Abstract
Objective:
To compare quality of life (QOL) across diagnoses associated with intellectual disability, construct QOL profiles and evaluate membership by diagnostic group, function and comorbidities.
Method:
Primary caregivers of 526 children with intellectual disability (age 5-18 years) and a diagnosis of cerebral palsy, autism spectrum disorder, Down syndrome, CDKL5 deficiency disorder or Rett syndrome completed the Quality of Life Inventory-Disability (QI-Disability) questionnaire. Latent profile analysis of the QI-Disability domain scores was conducted.
Results:
The mean (SD) total QOL score was 67.8 (13.4), ranging from 60.3 (14.6) for CDD to 77.5 (11.7) for Down syndrome. Three classes describing domain scores were identified: Class 1 was characterised by higher domain scores overall but poorer negative emotions scores; Class 2 by average to high scores for most domains but low independence scores; and Class 3 was characterised by low positive emotions, social interaction, and leisure and the outdoors scores, and extremely low independence scores. The majority of individuals with autism spectrum disorder and Down syndrome belonged to Class 1 and the majority with CDKL5 deficiency disorder belonged to Class 3. Those with better functional abilities (verbal communication and independent walking were predominately members of Class 1 and those with frequent seizures were more often members of Class 2 and 3.
Conclusion:
The profiles illustrated variation in QOL across a diverse group of children. QOL evaluations illustrate areas where interventions could improve QOL and provide advice to families as to where efforts may be best directed.
Keywords: intellectual disability, quality of life, latent profile analysis
1. INTRODUCTION
Almost one in 50 children in the population have intellectual disability (Bourke, de Klerk, Smith, & Leonard, 2016). Down syndrome is the most common known cause of intellectual disability, accounting for 6% of intellectual disability (Bourke, et al., 2016). However, there are multiple other genetic disorders, including both Rett syndrome (RTT) (H. Leonard, Cobb, & Downs, 2017) and CDKL5 deficiency disorder (CDD) (Fehr, et al., 2013), which affect much smaller numbers of individuals and vary in severity. For other children with intellectual disability categorisation may be according to diagnostic classification systems, such as cerebral palsy (CP) or autism spectrum disorder (ASD) (Yeargin-Allsopp, Murphy, Cordero, Decouflé, & Hollowell, 1997).
Quality of life (QOL) refers broadly to satisfaction with life in the context of culture and personal goals (WHO, 1995). QOL measures are multidimensional (Janssens, et al., 2016). Models that have been developed for individuals with intellectual disability, applied particularly to adults, and these include domains of physical, emotional, social, and financial wellbeing as well as aspects of personal development and activity (Felce & Perry, 1995; R. Schalock, Keith, Verdugo, & Gómez, 2011; R. L. Schalock, 2011). In the Schalock model, these domains include rights (R. Schalock, et al., 2011; R. L. Schalock, 2011), more explicitly representing the concepts in the United Nations Convention on the Rights of Persons with Disabilities (United Nations, 2006). Recent work has sought to understand this model in relation to QOL in children with intellectual disability when developing KidsLife, a proxy-report measure of QOL for children with intellectual disability (Gomez, et al., 2016).
We recently undertook four qualitative studies to further explore the domains of QOL important to children with intellectual disability. In-depth interviews were conducted with parents of six to 18-year-old children with either Down syndrome (n=17), Rett syndrome (n=21; a severe genetic neurodevelopmental disorder mainly affecting females (Neul, et al., 2010)), CP (n=18) or ASD (n=21) (Davis, et al., 2017; Epstein, et al., 2016; Epstein, Whitehouse, et al., 2019; Murphy, et al., 2017). These data enabled the development of the Quality of Life Inventory-Disability (QI-Disability), a parent-report measure of QOL for individuals with intellectual disability, which has been psychometrically validated with individuals with Down syndrome, RTT, CP and ASD, each with comorbid intellectual disability (Downs, et al., 2019; Epstein, Williams, et al., 2019). Good reliability has been demonstrated (Jacoby, et al., 2020).
