Sample characteristics

We included 89 individuals with ASD and 81 individuals with ADHD. Young people in the two groups were similar in age, sex (≈90% male), education (≈70% in education) and accommodation (≈90% lived at home) (Table 8). Caregiver sociodemographic characteristics in the two groups were also broadly similar (see Table 8). All of the caregivers were either a biological or an adoptive parent. Caregivers from the two samples showed similar sex and marital status distributions (72% of ASD caregivers were married vs. 78% of ADHD caregivers, χ² = 0.77; p = 0.38). In addition, around 35% of parents in both groups provided care for someone else aside from their child with ASD/ADHD (χ² = 0.28; p = 0.60). Caregivers from the two samples were also comparable in terms of age (ASD mean 49 years, SD 6 years vs. ADHD mean 48 years, SD 5 years, t = 1.65; p = 0.10). There were, however, significant differences in occupation of the two caregiver groups: parents of ASD were in higher occupational groups (managerial/professional and associate professional/administrative) (55% ASD vs. 42% ADHD, χ² = 12.54; p = 0.01).

TABLE 8

Descriptive characteristics of study sample and variables ASD (n = 89) and ADHD (n = 81)

Caregiver burden and study variables

Caregiver burden was high in both^68,69 groups, but it was significantly higher in ASD than ADHD (ASD mean 22.66, SD 8.84 vs. ADHD mean 17.80, SD 9.18, t = 3.52; p = 0.001). Family background (parental education) was significantly different, with 62% of parents in the ASD group being in the highest educational category, compared with 35% in ADHD (χ² = 12.58; p < 0.001). Primary stressors (i.e. symptoms, LDs and psychiatric comorbidities) also showed significant differences between the two groups. The mean current severity of ASD as measured by AQ score for the ASD group was 36 (SD 6), with 73% of this group showing AQ scores above the suggested cut-off point of 32. The mean BAARS-IV current symptom score for the ADHD group was 10.63 (SD 4.73). Using a cut-off point of > 5 on the BAARS-IV current symptom score, 58% met threshold for inattentiveness, 42% for hyperactivity and 36% for combined ADHD. Fifteen per cent of the ASD group had been previously diagnosed with a LD, compared with none of the ADHD group. In terms of psychiatric comorbidities, the mean Strengths and Difficulties Questionnaire (SDQ) score for the ASD group was 21 (SD 7), with 70% scoring in the abnormal range (> 16). Fifty-five per cent of the autism group also scored in the abnormal range on the hyperactivity subscale of the SDQ (> 6). The mean CIS-R score for the ADHD group was 8 (SD 6), with 27% per cent of the ADHD group scoring ≥ 12, indicating the presence of a common mental disorder.

Need, our measure of primary appraisal, also showed significant differences between the two groups, with the needs of those with an ASD (as rated by caregivers) being significantly higher (ASD mean 9.90, SD 3.55, vs. ADHD mean 5.01, SD 3.12, t = 9.51; p < 0.001). We used service use to conceptualise resources. There was little difference in this indicator between the two groups; 58% of the ASD group and 56% of the ADHD group were currently being seen by health services.

Tables 9–11 present the sequential multiple regression models used to examine the correlates of caregiver burden. Separate models were produced for each condition, as well as a model combining both conditions. Variables representing family background characteristics (parental education) were first entered into the model, followed by those capturing primary stressors (symptoms, a LD for ASD and psychiatric comorbidities), primary appraisal (need) and resources (use of services). For both samples, caregiver-rated need was a significant correlate of burden even after other factors were controlled for (see Tables 9–11). In addition, once need was controlled for, severity of disorder symptoms was no longer a significant predictor of burden for ASD, but remained significant for ADHD (see Table 10, model 3). Conversely, although psychiatric comorbidities were a significant predictor of burden for ASD, they were not for ADHD. Given that approximately half of the ASD group also met the threshold on the SDQ hyperactivity scale, we also investigated whether or not ADHD symptomology was associated with burden for the ASD group. When the SDQ hyperactivity subscale was included as a separate variable in the model (along with a variable comprising the total score from the other three subscales), the hyperactivity subscale was not significantly associated with burden.

