The impact of ADHD on the health and well-being of ADHD children and their siblings

15 Pages·2016·0.47 MB·English

by  Tessa Peasgood

Eur Child Adolesc Psychiatry
DOI 10.1007/s00787-016-0841-6
ORIGINAL CONTRIBUTION
The impact of ADHD on the health and well‑being of ADHD
children and their siblings
Tessa Peasgood1 · Anupam Bhardwaj2 · Katie Biggs1 · John E. Brazier1 ·
David Coghill3 · Cindy L. Cooper1 · David Daley4 · Cyril De Silva5 ·
Val Harpin6 · Paul Hodgkins7 · Amulya Nadkarni8 · Juliana Setyawan7 ·
Edmund J. S. Sonuga‑Barke9
Received: 30 October 2015 / Accepted: 7 March 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Childhood attention-deficit/hyperactivity dis- families with two different control groups. We controlled
order (ADHD) has been associated with reduced health for carers’ ADHD symptoms, their employment and rela-
and well-being of patients and their families. The authors tionship status and siblings’ ADHD symptoms. ADHD was
undertook a large UK survey-based observational study of associated with a significant deficit in the patient’s HRQoL
the burden associated with childhood ADHD. The impact (with a CHU-9D score of around 6 % lower). Children with
of ADHD on both the patient (N 476) and their siblings ADHD also have less sleep and were less happy with their
=
(N 337) on health-related quality of life (HRQoL) and family and their lives overall. No consistent decrement to
=
happiness was quantified using multiple standard meas- the HRQoL of the siblings was identified across the mod-
ures [e.g. child health utility-9D (CHU-9D), EuroQol- els, except that related to their own conduct problems.
5D-Youth]. In the analysis, careful statistical adjustments The siblings do, however, report lower happiness with life
were made to ensure a like-for-like comparison of ADHD overall and with their family, even when controlling for the
siblings own ADHD symptoms. We also find evidence of
elevated bullying between siblings in families with a child
with ADHD. Overall, the current results suggest that the
Electronic supplementary material The online version of this
article (doi:10.1007/s00787-016-0841-6) contains supplementary reduction in quality of life caused by ADHD is experienced
material, which is available to authorized users. both by the child with ADHD and their siblings.
* Katie Biggs
Keywords ADHD · Children · Siblings · Burden · Well-
[email protected]
being · CHU-9D · EQ-5D-Y · Life satisfaction · Sleep ·
1 School of Health and Related Research (ScHARR), Regent Health-related quality of life · Utility · Impact of ADHD on
Court, 30 Regent Street, Sheffield S1 4DA, UK
family outcomes
2 North East London Foundation Trust and University College
of London, London, UK
3 The Department of Psychiatry, The University of Dundee, Introduction
Dundee, UK
4 Division of Psychiatry & Applied Psychology, School Attention deficit-hyperactivity disorder (ADHD) is a com-
of Medicine, University of Nottingham, Nottingham, UK
mon childhood onset neurodevelopmental disorder, charac-
5 Medway NHS Foundation Trust, Kent, UK terized by developmentally inappropriate levels of inatten-
6 Sheffield Children’s NHS Foundation Trust, Sheffield, UK tion and/or hyperactivity and impulsivity that can continue
7 Global HEOR and Epidemiology, Shire, 725 Chesterbrook throughout life. A recent systematic review of prevalence
Boulevard, Wayne PA 19087, USA rates, using the DSM-IV diagnostic criteria, gave estimates
8 Lincolnshire Partnership NHS Foundation Trust, between 5.9 and 7.1 % of children worldwide; with males
Lincolnshire, UK more likely than females to meet the criteria for an overall
9 Department of Psychology, University of Southampton, diagnosis of ADHD and for each of the three DSM-IV sub-
Southampton, UK types [1]. Estimates of prevalence in the UK have tended to
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be slightly lower [2]. Children with ADHD face increased subjective well-being, such as satisfaction with life overall
difficulties in school having a higher risk of school expul- [25], few studies have examined this in relation to ADHD.
sion or drop out [3] and academic underperformance [4]. The aim of this study was to examine the impact of ADHD
Socially they may struggle with both peer [5] and family on health (particularly as measured by utility) and well-being
relationships [6], and be more at risk of both bullying and outcomes for patients themselves and their siblings through
being bullied [7]. Children with ADHD may develop signif- comparison with carefully matched control groups. In address-
icant conduct problems and antisocial behaviours (such as ing this we give careful attention to the fact that ADHD tends
fighting, early substance experimentation and adverse driv- to cluster in families [26, 27] and explore including controls
ing outcomes) and increased risk of developing oppositional for both parental and sibling’s own ADHD symptoms.
defiant disorder (ODD) and conduct disorder (CD) [8].
