Canadian children’s and youth’s adherence to the 24-h movement guidelines during the COVID-19 pandemic: A decision tree analysis

Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-2019(COVID-19)outbreak.Methods:Data for this study were from a nationally representative sample of 1472 Canadian parents(Meanage=45.12,SD=7.55)of children(511 years old)or youth(1217 years old).Data were collected in April 2020 via an online survey.Survey items assessed demographic,behavioral,social,micro-environmental,and macro-environmental characteristics.Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation(physical activity(PA),screen time,and sleep)were generated.Results:Results revealed specific combinations of adherence and non-adherence characteristics.Characteristics associated with adherence to the recommendation(s)included high parental perceived capability to restrict screen time,annual household income ofCAD 100,000,increases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,being a boy,having parents younger than 43 years old,and small increases in children’s and youth’s sleep duration since the COVID-19 outbreak began.Characteristics associated with non-adherence to the recommendation(s)included low parental perceived capability to restrict screen time,youth aged 1217 years,decreases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,primary residences located in all provinces except Quebec,low parental perceived capability to support children’s and youth’s sleep and PA,and annual household income ofCAD 99,999.Conclusion:Our results show that specific characteristics interact to contribute to(non)adherence to the movement behavior recommendations.Results highlight the importance of targeting parents’perceived capability for the promotion of children’s and youth’s movement behaviors during challenging times of the COVID-19 pandemic,paying particular attention to enhancing parental perceived capability to restrict screen time.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

Background： In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods： As useful methods to build a predictive model, Chi-squared automatic interaction detection （CHAID） decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1,2013 to December 31, 2014. Results： The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions： The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Three-Dimensional Computerized Tomography-Assisted Identification of Necrotic Volume, Distribution, Shape and Prognosis of Collapse in ONFH

Objective: We constructed 3D-model of ONFH in computer according to three-dimensional computerized tomography (3D-CT) data. We determined the location and volume of necrosis to investigate its clinical efficacy. Method: Totally 92 hips (59 cases) with ONFH (44 males, 15 females) were included, with mean age of 37.5 years (range from 26 to 58). Totally 20 cases (35 hips) were induced by corticosteroid (CTSs), 31 (49 hips) induced by alcohol, 4 (4 hips) induced by trauma and 4 (4 hips) idiopathic. All the hips were categorized into stage ARCO II. Finally diagnosed by MRI, all hips were scanned by CT to acquire data in DICOM format. The images were imported into software to extract 3D-shape of femoral heads, necrotic foci, their volumes and distribution in each quadrant. Deviation of volumes between digital image and biopsy specimen was analyzed by SAS9.1 package. Correlativity between collapse and volume of necrosis under specific pathogeneses was also analyzed. Among the cases necessitating total hip arthroplasty (THA) due to advancing to ARCO III, we randomly selected 8 of them to perform 3D-CT scanning thrice prior to surgical operation. Total femoral heads harvested were torn asunder. Cubic capacity of femoral heads and necrotic foci were hereby measured and compared with those acquired from digital models. Result: Through the digital model, necrotic foci were found mainly locating within the super lateral portion of femoral head, coinciding with those observed in biopsy specimen. Average volumetric ratio of digitally acquired necrosis focus/femoral head in 58 collapsed hips was 36.8%. The ratio of the 34 hips without collapse was 17.3%. In collapsed femoral heads, the distribution of necrosis focus was 23.4% in quadrant 1 (q1), 23.6% in q2, 12.1% in q3, 14.4% in q4, 9.0% in q5, 11.8% in q6, 1.6% in q7 and 3.9% in q8. In femoral heads without collapse, the distribution was 34.2% in q1, 29.6% in q2, 11.8% in q3, 11.3% in q4, 6.0% in q5, 6.0% in q6, 0.5% in q7 and 0.4% in q8. As for the average cubic capacities of femoral heads and necrotic foci, those acquired from the digital model and biopsy specimen had no significant difference in matched-pairs test (t = -1.49, P = 0.179 for femoral heads and t = -1.52, P = 0.172 for necrotic foci). There was significant difference (F = 2.720, P = 0.035 P was respectively 0.0001 and 0.0005). Decision tree model showed that 94.6% (53/56) hips would progress into collapse if the volumetric ratio of necrotic tissue was over 23.48%. Otherwise, if distribution in q2 was over 45.13%, 83.3% (5/6) hips would progress into collapse. No collapse (0/30) would occur if the distribution of necrotic tissue in q2 was under 45.13%. Conclusion: Digital 3D-model reconstructed from CT scanning can precisely incarnate spatial orientation of necrotic foci in femoral head. Multinomial logistic regression and decision-making tree shows that volumetric ratio of necrotic tissues plays an important role in anticipating collapse of femoral head.

