Accepted Manuscript The association of fitness and school absenteeism across gender and poverty: A prospective multilevel analysis in New York City middle schools Emily M. D’Agostino, DrPH, Sophia E. Day, MA, Kevin J. Konty, PhD, Michael Larkin, MA, Subir Saha, PhD, Katarzyna Wyka, PhD PII:
S1047-2797(17)30336-8
DOI:
10.1016/j.annepidem.2017.12.010
Reference:
AEP 8339
To appear in:
Annals of Epidemiology
Received Date: 12 June 2017 Revised Date:
8 December 2017
Accepted Date: 21 December 2017
Please cite this article as: D’Agostino EM, Day SE, Konty KJ, Larkin M, Saha S, Wyka K, The association of fitness and school absenteeism across gender and poverty: A prospective multilevel analysis in New York City middle schools, Annals of Epidemiology (2018), doi: 10.1016/ j.annepidem.2017.12.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Fitness, poverty and gender on absenteeism
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Emily M. D’Agostino, DrPHa; Sophia E. Day, MAb; Kevin J. Konty, PhDb; Michael Larkin, MAc; Subir Saha, PhDc; Katarzyna Wyka, PhDa CUNY Graduate School of Public Health and Health Policy, 55 W 125th Street, New York, NY 10027, USA b NYC Department of Health and Mental Hygiene, Office of School Health, 1 Court Square, 20th Floor Queens, NY 11101, USA c NYC Department of Education, Office of School Wellness, 335 Adams Street, 28th Floor Brooklyn, NY 11201, USA
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Present address
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Address for correspondence: Emily D’Agostino, DrPH, MS, MA Miami-Dade Department of Parks, Recreation and Open Spaces1 275 NW 2nd Street Suite 416 Miami, Florida 33128 email:
[email protected] Office: 305-755-7938 | Mobile: 646-853-1223| Fax: 305-755-7864
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The association of fitness and school absenteeism across gender and poverty: A prospective multilevel analysis in New York City middle schools
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ACCEPTED MANUSCRIPT Fitness, poverty and gender on absenteeism Abstract
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Purpose
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One-fifth to one-third of students in high-poverty, urban school districts do not attend school
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regularly (missing ≥6 days per year). Fitness is shown to be associated with absenteeism,
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although this relationship may differ across poverty and gender subgroups.
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Methods
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Six cohorts of New York City public school students were followed from grades 5-8 during
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2006/7-2012/13 (n=349,381). Stratified three-level longitudinal generalized linear mixed models
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were used to test the association of fitness changes and one-year lagged child-specific days
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absent across gender and poverty.
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Results
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In girls attending schools in high/very high poverty areas, greater improvements in fitness the
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year prior were associated with greater improvements in attendance (p=0.034). Relative to the
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reference group (>20% decrease in fitness composite percentile scores from the prior year), girls
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with a large increase in fitness (>20%) demonstrated 10.3% fewer days absent (Incidence Rate
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Ratio (IRR) 95% CI: 0.834, 0.964), followed by those who had a 10-20% increase in fitness
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(9.2%, IRR 95% CI: 0.835, 0.987), no change (5.4%, IRR 95% CI: 0.887, 1.007) and a 10-20%
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decrease in fitness (3.8%, IRR 95% CI: 0.885, 1.045). In girls attending schools in low/mid
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poverty areas, the fitness-attendance relationship was also positive, but no clear trend emerged.
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In boys, the fitness-attendance relationship was also positive but not significant in either poverty
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group.
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Conclusions
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ACCEPTED MANUSCRIPT Fitness, poverty and gender on absenteeism Fitness improvements may be more important to attendance improvements in high/very high
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poverty girls compared with low/mid poverty girls, and both high/very high and low/mid poverty
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boys. Expanding school-based physical activity programs for youth particularly in high-poverty
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neighborhoods may increase student attendance.
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54 Key words
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Poverty Areas; Gender; Schools; School-Age Population; Cardiorespiratory Fitness; Fitness
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Testing; Youth; Absenteeism
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List of abbreviations
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NYC, New York City; DOE, Department of Education; Fitnessgram, NYC FITNESSGRAM;
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DOHMH, Department of Health and Mental Hygiene; PACER, Progressive Aerobic
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Cardiovascular Endurance Run; BMI, Body Mass Index; CDC, Centers for Disease Control and
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Prevention; ICC, Intraclass Correlation; IRR, Incidence Rate Ratio.