Using QI-Disability, QOL scores for individuals with Down syndrome were higher than for individuals with RTT, CP and ASD, in part because of high scores for social interactions (Downs, et al., 2019). We have also found that children with better functional abilities and independence in daily activities had higher QOL scores (Williams, et al., 2021) as did those with less severe comorbidities, particularly sleep disturbances, frequent seizures and recurrent pain (Reddihough, et al., 2021). Using QI-Disability, the effects of seizures and medication side effects were associated with QOL scores in 176 children with a genetically caused developmental epileptic encephalopathy (Cohen, et al., 2022). Another recent study involved 29 individuals whose disability was severe, the majority associated with a genetic variant or cerebral palsy, and capacity to participate in motor tasks with support contributed to better QOL (Mensch, et al., 2019). Therefore, there is evidence that specific aetiologies, levels of function and comorbidities influence QOL in individuals with intellectual disability. The QI-Disability domains are also relevant for individuals with CDD, a condition that is generally associated with epilepsy from a young age, severe intellectual disability and major impairments (Tangarorang, Leonard, Epstein, & Downs, 2019).
Domain scores have potential to illustrate patterns of QOL, not apparent from the total score alone. Latent profile analysis is a statistical technique that identifies subgroups of participants where members of a subgroup or class can share distinct profiles of domain scores. Membership of a particular profile class could be associated with phenotypic characteristics. For example, developmental profile groups were derived from subdomain scores of the Australian Early Development Census, identifying groups with higher-than-average scores on some domains and lower than average scores on others, and predicting later mental health outcomes at age 13 years (Green, et al., 2019). To our knowledge, latent class profiles have not previously been examined for QOL in children with intellectual disability. It is possible that factors other than diagnosis are associated with different QOL profiles in intellectual disability, and group membership could be a useful way of understanding QOL with respect to disease natural history and therapeutic interventions.
In this study, we aimed to (1) compare total QOL scores between diagnostic groups, (2) construct QOL profiles using latent class analysis and (3) evaluate membership to each of the profiles by factors known to be associated with QOL including diagnostic group, functional abilities, and comorbidities.
2. METHODS
2.1. STUDY DESIGN AND PROCEDURE
A cross-sectional study design was used. Caregivers of a child with confirmed intellectual disability (aged 5-18 years) and a diagnosis of either ASD, CP, Down syndrome, RTT or CDD completed the QI-Disability instrument from April 2018 to February 2020. Families were contacted through population-based registries and other data sources, including the WA Autism Biological Registry (Taylor, et al., 2013), the WA Autism Register (Glasson, 2002), the Victorian Cerebral Palsy Register (Reid, et al., 2016) and the Australian Rett Syndrome Database (Downs, et al., 2016). For CDD families, the International CDKL5 Disorder Database (Fehr, et al., 2015) served as the data source for this study. Caregivers of individuals with Down syndrome born from 1980 to 2004 were also invited to participate via community organizations and networks, social media advertisements and network sampling (Williams, et al., 2021).
Following initial telephone contact with the caregivers, the questionnaire was administered using the Research Electronic Data Capture (REDCap™) tool. Some families provided data using a paper format or telephone interview with a member of the research team with a psychology qualification (MPsych, ClinPsych).
Approval for this study was provided by Human Research Ethics Committee at The University of Western Australia (RA/4/20/4276, RA/4/1/5024), the Child and Adolescent Services (RGS2390) and University of Colorado (COMIRB19-2756). Primary caregivers documented informed consent to participate in the study.
2.2. VARIABLES
Diagnostic group was classified as RTT, Down syndrome, CDD, CP or ASD, each with a confirmed diagnosis indicated in database records. Mobility was classified as “Able to walk without assistance”, “Assistance required when walking” or “Unable to walk”. Communication was classified as “Full sentences used”, “Some words used”, “Non-verbal communication only” or “No communication”.
