- Email: [email protected]
Improvements in Health-Related Quality of Life Among School-Based Health Center Users in Elementary and Middle School
Improvements in Health-Related Quality of Life Among School-Based Health Center Users in Elementary and Middle School Terrance J. Wade, PhD; Mona E. Mansour, MD; Kristin Line, MEd; Tracy Huentelman, MA; Kathryn N. Keller, MPA Objective.—To examine the role of school-based health centers (SBHCs) on changes in student health-related quality of life (HRQOL) over a 3-year period among elementary and middle school students. Methods.—Design: Three-year longitudinal prospective study. Setting: Four elementary schools with newly implemented SBHCs and 4 elementary comparison schools matched for rural/urban and state, percentage of nonwhite students, and percentage of free or reduced-price lunch–eligible students. Participants: Randomly selected student-parent dyads (n ¼ 579) who responded in all 3 years from 4 intervention schools and 4 comparison schools randomly selected from school enrollment lists. Students in intervention schools were further divided into SBHC users and nonusers. Intervention: SBHC. Measures: The outcome, HRQOL, was measured annually by student self-reported and parent proxy-reported scores using the PedsQL 4.0. School covariates included region and state; individual covariates included child age, gender, race, health insurance, chronic health conditions, family income, and parental marital status.
Results.—Adjusting for school- and individual-level covariates, there was a significant improvement in student-reported HRQOL over the 3 years for the SBHC user group compared with the comparison school group. Other significant predictors of student-reported HRQOL included student age, gender, health insurance, and household income. There were no differences across groups by using parent proxy reports of HRQOL.
S
school-aged children and children in rural settings.10 The recent SBHC census indicated that 48% of SBHCs serve elementary grades, and 27% are located in rural areas.4 Despite the increase in the number of SBHCs that target elementary school-aged children, research on the delivery of services to this age group has been slow to appear in the literature.3 At present, researchers have found that SBHCs improve elementary student health service access3,11 and reduce emergency department use.12 But unlike research on adolescents that reports improved student health with SBHC use,8,13 it is unclear whether these improvements in health status translate to younger students in elementary and middle schools. This study addresses this deficit and examines the role of SBHCs in elementary schools on student health and improvements in health over time by using student self-reported and parent proxy-reported health-related quality of life (HRQOL).
Conclusions.—The SBHC model of health care delivery improves student-reported HRQOL among younger, elementary, and middle school children. Moreover, it appears to have more influence on those children that generally have impeded access to care and who can most benefit from it, specifically those without private health insurance and with lower income levels. KEY WORDS: elementary school; health-related quality of life; school-based health center; school health Ambulatory Pediatrics 2008;8:241–249
ince the 1970s, school-based health centers (SBHC) have steadily increased in number across the United States.1–3 In fact, the latest census of SBHCs conducted in 2004–2005 by the National Assembly of School Based Health Care identifies more than 1709 SBHC programs in the United States.4 This shift in the delivery of health care to children is premised on the assumption that access to health services in school increases use and overall health among children, especially for those children whose access to care is otherwise limited.5–9 Traditionally, the majority of SBHCs have been established to serve middle and high schools, and generally urban areas, but an increasing number of SBHCs provide services to elementary From the Departments of Community Health Sciences and Child and Youth Studies, Brock University, St Catharines, Ontario, Canada (Dr Wade); Department of Pediatrics, University of Cincinnati College of Medicine, Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio (Dr Mansour); Division of Health Policy and Clinical Effectiveness, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio (Ms Line and Ms Huentelman); and Health Foundation of Greater Cincinnati, Cincinnati, Ohio (Ms Keller). Address correspondence to Terrance J. Wade, Department of Community Health Sciences, Brock University, 500 Glenridge Ave, St Catharines, Ontario, L2S 3A1, Canada (e-mail: [email protected]). Received for publication September 25, 2007; accepted February 18, 2008. AMBULATORY PEDIATRICS Copyright Ó 2008 by Academic Pediatric Association
METHODS Sample The data come from a 3-year longitudinal study that evaluated the effect of newly implemented SBHCs in elementary and middle schools on student health.14 The structural attributes of each SBHC are available from the authors. The study was reviewed and approved by the
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appropriate institutional review board. The study population consisted of all students enrolled at 4 selected public schools, 2 in southwest Ohio and 2 in northern Kentucky, that implemented a SBHC in 2000–2001. These 4 schools were selected from 8 funded SBHCs to provide a representative cross section of urban and rural schools funded across each state. Four comparison schools without SBHCs were selected matched on rural/urban and state, and percentage of nonwhite and free- or reduced-price lunch–eligible students. The resulting distribution is one intervention school and one comparison school in each grouping (ie, Kentucky rural, Kentucky urban, Ohio rural, Ohio urban). From the school enrollment lists, a simple random sample of parents from each school was selected in year 1. On the basis of power calculations for the final longitudinal sample, and assuming a 30% yearly attrition rate, the minimum first year parent-child dyad sample size was estimated to be 675 for both the intervention and comparison groups. Informed consent of parents for their participation as well as their verbal permission to allow us to interview their child in school were obtained in each year at the time of the telephone interview. Students provided informed assent at the time of the school interview. For year 1, a total of 1599 parents completed an interview. From this baseline parent sample, 239 children were not interviewed in year 1 for the following reasons: 164 legal guardians did not grant permission to have their child interviewed, 25 surveys were completed by someone other than the child’s legal guardian, 47 children whose parents gave permission were unavailable for interview as a result of withdrawal from school, suspensions, or repeated absences, and data for 3 cases were lost as a result of a technical problem with the computer interview software. The final baseline sample for year 1 was 678 intervention and 682 comparison parent-student dyads in kindergarten to grade 6, for a total of 1360. In year 2, we reinterviewed 803 parent-student dyads for a follow-up response rate 59.0%. In the final year, we successfully reinterviewed 588 of the original year 1 parent-student dyads for a response rate of 43% of the original year 1 sample and 73% of the year 2 sample. Attrition analysis revealed no differences between groups in HRQOL on years where data were available. Although there were differences in HRQOL across state, residence, income status, and gender, these did not differ across attrition groups. The Figure presents the sample disposition over the 3 years identifying points of attrition and the final sample size for each group. Measures Data were collected by questionnaires administered annually for 3 years, providing 3 points of longitudinal data for analysis. Students were administered a questionnaire in person at their school by the SBHC evaluation project staff that included a self-reported HRQOL measure. The parent completed an annual telephone questionnaire that included a proxy HRQOL measure of their child as well as additional demographic data and questions about chronic health conditions of the child.
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The primary outcome for the study was overall student HRQOL as perceived by parents and students. Secondary outcome measures included student and parent perceptions of student physical and psychosocial dimensions of health. Total HRQOL was measured by the 23-item PedsQL 4.0, a field-tested, age-standardized tool designed specifically for use with community and school populations.15 The PedsQL4.0 provided a measure of students’ overall HRQOL from 0 (the lowest) to 100 (the highest), which can also be decomposed into 2 dimensions: physical and psychosocial, containing 8 and 15 items, respectively. The minimal clinically important differences on the PedsQL 4.0 for the total scale and the physical and psychosocial dimensions are 4.36, 6.66, and 5.30 for student selfreport and 4.5, 6.9, and 5.5 for parent report, respectively.16 The reliability coefficients for the scales for both students and parent proxies were consistent with those of Varni and colleagues16,17 and ranged from a ¼ .72 (physical scale, child report) to a ¼ .88 (total scale for both student and parent report). Independent variables included SBHC group, time, and both school-level and individual-level factors. In order to examine whether those receiving SBHC services at least once within the 3 years differed from students attending SBHC schools but not utilizing services, we separated students into 3 SBHC groups: SBHC users, SBHC nonusers attending a school or school district where a SBHC was operating, and comparison school students (Figure). Time was treated as a continuous variable from 1 to 3 measured by school year to indicate year 1, year 2, and year 3, respectively. School-level factors were included to compare across states (southwestern Ohio and northern Kentucky), and urban versus rural school districts. Individual-level factors were broken down into 2 categories: sociodemographic factors and chronic illness. Sociodemographic factors consisted of child age (years), gender, race (white, black, other), health insurance status (private insurance, no health insurance, and public insurance including Child Health Insurance Program (CHIP), Kentucky Child Health Insurance Program (KCHIP), Healthy Start, Medicaid, and Medicare), household income, and parental marital status (married, single, separated/divorced). Chronic illnesses included parent proxy-reported asthma, attention-deficit/hyperactivity disorder, learning disabilities, and other chronic illnesses. Other chronic illnesses reported by parents included developmental delay or mental retardation, sickle cell, seizure disorder or epilepsy, headaches, and diabetes. The child was coded as having a specific chronic illness in year 1 if the parent reported being told at any time by a health professional that their child had this illness on a list read by the interviewer. In years 2 and 3, parents were asked since the time of their last interview whether they had been told for the first time that the child had one of these chronic health conditions. Analytic Strategy To compare groups over time, we used a generalized linear modeling technique within a panel regression framework. This technique specified the measure of each
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Figure. Sample decomposition.