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Only 25% of youth ages 12-15 meet the recommended daily 60 minutes or more of moderate to
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vigorous physical activity [1]. Schools in the US have replaced physical education and recess
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with non-physical instructional time due in part to an increasing emphasis on high-stakes testing
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[2]. This raises particular concern given a well-established link between school-based physical
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activity and health [3-7], and potential associations with academics [8-11] and attendance [8, 10-
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12].
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Schools in high-poverty areas, in particular, may hold a critical role in providing youth with safe
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and accessible physical activity [13, 14]. Environmental factors, including area poverty and the
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built environment, are shown in the literature to be associated with children’s tendency to
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participate in school- and neighborhood-based physical activity [13-16]. Neighborhood factors
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may contribute to opportunities for safe, attractive, and accessible physical activity [15, 17].
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School contextual factors may also impact community norms and attitudes pertaining to
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children’s school absences [18, 19]. Given one-fifth to one-third of students in high-poverty,
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urban school districts do not attend school regularly (missing ≥6 days per year), high-poverty
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subgroups may benefit from physical activity interventions. However, no literature has examined
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the potential modification of area poverty on the youth fitness-school absenteeism relationship.
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Disparities in both physical activity and absenteeism also persist across gender. Only 18% of
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high school girls compared with 37% of boys are reported to meet physical activity guidelines
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[20]. Numerous studies demonstrate low self-esteem in adolescent girls is significantly
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associated with both lower physical activity levels [21] and absenteeism [22, 23]. However, no
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papers were identified that examined gender differences in the fitness-absenteeism relationship.
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Given this literature, nuanced research on gender differences in the association of fitness and
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attendance are warranted.
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This study examined gender differences by area poverty on the longitudinal fitness-attendance
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association in 6 cohorts of New York City (NYC) Department of Education (DOE) middle
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school students each followed consecutively over 4 years. It was hypothesized that
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improvements in fitness (cardiorespiratory, muscular endurance, and muscular strength fitness
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composite percentile scores) would predict lower subsequent absenteeism (one-year lagged days
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absent) after accounting for potential individual- and school-level confounders, clustering, and
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time-dependent interactions. It was further hypothesized that higher area poverty and female
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gender would predict a stronger magnitude of association of fitness and absenteeism.
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Data sources and study population
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Data for this study were drawn from the NYC FITNESSGRAM (Fitnessgram) dataset jointly
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managed by NYC Department of Education (DOE) and Department of Health and Mental
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Hygiene (DOHMH) [24]. It is comprised of annual fitness assessments collected by DOE for
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approximately 870,000 NYC public school students per year (grades K-12) starting in 2006-07.
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This study was approved by the City University of New York and DOHMH IRBs and was
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determined by these boards to be public health surveillance that is not research, and therefore
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exempt from the requirement for obtaining written informed consent.
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ACCEPTED MANUSCRIPT Fitness, poverty and gender on absenteeism The Fitnessgram is based on the Cooper Institute’s FitnessGram®, which is demonstrated to
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have both strong reliability and validity [25]. Fitnessgram performance tests provide a health
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assessment related to present and future health outcomes. NYC schools are mandated to have
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≥85% of eligible students complete the Fitnessgram assessment each year. Inclusion criteria for
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this study included enrollment in a NYC public school for ≥2 consecutive years while in grades
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6-8 during the study period (2006/07-2012/13) while attending a school that collected
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Fitnessgram measurements (for Sample Selection Flow Chart see Figure A.1). Student cohorts
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were defined based on year of initiating grade 6. Fitness-change data from grades 5-6, 6-7 and
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7-8 was paired with days absent per year for grades 6, 7, and 8, respectively. The final sample of
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6-8th graders included 349,381 unique students nested in 624 schools (mean school
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population=541 students; SD=632). Students in 6th, 7th and 8th grades contributed 177,281,
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220,769, and 186,135 student-years, respectively.
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The primary exposure was a categorical variable representing age- and gender-specific percent
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change in fitness composite percentile scores based on the sum of percentile scores for the
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Progressive Aerobic Cardiovascular Endurance Run (PACER), muscle strength and endurance
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(curl-up and push-up) tests [24]. Scores were converted to percentiles to account for expected
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improvements in performance with increasing age and gender. The fitness variable was
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categorized as >20% decrease, 10-20% decrease, 20%
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increase in performance from the year prior consistent with longitudinal research on fitness and
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academic outcomes drawing from the Fitnessgram dataset [9].
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The primary outcome for this analysis was child-level number of days absent per year. Annual
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enrollment and attendance records were matched to their Fitnessgram results by a unique student
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identifier.
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Gender was based on parent report. Consistent with NYC DOHMH guidelines [26], a school
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neighborhood’s socioeconomic status was defined according to American Community Survey
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2007-2012 data as the percentage of households in the school zip code living below the federal
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poverty threshold (low (30%)
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area poverty) [27].
