bs_bs_banner

Journal of Intellectual Disability Research 851

doi: 10.1111/jir.12100

volume 58 part 9 pp 851–863 september 2014

Obesity and associated factors in adults with intellectual disability K. Hsieh,1 J. H. Rimmer2 & T. Heller1 1 Department of Disability and Human Development, University of Illinois at Chicago, Chicago, Illinois, USA 2 Health Promotion and Rehabilitation Sciences, University of Alabama at Birmingham, Birmingham, Alabama, USA

Abstract Background We examined the prevalence of obesity in adults with intellectual disabilities (ID) compared with the general population, and the factors associated with obesity and weight management status, comparing individuals with ID who were overweight or obese to those who were not. Methods We analysed baseline data (n = 1450) from the ongoing 4-year Longitudinal Health and Intellectual Disabilities Study (LHIDS) using a multivariate approach. Measures included body mass index (BMI), demographics, level of ID, diagnoses related to ID, health behaviours (i.e. physical activity, dietary habits, smoking, and alcohol consumption), various health parameters (e.g. mobility limitation, medications), and residential type and location. Results Compared with the general population, adults (≥18 years) with ID had a higher prevalence of obesity (38.3% vs. 28%) and morbid obesity (7.4% vs. 4.2%). Being female (AOR = 1.40, 95% CI = 1.09–1.81), having Down syndrome

Correspondence: Dr Kelly Hsieh, Rehabilitation Research and Training Center on Developmental Disabilities and Health, Department of Disability and Human Development, University of Illinois at Chicago, 1640 W. Roosevelt Road, Rm# 708, Chicago, Illinois, USA (e-mail: [email protected]).

(AOR = 2.53, 95% CI = 1.86–3.45), taking medications that cause weight gain (AOR = 1.80, 95% CI = 1.38–2.37), engaging in less moderate physical activity (AOR = 0.89, 95% CI = 0.79–0.99), and drinking greater amounts of soda (AOR = 1.20, 95% CI = 1.02–1.42) were associated with higher rates of obesity. Conclusion Adults with ID, in general, have a high risk of developing obesity, and women with ID have a high risk of developing morbid obesity. Health promotion initiatives should target individuals with the greatest risk. Keywords adults, associated factors, body mass index, intellectual disability, obesity

Introduction Obesity has become an epidemic in the USA and other countries around the world. According to the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS), prevalence estimates of obesity in the general population have dramatically increased from less than 15% to over 35% during the past 20 years (Centers for Disease Control and Prevention 2012). Obesity is not only related to adverse health consequences, such as type 2 diabetes, hypertension,

© 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd

Journal of Intellectual Disability Research

volume 58 part 9 september 2014

852 K. Hsieh et al. • Obesity and ID

cardiovascular disease, and early mortality, it is also associated with psychosocial problems and socio-economic burden (Finkelstein et al. 2003, 2010; Dixon 2010). If the trend continues, future obesity-related healthcare costs will cause a tremendous economic burden on society (Wang et al. 2008). Several studies have reported that the prevalence of obesity among adults with intellectual disability (ID) ranges from 26.5% to 58.5% in the USA (Rimmer et al. 1993; Yamaki 2005; Rimmer & Yamaki 2006). This indicates that, compared with the general population, obesity is a significantly greater problem in this population (Yamaki 2005; de Winter et al. 2012). Obesity is considered one of the most preventable health or secondary conditions among people with disabilities (Rimmer et al. 2011). Rimmer et al. (2011) developed a conceptual framework for understanding nonmodifiable antecedents (e.g. socio-demographics, pre-existing conditions), modifiable risk factors, and the consequences of secondary conditions. Nonmodifiable, modifiable, and secondary conditions can all lead to poor outcome trajectories at the individual level (e.g. lower healthrelated quality of life, reduced community participation, decreased employment) and/or societal level (e.g. increased cost of health care and increased health disparities). Key nonmodifiable antecedents noted in the research literature include gender (Emerson 2005; Moran et al. 2005; Yamaki 2005; Melville et al. 2007; Bhaumik et al. 2008; Stancliffe et al. 2011; de Winter et al. 2012), age (Flegal et al. 2010; Stancliffe et al. 2011), severity and type of ID (Emerson 2005; Melville et al. 2007, 2008; Stancliffe et al. 2011; de Winter et al. 2012), and genetic syndrome (Bhaumik et al. 2008; de Winter et al. 2012). Modifiable risk factors for obesity include personal and environmental variables. Personal risk factors identified in the general population include: taking medications such as antipsychotics, antidepressants, antihypertensives, or diabetes-related medications that can cause weight gain (Cohen et al. 2001; Hellings et al. 2001; Bokszanska et al. 2003); reduced or no physical activity (Handschin & Spiegelman 2008; Duncan et al. 2012); sedentary behaviours such as television (TV) watching (Fung

