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Correlates of wellness among youth with functional disabilities
Disability and Health Journal 8 (2015) 223e230 www.disabilityandhealthjnl.com
Research Paper
Correlates of wellness among youth with functional disabilities K.S. Menear, Ph.Da,*, J.K. Preskitt, Ph.Db, S.S. Goldfarb, Ph.Db, and N. Menachemi, Ph.Db a
Department of Human Studies, School of Education, University of Alabama at Birmingham, 1720 2nd Ave. S., EB 207, Birmingham, AL 35294-1250, USA b Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, RPHB 330, 1720 2nd Ave. S., Birmingham AL 35294, USA
Abstract Background: The literature is more informative on the impediments to wellness among youth with functional limitations and less instructive on the state of wellness for this population. Objective: To explore overall wellness, and each sub-dimension of wellness, in a national sample of youth with functional limitations and to determine how demographic characteristics are associated with wellness. Methods: Using a previously validated screening instrument, we identify youth with functional limitations aged 12 to 17 represented in the 2011/12 National Survey of Children’s Health. Survey items were coded to operationalize an overall wellness score comprised of four sub-dimensions of wellness (i.e., physical, intellectual, emotional, and social). Results: The mean overall wellness score was 26.7 (out of 40) and had an approximate normal distribution. Mean raw scores for each sub-dimension were as follows: social 5 2.79 (out of 4; 69.7%); emotional 5 4.09 (out of 6; 68.2%); intellectual 5 3.79 (out of 8; 47.4%); and physical 5 6.30 (out of 8; 78.7%). Lower wellness scores were associated with older age among youth, increasing number of chronic health conditions, lower income, single mother homes, and youth whose mother reported fair or poor mental health status (all p ! 0.05). Higher wellness scores were positively associated with mother’s education ( p ! 0.001). Conclusions: Program planners should consider interventions that target youth with functional limitations shown to be at particular risk for lower overall wellness and promote family involvement and comprehensive supports, including maternal educational attainment, mental health screening, and referral. Ó 2015 Elsevier Inc. All rights reserved. Keywords: Youth with functional disabilities; Wellness; 2011/12 National Survey of Children’s Health
Youth with special health care needs or disabilities are at higher risk for physical, developmental, behavioral, or emotional conditions and as a result require more health care services than their peers.1,2 Youth with functional limitations due to health conditions expected to last 12 months or more are a unique subset of youth with special health care needs, accounting for an estimated 4.3% of youth in this group.2 Successful health management of this population is multifaceted and includes quality improvement efforts within clinical practices and health systems, partnerships across the entire service team and related researchers, training and education for everyone involved, and advocacy for youth with functional limitations and their caregivers.3 Wellness is viewed as a holistic4e6 and multi-dimensional6e8 concept which encompasses more than the absence of disease.9,10 Wellness relates to functional abilities as There are no conflicts of interest. The article has not been submitted elsewhere for publication or presentation. * Corresponding author. Tel.: þ1 205 975 7409. E-mail address: [email protected] (K.S. Menear). 1936-6574/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.dhjo.2014.10.001
well as life balance,4e6 thereby aligning well with other comprehensive frameworks for describing health, including the International Classification of Functioning, Disability, and Health (ICF).11 ICF classifies health across multiple domains according to body structure and function, as well as an individual’s capacity to perform activities and participate in society.11 Similarly, wellness is commonly viewed as being comprised of various sub-dimensions, which can include social, emotional, physical, and intellectual wellness,8,9,12e19 though the identification of the specific dimensions of wellness has varied in number and definition in the literature.6e8 Wellness expands the traditional understanding of health and functioning to include a context of actively engaging in activities that build and support an individual’s potential or capacity to be healthy.12,14,20 For youth without disabilities, wellness is often maintained at the population level in large part through mandated physical education programs in schools and federally-mandated school wellness policies required by the Child Nutrition and WIC Reauthorization Act of 2004 (Public Law 108e265). Given the recognition that wellness is an important component of healthy living, it is troubling
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that researchers have documented challenges when implementing wellness programs for children with disabilities21 and children with functional limitations.22 Moreover, researchers have reported disparities in the delivery of preventive health services for youth with functional limitations.23 School wellness policies focus on physical activity, nutrition, and other school-based activities that promote student wellness. Overall the literature is more informative on the impediments to wellness among youth with functional limitations and is less instructive on the state of wellness for this population. Children with functional limitations were more likely than other children with special health care needs to experience participation restrictions related to school attendance, participation in organized activities, working for pay, and volunteering.22 Children with severe functional limitations experienced significantly greater odds of delayed health care, unmet health care and care-coordination needs, referral problems, dissatisfaction with care, difficulty using services, worse health insurance experiences and greater impact on family (financial problems, employment challenges, need to provide home care) than children with special health care needs who had some or no limitations.24 Research has also indicated that functional limitation, family poverty, and being uninsured were significantly associated with greater caregiver burden and less preventative dental care.25 Additionally identified impediments to the health of youth with functional limitations have included living in families with limited resources and experiencing greater exposure to secondhand smoke, less access to health care, and lower health status.26 While we have indications that factors such as social disadvantage, restricted access, decreased participation, unhealthy home environment, and financial resources impact the health care needs and experiences of youth with functional limitations, we do not know their overall state of wellness. The purpose of this paper is threefold. First, we operationalize a measure of wellness across multiple dimensions to determine the extent of wellness in a national sample of youth with functional limitations aged 12e17 years. Second, we calculate measures of the various sub-dimensions of wellness and determine which sub-dimensions are most prevalent in this population. Lastly, we explore how different characteristics of youth with functional limitations, their caregivers, or other demographic information are associated with wellness among this group. This information will be valuable to program directors, policymakers, and clinicians interested in understanding and improving the state of wellness for youth with functional limitations. Methods Consistent with the aims of the current study, we utilized nationally representative youth survey data that includes questions that allowed us to operationalize measures that represent the commonly cited sub-dimensions of wellness.
