Chronic health conditions and school performance among children and youth

Chronic health conditions and school performance among children and youth

Annals of Epidemiology 23 (2013) 179e184 Contents lists available at SciVerse ScienceDirect Annals of Epidemiology journal homepage: www.annalsofepi...

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Annals of Epidemiology 23 (2013) 179e184

Contents lists available at SciVerse ScienceDirect

Annals of Epidemiology journal homepage: www.annalsofepidemiology.org

Chronic health conditions and school performance among children and youth Casey Crump MD, PhD a, *, Diana Rivera b, Rebecca London PhD c, Melinda Landau MSN d, Bill Erlendson PhD d, Eunice Rodriguez DrPH b a

Department of Medicine, Stanford University, Stanford, California Department of Pediatrics, Stanford University, Stanford, California c John W. Gardner Center, Graduate School of Education, Stanford University, Stanford, California d San Jose Unified School District, San Jose, California b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 September 2012 Accepted 2 January 2013 Available online 12 February 2013

Purpose: Chronic health conditions are common and increasing among U.S. children and youth. We examined whether chronic health conditions are associated with low school performance. Methods: This retrospective cohort study of 22,730 children and youth (grades 2e11) in San Jose, California, was conducted from 2007 through 2010. Health conditions were defined as chronic if reported in each of the first 2 years, and school performance was measured using standardized English language arts (ELA) and math assessments. Results: Chronic health conditions were independently associated with low ELA and math performance, irrespective of ethnicity, socioeconomic status, or grade level. Adjusted odds ratios for the association between any chronic health condition and low (“basic or below”) performance were 1.25 (95% confidence interval [CI], 1.16e1.36; P < .001) for ELA and 1.28 (95% CI, 1.18e1.38; P < .001) for math, relative to students without reported health conditions. Further adjustment for absenteeism had little effect on these results. The strongest associations were found for ADHD, autism, and seizure disorders, whereas a weak association was found for asthma before but not after adjusting for absenteeism, and no associations were found for cardiovascular disorders or diabetes. Conclusions: Chronic neurodevelopmental and seizure disorders, but not cardiovascular disorders or diabetes, were independently associated with low school performance among children and youth. Ó 2013 Elsevier Inc. All rights reserved.

Keywords: Achievement Chronic disease Schools Students

Introduction Chronic health conditions have been measured in various ways [1], but are often defined as conditions that have lasted or are expected to last more than 3 months [2] or 1 year [3], and involve functional limitations or medical needs greater than usual for one’s age. By virtually any measure, chronic health conditions have increased among children and youth in the United States during the past 50 years [4]. The prevalence of chronic conditions resulting in physical disability has increased from 1.8% to more than 7% since 1960, now affecting more than 5 million children and youth [5]. Other measures including conditions without physical disability indicate a prevalence of at least 15% to 18% [4]. Although some of the reported increases may be owing to changes in definitions and ascertainment, a substantial part has resulted from improved survival of children with serious congenital or acquired illnesses [1], as well as increasing incidence of neurodevelopmental disorders * Corresponding author. 211 Quarry Road, Suite 405, MC 5985, Palo Alto, CA 94304-1426. Tel.: 650-723-6963; Fax: 650-498-7750. E-mail address: [email protected] (C. Crump). 1047-2797/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.annepidem.2013.01.001

and asthma, which now affect 15% [6] and 9% [7] of U.S. children, respectively. The growth of such conditions is expected to have major economic and social consequences as these individuals enter adulthood, owing to higher health care expenditures and lower workforce participation and productivity [4]. Although the physical effects of chronic health conditions have been widely studied [1], less is known about cognitive outcomes such as school performance. These outcomes are particularly important because educational attainment is itself a determinant of future health [8]. To the extent that chronic health conditions in early life adversely affect school performance, a reciprocal relationship between education and health is reinforced, leading to greater disparities in each [9]. With few exceptions like attentiondeficit hyperactivity disorder (ADHD) [10e12] and autism [13], the academic effects of chronic conditions are unclear because most studies have been small and have yielded inconsistent results. Some [14, 15] but not all [16e20] authors have reported that asthma is associated with low academic performance. Studies of other conditions, including seizure disorders and diabetes, have also yielded discrepant results [21, 22]. These studies have had various limitations, including insufficient sample sizes, inadequate

