Search

Population-level surveillance of student weight status (particularly monitoring students with a body mass index (BMI) ≥95th percentile) remains of public health interest. However, there is mounting concern about objectively measuring student BMI in schools. Using data from the nations largest school district, we determined how closely students self-reported BMI approximates objectively-measured BMI, aggregated at the school level, to inform decision-making related to school BMI measurement practices. Using non-matched data from n = 82,543 students with objective height/weight data and n = 7676 with self-reported height/weight from 84 New York City high schools (88% non-white and 75% free or reduced-price meal-eligible enrollment), we compared school-level mean differences in height, weight, BMI, and proportion of students by weight status, between objective and self-reported measures. At the school-level, the self-reported measurement significantly underestimated weight (-1.38 kg; 95% CI: -1.999, -0.758) and BMI (-0.38 kg/m2; 95% CI: -0.574, -0.183) compared to the objective measurement. Based on the objective measurement, 12.1% of students were classified as having obesity and 6.3% as having severe obesity (per CDC definition); the self-report data yielded 2.5 (95% CI: -1.964, -0.174) and 1.4 (95% CI: -2.176, -0.595) percentage point underestimates in students with obesity and severe obesity, respectively. This translates to 13% of students with obesity and 21% of students with severe obesity being misclassified if using self-reported BMI. School-level high school students self-reported data underestimate the prevalence of students with obesity and severe obesity and is particularly poor at identifying highest-risk students based on BMI percentile.

This study assessed associations between school nurse workload and student health and academic outcomes. We hypothesized that lower school nurse workload would be associated with better student outcomes, with associations being greater for members of groups who experience health disparities. Our methods entailed secondary analysis of data for New York City school students in kindergarten through 12th grade during 2015-2016 (N = 1,080,923), using multilevel multivariate regression as the analytic approach. Results demonstrated lower school nurse workload was associated with better outcomes for student participation in asthma education but not chronic absenteeism, early dismissals, health office visits, immunization compliance, academic achievement, or overweight/obesity. Our findings suggest school nurses may influence proximal outcomes, such as participation in disease-related education, more easily than downstream outcomes, such as absenteeism or obesity. While contrary to our hypotheses, results align with the fact that school nurses deliver community-based, population health--focused care that is inherently complex, multilevel, and directly impacted by social determinants of health. Future research should explore school nurses' perspectives on what factors influence their workload and how they can best impact student outcomes.

NYC FITNESSGRAM, monitored by the New York City (NYC) Department of Education and the NYC Department of Health and Mental Hygiene, functions as the NYC Department of Educations citywide youth fitness surveillance program. Here we present the methods, characteristics, and data used in this surveillance system to monitor physical fitness in public school students (grades kindergarten through 12; initiated in 2006; n = 6,748,265 observations; mean sample of 519,097 observations per year to date) in New York, New York. Youth physical fitness prevalence estimates, longitudinal trends, and spatial analyses may be investigated using continuous fitness composite percentile scores and Cooper Institute for Aerobic Research-defined sex- and age-specific Healthy Fitness Zones. Healthy Fitness Zones are based on individual-child fitness test performance, with standard errors clustered at the school and student levels and adjusted for sociodemographic characteristics. Results may be used to show trends in youth fitness attainment over time and highlight disparities in the fitness prevalence of NYC students. In sum, continuous fitness composite percentile scores offer the opportunity for prospective tracking of shifts in youth physical fitness on a population scale and across subpopulations. NYC FITNESSGRAM can accompany a growing body of surveillance tools demonstrating the potential for population-level surveillance tools to promote global public health.

The Office of School Health, a joint program of the Departments of Health and Education, administers New York City's (NYC) body mass index (BMI) surveillance system to monitor childhood obesity. We describe the context, importance, and process for creating a multi-agency, school-based BMI surveillance system using BMI collected from annual FITNESSGRAM® physical fitness assessments conducted as part of a larger physical activity and wellness curriculum in NYC public schools. We also summarize our current system and methodology, highlighting the types of data and data sources that comprise the system and partnership between the Departments of Health and Education that enable data sharing. Strategies for addressing threats to data quality, including missing data, biologically implausible values, and imprecise/subjective weight or height equipment are discussed. We also review current and future surveillance data products, and provide recommendations for collecting, analyzing, interpreting, and reporting BMI data for childhood obesity surveillance. Collaboration between Departments of Health and Education as well as attention to safeguards of BMI reporting and data quality threats have enabled NYC to collect high quality BMI data to accurately monitor childhood obesity trends. These findings have implications for youth BMI surveillance systems in the United States and globally.