et al. 2000; Hu et al. 2003); poor diet including consuming fast food (Rosenheck 2008); sugar sweetened beverages (Malik et al. 2006); and high energy/low nutrient or high fat food intake (Togo et al. 2001). Although some studies have shown that antipsychotic medication is related to weight gain in adults with ID (Cohen et al. 2001; Hellings et al. 2001; Bokszanska et al. 2003), a population-based study did not find an association between taking medication for anxiety, depression, epilepsy, behaviour problems, and/or sleep problems with obesity among adults with ID (Bhaumik et al. 2008). Draheim et al. (2002) reported that adults with ID who were less physically active and had a higher fat intake compared to adults with ID who were more physically active and had a lower fat intake were less likely to develop abdominal obesity. Environmental factors related to obesity include living in rural counties (Jackson et al. 2005) for the general population, and residential type for adults with ID (Rimmer & Yamaki 2006; Melville et al. 2007; Bhaumik et al. 2008; Stancliffe et al. 2011). Individuals living in less restrictive community settings have a higher rate of obesity than those who live in more restrictive institutional settings (Rimmer et al. 1993, 1995; Lewis et al. 2002). There has been extensive research on modifiable factors for obesity in the general population but limited research on risk factors for obesity in the ID population. To further advance our understanding of the factors associated with the higher incidence of obesity among adults with ID, this study examined the relationships between nonmodifiable and modifiable risk factors/antecedents for obesity by addressing four primary research questions: (1) What is the body weight status among adults with ID by age, sex, level of ID, diagnostic group, and residential type? (2) Does the prevalence rate of overweight and/or obesity among adults with ID differ from the rates of the general population? (3) What is the weight management status for those who are overweight or obese? (4) What are the modifiable factors (e.g. use of medications, health risk behaviours, and environmental factors) that are associated with obesity after adjusting for nonmodifiable antecedents (e.g. age, gender, mobility limitation, Down syndrome, and cerebral palsy)?

© 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd

volume 58 part 9 september 2014

Journal of Intellectual Disability Research 853 K. Hsieh et al. • Obesity and ID

Methods Participants The mean age of participants was 37.1 years (range = 18–86 years). The number of male and female participants was almost equally distributed (men = 55.2%, women = 44.8%). The majority of participants were white (88.5%), employed (60.4%), and lived in moderately supported residential types with family members, relatives, or guardians (57.4%). Chi-squared tests revealed significant dif-

ferences between women and men in age, diagnosis [e.g. ID without aetiology diagnosis, autism/ pervasive developmental disorder (PDD)], and living arrangement. There were no group differences in race, level of ID, and employment status (Table 1). Approximately 48% of informants were parents, 20.6% were healthcare providers/Manager Care Organization (MCO) staff, 12.1% were residential/day programme/social service staff/social workers, 8.5% were relatives, 3.4% were non-related live-in caregivers, and less than 1% were volunteers.

Table 1 Participant characteristics

Female (n = 650)

Male (n = 800)

Mean ± SD or % (n) Average age (years) Age group (years) 18–39 40–59 ≥60 Race White Black Hispanic Other Conditions related to ID Intellectual disability Autism/PDD Cerebral palsy Down syndrome Other Mobility limitation Level of intellectual disability† Borderline Mild Moderate Severe/profound Unknown Residential type Least supported Moderately supported Most supported Employment‡ Yes

38.1 ± 14.5

36.3 ± 13.8

57.8 (376) 33.1 (215) 9.1 (59)

64.0 (512) 28.9 (231) 7.1 (57)

88.5 (568) 6.2 (40) 3.6 (23) 1.7 (11)