individual at a specific time point. We inserted a groupby-time interaction with time coded as a continuous variable (ie, value of 1, 2, or 3 corresponding to each year). This tested for significant linear changes in HRQOL over all 3 years comparing SBHC users to SBHC nonusers and comparison school students. This technique controls for within-subject serial autocorrelation (similarity within an individual over the 3 years) to permit comparisons across groups. We did not compare differences in HRQOL by frequency of utilization because preliminary analyses indicated no statistically significant association between frequency of SBHC use and HRQOL (ie, high utilizers versus low utilizers) (not shown). First, bivariate analyses were conducted to test the effect of SBHC utilization (SBHC group) on student selfreported and parent proxy-reported total HRQOL, physical HRQOL, and psychosocial HRQOL over the 3 years. Second, we present full regression models that examined differences across groups over time by using the group
time interaction and adjusting for school- and individuallevel covariates. In addition, we tested all possible 3-way interactions to assess whether the relationship between SBHC and time differed across various school-level or individual-level covariates (eg, region, age). We conducted 2 sets of analyses, one without income included as a variable and one that included income. We conduct these 2 regression analyses for each outcome for 3 reasons. First, the inclusion of income results in an additional loss of cases as a result of some people’s reluctance to respond to this variable (loss of 44 additional cases, or 7.6% of final sample). Second, health insurance status, race, family structure, and income are multicollinear. A simple multivariate regression of income on both race and insurance status accounted for more than 35% of its explained variance. Finally, from a conceptual view, income is an underlying factor that usually drives the effect of family structure, race, and health insurance. For example, although it is important to examine whether family
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structure predicts HRQOL, it is not important principally because of the family form itself, but because of the financial disadvantage that accompanies it.18 The approach taken here provides insight in assessing how these factors may serve as a proxy for financial disadvantage. All analyses were conducted by SAS 8.2 proc mixed procedure.19 The SBHC user group as the primary intervention group was coded as the reference group. All interpretations of regression coefficients compared the non-SBHC user group and the comparison school student group to the SBHC user group. To assess the SBHC utilization time effect on the outcome, we used the following equation: HRQOL ¼ ai þ b1 ðSBHC nonuserÞ þ b2 ðcomparisonÞ þ b3 ðtimeÞ þ b4 ðSBHC nonuser timeÞ þ b5 ðcomparison timeÞ where ai is the intercept, SBHC nonuser equals 1 for students in intervention schools who did not use the SBHC, comparison equals 1 for those in comparison schools,
and time equals 1 for year 1, 2 for year 2, and 3 for year 3. Note that this coding is set up so that the SBHC user equals 0 for both b1 and b2. This defaults the intervention group as the reference category for all interpretations of regression coefficients. For example, when comparing the SBHC comparison group to the SBHC user intervention group, the above equation would be reduced to: HRQOL ¼ ai þ b2 þ ðb3 þ b5 ÞðtimeÞ where the comparison would equal 1, b2 would be the average difference between the intervention and comparison groups in year 1, and (b3 þ b5) would be the annual change in HRQOL for the comparison group relative to the intervention group for each unit in time. RESULTS Table 1 presents descriptive data across the school-level and individual-level variables for the 3-year longitudinal
Table 1. Longitudinal Sample Characteristics Across Years (N ¼ 579) Characteristic
Year 1, n (%)
Year 2, n (%)
Year 3, n (%)
. .
. .
342 (59.1) 237 (40.9)
. .
. .
219 (37.8) 360 (62.2)
. .
. .
9.4 (2.2) [Gr1;Gr7]
10.4 (2.2) [Gr2;Gr8]
298 (51.5) 281 (48.5)
. .
. .
73 (12.6) 491 (84.8) 15 (2.6) 40 726 (25 498)
. . . 41 584 (26 469)
. . . 42 030 (27 129)
398 (68.7) 97 (16.8) 84 (14.5)
403 (69.6) 95 (16.4) 81 (14.0)
390 (67.4) 111 (19.2) 78 (13.5)
123 (21.3) 431 (74.7) 23 (4.0)
123 (21.3) 429 (74.5) 24 (4.2)
137 (23.7) 424 (73.2) 18 (3.1)
96 (16.6) 42 (7.3)
115 (19.9) 53 (9.2)
128 (22.1) 63 (10.9)
42 (7.3) 55 (9.5)
52 (9.0) 81 (14.0)
61 (10.4) 98 (16.9)
Group School-based health center user School-based health center non-user Comparison
164 (28.3) 287 (49.6) School-Level Factors
State Ohio Kentucky Region Urban Rural Age, y, mean (SD) [range]* Gender Male Female Race Black White Other Household income,† mean (SD) Marital status Married Separated/divorced Single, never married Health insurance status Public‡ Private None Chronic disease Asthma Attention-deficit/hyperactivity disorder/attention-deficit disorder Learning disability Other chronic illness§
Individual-Level Factors 8.4 (2.2) [K;Gr6]
*Average age, standard deviation (SD), and age range for each year is reported. †Average income is calculated by using the median value for each income category. Sample size for this variable is N ¼ 535. ‡Public health insurance included Child Health Insurance Program, Kentucky Child Health Insurance Program, Medicaid, Medicare, or Healthy Start. §Other chronic illnesses reported by parents included developmental delay or mental retardation, sickle cell, seizure disorders or epilepsy, headaches, and diabetes.
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Table 2. Student Self-Reported and Parent Proxy-Reported Student Health-Related Quality of Life (HRQOL) (PedsQL Scores) for Years 1, 2, and 3† Year 1 Health Dimension Student self-report Physical Psychosocial Total HRQOL Parent report Physical Psychosocial Total HRQOL
Year 2
Year 3
User‡
Nonuser§
Comparisonk
User‡
Nonuser§
Comparisonk
User‡
Nonuser§
Comparisonk
82.6 69.6** 74.1*
83.2 75.2 78.0
84.9 74.6 78.2
83.8 70.0 74.8
83.7 73.9 77.3
83.4 71.9 75.8
84.7 73.7 77.5
84.8 74.5 76.1
83.9 73.3 77.0
88.7 81.4 84.0
91.0 83.4 85.9
90.0 81.7 84.6
85.2* 79.0 81.2*
89.00 82.0 84.5
89.5 80.8 83.8
87.5 79.8 82.5
89.6 80.6 83.7
89.4 81.4 84.2
*Significant difference at P < .05 from comparison group (2-tailed). **Significant difference at P < .05 from SBHC nonusers and comparison group (2-tailed). †Data from longitudinal parent and student surveys. ‡School-based health center (SBHC) user sample size, N ¼ 128. §SBHC nonuser sample size, N ¼ 164. kComparison schools sample size, N ¼ 287.
sample. Variables with descriptive data presented in year 1 only are constant across all 3 years. With regards to age, although it changes each year, it changes by a constant amount for each child, with only the grade range changing from kindergarten to grade 6 in year 1 to grade 2 to grade 8 in year 3. Marital status and insurance status can vary over time, and the percentage of children who are identified as having a chronic illness may change over time as new cases are identified. Table 2 presents the bivariate comparison of student self-reported and parent proxy-reported student HRQOL across SBHC users, nonusers, and comparison school students over 3 years. For student self-reported HRQOL in year 1, SBHC users scored significantly lower than comparison students on total HRQOL and lower than comparison students and nonusers on psychosocial health. There were no differences in years 2 or 3 as the average HRQOL score increased among SBHC users and decreased among the other 2 groups, indicating an intervention effect. Among parents, the only differences were in year 2, where parents of SBHC users rated the children’s physical and total HRQOL significantly lower than parents of children in comparison schools. Overall, parent proxy reports were much higher than student self-reports across all dimensions of HRQOL. Tables 3 and 4 present the fully adjusted panel regression models for student self-reported HRQOL and parent proxy student HRQOL, respectively. In Table 3, adjusting for school-level and individual-level factors, comparison school students report significantly higher total and psychosocial HRQOL scores compared with the SBHC user group. The average differences between the SBHC user group and comparison group for both total score and psychosocial increased was larger than the unadjusted means in Table 2 and were both statistically and clinically significant. In addition, the group time interaction is significant across all 3 measures of HRQOL. This indicates that there is a significant improvement over the 3 years for the SBHC user group compared with the comparison group. Although the same pattern is observed for the
SBHC user group compared with the SBHC nonuser group, the effect is not significant. In addition to the group effects, students reported significantly lower HRQOL scores if they were younger, female, from a single-parent (never married) household, and had lower household income. Living in Kentucky appears to result in a modestly lower level of student self-reported total HRQOL and physical HRQOL. Interestingly, when income is included, the effect of marital status is no longer significant, suggesting that its effect is a result of socioeconomic disadvantage experienced in these households. Finally, there were no significant 3-way interactions between group, time, and any of the school-level or individual-level factors, suggesting that the significant change over time across groups is similar across all school-level and individual-level characteristics (data not shown). Table 4 illustrates that there are no significant effects for SBHC group across any parent proxy HRQOL, measure mirroring the unadjusted results in Table 2. Moreover, parents do not appear to rate the health of their children differentially across school groups. Among school-level factors, parents with children attending rural schools rate their child’s HRQOL higher. Among individual-level factors, the presence of chronic health conditions is significantly associated with lower HRQOL. Moreover, parent proxy-reported student HRQOL is lower with lower household income and with children who have public health insurance. Interestingly, in the models that include income, the significant effect of public health insurance status is reduced or disappears, indicating that this effect is a result of socioeconomic disadvantage. Finally, there were no significant 3-way interactions indicating that the effect between groups on HRQOL does not differ across any school-level or individual-level factors (data not shown).
