- Email: [email protected]
Disability and Discussions of Health-Related Behaviors Between Youth and Health Care Providers
Journal of Adolescent Health 57 (2015) 81e86
www.jahonline.org Original article
Disability and Discussions of Health-Related Behaviors Between Youth and Health Care Providers Elisabeth M. Seburg, M.P.H. a, *, Barbara J. McMorris, Ph.D. b, Ann W. Garwick, Ph.D., R.N. b, and Peter B. Scal, M.D., M.P.H. a, c a
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota School of Nursing, University of Minnesota, Minneapolis, Minnesota c Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota b
Article history: Received December 2, 2014; Accepted March 5, 2015 Keywords: Mobility limitation; Adolescent health care; Preventive services; Health behaviors
A B S T R A C T
Purpose: The purpose of this study was to examine the likelihood of discussing health-related behaviors with health care providers (HCPs), comparing youth with and without mobility limitations (MLs). Methods: Analyses were conducted using baseline data from the MyPath study. Adolescents and young adults between the ages of 16 and 24 years completed a survey about their health care and health-related experiences. Analyses assessed the relationship between mobility status and discussing health-related behaviors with an HCP. Secondary analyses examined the extent to which adolescents and young adults’ engagement in these behaviors was associated with these discussions. Results: Overall, we found low rates of discussions about the following topics: substance use, sexual and reproductive health, healthy eating, weight, and physical activity. Adolescents and young adults with MLs were less likely to report discussing substance use and sexual and reproductive health, but were more likely to discuss healthy eating, weight, and physical activity than peers without MLs. Those adolescents and young adults who reported substance use had higher odds of discussing this topic and those who reported having sexual intercourse had higher odds of discussing sexual and reproductive health. Conclusions: Results suggest mobility status and a young person’s engagement in health risk and promoting behaviors are associated with the likelihood of discussing these behaviors with an HCP. It is important that HCPs view adolescents and young adults with MLs as needing the same counseling and guidance about health-related behaviors as any young person presenting him/herself for treatment. Ó 2015 Society for Adolescent Health and Medicine. All rights reserved.
Providing preventive counseling is a critical component of adolescent health services. Yet, many young people who experience difficulty with physical functioning because of a mobility Conflicts of Interest: Funding for this study comes from the Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities 1U48DP001939 (P.B.S., PI). * Address correspondence to: Elisabeth M. Seburg, M.P.H., HealthPartners Institute for Education and Research, 8170 33rd Ave S. Mail stop 23301A, PO Box 1524, Bloomington, MN 55440-1524. E-mail address: [email protected] (E.M. Seburg). 1054-139X/Ó 2015 Society for Adolescent Health and Medicine. All rights reserved. http://dx.doi.org/10.1016/j.jadohealth.2015.03.004
IMPLICATIONS AND CONTRIBUTION
Few adolescents and young adultsdand especially few of those with mobility limitations (MLs)ddiscussed health-related behaviors with a health care provider. This community-based study confirms the need to develop and implement strategies to improve counseling in clinical settings, especially for youth with MLs.
limitation (ML) do not receive information or counseling regarding their health behaviors [1e3]. The low rates of routine counseling for health behaviors in this population are concerning for several reasons. Adolescents and young adults with MLs, similar to peers without disabilities, experience the physiologic and psychosocial changes of adolescence and engage in and establish health-related behavior patterns that influence their health trajectories across the life span [4,5]. In addition, the risk for poor health outcomes related to health risk behaviors may be amplified by the interaction between chronic condition, treatments, and behavior [3]. For
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example, alcohol use can influence bowel management among those with spina bifida [6], whereas recreational substance use can compromise effectiveness of medications prescribed for health conditions or symptom management [7]. Furthermore, low engagement in health promoting behaviors, such as eating a balanced, nutritious diet and engaging in regular physical activity, are associated with greater risk for obesity. Adolescents and young adults with MLs are at a higher risk for obesity than peers without disabilities [8,9]. In addition to increasing the risk for many chronic health conditions [10], obesity can negatively affect condition management and progression (e.g., pressure sores and muscle wasting) and limit a young person’s capacity to perform self-care and activities of daily living [11]. Also of concern are the low rates of screening, counseling, and education about sexual and reproductive health for those with MLs. Similar to peers without disabilities, adolescents and young adults with MLs experience sexual development, engage in sexual activity, and have romantic and sexual relationships [12,13]. Likewise, these young people are exposed to negative health consequences associated with risky sexual behaviors [13]. Indeed, adolescents and young adults with disabilities experience greater risk of sexual abuse than those without disabilities [13]. Sexual development, sexuality, and reproductive health are relevant topics that should be routinely discussed and assessed in the health care setting for these young people [12]. As young people with MLs navigate their way through adolescence and the transition to adulthood, information and education about health behaviors should be viewed as important and relevant to their health and well-being. Health care providers (HCPs) are in a key position to provide screening, anticipatory guidance, and counseling regarding these behaviors, in the context of a young person’s specific health condition [14]. The benefits of preventive counseling in a health care setting are well established, and young people themselves express a desire for this care [2,15,16]. However, provision of these services for adolescents and young adults with MLs remains persistently low. Studies have identified barriers to providing preventive counseling [17], such as time constraints [18], provider training [19], and provider perceptions of which young people would benefit from these services. However, further research is needed to examine the extent to which adolescents and young adults with MLs discuss these topics with their HCPs and the factors that influence the likelihood of these conversations. The present study seeks to address research gaps using a community-based sample of adolescents and young adults, aged 16e24 years, with and without MLs. The study’s first aim was to assess the impact of having a ML on the extent to which young people discuss health behaviors with their HCP. The second aim was to examine whether adolescents and young adults’ own engagement in these behaviors is associated with the likelihood of talking to providers about sexual and reproductive health, substance use, healthy eating, and physical activity. This is the first study, to our knowledge, to investigate whether young people who are engaging in a particular behavior are more likely to discuss that behavior with an HCP. Methods Participants Data are drawn from the baseline survey of the MyPath study, a prospective, longitudinal study of the health care and
health-related experiences of a nonprobability, community-based sample of adolescents and young adults, collected between March 2011 and November 2012 in the upper Midwest region of the United States. To enroll adequate numbers of adolescents and young adults with and without MLs, separate recruitment strategies were used. Potential participants with MLs were recruited through targeted mailings sent from more than 120 study partners (HCPs, clinics, state agencies, school districts, colleges and universities, nonprofits and community organizations). Messages introduced the study and directed individuals to the research team to enroll. Our research team never had access to the names, contact information, or the number of individuals contacted; thus, we are unable to calculate a response rate. The cohort of participants without MLs was recruited through mailings to households in a five-state region (Minnesota, Wisconsin, Iowa, South Dakota, and North Dakota) with a high likelihood of having a 16- to 24-year-old living in the home. The list was purchased from Genesys Sampling Systems (Horsham, PA). Adult participants and parents of minors provided informed consent, and minors provided assent. Institutional review boards from the participating sites (when appropriate) approved study procedures. Baseline data consisted of responses to the MyPath questionnaire (available for completion online, paper, and telephone) from 786 adolescents and young adults. All participants were between the ages of 16 and 24 years at the time of enrollment; those who did not speak English or did not have the cognitive ability to complete the survey were excluded. For this analysis, the analytic sample comprised 557 participants (287 with MLs and 270 without MLs) who reported having seen an HCP in the 6 months before completing the survey. Measures Participants responded to questions assessing gender, age, ethnicity, race, and parent education level. Race was dichotomized into white or nonwhite; highest education level achieved by either parent was also dichotomized (college graduate or not a college graduate). In the present study, ML status was defined by self-report of having any of three specific conditions (spina bifida, cerebral palsy, or muscular dystrophy) and/or reporting any ML as measured by an adaption of the Gross Motor Functioning Classification System (GMFCS) [20]. The GMFCS selfreport youth version is a tool for categorizing the gross motor functioning of youth based on the way they usually engage in each activity assessed. Thus, it assesses functioning within youths’ environmental context reflecting the lived experience rather than gross motor assessment of capacity in a clinic setting. Youth are asked to select the GMFCS category that best describes their functioning. The five categories range from the most limited (“I have difficulty sitting on my own and have difficulty achieving any voluntary control of movement”) to the least (“Can walk on my own without using walking aids but am limited in speed, balance and coordination”) [20]. For this study, we provided a sixth category representing youth who perceive no limitation in their mobility. The survey assessed whether participants had a usual source of care and health insurance and the type of HCP they usually saw for checkups or if sick or hurt. Questions about specific health behaviors were adapted for the study from the Centers for Disease Control and Prevention Youth Risk Behavior Survey [21] and the Minnesota Student Survey [22]. Participants responded to questions regarding use of alcohol, cigarette, and marijuana use during the past 30 days
c
a
b
<.001 .001 .886 .357 .005 (35) (31) (73) (47) (80) 26 23 16 35 60
.171 .219 .004 57 (76) 73 (99) 60 (86)
.037 1.000 .107 .019 (53) (1) (95) (80) 40 1 69 60
(9) (11) (75) (40) (61) 10 12 9 43 66
74 (67) 111 (100) 104 (93)
(38) (2) (87) (64)
Percentages are calculated on valid sample sizes for each variable; these range from 70 to 107 for 16- to 18-year-olds and from 172 to 191 for 19- to 24-year-olds because of missing data. Mobility limitation status defined by diagnoses (cerebral palsy, muscular dystrophy, and spina bifida) or adapted Gross Motor Functioning Classification System level > 0. Based on chi-square tests.
