Matern Child Health J DOI 10.1007/s10995-014-1616-7
Availability of State-Based Obesity Surveillance Data on High School Students with Disabilities in the United States Kiyoshi Yamaki • Brienne Davis Lowry • Emilie Buscaj • Leigh Zisko • James H. Rimmer
Ó Springer Science+Business Media New York 2014
Abstract The aim of this study was to assess the availability of public health surveillance data on obesity among American children with disabilities in state-based surveillance programs. We reviewed annual cross-sectional datasets in state-level surveillance programs for high school students, implemented 2001–2011, for the inclusion of weight and height and disability screening questions. When datasets included a disability screen, its content and consistency of use across years were examined. We identified 54 surveillance programs with 261 annual datasets containing obesity data. Twelve surveillance programs in 11 states included a disability screening question that could be used to extract obesity data for high school students with disabilities, leaving the other 39 states with no state-level obesity data for students with disabilities. A total of 43 annual datasets, 16.5 % of the available datasets, could be used to estimate the obesity status of students with disabilities. The frequency of use of disability questions varied across states, and the content of the questions often changed across years and within a state. We concluded that state surveillance programs rarely contained questions that could be used to identify high school students with disabilities. This limits the availability of data that can be used to monitor obesity and related health statuses among this population in the majority of states.
K. Yamaki (&) B. D. Lowry E. Buscaj L. Zisko Department of Disability and Human Development, College of Applied Health Sciences, University of Illinois at Chicago, 1640 W. Roosevelt Road, Chicago, IL 60608, USA e-mail:
[email protected] J. H. Rimmer University of Alabama at Birmingham/Lakeshore Foundation Research Collaborative, 4000 Ridgeway Dr, Birmingham, AL 35209, USA
Keywords High school Disability Obesity State Surveillance
Introduction Obesity is a critical health problem among American children. One in five children is obese [1]. This ratio increases to one in three when including children who are overweight. Childhood obesity leads to an increased risk of adverse health consequences, including higher cholesterol [2, 3], hypertension [4, 5], diabetes [6, 7], asthma [8, 9], obstructive sleep apnea [10], joint disease and musculoskeletal pain [11, 12], gastrointestinal problems, liver and gallbladder problems, and early maturation [2, 13, 14]. Obese children may also experience poorer psychological and emotional health, suffering from depression, low selfesteem, limited peer relationships, and body dissatisfaction [2, 15, 16]. Because obese children are likely to become obese adults and have health consequences that track into their adult lives [2], childhood obesity can have a longlasting impact on quality of life beyond childhood. Obesity is also a significant health concern among children with disabilities. Researchers have reported that children with certain genetic conditions, such as spina bifida [17] and Down syndrome [18], have a higher prevalence of obesity than do children in the general population. Children with mobility limitations have also been shown to have a higher rate of obesity compared with children without such limitations [19, 20]. Children with cognitive limitations such as intellectual disability [21, 22], autism [23–27], learning disabilities [19, 26], and attention deficit hyperactivity disorder (ADHD) [26, 28–31] also have an elevated prevalence of obesity. Environmental risk factors contributing to obesity in children in general, such as low
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socioeconomic status, unhealthy diet, and sedentary lifestyle, may also affect children with disabilities, who may be more susceptible to such factors owing to their physical, cognitive, and other functional limitations [20, 21, 32]. Similar to children without disabilities, children with disabilities who have excess body weight have been shown to be more likely than their healthy weight counterparts to develop additional chronic health conditions [26, 33]. Thus, the current and future health consequences of obesity could be more intense for these children, who already have impairments and related functional limitations, than for children without disabilities [20]. Although research has clearly suggested that children with disabilities face a higher risk of obesity than their peers without disabilities, relatively little attention has been directed toward this vulnerable subgroup of children. One potential reason for this lack of attention could be the paucity of reliable and ongoing population-based data on obesity in children with disabilities. Clinical data, which has frequently been used to demonstrate the higher prevalence of obesity in children with disabilities, typically relies on a small number of subjects with specific disability etiologies and lacks the generalizability needed to capture the extent of the problem on the large scale. The availability of national-level population data on obesity in this population has been severely limited owing to the inconsistent inclusion of questions identifying disability in survey questionnaires. When disability questions are included in national surveillance programs, the number of children with disabilities sampled is routinely small, making it difficult to produce reliable population-level estimates [34]. The purpose of the present study was to investigate the availability of obesity surveillance data for children with disabilities across the 50 states in the United States. Although a few national surveillance programs have been used to monitor obesity in children with disabilities [19, 20], data from such programs are insensitive to divergence across states and have had limited utility to state-level data users. State-specific health surveillance data are most useful to identify subpopulations at risk, monitor trends, and evaluate interventions addressing the health needs of residents in specific jurisdictions. Despite its utility, there is little information on the availability of state-level surveillance data on obesity in children with disabilities. By systematically examining state surveillance programs, we aimed to facilitate the collection and use of such data that will enable maternal and child health stakeholders and policymakers in each state to better understand the health needs of children with disabilities and to develop programs and policies accordingly. We also aimed to highlight any gaps in the existing data that may be brought to the attention of individual state surveillance program administrators.