ADHD may affect a child’s emotional well-being in several
ways, including feelings of anxiety [9], lower self-esteem Methods
[10] poorer psychosocial health [11], and poorer over-
all quality of life [12–14]. Given the breadth of impact of The study obtained ethical approval from the Sheffield
ADHD, there are likely to be important implications for the Ethics Research Committee, research governance was
well-being of those with whom they spend time, particularly approved in each research site, and written consent was
their siblings. Having a sibling with ADHD has been found obtained from all participants.
to impact upon children’s well-being and the quality of their
family life [15]. A qualitative study by Kendall [16] found Study participants
that the impact upon siblings focused on feelings of victimi-
zation (from aggressive and annoying acts by their sibling), A large cross-section, observational survey was conducted
caretaking (expectations of parents that they befriend and across 15 ADHD centres/clinics in England and Scotland
protect their siblings), and feelings of anxiety and sorrow. (Coventry, Derby, Dundee, Durham, Leicester, Lincoln,
Siblings describe the constant disruption to family life as Medway, Newcastle, Tyne area, North Essex, Nottingham,
“chaotic, conflictual and exhausting” [16, p. 7]. Rotherham, Sheffield, Southampton, South Staffordshire)
For policy makers there is increasing interest in knowing from December 2010 to September 2012. Families were
whether these health and quality of life burdens can be iden- invited to participate in the study if they had a child (or
tified using preference based measures of health-related children), aged 6–18, with a current diagnosis of ADHD
quality of life (HRQoL), like the EuroQol-5D (EQ-5D) [17, and attending one of the ADHD clinics. The children had
18]. These provide information that can be used to examine all received a clinical diagnosis of ADHD although cen-
the cost-effectiveness of new interventions in terms of cost tres/clinics varied in their diagnostic protocols and instru-
per quality adjusted life year (QALY) by agencies such as ments used. This sample was representative of a typical UK
NICE in the UK [19] and related organizations in Australia, ADHD-clinic population receiving treatment. The sampling
Canada, Netherlands and others [20]. Instruments which frame covered a wide geographical area and included both
provide the ‘Q’, or utility,1 part of the QALY allow health specialist mental health services (CAMHS) and specialist
states to be valued on a scale where 0 is equivalent to being paediatric clinics. Data relating to parents or carers and sib-
dead and 1 is equivalent to full health. There is only mini- lings living with the child with ADHD were also collected.
mal evidence within the existing literature on the utility val- Children with a formal diagnosis of CD were excluded
ues associated with ADHD-related health states [21–24]. to maintain a tight focus within the study on ADHD and
These existing studies used either utility instruments com- remove its confounding effect on the impact of ADHD. The
pleted by parents as a proxy or values from parent or adult combination of ADHD and CD may be aetiologically dis-
preferences towards ADHD-related vignettes. There are no tinct from ADHD alone [28].
utility values available that are based on children assessing There were two different control groups. The first con-
themselves directly. Furthermore, despite the growing inter- trol group was taken from wave 1 of the Youth Panel
est in the impact of health conditions on measures of (2009–2010) of ‘Understanding Society: The UK’s House-
hold Longitudinal Survey’ (USoc), a multi-topic household
survey in the UK, conducted by the Institute for Social and
Economic Research (ISER) [29]. This offered a large sam-
1 The term ‘utility’ in this context is used in a very general sense as ple of 10–15 year olds, although only some of the instru-
a reflection of value or how ‘good’ a state would be to live in. It is ments used in the current study are available in this dataset.
drawn either from direct judgement the individual (or their proxy)
A second control group of families was recruited from the
makes about their own quality of life, or through taking the average
judgement from a sample of individuals about health states described
to them.