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adjustment for confounding, and the inability to examine these associations across a wide range of ages among children and youth. We sought to address these limitations by conducting the largest study of this issue to date. A cohort of 22,730 children and youth was followed up from 2007 through 2010 to examine (1) whether chronic health conditions were associated with absenteeism, after adjusting for potential sociodemographic confounders and (2) whether chronic health conditions were associated with low school performance, after adjusting for potential sociodemographic confounders and further adjusting for absenteeism. We hypothesized that chronic health conditions would be independently associated with increased absenteeism, and with low school performance after adjusting for potential confounders and absenteeism. Methods This study was based on demographic, health, and academic performance data obtained from school records for students enrolled in the San Jose Unified School District in San Jose, California, during the 2007/2008, 2008/2009, and 2009/2010 school years. This district includes 52 schools (27 elementary, 7 middle, 7 high, and 11 alternative) and has a diverse student population, with 43.5% enrolled in the Free and Reduced Price Lunch program (indicating low-income status) and 26.1% not yet English proficient. The district as a whole did not meet the proficiency gains required by No Child Left Behind in 2010. Academic performance was measured using the California Standards Test (CST), a standardized assessment of English language arts (ELA) and math given annually in the spring to all students in grades 2 through 11. A total of 32,548 students were enrolled in these grades during the 2007/2008, 2008/2009, and/or 2009/2010 school years. Of this total, we excluded 540 students (1.7%) because they did not take the CST during the study period. We excluded 9278 others (28.5%) because they were enrolled in the district for only 1 year and therefore we were unable to assess the chronicity of reported health conditions. A sensitivity analysis (described below) was performed to assess the effect of this exclusion on the results. A total of 22,730 students (69.8%) remained for inclusion in the main analyses. All data were deidentified and kept strictly anonymous. This study was approved by the San Jose Unified School District and the Institutional Review Board of Stanford University, and met all regulations of the Family Educational Rights and Privacy Act. Chronic health conditions (predictor variables) The health status of each student was ascertained using a parental survey administered by school nursing staff. Parents were required to complete a written health information survey at the beginning of the 2007/2008 and 2008/2009 school years. This survey, provided in English and Spanish, asks parents to identify specific conditions that apply to their child from a list that includes asthma, ADHD, seizures, heart problems, and diabetes. Parents were also asked to list “any other current medical conditions” and any medications taken by the child at home or at school. The response rate each year was 95%. In addition, the survey ascertained whether a student had a physician-prescribed treatment plan involving medication use at school; health conditions among such students (1.2% of the cohort) were verified by nursing staff with the prescribing physician. Health conditions were defined as chronic if they were reported in each of the first 2 study years (i.e., preceding measurement of the outcome) and were expected to result in medical needs or services