<.001 <.001 .219 .117 <.001 (73) (62) (45) (46) (80) 142 119 53 89 156 (33) (17) (32) (38) (47) 58 29 9 65 81
.639 .049 <.001 146 (75) 186 (95) 164 (85) 128 (73) 174 (99) 169 (96)
(76) (2) (94) (70) 148 4 179 134 (53) (4) (93) (56) 94 7 160 96
Mobility limitationb (N ¼ 176), n (%) p value No mobility limitation (N ¼ 75), n (%) Mobility limitation (N ¼ 111), n (%)
Age, 16e18 years
42 2 96 69
Female (vs. male) Hispanic (vs. not) White race (vs. nonwhite) Either parent is at least college graduate (vs. less than college graduate) Urban residence (vs. rural) Have usual place or provider for health care Have health insurance Health behaviors Used alcohol, cigarettes, or marijuana, past 30 days Had sexual intercourse, past 6 months Used condom, last sexual intercourse Consumed 2 servings of fruits or vegetables, per day Exercised moderately or vigorously 3 times, past week
Characteristics of the analysis sample (N ¼ 557) are presented in Table 1, stratified by age group. The overall mean age was 20.2 years. For the entire sample, participants with MLs reported having diagnoses of cerebral palsy (n ¼ 131, 45%), muscular dystrophy (n ¼ 45, 16%), spina bifida (n ¼ 57, 20%), and nonspecified functional ML (n ¼ 54, 19%). Almost three-fourths
Characteristics
Sample demographics and health characteristics
Table 1 Characteristics of analysis sample (N ¼ 557)a
Results
b
Statistical analysis All statistical analyses were conducted in IBM Statistics 20 (IBM Corp., Armonk, NY) and were run separately for adolescents between the ages of 16e18 years and young adults between the ages of 19e24 years because we expected developmental and normative differences between the age groups. Bivariate statistics tested for differences in demographics, health characteristics, and engagement in health behaviors between adolescents and young adults with and without MLs, using chi-square as the test statistic. For Aim 1, simple logistic regression models were used to examine the likelihood of discussing health behaviors with an HCP. For Aim 2, we used multivariable logistic regressions to test for the independent effects of engagement in health behaviors and ML status on the likelihood of discussing these health behaviors, adjusting for demographic characteristics (i.e., gender, race, urban residence, and parental education). We tested for potential moderating effects of engaging in the behavior on relationships between mobility status and HCP discussion by including interaction terms (limitation status engagement in behavior) in models; however, no interaction terms were statistically significant and results are not reported here.
83
c
Age, 19e24 years
No mobility limitation (N ¼ 195), n (%)
p valuec
(ordinal responses ranged from 0 to 30 days). Responses were recoded to create a dichotomous measure of any substance use. Sexual behavior was assessed by asking about sexual intercourse in the past 6 months (yes/no), and affirmative responses were followed up with whether they used a condom during their last instance of sexual activity (yes/no). Questions separately assessed participants’ fruit and vegetable intake over the past 7 days. Ordered categories ranging from “I did not eat fruits/vegetables” to “4 or more times per day” were recoded into a dichotomous measure of eating fruits or vegetables two or more times per day or less than two times per day. Two questions measured exercising behavior. Participants reported the number of days in the past week that they engaged in vigorous physical activity (that makes you sweat) for at least 20 minutes and moderate physical activity (that does not make you sweat) for at least 30 minutes. Participation in physical activity was operationalized as one dichotomous variable (moderate or vigorous exercise three or more times per week or two or fewer times per week). The survey included three questions about preventive health services in the past 6 months, which were adapted from the Foundation for Accountability’s Young Adult Health Care Survey [23]. These dichotomous outcomes indicate whether the following three topics were discussed with an HCP: tobacco, alcohol, or illegal drugs; sexually transmitted diseases or pregnancy prevention; and healthy weight, healthy eating, or physical activity.
<.001 .261 .790 .005
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(74%) used an assistive device in the past 6 months, and a small number (6%) with a diagnosis of cerebral palsy, muscular dystrophy, or spina bifida reported having no ML as measured by the GMFCS (results not shown). In terms of demographic characteristics, this is a homogenous sample of young people from the Midwest. Participants tended to be white, come from families where parents have college educations, and live in urban areas. Comparisons between study groups revealed statistically significant between-group differences, with participants with MLs more likely to be male (p < .01) and less likely to have a parent with at least a college education (p < .02) than their peers without MLs. With regard to health care, the vast majority had a usual place or provider of health care and health insurance, although some significant differences were noted by limitation status. For example, young people without MLs were slightly less likely to report having health insurance than their peers with MLs. About half of the entire analysis sample reported usually seeing a family physician or general doctor (54%), and a smaller proportion saw a general pediatrician (15%), general adult physician or internist (8%), nurse practitioner (6%), or physician’s assistant (4%; results not shown). Among those with MLs, one in 10 (11%) reported usually seeing a physician specialist, about half (45%) of whom treat all age groups and one third (29%) of whom treat only children and teens (results not shown). Table 1 also displays bivariate relationships between health behaviors and ML among younger (ages, 16e18 years) and older (ages, 19e24 years) participants. For both age groups, those without MLs were more likely than young people with MLs to report using any substances in the past month, having had sexual intercourse, and engaging in moderate or vigorous physical activity at least three times in the past week. Rates of condom use for sexually active participants and fruit and vegetable intake did not differ by ML status in either age group. Preventive health services Age-stratified, unadjusted rates and odds of youtheHCP discussions regarding health behaviors are provided in Table 2. In both age groups, young people without MLs were more likely to report discussions of substance use (16- to 18-year-olds, odds ratio
Table 2 Age-stratified rates and odds of patienteprovider discussions about healthrelated behaviors by functional mobility status Discussion topic
Tobacco, alcohol, or illegal drugs Mobility limitation No limitation STDs or pregnancy prevention Mobility limitation No limitation Healthy eating, healthy weight, or physical activity Mobility limitation No limitation
Age, 16e18 years
Age, 19e24 years
%
Odds ratios (95% CI)
%
Odds ratios (95% CI)
36 52
Ref. 1.89 (1.03e3.47)
25 46
Ref. 2.59 (1.66e4.04)
28 44
Ref. 2.02 (1.08e3.79)
25 49
Ref. 2.86 (1.83e4.46)
67 51
Ref. .53 (.29e.97)
55 47
Ref. .72 (.48e1.08)
Bolded estimates reflect significant differences at p < .05. CI ¼ confidence interval; STDs = sexually transmitted diseases.