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South Yorkshire Cohort (SYC)2 [30], which enlisted 18,000 EuroQol‑5D‑Youth (EQ‑5D‑Y)
patients via GP practices across South Yorkshire. A sample
of families from this cohort were sent a request to partici- The EQ-5D-Y [33] is an age-appropriate generic HRQoL
pate in our study and those who responded positively were instrument, derived from adapting the adult EQ-5D instru-
sent the full set of questionnaires. Families with a child ment. The EQ-5D-Y instrument comprises five questions
with a diagnosis of ADHD were excluded. These families dealing with various aspects of physical and emotional
completed the same survey instruments as the ADHD-fam- health (walking about, washing/dressing, usual activi-
ily group. ties, pain/discomfort, worried/sad/unhappy), for which the
Five hundred and forty-nine families with a child with response to each is one of three possible degrees of impair-
ADHD consented to the study. Of these no information ment. There is no recommended value set to derive a util-
was collected on 4 families, and only medical information ity score from the EQ-5D-Y profile. The EQ-VAS, usually
on the children with ADHD was collected for 90 families. asked alongside the EQ-5D, is a visual analogue scale for
Questionnaire data was collected on 455 ADHD-group recording an individual’s rating for their current health.
families. Of these 394 had 1 child aged 6–18 diagnosed This is anchored at the bottom at 0 (the worst health you
with ADHD, 51 had 2 children diagnosed with ADHD, 8 can imagine) and at the top at 100 (the best health you can
had 3, 1 had 4 and 1 had 5. Sufficient questionnaire data imagine).
was collected on 476 of these 529 children (90 %) for them
to be included in the analysis. Of the 455 ADHD-group Life satisfaction
families there were a total of 392 eligible siblings (aged
6–18, living at home, without a diagnosis of ADHD) of Single questions were asked about how happy children
which sufficient questionnaire data was collected on 337 were with their family and with their life overall to reflect
(86 %). Data on 123 control families were collected from the child’s view of overall well-being and family life. These
the SYC. Of these families 61 had 1 child, 51 had 2 chil- were taken from Understanding Society3 where they had
dren, 10 had 3 children and 1 had 4 children. Some ques- been piloted successfully with children. Each question uses
tionnaire data was collected on 196 of these 197 eligible a 1–7 response scale based on ‘smiley’ faces.
children. Only those children with siblings in the eligible
age group were compared to the siblings in the ADHD fam- Bullying
ily group (N 136).
=
Four questions were asked about how often brothers and
Instruments sisters perform acts of bullying at home (hit, kick or push
you; take your belongings; call you nasty names; and make
Child health utility‑9D (CHU‑9D) fun of you), with the response options of never; not much
(1–3 times in the last 6 months); quite a lot (more than 4
The CHU-9D [31] is a paediatric generic preference-based times in the last 6 months); and a lot (a few times every
measure of HRQoL for children aged 7–17. The CHU- week). The same questions were asked for how often the
9D has nine attributes: worried, sad, pain, tired, annoyed, child performs those acts towards their siblings. These
schoolwork/homework, sleep, daily routine and able to join questions were also taken from Understanding Society.
in activities, with five response levels for each. The CHU-
9D descriptive system was developed from qualitative Sleep
interviews with school age children.
A set of preference weights has been derived from Parents/carers reported the typical bed time and getting up
the application of the standard gamble method from 300 time of their children with ADHD, and all children from
members of the UK adult population [32]. This gives esti- the SYC-control group.
mates for the importance of a change in one item versus a
change in another item and versus extending years of life,
as perceived by adults. The tariff generates a score for each
CHU-9D health state on a scale on which 0 is equivalent to
being dead and 1 represents full health. 3 “The next few questions are about how you feel about your life.
The faces express different types of feelings. Below each face is
a number where ‘1’ is completely happy and ‘7’ is not at all happy.
Please tick the box which comes closest to how you feel about each
of the following things…your family/which best describes how you
feel about your life as a whole?” (understanding society, youth ques-
2 http://clahrc-sy.nihr.ac.uk/south-yorkshire-cohort.html. tionnaire).
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Adult ADHD is unavailable. Throughout the matching and the regression
adjustment we still relied on an assumption that there were
The Adult Self Report Scale (ASRS v1.1; [34]) was com- no important unobservable differences between the fami-
pleted by parents/carers. This is a six-item screener, lies with a child with ADHD and those without.