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This study focused on the availability of data for high school students with disabilities. Adolescence is a critical period during which children are at increased risk of obesity and related health conditions that are likely to continue into adulthood [35, 36]. Specifically, we addressed the following three questions: (1) Which state-level surveillance programs have collected obesity information on high school students? (2) Under these programs, what proportion of yearly datasets have included obesity information for students with disabilities? and (3) Are there any state-level surveillance programs that could be used to identify obesity trends among high school students with disabilities?
Methods We reviewed annual datasets from state-based health surveillance programs administered 2001–2011 using the following procedures. First, we conducted a search to identify state-based Youth Risk Behavior Surveys (YRBS), a schoolbased health surveillance program for high school students that includes body mass index (BMI) [37]. Second, we used Google’s search engine to identify additional state-level health surveillance programs that include obesity data for high school students using the key words: ‘‘youth,’’ ‘‘high school,’’ ‘‘health,’’ ‘‘obesity,’’ ‘‘body weight/height,’’ ‘‘BMI,’’ ‘‘surveillance,’’ ‘‘survey,’’ ‘‘student health survey,’’ ‘‘health statistics,’’ and ‘‘adolescent.’’ We also searched individual states’ government websites. Third, for each surveillance program identified, we reviewed annual datasets for the inclusion of students’ BMI or body weight and height using questionnaires, code books, and published reports. Fourth, we examined the same documents to see whether the datasets included a question on students’ disability status. We searched for survey questions that included the following key words: ‘‘disability,’’ ‘‘impairment,’’ ‘‘health problem,’’ ‘‘limitation,’’ ‘‘long term health condition,’’ and ‘‘special education.’’ Fifth, when a question was identified, we examined the content of the question and its frequency of use across years. Finally, we contacted state surveillance coordinators via telephone or email to confirm our findings. The Institutional Review Board of the University of Illinois at Chicago approved this study protocol.
Results Surveillance Programs with Obesity Data The 54 state-level surveillance programs with obesity data for high school students conducted from 2001 to 2011 are summarized in Table 1. Forty-six programs were state-
Matern Child Health J
based YRBS or included YRBS in part. We identified at least one surveillance program for each state. Georgia, Maine, Massachusetts, and Vermont had two programs each. Almost all surveillance programs collected data every other year. The California Healthy Kids Survey (HKS) and the Georgia Student Health Survey II (SHS II) were fielded every year. The Oregon Healthy Teens Survey (HTS) was fielded every year except 2010. The Minnesota Student Survey (SS) was fielded every 3 years. With a few exceptions, most programs collected data from a representative sample of 9th–12th grade students. The Oregon HTS included a sample of 11th grade students only, the California HKS used a representative sample of 9th and 11th grade students, and the Minnesota SS was administered to 9th and 12th grade students. Cross-Sectional Data Available from Surveillance Programs Table 1 also summarizes the available obesity datasets by surveillance program and year of data collection. Across the 54 surveillance programs, we initially identified 305 yearly datasets. Of those datasets, 38 (depicted by s in the table) failed to reach sufficient sample size to make reliable state-level population estimates, as determined by criteria set by state surveillance administrators. There were also six instances in which the surveillance programs did not include obesity information (depicted by h in the table) for certain years. We excluded these 44 datasets from the present analysis. The remaining 261 population-level datasets (depicted by d in the table) contain information from which state level obesity estimates for high school students can be produced. These datasets