88.6 (697) 6.6 (52) 2.3 (18) 2.5 (20)

50.2 (305) 6.9 (42) 13.7 (83) 25.9 (157) 3.3 (20) 5.1 (33)

43.2 (323) 15.5 (116) 11.2 (84) 24.1 (180) 5.9 (44) 4.3 (34)

14.0 (86) 31.3 (192) 24.3 (149) 7.8 (48) 22.6 (139)

12.7 (97) 31.8 (243) 23.2 (177) 9.3 (71) 22.9 (175)

29.7 (193) 53.1 (345) 17.2 (112)

27.1 (217) 61.0 (488) 11.9 (95)

58.1 (376)

62.3 (495)

χ2 or F 5.74* 5.98

Total (n = 1450) 37.1 ± 14.1 61.2 (888) 30.8 (446) 8.0 (116)

3.27 88.5 (1265) 6.4 (92) 2.9 (41) 2.2 (31) 31.61***

0.53 1.51

46.4 (628) 11.7 (158) 12.3 (167) 24.9 (337) 4.7 (64) 4.7 (67) 13.3 (183) 31.6 (435) 23.7 (326) 8.6 (119) 22.8 (314)

11.96** 28.3 (410) 57.4 (833) 14.3 (207) 2.66

60.4 (871)

Note: * P < 0.05, ** P < 0.01, *** P < 0.001. † Level of ID: 23% of participants had missing data on level of ID. ‡ Employment was defined as any paid job including: sheltered workshops, a supervised or supported on-site job, individualised or supported employment in the community, home-based employment, etc. ID, intellectual disability; PDD, pervasive developmental disorder.

© 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd

volume 58 part 9 september 2014

Journal of Intellectual Disability Research 854 K. Hsieh et al. • Obesity and ID

Thirty-three per cent of informants completed surveys with assistance from the adult with ID.

extreme obesity was defined as a BMI equal to or greater than 40 kg/m2.

Procedure

Nonmodifiable antecedents

This study used baseline data from a large-scale longitudinal study, the Longitudinal Health and Intellectual Disabilities study (LHIDS) (Hsieh et al. 2012). Adults with ID were recruited through various organisations (e.g. Special Olympics, Easter Seals, The Arc, managed care organisations in the Midwest) in the USA between March 2010 and January 2011. To broaden the participant sample, a mixed method (mail and online surveys) data collection procedure was used. A total of 2841 surveys were distributed (2182 paper and 659 online), and 1619 surveys were completed and returned (1183 paper and 436 online). The overall response rate was 56.9%; the response rate was 54% for the paper survey and 66.2% for the online survey. After excluding missing data on age, body weight, and height, a total sample of 1450 was used for analysis. Within our convenience sample, 57% of participants were from the Midwest region, 20% from the Northeast, 17% from the South, and 6% from the West. Informants (i.e. caregivers, agency staff, residential staff, nurses) who were familiar with the adults with ID were invited to complete the survey on behalf of the adult with ID as proxies. A full description of the survey’s development, design, recruitment and sample has been published elsewhere (Hsieh et al. 2012).

Nonmodifiable antecedents included age, sex, autism, Down syndrome, cerebral palsy, and mobility limitation. Age was divided into three groups (18–39, 40–59, and 60 and over). Mobility limitation was defined as needing a walking aid and/or use of a wheelchair. We did not include the level of ID in the multivariate regression model; nearly 23% of informants (the majority were parents) did not know the level of ID of their child or resident.

Measures Obesity status was used as the dependent variable. The independent variables were nonmodifiable antecedents, modifiable personal risk factors, and environmental factors. Obesity status Obesity status was determined by Body Mass Index (BMI), which was calculated using the following formula: BMI = weight (kg)/[height (m)]2. Weight and height were informant reported. Overweight was defined as a BMI equal to or greater than 25 and less than 30 kg/m2. Obesity was defined as a BMI equal to or greater than 30 kg/m2. Morbid or