Evidence-Based Use of Antibiotics in Veal Calves with Diarrhea

Diarrhea is the leading cause of mortality in beef and dairy calves during the first week of life and results in substantial financial loss [1]. Diarrhea is a multifactorial disease and can be infectious or non-infectious. However, in the majority of calves, infectious organisms, especially Cryptosporidium parvum, rotavirus, coronavirus, and E. coli, are the primary cause [2]. The aim of this study was to generate a decision tree, based on prevalence, diagnostic testing and treatment and to estimate associated costs or risk. For each of the four main pathogens, two principal approaches are outlined and compared. The first approach relies on a detailed diagnostic workup and allows for specific etiological treatment. The second approach relies on the trial-and-error method, which involves the use of a first-choice antibiotic, followed by a second- and third-choice antibiotic if the previous ones failed to resolve the disease. In Switzerland, the prevalence of diarrheic calves infected with E. coli is approximately 1% suggesting that the use of antimicrobials for the treatment of scouring calves, in the absence of a diagnostic workup, is not always justified. However, for all four major pathogens, the trial-and-error method affords cheaper treatment compared with treatment based on an etiological diagnosis. This creates a quandary in view of the current worldwide efforts to reduce the use of antibiotics in animal agriculture.

Evidence-Based Use of Antibiotics in Meat Calves

Abuse of antibiotics is an increasing commonly feature in the media. Widespread preventive use of antibiotics without diagnostics in meat calf husbandry is a major public health concern. In this study, we compare a “trial and error” method, comprising of a first choice antibiotic, followed by a second and third choice (as recommended by the WHO), with a method which utilizes an antibiotic resistance test to first identify the best antibiotic out of first, second or third choice alternatives using decision tree analysis. Data were collected from farms with a known calf herd problem along with antibiograms from those herds. Samples were analysed for resistance to antibiotics against calf pneumonia on a herd level, rather than for resistance against specific antibiotics. Resistance tests were performed on batch samples composed of at least three diseased animals. A deep nasal swap was taken. In nasal swaps only ++ or +++ growth in all 3 samples were used for diagnosis. Other growth of bacteria was considered as contamination. A comparison of resistance rates across a range of antibiotics between farms with known calf pneumonia and calf diarrhoea issues was performed. The decision tree analysis presented provides strong support in favour of an evidence-based approach to antimicrobial treatment by using an antimicrobial resistance test, providing an advantage of 58% per meat calf against the “trial and error” method, giving a financial gain of some CHF 320.09 under Swiss economic circumstances.

Diffusion-weighted magnetic resonance imaging with short T1 inversion recovery-echo planar imaging combined with dual-head coincidence single photon emission computed tomography for diagnosing solitary pulmonary nodule

Positron emission tomography （PET） using 18F-fluorodeoxyglucose （FDG） has not been widely used in China for expensive cost （$1200）. Dual-head coincidence single photon emission computed tomography （DHC-SPECT） can depict many of the lesions depicted with a PET scanner in the lungs, which is used in place of PET-CT for discriminating malignant from benign pulmonary nodules in many studies. However, DHC-SPECT has inevitable false-negative results because the sensitivity for small lesions less than 2.0 cm is limited, and has high false-positive rate for active inflammatory nodules. Furthermore,DHC-SPECT also has a considerably higher cost （$300 in China） than other imaging examination.

Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables:A recursive partitioning approach

Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption.However,most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability.Those methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous relationship.Therefore,to identify the energy consumption of the heterogeneous relationships in actual buildings,this study applies the MOdel-Based recursive partitioning(MOB)algorithm to the 2012 CBECS survey data,which would offer representative information about actual commercial building characteristics and energy consumption.With resultant tree-structured subgroups,the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy consumptions.The results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in U.S.office buildings.