Specifically, the 2011/12 National Survey of Children’s Health (NSCH)27 collected by the Centers for Disease Control and Prevention (CDC) was conducted via telephone interviews by trained interviewers, incorporating both landlines and cell phones and utilizing a sampling design focused on reaching U.S. households including at least one child aged 0e17 years at the time of the contact. Responses were given by the person in the household who knew the most about the child or youth’s health. A total of 95,677 interviews were conducted between February 2011 and June 2012, with representation from all US states (Centers for Disease Control and Prevention, National Center for Health Statistics, State and Local Area Integrated Telephone Survey. 2011e2012 National Survey of Children’s Health Frequently Asked Questions. April 2013. Available from URL: http://www.cdc.gov/nchs/ slaits/nsch.htm). From the survey, youth with any special health care needs were, first, identified through the use of a validated screening instrument, which includes five items and follow-up questions related to consequences of ongoing health conditions.28 From that group, youth with one or more functional limitations due to health conditions expected to last 12 months or more were selected for inclusion in this study (‘‘yes’’ to questions K2Q16, K2Q17, and K2Q18). The strength of the National Survey of Children’s Health is its utilization of a complex sampling design and population weights to be nationally representative of children and youth ages 0e17 years. There are a unique set of survey items for youth 12e17 years which fit our interest with the adolescent years. The survey design elements and analytical techniques allow for national-level generalizations. This study was exempted from review by the university’s Institutional Review Board as it utilizes a publiclyavailable, de-identified dataset. Dependent variable Based on extensive review of the wellness literature, we reviewed all questions on the NSCH and identified items relating to sub-dimensions of wellness that could reasonably be developed based on available data, including physical, intellectual, emotional, and social wellness. The NSCH did not contain survey items to operationalize other additionally common sub-dimensions of wellness (e.g., spiritual and environmental wellness). Based on definitions from the literature,6e8,29e31 questions were identified from the NSCH survey that represented each of the subdimensions of wellness. (Domains, descriptions, questions and coding are presented in the Appendix.) We evaluated each survey item to determine if it operationalized wellness for 12e17 year olds. Then, each author presented questions for a unique sub-dimension of wellness and identified any sub-dimensions that could not be reasonably developed using this survey. The authors reached consensus through extensive discussion about overall question selection and
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assignment under each sub-domain. Final group decisions were based on the literature, conceptual fit, discussion with other content experts, and our own expertise. In the end, 17 questions were selected to represent 4 sub-dimensions of wellness: social, emotional, intellectual, and physical. Given our methods, breadth of domains included, and analyses, we suggest that the index has good face validity. We then computed scores for each survey respondent on each wellness sub-dimension. The intellectual and physical sub-dimensions ranged on a scale from 0 to 8 points, the emotional sub-dimension ranged from 0 to 6 points, and the social sub-dimension ranged from 0 to 4 points. For example, the intellectual sub-dimension has a range of 0e8 points possible based upon responses to 4 questions. The first, ‘‘Since starting kindergarten, has [he/she] repeated any grades’’, is coded 1 for ‘‘no’’ and 0 for ‘‘yes.’’ The next question in the sub-dimension asks, ‘‘On an average weekday, about how much time does [CHILD’S NAME] usually spend reading for pleasure?’’ and is coded 3 for ‘‘over 30 min’’, 2 for ‘‘16e30 min’’, 1 for ‘‘1e15 min’’, and 0 for ‘‘None/0 min’’. The final survey item in the subdimension is, ‘‘I am going to read a list of items that sometimes describe children. For each item, please tell me how often this is true for [CHILD’S NAME] during the past month: 1) [He/She] cares about doing well in school’’, which is coded 2 for ‘‘Always’’, 1 for ‘‘Usually’’, and 0 for ‘‘Never/ Rarely/Sometimes’’; ‘‘2) [He/She] does all required homework’’, which is coded 2 for ‘‘Always’’, 1 for ‘‘Usually’’, and 0 for ‘‘Never/Rarely/Sometimes’’’’. The physical, emotional, and social sub-dimensions were calculated in a similar way. After we constructed raw scores for each sub-dimension for each survey respondent, we applied a multiplier to weight each sub-dimension to contribute equally (10 points) on a summated scale to create a total, standardized wellness score. The total wellness score could range from 0 to 40 points. This federal survey asked these survey items of all 12e17 year olds. This provided a dataset that is as homogenous as possible for this critical developmental stage (i.e., adolescence) for topics surrounding health and wellness. The respondents were the caregivers who knew the child best.
others), mother’s education level, and mother’s physical and mental health status, all of which were selected because the survey caller asked to speak to the caregiver most familiar with the child and therefore most of the respondents were mothers. Family income as a percentage of the federal poverty level (FPL) was also used as a predictor variable. Given that nearly all youth with functional limitations reported having insurance at the time of the interview, this variable was not included as a predictor.
Predictor variables
The study sample consisted of a total of 2000 youth with functional limitations who were between the ages of 12 and 17 years (See Table 1). The distribution across the younger (12e14 year) age group and the older (15e17 year) age group was approximately equal (49% and 51%, respectively). Youth were a majority white (66.1%) and nonHispanic (81.4%). About half (49.4%) had a normal weight based on BMI. Over half of youth in sample had three or more chronic health conditions (55.9%), and more than three quarters of respondents rated the severity of at least one of their youth’s health conditions as moderate or severe (77.8%). Over one quarter (28.3%) lived in a single mother home, and most mothers had more than a high school
In order to examine the correlates of wellness, we utilized various survey questions from the NSCH as predictor variables including: demographic characteristics (i.e., age, gender, race, ethnicity), BMI status (i.e., healthy weight, underweight, overweight, and obese), and characteristics associated with the individual youth’s functional limitation or chronic condition (i.e., number of health conditions reported, whether the respondent indicated a moderate or severe rating for any health condition reported, and qualification reason). Parental characteristics used as predictor variables included: family structure (single mother vs.
Data analysis Data analyses were conducted using Stata 12 to accommodate the complex sampling design and weighting techniques of the survey. Using the ‘subpop’ procedure to assure correct standard error calculations, analyses were limited to youth with functional limitations who were ages 12e17 years at the time of the survey. The cut point for statistical significance was set at p ! 0.05. We began our analyses by calculating descriptive statistics, including frequencies and means. We then performed t-test analyses to examine the differences in mean total wellness scores and raw sub-dimension scores between youth with and without functional limitations. Finally, we then utilized a four-stage step-wise linear regression model to examine the associations between each predictor variable and the overall wellness construct score for youth with functional limitations. The four stages sequentially added additional control variables, allowing the examination of the stability of association of each predictor variable as additional predictors were added. Model 1 included youth characteristics only, while Model 2 added condition characteristics. Model 3 also included parent characteristics and Model 4 incorporated family income. Finally, we developed multivariable linear regression models using all predictor variables (the fully-adjusted model) to assess associations between each predictor variable and each sub-dimension of wellness (e.g., physical, intellectual, emotional, and social) for the group of youth with functional limitations.
Results
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Table 1 Demographics of a sample of U.S. youth with functional limitations ages 12e17 years, National Survey of Children’s Health, 2011/12 (n 5 2000) Variable N (%) Age 12e14 15e17 BMI Underweight Overweight Obese Healthy weight Gender Female Male Ethnicity Non-Hispanic Hispanic Race Other White Moderate or severe rating for any condition Number of health conditions None 1 2 3 or more Single mother home Mother has more than high school education Mother has fair or poor health status Mother has fair or poor mental health status Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater Has health insurance Private insurance Public insurance
898 (49.3) 1102 (50.7) 127 309 453 1062
(5.9) (18.2) (26.6) (49.4)
797 (40.2) 1199 (59.8) 1773 (81.4) 194 (18.6) 499 (33.9) 1460 (66.1) 1539 (77.8) 162 399 274 1165 488 1244 436 275
(7.3) (20.0) (16.8) (55.9) (28.3) (65.4) (24.3) (15.2)
450 418 318 228 586 1928 987 941
(33.1) (19.8) (13.2) (11.0) (24.9) (96.8) (52.2) (47.8)
education (65.4%). Nearly 25% of mothers reported fair or poor physical health status (24.3%), while over 15% reported fair or poor mental health status (15.2%). About 25% of youths lived in homes at or above 400% FPL (24.9%), but over 50% lived in homes below 200% FPL (combined, 0e99% 5 33.1%; 100e199% 5 19.8%). Almost all youth with a functional limitation (96.8%) had health insurance at the time of the interview, with about equal percentages having private versus public insurance. The distribution of the overall wellness scores for youth with functional limitations in the sample is displayed in Fig. 1. The distribution, though slightly skewed to the right, approximates normality. The mean overall wellness score was 26.7 (on a maximum 40-point scale), with no youth scoring below a total of 6.25 points. Table 2 displays analyses of the difference in mean scores (overall wellness and raw sub-dimensions) between groups of youth with and without functional limitations. All differences were significant based on t-test analyses ( p ! 0.001), with youth without functional limitations
Fig. 1. Measurement of a wellness construct for a sample of U.S. youth with functional limitations ages 12e17 years, National Survey of Children’s Health, 2011/2012
scoring lower on overall wellness and across all subdimensions. Specifically for youth with functional limitations, mean raw scores for each sub-dimension were as follows: social 5 2.79 (out of 4; 69.7%); emotional 5 4.09 (out of 6; 68.2%); intellectual 5 3.79 (out of 8; 47.4%); and physical 5 6.30 (out of 8; 78.7%). Linear regression coefficients for predictor variables and the overall wellness score are presented in Table 3. Across all models, several predictors had a consistently stable relationship with the overall wellness score. Several youth characteristics were associated with significantly lower wellness scores. For example, lower scores were observed for older youth (ages 15e17 years; b 5 1.55, p ! 0.01). Lower scores were also associated with increasing number of health conditions reported (b 5 1.70, p ! 0.001). Lower scores were also associated with select parent characteristics. Youth living in single mother homes had lower overall wellness scores (b 5 1.54, p ! 0.05), as did those whose mother reported fair or poor mental health (b 5 3.66, p ! 0.001). Higher maternal education had a positive association with youth wellness scores (b 5 3.25, p ! 0.001). Family income at 300e399% FPL was associated with lower overall wellness scores (b 5 1.75, p ! 0.05). Fully-adjusted linear regression models for each subdimension of wellness are displayed in Table 4. Only one predictor was associated with scores consistently across all sub-dimensions. Youth whose mothers had more than a high school education had higher scores, ranging from 0.33 to 0.67 point increases across the sub-dimensions. In contrast, several predictors were associated with lower scores for certain sub-dimensions of wellness, but not others. For example, older youth had lower scores on the intellectual and physical sub-dimensions (b 5 0.69, p ! 0.001; b 5 0.40, p ! 0.01, respectively). Also, youth whose mothers reported fair or poor mental health
K.S. Menear et al. / Disability and Health Journal 8 (2015) 223e230 Table 2 Analysis of differences in mean standardized overall wellness and subdimension scores for a sample of U.S. youth with and without functional limitations, ages 12e17 years, National Survey of Children’s Health, 2011/12 Youth with Youth without functional functional limitations limitations (n 5 2000) (n 5 32,601) Mean Total wellness score e 26.7 standardized (0e40) Social sub-dimension (0e4) 2.79 Emotional sub-dimension (0e6) 4.09 Intellectual sub-dimension (0e8) 3.79 Physical sub-dimension (0e8) 6.30 a
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b 5 0.38, p ! 0.001; and b 5 0.64, p ! 0.001, respectively), but not the physical. Finally, several lower income levels were associated with 0.44e0.91 point reductions for the emotional and intellectual sub-dimensions, but no significant associations were observed with the social or physical sub-dimensions of wellness.
