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

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.

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 ◽  
...  

2015 ◽  
Vol 39 (7) ◽  
pp. 1041-1048 ◽  
Author(s):  
E Isganaitis ◽  
S L Rifas-Shiman ◽  
E Oken ◽  
J M Dreyfuss ◽  
W Gall ◽  
...  

2018 ◽  
Vol 90 (6) ◽  
pp. 358-367 ◽  
Author(s):  
Éadaoin M. Butler ◽  
José G.B. Derraik ◽  
Rachael W. Taylor ◽  
Wayne S. Cutfield

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.


Author(s):  
Piotr Socha ◽  
Christian Hellmuth ◽  
Dariusz Gruszfeld ◽  
Hans Demmelmair ◽  
Peter Rzehak ◽  
...  

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Kapp ◽  
A Frech ◽  
B Hall ◽  
A Kemner

Abstract Background Low strength of maternal-infant relationship (MIR) is consistently associated with early childhood obesity risk. Because obesity often persists once it develops, primary prevention is needed early. Home visiting programs support families with social determinants of health (SDH) and adverse childhood experiences (ACEs); SDH and ACEs contribute to health inequities. Addressing SDH and ACEs may facilitate improvements in MIR and ultimately mitigate early childhood obesity risk. Limited to no research has examined the association between ACEs, SDH, and MIR. In the context of a national, evidence-based home visiting program, we asked: are SDH and ACEs associated with low MIR? Methods This sample includes 6,972 children ages 0–<24 months enrolled in the Parents as Teachers home visiting program across the United States from sites using the Life Skills Progression (LSP) instrument through February 2020. Low MIR is dichotomized from a 1-5 scale, with low scores reflecting low nurturing, bonding, and responsiveness. We used the literature, theory, and a stepwise logistic regression model-building process to identify a parsimonious model for MIR. Results Preliminary results reflect 34.2% Hispanic or Latino, 22.7% non-Hispanic Black, 35.3% non-Hispanic Other race; 83.9% low income; 36.9% low education; and 13.4% mothers scoring low for MIR. Notable findings from modeling include: physical ACEs, captured here as child abuse or neglect (OR: 5.01, 95% CI: 4.10-6.11); mental illness ACEs, captured here as a mother/parent with mental illness (OR: 1.31, 95% CI: 1.05-1.63), or the mother/parent treated violently (OR: 1.95, 95% CI: 1.56-2.40). Protective associations include mothers' support of child development and self-esteem scores. Conclusions Understanding the complex interplay of SDH, ACEs, and MIR is critical for developing interventions that address “upstream” family characteristics in order to mitigate early childhood obesity risk. ACEs play a predominant role. Key messages This is the first known study to concurrently examine maternal-infant relationship, social determinants of health, and adverse childhood experiences. Home visiting programs may be critical partners in addressing these needs given their reach.


2020 ◽  
Vol 120 (3) ◽  
pp. 371-385 ◽  
Author(s):  
M. Pia Chaparro ◽  
May C. Wang ◽  
Christopher E. Anderson ◽  
Catherine M. Crespi ◽  
Shannon E. Whaley

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Éadaoin M. Butler ◽  
◽  
Avinesh Pillai ◽  
Susan M. B. Morton ◽  
Blake M. Seers ◽  
...  

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.


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