DISCUSSION This study demonstrates the positive effect that SBHCs have on the health and HRQOL among elementary and
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Table 3. Panel Regression of Student Self-Reported Total, Physical, and Psychosocial Health-Related Quality of Life (HRQOL) Scores on School-Based Health Center (SBHC) Use and School-Level and Individual-Level Factors Over Time‡ Total HRQOL
Factor Non-SBHC user Comparison SBHC user Time Non-SBHC user time{ Comparison time{ SBHC user time
Without Income§ (N ¼ 579) 3.36 6.14** . 1.32 1.10 2.35** .
Physical HRQOL
With Incomek (N ¼ 535) Intervention 1.95 5.90* . 1.09 0.75 2.35** .
Psychosocial HRQOL
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
3.45 1.16 . 0.88 0.10 1.82* .
2.93 2.75 . 0.39 0.45 1.60† .
7.60** 5.91* . 1.58† 1.68 2.65* .
4.71 7.64** – 1.49 1.42 2.77** .
2.09
. 2.06
School-Level Factors State Ohio Kentucky Region Urban Rural
2.13†
. 2.20
2.20†
. 2.43†
. 0.17
. 0.67
. 1.17
. 1.59
. 0.31
– 0.22
Individual-Level Factors Health insurance Private Public None Household income Marital status Married Single (never married) Separated/divorced Chronic illness Asthma Attention-deficit/hyperactivity disorder Learning disability Other
. 1.93† 1.51 .
. 1.14 1.40 0.05**
. 0.79 1.59 .
. 0.09 1.07 0.04†
. 2.68* 1.69 .
. 1.80 1.80 0.05*
3.30* 1.18
. 2.58 0.49
3.22* 1.18
. 2.55 0.74
3.32† 1.25
. 2.55 0.39
0.50 0.40 1.74 1.53
0.54 0.06 1.31 1.69
0.94 0.97 3.40 1.83
0.98 1.62 2.77 1.66
0.25 1.16 0.85 1.37
0.27 0.86 0.51 1.67
*P < .05. **P < .01. *** P < .001. †P < .10 (2-tailed). ‡Regression coefficients are unstandardized regression coefficients representing unit changes in the PedsQL measure of HRQOL. Regression models adjusted for age, gender, and race. §Household income variable not included in regression model. kHousehold income included in regression model. {Reported coefficients are intervention time interactions with 1 df. SBHC user is the reference category.
middle school students, consistent with findings of previous work on adolescents.8,13 Moreover, the positive effect of SBHCs appears to have more influence on those children that generally have impeded access to care and who can most benefit from it: students without private health insurance and with lower income levels.20,21 Because many students who utilize SBHCs are also socioeconomically disadvantaged and are less likely to have access to or receive primary care, SBHCs in elementary and middle school settings may offer students similar benefits previously found among disadvantaged adolescent populations.5–9 As such, the integration of SBHCs into elementary schools may serve to improve HRQOL at a younger age, which could have positive short-term and long-term health and educational benefits for the children and more effective utilization of health care resources. For example, researchers have found that children with asthma who use
the SBHC have lower emergency department utilization rates, fewer absences, and better maintenance of their chronic illness.22,23 Others have also found that SBHCs reduce emergency department utilization7,12 and Medicaid costs.8,12,24 In fact, the increase in numbers each year across all chronic illnesses in our results may be partly related to the presence of the SBHC to identify conditions that may have otherwise gone unrecognized. It is notable that a large improvement was found for psychosocial HRQOL. Indeed, this finding is consistent with previous work examining SBHCs for adolescents that identifies large needs in psychosocial health services and the opportunity to realize large gains in mental health.2,13 The specific SBHCs in this analysis provide on-site diagnoses as well as improved access to mental health services through on-site care or through a referral basis. Both of these services provide more opportunity for identification
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Table 4. Panel Regression of Parent Proxy-Reported Total, Physical, and Psychosocial Student Health-Related Quality of Life (HRQOL) Scores on School-Based Health Center (SBHC) Use and School-Level and Individual-Level Factors Over Time‡ Total HRQOL
Factor
Without Income§ (N ¼ 579)
Physical HRQOL
With Incomek (N ¼ 535)
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Psychosocial HRQOL Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Intervention Non-SBHC user Comparison SBHC user Time Non-SBHC user time{ Comparison time{ SBHC user time
1.30 0.65 . 0.92† 0.05 0.39 .
1.53 0.04 . 0.53 0.20 0.01 .
1.36 0.06 . 0.59 0.03 0.40 .
1.42 0.85 . 0.11 0.05 0.06 .
1.47 0.78 . 1.10† 0.08 0.38 .
1.82 0.35 . 0.75 0.35 0.02 .
School-Level Factors State Ohio Kentucky Region Urban Rural
1.49
. 1.22
1.24
. 0.95
1.63
. 1.39
3.78**
. 2.67*
3.33*
. 2.28†
3.91**
. 2.80*
Individual-Level Factors Health insurance Private Public None Household income Marital status Married Single (never married) Separated/divorced Chronic illness Asthma attention-deficit/hyperactivity disorder Learning disability Other
2.26* 2.18 .
. 1.70 2.14 0.05***
1.95† 0.24 .
. 1.32 0.41 0.04**
2.52** 3.44* .
. 1.93† 3.50* 0.05***
1.29 1.72†
. 1.59 1.03
2.32 2.05†
. 2.44 1.63**
0.98 1.52
. 1.22 0.62
3.38** 10.00***
3.18** 9.78***
3.48** 4.12†
3.16** 4.33*
6.10** 5.30***
5.78** 5.90***
2.50 6.10***
1.80 6.06***
3.34** 13.08***
3.21* 12.67***
8.06*** 4.90**
7.89*** 5.83***
*P < .05. **P < .01. ***P < .001. †P < .10 (2-tailed). ‡Regression coefficients are unstandardized coefficients representing unit changes in the PedsQL measure of HRQOL. Regression models adjusted for age, gender, and race. §Household income variable not included in regression model. kHousehold income included in regression model. {Reported coefficients are intervention time interactions with 1 df. SBHC user is the reference category.