Modifiable personal factors Medications that cause weight gain Taking any medications for depression, hypertension, anxiety, epilepsy, diabetes, or sleep disorder (i.e. medications that are associated with weight gain) were coded as ‘1’ for yes and ‘0’ for none. We asked informants whether they were taking medications for confirmed health conditions; however, we did not ask them to list the medications that adults with ID were currently taking. Physical activity Physical activity was measured with the following question: ‘On average, how many days a week does he/she do moderate physical activities for at least 30 minutes?’ The definition of moderate physical activity and examples of moderate physical activities were provided. Scores ranged from 1 (never) to 4 (4 or more times a week). Special Olympics participation We assessed Special Olympics participation by asking, ‘Does he/she currently participate in the Special Olympics?’ Responses were coded 1 (yes) or 0 (no). Hours of television watching To assess hours of TV-watching per day, we asked, ‘On an average day, how many total hours does he/she spend watching TV?’ It was rated from 0 (0 hour) to 9 (9 or more hours).

© 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd

volume 58 part 9 september 2014

Journal of Intellectual Disability Research 855 K. Hsieh et al. • Obesity and ID

Dietary habits

Data analysis

Dietary habits included daily fruit/vegetable intake, eating foods high in fat or cholesterol, and the quantity of soda consumption. Fast food intake was identified by weekly consumption. Responses were rated from 1 (rarely/never) to 5 (7 or more servings) for fruit/vegetable daily intake, cholesterol, and high fat foods. Soda consumption was rated from 1 (rare/ never) to 5 (7 or more drinks) a day. One drink was equal to 12 ounces. The fast food intake was rated from 1 (rarely/never) to 4 (7 or more times a week).

Descriptive statistics were used to examine body weight status and obesity prevalence and data were compared with the 2010 National Health Interview Survey (NHIS, 95% confidence intervals). To examine whether there were group (obesity vs. nonobesity) differences among the independent variables (e.g. antecedents, personal factors, environmental factors), a series of univariate logistic regressions were employed. Using a traditional P value level of 0.05 can fail to identify variables of known importance (Bendel & Afifi 1977; Mickey & Greenland 1989). Therefore, to identify potential covariates and confounders, independent variables with a P value cut-off point of 0.25 on the Wald test for univariate analysis were included in the multivariate model. As a result, four independent variables – mobility limitation, Special Olympics participation, fast food intake, and current smoking status – failed to meet the cut-off criteria and were excluded from the multivariate logistic regression. The multivariate logistic regression was conducted using the block entry method to examine the associated factors for obesity with adjustment for nonmodifiable antecedents (age, sex, Down syndrome diagnosis, cerebral palsy, and mobility limitation). The independent variables included modifiable personal risk factors (use of medications, physical activity, dietary habits, smoking, and alcoholic consumption) and environmental factors (residential type and residential location). A significance level at a P value of .05 was used for all analyses.

Smoking We asked, ‘Does he/she currently smoke cigarettes?’ Scoring was 1 (yes) for a current smoker and 0 (no) for a non-current smoker. Alcoholic beverage consumption The frequency of alcoholic beverage was assessed with the question: ‘How often does he/she drink alcoholic beverages?’ Responses were rated from 0 (does not drink alcohol) to 4 (daily).

Environmental factors Residential type Residential type was categorised by three levels of support: low (living in their own home or supportive living), moderate (living with family member or relative or guardian), and high (living in a foster care home or group home). Residential location Residential location was classified into rural and urban areas based on the participant’s zip code using Census 2000 definitions. The file for urban/ rural classification was downloaded for Zip Code Tabulation Areas (ZCTAs). The file contains population numbers for three types of areas: urbanised area, urban cluster, and rural. The data were collapsed into one dichotomous variable. Zip codes with populations in the urbanised area or urban cluster were classified as urban and the remaining sample as rural. Participant zip codes were matched to the ZCTAs so that they contained the urban/ rural classification.

Results Prevalence of obesity Obesity and overweight status, men and women combined, was 38.3% and 28.9%, respectively. There was a significantly higher prevalence of obesity among women (43.2%) with ID compared with men with ID (34.3%) The prevalence of overweight status was higher in men (31.6%) than women (25.5%). Obesity prevalence was highest in the 40- to 59-year-old age group. The prevalence of morbid obesity among women was double that of men (10.9% vs. 4.5%). The prevalence of morbid

© 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd

volume 58 part 9 september 2014

Journal of Intellectual Disability Research 856 K. Hsieh et al. • Obesity and ID

Table 2 Body weight status by intellectual disability related diagnosis, residential type, and severity of intellectual disability (n = 1450)