A diagnosis of epilepsy was classified as “yes” or “no”, and if yes, the frequency of seizures was described as “controlled”, “less than weekly” or “daily or weekly”. Select domains from the Sleep Disturbance Scale for Children (Bruni, et al., 1996), were used to describe sleep. For this study, the “Disorders of Initiating and Maintaining Sleep” and “Sleep Breathing Disorders” subscales represent sleep issues that have been shown to be problematic for children with ASD (Souders, et al., 2017), CP (Simard-Tremblay, Constantin, Gruber, Brouillette, & Shevell, 2011), Down syndrome (Stores & Stores, 2013) and RTT (Boban, et al., 2016). Subscale scores were compared with normative data previously reported (Bruni, et al., 1996) to calculate z-scores and then t-scores (Bruni, et al., 1996). T-scores were then classified as normal (<70) or abnormal (=>70). Primary caregivers reported the presence of constipation which was coded as “yes” if it was present or if it was not present but was being treated. Nights in hospital over previous 12 months were classified as “None”, “1 night”, “2-5 nights” or “>5 nights”.
The parent-report QI-Disability questionnaire is a 32-item parent-report measure assessing the QOL of children with intellectual disability and initial reliability and validity of the instrument has previously been published (Downs, et al., 2019; Epstein, Williams, et al., 2019). The questionnaire comprises six domains: Social Interaction (7 items), Positive Emotions (4 items), Negative Emotions (7 items), Physical Health (4 items), Leisure and the Outdoors (5 items), and Independence (5 items). Items were rated on a 5-point Likert scale and caregivers asked to recall observations of their child’s well-being and enjoyment of life over the past month. Items were linearly transformed to a scale of 0 to 100, with higher scores representing better QOL. Domain scores were calculated by averaging item scores and total scores calculated by averaging domain scores.
2.3. PARTICIPANTS
Of the 706 families, 526 (74.5%) provided a questionnaire with adequate QI-Disability data (maximum one missing item in the Positive Emotions or Social Interactions domain) (Jacoby, et al., 2022). Participants were from Australia (cerebral palsy, Down Syndrome, Rett Syndrome, CDKL5 deficiency disorder) and North America and Europe (CDKL5 deficiency disorder). This included families of 130/162 (80.2%) children with ASD, 151/229 (65.9%) children with CP, 89/97 (91.8%) children with Down syndrome, 81/122 (66.4%) children with CDD, and 75/96 (78.1%) children with Rett syndrome. The largest proportions had CP (28.7%) or ASD (24.5%) and smaller proportions had Down syndrome (16.9%), CDD (15.4%) or RTT (14.2%). Slightly less than half (46.4%) were aged 12-18 years and slightly more than half (54.9%) were female. Nearly two thirds (314, 60.2%) were able to walk with no assistance whereas slightly more than one third (181, 34.7%) could speak in sentences. Nearly half had been diagnosed with epilepsy (265, 48.6%), had an abnormal Disorders of Initiating and Maintaining Sleep score (249, 48.4%) or constipation (233, 44.6%). Smaller proportions of individuals had abnormal Sleep Breathing Disorder scores (116, 23%) or had spent at least one night in hospital over the previous 12 months (158, 30.4%). Descriptive data are presented in Table 1.
Table 1:
Frequency distribution (%) of categorical variables and mean (SD) values for quality of life scores for individuals in the study (n=526).