TABLE 9

Linear regression model for predictors of caregiver burden (ASD, n = 89)

TABLE 11

Linear regression model for predictors of caregiver burden (ASD and ADHD, n = 170)

TABLE 10

Linear regression model for predictors of caregiver burden (ADHD, n = 81)

In the combined model need and comorbid psychopathology were significant predictors of burden, whereas having a diagnosis of either an ASD or ADHD was not (see Table 11). The models show 34% of the variance in caregiver burden among those caring for a young person with an ASD (see Table 9) in comparison with 23% in the ADHD group (see Table 10). Further models were run for both disorders using the young person’s assessment of their needs; however, no relationship was found with parental burden.

In order to further explore the relationship between need and burden the models above were run with unmet and met need instead of total need (not shown). The results showed that only unmet need was significantly associated with caregiver burden. The models using unmet need instead of total need accounted for 42% of the variance in burden for the ASD group and 26% for the ADHD group. To investigate these results further, additional bivariate correlations were calculated between individual CANDID items and caregiver burden. Table 12 shows the individual domains of unmet needs, which were significantly correlated with burden in the two groups. Unmet needs, related to depression/anxiety, inappropriate behaviour, exploitation risk, aggression/violence and daytime activities, were significantly correlated with burden for both samples. Specific to the ASD sample were significant correlations between burden and unmet needs relating to social relationships, mental health problems, safety of self and communication.

TABLE 12

Significant correlations between items rated by parent as an unmet need and caregiver burden (ASD n = 89 and ADHD n = 81)

Strengths and weaknesses

This study has a number of limitations. First, we did not have a direct measure of maladaptive behaviour, although it is likely that the measures used (e.g. of need and psychiatric comorbidities) also captured behavioural difficulties. Second, our ASD sample appears to be higher functioning than the ASD populations that have been investigated in other studies. For example, approximately 15% of our sample was diagnosed with a LD, in comparison with a prevalence of 56% found by others in a population cohort of children.⁷ In addition, there is increasing recognition that many individuals with ASD do not have ID. Even so, caregivers of these higher-functioning individuals still describe a very significant level of burden. Hence, public health policies should not assume that relatively higher-functioning ASD individuals and their caregivers do not also have significant (and unmet) needs. Third, the sex balance of the young people in both the ASD and ADHD samples was not representative of the respective populations. A male to female ratio of 3.3 : 1 was reported in a study of children with ASD⁶² and a male to female ratio of 1.6 : 1 was reported in a study of adults with ADHD.⁶³

By contrast, our samples had ratios of approximately 9 : 1 (male : female). In addition, almost all caregivers in the study were mothers. Given that studies in ASD and ADHD have found having a male child to be associated with higher levels of burden,^68,69 and that levels of burden have been reported to be higher in mothers than in fathers,⁷⁴ our results may overestimate the level of burden in this population. Fourth, although a measure of ADHD symptomology was included for the ASD sample (the hyperactivity/inattention subscale of the SDQ), no corresponding measure of ASD symptomology was included for the ADHD sample. Although the ADHD sample was screened for ASD in childhood, mild autism traits could still have been present; however, we were unable to consider the effect of these traits on caregiver burden. Fifth, this initial study was cross-sectional and is therefore able to examine associations of need and caregiver burden at only one point in time. Future longitudinal research is required to determine whether or not input from formal services significantly reduces parental burden. Sixth, there was some reduction in sample size due to missing data. As the CIS-R in the ADHD group was taken from the young person’s interview, analyses focused on those families with both a parent and young person interview (i.e. out of 86 paired interviews, 81 contained complete information). For the ASD group, analyses focused on the parent interviews as these contained the necessary measures. The sample size for this group dropped from 101 to 89, largely as a result of missing data on the SDQ. Seventh, differences in the relationship between comorbidities and caregiver burden in the two groups may be due to differences in the way that this information was collected: through self-reports in the ADHD group using the CIS-R and through informant reports in the ASD group using the SDQ. Finally, our initial analyses were cross-sectional – but we need to also examine change. As all data have now been collected, our future focus will be examining changes over time as outlined in research questions 2 and 3. Thus, we will investigate how needs and associated comorbidities have changed over time in this clinical group and examine the demographic and medical factors associated with this change. Our final analysis, which has not yet begun, will be to examine the main family carer’s role in meeting needs and how this has changed over time and whether or not this is related to changes in service use. We will assess correlates associated with this change and the consequences of changes in the family carer’s role for their own well-being.