FITNESSGRAM® is the most widely used criterion-referenced tool to assess/report on student health-related fitness across the US. Potential weight-related biases with the two most common tests of musculoskeletal fitness-the trunk extension and Back-Saver Sit-and-Reach (sit-and-reach)-have been hypothesized, though have not been studied. To determine the association between musculoskeletal fitness test performance and weight status, we use data from 571,133 New York City public school 4th-12th grade students (85% non-White; 75% qualified for free or reduced-price meals) with valid/complete 2017-18 FITNESSGRAM® data. Adjusted logistic mixed effects models with a random effect for school examined the association between weight status and whether a student was in the Healthy Fitness Zone (HFZ; met sex- and age-specific criterion-referenced standards) for the trunk extension and sit-and-reach. Compared to students with normal weight, the odds of being in the HFZ for trunk extension were lower for students with underweight (OR = 0.77; 95% CI: 0.741, 0.795) and higher for students with overweight (OR = 1.10; 95% CI: 1.081, 1.122) and obesity (OR = 1.11; 95% CI: 1.090, 1.13). The odds of being in the HFZ for sit-and-reach were lower for students with underweight OR = 0.85; 95% CI: 0.826, 0.878), overweight (OR = 0.83; 95% CI: 0.819, 0.844) and obesity (OR = 0.65; 95% CI: 0.641, 0.661). Students with overweight and obesity perform better on the trunk extension, yet worse on the sit-and-reach, compared to students with normal weight. Teachers, administrators, and researchers should be aware of the relationship of BMI with student performance in these assessments.

Standardized physical fitness monitoring provides a more accurate proxy for youth health when compared with physical activity. Little is known about the utilization of broad-scale individual-level youth physical fitness testing to explore health disparities. We examined longitudinal trends in population-level fitness for 4th-12th grade New York City youth during 2006/7-2016/17 (average n = 510,293 per year). Analyses were performed in 2019. The primary outcome was whether or not youth achieved sex-/age-specific performance levels (called the Healthy Fitness Zone) on the aerobic capacity, muscular strength and muscular endurance tests using the NYC FITNESSGRAM. The Cooper Institute's most recent Healthy Fitness Zone criteria were applied to all tests and years. Prevalence estimates were weighted, accounted for school clustering, adjusted for student-level sociodemographics, and run by sociodemographic subgroups and year. The overall prevalence for meeting 3 Healthy Fitness Zones increased from 15.5% (95%CI: 13.9%-17.0%) in 2006/7 to 23.3% (95%CI: 22.2%-24.4%) in 2016/17 for students in grades 4-12. Fitness for all student groups increased over time, although Hispanic and non-Hispanic black girls consistently had the lowest prevalence of meeting 3 Healthy Fitness Zones as compared to all other race/sex subgroups. Also, 9th-12th graders had a lower prevalence of meeting 3 Healthy Fitness Zones as compared to 4th-8th graders. Given forecasted sharp increases in cardiovascular disease prevalence, routine youth fitness surveillance using standardized, criterion referenced methods can identify important fitness disparities and inform interventions.

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.

Background: Socioeconomic status (SES) impacts health outcomes. The Youth Risk Behavior Survey (YRBS), like many school-based data sources, lacks individual-level poverty information. We propose using school-level percentages of student eligibility for free/reduced-price meals (%FRPM) as a proxy for individual-level poverty. Methods: Using the New York City (NYC) 2009 YRBS, we created school-level poverty quartiles to append to individual YRBS records by ranking schools by %FRPM. We compared this with 2 other school-level poverty measures using students' home and school neighborhood-level poverty and measured the association of these 3 school-level proxies with individual's household income. Last, we evaluated health outcomes by race/ethnicity and poverty to demonstrate the importance of accounting for poverty. Results: The school-level measure that used %FRPM had the strongest association with household income. When the school-level individual poverty proxy was included in illustrative analyses using YRBS data, patterns by poverty within race/ethnicity emerged that were not seen when looking at race/ethnicity alone. Conclusions: Using a poverty measure to analyze school-based data will provide a better understanding of the impact of SES on health outcomes. Based on our evaluation, when individual-level information is not available, we propose using school-level %FRPM, which are publicly available throughout the United States.