Mean
p-valuea
Discussion
30.4
!0.001
Our analysis suggests that youth with functional limitations had lower overall wellness and sub-dimension scores compared to youth without functional limitations. However, the average youth with functional limitations obtained at least half of the points possible on the overall wellness scale and within each sub-dimension except intellectual. Variation exists and may be explained by individual and parental or familial characteristics. Our results indicate the vulnerability that youth with functional limitations may face regarding their overall wellness. Given the impact that wellness may have on physical health, academic progress, and coping skills, among other chronological and developmental growth areas common to youth, awareness of the impact wellness may have is a vital to
!0.001 !0.001 !0.001 !0.001
3.16 4.83 4.87 6.59
Based t-test of difference in mean values between the two groups.
status had lower scores on the social, emotional, and physical sub-dimensions (b 5 0.43, p ! 0.05; b 5 0.99, p ! 0.01; and b 5 0.52, p ! 0.01, respectively). An increase in the number of health conditions reported was associated with lower scores for the social, emotional, and intellectual sub-dimensions (b 5 0.13, p ! 0.05;
Table 3 Linear regression results; wellness in a sample of U.S. youth with functional limitations ages 12e17 years; 2011/12 National Survey of Children’s Health Model 1 Model 2 Model 3 Model 4 Variable coefficient (SE) coefficient (SE) coefficient (SE) coefficient (SE) Youth characteristics Age 12e14 15e17 Underweight Overweight Obese Male Hispanic White Condition characteristics Moderate or severe rating for any condition Number of health conditions Parent characteristics Other family structure Single mother home Mother has more than high school education Mother has fair or poor physical health status Mother has fair or poor mental health status Other demographics Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater
SE 5 Standard error. *p ! 0.05, **p ! 0.01, ***p ! 0.001. a Number observations in regression model.
Ref 1.76 2.15 1.15 0.47 1.24 0.50 1.67
(0.67)** (1.31) (0.76) (0.99) (0.71) (1.25) (0.75)*
Ref 1.91 1.68 0.89 0.00 0.44 0.44 1.96
(0.57)** (1.29) (0.66) (0.85) (0.60) (1.05) (0.18)**
Ref 1.45 1.48 1.03 0.04 0.24 1.24 1.05
(0.55)** (1.13) (0.70) (0.79) (0.58) (1.04) (0.64)
Ref 1.55 1.53 1.02 0.02 0.40 1.31 1.05
(0.54)** (1.16) (0.70) (0.73) (0.58) (0.98) (0.62)
e e
0.95 (0.94) 2.10 (0.42)***
0.32 (0.85) 1.70 (0.34)***
0.07 (0.79) 1.70 (0.33)***
e
e
Ref 1.54 3.25 0.11 3.66
e e e
e e e
Ref 1.59 3.29 0.17 3.70
e
e
e
F 5 4.34 p ! 0.000 R2 5 0.056 1779a
F 5 13.05 p ! 0.000 R2 5 0.183 1779a
F 5 11.10 p ! 0.000 R2 5 0.293 1589a
(0.61)** (0.64)*** (0.71) (0.99)***
(0.70)* (0.74)*** (0.72) (0.95)***
0.56 (1.16) 1.44 (0.79) 1.21 (0.79) 1.75 (0.69)* Ref F 5 9.52 p ! 0.000 R2 5 0.302 1589a
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Table 4 Linear regression results; sub-dimensions of wellness in a sample of U.S. youth with functional limitations ages 12e17 years; 2011/12 National Survey of Children’s Health (Model 4) Social sub-dimension Emotional sub-dimension Intellectual sub-dimension Physical sub-dimension Variable coefficient (SE) coefficient (SE) coefficient (SE) coefficient (SE) Youth characteristics Age 12e14 15e17 Underweight Overweight Obese Male Hispanic White Condition characteristics Moderate or severe rating for any condition Number of health conditions Parent characteristics Other family structure Single mother home Mother has more than high school education Mother has fair or poor physical health status Mother has fair or poor mental health status Other demographics Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater
Ref 0.09 0.12 0.08 0.09 0.04 0.05 0.15
(0.10) (0.24) (0.15) (0.13) (0.11) (0.12) (0.12)
Ref 0.01 0.14 0.44 0.11 0.19 0.48 0.15
(0.14) (0.23) (0.19)* (0.18) (0.15) (0.24) (0.17)
Ref 0.69 0.45 0.07 0.16 0.43 0.49 0.23
(0.18)*** (0.33) (0.22) (0.22) (0.19)* (0.29) (0.20)
0.06 (0.12) 0.13 (0.05)*
0.10 (0.19) 0.38 (0.08)***
0.14 (0.23) 0.64 (0.10)***
Ref 0.09 0.33 0.15 0.43
Ref 0.28 0.41 0.00 0.99
Ref 0.30 0.67 0.00 0.27
(0.12) (0.13)* (0.12) (0.19)*
0.08 (0.19) 0.05 (0.15) 0.16 (0.15) 0.00 (0.20) Ref F 5 2.51 p ! 0.000 R2 5 0.086
(0.16) (0.16)* (0.19) (0.29)**
0.41 (0.22) 0.44 (0.18)* 0.29 (0.19) 0.91 (0.32)** Ref F 5 10.17 p ! 0.000 R2 5 0.252
(0.21) (0.22)** (0.22) (0.29)
0.03 (0.32) 0.50 (0.24)* 0.32 (0.27) 0.15 (0.25) Ref F 5 8.80 p ! 0.000 R2 5 0.225
Ref 0.40 0.32 0.03 0.23 0.10 0.03 0.03
(0.11)** (0.22) (0.17) (0.15) (0.11) (0.23) (0.14)
0.13 (0.17) 0.01 (0.07) Ref 0.23 0.55 0.42 0.52
(0.15) (0.16)*** (0.16)** (0.19)**
0.27 (0.23) 0.13 (0.19) 0.06 (0.15) 0.09 (0.18) Ref F 5 4.20 p ! 0.000 R2 5 0.209
SE 5 Standard error. *p ! 0.05, **p ! 0.01, ***p ! 0.001.