based on the DSM-IV TR criteria, with five responses for There were two independent variables of interest: having
each item. Where an individual has four or more posi- a diagnosis of ADHD and having a sibling with a diagnosis
tive responses this was taken as indicating possible adult of ADHD. The regressions controlled for a broad range of
ADHD. child and household characteristics (age, gender, the num-
ber of children in the household, education level of carers,
Strengths and Difficulties Questionnaire (SDQ) and employment and income deprivation within the local
area5 and an adult ADHD screen). We ran the models with
Parents completed the SDQ [35], a behavioural screening and without controlling for the primary carers ADHD
questionnaire for 3–16-year olds consisting of five sub- screen to see how this affected results. We also ran the
scales each with five items. A score of 0–10 is given for models with and without parental relationship and employ-
each sub-scale (emotional symptoms, conduct problems, ment status. Since these could have been caused, in part, by
hyperactivity, peer problems, and prosocial).4 Children can living with a child with ADHD [39] controlling for these
be classified into ‘unlikely’, ‘possible’ and ‘probable’ for family level factors may produce an underestimate of the
each sub-scale, although such classification would usually full impact of childhood ADHD. For siblings we also
be based on completion by teachers and children in addi- included their own ADHD symptoms through including the
tion to parents. The SDQ does not provide a full evaluation hyperactivity sub-score and the conduct problems sub-
or diagnosis of ADHD status, however, it has been found to score of the SDQ as an additional control. Siblings without
be a good screening measure in a UK community setting a diagnosis of ADHD may still experience some ADHD
[36]. symptoms hence any deficit in health or well-being could
have arisen due to their own ADHD symptoms rather than
Statistical methods as a consequence of living with a sibling with ADHD. We
show our regressions with and without the inclusion of the
Since there were considerable difference in the background sibling SDQ sub-scores as it is also possible that rather
characteristics of the ADHD-family group and the control than being caused by the siblings own-ADHD this behav-
groups (see Table 1) we took measures to ensure our com- iour could be causally related to the presence of a sibling
parisons were carefully controlled. First we used a process with ADHD, for example, through copying older siblings,
called coarsened exact matching (CEM) [37]. Children or reduced parenting time for the non-ADHD siblings, or
were allocated to a subgroup based on their gender, age arise from the general level of family disruption.
(3 groups), and the highest education attainment of their Each child outcome measure (CHU-9D, EQ-5D-Y pro-
primary carer (2 groups), all of which are characteristics file, happiness with life and family scores, hours of sleep,
which are unlikely to have been caused by the child having sibling bullying) is treated as a dependent variable and
ADHD. Children were only included in the analysis where modeled as a function of child and family characteristics.
a good match could be found for them. The observations We adopt a model that is suitable to each outcome meas-
were then assigned a weight in proportion to the number of ure in question, with consideration given to the nature and
ADHD-family group and control group observations within distribution of the outcome measure. Life and family hap-
each subgroup. This matching process created a better piness, and hours of sleep are treated as cardinal and ana-
covariate balance between the ADHD-family group and the lysed using linear models (OLS). Responses to bullying
control groups. Any remaining imbalance in observed vari- questions and EQ-5D-Y are analysed using ordered logit
ables was further controlled for using standard weighted models. The bounded nature of the CHU-9D utility instru-
regression models. The more accurate the match, the less ments which cannot go above one at full health and the
emphasis is put on getting the assumptions implicit in positive skew of the data with many values at full health
the regression models correct (hence it is less sensitive make it suitable for analysis with the tobit model [40].
to choices about whether to include interaction or higher Robust standard errors are used, clustered at the household
order terms, for example) [38]. Due to discarding data that
does not have a good match the model does not extrapolate
counterfactual outcomes to areas where good information
5 The local level income and employment deprivation was derived
from the 2010 proportion of the population income deprived accord-
4 Up to two missing values were tolerated and the score calculated ing to benefit claims at the lower level super outcome area (LSOA) in
pro-rata. England and at the slightly smaller data zone level for Scotland.