| All (n=526) | Cerebral Palsy (n=151) | Autism spectrum disorder (n=130) | Down syndrome (n=89) | CDKL5 Deficiency Disorder (n=81) | Rett syndrome (n=75) | ||
|---|---|---|---|---|---|---|---|
| N (%) | |||||||
| Age (N=526) | 5 to 11 years | 281 (53.4) | 65 (43.1) | 75 (57.7) | 52 (58.4) | 51 (63.0) | 38 (50.7) |
| 12 to 18 years | 245 (46.6) | 86 (57.0) | 55 (42.3) | 37 (41.6) | 30 (37.0) | 37 (49.3) | |
| Sex (N=525) | Female | 288 (54.9) | 60 (39.7) | 33 (25.4) | 53 (59.6) | 67 (83.8) | 75 (100) |
| Mobility (N=526) | Walks with no assistance | 317 (60.3) | 53 (35.1) | 128 (98.5) | 88 (98.9) | 26 (32.1) | 22 (29.3) |
| Walks with assistance | 46 (8.8) | 19 (12.6) | 2 (1.5) | 0 | 10 (12.4) | 15 (20.0) | |
| Unable to walk | 163 (31.0) | 79 (52.3) | 0 | 1 (1.1) | 45 ( 55.6) | 38 (50.7) | |
| Communication (n-526) | Full sentences | 181 (34.4) | 45 (29.8) | 76 (58.5) | 48 (53.9) | 4 (4.9) | 8 (10.7) |
| Some words | 97 (18.4) | 18 (11.9) | 28 (21.5) | 34 (38.2) | 11 (13.6) | 6 (8.0) | |
| Nonverbal only | 165 (31.4) | 49 (32.5) | 19 (14.6) | 6 (6.7) | 44 (54.3) | 47 (62.7) | |
| None | 83 (15.4) | 39 (25.8) | 7 (5.4) | 1 (1.1) | 22 (27.6) | 14 (19.0) | |
| Seizure Frequency (N=519) | None | 265 (51.1) | 62 (41.1) | 108 (83.1) | 84 (94.4) | 0 | 11 (16.2) |
| Controlled | 46 (8.9) | 25 (16.6) | 6 (4.6) | 3 (3.4) | 2 (2.5) | 10 (14.7) | |
| Less than once a week | 86 (16.6) | 35 (23.1) | 9 (6.9) | 0 | 21 (25.9) | 21 (30.9) | |
| Daily or weekly | 122 (23.5) | 29 (19.2) | 7 (5.4) | 2 (2.3) | 58 (71.6) | 26 (38.2) | |
| DIMS (N=515) | Abnormal | 249 (48.4) | 77 (52.7) | 67 (51.9) | 30 (34.1) | 45 (56.3) | 30 (41.7) |
| SBD (N=504) | Abnormal | 116 (23.0) | 36 (24.2) | 23 (19.0) | 32 (36.8) | 11 (13.8) | 14 (20.9) |
| Constipation (N=526) | Yes | 236 (44.9) | 74 (49.0) | 47 (36.2) | 35 (39.3) | 43 (53.1) | 37 (49.3) |
| Nights in Hospital (N=523) | None | 363 (69.4) | 72 (48.0) | 117 (90.0) | 62 (69.7) | 64 (80.0) | 64 (79.0) |
| 1 night | 32 (6.1) | 7 (4.7) | 5 (3.9) | 12 (13.5) | 4 (5.0) | 5 (6.2) | |
| 2-5 nights | 46 (8.8) | 23 (15.3) | 6 (4.6) | 7 (7.9) | 4 (5.0) | 4 (4.9) | |
| >5 nights | 82 (15.7) | 48 (32.0) | 2 (1.5) | 8 (9.0) | 8 (10.0) | 8 (9.9) | |
| Mean (SD) | |||||||
| Quality of life (/100) (N=526) | Total score | 67.8 (13.4) | 66.6 (13.5) | 68.3 (10.9) | 77.5 (11.7) | 60.3 (14.6) | 66.2 (11.4) |
| Physical Health | 70.4 (16.4) | 67.2 (16.6) | 74.6 (15.6) | 71.6 (16.6) | 72.1 (14.4) | 66.5 (17.1) | |
| Positive Emotions | 75.5 (18.7) | 75.2 (19.2) | 74.1 (16.3) | 86.6 (14.7) | 66.1 (21.3) | 75.3 (16.8) | |
| Negative Emotions | 66.2 (18.9) | 67.6 (19.8) | 57.6 (18.6) | 70.9 (16.4) | 71.3 (18.0) | 67.5 (16.7) | |
| Social Interaction | 67.7 (21.1) | 70.9 (20.1) | 61.5 (17.7) | 80.0 (15.9) | 52.4 (25.2) | 74.4 (15.0) | |
| Leisure | 69.7 (21.0) | 64.5 (21.2) | 72.5 (18.7) | 78.3 (17.5) | 65.4 (25.2) | 69.5 (19.3) | |
| Independence | 57.5 (26.4) | 54.2 (26.0) | 69.8 (16.3) | 77.7 (17.7) | 34.4 (26.8) | 44.0 (20.6) | |
DIMS: Disorders of Initiating and Maintaining Sleep
SBD: Sleep Breathing Disorders
3. ANALYSIS
Total QI-Disability scores were compared across diagnostic groups using one-way ANOVA. We used the STATA module ‘gsem’ to conduct latent profile analysis of QI-Disability domain scores. A series of models were fitted, each with a categorical latent variable corresponding to a specific number of classes and the six observed variables comprising the domain scores. An exchangeable covariance structure was specified to constrain the covariances between domain scores to be equal within each latent profile. The number of classes was increased, starting at one, and model fits were compared using the Bayesian Information Criterion (BIC) and other model fit indices. Posterior probabilities of membership of each class, according to the best fitting model, were calculated for each individual and membership assigned according to the highest probability. Model entropy and univariate entropy values for the best fitting model were calculated. Mean domain scores with 95% confidence intervals were calculated for each class, stratified by levels of the diagnosis, functional ability, and comorbidity variables. Linear regression analysis was used to compare the latent profile domain scores. For a small number of respondents, missing data was imputed according to the missing data rule for QI-Disability (Jacoby, et al., 2022) which allows for one missing item in only two specified domains (Positive Emotions and Social Interactions). Children with more missing QI-Disability data were excluded from the analysis. Statistical analyses were performed using STATA 16.0 (StataCorp LLC, College Station, Texas).