understanding how to facilitate their developmental progression. As expected, youth with more chronic health conditions had lower overall wellness scores and lower scores for the social, emotional and intellectual sub-dimensions. This finding is consistent with previous work that found youth with functional limitations or those who qualified as having a special health care need based on more screener items had poorer overall health and more complex health care needs,30 and qualifying with three or more special health care needs was the strongest predictor of lower healthrelated quality of life.31 It is somewhat surprising that lower scores were not also noted in the physical sub-dimension, though this may be a function of small sample size and the inclusive content of this sub-dimension which more broadly captures topics that contribute to physical wellness, such as preventive visits and health behaviors. It is a reassuring finding that mean raw scores for the physical subdimension were the highest in terms of percent attainment across the four sub-dimensions of wellness. Perhaps youth with functional limitations who have multiple chronic conditions, along with their caregivers, are placing greater emphasis on these activities that support the physical aspects of wellness. However, youth with functional
limitations, those with worse health status, and those exposed to social disadvantage may be met with participation challenges in school and community.22 Youth whose mothers reported fair or poor mental health status had a nearly 4-point (equating to a 10%) reduction in overall wellness score and had lower scores across all subdimensions except intellectual. This finding is supported by related literature. Churchill, Villareale, Monaghan, Sharp, and Kieckhefer32 reported increased risk of depressive symptoms in parents of youth with special health care needs, especially if they are single and unemployed. Gaskin and Mitchell33 indicated that youth with special health care needs with parents who have symptoms of depression are significantly more likely to experience challenges obtaining needed medical and mental health care services. As expected, maternal education was positively associated with wellness. Youth whose mother had education above the high school level had higher overall wellness scores and higher scores for all sub-dimensions. Lykens, Fulda, Bae, and Singh34 found that maternal education significantly affected whether children received all needed specialty care when the analysis was stratified by socioeconomic status. Porterfield and McBride35 found that parents of youth with special health needs who had lower income
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and were less educated were less likely than higher-income and more-educated parents to indicate their children needed specialized health services. Older youth with functional limitations had lower overall wellness scores and lower scores in the intellectual and physical sub-dimensions. This finding may be related to general expectations and available activities for older youth coupled with the natural increase in desired autonomy during adolescence.36 Lower income was associated with lower overall wellness scores and lower scores across two of the four subdimensions (emotional and intellectual), albeit the specific FPL percentages varied. This is not surprising given the literature that indicates an association between lower socioeconomic status and lower health-related quality of life,37,38 the documented financial burden of raising youth with special health care needs,39,40 the impact of state-level income inequality on family burden related to the health care of special needs children,41 the difficulty of being able to work full time for parents caring for the special needs of their children,24,42e45 and disproportionately higher rates of unmet mental health needs of youth with special health care needs and their families of lower socioeconomic status.46 As our sample of youth with functional limitations is a subset of youth with special health care needs, we would expect similar findings to the larger group related to income associations. Interestingly, race and ethnicity were not associated with wellness scores as we would have expected. Once models were adjusted for all other variables, there were no significant differences between White and non-White youth or for Hispanic and non-Hispanic youth in overall wellness scores or for any sub-dimension. These results are in contrast to previous literature that found children with autism (a special health care need) who are Latino or Black face greater challenges with obtaining high-quality health care.47 Further, Inkelas et al48 found that youth with a chronic mental, behavioral, or developmental problem that were African-American had greater unmet mental health needs. Nqui & Flores49 found greater disparities and risk factors for unmet specialty, dental, mental, and allied health care needs among Black and Hispanic youth with special health care needs compared to their White counterparts. Limitations There are several limitations that are important to mention. Our data is limited by the self-reported nature of the survey instrument. We recognize the potential for recall bias, and the fact that the telephone survey method used achieved a limited response rate. Moreover, the survey itself was not designed to measure wellness; instead, we utilize select questions to operationalize various dimensions that were related to the theoretical concept of wellness. There may be other aspects of wellness or indicators of sub-dimensions that cannot be captured in this survey based on available questions. Also, the cross-sectional design of
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our analysis allows us to identify associations only. Therefore, none of the relationships we present can be interpreted as causal. Additionally, we recognize the domains have not been empirically validated although they are theoretically grounded. Future work should empirically validate the domains. Conclusions Our study presents an objective measure of wellness that is expanded beyond condition-based health-related quality of life. In light of our findings, program planners should consider interventions that target youth with functional limitations shown to be at particular risk for lower overall wellness, including older youth, those in lower income families, and those with more chronic health conditions. Further, our findings related to maternal educational level and mental health status highlight the importance of family involvement and comprehensive support, including support for educational attainment and screening for mental health issues among maternal caregivers, with referral for services as needed.
Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.dhjo.2014.10.001. References 1. McPherson M, Arango P, Fox H, et al. A new definition of children with special health care needs. Pediatrics. 1998;102(1):137e139. 2. National Survey of Children with Special Health Care Needs. Data Resource Center; 2009/10. 3. Szilagyi PG, Kuhlthau KA. Children with special health care needs: a celebration of success!. Acad Pediatr. 2011;11(2):94e95. 4. Cicchetti DE, Rappaport JE, Sandler IE, Weissberg RP. The Promotion of Wellness in Children and Adolescents. Washington, DC: Child Welfare League of America; 2000. 5. Dunn HL. High-level Wellness. Thorofare, NJ: Charles B. Slack, Inc; 1977. 6. Spurr S, Bally J, Ogenchuk M, Walker K. A framework for exploring adolescent wellness. Pediatr Nurs. 2012;38(6):320e326. 7. Kiefer RA. An integrative review of the concept of well-being. Holist Nurs Pract. 2008;22(5):244e252. 8. Whipple K, Combs S, Dowd D, Elliott S. Using the dimensions of health to assess motivation among running moms. Health Care Women Int. 2011;32(5):384e401. 9. Mcloughlin CS, Kubick RJ. Wellness promotion as a life-long endeavor: promoting and developing life competencies from childhood. Psychol Sch. 2004;41(1):131e141. 10. World Health Organization. Towards a Common Language for Functioning, Disability and Health: ICF; 2002:1e23. 11. Seligman ME. Positive health. Appl Psych. 2008;57(s1):3e18. 12. Breslow L. Health measurement in the third era of health. Am J Public Health. 2006;96(1):17e19. 13. Bruess C, Richardson G. Decisions for Health. Dubuque, IA: W.C. Brown Publishers; 1992. 14. Corbin CB, Pangrazi RP. Toward a uniform definition of wellness: a commentary. Pres Counc Phys Fit Sports Res Dig; 2001:2e10.
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15. Corbin CB, Pangrazi RP, Franks BD. Definitions: health, fitness, and physical activity. Pres Counc Phys Fit Sports Res Dig. 2000;3(9):1e8. 16. Edlin G, Golanty E. Health and Wellness, a Holistic Approach. Boston: Jones and Bartett Publishers; 1992. 17. Foster TW, Levitov JE. Wellness during midlife and older adulthood: a different perception. Adultspan Journal. 2012;11(2):66e76. 18. McMahon S, Fleury J. Wellness in older adults: a concept analysis. Nurs Forum. 2012;47(1):39e51. 19. Payne WA, Hahn DB. Understanding Your Health. 6th ed. St. Louis: McGraw-Hill; 2000. 20. Larson JS. The conceptualization of health. Med Care Res Rev. 1999;56(2):123e136. 21. Breen L, Wildy H, Saggers S. Challenges in implementing wellness approaches in childhood disability services: Views from the field. Int J Disabil Dev Educ. 2011;58(2):137e153. 22. Houtrow A, Jones J, Ghandour R, Strickland B, Newacheck P. Participation of children with special health care needs in school and the community. Acad Pediatr. 2012;12(4):326e334. 23. Houtrow AJ, Kim SE, Chen AY, Newacheck PW. Preventive health care for children with and without special health care needs. Pediatrics. 2007;119(4):e821ee828. 24. Nageswaran S, Silver EJ, Stein RE. Association of functional limitation with health care needs and experiences of children with special health care needs. Pediatrics. 2008;121(5):994e1001. 25. Chi DL, McManus BM, Carle AC. Caregiver burden and preventive dental care use for US children with special health care needs: a stratified analysis based on functional limitation. Matern Child Heallth J. 2013;18(4):1e9. 26. Hogan DP, Rogers ML, Msall ME. Functional limitations and key indicators of well-being in children with disability. Arch Pediatr Adolesc Med. 2000;154(10):1042e1048. 27. National Survey of Children’s Health. Data Resource Center; 2011. 28. Bethell CD, Read D, Stein RE, Blumberg SJ, Wells N, Newacheck PW. Identifying children with special health care needs: development and evaluation of a short screening instrument. Ambul Pediatr. 2002;2(1):38e48. 29. Hales D. An Invitation to Health: Brief, CengageBrain.com; 2008. 30. Hoeger WW, Hoeger SA. Lifetime Physical Fitness and Wellness: A Personalized Program, CengageBrain.com; 2008. 31. Jutras S, Lepage G. Parental perceptions of contributions of school and neighborhood to children’s psychological wellness. J Community Psychol. 2006;34(3):305e325. 32. Churchill SS, Villareale NL, Monaghan TA, Sharp VL, Kieckhefer GM. Parents of children with special health care needs who have better coping skills have fewer depressive symptoms. Matern Child Health J. 2010;14(1):47e57. 33. Gaskin DJ, Mitchell JM. Health status and access to care for children with special health care needs. J Ment Health Policy Econ. 2005;8(1): 29e35. 34. Lykens KA, Fulda KG, Bae S, Singh KP. Differences in risk factors for children with special health care needs (CSHCN) receiving needed specialty care by socioeconomic status. BMC Pediatr. 2009;9(1):48.