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up 8)
ADHD family grosiblings (10–N15 years) ( 17= 12.4 (SD 1.7)Range 10–15 45.2 7.5 (SD 7.7) 5.8 (SD 7.1) 32.6 10.5 16.7 72.5 59.1 n38.4 % ( 172)= n31.8 % ( 88)=
7)
USoc (those with appropriately aged Nsiblings) ( 3,47= 12.8 (SD 1.7)Range 10–15 49.8 Not available Not available 33.0 10.0 17.7 76.1 38.0 Not available Not available
6)
HD, siblings and their controls, prior to matching Siblings and their controls ADHD-family group SYC (those with ADHD-family group Nsiblings ( 337)appropriate aged diagnosed children =Nsiblings) ( 13(10–15 years only) =N( 307)= 12.4 (SD 1.6)11.8 (SD 3.6)11.9 (SD 3.3)Range 10–15Range 5–18Range 6–18 87.65049.6 21.1 (SD 5.5)7.8 (SD 7.8)4.1 (SD 4.7) 19.7 (SD 6.4)7.0 (SD 7.7)2.7 (SD 4.4) 27.729.180.1 10.110.58.2 16.817.111.4 65.472.491.2 59.955.311.8 nnn35.7 % ( 297)37.3 % ( 327)6.1 % ( 132)=== nnn31.2 % ( 128)32.9 % ( 167)7.2 % ( 97)=== are employment deprived
Table 1 Background characteristics and descriptive data on the children with AD Children with ADHD and their controls NUSoc (10–15 years ADHD-family group SYC ( 196)=Nonly) ( 4,234)diagnosed children =N( 476)= Age (mean)11.8 (SD 2.9)11.8 (SD 2.9)12.5 (SD 1.7)Range 6–18Range 6–18Range 10–15 Male (%)83.253.649.7 Inattentive ADHD 21.2 (SD 5.4)4.4 (SD 4.8)Not availablerating score (mean) Hyperactive ADHD 20.0 (SD 6.3)2.9 (SD 4.4)Not availablerating score (mean) 28.274.033.0Primary carer has further or higher education (%) Employment depri-10.78.210.6avation (%)b 17.111.417.5Income deprivation(%) 67.487.872.9Primary carer cohab-iting with partner (%) Primary carer not 58.211.735.7employed (%) Primary carer posi-nnNot available36.4 % ( 459)6.4 % ( 187)==tive ADHD screen nnNot available36.7 % ( 210)9.0 % ( 134)Secondary carer ==positive ADHD screen a Proportion of working age population in LSOA or data zone (for Scotland) that b Proportion of population in LSOA or data zone that are income deprived
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level to account for households with more than one child
with ADHD or more than one eligible sibling. 2)
Results ADHD family group siblings (10–15 years) N( 178)= 82.70 (SD 15.6Range 29–100n 158=0.85 (SD 0.10) Range 0.35–1n 148=5.32 (SD 1.45)n 156=5.78 (SD 1.29)n 157=NA
Study population 7)
TbTthohheyeeirs .bAs aTiDbchlkHieng DgrSosY-u faaCnnmd-dc i cltoyhhn eat grrroareocls tupgeperroc istcutioivpcne sc t caoooinnfn tsatt rhioanel e scgd ahr erpielard itsmreherona rwp ywen ric ctiahenr n eTAtraasDgb weHle iD ot1hf., USoc (those with appropriately aged Nsiblings) ( 3,47= NA NA 6.38 (SD 1.03)n 3,457=6.38 (SD 1.03)n 3,457=NA
geablhnTmtmarm reiehorgaoeptelelTwptahroyd c etglhls e meoh iirebsegtryt oo siicanehevmue eatt dtectwp din hid, nth lu nhAeetiaiciconfo eenrlUDfa u nt geatic tdSH obm ir roeSpce eoeDtnpnor Y achfo ohcc t-eoCeaccesroa ut ctAoesbv-aatnr sncaian iDetsdiotnnriecn rn iHne onpeaamigwtln n.trDr oeueaiodr Tsftn-ewnnl i fhtl tatedaahe,whael s mtie tesn iwgichnoe otdiahmeh nhl r rayt di soAeteanp dihh rdgtf Dpric efw-rpctoleh coHhih eprAeioaueernaDssnn penDr g dtr -wa cc rHife mcepanoalegstserDm lieor toe sgeergwr-ssichiesrfl r seymeals towtidsir mlikugoe sicuetekp r nisorrololel ,ee y yew flut mp y s haptachsa gone o.o irtr gdcsovdthcoito ha eoacuato rhblvunrpisidkeees---,. Siblings and their controls ADHD-family group SYC (those with Nsiblings ( 337)appropriate aged =Nsiblings) ( 136)= 87.74 (SD 13.65) 83.66 (SD 16.60)Range 35–100Range 5–100nn 133 291==0.88 (SD 0.10) 0.86 (SD 0.10) Range 0.39–1Range 0.35–1nn 131 268==5.40 (SD 1.51)5.86 (SD 1.13)nn 290 133==5.76 (SD 1.34)6.41 (SD 0.91)nn 291 133==NANA
