scholarly journals Prediction Models for Early Childhood Obesity: Applicability and Existing Issues

2018 ◽  
Vol 90 (6) ◽  
pp. 358-367 ◽  
Author(s):  
Éadaoin M. Butler ◽  
José G.B. Derraik ◽  
Rachael W. Taylor ◽  
Wayne S. Cutfield
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Childhood Obesity ◽  
Statistical Models ◽  
Prediction Models ◽  
False Negative ◽  
Targeted Interventions ◽  
Wide Range ◽  
Early Childhood Obesity ◽  
The Impact

Statistical models have been developed for the prediction or diagnosis of a wide range of outcomes. However, to our knowledge, only 7 published studies have reported models to specifically predict overweight and/or obesity in early childhood. These models were developed using known risk factors and vary greatly in terms of their discrimination and predictive capacities. There are currently no established guidelines on what constitutes an acceptable level of risk (i.e., risk threshold) for childhood obesity prediction models, but these should be set following consideration of the consequences of false-positive and false-negative predictions, as well as any relevant clinical guidelines. To date, no studies have examined the impact of using early childhood obesity prediction models as intervention tools. While these are potentially valuable to inform targeted interventions, the heterogeneity of the existing models and the lack of consensus on adequate thresholds limit their usefulness in practice.

Childhood obesity: how long should we wait to predict weight?

2018 ◽  
Vol 31 (5) ◽  
pp. 497-501 ◽  
Author(s):  
Éadaoin M. Butler ◽  
José G.B. Derraik ◽  
Rachael W. Taylor ◽  
Wayne S. Cutfield
Keyword(s):  
Early Childhood ◽  
Childhood Obesity ◽  
Prediction Models ◽  
Population Based ◽  
Future Research ◽  
Clinical Prediction ◽  
Obesity Risk ◽  
Clinical Prediction Models ◽  
Predictive Capacity ◽  
Early Childhood Obesity

AbstractObesity is highly prevalent in children under the age of 5 years, although its identification in infants under 2 years remains difficult. Several clinical prediction models have been developed for obesity risk in early childhood, using a number of different predictors. The predictive capacity (sensitivity and specificity) of these models varies greatly, and there is no agreed risk threshold for the prediction of early childhood obesity. Of the existing models, only two have been practically utilized, but neither have been particularly successful. This commentary suggests how future research may successfully utilize existing early childhood obesity prediction models for intervention. We also consider the need for such models, and how targeted obesity intervention may be more effective than population-based intervention.

scholarly journals Acceptability of early childhood obesity prediction models to New Zealand families

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225212
Author(s):  
Éadaoin M. Butler ◽  
José G. B. Derraik ◽  
Marewa Glover ◽  
Susan M. B. Morton ◽  
El-Shadan Tautolo ◽  
...  
Keyword(s):  
Early Childhood ◽  
New Zealand ◽  
Childhood Obesity ◽  
Prediction Models ◽  
Early Childhood Obesity

scholarly journals Racial and Ethnic Disparities in Early Childhood Obesity

2018 ◽  
pp. 58-72
Author(s):  
Inyang A. Isong ◽  
Sowmya R. Rao ◽  
Marie-Abèle Bind ◽  
Mauricio Avendaño ◽  
Ichiro Kawachi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Weight Gain ◽  
Childhood Obesity ◽  
Ethnic Disparities ◽  
Z Score ◽  
Z Scores ◽  
Obesity Risk Factors ◽  
Early Childhood Obesity ◽  
Infant Weight Gain