were found more often in odd-numbered years when the YRBS were fielded. Looking at odd-numbered years only, the number of datasets doubled from 25 in 2001 to 50 in 2011. Cross-Sectional Data on Students with Disabilities Table 2 summarizes the findings on the 12 surveillance programs that included screening questions to identify students with disabilities, listed by year of survey administration. The Montana YRBS and the North Carolina YRBS included a screening question consistently in every data collection since 2001. The Massachusetts YRBS and the Vermont YRBS similarly included a screening question during five out of six data collection years. Vermont included the screen consistently from 2001 until the question was dropped in 2011. The other eight surveillance programs included screening questions only two to three times during this period. There were 43 state-level yearly datasets that would allow for the estimation of obesity status among high school students with disabilities. This
amounts to 16.5 % of the available datasets for high school students in general over the same period. Disability Screening Questions: Variations Across Surveillance Programs Table 3 summarizes the variation in the disability screening questions used in the 12 surveillance programs. Questions used to screen students with disabilities varied significantly across programs and years. With the exception of the Montana and Vermont YRBS, the majority of programs included multiple questions to address the presence of disabilities among students. The most frequently used approach was the inclusion of paired questions asking about the presence of learning disabilities or emotional problems and physical disabilities or long-term health problems. This approach was found in eight surveillance programs: the Delaware YRBS, the Maine IYHS, the Massachusetts YRBS, the Massachusetts YHS, the Minnesota SS, the North Dakota YRBS, the Rhode Island YRBS, and the Washington HYS. Another common approach used by six programs (the Maine IYHS, the Massachusetts YHS, the North Carolina YRBS, the North Dakota YRBS, the Oregon HTS, and the Washington HYS) was to assess the presence of activity limitations owing to a disability, impairment, or health condition. The third most common approach was to identify students with disabilities through their access to special education services (the Massachusetts YRBS, the Minnesota SS, the Montana YRBS, the North Dakota YRBS, and the Vermont YRBS). Three programs (the Maine IYHS, the Massachusetts YHS, and the Washington HYS) included a question about the perceptions of others, asking students whether other people considered her/him to have a disability. The 2006 and 2010 Washington HYS and all years of the California HKS asked students whether they had experienced harassment and bullying because of their disability. Because this question would capture only a segment of students with disabilities, we excluded these datasets from the present analysis. Disability Screening Questions: Consistency Within Surveillance Programs Table 3 also illustrates the consistency of use of disability screening questions across years within each of the 12 surveillance programs. Three programs (the Montana YRBS, the North Carolina YRBS, and the Vermont YRBS) repeated the same or nearly the same question to screen for disabilities five to six times. This allows data users to track obesity in high school students with disabilities at the state level over a decade. In contrast, while the Massachusetts YRBS included disability screening questions five times,
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Matern Child Health J Table 1 State-level surveillance programs for high school students with obesity data by state and year of data collection State
Surveillance program
Year 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Number of obesity data sets
AL
YRBS
d
–
d
–
d
–
s
–
d
–
d
5
AK
YRBS
–
–
d
–
s
–
d
–
d
–
d
4
AZ AR
YRBS YRBS
– d
– –
d s
– –
d d
– –
d d
– –
d d
– –
d d
5 5 11
CA
Healthy Kids Survey
d
d
d
d
d
d
d
d
d
d
d
CO
YRBS
s
–
s
–
d
–
s
–
d
–
d
3
CT
School Health Survey (YRBS)
–
–
s
–
d
–
d
–
d
–
d
4
DE
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
FL
YRBS
d
–
d
–