35. Porterfield SL, McBride TD. The effect of poverty and caregiver education on perceived need and access to health services among children with special health care needs. Am J Public Health. 2007;97(2): 323e329. 36. Berk LE. Physical Development in Adolescence. Infants, Children, and Adolescents. 7th ed. Boston: Ally-Bacon; 2012:528e563. 37. Chen H-Y, Cisler RA. Assessing health-related quality of life among children with special health care needs in the United States. Child Health Care. 2011;40(4):311e325. 38. Simon AE, Chan KS, Forrest CB. Assessment of children’s healthrelated quality of life in the United States with a multidimensional index. Pediatrics. 2008;121(1):e118ee126. 39. Lindley LC, Mark BA. Children with special health care needs: Impact of health care expenditures on family financial burden. J Child Fam Stud. 2010;19(1):79e89. 40. Parish SL, Shattuck PT, Rose RA. Financial burden of raising CSHCN: association with state policy choices. Pediatrics. 2009;124(s4):S435eS442. 41. Parish SL, Rose RA, Dababnah S, Yoo J, Cassiman SA. State-level income inequality and family burden of US families raising children with special health care needs. Soc Sci Med. 2012;74(3): 399e407. 42. Blanchard LT, Gurka MJ, Blackman JA. Emotional, developmental, and behavioral health of American children and their families: a report from the 2003 National Survey of Children’s Health. Pediatrics. 2006;117(6):e1202ee1212. 43. DeRigne L. The employment and financial effects on families raising children with special health care needs: an examination of the evidence. J Pediatr Health Care. 2012;26(4):283e290. 44. Lollar DJ, Hartzell MS, Evans MA. Functional difficulties and health conditions among children with special health needs. Pediatrics. 2012;129(3):e714ee722. 45. Phelps RA, Pinter JD, Lollar DJ, Medlen JG, Bethell CD. Health care needs of children with down syndrome and impact of health system performance on children and their families. J Dev Behav Pediatr. 2012;33(3):214e220. 46. Ganz ML, Tendulkar SA. Mental health care services for children with special health care needs and their family members: prevalence and correlates of unmet needs. Pediatrics. 2006;117(6): 2138e2148. 47. Maga~na S, Parish SL, Rose RA, Timberlake M, Swaine JG. Racial and ethnic disparities in quality of health care among children with autism and other developmental disabilities. Intellect Dev Disabil. 2012;50(4):287e299. 48. Inkelas M, Raghavan R, Larson K, Kuo AA, Ortega AN. Unmet mental health need and access to services for children with special health care needs and their families. Ambul Pediatr. 2007;7(6): 431e438. 49. Nqui EM, Flores G. Unmet needs for specialty, dental, mental, and allied health care among children with special health care needs: are there racial/ethnic disparities? J Health Care Poor Underserved. 2007;18(5):931e949.
Research Paper
Correlates of wellness among youth with functional disabilities K.S. Menear, Ph.Da,*, J.K. Preskitt, Ph.Db, S.S. Goldfarb, Ph.Db, and N. Menachemi, Ph.Db a
Department of Human Studies, School of Education, University of Alabama at Birmingham, 1720 2nd Ave. S., EB 207, Birmingham, AL 35294-1250, USA b Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, RPHB 330, 1720 2nd Ave. S., Birmingham AL 35294, USA
Abstract Background: The literature is more informative on the impediments to wellness among youth with functional limitations and less instructive on the state of wellness for this population. Objective: To explore overall wellness, and each sub-dimension of wellness, in a national sample of youth with functional limitations and to determine how demographic characteristics are associated with wellness. Methods: Using a previously validated screening instrument, we identify youth with functional limitations aged 12 to 17 represented in the 2011/12 National Survey of Children’s Health. Survey items were coded to operationalize an overall wellness score comprised of four sub-dimensions of wellness (i.e., physical, intellectual, emotional, and social). Results: The mean overall wellness score was 26.7 (out of 40) and had an approximate normal distribution. Mean raw scores for each sub-dimension were as follows: social 5 2.79 (out of 4; 69.7%); emotional 5 4.09 (out of 6; 68.2%); intellectual 5 3.79 (out of 8; 47.4%); and physical 5 6.30 (out of 8; 78.7%). Lower wellness scores were associated with older age among youth, increasing number of chronic health conditions, lower income, single mother homes, and youth whose mother reported fair or poor mental health status (all p ! 0.05). Higher wellness scores were positively associated with mother’s education ( p ! 0.001). Conclusions: Program planners should consider interventions that target youth with functional limitations shown to be at particular risk for lower overall wellness and promote family involvement and comprehensive supports, including maternal educational attainment, mental health screening, and referral. Ó 2015 Elsevier Inc. All rights reserved. Keywords: Youth with functional disabilities; Wellness; 2011/12 National Survey of Children’s Health
Youth with special health care needs or disabilities are at higher risk for physical, developmental, behavioral, or emotional conditions and as a result require more health care services than their peers.1,2 Youth with functional limitations due to health conditions expected to last 12 months or more are a unique subset of youth with special health care needs, accounting for an estimated 4.3% of youth in this group.2 Successful health management of this population is multifaceted and includes quality improvement efforts within clinical practices and health systems, partnerships across the entire service team and related researchers, training and education for everyone involved, and advocacy for youth with functional limitations and their caregivers.3 Wellness is viewed as a holistic4e6 and multi-dimensional6e8 concept which encompasses more than the absence of disease.9,10 Wellness relates to functional abilities as There are no conflicts of interest. The article has not been submitted elsewhere for publication or presentation. * Corresponding author. Tel.: þ1 205 975 7409. E-mail address: [email protected] (K.S. Menear). 1936-6574/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.dhjo.2014.10.001
well as life balance,4e6 thereby aligning well with other comprehensive frameworks for describing health, including the International Classification of Functioning, Disability, and Health (ICF).11 ICF classifies health across multiple domains according to body structure and function, as well as an individual’s capacity to perform activities and participate in society.11 Similarly, wellness is commonly viewed as being comprised of various sub-dimensions, which can include social, emotional, physical, and intellectual wellness,8,9,12e19 though the identification of the specific dimensions of wellness has varied in number and definition in the literature.6e8 Wellness expands the traditional understanding of health and functioning to include a context of actively engaging in activities that build and support an individual’s potential or capacity to be healthy.12,14,20 For youth without disabilities, wellness is often maintained at the population level in large part through mandated physical education programs in schools and federally-mandated school wellness policies required by the Child Nutrition and WIC Reauthorization Act of 2004 (Public Law 108e265). Given the recognition that wellness is an important component of healthy living, it is troubling
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that researchers have documented challenges when implementing wellness programs for children with disabilities21 and children with functional limitations.22 Moreover, researchers have reported disparities in the delivery of preventive health services for youth with functional limitations.23 School wellness policies focus on physical activity, nutrition, and other school-based activities that promote student wellness. Overall the literature is more informative on the impediments to wellness among youth with functional limitations and is less instructive on the state of wellness for this population. Children with functional limitations were more likely than other children with special health care needs to experience participation restrictions related to school attendance, participation in organized activities, working for pay, and volunteering.22 Children with severe functional limitations experienced significantly greater odds of delayed health care, unmet health care and care-coordination needs, referral problems, dissatisfaction with care, difficulty using services, worse health insurance experiences and greater impact on family (financial problems, employment challenges, need to provide home care) than children with special health care needs who had some or no limitations.24 Research has also indicated that functional limitation, family poverty, and being uninsured were significantly associated with greater caregiver burden and less preventative dental care.25 Additionally identified impediments to the health of youth with functional limitations have included living in families with limited resources and experiencing greater exposure to secondhand smoke, less access to health care, and lower health status.26 While we have indications that factors such as social disadvantage, restricted access, decreased participation, unhealthy home environment, and financial resources impact the health care needs and experiences of youth with functional limitations, we do not know their overall state of wellness. The purpose of this paper is threefold. First, we operationalize a measure of wellness across multiple dimensions to determine the extent of wellness in a national sample of youth with functional limitations aged 12e17 years. Second, we calculate measures of the various sub-dimensions of wellness and determine which sub-dimensions are most prevalent in this population. Lastly, we explore how different characteristics of youth with functional limitations, their caregivers, or other demographic information are associated with wellness among this group. This information will be valuable to program directors, policymakers, and clinicians interested in understanding and improving the state of wellness for youth with functional limitations. Methods Consistent with the aims of the current study, we utilized nationally representative youth survey data that includes questions that allowed us to operationalize measures that represent the commonly cited sub-dimensions of wellness.
