Context, importance, and process for creating a body mass index surveillance system to monitor childhood obesity within the New York City public school setting

The Association Between a Neighborhood Adverse Childhood Experiences Index and Body Mass Index Among New York City Youth

Background: The role of neighborhood factors in the association between adverse childhood experiences (ACEs) and body mass index (BMI) has not been widely studied. A neighborhood ACEs index (NAI) captures neighborhood environment factors associated with ACE exposure. This study examined associations between BMI and an NAI among New York City (NYC) youth. An exploratory objective examined the NAI geographic distribution across NYC neighborhoods. Methods: Data for students attending NYC public general education schools in kindergarten-12th grade from 2006-2017 (n = 1,753,867) were linked to 25 geospatial datasets capturing neighborhood characteristics for every census tract in NYC. Multivariable hierarchical linear regression tested associations between BMI and the NAI; analyses also were conducted by young (<8 years), school age (8-12 years), and adolescent (>12 years) subgroups. In addition, NAI was mapped by census tract, and local Moran's I identified clusters of high and low NAI neighborhoods. Results: Higher BMI was associated with higher NAI across all sex and age groups, with largest magnitude of associations for girls (medium NAI vs. low NAI: unstandardized β = 0.112 (SE 0.008), standardized β [effect size]=0.097, p < 0.001; high NAI vs. low NAI: unstandardized β = 0.195 (SE 0.008), standardized β = 0.178, p < 0.001) and adolescents (medium NAI vs. low NAI: unstandardized β = 0.189 (SE 0.014), standardized β = 0.161, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.364 (SE 0.015), standardized β = 0.334, p < 0.001 for adolescent girls; medium NAI vs. low NAI: unstandardized β = 0.122 (SE 0.014), standardized β = 0.095, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.217 (SE 0.015), standardized β = 0.187, p < 0.001 for adolescent boys). Each borough of NYC included clusters of neighborhoods with higher and lower NAI exposure, although clusters varied in size and patterns of geographic dispersion across boroughs. Conclusions: A spatial index capturing neighborhood environment factors associated with ACE exposure is associated with higher BMI among NYC youth. Findings complement prior literature about relationships between neighborhood environment and obesity risk, existing research documenting ACE-obesity associations, and the potential for neighborhood factors to be a source of adversity. Collectively, evidence suggests that trauma-informed place-based obesity reduction efforts merit further exploration as potential means to interrupt ACE-obesity associations.

Associations Between Neighborhood Opportunity and Indicators of Physical Fitness for New York City Public School Youth

Background: Fewer than 1/4th of US children and adolescents meet physical activity (PA) guidelines, leading to health disparities that track into adulthood. Neighborhood opportunity may serve as a critical modifiable factor to improve fitness attainment and reduce these disparities. We drew data from the Child Opportunity Index to examine associations between neighborhood indicators of opportunity for PA and multiple fitness indicators among New York City public school youth. Methods: Multilevel generalized linear mixed models were used to estimate the overall and sex-stratified associations between neighborhood indicators (green space, healthy food, walkability, commute time) and indicators for physical fitness [curl-ups, push-ups, Progressive Aerobic Cardiovascular Endurance Run (PACER), sit-and-reach] using the New York City FITNESSGRAM data set. Results: The analytic sample [n = 299,839; median (interquartile range) age = 16 (12-17)] was 50.1% female, 37.5% Hispanic, 26.2% non-Hispanic Black, and most (69.5%) qualified for free/reduced price school meals. Neighborhood indicators were positively associated with higher values of indicators for physical fitness. The strongest associations were observed between walkability and both BMI and PACER, and commute time with BMI, push-ups, and PACER. For example, walkability had the greatest magnitude of effects for BMI and muscular strength and endurance (BMI: β: -0.75, 95% confidence interval, CI: -1.01 to -0.49; PACER: β: 1.98, 95% CI: 1.59 to 2.37), and particularly for girls compared with boys (BMI, girls: β: -0.91, 95% CI: -1.22 to -0.66); BMI, boys: β: -0.56, 95% CI: -0.86 to -0.25); PACER, girls: β: 2.11, 95% CI: 1.68 to 2.54; push-ups, boys: β: 1.71, 95% CI: 1.31 to 2.12). Conclusion: Neighborhood indicators were associated with multiple measures of youth fitness. Continued research on neighborhood opportunity and youth fitness may better inform place-based public health interventions to reduce disparities.

Increasing disparities in obesity and severe obesity prevalence among public elementary and middle school students in New York City, school years 2011-12 through 2019-20

Recent national trends in the United States indicate a significant increase in childhood obesity, a major public health concern with documented physical and mental comorbidities and sociodemographic disparities. We aimed to estimate the prevalence of obesity and severe obesity among youth in New York City (NYC) before the COVID-19 pandemic and examine time trends overall and by key characteristics. We included all valid height and weight measurements of kindergarten through 8th grade public school students aged 5 to 15 from school years 2011-12 through 2019-20 (N = 1,370,890 unique students; 5,254,058 observations). Obesity and severe obesity were determined using age- and sex-specific body mass index percentiles based on the Centers for Disease Control and Prevention growth charts. Analyses were performed using multivariate logistic regression models with repeated cross-sectional observations weighted to represent the student population for each year and clustered by student and school. Among youth attending public elementary and middle schools in NYC, we estimate that 20.9% and 6.4% had obesity and severe obesity, respectively, in 2019-20. While consistent declines in prevalence were observed overall from 2011-12 to 2019-20 (2.8% relative decrease in obesity and 0.2% in severe obesity, p<0.001), increasing trends were observed among Black, Hispanic, and foreign-born students, suggesting widening disparities. Extending previous work reporting prevalence estimates in this population, nearly all groups experienced significant increases in obesity and severe obesity from 2016-17 to 2019-20 (relative change = 3.5% and 6.7%, respectively, overall; p<0.001). Yet, some of the largest increases in obesity were observed among those already bearing the greatest burden, such as Black and Hispanic students and youth living in poverty. These findings highlight the need for greater implementation of equity-centered obesity prevention efforts. Future research should consider the influence of the COVID-19 pandemic and changes in clinical guidance on childhood obesity and severe obesity in NYC.