d
–
d
–
d
–
d
6 5
GA
Student Health Survey (YRBS)
s
–
d
–
d
–
d
–
d
–
d
GA
Student Health Survey II
–
–
–
–
–
–
d
d
d
d
d
5
HI
YRBS
s
–
s
–
d
–
d
–
d
–
d
4
ID
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
IL
YRBS
s
–
d
–
s
–
d
–
d
–
d
4
IN
YRBS
s
–
d
–
d
–
d
–
d
–
d
5
IA
YRBS
s
–
s
–
d
–
d
–
s
–
d
3
KS
YRBS
s
–
s
–
d
–
d
–
d
–
d
4
KY LA
YRBS YRBS
s s
– –
d s
– –
d –
– –
d d
– –
d d
– –
d d
5 3
ME
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
ME
Integrated Youth Health Survey
–
–
–
–
–
–
–
–
d
–
d
2
MD
YRBS
–
–
–
–
d
–
d
–
d
–
d
4
MA
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
MA
Youth Health Survey
–
–
–
–
–
–
d
–
d
–
d
3
MI
YRBS
d
–
d
–
d
–
d
–
d
–
d
6 2
MN
Student Survey
h
–
–
h
–
–
d
–
–
d
–
MS
YRBS
d
–
d
–
s
–
d
–
d
–
d
5
MO
YRBS
d
–
d
–
d
–
d
–
d
–
s
5
MT
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
NE
YRBS
s
–
d
–
d
–
s
–
s
–
d
3
NV
YRBS
h
–
h
–
h
–
d
–
d
–
d
3
NH
YRBS
s
–
d
–
d
–
d
–
d
–
d
5
NJ
YRBS
d
–
s
–
d
–
s
–
d
–
d
4
NM
Youth Risk and Resilience Survey (YRBS)
d
–
s
–
d
–
d
–
d
–
d
5
NY
YRBS
s
–
d
–
d
–
d
–
d
–
d
5
NC
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
ND
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
OH
YRBS
d
–
d
–
d
–
d
–
s
–
d
5
OK
YRBS
–
–
d
–
d
–
d
–
d
–
d
5
OR
Oregon Healthy Teens
d
d
d
d
d
d
d
d
d
–
d
10
PA
YRBS
–
–
–
–
–
–
–
–
d
–
s
1
RI
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
SC
YRBS
s
–
s
–
d
–
d
–
d
–
d
4
SD
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
TN TX
YRBS YRBS
s d
– –
d d
– –
d d
– –
d d
– –
d d
– –
d d
5 6
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Matern Child Health J Table 1 continued State
Surveillance program
Year 2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Number of obesity data sets
UT
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
VT
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
VT
YHS
–
–
–
h
–
d
–
d
–
–
d
3
VA
YRBS
–
–
–
–
–
–
–
–
s
–
d
1
WA
Healthy Youth Survey
–
d
–
d
–
d
–
d
–
d
–
5
WV
YRBS
s
–
d
–
d
–
d
–
d
–
d
5
WI
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
WY
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
25
3
34
3
41
4
45
5
47
4
50
261
Number of obesity data sets d
d Year in which state-level weighted obesity data is available s Year in which state-level weighted obesity data is not available due to small sample size h Year in which surveillance program did not collect obesity data – Year in which surveillance was not conducted
the use of questions addressing different constructs of disability in different years makes it difficult to define the population of students with disabilities consistently. The remaining surveillance programs included an identical or almost identical set of screening questions two to three times consecutively. This frequently occurred from 2007 forward, suggesting an increasing trend toward the continual use of the same disability construct within surveillance programs in recent years. In summary, we identified 54 health surveillance programs that included obesity data for high school students from 2001 to 2011. Across these surveillance programs, there were 261 single-year cross-sectional datasets from which state-level population estimates for obesity could be created. Twelve surveillance programs across 11 states included a disability screening question that could be used to extract obesity data for high school students with disabilities, leaving the other 39 states with no population-level obesity data for students with disabilities. The number of single-year datasets that could be used to estimate obesity status in students with disabilities has increased over the years. However, the total number of these datasets found during this period was only 43, or 16.5 % of the available datasets. When states did include disability screening questions in their surveillance programs, the questions were not always asked in every implementation and the content of the questions was not consistent over time. Only three programs used consistent disability screening questions and were able to provide cross-sectional obesity data on students with disabilities six times since 2001. Seven programs were able to provide two to three datasets collected from 2007 to 2011.