Specifically, the 2011/12 National Survey of Children’s Health (NSCH)27 collected by the Centers for Disease Control and Prevention (CDC) was conducted via telephone interviews by trained interviewers, incorporating both landlines and cell phones and utilizing a sampling design focused on reaching U.S. households including at least one child aged 0e17 years at the time of the contact. Responses were given by the person in the household who knew the most about the child or youth’s health. A total of 95,677 interviews were conducted between February 2011 and June 2012, with representation from all US states (Centers for Disease Control and Prevention, National Center for Health Statistics, State and Local Area Integrated Telephone Survey. 2011e2012 National Survey of Children’s Health Frequently Asked Questions. April 2013. Available from URL: http://www.cdc.gov/nchs/ slaits/nsch.htm). From the survey, youth with any special health care needs were, first, identified through the use of a validated screening instrument, which includes five items and follow-up questions related to consequences of ongoing health conditions.28 From that group, youth with one or more functional limitations due to health conditions expected to last 12 months or more were selected for inclusion in this study (‘‘yes’’ to questions K2Q16, K2Q17, and K2Q18). The strength of the National Survey of Children’s Health is its utilization of a complex sampling design and population weights to be nationally representative of children and youth ages 0e17 years. There are a unique set of survey items for youth 12e17 years which fit our interest with the adolescent years. The survey design elements and analytical techniques allow for national-level generalizations. This study was exempted from review by the university’s Institutional Review Board as it utilizes a publiclyavailable, de-identified dataset. Dependent variable Based on extensive review of the wellness literature, we reviewed all questions on the NSCH and identified items relating to sub-dimensions of wellness that could reasonably be developed based on available data, including physical, intellectual, emotional, and social wellness. The NSCH did not contain survey items to operationalize other additionally common sub-dimensions of wellness (e.g., spiritual and environmental wellness). Based on definitions from the literature,6e8,29e31 questions were identified from the NSCH survey that represented each of the subdimensions of wellness. (Domains, descriptions, questions and coding are presented in the Appendix.) We evaluated each survey item to determine if it operationalized wellness for 12e17 year olds. Then, each author presented questions for a unique sub-dimension of wellness and identified any sub-dimensions that could not be reasonably developed using this survey. The authors reached consensus through extensive discussion about overall question selection and
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assignment under each sub-domain. Final group decisions were based on the literature, conceptual fit, discussion with other content experts, and our own expertise. In the end, 17 questions were selected to represent 4 sub-dimensions of wellness: social, emotional, intellectual, and physical. Given our methods, breadth of domains included, and analyses, we suggest that the index has good face validity. We then computed scores for each survey respondent on each wellness sub-dimension. The intellectual and physical sub-dimensions ranged on a scale from 0 to 8 points, the emotional sub-dimension ranged from 0 to 6 points, and the social sub-dimension ranged from 0 to 4 points. For example, the intellectual sub-dimension has a range of 0e8 points possible based upon responses to 4 questions. The first, ‘‘Since starting kindergarten, has [he/she] repeated any grades’’, is coded 1 for ‘‘no’’ and 0 for ‘‘yes.’’ The next question in the sub-dimension asks, ‘‘On an average weekday, about how much time does [CHILD’S NAME] usually spend reading for pleasure?’’ and is coded 3 for ‘‘over 30 min’’, 2 for ‘‘16e30 min’’, 1 for ‘‘1e15 min’’, and 0 for ‘‘None/0 min’’. The final survey item in the subdimension is, ‘‘I am going to read a list of items that sometimes describe children. For each item, please tell me how often this is true for [CHILD’S NAME] during the past month: 1) [He/She] cares about doing well in school’’, which is coded 2 for ‘‘Always’’, 1 for ‘‘Usually’’, and 0 for ‘‘Never/ Rarely/Sometimes’’; ‘‘2) [He/She] does all required homework’’, which is coded 2 for ‘‘Always’’, 1 for ‘‘Usually’’, and 0 for ‘‘Never/Rarely/Sometimes’’’’. The physical, emotional, and social sub-dimensions were calculated in a similar way. After we constructed raw scores for each sub-dimension for each survey respondent, we applied a multiplier to weight each sub-dimension to contribute equally (10 points) on a summated scale to create a total, standardized wellness score. The total wellness score could range from 0 to 40 points. This federal survey asked these survey items of all 12e17 year olds. This provided a dataset that is as homogenous as possible for this critical developmental stage (i.e., adolescence) for topics surrounding health and wellness. The respondents were the caregivers who knew the child best.
others), mother’s education level, and mother’s physical and mental health status, all of which were selected because the survey caller asked to speak to the caregiver most familiar with the child and therefore most of the respondents were mothers. Family income as a percentage of the federal poverty level (FPL) was also used as a predictor variable. Given that nearly all youth with functional limitations reported having insurance at the time of the interview, this variable was not included as a predictor.
Predictor variables
The study sample consisted of a total of 2000 youth with functional limitations who were between the ages of 12 and 17 years (See Table 1). The distribution across the younger (12e14 year) age group and the older (15e17 year) age group was approximately equal (49% and 51%, respectively). Youth were a majority white (66.1%) and nonHispanic (81.4%). About half (49.4%) had a normal weight based on BMI. Over half of youth in sample had three or more chronic health conditions (55.9%), and more than three quarters of respondents rated the severity of at least one of their youth’s health conditions as moderate or severe (77.8%). Over one quarter (28.3%) lived in a single mother home, and most mothers had more than a high school
In order to examine the correlates of wellness, we utilized various survey questions from the NSCH as predictor variables including: demographic characteristics (i.e., age, gender, race, ethnicity), BMI status (i.e., healthy weight, underweight, overweight, and obese), and characteristics associated with the individual youth’s functional limitation or chronic condition (i.e., number of health conditions reported, whether the respondent indicated a moderate or severe rating for any health condition reported, and qualification reason). Parental characteristics used as predictor variables included: family structure (single mother vs.