OBJECTIVES The prevalence of childhood obesity is significantly higher among racial and/or ethnic minority children in the United States. It is unclear to what extent well-established obesity risk factors in infancy and preschool explain these disparities. Our objective was to decompose racial and/or ethnic disparities in children’s weight status according to contributing socioeconomic and behavioral risk factors. METHODS We used nationally representative data from ~10 700 children in the Early Childhood Longitudinal Study Birth Cohort who were followed from age 9 months through kindergarten entry. We assessed the contribution of socioeconomic factors and maternal, infancy, and early childhood obesity risk factors to racial and/or ethnic disparities in children’s BMI z scores by using Blinder-Oaxaca decomposition analyses. RESULTS The prevalence of risk factors varied significantly by race and/or ethnicity. African American children had the highest prevalence of risk factors, whereas Asian children had the lowest prevalence. The major contributor to the BMI z score gap was the rate of infant weight gain during the first 9 months of life, which was a strong predictor of BMI z score at kindergarten entry. The rate of infant weight gain accounted for between 14.9% and 70.5% of explained disparities between white children and their racial and/or ethnic minority peers. Gaps in socioeconomic status were another important contributor that explained disparities, especially those between white and Hispanic children. Early childhood risk factors, such as fruit and vegetable consumption and television viewing, played less important roles in explaining racial and/or ethnic differences in children’s BMI z scores. CONCLUSIONS Differences in rapid infant weight gain contribute substantially to racial and/or ethnic disparities in obesity during early childhood. Interventions implemented early in life to target this risk factor could help curb widening racial and/or ethnic disparities in early childhood obesity.

scholarly journals A prediction model for childhood obesity in New Zealand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Éadaoin M. Butler ◽  
◽  
Avinesh Pillai ◽  
Susan M. B. Morton ◽  
Blake M. Seers ◽  
...  
Keyword(s):  
Early Childhood ◽  
New Zealand ◽  
Childhood Obesity ◽  
Prediction Model ◽  
Pacific Islands ◽  
Prediction Models ◽  
External Validation ◽  
False Positives ◽  
Predictive Values ◽  
Early Childhood Obesity

AbstractSeveral early childhood obesity prediction models have been developed, but none for New Zealand's diverse population. We aimed to develop and validate a model for predicting obesity in 4–5-year-old New Zealand children, using parental and infant data from the Growing Up in New Zealand (GUiNZ) cohort. Obesity was defined as body mass index (BMI) for age and sex ≥ 95th percentile. Data on GUiNZ children were used for derivation (n = 1731) and internal validation (n = 713). External validation was performed using data from the Prevention of Overweight in Infancy Study (POI, n = 383) and Pacific Islands Families Study (PIF, n = 135) cohorts. The final model included: birth weight, maternal smoking during pregnancy, maternal pre-pregnancy BMI, paternal BMI, and infant weight gain. Discrimination accuracy was adequate [AUROC = 0.74 (0.71–0.77)], remained so when validated internally [AUROC = 0.73 (0.68–0.78)] and externally on PIF [AUROC = 0.74 [0.66–0.82)] and POI [AUROC = 0.80 (0.71–0.90)]. Positive predictive values were variable but low across the risk threshold range (GUiNZ derivation 19–54%; GUiNZ validation 19–48%; and POI 8–24%), although more consistent in the PIF cohort (52–61%), all indicating high rates of false positives. Although this early childhood obesity prediction model could inform early obesity prevention, high rates of false positives might create unwarranted anxiety for families.

Stress, anxiety, depression and burnout in frontline health care workers during COVID-19 pandemic: a brief systematic review and new data from Russia

2021 ◽  
Author(s):  
Ekaterina Mosolova ◽  
Dmitry Sosin ◽  
Sergey Mosolov
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Female Gender ◽  
Assessment Tools ◽  
Care Workers ◽  
Younger Age ◽  
Wide Range ◽  
Associated Risk Factors ◽  
Anxiety Depression ◽  
The Impact

During the COVID-19 pandemic, healthcare workers (HCWs) have been subject to increased workload while also exposed to many psychosocial stressors. In a systematic review we analyze the impact that the pandemic has had on HCWs mental state and associated risk factors. Most studies reported high levels of depression and anxiety among HCWs worldwide, however, due to a wide range of assessment tools, cut-off scores, and number of frontline participants in the studies, results were difficult to compare. Our study is based on two online surveys of 2195 HCWs from different regions of Russia during spring and autumn epidemic outbreaks revealed the rates of anxiety, stress, depression, emotional exhaustion and depersonalization and perceived stress as 32.3%, 31.1%, 45.5%, 74.2%, 37.7% ,67.8%, respectively. Moreover, 2.4% of HCWs reported suicidal thoughts. The most common risk factors include: female gender, nurse as an occupation, younger age, working for over 6 months, chronic diseases, smoking, high working demands, lack of personal protective equipment, low salary, lack of social support, isolation from families, the fear of relatives getting infected. These results demonstrate the need for urgent supportive programs for HCWs fighting COVID-19 that fall into higher risk factors groups.

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