greater than usual for the student’s age. Measurement of prevalence was not an aim of this study. This definition may underestimate the prevalence of chronic conditions but is expected to improve their positive predictive value and thereby improve the validity of the measured associations. Specifically, the following conditions were examined as predictor variables: (1) Asthma, (2) seizure disorders, (3) ADHD, (4) autism, (5) mental health disorders, (6) cardiovascular disorders, and (7) diabetes mellitus. We also examined in a combined category “other chronic conditions” that were not reported in sufficient numbers to allow the analysis of each separately. These included autoimmune disorders, blood disorders, cancer, endocrine disorders other than diabetes, kidney or urologic disorders, gastrointestinal disorders, cystic fibrosis, respiratory disorders other than asthma, neurologic disorders other than seizures, genetic syndromes, chronic orthopedic disorders, frequent ear infections, frequent headaches, and severe vision or hearing disorders. The reference category for all analyses consisted of students who had none of these conditions reported during the study period. Students with conditions reported in only 1 year were not included in the reference group to reduce misclassification (i.e., false negatives), because an unknown number of these conditions may have been chronic. School absenteeism and performance (outcome variables) The study outcomes were (1) the number of full-day school absences per year (regardless of reason), based on school attendance records and (2) CST results for ELA and math from the 2008/ 2009 and 2009/2010 school years. The California Department of Education reports CST results as numerical scores ranging from 200 to 600, but these scores are not directly comparable across grades or school years. We therefore measured academic performance in three ways: (1) CST performance levels dichotomized as “basic or below” versus “proficient or advanced” (a standard metric used by the California Department of Education to determine which schools have met the “No Child Left Behind” performance goals); (2) CST scores categorized into five ordered performance levels (advanced, proficient, basic, below basic, and far below basic), which are determined each year by the California Department of Education; and (3) numerical CST scores converted to z-scores, indicating the number of standard deviations above or below the mean CST score for a particular grade and school year. Other study variables Low socioeconomic status and other demographic factors have previously been associated with chronic health conditions [23, 24] and with school absenteeism and/or low school performance [25]. Based on these previously reported associations, the following were identified from school records as potential confounders and included as adjustment variables: Age (modeled as a continuous variable by date of birth); gender (female or male); ethnicity (American Indian/Alaskan, Asian, Black, Filipino, Hispanic, Pacific Islander, White, or multiple/unknown); primary language (English or other); grade level (2e5, 6e8, or 9e11; modeled as a categorical variable to allow for a nonlinear effect); special education (yes or no, identified in each study year; information on Individualized Educational Plans or a 504 Plan was unavailable); participation in the federal Free or Reduced Price Lunch program (yes or no, identified in each study year; eligibility is determined by a family income below 185% of the federal poverty line); and parental education level (self-reported, highest-attained education level by either parent: Not a high school graduate, high school graduate, some college, college graduate, graduate school, or unknown).

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Statistical analysis Generalized estimating equations (GEE) for a negative binomial distribution were used to estimate incidence rate ratios and 95% confidence intervals (CIs) for the number of school absences per year, comparing individuals with chronic health conditions with those without reported conditions. Negative binomial models are a standard method for analyzing count data that are overdispersed (variance larger than the mean) [26], and were a significantly better fit for these data than Poisson models. GEE for a binomial distribution was used to estimate odds ratios (ORs) and 95% CIs for the association between chronic health conditions and CST performance levels either dichotomized or in five ordered categories (as defined above). GEE for a Gaussian distribution was also used to estimate coefficients and 95% CIs for the difference in CST z-scores, comparing individuals with chronic health conditions with those without reported conditions. Robust standard errors were used in all models to account for intrasubject correlation across different school years, and among students with more than one reported condition or whose participation in special education or in the Free or Reduced Price Lunch program changed during the study period. Analyses were conducted first unadjusted and then using two different adjusted models: The first included age, gender, ethnicity, language, grade level, special education, participation in the Free or Reduced Price Lunch program, and parental education; and the second included the same variables as well as the number of school absences as a continuous variable. School absences were alternatively modeled as a categorical variable (0e1, 2e4, 5e9, 10), but the results were unchanged. We explored first-order interactions between chronic health conditions and each of the covariates with respect to CST performance using a likelihood ratio test. All statistical tests were two-sided and used an a-level of 0.05. All analyses were conducted using Stata version 11.0 [27]. We performed a sensitivity analysis to assess the effect of excluding students who were enrolled for only 1 year (n ¼ 9278; 28.5%). As noted, these students were excluded from the main analyses because we were unable to assess the chronicity of reported health conditions. In this sensitivity analysis, we repeated the main analyses after including these students with the main cohort and examining a range of putative prevalences (10%e25%) of chronic health conditions among these students. Results In this cohort of 22,730 children and youth, 50.2% were Hispanic, 27.6% were White, 13.8% were Asian, 3.4% were Black, and 5.0% were other ethnicities. Nearly half (46.7%) participated in the Free and Reduced Price Lunch program. Chronic health conditions were identified in a total of 2891 (12.7%) students. Asthma was the most prevalent and was identified in 1387 (6.1%) students. Compared with students without chronic conditions, those with chronic conditions were more likely to be male, be in higher grade levels, be White or Black, be in special education, or speak English as their primary language (Table 1). School absenteeism Associations between chronic health conditions and absenteeism are presented in Table 2. All conditions except autism were associated with increased absenteeism. After adjusting for covariates, the absentee rate among students with any chronic health condition was 1.30 times larger (95% CI, 1.26e1.35; P < .001) than among those without reported health conditions. Students with mental health disorders had the highest absentee rate (adjusted incidence rate ratio, 1.88; 95% CI, 1.59e2.23; P < .001). In addition,