Discussion The present study explored the availability of obesity surveillance data for high school students with disabilities through health surveillance programs across the United States. Although all states regularly conduct comprehensive health surveillance on high school students, including the collection of data on obesity, our findings suggest that, in most states, students with disabilities were not given an opportunity to self-identify. The number of states providing such an opportunity has, however, increased in recent years. Of the few states in which data on obesity and disability status were collected for high school students, variation in and sporadic implementation of the disability screening questions made it difficult to track changes in this population. It appears that two separate but closely connected issues contribute to the current void of obesity data for students with disabilities in state surveillance programs: (1) the inability to identify students with disabilities and (2) variations in operational definitions of disability. The inability to screen students with disabilities found in the present study was consistent with previous findings for national-level data collection systems. McGrew et al. found that students with disabilities were systematically excluded from national-level data collection on educational outcomes, primarily owing to their inability to respond to educational tests or surveys. These researchers estimated that national data included only one-third to one-half of students with disabilities and argued that this omission hampered policymakers’ ability to monitor the impact of educational reform on this population [38]. Other researchers revealed that the data addressing the Healthy
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Matern Child Health J Table 2 State-level surveillance programs with obesity data for high school students with disabilities by state and year of data collection State
Surveillance program
Year 2001 2002
2003
2004
2005
2006 2007
2008
2009
2010
2011
Number of data sets
DE
YRBS
h
–
h
–
h
–
h
–
d
–
d
2
ME
Integrated Youth Health Survey
–
–
–
–
–
–
–
–
d
–
d
2
MA
YRBS
h
–
d
–
d
–
d
–
d
–
d
5
Youth Health Survey
–
–
–
–
–
–
d
–
d
–
d
3
MN
Student Survey
–
–
–
–
–
–
d
–
–
d
–
2
MT
YRBS
d
–
d
–
d
–
d
–
d
–
d
6
NC ND
YRBS YRBS
d –
– –
d h
– –
d d
– –
d h
– –
d d
– –
d d
6 3
OR
Oregon Healthy Teens
h
h
h
d
h
d
h
d
h
–
h
3
RI
YRBS
h
–
h
–
h
–
d
–
d
–
d
3
VT
YRBS
d
–
d
–
d
–
d
–
d
–
h
5
WA
Healthy Youth Survey
–
d
–
d
–
h
–
d
–
h
–
3
Number of data sets with disability screening questions
3
1
4
2
5
1
7
2
9
1
8
43
2006
2007
2008
d Year in which data included disability screening question(s) h Year in which data did not include disability screening question(s) – Year in which surveillance was not conducted
Table 3 Disability screening questions used in state surveillance programs across years State
DE
ME
MA
Surveillance program
Disability construct addressed
YRBS
Emotional problems or LD
Integrated Youth Health Survey
YRBS
Year 2001
2002
2003
2009 2010
2011
d1
d2
Physical disabilities or health problems
d
3
d4
Emotional problems or LD
d5
d5
Physical disabilities or health problems
d
6
d6
Perception of others
d7
d7
8
d d9
d8 d9
d6
d6
d2
d2
d2
6
6
d6
Activity limitations Emotional problems or LD d6
Physical disabilities or health problems
d10
Special education service Youth Health Survey
MN
Student Survey
2004 2005
Emotional problems or LD Physical disabilities or health problems
d
Perception of others
d7
Activity limitations Physical disabilities or health problems
d8 d4
d4
Mental/emotional problems problem
d12
d12
NC
YRBS YRBS
Special education service Self-perceived disability Activity limitations Trouble learning
123
d
d13
Special education service MT
d11
14
d
15
d
16
d
17
d
d
14
d
15
d
16
d
17
14
d
15
d
16
d
17
d
14
d
15
d
d13 d
14
d14
d
15
d15
16
d16
17
d18
d d
Matern Child Health J Table 3 continued State
ND
Surveillance program YRBS
Disability construct addressed
Year 2001
2002
2003
2004 2005
2006
2007
2008
2009 2010
Emotional problems or LD
d
Physical disabilities or health problems
d6
8
d
Activity limitations Oregon Healthy Teens Survey
d20
Activity limitations
d
Assistive device
d8
YRBS
d22
WA
YRBS Healthy Youth Survey
Special education service
d22 2
d2
d2
6
6
d6
d
Emotional problems or LD
d
Physical disabilities or health problems VT
d8
21
Etiology RI
d19 d14
Special education service OR
2011
2
d23
d24 2
d24 2
d
d24
d24
Emotional problems or LD
d
d
Physical disabilities or health problems
d6
d6
d6
Activity limitations
d8
d8
d8
7
7
d7
Perception of others
d
d
d
2
1
Ever been diagnosed with a learning disability?