4. RESULTS
The mean (SD) total QOL score was 67.8 (13.4) with mean (SD) values ranging from 60.3 (14.6) for individuals with CDD to 77.5 (11.7) for individuals with Down syndrome (Table 1). A significant variance in the total score was accounted for by diagnostic group (F(4,521)=21.43, p<0.001).
The model specifying three classes was identified as representing the best fit based on minimum BIC (Supplementary Table 1). Entropy for this model was 0.78 representing satisfactory classification accuracy. Univariate entropies for the QI-Disability domain indicators varied between 0.23 and 0.92. Univariate entropy indicates how well the domain scores, considered individually, identify the latent profiles. There are no cut-offs for univariate entropy scores (Supplementary Table 2).
The three classes, based on most likely membership, were as follows: Class 1 (n=341, 64.8%) was characterised by high scores on all domains although less so for the Negative Emotions domain. Compared to children in Class 1, children in Class 2 (n=137, 26.0%) had lower Physical Health (−3.59, 95% CI −6.83, −0.35), Leisure and the Outdoors (−8,49, 95% CI −12.46, −4.72) and Independence (−42.03, 95%CI 45.07, −38.77) scores, but higher Negative Emotions scores (10.90, 95% CI 7.32, 14.47). Also compared to children in Class 1, children in Class 3 (n=48, 9.1%) had even greater differences in scores with lower Positive Emotions (−26.64, 95%CI −31.79, −21.49), Social Interaction (−40.23 95%CI −45.58, −34.88), Leisure and the Outdoors (−30.94 95%CI −36.69, −25.20) and Independence (−52.54 95%CI −57.17, −47.91) scores (Table 2). The mean domain scores for each class are shown in Table 2, the pattern of scores for each class is shown in Figure 1.
Table 2:
Mean (95% CI) QI-Disability total and domain scores for each latent class and comparison of class scores relative to Class 1 using linear regression analysis.