Variables

Under weight

Normal weight

Overweight

Obesity

Morbid obesity

% (95% CI) 2010 NHIS population All ages 18 and over 18–39 40–59 ≥60 Male Female

1.8 (1.6–2.0) 2.2 (1.9–2.6) 1.1 (0.8–1.4) 2.0 (1.6–2.3) 1.0 (0.8–1.2) 2.5 (2.2–2.8)

35.6 (34.9–36.3) 43.0 (41.9–44.1) 29.3 (28.1–30.5) 32.9 (31.6–34.2) 29.5 (28.5–30.6) 41.5 (40.5–42.4)

34.6 (33.9–35.4) 30.9 (29.7–32.0) 36.9 (35.7–38.1) 37.5 (36.2–38.8) 41.2 (40.2–42.3) 28.3 (27.4–29.2)

28.0 (27.3–28.7) 23.9 (22.9–24.9) 32.7 (31.5–33.9) 27.7 (26.4–28.9) 28.3 (27.3–29.3) 27.7 (26.8–28.6)

4.2 (3.9–4.5) 3.8 (3.4–4.2) 5.2 (4.6–5.8) 3.5 (3.0–4.0) 3.0 (2.6–3.4) 5.4 (5.0–5.9)

% (n) All study population Age group 18–39 40–59 ≥60 Gender Male Female ID diagnostic groups ID with unknown aetiology Autism/PDD Cerebral palsy Down syndrome Residential type Least supported Moderately supported Most supported Level of intellectual disability Borderline Mild Moderate Severe/profound

4.1 (60)

28.7 (416)

28.9 (419)

5.4 (48) 2.0 (9) 2.6 (3)

30.9 (274) 22.2 (99) 37.1 (43)

26.8 (238) 33.6 (150) 26.7 (31)

4.3 (34) 4.0 (26)

29.9 (239) 27.2 (177)

31.6 (253) 25.5 (166)

3.5 (22) 6.3 (10) 12.0 (20) –

30.6 (192) 37.3 (59) 37.7 (63) 15.7 (53)

29.6 (186) 29.1 (46) 28.1 (47) 30.9 (104)

2.4 (10) 5.4 (45) 2.4 (5)

25.6 (105) 29.3 (244) 32.4 (67)

30.2 (124) 28.0 (233) 30.0 (62)

3.3 (6) 3.2 (14) 3.1 (10) 10.1 (12)

28.4 (52) 29.4 (128) 26.4 (86) 32.8 (39)

33.9 (62) 26.7 (116) 26.7 (87) 31.1 (37)

38.3 (555) 27.63*** 36.9 (328) 42.2 (188) 33.6 (39) 13.08** 34.3 (274) 43.2 (281) 110.65*** 36.3 (228) 27.2 (43) 22.2 (37) 53.4 (180) 12.53 41.7 (171) 37.3 (311) 35.3 (73) 26.75** 34.4 (63) 40.7 (177) 43.9 (143) 26.1 (31)

7.4 (107) 7.3 (65) 7.8 (35) 6.0 (7) 4.5 (36) 10.9 (71) 8.1 (51) 1.9 (3) 1.8 (3) 10.4 (35) 9.3 (38) 7.2 (60) 4.3 (9) 6.6 (12) 7.1 (31) 5.5 (18) 5.0 (6)

Note: * P < 0.05, ** P < 0.01, *** P < 0.001 for chi-squared tests. Level of ID: 23% of participants had missing data on level of ID. ID, intellectual disability; NHIS, National Health Interview Survey; PDD, pervasive developmental disorder.

obesity for the 18- to 39-year-old age group was comparable with the 40- to 59-year-old age group (Table 2). Diagnostic group The highest obesity prevalence within the diagnostic groups was among individuals with Down syndrome (53.4%), followed by ID with unknown aetiology, Autism or PDD, and cerebral palsy. The prevalence

of morbid obesity for adults with Down syndrome was 10.4% (vs. 8.1% for those with ID with unknown aetiology). Only a small percentage (

Obesity and associated factors in adults with intellectual disability.

We examined the prevalence of obesity in adults with intellectual disabilities (ID) compared with the general population, and the factors associated w...
130KB Sizes 0 Downloads 0 Views