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Table 1 Student characteristics by health status (N ¼ 22,730) Chronic health conditions* (n ¼ 2891), n (%) Gender Female 1201 Male 1690 Grade level 2e5 1101 6e8 906 9e11 884 Ethnicity American Indian or Alaskan 30 Asian 215 Black 147 Filipino 46 Hispanic 1420 Pacific Islander 24 White 951 Multiple or unknown 58 Primary language English 1945 Other 946 Special education No 2254 Yes 637 Free or reduced price lunch program No 1585 Yes 1306 Parental education Not a high school graduate 389 High school graduate 456 Some college 651 College graduate 618 Graduate school 408 Unknown 369

No chronic health conditions* (n ¼ 19,839), n (%)

(41.5) (58.5)

9768 (49.2) 10,071 (50.8)

(38.1) (31.3) (30.6)

8337 (42.0) 5879 (29.6) 5623 (28.4)

(1.0) (7.5) (5.1) (1.6) (49.1) (0.8) (32.9) (2.0)

160 2921 633 419 9988 97 5326 295

(0.8) (14.7) (3.2) (2.1) (50.3) (0.5) (26.9) (1.5)

(67.3) (32.7)

9614 (48.5) 10,225 (51.5)

(78.0) (22.0)

18,321 (92.3) 1518 (7.7)

(54.8) (45.2)

10,544 (53.1) 9295 (46.9)

(13.4) (15.8) (22.5) (21.4) (14.1) (12.8)

3433 2992 3283 4314 3252 2565

(17.3) (15.1) (16.6) (21.7) (16.4) (12.9)

* Health conditions were defined as chronic if reported in each of the first 2 study years.

the number of absences was inversely associated with ELA and math performance, whether modeled as continuous z-scores or dichotomized (P < .001 in adjusted model for ELA or math; data not shown). School performance Associations between chronic health conditions and ELA or math performance are presented in Table 3. ELA and math performance was first examined after dichotomizing to a standard metric of “basic or below” versus “proficient or advanced” performance levels. After adjusting for sociodemographic factors, ORs for the association between any chronic health condition and “basic or below” performance were 1.25 (95% CI, 1.16e1.36; P < .001) for ELA and 1.28 (95% CI, 1.18e1.38, P < .001) for math, relative to students without reported health conditions (Table 3, adjusted model 1). Further adjustment for the number of school absences had only a modest effect on these results (Table 3, adjusted model 2). Among specific conditions in the fully adjusted model, the strongest associations were between ADHD and low ELA or math performance (ELA: OR, 1.70 [95% CI, 1.40e2.05; P < .001]; math: OR, 1.90 [95% CI, 1.57e2.29; P < .001]), between autism and low ELA or math performance (ELA: OR, 1.79 [95% CI, 1.14e2.81; P ¼ .01]; math: OR, 1.64 [95% CI, 1.00e2.70; P ¼ .05]), and between seizure disorders and low ELA performance (OR, 1.57; 95% CI, 1.01e2.45; P ¼ .04). Asthma was weakly associated with low ELA and math performance after adjusting for sociodemographic factors, but not after further adjusting for absences. In addition, mental health disorders were associated with low math performance before but not after adjusting for absences. No association was found between cardiovascular disorders or diabetes and either ELA or math performance.