| Class 1 (n=341) | Class 2 (n=137) | Class 3 (n=48) | ||||
|---|---|---|---|---|---|---|
| Mean (95%CI) | Mean (95%CI) | Mean Difference (95%CI) P value |
Mean (95%CI) | Mean Difference (95%CI) P value |
||
| Total | 71.7 (70.4,73.0) | Ref | 64.8 (63.0, 66.7) | −6.92 (−9.23, −4.61) <0.001 |
48.7 (46.12,51.3) | −23.03 (−26.55, −19.51) <0.001 |
| Physical Health | 71.7 (70.0, 73.4) | Ref | 68.1 (65.1,71.1) | −3.59 (−6.83, −0.35) 0.030 |
67.8 (63.8, 71.9) | −3.86 (−8.00, 1.08) 0.125 |
| Positive Emotions | 77.3 (75.4, 79.1) | Ref | 79.6 (76.8, 82.4) | 2.32 (−1.06, 5.69) 0.178 |
50.7 (46.2, 55.1) | −26.64 (−31.79, −21.49) <0.001 |
| Negative Emotions | 61.9 (59.9, 63.9) | Ref | 72.8 (69.8, 75.9) | 10.90 (7.32, 14.47) <0.001 |
78.0 (74.7, 81.3) | 16.04 (10.59, 21.48) <0.001 |
| Social Interactions | 71.6 (69.6, 73.6) | Ref | 71 (68.3, 73.6) | −0.61 (−4.12, 2.90) 0.733 |
31.3 (26.9, 35.7) | −40.23 (−45.58, −34.88) <0.001 |
| Leisure and the Outdoors | 74.7 (72.7, 76.7) | Ref | 66.2 (63.0, 69.4) | −8.49 (−12.26, −4.72) <0.001 |
43.8 (37.7, 49.8) | −30.94 (−36.69, −25.20) <0.001 |
| Independence | 73.3 (71.6, 74.9) | Ref | 31.2 (28.8, 33.7) | −42.03 (45.07, −38.99) <0.001 |
20.7 (16.5, 25.0) | −52.54 (−57.17, −47.91) <0.001 |
Figure 1:
Mean QI-Disability domain scores for three latent classes of 526 individuals with intellectual disability.
Table 3 presents the proportion of individuals in each class according to their diagnosis and functional and comorbidity categories. Most individuals with ASD (92.3%) and Down syndrome (93.3%) were in Class 1, and approximately half of individuals with CP and RTT were present in Class 2 (40.4%, 53.3% respectively). Individuals with CDD were spread across all classes but the largest proportion was in Class 3 (43.2% of individuals with CDD). Those with better functional abilities (verbal communication and walking without assistance) and less severe epilepsy were predominately members of Class 1, while those individuals who had spent >5 nights in hospital in the previous 12 months were more likely to be in Class 2 or Class 3 than those with fewer hospital stays. Otherwise, abnormal “Disorders of Initiating and Maintaining Sleep” and “Sleep Breathing Disorder” scores and the presence of constipation had little effect on class membership (Table 3).
Table 3:
Number (%) of individuals in each class (most likely membership) by diagnostic group, level of function and comorbidity (row percentage values)
| n | Class 1 (n=341) | Class 2 (n=137) | Class 3 (n=48) | ||
|---|---|---|---|---|---|
| Diagnosis | Cerebral palsy | 151 | 80 (53.0) | 61 (40.4) | 10 (6.6) |
| Autism spectrum disorder | 130 | 120 (92.3) | 8 (6.2) | 2 (1.5) | |
| Down syndrome | 89 | 83 (93.3) | 6 (6.7) | 0 | |
| CDKL5 Deficiency Disorder | 81 | 24 (29.6) | 22 (27.1) | 35 (43.2) | |
| Rett syndrome | 75 | 34 (45.3) | 40 (53.3) | 1 (1.3) | |
| Mobility | Walks with no assistance | 317 | 278 (87.7) | 32 (10.1) | 7 (2.2) |
| Walks with assistance | 46 | 20 (43.5) | 20 (43.5) | 6 (13.0) | |
| Unable to walk | 163 | 43 (26.4) | 85 (52.2) | 35 (21.5) | |
| Communication | Full sentences | 181 | 172 (95.0) | 8 (4.4) | 1 (0.6) |
| Some words | 97 | 81 (83.5) | 16 (16.5) | 0 | |
| Non-verbal only | 165 | 67 (40.6) | 69 (41.8) | 29 (17.6) | |
| None | 83 | 21 (25.3) | 44 (53.0) | 18 (21.7) | |
| Seizure Frequency | No epilepsy | 265 | 224 (84.5) | 38 (14.3) | 3 (1.1) |
| Controlled | 46 | 27 (58.7) | 17 (37.0) | 2 (4.4) | |
| Less than Weekly | 86 | 46 (53.5) | 30 (34.9) | 10 (11.6) | |
| Daily or Weekly | 122 | 42 (34.4) | 47 (38.5) | 33 (27.1) | |
| DIMS | Normal | 266 | 175 (65.8) | 66 (24.8) | 25 (9.4) |
| Abnormal | 249 | 160 (64.3) | 66 (26.5) | 23 (9.2) | |
| SBD | Normal | 388 | 250 (64.4) | 104 (26.8) | 34 (8.8) |
| Abnormal | 116 | 76 (65.5) | 27 (23.3) | 13 (11.2) | |
| Constipation | No | 290 | 196 (67.6) | 65 (22.4) | 29 (10.0) |
| Yes | 236 | 145 (61.4) | 72 (30.5) | 19 (8.1) | |
| Nights in hospital (over previous 12m) | None | 363 | 254 (70.0) | 76 (20.9) | 33 (9.1) |
| 1 night | 32 | 25 (78.1) | 5 (15.6) | 2 (6.3) | |
| 2-5 nights | 46 | 29 (63.0) | 13 (28.3) | 4 (8.7) | |
| >5 nights | 82 | 31 (37.8) | 42 (51.2) | 9 (11.0) |
Mean QI-Disability domain scores with 95% confidence intervals for each class, stratified by levels of the diagnosis, functional ability, and comorbidity variables, are presented in Supplementary Tables 3–5.