Data analysis Data analyses were conducted using Stata 12 to accommodate the complex sampling design and weighting techniques of the survey. Using the ‘subpop’ procedure to assure correct standard error calculations, analyses were limited to youth with functional limitations who were ages 12e17 years at the time of the survey. The cut point for statistical significance was set at p ! 0.05. We began our analyses by calculating descriptive statistics, including frequencies and means. We then performed t-test analyses to examine the differences in mean total wellness scores and raw sub-dimension scores between youth with and without functional limitations. Finally, we then utilized a four-stage step-wise linear regression model to examine the associations between each predictor variable and the overall wellness construct score for youth with functional limitations. The four stages sequentially added additional control variables, allowing the examination of the stability of association of each predictor variable as additional predictors were added. Model 1 included youth characteristics only, while Model 2 added condition characteristics. Model 3 also included parent characteristics and Model 4 incorporated family income. Finally, we developed multivariable linear regression models using all predictor variables (the fully-adjusted model) to assess associations between each predictor variable and each sub-dimension of wellness (e.g., physical, intellectual, emotional, and social) for the group of youth with functional limitations.
Results
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Table 1 Demographics of a sample of U.S. youth with functional limitations ages 12e17 years, National Survey of Children’s Health, 2011/12 (n 5 2000) Variable N (%) Age 12e14 15e17 BMI Underweight Overweight Obese Healthy weight Gender Female Male Ethnicity Non-Hispanic Hispanic Race Other White Moderate or severe rating for any condition Number of health conditions None 1 2 3 or more Single mother home Mother has more than high school education Mother has fair or poor health status Mother has fair or poor mental health status Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater Has health insurance Private insurance Public insurance
898 (49.3) 1102 (50.7) 127 309 453 1062
(5.9) (18.2) (26.6) (49.4)
797 (40.2) 1199 (59.8) 1773 (81.4) 194 (18.6) 499 (33.9) 1460 (66.1) 1539 (77.8) 162 399 274 1165 488 1244 436 275
(7.3) (20.0) (16.8) (55.9) (28.3) (65.4) (24.3) (15.2)
450 418 318 228 586 1928 987 941
(33.1) (19.8) (13.2) (11.0) (24.9) (96.8) (52.2) (47.8)
education (65.4%). Nearly 25% of mothers reported fair or poor physical health status (24.3%), while over 15% reported fair or poor mental health status (15.2%). About 25% of youths lived in homes at or above 400% FPL (24.9%), but over 50% lived in homes below 200% FPL (combined, 0e99% 5 33.1%; 100e199% 5 19.8%). Almost all youth with a functional limitation (96.8%) had health insurance at the time of the interview, with about equal percentages having private versus public insurance. The distribution of the overall wellness scores for youth with functional limitations in the sample is displayed in Fig. 1. The distribution, though slightly skewed to the right, approximates normality. The mean overall wellness score was 26.7 (on a maximum 40-point scale), with no youth scoring below a total of 6.25 points. Table 2 displays analyses of the difference in mean scores (overall wellness and raw sub-dimensions) between groups of youth with and without functional limitations. All differences were significant based on t-test analyses ( p ! 0.001), with youth without functional limitations
Fig. 1. Measurement of a wellness construct for a sample of U.S. youth with functional limitations ages 12e17 years, National Survey of Children’s Health, 2011/2012
scoring lower on overall wellness and across all subdimensions. Specifically for youth with functional limitations, mean raw scores for each sub-dimension were as follows: social 5 2.79 (out of 4; 69.7%); emotional 5 4.09 (out of 6; 68.2%); intellectual 5 3.79 (out of 8; 47.4%); and physical 5 6.30 (out of 8; 78.7%). Linear regression coefficients for predictor variables and the overall wellness score are presented in Table 3. Across all models, several predictors had a consistently stable relationship with the overall wellness score. Several youth characteristics were associated with significantly lower wellness scores. For example, lower scores were observed for older youth (ages 15e17 years; b 5 1.55, p ! 0.01). Lower scores were also associated with increasing number of health conditions reported (b 5 1.70, p ! 0.001). Lower scores were also associated with select parent characteristics. Youth living in single mother homes had lower overall wellness scores (b 5 1.54, p ! 0.05), as did those whose mother reported fair or poor mental health (b 5 3.66, p ! 0.001). Higher maternal education had a positive association with youth wellness scores (b 5 3.25, p ! 0.001). Family income at 300e399% FPL was associated with lower overall wellness scores (b 5 1.75, p ! 0.05). Fully-adjusted linear regression models for each subdimension of wellness are displayed in Table 4. Only one predictor was associated with scores consistently across all sub-dimensions. Youth whose mothers had more than a high school education had higher scores, ranging from 0.33 to 0.67 point increases across the sub-dimensions. In contrast, several predictors were associated with lower scores for certain sub-dimensions of wellness, but not others. For example, older youth had lower scores on the intellectual and physical sub-dimensions (b 5 0.69, p ! 0.001; b 5 0.40, p ! 0.01, respectively). Also, youth whose mothers reported fair or poor mental health
K.S. Menear et al. / Disability and Health Journal 8 (2015) 223e230 Table 2 Analysis of differences in mean standardized overall wellness and subdimension scores for a sample of U.S. youth with and without functional limitations, ages 12e17 years, National Survey of Children’s Health, 2011/12 Youth with Youth without functional functional limitations limitations (n 5 2000) (n 5 32,601) Mean Total wellness score e 26.7 standardized (0e40) Social sub-dimension (0e4) 2.79 Emotional sub-dimension (0e6) 4.09 Intellectual sub-dimension (0e8) 3.79 Physical sub-dimension (0e8) 6.30 a
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b 5 0.38, p ! 0.001; and b 5 0.64, p ! 0.001, respectively), but not the physical. Finally, several lower income levels were associated with 0.44e0.91 point reductions for the emotional and intellectual sub-dimensions, but no significant associations were observed with the social or physical sub-dimensions of wellness.
Mean
p-valuea
Discussion
30.4
!0.001
Our analysis suggests that youth with functional limitations had lower overall wellness and sub-dimension scores compared to youth without functional limitations. However, the average youth with functional limitations obtained at least half of the points possible on the overall wellness scale and within each sub-dimension except intellectual. Variation exists and may be explained by individual and parental or familial characteristics. Our results indicate the vulnerability that youth with functional limitations may face regarding their overall wellness. Given the impact that wellness may have on physical health, academic progress, and coping skills, among other chronological and developmental growth areas common to youth, awareness of the impact wellness may have is a vital to
!0.001 !0.001 !0.001 !0.001
3.16 4.83 4.87 6.59
Based t-test of difference in mean values between the two groups.
status had lower scores on the social, emotional, and physical sub-dimensions (b 5 0.43, p ! 0.05; b 5 0.99, p ! 0.01; and b 5 0.52, p ! 0.01, respectively). An increase in the number of health conditions reported was associated with lower scores for the social, emotional, and intellectual sub-dimensions (b 5 0.13, p ! 0.05;
Table 3 Linear regression results; wellness in a sample of U.S. youth with functional limitations ages 12e17 years; 2011/12 National Survey of Children’s Health Model 1 Model 2 Model 3 Model 4 Variable coefficient (SE) coefficient (SE) coefficient (SE) coefficient (SE) Youth characteristics Age 12e14 15e17 Underweight Overweight Obese Male Hispanic White Condition characteristics Moderate or severe rating for any condition Number of health conditions Parent characteristics Other family structure Single mother home Mother has more than high school education Mother has fair or poor physical health status Mother has fair or poor mental health status Other demographics Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater
SE 5 Standard error. *p ! 0.05, **p ! 0.01, ***p ! 0.001. a Number observations in regression model.