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Covariates included time, race/ethnicity, place of birth and school size. These covariates are
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shown in the literature to predict both fitness and absenteeism [8, 28-30]. Time (calendar year) at
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height and weight measurement was treated as a continuous variable. Race/ethnicity was based
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on parent report and was grouped into 5 categories: Hispanic, non-Hispanic black, non-Hispanic
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white, Asian/Pacific Islander, and other (including multiple races). A binary school size variable
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was based on count of individuals attending each student’s school. Per the literature, schools
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with less than 400 students were considered small schools, and schools with 400 or more
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students were considered non-small schools [30].
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Change in obesity status from the year prior (obese to not obese, consistently not obese,
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consistently obese, not obese to obese) was also included as a potential confounder based on the
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literature [8]. BMI is collected annually as part of the Fitnessgram curriculum. Obesity was
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defined as having a BMI ≥ 95th percentile for youth in the same gender and age group according
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to CDC guidelines [31]. Change in obesity status category was chosen as a covariate in lieu of
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changes in BMI percentile to capture meaningful shifts in body composition associated with
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school outcomes [32].
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159 Statistical methods
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Descriptive statistics were computed to summarize sample characteristics. Next, trends in
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absenteeism (days absent) by fitness, grade, and demographic characteristics were examined.
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Mixed models methodology was used to assess between-school variability in student
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absenteeism. First, in order to determine the extent of variation in absenteeism at the school-
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level, unconditional three-level longitudinal linear mixed models with random-intercepts were fit
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to the data for all students and stratified by gender. The school-level intraclass correlation (ICC)
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was calculated as the ratio of the variance for the school, divided by the sum of the 3 variance
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parameter estimates, represented as: σ2school / (σ2student+ σ2school+ σ2ε) . Although univariate
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distributions for the days absent variable demonstrated a long right-tailed Poisson distribution,
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the ICC was calculated based on a linear mixed model given the ICC is not well defined for
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Poisson models [33].
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Next, the longitudinal association of change in fitness and lagged number of days absent per year
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was assessed by fitting stratified negative binomial longitudinal mixed models [34, 35] with
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random-intercepts, the exposure, child-specific change in fitness from the year prior, and an
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offset term representing total instructional days per school year. Crude and adjusted models
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were stratified by school-area poverty (low/mid vs. high/very high poverty) and gender (girls vs.
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Statistical significance of the overall association of the exposure, change in fitness and outcome,
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one-year lagged number of days absent per year was assessed using Type III Tests of Fixed
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Effects. Exponentiated beta coefficients (incidence rate ratios (IRR)) represented the association
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of each level of exposure (>20% increase, 10-20% increase, 20% decrease in fitness). A two
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sided P-value of 20% decrease (19%), 10-20% increase (12%), and 10-20% decrease
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(12%). There were 624 schools included in the analysis, including 365 (58%) small schools.
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Fitness, poverty and gender on absenteeism
Table 1. Demographic and fitness-change characteristics of in New York City public school students in grades 6-8 across gender and poverty subgroups (Nstudents=349381, Nobs=675266a), 2006/7-2012/13. Males n % 177355 51
High Povertyb
nc,d
nc,d
%e
nc,d
%e
nc,d
59134
34
43462
25
38611
19277 15888 10063
33 27 17
19332 13373 4266
45 31 10
21081 14652 947
13686
23
6331
15
1785
31756 12235 15143
54 21 26
47160 11965 20624 13364 44020 14233 23880
Low Povertyb
Medium Povertyb
High Povertyb
%e
nc,d
%e
nc,d
%e
nc,d
%e
nc,d
%e
23
31422
18
60085
34
45945
26
39899
22
20 21 39
19497 14652 11081
33 24 18
20810 13152 4807
45 29 11
22241 14538 1060
56 36 3
6597
21
14609
24
6979
15
1896
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55 6118 38 6484 3 12096 5
Very High Povertyb
22161 13488 7813
51 31 18
22630 13440 2541
59 21905 35 3035 7 6482
70 10 21
31564 12192 16329
53 20 28
22976 14386 8583
50 31 19
23161 14136 2602
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30816 18 Overall Race/Ethnicity 6095 20 Hispanic 6619 22 Non-Hispanic Black 11537 37 Non-Hispanic White Asian and/or Pacific 6412 21 Islander Language Spoken at Home 21568 70 English 3139 10 Spanish 6109 20 Other Language Place of Birth 27180 88 US 3628 12 Foreign f Change in Fitness (all years) 9957 16 20% Decrease 6935 11 10-20% Decrease 24291 40 1 race/ethnicity=177. dStudents in 6th, 7th and 8th grades contributed 177,281, 220,769, and 186,135 student-years, respectively. ePercents may not total 100 due to rounding. fChange in fitness composite percentile scores from the year prior was based on PACER (Progressive Aerobic Cardiovascular Endurance Run), Push-up and Curl-up Fitnessgram tests. gChange in obesity status from the year prior was defined according to CDC growth chart-derived norms for gender and age in months based on a historical reference population, and used to compute the BMI percentile for each child. Obesity was defined as having a BMI ≥ 95th percentile for youth in the same gender and age in months group.