[OR], 1.89; 95% confidence interval [CI], 1.03e3.47; 19- to 24-yearolds, OR, 2.59; 95% CI, 1.66e4.04), or sexual and reproductive health (16- to 18-year-olds; OR, 2.02; 95% CI, 1.08e3.79; 19- to 24year-olds; OR, 2.86; 95% CI, 1.83e4.46), with an HCP, compared with their peers with MLs. In contrast, adolescents without MLs were less likely to report discussions of healthy eating, weight, and physical activity with an HCP, compared with adolescents and young adults with MLs, although this difference was significant only in the younger age group (OR, .53; 95% CI, .29e.97). Regardless of age and ML status, rates of discussions were low for all adolescents and young adults, especially with regard to sexual and reproductive health and substance use. Engaging in a behavior was associated with discussing most of the health behavior topics with an HCP, after controlling for demographic characteristics and ML status (Table 3). Substance use was associated with discussing these behaviors in both age groups (16- to 18-year-olds, OR, 2.71; 95% CI, 1.12e6.55; 19- to 24-year-olds, OR, 1.74; 95% CI, 1.05e2.88), and, among older participants, those without MLs were almost twice as likely to discuss substance use, compared to those with MLs (OR, 1.93; 95% CI, 1.16e3.20). In models predicting the likelihood of discussions about sexual and reproductive health, participants in both age groups who reported having had sex in the past 6 months were more likely than other participants to report having discussed this topic with their HCP (16- to 18-year-olds, OR, 2.49; 95% CI, 1.06e5.86; 19- to 24-year-olds, OR, 2.99; 95% CI, 1.76e5.06). ML status was not associated with youtheHCP discussions about sexual and reproductive health. Discussions regarding healthy eating, weight, and physical activity were examined in two separate models, one using reported fruit and vegetable intake and the other using moderate/ vigorous exercise as the predictor. In the first model, among the younger group, having no ML was associated with lower odds of discussing this topic with an HCP (OR, .45; 95% CI, .23e.87). Among the older group, greater fruit or vegetable consumption, but not ML status was associated with having discussed this topic (OR, 2.12; 95% CI, 1.37e3.29). In the second model, younger participants without MLs, compared to those with MLs, were less likely to report discussions regarding nutrition, weight, and physical activity with an HCP (OR, .48; 95% CI, .25e.95). Among older participants, neither physical activity level nor ML status were predictors of discussions about this topic. Discussion Preventive counseling regarding health behaviors is an essential component of health care services for all young people [24], including those with MLs and chronic conditions [25]. Leading professional organizations [26] and public health agencies [27] recommend routine screening and anticipatory guidance by HCPs regarding health behaviors, including sexually transmitted diseases, pregnancy, substance use, nutrition, and physical activity because HCPs have a unique opportunity to promote healthy behaviors, screen for and assess the level of risk, and tailor interventions to best meet the needs of young people. Yet, as previous research has demonstrated, many opportunities for screening, counseling, and intervention are missed [28e32], especially among those with chronic health conditions. It is important to understand why rates of preventive health services related to health behaviors are low among adolescents and young adults, despite well-publicized recommendations. Identified barriers to preventive care for those with chronic health
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Table 3 Age-stratified logistic regressions on patienteprovider discussions about health-related behaviors
Model 1: Assessing the likelihood of discussion about tobacco, alcohol, or illegal drugs No limitation (vs. ML) Use of alcohol, cigarettes, or marijuana (vs. no use) Model 2: Assessing the likelihood of discussion about STDs or pregnancy prevention No limitation (vs. ML) Sexual intercourse (vs. not) Model 3: Assessing the likelihood of discussion about healthy eating, healthy weight, or physical activity No limitation (vs. ML) Consumed 2 servings of fruits or vegetables, per day (vs. not) Model 4: Assessing the likelihood of discussion about healthy eating, healthy weight, or physical activity No limitation (vs. ML) Exercised moderately or vigorously 3 times, past week (vs. not)
Age, 16e18 years
Age, 19e24 years
AOR (95% CI)a
AOR (95% CI)a
1.49 (.74e2.98) 2.71 (1.12e6.55)
1.93 (1.16e3.20) 1.74 (1.05e2.88)
1.94 (.95e3.97) 2.49 (1.06e5.86)
1.55 (.90e2.66) 2.99 (1.76e5.06)
.45 (.23e.87) 1.69 (.88e3.27)
.68 (.43e1.06) 2.12 (1.37e3.29)
.48 (.25e.95) .97 (.49e1.96)
.67 (.42e1.06) 1.29 (.80e2.08)
Bolded estimates reflect significant differences at p < .05. AOR ¼ adjusted odds ratio; CI ¼ confidence interval; ML ¼ mobility limitation; STDs = sexually transmitted diseases. a Each model was estimated separately and adjusted for mobility status, gender, race, parental education level, and urban residence.