and treatment than is available in the community.25 In fact, necessary mental health services have been repeatedly shown to be difficult to access in the community, especially among socioeconomically disadvantaged groups.21,26 These improvements in psychosocial health could also be due to SBHCs providing children with an increased sense of school connectedness, which has previously been shown to be linked to higher health status and HRQOL.27–30 In addition, SBHCs provide a friendly, welcoming environment for children with an opportunity to have positive relationships with multiple adults. As such, in addition to increased access to mental health services, it could also be working at various levels of increasing the developmental assets of children.31 Improvements in psychosocial health at the elementary age could have far-reaching consequences for these
children as they age. Some have found that social and emotional health at the elementary level are better predictors of academic performance than are early cognitive skills or family background.32,33 Moreover, because early middle school is also when the onset of mental health problems such as depression begins to increase, especially among girls,34 SBHCs are in an ideal position to identify children at an earlier stage of onset. There were 2 unexpected findings that require further attention. First, there was the high level of student mobility resulting in higher-than-expected attrition rates. This attrition was significantly higher in the inner-city schools. A high level of school mobility has been associated with significant socioeconomic disadvantage and a host of social, behavioral, and health problems among children.27 As such, it is these very children that are likely in the
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greatest need for the availability of health services in schools because of the difficulty in maintaining consistency of community service providers.27,35 Second, there is a notable dissonance between predictors of parent proxy-reported HRQOL and student self-reported HRQOL. For parents, the strongest predictor of their children’s HRQOL was the presence of a chronic illness, indicating that parents use it as a reference when evaluating their child’s health. Others have also found this effect, indicating that parents may not have insight into behaviors that are less observable, especially those associated with psychosocial health.27 Contrary to previous findings with the PedsQL, however, the presence of chronic illness had no significant effect on student self-reported HRQOL. However, this may be due to the lower ratings among these students generally, as their ratings are more closely aligned with the chronically ill population in previous validity studies of the PedsQL.16 The principal limitation associated with this study was the high level of attrition previously discussed having implications for the statistical power of the analysis. Beginning with 1360 parent-child dyads in wave 1, we were left with 588 cases, or 43% of the original sample—almost 100 cases below our estimated attrition. Those families who were lost to follow-up included both those who moved schools as well as those who refused to continue to participate in subsequent follow-up surveys (years 2 and 3). Those more likely to move schools were black and urban, and had lower household incomes. This pattern suggests that those students in more disadvantaged families were more likely to be lost to follow-up. The attrition rate among intervention schools was also significantly higher from year 2 to year 3 than among the comparison schools. This may be a result of the intervention sample consisting of the first year of SBHC grant awardees. The grantees were schools that, in addition to submitting a sound proposal, demonstrated the greatest level of need. This may have biased the intervention group as being more disadvantaged overall than the comparison group. However, as previously mentioned, there were no differences in attrition analyses for baseline HRQOL. In addition, the SBHCs in this study were newly implemented at the beginning of this study. It is plausible that if the SBHCs were more established or if the study measured HRQOL over a longer period of time, then the benefits of SBHCs on HRQOL may have been greater. Finally, because the significant 3-year change effect of the intervention group compared with the comparison school was a result of the combination of an increase in HRQOL among the SBHC users and a reduction among the comparison school students, we cannot rule out that the effect is simply regression toward some mean or average level of HRQOL among this student population. However, in previous work by Varni and colleagues16,17 that sought to establish the validity of the PedsQL, they found average student self-reported overall HRQOL scores among chronically ill children registered in the California Medicaid system to be 74.2, which was in line with the first-year score of the SBHC user group in this study. More-
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over, self-reports among healthy children in the California Medicaid system averaged 83.9, far above any of the groups in this study. As such, any regression toward the mean should have resulted in a substantial increase among all 3 groups in this sample toward the 83.9 score if they are comparable to Medicaid students in California. To conclude, the implementation of SBHCs in primary schools has a demonstrated positive impact on childreported HRQOL, even over the first 3 years of operation. Moreover, it appears that its effect is even more pronounced on psychosocial health, which could translate into more positive long-term benefits in learning, cognitive development, and mental health. The improvements observed in this study on the positive benefits of the SBHC health service delivery model on primary student HRQOL adds to the current research on adolescent student populations on the health benefits of school-based health centers. ACKNOWLEDGMENTS This project was funded by a grant from the Health Foundation of Greater Cincinnati (HFGC) under their School-Based Child Health Initiative. T.W. is currently supported by the Canada Research Chairs Program. We thank the HFGC School-Based Health Center Evaluation Project advisory group and the participating school districts, their staff and SBHCs, and all the students and parents who participated in this evaluation project.
REFERENCES 1. Brindis CD, Klein J, Schlitt J, Santelli J, Juszczak L, Nystrom RJ. School-based health centers: accessibility and accountability. J Adolesc Health. 2003;32:98–107. 2. Dryfoos JG. Schools as places for health, mental health, and social services. Teachers Coll Rec. 1993;94:540–567. 3. Kaplan DW, Brindis C, Naylor KE, Phibbs SL, Ahlstrand KR, Melinkovich P. Elementary school–based health center use. Pediatrics. 1998;101:E12. 4. Juszczak L, Schlitt J, Moore A. School-Based Health Centers: National Census School Year 2004–05. Washington, DC: National Assembly on School-Based Health Care; 2007. 5. Freidrich M. 25 years of school-based health centers. JAMA. 1999; 281:781–782. 6. Armbruster P, Gerstein SH, Fallon T. Bridging the gap between service need and service utilization: a school-based mental health program. Community Ment Health J. 1997;33:199–211. 7. Dryfoos JG. Safe Passage: Making It Through Adolescence in a Risky Society: What Parents, Schools, and Communities Can Do. New York, NY: Oxford University Press; 1998. 8. Santelli J, Kouzis A, Newcomer S. School-based health centers and adolescent use of primary care and hospital care. J Adolesc Health. 1996;19:267–275. 9. Tyack D. Health and social services in public schools: historical perspectives. Future Child. 1992;2:19–31. 10. Schlitt J, Juszczak L, Brindis C, et al. Creating Access to Care for Children and Youth: School-Based Health Center Census 1998– 1999. George Washington University and Emroy University. Washington, DC: National Assembly on School-Based Health Care; 2000. 11. Kaplan DW, Brindis CD, Phibbs SL, Melinkovich P, Naylor K, Ahlstrand K. A comparison study of an elementary school-based health center: effects on health care access and use. Arch Pediatr Adolesc Med. 1999;153:235–243. 12. Young TL, D’Angelo SL, Davis J. Impact of a school-based health center on emergency department use by elementary school students. J Sch Health. 2001;71:196–198. 13. Kisker EE, Brown RS. Do school-based health centers improve adolescents’ access to health care, health status, and risk-taking behavior? J Adolesc Health. 1996;18:335–343.
AMBULATORY PEDIATRICS 14. Health Foundation of Greater Cincinnati. A Prescription for Success: How SBHCS Affect Health Status and Healthcare Use and Cost. Cincinnati, Ohio: Health Foundation of Greater Cincinnati; 2005. 15. Varni JW, Seid M, Rode CA. The PedsQL: measurement model for the pediatric quality of life inventory. Med Care. 1999;37:126–139. 16. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3:329–341. 17. Varni JW, Seid M, Kurtin PS. The PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory Version 4.0 generic core scales in healthy and patient populations. Med Care. 2001;39:800–812. 18. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995; Spec No:80–94. 19. SAS Procedures Guide. Version 8, Vols. 1, 2, and 3. Cary, NC: SAS Publishing; 1999. 20. Newacheck PW, Hughes DC, Stoddard JJ. Children’s access to primary care: differences by race, income, and insurance status. Pediatrics. 1996;97:26–32. 21. Brindis C, Kapphahn C, McCarter V, Wolfe AL. The impact of health insurance status on adolescents’ utilization of school-based clinic services: implications for health care reform. J Adolesc Health. 1995;16: 18–25. 22. Guo JJ, Jang R, Keller KN, McCracken AL, Pan W, Cluxton RJ. Impact of school-based health centers on children with asthma. J Adolesc Health. 2005;37:266–274. 23. Webber MP, Carpiniello KE, Oruwairye T, Yungtai L, Burton WB, Appel DK. Burden of asthma in inner-city elementary schoolchildren: do school-based health centers make a difference? Arch Pediatr Adolesc Med. 2003;157:125–129. 24. Adams EK, Johnson V. An elementary school-based health clinic: can it reduce medicaid costs? Pediatrics. 2000;105:780–788.
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25. Gall G, Pagano ME, Desmond MS, Perrin JM, Murphy JM. Utility of psychosocial screening at a school-based health center. J Sch Health. 2000;70:292–298. 26. Newacheck PW, Taylor WR. Childhood chronic illness: prevalence, severity, and impact. Am J Public Health. 1992;82:364–371. 27. Mansour ME, Kotagal U, Rose B, et al. Health-related quality of life in urban elementary schoolchildren. Pediatrics. 2003;111:1372–1381. 28. Bonny AE, Britto MT, Klostermann BK, Hornung RW, Slap GB. School disconnectedness: identifying adolescents at risk. Pediatrics. 2000;106:1017–1021. 29. Vingilis E, Wade TJ, Adlaf E. What factors predict student self-rated physical health? J Adolesc. 1998;21:83–97. 30. Wade TJ, Brannigan A. The genesis of adolescent risk-taking: pathways through family, school, and peers. Can J Sociol. 1998;23: 1–19. 31. Scales PC. Building students’ developmental assets to promote health and school success. Clearinghouse J Educ Strategies Issues Ideas. 2000;74:84–88. 32. Ladd GW, Burgess KB. Charting the relationship trajectories of aggressive, withdrawn, and aggressive/withdrawn children during early grade school. Child Dev. 1999;70:910–929. 33. McClelland M, Morrison F, Holmes D. Children at risk for early academic problems: the role of learning-related social skills. Early Child Res Q. 2000;15:307–320. 34. Wade TJ, Cairney J, Pevalin DJ. The emergence of gender differences in depression among adolescents: National panel results from the USA, Canada and Great Britain. J Am Acad Child Adolesc Psychiatry. 2002;41:190–198. 35. Wade TJ, Cairney J. General issues: sociological contributions. In: Ammerman RT, ed. Child Psychopathology. New York, NY: John Wiley & Sons; 2006:47–63.