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2407 50299 7299 1941
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Obese to not obese Consistently not obese Consistently obese Not obese to obese Attending Small Schools (20% decrease 22573 12.02(12.36) 10-20% decrease 10526 11.63(11.91) 20% increase 23836 10.93(11.24)
n 22532 16414 55658 17418 23946
Grade 8 (nobs=213482)
n 18416 12475 40911 12386 17602
Mean(SD) 12.85(13.06) 11.40(11.69) 10.51(10.79) 10.49(11.04) 10.79(10.85) Mean(SD) 14.87(15.40) 13.81(14.73) 13.62(14.37) 12.64(13.39) 12.99(14.31)
Tabulated mean estimates based on days absent per year. bNmale students=177355, Nfemale students=172026, Nlow/mid c poverty students=181457, Nhigh/very high poverty students=167,917. Observations account for 1-3 years of fitness-change per d student. Based on percentage of households in the school zip code living below the federal poverty threshold (low (30%) area poverty) drawing from the e American Community Survey (ACS) 2007-2012. Nmissing Area Poverty=52. fChange in fitness composite percentile scores from the year prior was based on PACER (Progressive Aerobic Cardiovascular Endurance Run), Push-up and Curl-up Fitnessgram tests.
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n 17493 13707 48590 14108 17794
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Low/Mid Povertyde (nobs=355951) Change in Fitnessf n >20% decrease 20949 10-20% decrease 10937 20% increase 29954
Grade 7 (nobs=257201)
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Grade 6 (nobs=204583)
Twenty six percent and 22% of students attended a school in a high- or very high-poverty area,
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respectively. Table 2 shows higher absenteeism with increasing grade level and increasing
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poverty for students who had a decrease or no improvement vs. increase in fitness from the year
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prior. Students in the 6th grade show inverse dose-response trends between fitness and
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absenteeism, although higher grades do not show this trend.
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Table 3 shows higher absenteeism with decreasing fitness and increasing grade level across both
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genders. Boys showed more mean days absent than girls across all grades, although girls
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Table 3. Mean in absenteeisma for New York City public school students in grades 6-8, by level of fitness-change from the previous year across gender (Nstudents=349381b, Nobs= 675318c) Grade 7 (nobs=257203)
Grade 8 (nobs=213526)
22053 10446 29804 11509 27202
10.23(10.78) 9.75(10.57) 9.55(10.66) 9.32(10.06) 9.14(9.86)
23640 15492 50242 15536 22613
11.32(12.49) 10.30(11.77) 9.61(11.34) 9.40(10.91) 9.53(10.50)
Boys (nobs=340982) Change in Fitnessd >20% decrease 10-20% decrease 20% increase
n 21471 11017 32325 12174 26588
Mean (SD) 11.06(11.76) 10.60(11.20) 10.43(11.07) 10.08(10.65) 9.97(10.39)
n 23035 16023 51272 16401 22949
Mean (SD) 11.82(12.80) 10.94(12.00) 10.52(11.98) 10.02(11.04) 10.13(11.13)
n 17982 13129 44723 13593 18300
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13.51(13.77) 12.33(13.19) 11.60(12.28) 11.27(12.15) 11.80(12.85)
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Girls (nobs=334336) Change in Fitnessd >20% decrease 10-20% decrease 20% increase
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Grade 6 (nobs=204589)
Mean (SD) 14.27(14.93) 12.76(13.36) 12.27(13.00) 11.71(12.32) 11.97(12.65)
Tabulated mean estimates based on days absent per year. bNmale students=177355, Nfemale students=172026, Nlow/mid poverty c students=181457, Nhigh/very high poverty students=167917. Observations account for 1-3 years of fitness-change per student. d Change in fitness composite percentile scores from the year prior was based on PACER (Progressive Aerobic Cardiovascular Endurance Run), Push-up and Curl-up Fitnessgram tests.
showed greater increases in absenteeism with increasing grade and decreasing fitness. The overall
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mean absentee rate across all schools (n=624) based on the unconditional longitudinal
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model intercept was 12 days per year. ICC estimates demonstrated a large degree of clustering at
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the school-level (9% for models including all students; 10% for gender-stratified models; see
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Table A.1).