conditions include provider perceptions that services related to health risk behaviors are not relevant to the young person [29] and lack of expertise to counsel adolescents and young adults on these topics [33,34]. In our study of adolescents and young adults who had been seen by an HCP in the past 6 months, the majority had not discussed important preventive health topics with their HCP. Our first aim was to investigate the impact of having a ML on the likelihood of these discussions. We found that in unadjusted models, participants with MLs were less likely than their peers to engage in discussions with their HCP about alcohol, tobacco or drug use, and sexual and reproductive health topics. These results suggest that a young person’s mobility status may play an important role in whether health behaviors are discussed with an HCP. Adolescents and young adults with chronic health conditions may be concerned that discussions of health behaviors with HCPs will be shared with their parents [29,35,36]. Furthermore, HCPs may be hesitant to discuss topics, such as substance use and sexual activity, with young people that they have treated for many years [29]. Interestingly, younger participants with MLs were more likely than their peers to have discussed healthy eating, weight, and physical activity with their HCP. This suggests some adolescents and young adults with MLs receive broader messages about health from HCPs, which is promising, given the heightened risk for obesity among this population [8,37]. This finding may reflect the increasing recognition of the important role nutrition and exercise play in the health of adolescents and young adults with chronic health conditions. Our study’s second aim was to assess whether adolescents and young adults’ own engagement in health behaviors was associated with discussions of sexual and reproductive health, substance use, healthy eating, and physical activity with an HCP, after controlling for the young person’s mobility status and demographic characteristics. In adjusted models, a young person’s engagement in a behavior emerged as a significant predictor of related discussions with an HCP. Specifically, findings suggest that adolescents and young adults’ substance use and sexual activity were related to discussions of these topics, after controlling for mobility status and demographic characteristics. Among younger participants, mobility status was associated with discussions of nutrition,
weight, and physical activity with an HCP; greater fruit and vegetable intake was associated with a greater likelihood of discussing this topic, whereas adolescents and young adults without MLs had lower odds of discussing this topic. Among the older group, those without MLs were almost two times as likely to discuss substance use with their HCP, compared to peers with MLs. Overall, the relatively low rates of youtheHCP discussions regarding health behaviors in our sample are concerning. Although it is encouraging that adolescents and young adults who have a history of substance use or sexual activity are discussing these topics with providers at higher rates, all adolescents should receive screening and counseling regarding these behaviors. In our sample, rates of substance use were lower among participants with MLs, compared to those without MLs; however, risks from substance use in particular may be higher for adolescents and young adults with MLs than those for peers without disabilities because of potential interactions with prescription medications or condition management [6,7]. Participants with MLs reported low levels of participation in moderate and vigorous physical activity and significantly lower engagement than their counterparts without MLs. These results are similar to other findings and indicate a continuing need to focus on physical activity among young people with MLs [37e39]. Rates of substance use and sexual activity were lower among participants without MLs in our sample than prevalence estimates in national samples [40]. These lower rates could be related to several factors, including regional and seasonal variations in patterns of behavior, selection bias in the recruitment process, or self-report bias. Although our sample is fairly typical of the upper Midwest region, its homogenous nature limits generalizability of results. Findings are also limited by dichotomous classification of health behaviors, which may not fully describe the positive or negative implications of these behaviors. An important strength, however, is the large communitybased sample of adolescents and young adults with MLs. Because chronic health conditions that may limit mobility (e.g., cerebral palsy, muscular dystrophy, spina bifida) have low prevalence, nationally representative samples of adolescents often have too few participants with MLs to conduct statistically meaningful analyses. This study also includes both adolescents and young adults, which allowed us to investigate behaviors
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and discussions with HCPs across these age groups. The community-based aspect of the MyPath sample provides a unique opportunity to investigate the health needs of this group during the transition to adulthood, not just those enrolled in specialty health or education services. Participants see a wide variety of HCPs, which is not typical in clinical-based samples of young people with MLs, and may contribute to diversity in health care experiences. A noncategorical approach to defining ML was used; therefore, participants defined as having a ML are diverse in terms of the origins of their limitation. This study highlights successes and missed opportunities in health care services. Adolescents and young adults with MLs have higher rates of health care utilization than peers without disabilities, and HCPs have the opportunity to play a critical role in health promotion efforts. HCPs should regularly ask adolescents and young adults with MLs about substance use, provide education and information on sexual and reproductive health, and discuss opportunities to make healthy eating choices and enhance their physical activity. Integrating health promotion into clinic visits provides opportunities for adolescents and young adults with MLs to discuss these health topics with their HCP, tailored to the context of their individual health and wellness needs. Acknowledgments Poster presented at the 11th International Family Nursing Conference. Minneapolis, MN, June 2013. Poster presented at the Houston Healthcare Transition Research Consortium Research Day, Houston, TX, October 2012. References [1] Stevens SE, Steele CA, Jutai JW, et al. Adolescents with physical disabilities: Some psychosocial aspects of health. J Adolesc Health 1996;19:157e64. [2] Sawyer SM, Roberts KV. Sexual and reproductive health in young people with spina bifida. Dev Med Child Neurol 1999;41:671e5. [3] Sawyer SM, Drew S, Yeo MS, Britto MT. Adolescents with a chronic condition: Challenges living, challenges treating. Lancet 2007;369:1481e9. [4] Halfon N, Hochstein M. Life course health development: An integrated framework for developing health, policy, and research. Milbank Q 2002;80: 433e79. [5] Schulenberg JE, Maggs JL, O’Malley PM. How and why the understanding of developmental continuity and discontinuity is important. In: Mortimer JT, Shanahan MJ, eds. Handbook of the Life Course. New York: Kluwer Academic/Plenum Publishers; 2003:413e36. [6] Woodhouse CR. Myelomeningocele in young adults. BJU Int 2005;95: 223e30. [7] Ford JA, Workman J, Masoudi N, et al. Accessible substance abuse prevention for all children. In: Hollar D, ed. Handbook of Children With Special Health Care Needs. New York: Springer; 2012:353e68. [8] Rimmer JH, Rowland JL, Yamaki K. Obesity and secondary conditions in adolescents with disabilities: Addressing the needs of an underserved population. J Adolesc Health 2007;41:224e9. [9] Murphy NA, Carbone PS. Promoting the participation of children with disabilities in sports, recreation, and physical activities. Pediatrics 2008; 121:1057e61. [10] Dietz WH. Health consequences of obesity in youth: Childhood predictors of adult disease. Pediatrics 1998;101(3 Pt 2):518e25. [11] Liou TH, Pi-Sunyer FX, Laferrere B. Physical disability and obesity. Nutr Rev 2005;63:321e31. [12] Murphy NA, Elias ER. Sexuality of children and adolescents with developmental disabilities. Pediatrics 2006;118:398e403. [13] Cheng MM, Udry JR. Sexual behaviors of physically disabled adolescents in the United States. J Adolesc Health 2002;31:48e58.