5. DISCUSSION
QOL total scores varied between diagnostic groups in a large sample who represented a wide range of function and health from across the spectrum of intellectual disability. Based on the six QI-Disability domain scores (Downs, et al., 2019), we identified three latent profiles which demonstrated how factors other than diagnostic category alone can influence QOL. The latent profile model entropy was 0.78 indicating good differentiation between profiles and acceptable confidence in assigning individuals to profiles based on most likely membership.
Class 1 was the largest class and included individuals from each of the diagnostic groups, particularly those who could walk unassisted, communicate using sentences or did not have epilepsy. These children had relatively high scores across each of the QOL domains but the lowest scores for the Negative Emotions domain indicating the presence of some challenging behaviours such as being unsettled or upset, withdrawn, or showing signs of agitation or self-injury. Mental health problems are prevalent in intellectual disability (Buckley, et al., 2020; Glasson, et al., 2020) but those in Class 1 with apparent milder disability appeared more vulnerable to poorer emotional and behavioural wellbeing. The structure of Class 1 highlights relative strengths across the QOL domains for these children and the importance of supporting healthy emotions and behaviours to normalise QOL profiles.
Class 2 also included individuals from each of the diagnostic groups, but the proportions of children with ASD or Down syndrome were small. The majority were unable to walk, use sentences or words and had epilepsy. Compared to Class 1, children in Class 2 had higher Negative Emotions scores indicating fewer challenging behaviours, but lower Independence scores indicating less choice and control during daily activities (Downs, et al., 2019), consistent with greater functional impairments. Data suggest that implementing strategies that provide more opportunities for choice and control could be important mechanisms for building QOL in this group.
Class 3 was particularly characterised by low Positive Emotion, Social Interaction, and Independence scores. Individuals with Down syndrome or RTT were not represented in this class, possibly because individuals with Down syndrome have a greater repertoire of social interactions (Fidler & Nadel, 2007) and many individuals with RTT are able to use eye gaze for social communications (Neul, et al., 2010). Rather, individuals with CDD or CP and a small proportion with ASD populated this class, the highest proportion having CDD. Other factors, such as use of multiple and/or high doses of antiepileptic medications for refractory epilepsy (Helen Leonard, Junaid, Wong, Demarest, & Downs, 2021) and cortical visual impairment (Demarest, et al., 2019), may tend to impair these aspects of QOL. Our latent class analyses highlight the effects of a complex set of difficulties in individuals with very severe disability, that alone or in combination, threaten QOL. Greater support for alternative communication modalities could enable opportunities for social interactions and more choice and control could improve QOL in this extremely vulnerable group.
These latent class profiles are important because they illustrate patterns of strengths and difficulties in QOL across a diverse group of individuals with diagnoses associated with intellectual disability, with respect to diagnosis and clinical severity. The patterns indicate areas where interventions should be targeted to improve QOL, and domains where there could be potential to achieve meaningful change. For example, patterns could suggest needs for emotional and behavioural support, building capacity for greater choice and control in making decisions, or providing opportunities for social interactions and community activities. The pattern of domain scores could inform how therapists and families plan, discuss, and implement the goals and strategies for the child, with the goal of improving their child’s QOL. Notably, CDD was represented across classes, illustrating how variation in phenotype of this rare disorder (Fehr, et al., 2015) is associated with different patterns of QOL. Whether class membership is responsive to change following treatments, alteration of functioning or health status, or increasing age requires further longitudinal studies.