Results.—Adjusting for school- and individual-level covariates, there was a significant improvement in student-reported HRQOL over the 3 years for the SBHC user group compared with the comparison school group. Other significant predictors of student-reported HRQOL included student age, gender, health insurance, and household income. There were no differences across groups by using parent proxy reports of HRQOL.
S
school-aged children and children in rural settings.10 The recent SBHC census indicated that 48% of SBHCs serve elementary grades, and 27% are located in rural areas.4 Despite the increase in the number of SBHCs that target elementary school-aged children, research on the delivery of services to this age group has been slow to appear in the literature.3 At present, researchers have found that SBHCs improve elementary student health service access3,11 and reduce emergency department use.12 But unlike research on adolescents that reports improved student health with SBHC use,8,13 it is unclear whether these improvements in health status translate to younger students in elementary and middle schools. This study addresses this deficit and examines the role of SBHCs in elementary schools on student health and improvements in health over time by using student self-reported and parent proxy-reported health-related quality of life (HRQOL).
Conclusions.—The SBHC model of health care delivery improves student-reported HRQOL among younger, elementary, and middle school children. Moreover, it appears to have more influence on those children that generally have impeded access to care and who can most benefit from it, specifically those without private health insurance and with lower income levels. KEY WORDS: elementary school; health-related quality of life; school-based health center; school health Ambulatory Pediatrics 2008;8:241–249
ince the 1970s, school-based health centers (SBHC) have steadily increased in number across the United States.1–3 In fact, the latest census of SBHCs conducted in 2004–2005 by the National Assembly of School Based Health Care identifies more than 1709 SBHC programs in the United States.4 This shift in the delivery of health care to children is premised on the assumption that access to health services in school increases use and overall health among children, especially for those children whose access to care is otherwise limited.5–9 Traditionally, the majority of SBHCs have been established to serve middle and high schools, and generally urban areas, but an increasing number of SBHCs provide services to elementary From the Departments of Community Health Sciences and Child and Youth Studies, Brock University, St Catharines, Ontario, Canada (Dr Wade); Department of Pediatrics, University of Cincinnati College of Medicine, Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio (Dr Mansour); Division of Health Policy and Clinical Effectiveness, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio (Ms Line and Ms Huentelman); and Health Foundation of Greater Cincinnati, Cincinnati, Ohio (Ms Keller). Address correspondence to Terrance J. Wade, Department of Community Health Sciences, Brock University, 500 Glenridge Ave, St Catharines, Ontario, L2S 3A1, Canada (e-mail: [email protected]). Received for publication September 25, 2007; accepted February 18, 2008. AMBULATORY PEDIATRICS Copyright Ó 2008 by Academic Pediatric Association
METHODS Sample The data come from a 3-year longitudinal study that evaluated the effect of newly implemented SBHCs in elementary and middle schools on student health.14 The structural attributes of each SBHC are available from the authors. The study was reviewed and approved by the
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appropriate institutional review board. The study population consisted of all students enrolled at 4 selected public schools, 2 in southwest Ohio and 2 in northern Kentucky, that implemented a SBHC in 2000–2001. These 4 schools were selected from 8 funded SBHCs to provide a representative cross section of urban and rural schools funded across each state. Four comparison schools without SBHCs were selected matched on rural/urban and state, and percentage of nonwhite and free- or reduced-price lunch–eligible students. The resulting distribution is one intervention school and one comparison school in each grouping (ie, Kentucky rural, Kentucky urban, Ohio rural, Ohio urban). From the school enrollment lists, a simple random sample of parents from each school was selected in year 1. On the basis of power calculations for the final longitudinal sample, and assuming a 30% yearly attrition rate, the minimum first year parent-child dyad sample size was estimated to be 675 for both the intervention and comparison groups. Informed consent of parents for their participation as well as their verbal permission to allow us to interview their child in school were obtained in each year at the time of the telephone interview. Students provided informed assent at the time of the school interview. For year 1, a total of 1599 parents completed an interview. From this baseline parent sample, 239 children were not interviewed in year 1 for the following reasons: 164 legal guardians did not grant permission to have their child interviewed, 25 surveys were completed by someone other than the child’s legal guardian, 47 children whose parents gave permission were unavailable for interview as a result of withdrawal from school, suspensions, or repeated absences, and data for 3 cases were lost as a result of a technical problem with the computer interview software. The final baseline sample for year 1 was 678 intervention and 682 comparison parent-student dyads in kindergarten to grade 6, for a total of 1360. In year 2, we reinterviewed 803 parent-student dyads for a follow-up response rate 59.0%. In the final year, we successfully reinterviewed 588 of the original year 1 parent-student dyads for a response rate of 43% of the original year 1 sample and 73% of the year 2 sample. Attrition analysis revealed no differences between groups in HRQOL on years where data were available. Although there were differences in HRQOL across state, residence, income status, and gender, these did not differ across attrition groups. The Figure presents the sample disposition over the 3 years identifying points of attrition and the final sample size for each group. Measures Data were collected by questionnaires administered annually for 3 years, providing 3 points of longitudinal data for analysis. Students were administered a questionnaire in person at their school by the SBHC evaluation project staff that included a self-reported HRQOL measure. The parent completed an annual telephone questionnaire that included a proxy HRQOL measure of their child as well as additional demographic data and questions about chronic health conditions of the child.
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The primary outcome for the study was overall student HRQOL as perceived by parents and students. Secondary outcome measures included student and parent perceptions of student physical and psychosocial dimensions of health. Total HRQOL was measured by the 23-item PedsQL 4.0, a field-tested, age-standardized tool designed specifically for use with community and school populations.15 The PedsQL4.0 provided a measure of students’ overall HRQOL from 0 (the lowest) to 100 (the highest), which can also be decomposed into 2 dimensions: physical and psychosocial, containing 8 and 15 items, respectively. The minimal clinically important differences on the PedsQL 4.0 for the total scale and the physical and psychosocial dimensions are 4.36, 6.66, and 5.30 for student selfreport and 4.5, 6.9, and 5.5 for parent report, respectively.16 The reliability coefficients for the scales for both students and parent proxies were consistent with those of Varni and colleagues16,17 and ranged from a ¼ .72 (physical scale, child report) to a ¼ .88 (total scale for both student and parent report). Independent variables included SBHC group, time, and both school-level and individual-level factors. In order to examine whether those receiving SBHC services at least once within the 3 years differed from students attending SBHC schools but not utilizing services, we separated students into 3 SBHC groups: SBHC users, SBHC nonusers attending a school or school district where a SBHC was operating, and comparison school students (Figure). Time was treated as a continuous variable from 1 to 3 measured by school year to indicate year 1, year 2, and year 3, respectively. School-level factors were included to compare across states (southwestern Ohio and northern Kentucky), and urban versus rural school districts. Individual-level factors were broken down into 2 categories: sociodemographic factors and chronic illness. Sociodemographic factors consisted of child age (years), gender, race (white, black, other), health insurance status (private insurance, no health insurance, and public insurance including Child Health Insurance Program (CHIP), Kentucky Child Health Insurance Program (KCHIP), Healthy Start, Medicaid, and Medicare), household income, and parental marital status (married, single, separated/divorced). Chronic illnesses included parent proxy-reported asthma, attention-deficit/hyperactivity disorder, learning disabilities, and other chronic illnesses. Other chronic illnesses reported by parents included developmental delay or mental retardation, sickle cell, seizure disorder or epilepsy, headaches, and diabetes. The child was coded as having a specific chronic illness in year 1 if the parent reported being told at any time by a health professional that their child had this illness on a list read by the interviewer. In years 2 and 3, parents were asked since the time of their last interview whether they had been told for the first time that the child had one of these chronic health conditions. Analytic Strategy To compare groups over time, we used a generalized linear modeling technique within a panel regression framework. This technique specified the measure of each
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Figure. Sample decomposition.