[14] Suris J-C, Michaud P-A, Akre C, Sawyer SM. Health risk behaviors in adolescents with chronic conditions. Pediatrics 2008;122:e1113e8. [15] Klein JD, Wilson KM. Delivering quality care: Adolescents’ discussion of health risks with their providers. J Adolesc Health 2002;30:190e5. [16] Berman H, Harris D, Enright R, et al. Sexuality and the adolescent with a physical disability: Understandings and misunderstandings. Issues Compr Pediatr Nurs 1999;22:183e96. [17] Henry-Reid LM, O’Connor KG, Klein JD, et al. Current pediatrician practices in identifying high-risk behaviors of adolescents. Pediatrics 2010;125: e741e7. [18] Van Hook S, Harris SK, Brooks T, et al. The “Six T’s”: Barriers to screening teens for substance abuse in primary care. J Adolesc Health 2007;40: 456e61. [19] Lustig JL, Ozer EM, Adams SH, et al. Improving the delivery of adolescent clinical preventive services through skills-based training. Pediatrics 2001; 107:1100e7. [20] Palisano RJ, Rosenbaum P, Bartlett D, Livingston MH. Content validity of the expanded and revised Gross Motor Function Classification System. Dev Med Child Neurol 2008;50:744e50. [21] Centers for Disease Control and Prevention. 2010 Youth risk behavior survey. Available at: www.cdc.gov/yrbs. Accessed January 14, 2013. [22] Minnesota Center for Health Statistics, Minnesota Student Survey. Available at: http://www.health.state.mn.us/divs/chs/mss/. Accessed January 14, 2013. [23] Bethell C, Klein J, Peck C. Assessing health system provision of adolescent preventive services: The Young Adult Health Care Survey. Med Care 2001; 39:478e90. [24] Ozer EM, Urquhart JT, Brindis CD, et al. Young adult preventive health care guidelines: There but can’t be found. Arch Pediatr Adolesc Med 2012;166: 240e7. [25] Michaud P, Suris J, Viner R. The adolescent with a chronic condition. Part II: Healthcare provision. Arch Dis Child 2004;89:943e9. [26] American Medical Association Department of Adolescent Health. Guidelines for adolescent preventive services (GAPS): Clinical evaluation and management handbook. Chicago, IL: American Medical Association; 2000. [27] Hagan JF, Shaw JS, Duncan PM. Bright Futures: Guidelines for health supervision of infants, children, and adolescents. Elk Grove Village, IL: American Academy of Pediatrics; 2008. [28] Blum RW, Beuhring T, Wunderlich M, Resnick MD. Don’t ask, they won’t tell: The quality of adolescent health screening in five practice settings. Am J Public Health 1996;86:1767e72. [29] Britto MT, Garrett JM, Dugliss MA, et al. Preventive services received by adolescents with cystic fibrosis and sickle cell disease. Arch Pediatr Adolesc Med 1999;153:27e32. [30] Cabana MD, Rand CS, Powe NR, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999;282: 1458e65. [31] Neinstein LS, Irwin Jr CE. Young adults remain worse off than adolescents. J Adolesc Health 2013;53:559e61. [32] Irwin CE, Adams SH, Park MJ, Newacheck PW. Preventive care for adolescents: Few get visits and fewer get services. Pediatrics 2009;123:e565e72. [33] McDonagh J, Southwood T, Shaw K. Unmet education and training needs of rheumatology health professionals in adolescent health and transitional care. Rheumatology 2004;43:737e43. [34] East LJ, Orchard TR. Somebody else’s job: Experiences of sex education among health professionals, parents and adolescents with physical disabilities in Southwestern Ontario. Sex Disabil 2013;32:1e16. [35] Britto MT, Tivorsak TL, Slap GB. Adolescents’ needs for health care privacy. Pediatrics 2010;126:e1469e76. [36] Farrant B, Watson P. Health care delivery: Perspectives of young people with chronic illness and their parents. J Paediatr Child Health 2004;40: 175e9. [37] Neter JE, Schokker DF, de Jong E, et al. The prevalence of overweight and obesity and its determinants in children with and without disabilities. J Pediatr 2011;158:735e9. [38] Steele C, Kalnins I, Jutai J, et al. Lifestyle health behaviours of 11- to 16-year-old youth with physical disabilities. Health Educ Res 1996;11: 173e86. [39] Rimmer JA, Rowland JL. Physical activity for youth with disabilities: A critical need in an underserved population. Dev Neurorehabil 2008;11: 141e8. [40] Centers for Disease Control and Prevention. Youth risk behavior surveillancedUnited States, 2011. MMWR Surveill Summ 2012;61:1e162.
www.jahonline.org Original article
Disability and Discussions of Health-Related Behaviors Between Youth and Health Care Providers Elisabeth M. Seburg, M.P.H. a, *, Barbara J. McMorris, Ph.D. b, Ann W. Garwick, Ph.D., R.N. b, and Peter B. Scal, M.D., M.P.H. a, c a
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota School of Nursing, University of Minnesota, Minneapolis, Minnesota c Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota b
Article history: Received December 2, 2014; Accepted March 5, 2015 Keywords: Mobility limitation; Adolescent health care; Preventive services; Health behaviors
A B S T R A C T
Purpose: The purpose of this study was to examine the likelihood of discussing health-related behaviors with health care providers (HCPs), comparing youth with and without mobility limitations (MLs). Methods: Analyses were conducted using baseline data from the MyPath study. Adolescents and young adults between the ages of 16 and 24 years completed a survey about their health care and health-related experiences. Analyses assessed the relationship between mobility status and discussing health-related behaviors with an HCP. Secondary analyses examined the extent to which adolescents and young adults’ engagement in these behaviors was associated with these discussions. Results: Overall, we found low rates of discussions about the following topics: substance use, sexual and reproductive health, healthy eating, weight, and physical activity. Adolescents and young adults with MLs were less likely to report discussing substance use and sexual and reproductive health, but were more likely to discuss healthy eating, weight, and physical activity than peers without MLs. Those adolescents and young adults who reported substance use had higher odds of discussing this topic and those who reported having sexual intercourse had higher odds of discussing sexual and reproductive health. Conclusions: Results suggest mobility status and a young person’s engagement in health risk and promoting behaviors are associated with the likelihood of discussing these behaviors with an HCP. It is important that HCPs view adolescents and young adults with MLs as needing the same counseling and guidance about health-related behaviors as any young person presenting him/herself for treatment. Ó 2015 Society for Adolescent Health and Medicine. All rights reserved.
Providing preventive counseling is a critical component of adolescent health services. Yet, many young people who experience difficulty with physical functioning because of a mobility Conflicts of Interest: Funding for this study comes from the Centers for Disease Control and Prevention, National Center on Birth Defects and Developmental Disabilities 1U48DP001939 (P.B.S., PI). * Address correspondence to: Elisabeth M. Seburg, M.P.H., HealthPartners Institute for Education and Research, 8170 33rd Ave S. Mail stop 23301A, PO Box 1524, Bloomington, MN 55440-1524. E-mail address: [email protected] (E.M. Seburg). 1054-139X/Ó 2015 Society for Adolescent Health and Medicine. All rights reserved. http://dx.doi.org/10.1016/j.jadohealth.2015.03.004
IMPLICATIONS AND CONTRIBUTION
Few adolescents and young adultsdand especially few of those with mobility limitations (MLs)ddiscussed health-related behaviors with a health care provider. This community-based study confirms the need to develop and implement strategies to improve counseling in clinical settings, especially for youth with MLs.