2
Have long-term emotional problems or learning disabilities?
3
Ever been diagnosed with a long term physical disability which limits the things you can do?
4
Have a physical health condition or problem that has lasted at least 12 months?
5
Have any long-term emotional or behavioral problems?
6
Have any physical disabilities or long-term health problems?
7
Other people consider you to have any disabilities or long-term health problems, including physical health, emotional, or learning problems?
8
Limited in any activities because of any disabilities or long-term health problems, including physical health, emotional problems, or learning problems?
9
Have any long-term learning disabilities or emotional problems?
10 11
Receive special education services? Receive special education services or have IEP?
12
Have a mental or emotional health problem that has lasted at least 12 months?
13
Ever had an IEP?
14
Receive help from a resource teacher, speech therapist or other special education teacher at school?
15
Do you consider yourself to have a disability?
16
Limited in any way in any activities because of any impairment or health problem?
17
Because of any impairment or health problem, do you have any trouble learning, remembering, or concentrating?
18
Have trouble learning, remembering, or concentrating because of disability or health problem?
19
Have any long-term health problems
20
Limited in any way in any activities because of any physical, mental or emotional condition?
21
Use special equipment, such as a cane, a wheelchair, a special bed, or a special telephone?
22
Has a doctor, nurse, or other health professional ever told you that you have: physical condition, including developmental conditions (spina bifida, cerebral palsy, etc.), long-term injuries (spinal cord injury, etc.), blindness or problem seeing (other than needing glasses or contacts) or deafness or problem hearing, an emotional condition such as depression or anxiety, a learning disorder, attention deficit disorder, ADHD, or severe learning disability such as mental retardation?
23
Have been receiving services or support through an IEP (Individualized Education Plan) or a 504 Plan?
24
Have been receiving services or support through an IEP (Individualized Education Plan)?
People 2010 objectives for adolescents with disabilities were limited. Of 21 critical health objectives for adolescents, only one objective included data on adolescents with
chronic illness/physical disabilities. Data on adolescents with learning disabilities and emotional/behavior difficulties were available for four and five objectives, respectively
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[39]. These findings, combined with the present results, highlight the persistent scarcity of surveillance data on students with disabilities in both federal and state-level health data systems. There are several approaches that states could take to better track obesity in students with disabilities. In light of the fact that the majority of state surveillance programs were YRBS operated through a federal-state partnership, the Centers for Disease Control and Prevention (CDC), a leading federal agency, could facilitate the continuous inclusion of a disability screen across state YRBS. Oversampling is another commonly used approach in public health surveillance programs to produce a stable population-level estimate for relatively small subpopulation groups such as students with disabilities [40]. Collaboration with state departments of education may be helpful for oversampling students who access special education services. School-based BMI surveillance programs, which some states have conducted, could be another resource to explore [41, 42]. Though the mechanism of implementation for these programs may vary across states and the extent of inclusion of high school students with disabilities is unknown, they may provide information that is not available through the surveillance programs examined in the present study. Illinois, for example, has used mandatory school physical examination records to extract BMI data based on actual measurements of weight and height by a health professional, and California tracks students’ BMI along with their physical fitness data. Identifying students who access special education services in these datasets could provide reliable BMI data, along with other health status information, for subgroups of students with disabilities. Our finding of inconsistent definitions of disability within and across surveillance programs is consistent with previous observations made on national surveillance programs. Disability is a complex and multi-dimensional phenomenon. It includes etiologies (e.g., Down syndrome, spina bifida, spinal cord injury, and ADHD), the resulting limitations (e.g., intellectual disabilities, physical limitations, and sensory limitations), and difficulties with participation in school, social, and community activities associated with these conditions. Further, a recent social model of disability has emphasized the importance of contextual factors that contribute to disability [43]. It is difficult to summarize such a complex concept with a manageable set of questions that can be used in a surveillance program [44]. Consequently, the lack of consensus regarding an operational definition of disability across and within states may be an explanation for the use of different questions in various combinations to define the population of high school students with disabilities in state surveillance programs.