p
aAicoUnthleDSgdisl soHd.,ac rF D techson aherf.m i altFmmdhporelaiere lt Syn ct hohYgaefiCnnr o1dUg uc8 Snpor4eoo m sccnSuph eYclai tloreoCdimdfsr e opatinhnnna e rd at idh nAsr4iodsoD3n p1r9 Hpeo2 siDAf nu S-jgDlufYt aeHs7Cmdt3 D tii0chln-y hfe oa di glmf1rd ro0triohp–leuyepn1p i,5g n4-llrg,yeeo2 aaeu33vvap74r-- or to matching ADHD-family groudiagnosed chil-dren (10–15 years) N( 307)= 79.75 (SD 19.10)Range 12–100n 291=0.82 (SD 0.11) Range 0.45–1n 271=5.34 (SD 1.50)n 294=5.89 (SD 1.18)n 295=8.50 (SD 1.58)Range 3–12n 209=
ing a sample of 3,504 and 307, respectively. For the SYC s, pri
c3iUtnho9Sgem A o3pacD, a 4csHr7oia7sDmmo c nppfh aalieomrl idfs isrolsieyzninb e g tl fhirronoeofgu m msp2 a9ttchht8hcee ih laUidmnnrSgdaeot n rcc1 eha 3sainun3ndlg,dt e 3 r1rd eef sorsiponufe m ltdcther tetidohv p1eeip7nl ySi8 n.Yd gcrFC ho2oip 7lrldpe 1riat nheovngef- and their control USoc(10–15 years) N( 4,234)= NA NA 5.88 (SD 1.18) n 4,199=6.37 (SD 1.05)n 4,212=NA
s
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lsra−roeecnw0mmrde. e5aaeerii7n nnm feefwopoddrlar o sststy hhitammreedo eidnCElneagHQtdrl .y U- st VtIoa-nsA9 ticutDgShlsnu.e iad (Wfincmincodhoagl uen−d nmtep.5 l rn .ti8I(hm nc0e3oca6 )llrp uuyftrdmho imiercnn yatag hr2r feey)etr hl Etclehc Qa seorl-ssheiVeegra cAhbdAotiSeltnDyafi)d t Hciab(oirtuDtoynst Table 2 Descriptive EQ-VAS (0–100) CHU-9 (0–1) Happiness with life (1–7) Happiness with fam-ily (1–7) Hours of sleep per day
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Table 3 Marginal effects on health and well-being outcomes
Variables SYC control
(1) Standard controls (2) As (1) plus primary carer (3) As (2) plus relationship and (4) As (3) plus secondary carer
ADHD screen job status of primary carer ADHD screen
EQ-VAS
ADHD 6.930*** 6.301** 5.806** 3.883
− − − −
PC possible ADHD 0.349 0.318 2.882
−
PC partner at home 2.390 18.795
−
PC no job 4.588** 1.831
− −
SC possible ADHD 6.082*
−
N 602 578 573 315
Adj R2 0.0591 0.0601 0.0692 0.0817
CHU-9
ADHD 0.063*** 0.061*** 0.057*** 0.075***
− − − −
PC ADHD score 0.006 0.006 0.013
− − −
PC partner at home 0.013 0.032
− −
PC no job 0.024 0.004
− −
SC possible ADHD 0.021
−
N 569 546 541 298
Hours of sleep
ADHD 0.867*** 0.740*** 0.647*** 0.542**
− − − −
PC possible ADHD 0.445** 0.361** 0.469*
− − −
PC partner at home 0.440** 1.210**
PC no job 0.086 0.146
− −
SC possible ADHD 0.035
−
N 436 416 413 251
Adj R2 0.202 0.207 0.221 0.216
Children with ADHD and matched controls from the SYC-control
carer ADHD screen (column 4) increased the magnitude (column 3) reduces this further to about 39 min differ-
of effect CHU-9D but resulted in a loss of significance ence. Where the primary carer is cohabiting this results in
for the EQ-VAS, and gave a considerably smaller sample the child getting 26 min more sleep per night compared
size. with having a primary carer who is not in a co-habiting
We also considered predictors of individual item lev- relationship. Those children with primary carers who
els within the utility instruments, the CHU-9D and the screen positive for possible ADHD have about 22–27 min
EQ-5D-Y, using ordered logit models (see on-line supple- less sleep per night.