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Table 2 Incidence rate ratios for number of absences per year Chronic health conditions

n

No reported condition Any chronic condition Asthma Seizure disorders ADHD Autism Mental health disorders Cardiovascular disorders Diabetes Other chronic conditions

19,019 2891 1387 84 513 94 116 119 35 722

Adjusted*

Unadjusted IRR

95% CI

P

IRR

95% CI

P

Reference 1.45 1.43 1.44 1.32 1.07 2.28 1.46 1.50 1.55

d 1.40e1.50 1.37e1.50 1.19e1.73 1.23e1.42 0.90e1.28 1.92e2.71 1.23e1.73 1.17e1.93 1.44e1.66

d <.001 <.001 <.001 <.001 .45 <.001 <.001 .001 <.001

Reference 1.30 1.29 1.19 1.11 1.01 1.88 1.33 1.50 1.42

d 1.26e1.35 1.23e1.35 1.01e1.40 1.03e1.20 0.84e1.21 1.59e2.23 1.13e1.57 1.12e2.00 1.33e1.53

d <.001 <.001 .04 .005 .93 <.001 .001 .006 <.001

ADHD ¼ attention deficit hyperactivity disorder; CI ¼ confidence interval; IRR ¼ incidence rate ratio. * Adjusted for age, gender, ethnicity, language, grade level, special education, participation in the Free or Reduced Price Lunch program, and parental education.

CST performance was alternatively analyzed in five ordered categories using GEE ordinal logistic regression, and as continuous z-scores using GEE linear regression. These analyses have slightly greater statistical power but less ease of interpretation than those presented for a dichotomized outcome. The results overall were consistent with those reported, with comparable effect sizes and significance levels (data not shown). Significant first-order interactions were found between “any chronic health condition” and parental education level, participation in the Free or Reduced Price Lunch program, or ethnicity with respect to “basic or below” ELA or math performance (P < .001 for each of these interactions; data not shown). In particular, the association between chronic health conditions and low ELA or math performance was stronger among students with a high parental education level or nonparticipation in the Free or Reduced Price Lunch program. Chronic health conditions were associated with low ELA or math performance among all ethnicities, but the association was stronger among Asians, Blacks, and Whites than among Hispanics. No other interactions were found, including between chronic health conditions and special education status with respect to ELA (P ¼ .95) or math (P ¼ .16) performance. Only 1.2% of students had a physicianprescribed plan for medication use at school (ascertained by parental

survey), which for certain conditions may reflect greater severity, but this was not an effect modifier in these data. Sensitivity analyses in which we included students who were enrolled for only 1 year (n ¼ 9278; 28.5%) showed that most associations between chronic health conditions and low ELA or math performance were slightly attenuated, but all remained highly significant (P < .001 in unadjusted and adjusted models; eTable 1). Attenuation of these risk estimates toward the null hypothesis was expected because of increased misclassification of chronic health conditions among these students (i.e., they were not enrolled long enough to confirm the chronicity of reported health conditions), which was nondifferential with respect to the outcome (school performance). However, these sensitivity analyses suggest that exclusion of these students did not substantially affect the main findings or inferences. Discussion In this large cohort study, chronic health conditions were independently associated with low performance in ELA and math among children and youth, irrespective of ethnicity, socioeconomic status, or grade level. The strongest associations were observed for

Table 3 Odds ratios (ORs) for association between chronic health conditions and “basic or below” performance on the California standards test

English language arts No reported condition Any chronic condition Asthma Seizure disorders ADHD Autism Mental health disorders Cardiovascular disorders Diabetes Other chronic conditions Math No reported condition Any chronic condition Asthma Seizure disorders ADHD Autism Mental health disorders Cardiovascular disorders Diabetes Other chronic conditions

Adjusted model 2y

Adjusted model 1*

Unadjusted OR

95% CI

P

OR

95% CI

P

OR

95% CI

P

Reference 1.45 1.30 2.54 2.23 2.44 1.50 1.14 0.96 1.33

1.36e1.56 1.18e1.43 1.67e3.89 1.89e2.62 1.67e3.58 1.08e2.09 0.82e1.59 0.53e1.74 1.16e1.52