individual at a specific time point. We inserted a groupby-time interaction with time coded as a continuous variable (ie, value of 1, 2, or 3 corresponding to each year). This tested for significant linear changes in HRQOL over all 3 years comparing SBHC users to SBHC nonusers and comparison school students. This technique controls for within-subject serial autocorrelation (similarity within an individual over the 3 years) to permit comparisons across groups. We did not compare differences in HRQOL by frequency of utilization because preliminary analyses indicated no statistically significant association between frequency of SBHC use and HRQOL (ie, high utilizers versus low utilizers) (not shown). First, bivariate analyses were conducted to test the effect of SBHC utilization (SBHC group) on student selfreported and parent proxy-reported total HRQOL, physical HRQOL, and psychosocial HRQOL over the 3 years. Second, we present full regression models that examined differences across groups over time by using the group
time interaction and adjusting for school- and individuallevel covariates. In addition, we tested all possible 3-way interactions to assess whether the relationship between SBHC and time differed across various school-level or individual-level covariates (eg, region, age). We conducted 2 sets of analyses, one without income included as a variable and one that included income. We conduct these 2 regression analyses for each outcome for 3 reasons. First, the inclusion of income results in an additional loss of cases as a result of some people’s reluctance to respond to this variable (loss of 44 additional cases, or 7.6% of final sample). Second, health insurance status, race, family structure, and income are multicollinear. A simple multivariate regression of income on both race and insurance status accounted for more than 35% of its explained variance. Finally, from a conceptual view, income is an underlying factor that usually drives the effect of family structure, race, and health insurance. For example, although it is important to examine whether family
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structure predicts HRQOL, it is not important principally because of the family form itself, but because of the financial disadvantage that accompanies it.18 The approach taken here provides insight in assessing how these factors may serve as a proxy for financial disadvantage. All analyses were conducted by SAS 8.2 proc mixed procedure.19 The SBHC user group as the primary intervention group was coded as the reference group. All interpretations of regression coefficients compared the non-SBHC user group and the comparison school student group to the SBHC user group. To assess the SBHC utilization time effect on the outcome, we used the following equation: HRQOL ¼ ai þ b1 ðSBHC nonuserÞ þ b2 ðcomparisonÞ þ b3 ðtimeÞ þ b4 ðSBHC nonuser timeÞ þ b5 ðcomparison timeÞ where ai is the intercept, SBHC nonuser equals 1 for students in intervention schools who did not use the SBHC, comparison equals 1 for those in comparison schools,
and time equals 1 for year 1, 2 for year 2, and 3 for year 3. Note that this coding is set up so that the SBHC user equals 0 for both b1 and b2. This defaults the intervention group as the reference category for all interpretations of regression coefficients. For example, when comparing the SBHC comparison group to the SBHC user intervention group, the above equation would be reduced to: HRQOL ¼ ai þ b2 þ ðb3 þ b5 ÞðtimeÞ where the comparison would equal 1, b2 would be the average difference between the intervention and comparison groups in year 1, and (b3 þ b5) would be the annual change in HRQOL for the comparison group relative to the intervention group for each unit in time. RESULTS Table 1 presents descriptive data across the school-level and individual-level variables for the 3-year longitudinal
Table 1. Longitudinal Sample Characteristics Across Years (N ¼ 579) Characteristic
Year 1, n (%)
Year 2, n (%)
Year 3, n (%)
. .
. .
342 (59.1) 237 (40.9)
. .
. .
219 (37.8) 360 (62.2)
. .
. .
9.4 (2.2) [Gr1;Gr7]
10.4 (2.2) [Gr2;Gr8]
298 (51.5) 281 (48.5)
. .
. .
73 (12.6) 491 (84.8) 15 (2.6) 40 726 (25 498)
. . . 41 584 (26 469)
. . . 42 030 (27 129)
398 (68.7) 97 (16.8) 84 (14.5)
403 (69.6) 95 (16.4) 81 (14.0)
390 (67.4) 111 (19.2) 78 (13.5)
123 (21.3) 431 (74.7) 23 (4.0)
123 (21.3) 429 (74.5) 24 (4.2)
137 (23.7) 424 (73.2) 18 (3.1)
96 (16.6) 42 (7.3)
115 (19.9) 53 (9.2)
128 (22.1) 63 (10.9)
42 (7.3) 55 (9.5)
52 (9.0) 81 (14.0)
61 (10.4) 98 (16.9)
Group School-based health center user School-based health center non-user Comparison
164 (28.3) 287 (49.6) School-Level Factors
State Ohio Kentucky Region Urban Rural Age, y, mean (SD) [range]* Gender Male Female Race Black White Other Household income,† mean (SD) Marital status Married Separated/divorced Single, never married Health insurance status Public‡ Private None Chronic disease Asthma Attention-deficit/hyperactivity disorder/attention-deficit disorder Learning disability Other chronic illness§
Individual-Level Factors 8.4 (2.2) [K;Gr6]
*Average age, standard deviation (SD), and age range for each year is reported. †Average income is calculated by using the median value for each income category. Sample size for this variable is N ¼ 535. ‡Public health insurance included Child Health Insurance Program, Kentucky Child Health Insurance Program, Medicaid, Medicare, or Healthy Start. §Other chronic illnesses reported by parents included developmental delay or mental retardation, sickle cell, seizure disorders or epilepsy, headaches, and diabetes.
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Table 2. Student Self-Reported and Parent Proxy-Reported Student Health-Related Quality of Life (HRQOL) (PedsQL Scores) for Years 1, 2, and 3† Year 1 Health Dimension Student self-report Physical Psychosocial Total HRQOL Parent report Physical Psychosocial Total HRQOL
Year 2
Year 3
User‡
Nonuser§
Comparisonk
User‡
Nonuser§
Comparisonk
User‡
Nonuser§
Comparisonk
82.6 69.6** 74.1*
83.2 75.2 78.0
84.9 74.6 78.2
83.8 70.0 74.8
83.7 73.9 77.3
83.4 71.9 75.8
84.7 73.7 77.5
84.8 74.5 76.1
83.9 73.3 77.0
88.7 81.4 84.0
91.0 83.4 85.9
90.0 81.7 84.6
85.2* 79.0 81.2*
89.00 82.0 84.5
89.5 80.8 83.8
87.5 79.8 82.5
89.6 80.6 83.7
89.4 81.4 84.2
*Significant difference at P < .05 from comparison group (2-tailed). **Significant difference at P < .05 from SBHC nonusers and comparison group (2-tailed). †Data from longitudinal parent and student surveys. ‡School-based health center (SBHC) user sample size, N ¼ 128. §SBHC nonuser sample size, N ¼ 164. kComparison schools sample size, N ¼ 287.
sample. Variables with descriptive data presented in year 1 only are constant across all 3 years. With regards to age, although it changes each year, it changes by a constant amount for each child, with only the grade range changing from kindergarten to grade 6 in year 1 to grade 2 to grade 8 in year 3. Marital status and insurance status can vary over time, and the percentage of children who are identified as having a chronic illness may change over time as new cases are identified. Table 2 presents the bivariate comparison of student self-reported and parent proxy-reported student HRQOL across SBHC users, nonusers, and comparison school students over 3 years. For student self-reported HRQOL in year 1, SBHC users scored significantly lower than comparison students on total HRQOL and lower than comparison students and nonusers on psychosocial health. There were no differences in years 2 or 3 as the average HRQOL score increased among SBHC users and decreased among the other 2 groups, indicating an intervention effect. Among parents, the only differences were in year 2, where parents of SBHC users rated the children’s physical and total HRQOL significantly lower than parents of children in comparison schools. Overall, parent proxy reports were much higher than student self-reports across all dimensions of HRQOL. Tables 3 and 4 present the fully adjusted panel regression models for student self-reported HRQOL and parent proxy student HRQOL, respectively. In Table 3, adjusting for school-level and individual-level factors, comparison school students report significantly higher total and psychosocial HRQOL scores compared with the SBHC user group. The average differences between the SBHC user group and comparison group for both total score and psychosocial increased was larger than the unadjusted means in Table 2 and were both statistically and clinically significant. In addition, the group time interaction is significant across all 3 measures of HRQOL. This indicates that there is a significant improvement over the 3 years for the SBHC user group compared with the comparison group. Although the same pattern is observed for the
SBHC user group compared with the SBHC nonuser group, the effect is not significant. In addition to the group effects, students reported significantly lower HRQOL scores if they were younger, female, from a single-parent (never married) household, and had lower household income. Living in Kentucky appears to result in a modestly lower level of student self-reported total HRQOL and physical HRQOL. Interestingly, when income is included, the effect of marital status is no longer significant, suggesting that its effect is a result of socioeconomic disadvantage experienced in these households. Finally, there were no significant 3-way interactions between group, time, and any of the school-level or individual-level factors, suggesting that the significant change over time across groups is similar across all school-level and individual-level characteristics (data not shown). Table 4 illustrates that there are no significant effects for SBHC group across any parent proxy HRQOL, measure mirroring the unadjusted results in Table 2. Moreover, parents do not appear to rate the health of their children differentially across school groups. Among school-level factors, parents with children attending rural schools rate their child’s HRQOL higher. Among individual-level factors, the presence of chronic health conditions is significantly associated with lower HRQOL. Moreover, parent proxy-reported student HRQOL is lower with lower household income and with children who have public health insurance. Interestingly, in the models that include income, the significant effect of public health insurance status is reduced or disappears, indicating that this effect is a result of socioeconomic disadvantage. Finally, there were no significant 3-way interactions indicating that the effect between groups on HRQOL does not differ across any school-level or individual-level factors (data not shown).