ment Table S.2 and S.3). This found that the children with Comparisons to the SYC-control and to the USoc con-
ADHD reported significantly poorer outcomes in problems trol (see Table 4) both find children with ADHD are less
with school work, feeling annoyed, having had problems happy with their life overall; at least 0.5 on a 1–7 scale and
sleeping the previous night, daily routine, and joining in robust to the inclusion of additional controls. Satisfaction
with activities, but not in feeling sad, feeling worried, being with family is also significantly lower in both comparisons
in pain or feeling tired. For the EQ-5D-Y items the children with the magnitude slightly greater in the USoc compari-
with ADHD report more problems with washing/dressing, son ( 0.344 for the SYC and 0.488 in the USoc, for the
− −
usual activities, pain/discomfort and feeling worried/sad/ model with standard controls). Once primary carer employ-
unhappy, but no differences in mobility. ment status is included in the model only the USoc com-
Children with ADHD report 52 min less sleep per parison remains significant.
night than their matched SYC-controls in the model with There was a difference in reports of sibling bullying
standard controls. Including the primary carer ADHD between the children with ADHD and children from both
screen as a control reduces this to 44 min difference, and control groups. As the data is ordinal it was analysed using
further including employment, and relationship status order logit models which estimates the differences in the
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Eur Child Adolesc Psychiatry
Table 4 Marginal effects on subjective well-being outcomes SYC control groupUSoc control group (1) Standard con-(2) As (1) plus primary carer (3) As (2) plus relationship and (4) As (3) plus ADHD screen of (5) Standard controls(6) As (5) plus relationship and trolsADHD screenjob status of primary carersecondary carerjob status of main carer Happy with family ADHD0.344**0.294*0.2520.401***0.488***0.505***−−−−−− PC poss ADHD0.0900.0980.145−−− PC cohabiting0.1570.0110.197***−− PC no job0.273*0.2000.091*−− SC poss ADHD0.016 6065825773173,7803,433N 20.05300.05240.06220.08570.04380.0533R Adj Happy with life ADHD0.575***0.505**0.525***0.650**0.546***0.545***−−−−−− PC poss ADHD0.2010.2130.488**−−− PC cohabiting0.2520.3680.205***−− PC no job0.1400.0330.045−− SC poss ADHD0.058−N6055815763163,7703,423 20.04500.04000.04420.06570.02180.0282R Adj Children with ADHD and matched controls from the USoc The CHU-9D models use a weighted tobit and show average marginal effects. The other models use weighted OLS. Controls include: child’s age, gender, number of children in the household, % employment deprived in the area and % income deprived, primary carer having a further or higher education qualification, primary carer having A level or equivalent qualification, primary p p pcarer having some form of formal qualification below A level. A constant is also included. *** < 0.01, ** < 0.05, * < 0.1, these are based on robust standard errors which are clustered at the household level. Full details of these regressions are available from the authors
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Eur Child Adolesc Psychiatry
d probability of responding to each of the four response
n
a
nship carer icnagtesg orartihese.r Tthabanle e5a cshh omwasr ga insuaml emffaercyt offo rs iegancifih craenstp fionnsde-
models) plus relatioof primary csAiabDtelHignoDgr )yb .a uTcllrhyoeisn sUg a Slrloe cpfo ocurotre mdbp u(alblryiositnohgn tqofiu naednsstd ios ibngysn. icfiHhcoialwndtrelevyne rm,w otihtrhee
dered logit y (6) As (5) job status √ √ √ √ √ √ √ √ SibneYclioCdn egcniocnmeg spo aafrn icdsoh, niiln d firoennndl ysw tohintehl y mA osDidgHenlDi fiw citatahnk tislntyag n mdthoaerrdeir cfsorienbqtlruionelgnsst,
m or ciet ols calling them names.
aken fro nding So ard contr Siblings
YC and USoc (t Understa y carer (5) Stand √ √ √ √ √ √ √ √ TAi(nCaDgbH HlUeaD -6s9. iDTbslh,hi oEnew gQm s-wa VttiAchthehSe )idA,m ecDpxoaHcmceDtpp ato ruffio psrlooi vntnhis ne tfi ghCn eHwd HUinthRo-9 QiaDm o spLfioab rclm ittn heogeaf s mwhuaroievtshs-t
m S dar complete model. The siblings do, however, report substan-
ontrols fro with secon ta(ibnaedltl wy0 e.l5eo9nw7 e0 ir.n4 h4ta3hp eap ndindife fs0es.r7 ew6n2ti t)hm o onlid faee l s1o)– v7aen rsadcl law l(eibt,h ea tlwtthheeoeirun g fha0m .3thi2liy1s
ched c As (3) HD iTsh neo ts iabllwinagyss seixgpnriefiscsa ngt riena ttehre dsmissaallteisrf aScYtiCon c owmitpha ritshoenir.