<.001 <.001 <.001 <.001 <.001 .02 .43 .88 <.001

Reference 1.25 1.13 1.59 1.72 1.80 1.14 1.07 1.01 1.23

1.16e1.36 1.01e1.26 1.02e2.48 1.42e2.08 1.15e2.81 0.77e1.67 0.74e1.54 0.49e2.10 1.06e1.43

<.001 .03 .04 <.001 .01 .51 .73 .97 .008

Reference 1.22 1.09 1.57 1.70 1.79 1.00 1.03 0.95 1.18

1.12e1.32 0.98e1.22 1.01e2.45 1.40e2.05 1.14e2.81 0.67e1.50 0.71e1.49 0.46e1.99 1.01e1.37

<.001 .11 .04 <.001 .01 .99 .89 .90 .04

Reference 1.56 1.30 2.35 2.80 1.89 2.43 1.23 1.11 1.49

1.46e1.68 1.18e1.44 1.55e3.54 2.36e3.31 1.30e2.75 1.73e3.42 0.89e1.70 0.63e1.96 1.30e1.70

<.001 <.001 <.001 <.001 .001 <.001 .20 .71 <.001

Reference 1.28 1.11 1.41 1.91 1.59 1.63 1.01 0.88 1.24

1.18e1.38 1.00e1.24 0.89e2.23 1.58e2.31 0.96e2.62 1.13e2.36 0.73e1.41 0.44e1.76 1.06e1.44

<.001 .05 .14 <.001 .07 .01 .95 .72 .006

Reference 1.21 1.05 1.38 1.90 1.64 1.37 0.96 0.80 1.13

1.12e1.31 0.94e1.16 0.87e2.18 1.57e2.29 1.00e2.70 0.93e2.01 0.68e1.35 0.39e1.63 0.97e1.32

<.001 .41 .17 <.001 .05 .11 .82 .55 .12

ADHD ¼ attention deficit hyperactivity disorder; CI ¼ confidence interval. * Adjusted for age, gender, ethnicity, language, grade level, special education, participation in the Free or Reduced Price Lunch program, and parental education. y Adjusted for the same variables as above, and number of absences per year.

C. Crump et al. / Annals of Epidemiology 23 (2013) 179e184

ADHD, autism, and seizure disorders, and these were not explained by absenteeism. Asthma was weakly associated with low ELA and math performance, and mental disorders with low math performance, but this seemed to be explained by increased absenteeism. In contrast, cardiovascular disorders and diabetes were not associated with low ELA or math performance. We also found that the observed associations were stronger among students whose parents were more highly educated. This may have been owing to underdiagnosis and/or underreporting of health conditions among those whose parents were less educated. Lower concurrence between parental report and medical records has previously been reported among less educated compared with more educated parents [28]. To the extent that this occurred in the current study, the association between chronic health conditions and low school performance was biased toward the null hypothesis and therefore underestimated. The link between low educational attainment and poor health outcomes in adulthood has been well-documented [8, 9, 29]. The current study contributes more proximal evidence for the effects of chronic conditions in early life on school performance. Consistent with previous studies [30, 31], it suggests that neurodevelopmental disorders are a more important determinant of low school achievement than physical disorders. Broader behavioral problems in childhood have also been associated with low educational attainment and worse employment outcomes in young adulthood [32]. Developmental disorders (ADHD, autism, and other developmental delays) increased in prevalence from 12.8% to 15.0% between 1997 and 2008 among U.S. children and youth [6]. The substantial growth of these conditions will likely contribute to adverse employment outcomes and increased economic and health disparities as these individuals enter adulthood [4]. In contrast, we found that cardiovascular disorders and diabetes were not associated with low school performance. The academic effects of these conditions are relatively understudied, although several smaller studies have reported that diabetes is associated with low cognitive ability or school performance in childhood [21e33]. Asthma was weakly associated with low school performance in the current study, but this seemed to be largely explained by absenteeism. Previous studies of asthma have yielded discrepant results but were based on smaller sample sizes and varied widely in adjustment for confounding [14e20]. Additional large cohort studies with more detailed information on severity of illness are needed to further elucidate these relationships. Important strengths of this study included its large sample size and the inclusion of children and youth across a wide range of grade levels, enabling more robust and generalizable inferences. We were able to examine various chronic conditions to assess their relative importance, while adjusting for potential confounders. Information on school absences enabled the examination of absenteeism as a potential mechanism underlying the findings. Adjustment for an intermediate variable such as absenteeism can provide valid estimates of a direct causal effect of chronic health conditions on low school performance under certain assumptions, namely, the absence of unmeasured confounding in the associations between absenteeism or chronic health conditions and low school performance [34, 35]. Although we adjusted for potential confounders more thoroughly than most previous studies, unmeasured confounding cannot be excluded; therefore, the risk estimates that are adjusted for absenteeism should be interpreted with caution. Other study limitations include the ascertainment of chronic health conditions by parental survey rather than clinical diagnostic information, which was unavailable. Previous studies have found that parent- or student-reported asthma is a good indicator of current asthma [36, 37], but the reliability of parental report is less well-established for other conditions. To reduce misclassification,