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Efforts have been made to ensure the consistent use of disability screening questions in surveillance programs. The federal government recently standardized disability screening questions in federally sponsored surveillance programs [45]. Included among the standardized questions were six functional limitation questions, conceptually based on a model of disability from the International Classification of Functioning, Disability, and Health [43], and intended to accentuate differences between individuals with and without disabilities. Similar efforts to standardize the disability screening questions for high school students across state-based surveillance programs may contribute to a better understanding of the changes over time in obesity and other health characteristics of students with disabilities, effective program evaluation on the impact of obesity reduction strategies for this population in each state, and the targeted distribution of resources. The interpretation of the present findings is subject to at least the following three methodological limitations. First, the list of health surveillance programs collecting obesity data for high school students identified in this study might not be exhaustive because of the complexity of state-level surveillance programs. We based our search primarily on state-based YRBS programs, but the YRBS is sometimes combined with another surveillance program or is simply named differently in some states. In Connecticut, for example, the YRBS was renamed the Youth Behavior Component and combined with the Youth Tobacco Component to constitute the School Health Survey. In New Mexico, the YRBS was embedded within the Youth Risk and Resiliency Survey. In Georgia, the YRBS was renamed the Student Health Survey. Although we extensively sought out and examined survey questionnaires and other published reports from non-YRBS surveillance programs for the inclusion of body weight and height information, it is possible that there were surveillance programs that we did not discover, resulting in an underestimation of the number of surveillance programs and datasets with information on obesity. Second, some of the disability screening questions included in the present study may capture students who do not have disabilities. The questions selected in the study addressed the presence of long-term health problems or conditions. Included are, for example, ‘‘longterm health problems’’ (2011 North Dakota YRBS) and ‘‘a physical health condition or problem that has lasted at least 12 months’’ (2011 Delaware YRBS, 2007 and 2010 Minnesota SS). While these questions can certainly capture students with disabilities, they may also capture students who have chronic health conditions but do not have disabilities or activity limitations, such as those with mild asthma, allergies, and vision problems that can
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be corrected with glasses or contact lenses. The use of these questions as a disability screen may result in the overestimation of students with disabilities in certain states. Third, for the surveillance programs that collected obesity data on students with disabilities, the extent of these students’ participation is unknown. With the exception of the Minnesota SS, which included special schools in their sample [46], we did not find any documentation suggesting that states actively sought the inclusion of students with disabilities in their data collection. Typically, the method of data collection for these surveillance programs is a paper and pencil survey administered in a sampled classroom [37]. It is unclear whether these classrooms included students with disabilities. Further, we found no information from any state on whether they provided accommodations to facilitate the survey participation of students with disabilities in the classrooms. Thus, the students with disabilities included in these surveillance programs might not be representative of the larger population, and obesity data based on these students should be interpreted with caution. Population data from surveillance programs on children and youth can highlight points of concern, clarify service priorities, and, consequently, provide the foundation for decision making for maternal and child health policymakers [47]. Despite the current administration’s focus on the reduction of childhood obesity to ‘‘5 % by 2030’’ (p. 9) [48], our findings suggest that there is currently little state-level data with which to monitor changes among high school students with disabilities. One of the core public health beliefs is ‘‘what gets measured gets done’’ [39]. Obesity and other health data on adolescents with disabilities can be used to monitor their health status and health service needs. The continued absence of this type of data may impede the ability of maternal and child health advocates and policymakers across states to address a critical heath issue for this already vulnerable population and may widen existing health disparities as young people with disabilities progress toward adulthood. Acknowledgments The preparation of this manuscript was supported in part by Contract Number H133A100011 from the National Institute on Disability and Rehabilitation Research, US Department of Education to the University of Alabama-Birmingham. The authors would like to thank state YRBS surveillance coordinator Lisa Whittle, Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Division of Adolescent and School Health, and high school health surveillance coordinators across states for their technical assistance in obtaining datasets and confirming the information we found in our research.
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