limitation (ML) do not receive information or counseling regarding their health behaviors [1e3]. The low rates of routine counseling for health behaviors in this population are concerning for several reasons. Adolescents and young adults with MLs, similar to peers without disabilities, experience the physiologic and psychosocial changes of adolescence and engage in and establish health-related behavior patterns that influence their health trajectories across the life span [4,5]. In addition, the risk for poor health outcomes related to health risk behaviors may be amplified by the interaction between chronic condition, treatments, and behavior [3]. For
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example, alcohol use can influence bowel management among those with spina bifida [6], whereas recreational substance use can compromise effectiveness of medications prescribed for health conditions or symptom management [7]. Furthermore, low engagement in health promoting behaviors, such as eating a balanced, nutritious diet and engaging in regular physical activity, are associated with greater risk for obesity. Adolescents and young adults with MLs are at a higher risk for obesity than peers without disabilities [8,9]. In addition to increasing the risk for many chronic health conditions [10], obesity can negatively affect condition management and progression (e.g., pressure sores and muscle wasting) and limit a young person’s capacity to perform self-care and activities of daily living [11]. Also of concern are the low rates of screening, counseling, and education about sexual and reproductive health for those with MLs. Similar to peers without disabilities, adolescents and young adults with MLs experience sexual development, engage in sexual activity, and have romantic and sexual relationships [12,13]. Likewise, these young people are exposed to negative health consequences associated with risky sexual behaviors [13]. Indeed, adolescents and young adults with disabilities experience greater risk of sexual abuse than those without disabilities [13]. Sexual development, sexuality, and reproductive health are relevant topics that should be routinely discussed and assessed in the health care setting for these young people [12]. As young people with MLs navigate their way through adolescence and the transition to adulthood, information and education about health behaviors should be viewed as important and relevant to their health and well-being. Health care providers (HCPs) are in a key position to provide screening, anticipatory guidance, and counseling regarding these behaviors, in the context of a young person’s specific health condition [14]. The benefits of preventive counseling in a health care setting are well established, and young people themselves express a desire for this care [2,15,16]. However, provision of these services for adolescents and young adults with MLs remains persistently low. Studies have identified barriers to providing preventive counseling [17], such as time constraints [18], provider training [19], and provider perceptions of which young people would benefit from these services. However, further research is needed to examine the extent to which adolescents and young adults with MLs discuss these topics with their HCPs and the factors that influence the likelihood of these conversations. The present study seeks to address research gaps using a community-based sample of adolescents and young adults, aged 16e24 years, with and without MLs. The study’s first aim was to assess the impact of having a ML on the extent to which young people discuss health behaviors with their HCP. The second aim was to examine whether adolescents and young adults’ own engagement in these behaviors is associated with the likelihood of talking to providers about sexual and reproductive health, substance use, healthy eating, and physical activity. This is the first study, to our knowledge, to investigate whether young people who are engaging in a particular behavior are more likely to discuss that behavior with an HCP. Methods Participants Data are drawn from the baseline survey of the MyPath study, a prospective, longitudinal study of the health care and
health-related experiences of a nonprobability, community-based sample of adolescents and young adults, collected between March 2011 and November 2012 in the upper Midwest region of the United States. To enroll adequate numbers of adolescents and young adults with and without MLs, separate recruitment strategies were used. Potential participants with MLs were recruited through targeted mailings sent from more than 120 study partners (HCPs, clinics, state agencies, school districts, colleges and universities, nonprofits and community organizations). Messages introduced the study and directed individuals to the research team to enroll. Our research team never had access to the names, contact information, or the number of individuals contacted; thus, we are unable to calculate a response rate. The cohort of participants without MLs was recruited through mailings to households in a five-state region (Minnesota, Wisconsin, Iowa, South Dakota, and North Dakota) with a high likelihood of having a 16- to 24-year-old living in the home. The list was purchased from Genesys Sampling Systems (Horsham, PA). Adult participants and parents of minors provided informed consent, and minors provided assent. Institutional review boards from the participating sites (when appropriate) approved study procedures. Baseline data consisted of responses to the MyPath questionnaire (available for completion online, paper, and telephone) from 786 adolescents and young adults. All participants were between the ages of 16 and 24 years at the time of enrollment; those who did not speak English or did not have the cognitive ability to complete the survey were excluded. For this analysis, the analytic sample comprised 557 participants (287 with MLs and 270 without MLs) who reported having seen an HCP in the 6 months before completing the survey. Measures Participants responded to questions assessing gender, age, ethnicity, race, and parent education level. Race was dichotomized into white or nonwhite; highest education level achieved by either parent was also dichotomized (college graduate or not a college graduate). In the present study, ML status was defined by self-report of having any of three specific conditions (spina bifida, cerebral palsy, or muscular dystrophy) and/or reporting any ML as measured by an adaption of the Gross Motor Functioning Classification System (GMFCS) [20]. The GMFCS selfreport youth version is a tool for categorizing the gross motor functioning of youth based on the way they usually engage in each activity assessed. Thus, it assesses functioning within youths’ environmental context reflecting the lived experience rather than gross motor assessment of capacity in a clinic setting. Youth are asked to select the GMFCS category that best describes their functioning. The five categories range from the most limited (“I have difficulty sitting on my own and have difficulty achieving any voluntary control of movement”) to the least (“Can walk on my own without using walking aids but am limited in speed, balance and coordination”) [20]. For this study, we provided a sixth category representing youth who perceive no limitation in their mobility. The survey assessed whether participants had a usual source of care and health insurance and the type of HCP they usually saw for checkups or if sick or hurt. Questions about specific health behaviors were adapted for the study from the Centers for Disease Control and Prevention Youth Risk Behavior Survey [21] and the Minnesota Student Survey [22]. Participants responded to questions regarding use of alcohol, cigarette, and marijuana use during the past 30 days
c
a
b
<.001 .001 .886 .357 .005 (35) (31) (73) (47) (80) 26 23 16 35 60
.171 .219 .004 57 (76) 73 (99) 60 (86)
.037 1.000 .107 .019 (53) (1) (95) (80) 40 1 69 60
(9) (11) (75) (40) (61) 10 12 9 43 66
74 (67) 111 (100) 104 (93)
(38) (2) (87) (64)
Percentages are calculated on valid sample sizes for each variable; these range from 70 to 107 for 16- to 18-year-olds and from 172 to 191 for 19- to 24-year-olds because of missing data. Mobility limitation status defined by diagnoses (cerebral palsy, muscular dystrophy, and spina bifida) or adapted Gross Motor Functioning Classification System level > 0. Based on chi-square tests.
<.001 <.001 .219 .117 <.001 (73) (62) (45) (46) (80) 142 119 53 89 156 (33) (17) (32) (38) (47) 58 29 9 65 81
.639 .049 <.001 146 (75) 186 (95) 164 (85) 128 (73) 174 (99) 169 (96)
(76) (2) (94) (70) 148 4 179 134 (53) (4) (93) (56) 94 7 160 96
Mobility limitationb (N ¼ 176), n (%) p value No mobility limitation (N ¼ 75), n (%) Mobility limitation (N ¼ 111), n (%)
Age, 16e18 years
42 2 96 69
Female (vs. male) Hispanic (vs. not) White race (vs. nonwhite) Either parent is at least college graduate (vs. less than college graduate) Urban residence (vs. rural) Have usual place or provider for health care Have health insurance Health behaviors Used alcohol, cigarettes, or marijuana, past 30 days Had sexual intercourse, past 6 months Used condom, last sexual intercourse Consumed 2 servings of fruits or vegetables, per day Exercised moderately or vigorously 3 times, past week
Characteristics of the analysis sample (N ¼ 557) are presented in Table 1, stratified by age group. The overall mean age was 20.2 years. For the entire sample, participants with MLs reported having diagnoses of cerebral palsy (n ¼ 131, 45%), muscular dystrophy (n ¼ 45, 16%), spina bifida (n ¼ 57, 20%), and nonspecified functional ML (n ¼ 54, 19%). Almost three-fourths
Characteristics
Sample demographics and health characteristics
Table 1 Characteristics of analysis sample (N ¼ 557)a
Results
b
Statistical analysis All statistical analyses were conducted in IBM Statistics 20 (IBM Corp., Armonk, NY) and were run separately for adolescents between the ages of 16e18 years and young adults between the ages of 19e24 years because we expected developmental and normative differences between the age groups. Bivariate statistics tested for differences in demographics, health characteristics, and engagement in health behaviors between adolescents and young adults with and without MLs, using chi-square as the test statistic. For Aim 1, simple logistic regression models were used to examine the likelihood of discussing health behaviors with an HCP. For Aim 2, we used multivariable logistic regressions to test for the independent effects of engagement in health behaviors and ML status on the likelihood of discussing these health behaviors, adjusting for demographic characteristics (i.e., gender, race, urban residence, and parental education). We tested for potential moderating effects of engaging in the behavior on relationships between mobility status and HCP discussion by including interaction terms (limitation status engagement in behavior) in models; however, no interaction terms were statistically significant and results are not reported here.
83
c
Age, 19e24 years
No mobility limitation (N ¼ 195), n (%)
p valuec
(ordinal responses ranged from 0 to 30 days). Responses were recoded to create a dichotomous measure of any substance use. Sexual behavior was assessed by asking about sexual intercourse in the past 6 months (yes/no), and affirmative responses were followed up with whether they used a condom during their last instance of sexual activity (yes/no). Questions separately assessed participants’ fruit and vegetable intake over the past 7 days. Ordered categories ranging from “I did not eat fruits/vegetables” to “4 or more times per day” were recoded into a dichotomous measure of eating fruits or vegetables two or more times per day or less than two times per day. Two questions measured exercising behavior. Participants reported the number of days in the past week that they engaged in vigorous physical activity (that makes you sweat) for at least 20 minutes and moderate physical activity (that does not make you sweat) for at least 30 minutes. Participation in physical activity was operationalized as one dichotomous variable (moderate or vigorous exercise three or more times per week or two or fewer times per week). The survey included three questions about preventive health services in the past 6 months, which were adapted from the Foundation for Accountability’s Young Adult Health Care Survey [23]. These dichotomous outcomes indicate whether the following three topics were discussed with an HCP: tobacco, alcohol, or illegal drugs; sexually transmitted diseases or pregnancy prevention; and healthy weight, healthy eating, or physical activity.