How do looking patterns, anti-fat bias, and causal weight attributions relate to adults' judgements of child weight?

Prevailing weight-normative approaches to health pressure adults to visually categorise children's weight, despite little understanding of how such judgements are made. There is no evidence this strategy improves child health, and it may harm children with higher weights. To understand decision-making processes and identify potential mechanisms of harm we examined perceptual and attitudinal factors involved in adults' child weight category judgements. Eye movements of 42 adults were tracked while categorizing the weight of 40 computer-generated images of children (aged 4-5 & 10-11 years) varying in size. Questionnaires assessed child-focused weight bias and causal attributions for child weight. Participants' eye movement patterns resembled those previously reported for adult bodies. Categorisation data showed a perceptual bias towards the 'mid-range' category. For higher weight stimuli, participants whose category judgements most closely matched the stimulus's objective weight had higher child-focused anti-fat bias and weaker genetic attributions for child weight - i.e,. adults who 'label' higher weight in children in line with BMI categories report more stigmatising beliefs about such children, suggesting a possible mechanism of harm. Overall, adults' judgements reflect both unalterable perceptual biases and potentially harmful attitudinal factors, calling into question the feasibility and appropriateness of public health efforts to promote visual child weight categorisation.

Quantile Regression of Childhood Growth Trajectories: Obesity Disparities and Evaluation of Public Policy Interventions at the Local Level

Quantile regression applied to child growth trajectories has been proposed in the methodological literature but has only seen limited applications even though it is a promising framework for the evaluation of school-based policy interventions designed to address childhood obesity. Data that could be used to support such assessments, school-based collection of height and weight, has become increasingly common. Three states currently mandate annual collection and several other jurisdictions including California and New York City (NYC) collect BMI as part of physical fitness assessments. This has resulted in the establishment of extremely large databases that share important characteristics including the ability to define longitudinal growth curves by student with high coverage rates. In NYC public schools, starting in 2006, student records have been linked to registry, academic, and attendance data and across years resulting in a longitudinal dataset containing 9 cohorts with 2 million unique children. A high level of demographic and geographic detail allow for analysis of public policy at the local scale. We demonstrate the utility of quantile regression longitudinal growth curve models applied to BMI trajectories as a means of assessing policy interventions. Models consisting solely of age terms yield empirical curves similar to CDC growth charts; covariates modify these curves. Incorporating lag terms yields a distribution of possible growth trajectories and the effect of interventions can be explicitly quantified. We evaluate area-based and individual poverty measures, known strong correlates of child obesity, as a baseline assessment of the modeling framework. We then evaluate the impact of a real intervention (water jet installations). Our results indicate that students with access to water jets have a statistically significant leftward shift in the right tail of the BMI distribution relative to students without access to water jets. The absolute magnitude of the shift is comparable to the difference in BMI associated with student residential exposure to low versus extreme poverty.

Longitudinal Association between Weight Status, Aerobic Capacity, Muscular Strength, and Endurance among New York City Youth, 2010-2017

Background: Child weight status is inversely associated with fitness, but less is known about this relationship across fitness domains. This study examined the longitudinal association between weight status and fitness domains in a large, diverse sample of children. Methods: Data were drawn from the New York City Fitnessgram (2010-2011 to 2017-2018). Height and weight were collected annually and converted to weight status using Centers for Disease Control and Prevention growth charts. Aerobic capacity, muscular strength, and endurance were measured as age and sex standardized z-scores based on the fitness performance tests. Repeated-measures multilevel models were run testing the association between weight status and 1-year lagged fitness domains. Results: The sample included 917,554 children (51.8% male, 39.3% Hispanic, 29.9% non-Hispanic Black, 13.9%, 4.7%, and 1.7% class I, II, and III obesity, respectively). For each fitness domain, fitness scores decreased with increasing weight status across all demographic categories, with the lowest fitness scores observed in children with the most severe obesity, and highest magnitude of effects for aerobic capacity, and particularly among boys, non-Hispanic Whites, and older youth. For example, compared with youth with healthy weight, youth with overweight had 0.28 standard deviation lower aerobic capacity performance [confidence interval (95% CI): -0.29 to -0.28], followed by class 1 obesity (β = -0.57, 95% CI: -0.58 to -0.57), class 2 obesity (β = -0.88, 95% CI: -0.88 to -0.88), and class 3 obesity (β = -1.19, 95% CI: -1.20 to -1.18). Conclusions: Compared with youth with healthy weight, youth at every other weight status had lower subsequent fitness, with the magnitude of the relationship increasing as weight status increased. Future research should examine interventions targeting aerobic capacity to reduce fitness disparities.