This study assessed associations between school nurse workload and student health and academic outcomes. We hypothesized that lower school nurse workload would be associated with better student outcomes, with associations being greater for members of groups who experience health disparities. Our methods entailed secondary analysis of data for New York City school students in kindergarten through 12th grade during 2015–2016 (N = 1,080,923), using multilevel multivariate regression as the analytic approach. Results demonstrated lower school nurse workload was associated with better outcomes for student participation in asthma education but not chronic absenteeism, early dismissals, health office visits, immunization compliance, academic achievement, or overweight/obesity. Our findings suggest school nurses may influence proximal outcomes, such as participation in disease-related education, more easily than downstream outcomes, such as absenteeism or obesity. While contrary to our hypotheses, results align with the fact that school nurses deliver community-based, population health–focused care that is inherently complex, multilevel, and directly impacted by social determinants of health. Future research should explore school nurses' perspectives on what factors influence their workload and how they can best impact student outcomes. [ABSTRACT FROM AUTHOR]

Background: The effects of individual school dismissal on influenza transmission have not been well studied. During the spring 2009 novel H1N1 outbreak, New York City implemented an individual school dismissal policy intended to limit influenza transmission at schools with high rates of influenza-like illness (ILI). Methods: Active disease surveillance data collected by the New York City Health Department on rates of ILI in schools were used to evaluate the impact. Sixty-four schools that met the Health Department's criteria for considering dismissal were included in the analysis. Twenty-four schools that met criteria subsequently dismissed all classes for approximately 1 school week. A regression model was fit to these data, estimating the effect of school dismissal on rates of in-school ILI following reconvening, adjusting for potential confounders. Results: The model estimated that, on average, school dismissal reduced the rate of ILI by 7.1% over the entire average outbreak period. However, a large proportion of in-school ILI occurred before dismissal criteria were met. A separate model estimated that school absenteeism rates were not significantly affected by dismissal. Conclusion: Results suggest that individual school dismissal could be considered in situations where schools have a disproportionate number of high-risk students or may be unable to implement recommended preventive or infection control measures. Future work should focus on developing more sensitive indicators of early outbreak detection in schools and evaluating the impact of school dismissal on community transmission. (Contains 3 figures and 2 tables.)

Background: Extensive research demonstrates the benefits of fitness on children's health and academic performance. Although decreases in health-related fitness may increase school absenteeism, multiple years of prospective, child-level data are needed to examine whether fitness changes predict subsequent chronic absenteeism status. Methods: Six cohorts of New York City public school students were followed from grades 5-8 (2006/2007-2012/2013; N = 349,381). A longitudinal 3-level logistic generalized linear mixed model with random intercepts was used to test the association of individual children's changes in fitness and 1-year lagged chronic absenteeism. Results: The odds of chronic absenteeism increased 27% [odds ratio (OR) 95% confidence interval (CI), 1.25-1.30], 15% (OR 95% CI, 1.13-1.18), 9% (OR 95% CI, 1.07-1.11), and 1% (OR 95% CI, 0.98-1.04), for students who had a >20% decrease, 10%-20% decrease, <10% increase or decrease, and 10%-20% increase in fitness, respectively, compared with >20% fitness increase. Conclusion: These findings contribute important longitudinal evidence to a cross-sectional literature, demonstrating reductions in youth fitness may increase absenteeism. Given only 25%of youth aged 12-15 years achieve the recommended daily 60 minutes or more of moderate to vigorous physical activity, future work should examine the potential for youth fitness interventions to reduce absenteeism and foster positive attitudes toward lifelong physical activity. [ABSTRACT FROM AUTHOR]