<.001 .261 .790 .005
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(74%) used an assistive device in the past 6 months, and a small number (6%) with a diagnosis of cerebral palsy, muscular dystrophy, or spina bifida reported having no ML as measured by the GMFCS (results not shown). In terms of demographic characteristics, this is a homogenous sample of young people from the Midwest. Participants tended to be white, come from families where parents have college educations, and live in urban areas. Comparisons between study groups revealed statistically significant between-group differences, with participants with MLs more likely to be male (p < .01) and less likely to have a parent with at least a college education (p < .02) than their peers without MLs. With regard to health care, the vast majority had a usual place or provider of health care and health insurance, although some significant differences were noted by limitation status. For example, young people without MLs were slightly less likely to report having health insurance than their peers with MLs. About half of the entire analysis sample reported usually seeing a family physician or general doctor (54%), and a smaller proportion saw a general pediatrician (15%), general adult physician or internist (8%), nurse practitioner (6%), or physician’s assistant (4%; results not shown). Among those with MLs, one in 10 (11%) reported usually seeing a physician specialist, about half (45%) of whom treat all age groups and one third (29%) of whom treat only children and teens (results not shown). Table 1 also displays bivariate relationships between health behaviors and ML among younger (ages, 16e18 years) and older (ages, 19e24 years) participants. For both age groups, those without MLs were more likely than young people with MLs to report using any substances in the past month, having had sexual intercourse, and engaging in moderate or vigorous physical activity at least three times in the past week. Rates of condom use for sexually active participants and fruit and vegetable intake did not differ by ML status in either age group. Preventive health services Age-stratified, unadjusted rates and odds of youtheHCP discussions regarding health behaviors are provided in Table 2. In both age groups, young people without MLs were more likely to report discussions of substance use (16- to 18-year-olds, odds ratio
Table 2 Age-stratified rates and odds of patienteprovider discussions about healthrelated behaviors by functional mobility status Discussion topic
Tobacco, alcohol, or illegal drugs Mobility limitation No limitation STDs or pregnancy prevention Mobility limitation No limitation Healthy eating, healthy weight, or physical activity Mobility limitation No limitation
Age, 16e18 years
Age, 19e24 years
%
Odds ratios (95% CI)
%
Odds ratios (95% CI)
36 52
Ref. 1.89 (1.03e3.47)
25 46
Ref. 2.59 (1.66e4.04)
28 44
Ref. 2.02 (1.08e3.79)
25 49
Ref. 2.86 (1.83e4.46)
67 51
Ref. .53 (.29e.97)
55 47
Ref. .72 (.48e1.08)
Bolded estimates reflect significant differences at p < .05. CI ¼ confidence interval; STDs = sexually transmitted diseases.
[OR], 1.89; 95% confidence interval [CI], 1.03e3.47; 19- to 24-yearolds, OR, 2.59; 95% CI, 1.66e4.04), or sexual and reproductive health (16- to 18-year-olds; OR, 2.02; 95% CI, 1.08e3.79; 19- to 24year-olds; OR, 2.86; 95% CI, 1.83e4.46), with an HCP, compared with their peers with MLs. In contrast, adolescents without MLs were less likely to report discussions of healthy eating, weight, and physical activity with an HCP, compared with adolescents and young adults with MLs, although this difference was significant only in the younger age group (OR, .53; 95% CI, .29e.97). Regardless of age and ML status, rates of discussions were low for all adolescents and young adults, especially with regard to sexual and reproductive health and substance use. Engaging in a behavior was associated with discussing most of the health behavior topics with an HCP, after controlling for demographic characteristics and ML status (Table 3). Substance use was associated with discussing these behaviors in both age groups (16- to 18-year-olds, OR, 2.71; 95% CI, 1.12e6.55; 19- to 24-year-olds, OR, 1.74; 95% CI, 1.05e2.88), and, among older participants, those without MLs were almost twice as likely to discuss substance use, compared to those with MLs (OR, 1.93; 95% CI, 1.16e3.20). In models predicting the likelihood of discussions about sexual and reproductive health, participants in both age groups who reported having had sex in the past 6 months were more likely than other participants to report having discussed this topic with their HCP (16- to 18-year-olds, OR, 2.49; 95% CI, 1.06e5.86; 19- to 24-year-olds, OR, 2.99; 95% CI, 1.76e5.06). ML status was not associated with youtheHCP discussions about sexual and reproductive health. Discussions regarding healthy eating, weight, and physical activity were examined in two separate models, one using reported fruit and vegetable intake and the other using moderate/ vigorous exercise as the predictor. In the first model, among the younger group, having no ML was associated with lower odds of discussing this topic with an HCP (OR, .45; 95% CI, .23e.87). Among the older group, greater fruit or vegetable consumption, but not ML status was associated with having discussed this topic (OR, 2.12; 95% CI, 1.37e3.29). In the second model, younger participants without MLs, compared to those with MLs, were less likely to report discussions regarding nutrition, weight, and physical activity with an HCP (OR, .48; 95% CI, .25e.95). Among older participants, neither physical activity level nor ML status were predictors of discussions about this topic. Discussion Preventive counseling regarding health behaviors is an essential component of health care services for all young people [24], including those with MLs and chronic conditions [25]. Leading professional organizations [26] and public health agencies [27] recommend routine screening and anticipatory guidance by HCPs regarding health behaviors, including sexually transmitted diseases, pregnancy, substance use, nutrition, and physical activity because HCPs have a unique opportunity to promote healthy behaviors, screen for and assess the level of risk, and tailor interventions to best meet the needs of young people. Yet, as previous research has demonstrated, many opportunities for screening, counseling, and intervention are missed [28e32], especially among those with chronic health conditions. It is important to understand why rates of preventive health services related to health behaviors are low among adolescents and young adults, despite well-publicized recommendations. Identified barriers to preventive care for those with chronic health
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Table 3 Age-stratified logistic regressions on patienteprovider discussions about health-related behaviors
Model 1: Assessing the likelihood of discussion about tobacco, alcohol, or illegal drugs No limitation (vs. ML) Use of alcohol, cigarettes, or marijuana (vs. no use) Model 2: Assessing the likelihood of discussion about STDs or pregnancy prevention No limitation (vs. ML) Sexual intercourse (vs. not) Model 3: Assessing the likelihood of discussion about healthy eating, healthy weight, or physical activity No limitation (vs. ML) Consumed 2 servings of fruits or vegetables, per day (vs. not) Model 4: Assessing the likelihood of discussion about healthy eating, healthy weight, or physical activity No limitation (vs. ML) Exercised moderately or vigorously 3 times, past week (vs. not)
Age, 16e18 years
Age, 19e24 years
AOR (95% CI)a
AOR (95% CI)a
1.49 (.74e2.98) 2.71 (1.12e6.55)
1.93 (1.16e3.20) 1.74 (1.05e2.88)
1.94 (.95e3.97) 2.49 (1.06e5.86)
1.55 (.90e2.66) 2.99 (1.76e5.06)
.45 (.23e.87) 1.69 (.88e3.27)
.68 (.43e1.06) 2.12 (1.37e3.29)
.48 (.25e.95) .97 (.49e1.96)
.67 (.42e1.06) 1.29 (.80e2.08)
Bolded estimates reflect significant differences at p < .05. AOR ¼ adjusted odds ratio; CI ¼ confidence interval; ML ¼ mobility limitation; STDs = sexually transmitted diseases. a Each model was estimated separately and adjusted for mobility status, gender, race, parental education level, and urban residence.
