Lynch, Brian A

The prevalence of childhood obesity has increased over the past 3 decades. This study was designed to understand how childhood body mass index (BMI) influences later risk of obesity. We calculated BMIs for children residing in Olmsted County, Minnesota, between January 1, 2005 and December 31, 2012 using medical records data. We defined homogenous BMI trajectory clusters using a nonparametric hill-climbing algorithm. Overall, 16,538 (47%) children had >3 weight assessments at least 1 year apart and were included in the analyses. Within the 8-year follow-up period, children who were younger than 2 years and overweight had a 3- fold increase of obesity (adjusted hazard ratio [HR] = 3.24; 95% confidence interval [CI] = 2.69-3.89) and those aged 5 years and overweight had a 10-fold increased risk of obesity (adjusted HR = 9.97, 95% CI = 8.55-11.62). Three distinct BMI trajectories could be distinguished prior to 5 years of age. The risk of developing obesity in those who are overweight increased dramatically with increasing age. Interventions to prevent obesity need to occur prior to school age to prevent children from entering unhealthy BMI trajectories.

AbstractBackgroundChildhood obesity is underdiagnosed in primary care practices. Our study aimed to compare rates of documentation of diagnosis of obesity and counselling for nutrition and physical activity at an academic primary care practice prior to and following implementation of a body mass index (BMI)‐based electronic point of care clinical reminder tool.MethodsWe performed a retrospective record review of children aged 2–18 years undergoing well child visits during any of three calendar years (2009, 2011 and 2013). The electronic clinical reminder tool was implemented in September 2010. Records of children with BMI > 95th percentile were electronically searched for terms of documentation of diagnosis of obesity and nutrition and physical activity counselling. Multivariable logistic regression models were used to adjust for potential patient and provider confounders.ResultsAmong those with BMI ≥ 95th percentile, there was a statistically significant increase in documentation of diagnosis of obesity (45.5% in 2009 vs. 52.4% in 2011; P < 0.001; adjusted odds ratio 1.36; 95% CI 1.13–1.65) and in counselling for physical activity and nutrition (66.8% in 2009 vs. 75.2% in 2011; P < 0.001; adjusted odds ratio 1.33; 95% CI 1.06–1.66) following implementation of the tool. Compared with 2011, there was no statistically significant increase in documentation of obesity (50.4%) or nutrition and physical activity counselling (77.9%) in 2013.ConclusionsThe implementation of a point of care clinical reminder tool was associated with improvement in documentation of diagnosis of obesity and counselling for nutrition and physical activity. Further studies are needed to determine the impact of these automated tools on weight outcomes in children.

Background: Pediatric obesity is a significant public health problem with a prevalence of 16.9% among US children. School-based obesity interventions show promise for reducing adiposity in elementary age children. This pilot study evaluated the impact of the Let’s Go! 5-2-1-0 pediatric obesity intervention program in an elementary school setting. Methods: This was a cluster randomized controlled field trial comprising 8 classrooms of second- and third-grade children. The impact of implementation of a standardized 5-2-1-0 curriculum was evaluated in the classrooms by looking at health behavior (self-reported fruit and vegetable and sugar-containing beverage intake, and screen time), physical activity (steps measured by pedometer), and body mass index (BMI). Half of the classrooms were given 5-2-1-0 teaching over a 4-month period. Results: There was no statistical difference in improvement of healthy habits, BMI, or physical activity in the intervention group compared with the control group. Conclusions: The 5-2-1-0 intervention used in this study was feasible. There was no significant change in healthy habits, likely due to the small number of participants in the study. The intervention and control sites were in different classrooms and there could be factors such as teacher enthusiasm, socioeconomic factors, and individual traits affecting habits. Future studies could use pre- and post-intervention quizzes to assess healthy habits knowledge retention, involving the environments the child participates in outside of school, using research-grade pedometers or accelerometers for measuring activity data collection, and recruiting larger samples to ensure adequate statistical power.

Objective: The purpose of our study was to compare the clinical utility of administering 2 recommended developmental screening instruments, the Infant Developmental Inventory (IDI) and the Ages and Stages Questionnaire (ASQ), at 9-month well-child visits in paper format. Methods: Outcomes of the 2 screens, including correct completion and interpretation by clinician, time of visit, and screen outcome were compared. Results: Out of 33 children administered the ASQ and with documented scores, 12 (36.4%) did not receive passing scores, while 5 (12.2%) of the 41 children administered the IDI did not receive passing scores ( P = .014). Out of 41 IDI screens, 12 (29.3%) were completed incorrectly, while there were no ASQ screens completed incorrectly ( P < .001) by caregivers. Conclusion: In our pilot study, the ASQ is more often completed correctly by caregivers and identifies more children at risk for delay as compared with the IDI. Additional larger scale studies are needed to evaluate the usefulness of developmental screening tools when used within primary care practice.