Despite these differences, there were commonalities between the classes. “Disorders of Initiating and Maintaining Sleep” and “Sleep Breathing Disorder” scores from the sleep measure, and constipation were similar between classes, although there were different class prevalences for the frequency of epilepsy. Both sleep problems (Hollway & Aman, 2011) and constipation (Pawliuk, et al., 2020) are pervasive across disability groups. These results suggest that each diagnostic group was associated with complexities in their physical health needs.
Strengths and limitations
Strengths of our study include the large sample of individuals across a range of diagnoses, use of a validated QOL instrument, and evaluation by three latent class profiles that were robust according to fit statistics. There were generally high recruitment fractions and little missing data. Our sample was comparable to some population data. For example, approximately one third of girls with Rett syndrome will maintain the ability to walk independently (Downs, et al., 2016) and more than one third of children with Down syndrome experience Sleep Breathing Disorders (Lal, White, Joseph, van Bakergem, & LaRosa, 2015), consistent with our observations. However, it would be impossible to capture all diagnostic groups associated with intellectual disability. Whilst our sample represents the range of differing functional abilities, medical comorbidities and different needs for autonomy across the range of diagnostic groups we selected, generalisability to the full spectrum of intellectual disability is likely not possible. For example, we did not include children with a mild intellectual disability who have no diagnosis and this much larger group of children is likely to be more independent and to experience fewer medical comorbidities. There may be other aspects of physical health that we were not able to characterise across the groups such as autonomic dysfunction, which is common in RTT (Mackay, et al., 2017) and visual function, with cortical visual impairment highly prevalent in CDD (Helen Leonard, et al., 2022). We acknowledge that QOL was measured using proxy-report and this may differ from what the individual would report. We note that parent/proxy reports provide important guidance for health and disability supports and services in paediatric practice, particularly if the child has a degree of intellectual disability where their ability to express QOL is less understood. Finally, we recommend that latent profile analysis on QOL be replicated for different and even larger samples of children with intellectual disability, particularly those with a mild level of impairment.
6. CONCLUSION
These findings have implications for how clinical supports are provided. While many health and disability systems provide access to supports based upon the presence of a diagnosis, the current study demonstrates variability in the QOL of individuals within a given diagnostic category and commonalities between diagnostic categories. Given that improving QOL is a valued target for most interventions, the current findings highlight the importance of an individual’s experiences, rather than the presence or absence of a diagnostic label, in determining access to intervention and support services. Our data provide evidence for using the QOL tool to identify domains in which interventions might be best targeted.
Supplementary Material
What this paper adds.
This paper advances the field by identifying patterns of quality of life, based on six Quality of Life Inventory-Disability domain scores, in a large sample of children with conditions associated with intellectual disability. Class membership was influenced by diagnostic group, but also by levels of functional abilities and comorbidities within diagnoses. Profiles could illustrate areas where interventions and supports may be best directed especially in children with moderate or severe intellectual disability.
Acknowledgements
We extend our thanks to the families for their participation in this study. This study was funded by the National Health and Medical Research Council (#1103745), the International Foundation for CDKL5 Research, and the National Institute for Health (U01NS114312-01A1). We acknowledge the support of the WA Autism Registry. The Victorian Cerebral Palsy Register receives funding from the Victorian Department of Health and Human Services and from the Victorian Government’s Operational Infrastructure Support Program for support for register staff. The authors acknowledge the support of Disability Services Commission WA in establishing the Down syndrome database, and community organizations Developmental Disability WA and the Down Syndrome Association of Western Australia for their support. We thank the Australian Paediatric Surveillance Unit (APSU) and the Rett Syndrome Association of Australia for their ongoing support in case ascertainment for the Australian Rett Syndrome Database. We acknowledge for ongoing support from the International Foundation for CDKL5 Research since the International CDKL5 Disorder Database was established in 2012.
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