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we defined chronic health conditions as those that were reported in each of the first 2 years, and the reference group consisted of students without reported health conditions. This may have underestimated the prevalence of chronic health conditions (which was not a study aim), but is expected to improve the validity of the measured associations. Information on intelligence or specific learning disorders was unavailable; therefore, we were unable to assess these factors more directly. However, we found that chronic health conditions were associated with low school performance irrespective of whether the affected student was in regular or special education. Special education was measured only as a dichotomous variable, and residual confounding or effect modification by this factor or by specific underlying learning disorders is possible. Information on disease severity was also unavailable. The weak association we found between asthma and low school performance may have been stronger among those with severe asthma [20]. A small percentage of students had a physician-prescribed plan for medication use at school, which may reflect more severe illness, but this was not an important effect modifier in this cohort. In conclusion, this large cohort study found that chronic neurodevelopmental and seizure disorders, but not cardiovascular disorders or diabetes, were independently associated with low school performance among children and youth, irrespective of ethnicity, socioeconomic status, or grade level. The substantial growth of these conditions among U.S. children and youth is expected to contribute to increased economic and health disparities. Targeted educational, medical, and social support interventions are needed at early ages to reduce disparities from these conditions. Acknowledgments Supported in part by grants from the Lucile Packard Foundation for Children’s Health and the Lucile Packard Children’s Hospital, Palo Alto, California. The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript. There were no conflicts of interest. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.annepidem.2013.01.001. References [1] van der Lee JH, Mokkink LB, Grootenhuis MA, Heymans HS, Offringa M. Definitions and measurement of chronic health conditions in childhood: a systematic review. JAMA 2007;297(24):2741e51. [2] Perrin EC, Newacheck P, Pless IB, et al. Issues involved in the definition and classification of chronic health conditions. Pediatrics 1993;91(4):787e93. [3] Stein RE, Bauman LJ, Westbrook LE, Coupey SM, Ireys HT. Framework for identifying children who have chronic conditions: the case for a new definition. J Pediatr 1993;122(3):342e7. [4] Perrin JM, Bloom SR, Gortmaker SL. The increase of childhood chronic conditions in the United States. JAMA 2007;297(24):2755e9. [5] National Center of Health Statistics. Health United States with chartbook on trends in the health of Americans. Hyattsville (MD): National Center of Health Statistics; 2006. [6] Boyle CA, Boulet S, Schieve LA, et al. Trends in the prevalence of developmental disabilities in US children, 1997-2008. Pediatrics 2011;127(6): 1034e42. [7] Akinbami L. The state of childhood asthma, United States, 1980-2005. Adv Data 2006;(381):1e24. [8] Mechanic D. Population health: challenges for science and society. Milbank Q 2007;85(3):533e59. [9] Fiscella K, Kitzman H. Disparities in academic achievement and health: the intersection of child education and health policy. Pediatrics 2009;123(3): 1073e80.

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