conditions include provider perceptions that services related to health risk behaviors are not relevant to the young person [29] and lack of expertise to counsel adolescents and young adults on these topics [33,34]. In our study of adolescents and young adults who had been seen by an HCP in the past 6 months, the majority had not discussed important preventive health topics with their HCP. Our first aim was to investigate the impact of having a ML on the likelihood of these discussions. We found that in unadjusted models, participants with MLs were less likely than their peers to engage in discussions with their HCP about alcohol, tobacco or drug use, and sexual and reproductive health topics. These results suggest that a young person’s mobility status may play an important role in whether health behaviors are discussed with an HCP. Adolescents and young adults with chronic health conditions may be concerned that discussions of health behaviors with HCPs will be shared with their parents [29,35,36]. Furthermore, HCPs may be hesitant to discuss topics, such as substance use and sexual activity, with young people that they have treated for many years [29]. Interestingly, younger participants with MLs were more likely than their peers to have discussed healthy eating, weight, and physical activity with their HCP. This suggests some adolescents and young adults with MLs receive broader messages about health from HCPs, which is promising, given the heightened risk for obesity among this population [8,37]. This finding may reflect the increasing recognition of the important role nutrition and exercise play in the health of adolescents and young adults with chronic health conditions. Our study’s second aim was to assess whether adolescents and young adults’ own engagement in health behaviors was associated with discussions of sexual and reproductive health, substance use, healthy eating, and physical activity with an HCP, after controlling for the young person’s mobility status and demographic characteristics. In adjusted models, a young person’s engagement in a behavior emerged as a significant predictor of related discussions with an HCP. Specifically, findings suggest that adolescents and young adults’ substance use and sexual activity were related to discussions of these topics, after controlling for mobility status and demographic characteristics. Among younger participants, mobility status was associated with discussions of nutrition,
weight, and physical activity with an HCP; greater fruit and vegetable intake was associated with a greater likelihood of discussing this topic, whereas adolescents and young adults without MLs had lower odds of discussing this topic. Among the older group, those without MLs were almost two times as likely to discuss substance use with their HCP, compared to peers with MLs. Overall, the relatively low rates of youtheHCP discussions regarding health behaviors in our sample are concerning. Although it is encouraging that adolescents and young adults who have a history of substance use or sexual activity are discussing these topics with providers at higher rates, all adolescents should receive screening and counseling regarding these behaviors. In our sample, rates of substance use were lower among participants with MLs, compared to those without MLs; however, risks from substance use in particular may be higher for adolescents and young adults with MLs than those for peers without disabilities because of potential interactions with prescription medications or condition management [6,7]. Participants with MLs reported low levels of participation in moderate and vigorous physical activity and significantly lower engagement than their counterparts without MLs. These results are similar to other findings and indicate a continuing need to focus on physical activity among young people with MLs [37e39]. Rates of substance use and sexual activity were lower among participants without MLs in our sample than prevalence estimates in national samples [40]. These lower rates could be related to several factors, including regional and seasonal variations in patterns of behavior, selection bias in the recruitment process, or self-report bias. Although our sample is fairly typical of the upper Midwest region, its homogenous nature limits generalizability of results. Findings are also limited by dichotomous classification of health behaviors, which may not fully describe the positive or negative implications of these behaviors. An important strength, however, is the large communitybased sample of adolescents and young adults with MLs. Because chronic health conditions that may limit mobility (e.g., cerebral palsy, muscular dystrophy, spina bifida) have low prevalence, nationally representative samples of adolescents often have too few participants with MLs to conduct statistically meaningful analyses. This study also includes both adolescents and young adults, which allowed us to investigate behaviors
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and discussions with HCPs across these age groups. The community-based aspect of the MyPath sample provides a unique opportunity to investigate the health needs of this group during the transition to adulthood, not just those enrolled in specialty health or education services. Participants see a wide variety of HCPs, which is not typical in clinical-based samples of young people with MLs, and may contribute to diversity in health care experiences. A noncategorical approach to defining ML was used; therefore, participants defined as having a ML are diverse in terms of the origins of their limitation. This study highlights successes and missed opportunities in health care services. Adolescents and young adults with MLs have higher rates of health care utilization than peers without disabilities, and HCPs have the opportunity to play a critical role in health promotion efforts. HCPs should regularly ask adolescents and young adults with MLs about substance use, provide education and information on sexual and reproductive health, and discuss opportunities to make healthy eating choices and enhance their physical activity. Integrating health promotion into clinic visits provides opportunities for adolescents and young adults with MLs to discuss these health topics with their HCP, tailored to the context of their individual health and wellness needs. Acknowledgments Poster presented at the 11th International Family Nursing Conference. Minneapolis, MN, June 2013. Poster presented at the Houston Healthcare Transition Research Consortium Research Day, Houston, TX, October 2012. References [1] Stevens SE, Steele CA, Jutai JW, et al. Adolescents with physical disabilities: Some psychosocial aspects of health. J Adolesc Health 1996;19:157e64. [2] Sawyer SM, Roberts KV. Sexual and reproductive health in young people with spina bifida. Dev Med Child Neurol 1999;41:671e5. [3] Sawyer SM, Drew S, Yeo MS, Britto MT. Adolescents with a chronic condition: Challenges living, challenges treating. Lancet 2007;369:1481e9. [4] Halfon N, Hochstein M. Life course health development: An integrated framework for developing health, policy, and research. Milbank Q 2002;80: 433e79. [5] Schulenberg JE, Maggs JL, O’Malley PM. How and why the understanding of developmental continuity and discontinuity is important. In: Mortimer JT, Shanahan MJ, eds. Handbook of the Life Course. New York: Kluwer Academic/Plenum Publishers; 2003:413e36. [6] Woodhouse CR. Myelomeningocele in young adults. BJU Int 2005;95: 223e30. [7] Ford JA, Workman J, Masoudi N, et al. Accessible substance abuse prevention for all children. In: Hollar D, ed. Handbook of Children With Special Health Care Needs. New York: Springer; 2012:353e68. [8] Rimmer JH, Rowland JL, Yamaki K. Obesity and secondary conditions in adolescents with disabilities: Addressing the needs of an underserved population. J Adolesc Health 2007;41:224e9. [9] Murphy NA, Carbone PS. Promoting the participation of children with disabilities in sports, recreation, and physical activities. Pediatrics 2008; 121:1057e61. [10] Dietz WH. Health consequences of obesity in youth: Childhood predictors of adult disease. Pediatrics 1998;101(3 Pt 2):518e25. [11] Liou TH, Pi-Sunyer FX, Laferrere B. Physical disability and obesity. Nutr Rev 2005;63:321e31. [12] Murphy NA, Elias ER. Sexuality of children and adolescents with developmental disabilities. Pediatrics 2006;118:398e403. [13] Cheng MM, Udry JR. Sexual behaviors of physically disabled adolescents in the United States. J Adolesc Health 2002;31:48e58.
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