Ref 1.76 2.15 1.15 0.47 1.24 0.50 1.67
(0.67)** (1.31) (0.76) (0.99) (0.71) (1.25) (0.75)*
Ref 1.91 1.68 0.89 0.00 0.44 0.44 1.96
(0.57)** (1.29) (0.66) (0.85) (0.60) (1.05) (0.18)**
Ref 1.45 1.48 1.03 0.04 0.24 1.24 1.05
(0.55)** (1.13) (0.70) (0.79) (0.58) (1.04) (0.64)
Ref 1.55 1.53 1.02 0.02 0.40 1.31 1.05
(0.54)** (1.16) (0.70) (0.73) (0.58) (0.98) (0.62)
e e
0.95 (0.94) 2.10 (0.42)***
0.32 (0.85) 1.70 (0.34)***
0.07 (0.79) 1.70 (0.33)***
e
e
Ref 1.54 3.25 0.11 3.66
e e e
e e e
Ref 1.59 3.29 0.17 3.70
e
e
e
F 5 4.34 p ! 0.000 R2 5 0.056 1779a
F 5 13.05 p ! 0.000 R2 5 0.183 1779a
F 5 11.10 p ! 0.000 R2 5 0.293 1589a
(0.61)** (0.64)*** (0.71) (0.99)***
(0.70)* (0.74)*** (0.72) (0.95)***
0.56 (1.16) 1.44 (0.79) 1.21 (0.79) 1.75 (0.69)* Ref F 5 9.52 p ! 0.000 R2 5 0.302 1589a
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Table 4 Linear regression results; sub-dimensions of wellness in a sample of U.S. youth with functional limitations ages 12e17 years; 2011/12 National Survey of Children’s Health (Model 4) Social sub-dimension Emotional sub-dimension Intellectual sub-dimension Physical sub-dimension Variable coefficient (SE) coefficient (SE) coefficient (SE) coefficient (SE) Youth characteristics Age 12e14 15e17 Underweight Overweight Obese Male Hispanic White Condition characteristics Moderate or severe rating for any condition Number of health conditions Parent characteristics Other family structure Single mother home Mother has more than high school education Mother has fair or poor physical health status Mother has fair or poor mental health status Other demographics Income 0e99% FPL 100e199% FPL 200e299% FPL 300e399% FPL 400% FPL or greater
Ref 0.09 0.12 0.08 0.09 0.04 0.05 0.15
(0.10) (0.24) (0.15) (0.13) (0.11) (0.12) (0.12)
Ref 0.01 0.14 0.44 0.11 0.19 0.48 0.15
(0.14) (0.23) (0.19)* (0.18) (0.15) (0.24) (0.17)
Ref 0.69 0.45 0.07 0.16 0.43 0.49 0.23
(0.18)*** (0.33) (0.22) (0.22) (0.19)* (0.29) (0.20)
0.06 (0.12) 0.13 (0.05)*
0.10 (0.19) 0.38 (0.08)***
0.14 (0.23) 0.64 (0.10)***
Ref 0.09 0.33 0.15 0.43
Ref 0.28 0.41 0.00 0.99
Ref 0.30 0.67 0.00 0.27
(0.12) (0.13)* (0.12) (0.19)*
0.08 (0.19) 0.05 (0.15) 0.16 (0.15) 0.00 (0.20) Ref F 5 2.51 p ! 0.000 R2 5 0.086
(0.16) (0.16)* (0.19) (0.29)**
0.41 (0.22) 0.44 (0.18)* 0.29 (0.19) 0.91 (0.32)** Ref F 5 10.17 p ! 0.000 R2 5 0.252
(0.21) (0.22)** (0.22) (0.29)
0.03 (0.32) 0.50 (0.24)* 0.32 (0.27) 0.15 (0.25) Ref F 5 8.80 p ! 0.000 R2 5 0.225
Ref 0.40 0.32 0.03 0.23 0.10 0.03 0.03
(0.11)** (0.22) (0.17) (0.15) (0.11) (0.23) (0.14)
0.13 (0.17) 0.01 (0.07) Ref 0.23 0.55 0.42 0.52
(0.15) (0.16)*** (0.16)** (0.19)**
0.27 (0.23) 0.13 (0.19) 0.06 (0.15) 0.09 (0.18) Ref F 5 4.20 p ! 0.000 R2 5 0.209
SE 5 Standard error. *p ! 0.05, **p ! 0.01, ***p ! 0.001.
understanding how to facilitate their developmental progression. As expected, youth with more chronic health conditions had lower overall wellness scores and lower scores for the social, emotional and intellectual sub-dimensions. This finding is consistent with previous work that found youth with functional limitations or those who qualified as having a special health care need based on more screener items had poorer overall health and more complex health care needs,30 and qualifying with three or more special health care needs was the strongest predictor of lower healthrelated quality of life.31 It is somewhat surprising that lower scores were not also noted in the physical sub-dimension, though this may be a function of small sample size and the inclusive content of this sub-dimension which more broadly captures topics that contribute to physical wellness, such as preventive visits and health behaviors. It is a reassuring finding that mean raw scores for the physical subdimension were the highest in terms of percent attainment across the four sub-dimensions of wellness. Perhaps youth with functional limitations who have multiple chronic conditions, along with their caregivers, are placing greater emphasis on these activities that support the physical aspects of wellness. However, youth with functional
limitations, those with worse health status, and those exposed to social disadvantage may be met with participation challenges in school and community.22 Youth whose mothers reported fair or poor mental health status had a nearly 4-point (equating to a 10%) reduction in overall wellness score and had lower scores across all subdimensions except intellectual. This finding is supported by related literature. Churchill, Villareale, Monaghan, Sharp, and Kieckhefer32 reported increased risk of depressive symptoms in parents of youth with special health care needs, especially if they are single and unemployed. Gaskin and Mitchell33 indicated that youth with special health care needs with parents who have symptoms of depression are significantly more likely to experience challenges obtaining needed medical and mental health care services. As expected, maternal education was positively associated with wellness. Youth whose mother had education above the high school level had higher overall wellness scores and higher scores for all sub-dimensions. Lykens, Fulda, Bae, and Singh34 found that maternal education significantly affected whether children received all needed specialty care when the analysis was stratified by socioeconomic status. Porterfield and McBride35 found that parents of youth with special health needs who had lower income
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and were less educated were less likely than higher-income and more-educated parents to indicate their children needed specialized health services. Older youth with functional limitations had lower overall wellness scores and lower scores in the intellectual and physical sub-dimensions. This finding may be related to general expectations and available activities for older youth coupled with the natural increase in desired autonomy during adolescence.36 Lower income was associated with lower overall wellness scores and lower scores across two of the four subdimensions (emotional and intellectual), albeit the specific FPL percentages varied. This is not surprising given the literature that indicates an association between lower socioeconomic status and lower health-related quality of life,37,38 the documented financial burden of raising youth with special health care needs,39,40 the impact of state-level income inequality on family burden related to the health care of special needs children,41 the difficulty of being able to work full time for parents caring for the special needs of their children,24,42e45 and disproportionately higher rates of unmet mental health needs of youth with special health care needs and their families of lower socioeconomic status.46 As our sample of youth with functional limitations is a subset of youth with special health care needs, we would expect similar findings to the larger group related to income associations. Interestingly, race and ethnicity were not associated with wellness scores as we would have expected. Once models were adjusted for all other variables, there were no significant differences between White and non-White youth or for Hispanic and non-Hispanic youth in overall wellness scores or for any sub-dimension. These results are in contrast to previous literature that found children with autism (a special health care need) who are Latino or Black face greater challenges with obtaining high-quality health care.47 Further, Inkelas et al48 found that youth with a chronic mental, behavioral, or developmental problem that were African-American had greater unmet mental health needs. Nqui & Flores49 found greater disparities and risk factors for unmet specialty, dental, mental, and allied health care needs among Black and Hispanic youth with special health care needs compared to their White counterparts. Limitations There are several limitations that are important to mention. Our data is limited by the self-reported nature of the survey instrument. We recognize the potential for recall bias, and the fact that the telephone survey method used achieved a limited response rate. Moreover, the survey itself was not designed to measure wellness; instead, we utilize select questions to operationalize various dimensions that were related to the theoretical concept of wellness. There may be other aspects of wellness or indicators of sub-dimensions that cannot be captured in this survey based on available questions. Also, the cross-sectional design of
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our analysis allows us to identify associations only. Therefore, none of the relationships we present can be interpreted as causal. Additionally, we recognize the domains have not been empirically validated although they are theoretically grounded. Future work should empirically validate the domains. Conclusions Our study presents an objective measure of wellness that is expanded beyond condition-based health-related quality of life. In light of our findings, program planners should consider interventions that target youth with functional limitations shown to be at particular risk for lower overall wellness, including older youth, those in lower income families, and those with more chronic health conditions. Further, our findings related to maternal educational level and mental health status highlight the importance of family involvement and comprehensive support, including support for educational attainment and screening for mental health issues among maternal caregivers, with referral for services as needed.
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