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Longitudinal association of fitness-change, absenteeism and school-area poverty across gender
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Crude models examining the fitness-absenteeism association stratified by school-area poverty
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and gender showed significant associations between change in fitness and lagged absenteeism for
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all poverty levels across both genders (p20% Increase 10-20% Increase 20% Decrease
IRR (model 1)e,f
BOYS (nobs=340982) Change in Fitnessd >20% Increase 10-20% Increase 20% Decrease
IRR (model 3)e,h
95% CI lower upper 0.821 0.912 0.831 0.936 0.850 0.933 0.888 1.003 . .
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Table 4. Crude longitudinal association of fitness-change and absenteeism in New York City public school students in grades 6-8 (Nstudents=349381a, Nobs=675266b, 624 schools) across school area poverty and gender. Low/Mid School-Area Povertyc High/Very High School-Areac (nobs=355951) Poverty (nobs=319315)
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0.877 0.885 0.916 0.936 .
95% CI lower upper 0.817 0.941 0.818 0.958 0.860 0.974 0.864 1.015 . .
IRR (model 2)e,g 0.897 0.915 0.966 0.971 .
IRR (model 4)e,i 0.891 0.906 0.962 0.971 .
95% CI lower upper 0.847 0.950 0.856 0.979 0.919 1.016 0.909 1.037 . .
95% CI lower upper 0.819 0.969 0.823 0.997 0.894 1.036 0.882 1.069 . .
Abbreviation: IRR, Incidence rate ratios. aNmale students=177355, Nfemale students=172026, Nlow/mid poverty b students=181457, Nhigh/very high poverty students=167917. Observations account for 1-3 years of fitness-change per c student. Based on percentage of households in the school zip code living below the federal poverty threshold (low (30%) area poverty) drawing from the American Community Survey (ACS) 2007-2012. dChange in fitness composite percentile scores from the year prior was based on PACER (Progressive Aerobic Cardiovascular Endurance Run), Push-up and Curl-up Fitnessgram tests. eExponentiated beta coefficients (IRR) represented the association of each level of exposure (>20% increase, 10-20% increase, 20% decrease in fitness). P-values were calculated using adjusted negative binomial longitudinal mixed models; Statistical significance of number of days absent per year was assessed using Type III Tests of Fixed Effects. A two sided P-value of 20% Increase 10-20% Increase 20% Decrease
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Table 5. Adjusteda longitudinal association of fitness-change and absenteeism in New York City public school students in grades 6-8 (Nstudents=349381b, Nobs=675266c, 624 schools) across school-area poverty and gender. Low/Mid School-Area Povertyd High/Very High School-Area (model 5) Povertyd (model 6) IRRf,g 95% CI IRRf,h 95% CI Girls (nobs=334336)
upper 0.975 0.862 0.979 1.039 .
IRRf,j
0.893 0.898 0.941 0.957 .
95% CI
lower 0.786 0.775 0.840 0.826 .
upper 1.016 1.041 1.060 1.110 .
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Abbreviation: IRR, Incidence rate ratios. aAdjusted for individual-level race/ethnicity, grade, calendar year, change in obesity status from the year prior, place of birth (US (not NYC), NYC, or foreign), group-level school size, and including interactions (Grade*Ethnicity and Grade*Place of Birth). bNmale students=177355, Nfemale students=172026, Nlow/mid poverty students=181457, Nhigh/very high poverty students=167917. c Observations account for 1-3 years of fitness-change per student. dBased on percentage of households in the school zip code living below the federal poverty threshold (low (30%) area poverty) drawing from the American Community Survey (ACS) 2007-2012. eChange in fitness composite percentile scores from the year prior was based on PACER (Progressive Aerobic Cardiovascular Endurance Run), Push-up and Curl-up Fitnessgram tests. fExponentiated beta coefficients (IRR) represented the association of each level of exposure (>20% increase, 10-20% increase, 20% decrease in fitness). P-values were calculated using adjusted negative binomial longitudinal mixed models; Statistical significance of number of days absent per year was assessed using Type III Tests of Fixed Effects. A two sided P-value of 20% Increase
10-20% Increase
20% decrease in fitness)
Note. The graph illustrates the relationship between fitness change and absenteeism in girls by
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school-area poverty. As shown above, relative to girls attending schools in high/very high
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poverty areas who had a large decrease in fitness (>20% from the year prior), those who had a
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large increase (>20%), small increase (10-20%),
