented more burnout (29% vs 18.6%, p < 0.001) and more PTSD levels (14.8% vs 10%, p = 0.03). Younger professionals were also more burned out ( p < 0.05 in all three dimensions). CONCLUSIONS: The prevalence of mental health disorders in Brazilian PICU workers during the first 2020 peak of COVID-19 was as high as those described in adult ICU workers. Some subgroups, particularly those reporting previous mental disorders and younger professionals, should receive special attention to prevent future crises.

Multicenter validation of PIM3 and PIM2 in Brazilian pediatric intensive care units.

Objective: To validate the PIM3 score in Brazilian PICUs and compare its performance with the PIM2. Methods: Observational, retrospective, multicenter study, including patients younger than 16 years old admitted consecutively from October 2013 to September 2019. We assessed the Standardized Mortality Ratio (SMR), the discrimination capability (using the area under the receiver operating characteristic curve - AUROC), and the calibration. To assess the calibration, we used the calibration belt, which is a curve that represents the correlation of predicted and observed values and their 95% Confidence Interval (CI) through all the risk ranges. We also analyzed the performance of both scores in three periods: 2013-2015, 2015-2017, and 2017-2019. Results: 41,541 patients from 22 PICUs were included. Most patients aged less than 24 months (58.4%) and were admitted for medical conditions (88.6%) (respiratory conditions = 53.8%). Invasive mechanical ventilation was used in 5.8%. The median PICU length of stay was three days (IQR, 2-5), and the observed mortality was 1.8% (763 deaths). The predicted mortality by PIM3 was 1.8% (SMR 1.00; 95% CI 0.94-1.08) and by PIM2 was 2.1% (SMR 0.90; 95% CI 0.83-0.96). Both scores had good discrimination (PIM3 AUROC = 0.88 and PIM2 AUROC = 0.89). In calibration analysis, both scores overestimated mortality in the 0%-3% risk range, PIM3 tended to underestimate mortality in medium-risk patients (9%-46% risk range), and PIM2 also overestimated mortality in high-risk patients (70%-100% mortality risk). Conclusions: Both scores had a good discrimination ability but poor calibration in different ranges, which deteriorated over time in the population studied.