Data Missingness and Equity Implications in the Nation's Largest Student Fitness Surveillance System

The New York City School Based Physical Fitness Testing Programs, 2006–2020

Bibliographic Data

ID21354409
AuthorsHannah R Thompson (0009-0009-6874-9556, Department of Community Health Science School of Public Health, University of California Berkeley California USA, corresponding author), Joni Ladawn Ricks‐Oddie (Center for Statistical Consulting Department of Statistics, University of California Irvine Irvine California USA), Joni Ricks‐Oddie (0000-0002-9305-7692, University of California, Irvine), Margaret Schneider (0000-0002-8314-0732, Joe C. Wen School of Population & Public Health University of California Irvine Irvine California USA), Sophia E Day (0000-0003-1885-5907, Office of School Health, Data Science and Research New York City Department of Health and Mental Hygiene New York New York USA), Kira Argenio (0000-0001-8272-2565, Office of School Health, Data Science and Research New York City Department of Health and Mental Hygiene New York New York USA), Kevin Konty (0000-0002-7156-7034, Office of School Health, Data Science and Research New York City Department of Health and Mental Hygiene New York New York USA), Shlomit Radom‐Aizik (0009-0004-4023-7026, Pediatric Exercise and Genomics Research Center Department of Pediatrics, School of Medicine, University of California Irvine Irvine California USA), Yawen Guo (0009-0006-4610-5678, Department of Informatics University of California, Irvine Irvine California USA), Dan M Cooper (0000-0003-4022-0043, Pediatric Exercise and Genomics Research Center Department of Pediatrics, School of Medicine, University of California Irvine Irvine California USA)
Year2025
Volume95
Issue7
Pages498-509
Publication date2025-07-01
Open AccessYes
TypeARTICLE
VenueJournal of School Health (JOURNAL)
Journal identifiersISSN: 0022-4391 • E-ISSN: 1746-1561
PublisherWiley (PUBLISHER • GB)
DOI10.1111/josh.70021
PMID40386883
OpenAlexW4410481521
LanguageEN
References cited31

BACKGROUND: Data missingness can bias interpretation and outcomes resulting from data use. We describe data missingness in the longest-standing US-based youth fitness surveillance system (2006/07-2019/20). METHODS: This observational study uses the New York City FITNESSGRAM (NYCFG) database from 1,983,629 unique 4th-12th grade students (9,147,873 student-year observations) from 1756 schools. NYCFG tests for aerobic capacity, muscular strength, and endurance were administered annually. Mixed effects models determined the prevalence of missingness by demographics, and associations between demographics and missingness. RESULTS: Across years, 20.1% of students were missing data from all three tests (11.7% for elementary students, 15.6% middle, and 36.3% high). Missingness did not differ by sex, but differed significantly by race/ethnicity and student home neighborhood socioeconomic status. CONCLUSION: The nation's largest youth fitness surveillance system demonstrates the highest fitness data missingness among high school students, with more than 1/3 of students missing data. Non-Hispanic Black students and those with very poor home neighborhood SES, across all grade levels, have the highest odds of missing data. IMPLICATIONS FOR SCHOOL HEALTH: Strategies to better understand and ameliorate the causes of school-based fitness testing data missingness will increase overall data quality and begin to address health inequities in this critical metric of youth health.

Ethnic group · Health equity · Logistic regression · Missing data · Odds · Population · Public health · Socioeconomic status · Sociology · Statistics · Demography · Educational Assessment and Improvement · Mathematics · Medicine · Obesity, Physical Activity, Diet · Physical Activity and Health · Psychology · Gerontology

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