DISCUSSION This study demonstrates the positive effect that SBHCs have on the health and HRQOL among elementary and
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Table 3. Panel Regression of Student Self-Reported Total, Physical, and Psychosocial Health-Related Quality of Life (HRQOL) Scores on School-Based Health Center (SBHC) Use and School-Level and Individual-Level Factors Over Time‡ Total HRQOL
Factor Non-SBHC user Comparison SBHC user Time Non-SBHC user time{ Comparison time{ SBHC user time
Without Income§ (N ¼ 579) 3.36 6.14** . 1.32 1.10 2.35** .
Physical HRQOL
With Incomek (N ¼ 535) Intervention 1.95 5.90* . 1.09 0.75 2.35** .
Psychosocial HRQOL
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
3.45 1.16 . 0.88 0.10 1.82* .
2.93 2.75 . 0.39 0.45 1.60† .
7.60** 5.91* . 1.58† 1.68 2.65* .
4.71 7.64** – 1.49 1.42 2.77** .
2.09
. 2.06
School-Level Factors State Ohio Kentucky Region Urban Rural
2.13†
. 2.20
2.20†
. 2.43†
. 0.17
. 0.67
. 1.17
. 1.59
. 0.31
– 0.22
Individual-Level Factors Health insurance Private Public None Household income Marital status Married Single (never married) Separated/divorced Chronic illness Asthma Attention-deficit/hyperactivity disorder Learning disability Other
. 1.93† 1.51 .
. 1.14 1.40 0.05**
. 0.79 1.59 .
. 0.09 1.07 0.04†
. 2.68* 1.69 .
. 1.80 1.80 0.05*
3.30* 1.18
. 2.58 0.49
3.22* 1.18
. 2.55 0.74
3.32† 1.25
. 2.55 0.39
0.50 0.40 1.74 1.53
0.54 0.06 1.31 1.69
0.94 0.97 3.40 1.83
0.98 1.62 2.77 1.66
0.25 1.16 0.85 1.37
0.27 0.86 0.51 1.67
*P < .05. **P < .01. *** P < .001. †P < .10 (2-tailed). ‡Regression coefficients are unstandardized regression coefficients representing unit changes in the PedsQL measure of HRQOL. Regression models adjusted for age, gender, and race. §Household income variable not included in regression model. kHousehold income included in regression model. {Reported coefficients are intervention time interactions with 1 df. SBHC user is the reference category.
middle school students, consistent with findings of previous work on adolescents.8,13 Moreover, the positive effect of SBHCs appears to have more influence on those children that generally have impeded access to care and who can most benefit from it: students without private health insurance and with lower income levels.20,21 Because many students who utilize SBHCs are also socioeconomically disadvantaged and are less likely to have access to or receive primary care, SBHCs in elementary and middle school settings may offer students similar benefits previously found among disadvantaged adolescent populations.5–9 As such, the integration of SBHCs into elementary schools may serve to improve HRQOL at a younger age, which could have positive short-term and long-term health and educational benefits for the children and more effective utilization of health care resources. For example, researchers have found that children with asthma who use
the SBHC have lower emergency department utilization rates, fewer absences, and better maintenance of their chronic illness.22,23 Others have also found that SBHCs reduce emergency department utilization7,12 and Medicaid costs.8,12,24 In fact, the increase in numbers each year across all chronic illnesses in our results may be partly related to the presence of the SBHC to identify conditions that may have otherwise gone unrecognized. It is notable that a large improvement was found for psychosocial HRQOL. Indeed, this finding is consistent with previous work examining SBHCs for adolescents that identifies large needs in psychosocial health services and the opportunity to realize large gains in mental health.2,13 The specific SBHCs in this analysis provide on-site diagnoses as well as improved access to mental health services through on-site care or through a referral basis. Both of these services provide more opportunity for identification
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SBHC and Elementary Student HRQOL
247
Table 4. Panel Regression of Parent Proxy-Reported Total, Physical, and Psychosocial Student Health-Related Quality of Life (HRQOL) Scores on School-Based Health Center (SBHC) Use and School-Level and Individual-Level Factors Over Time‡ Total HRQOL
Factor
Without Income§ (N ¼ 579)
Physical HRQOL
With Incomek (N ¼ 535)
Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Psychosocial HRQOL Without Income§ (N ¼ 579)
With Incomek (N ¼ 535)
Intervention Non-SBHC user Comparison SBHC user Time Non-SBHC user time{ Comparison time{ SBHC user time
1.30 0.65 . 0.92† 0.05 0.39 .
1.53 0.04 . 0.53 0.20 0.01 .
1.36 0.06 . 0.59 0.03 0.40 .
1.42 0.85 . 0.11 0.05 0.06 .
1.47 0.78 . 1.10† 0.08 0.38 .
1.82 0.35 . 0.75 0.35 0.02 .
School-Level Factors State Ohio Kentucky Region Urban Rural
1.49
. 1.22
1.24
. 0.95
1.63
. 1.39
3.78**
. 2.67*
3.33*
. 2.28†
3.91**
. 2.80*
Individual-Level Factors Health insurance Private Public None Household income Marital status Married Single (never married) Separated/divorced Chronic illness Asthma attention-deficit/hyperactivity disorder Learning disability Other
2.26* 2.18 .
. 1.70 2.14 0.05***
1.95† 0.24 .
. 1.32 0.41 0.04**
2.52** 3.44* .
. 1.93† 3.50* 0.05***
1.29 1.72†
. 1.59 1.03
2.32 2.05†
. 2.44 1.63**
0.98 1.52
. 1.22 0.62
3.38** 10.00***
3.18** 9.78***
3.48** 4.12†
3.16** 4.33*
6.10** 5.30***
5.78** 5.90***
2.50 6.10***
1.80 6.06***
3.34** 13.08***
3.21* 12.67***
8.06*** 4.90**
7.89*** 5.83***
*P < .05. **P < .01. ***P < .001. †P < .10 (2-tailed). ‡Regression coefficients are unstandardized coefficients representing unit changes in the PedsQL measure of HRQOL. Regression models adjusted for age, gender, and race. §Household income variable not included in regression model. kHousehold income included in regression model. {Reported coefficients are intervention time interactions with 1 df. SBHC user is the reference category.
and treatment than is available in the community.25 In fact, necessary mental health services have been repeatedly shown to be difficult to access in the community, especially among socioeconomically disadvantaged groups.21,26 These improvements in psychosocial health could also be due to SBHCs providing children with an increased sense of school connectedness, which has previously been shown to be linked to higher health status and HRQOL.27–30 In addition, SBHCs provide a friendly, welcoming environment for children with an opportunity to have positive relationships with multiple adults. As such, in addition to increased access to mental health services, it could also be working at various levels of increasing the developmental assets of children.31 Improvements in psychosocial health at the elementary age could have far-reaching consequences for these
children as they age. Some have found that social and emotional health at the elementary level are better predictors of academic performance than are early cognitive skills or family background.32,33 Moreover, because early middle school is also when the onset of mental health problems such as depression begins to increase, especially among girls,34 SBHCs are in an ideal position to identify children at an earlier stage of onset. There were 2 unexpected findings that require further attention. First, there was the high level of student mobility resulting in higher-than-expected attrition rates. This attrition was significantly higher in the inner-city schools. A high level of school mobility has been associated with significant socioeconomic disadvantage and a host of social, behavioral, and health problems among children.27 As such, it is these very children that are likely in the
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greatest need for the availability of health services in schools because of the difficulty in maintaining consistency of community service providers.27,35 Second, there is a notable dissonance between predictors of parent proxy-reported HRQOL and student self-reported HRQOL. For parents, the strongest predictor of their children’s HRQOL was the presence of a chronic illness, indicating that parents use it as a reference when evaluating their child’s health. Others have also found this effect, indicating that parents may not have insight into behaviors that are less observable, especially those associated with psychosocial health.27 Contrary to previous findings with the PedsQL, however, the presence of chronic illness had no significant effect on student self-reported HRQOL. However, this may be due to the lower ratings among these students generally, as their ratings are more closely aligned with the chronically ill population in previous validity studies of the PedsQL.16 The principal limitation associated with this study was the high level of attrition previously discussed having implications for the statistical power of the analysis. Beginning with 1360 parent-child dyads in wave 1, we were left with 588 cases, or 43% of the original sample—almost 100 cases below our estimated attrition. Those families who were lost to follow-up included both those who moved schools as well as those who refused to continue to participate in subsequent follow-up surveys (years 2 and 3). Those more likely to move schools were black and urban, and had lower household incomes. This pattern suggests that those students in more disadvantaged families were more likely to be lost to follow-up. The attrition rate among intervention schools was also significantly higher from year 2 to year 3 than among the comparison schools. This may be a result of the intervention sample consisting of the first year of SBHC grant awardees. The grantees were schools that, in addition to submitting a sound proposal, demonstrated the greatest level of need. This may have biased the intervention group as being more disadvantaged overall than the comparison group. However, as previously mentioned, there were no differences in attrition analyses for baseline HRQOL. In addition, the SBHCs in this study were newly implemented at the beginning of this study. It is plausible that if the SBHCs were more established or if the study measured HRQOL over a longer period of time, then the benefits of SBHCs on HRQOL may have been greater. Finally, because the significant 3-year change effect of the intervention group compared with the comparison school was a result of the combination of an increase in HRQOL among the SBHC users and a reduction among the comparison school students, we cannot rule out that the effect is simply regression toward some mean or average level of HRQOL among this student population. However, in previous work by Varni and colleagues16,17 that sought to establish the validity of the PedsQL, they found average student self-reported overall HRQOL scores among chronically ill children registered in the California Medicaid system to be 74.2, which was in line with the first-year score of the SBHC user group in this study. More-
AMBULATORY PEDIATRICS
over, self-reports among healthy children in the California Medicaid system averaged 83.9, far above any of the groups in this study. As such, any regression toward the mean should have resulted in a substantial increase among all 3 groups in this sample toward the 83.9 score if they are comparable to Medicaid students in California. To conclude, the implementation of SBHCs in primary schools has a demonstrated positive impact on childreported HRQOL, even over the first 3 years of operation. Moreover, it appears that its effect is even more pronounced on psychosocial health, which could translate into more positive long-term benefits in learning, cognitive development, and mental health. The improvements observed in this study on the positive benefits of the SBHC health service delivery model on primary student HRQOL adds to the current research on adolescent student populations on the health benefits of school-based health centers. ACKNOWLEDGMENTS This project was funded by a grant from the Health Foundation of Greater Cincinnati (HFGC) under their School-Based Child Health Initiative. T.W. is currently supported by the Canada Research Chairs Program. We thank the HFGC School-Based Health Center Evaluation Project advisory group and the participating school districts, their staff and SBHCs, and all the students and parents who participated in this evaluation project.
REFERENCES 1. Brindis CD, Klein J, Schlitt J, Santelli J, Juszczak L, Nystrom RJ. School-based health centers: accessibility and accountability. J Adolesc Health. 2003;32:98–107. 2. Dryfoos JG. Schools as places for health, mental health, and social services. Teachers Coll Rec. 1993;94:540–567. 3. Kaplan DW, Brindis C, Naylor KE, Phibbs SL, Ahlstrand KR, Melinkovich P. Elementary school–based health center use. Pediatrics. 1998;101:E12. 4. Juszczak L, Schlitt J, Moore A. School-Based Health Centers: National Census School Year 2004–05. Washington, DC: National Assembly on School-Based Health Care; 2007. 5. Freidrich M. 25 years of school-based health centers. JAMA. 1999; 281:781–782. 6. Armbruster P, Gerstein SH, Fallon T. Bridging the gap between service need and service utilization: a school-based mental health program. Community Ment Health J. 1997;33:199–211. 7. Dryfoos JG. Safe Passage: Making It Through Adolescence in a Risky Society: What Parents, Schools, and Communities Can Do. New York, NY: Oxford University Press; 1998. 8. Santelli J, Kouzis A, Newcomer S. School-based health centers and adolescent use of primary care and hospital care. J Adolesc Health. 1996;19:267–275. 9. Tyack D. Health and social services in public schools: historical perspectives. Future Child. 1992;2:19–31. 10. Schlitt J, Juszczak L, Brindis C, et al. Creating Access to Care for Children and Youth: School-Based Health Center Census 1998– 1999. George Washington University and Emroy University. Washington, DC: National Assembly on School-Based Health Care; 2000. 11. Kaplan DW, Brindis CD, Phibbs SL, Melinkovich P, Naylor K, Ahlstrand K. A comparison study of an elementary school-based health center: effects on health care access and use. Arch Pediatr Adolesc Med. 1999;153:235–243. 12. Young TL, D’Angelo SL, Davis J. Impact of a school-based health center on emergency department use by elementary school students. J Sch Health. 2001;71:196–198. 13. Kisker EE, Brown RS. Do school-based health centers improve adolescents’ access to health care, health status, and risk-taking behavior? J Adolesc Health. 1996;18:335–343.
AMBULATORY PEDIATRICS 14. Health Foundation of Greater Cincinnati. A Prescription for Success: How SBHCS Affect Health Status and Healthcare Use and Cost. Cincinnati, Ohio: Health Foundation of Greater Cincinnati; 2005. 15. Varni JW, Seid M, Rode CA. The PedsQL: measurement model for the pediatric quality of life inventory. Med Care. 1999;37:126–139. 16. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3:329–341. 17. Varni JW, Seid M, Kurtin PS. The PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory Version 4.0 generic core scales in healthy and patient populations. Med Care. 2001;39:800–812. 18. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995; Spec No:80–94. 19. SAS Procedures Guide. Version 8, Vols. 1, 2, and 3. Cary, NC: SAS Publishing; 1999. 20. Newacheck PW, Hughes DC, Stoddard JJ. Children’s access to primary care: differences by race, income, and insurance status. Pediatrics. 1996;97:26–32. 21. Brindis C, Kapphahn C, McCarter V, Wolfe AL. The impact of health insurance status on adolescents’ utilization of school-based clinic services: implications for health care reform. J Adolesc Health. 1995;16: 18–25. 22. Guo JJ, Jang R, Keller KN, McCracken AL, Pan W, Cluxton RJ. Impact of school-based health centers on children with asthma. J Adolesc Health. 2005;37:266–274. 23. Webber MP, Carpiniello KE, Oruwairye T, Yungtai L, Burton WB, Appel DK. Burden of asthma in inner-city elementary schoolchildren: do school-based health centers make a difference? Arch Pediatr Adolesc Med. 2003;157:125–129. 24. Adams EK, Johnson V. An elementary school-based health clinic: can it reduce medicaid costs? Pediatrics. 2000;105:780–788.
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25. Gall G, Pagano ME, Desmond MS, Perrin JM, Murphy JM. Utility of psychosocial screening at a school-based health center. J Sch Health. 2000;70:292–298. 26. Newacheck PW, Taylor WR. Childhood chronic illness: prevalence, severity, and impact. Am J Public Health. 1992;82:364–371. 27. Mansour ME, Kotagal U, Rose B, et al. Health-related quality of life in urban elementary schoolchildren. Pediatrics. 2003;111:1372–1381. 28. Bonny AE, Britto MT, Klostermann BK, Hornung RW, Slap GB. School disconnectedness: identifying adolescents at risk. Pediatrics. 2000;106:1017–1021. 29. Vingilis E, Wade TJ, Adlaf E. What factors predict student self-rated physical health? J Adolesc. 1998;21:83–97. 30. Wade TJ, Brannigan A. The genesis of adolescent risk-taking: pathways through family, school, and peers. Can J Sociol. 1998;23: 1–19. 31. Scales PC. Building students’ developmental assets to promote health and school success. Clearinghouse J Educ Strategies Issues Ideas. 2000;74:84–88. 32. Ladd GW, Burgess KB. Charting the relationship trajectories of aggressive, withdrawn, and aggressive/withdrawn children during early grade school. Child Dev. 1999;70:910–929. 33. McClelland M, Morrison F, Holmes D. Children at risk for early academic problems: the role of learning-related social skills. Early Child Res Q. 2000;15:307–320. 34. Wade TJ, Cairney J, Pevalin DJ. The emergence of gender differences in depression among adolescents: National panel results from the USA, Canada and Great Britain. J Am Acad Child Adolesc Psychiatry. 2002;41:190–198. 35. Wade TJ, Cairney J. General issues: sociological contributions. In: Ammerman RT, ed. Child Psychopathology. New York, NY: John Wiley & Sons; 2006:47–63.