bullying; children with ADHD versus mat arer (3) As (2) with relationship and (4) job status of primary carerAD ×× ×× ×× ×× ××√√ ×× ×× ftcssotprshhaetuuhvlalemeebrieanOt lp -rticdidUsreoawlhriaycdlnesSilrno l i ndtod notrih osecrcgfwca es o oontt,niicnh hnstf o ahd eettwtamh r u hAdo SieEcastple DhDtsiQtacfi m rhQtAHapc-hiViirirs DlDleetooAacd bHo ronrirSan elefn meDn dc nwdm0adlu .auun.ew,csg5c d d,twnit5e is toi4edhahtpthne s u re(Afe o dncoi aibnDe odrC cl leedtHotuHshnomeinmDebUt fiti tilrnfi hicr-iot( 9neieehf2ltDdg lmo )aid s orn.pswe) bfgp.,eWfi y e xiaP0cf n ahrosi.epeet4meerr as e4 nhosnrpo 8 ae ftewlwo hp negtnfasnt,hia o t lt atuhy mcrSietvs o DaliottefminhchofrlQhgyeeee-r
of y c additional point on the 0–10 scale lowers the CHU-9 by
ncy mar 0.010 and the VAS by 1.620. Both control groups find a
ue pri strong negative impact of own conduct problems on hap-
nces in freq As (1) with HD control pncoiengmeasptsiav rewis ioitnmh palilafsceot. fiOonfnd lsyh yatp hseeirg aUncitSfiivocicaty nc tlosymy mnpeapgrtaiostmoivnse . arleTslhoae ti fionUndsShso ipca
e on differe hire Cohort (2) ntrolsAD × × × × ×√ × × birleeylt.aw tHieooenwns heoviwpesrn,. tchoen ddiurecct tiporno bolfe mcasu saanldit yh aisp pnionte cslse awr iitnh tfhaemse-
nificanc h Yorks dard co forT tahbel sei b7l isnhgosw. Ts hae sSuYmCm caormy poafr itshoen b fiunlldysi nsgib lcionmgsp oafr icsohnils-
e sig Sout (1)Stan × × × × ×√ √ × dren with ADHD report increased frequency of their sib-
h lings calling them names and taking their belongings. How-
Table 5 Summary of t Hit, kick or push you Take your belongings Call you nasty names Make fun of you Hit, kick or push them Take their belongings Call them nasty names Make fun of them eobtUahvlufSle el cmqlory,ouc sint enehctsglreotvo.ism eoleTssn p. ehs acf,Nerf ab ielossoclo iittnbsnshigl giat finnortngeih afi dsenn csioadr a s l nlsfibotgro ronn odmgritifiehfe fpcreteh oarsr/enreist gtni slsnciyitbniee filc mricrisnseao g annrfssetao.e suwdbtnyu idt flhlr nye iatniqhnm uegpee hfnsauy.cc lsylrTi o cshoseaesftl
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Eur Child Adolesc Psychiatry
y
arb
mo
prind j
nd p a
us anshi
(7) As (6) plcarer relatioct status NA NA 0.595***− 0.013−0.133***−0.153*** 0.176*** 2,993 0.125 0.587***− 0.070***−
u
d
Q on
SDnd c
ntrol (6) As (5) plus hyperactivity aproblems NA NA 0.554***− 0.021**−0.131***− 3,312 0.119 0.562***− 0.070***−
o
c
y
ciet ols
So ntr
Understanding (5) Standard co NA NA 0.567***− 3,344 0.056 0.521***−
D
c-H
seD
s A
3) plucarer *** * ** *** *** *** **
(4) As (ondary screen 2.820−1.048 0.313−2.172−NA 2.101−5.277−221 0.154 0.035−0.004−0.000 0.018−NA 0.006 0.004−210 0.762−0.091 0.010−0.164− 0.254 0.280 219 0.597−0.061 0.022
p
ary nshi
mo
2) plus prib and relati ** *** *
(3) As (carer jostatus 0.828−1.296 0.491−1.783−3.439−3.591− 362 0.108 0.000 0.012 0.004−0.011−0.011−0.024− 340 0.455−0.290 0.017−0.062−0.016−0.238− 361 0.321−0.130 0.021
mes ol pri-HD
well-being outco hire Cohort contr (2) As (1) plus mary carer ADscreen 0.928−1.511 0.620−1.620**− 365 0.0981 0.002−0.010 0.005*−0.010***− 343 0.499*−0.247 0.038−0.039− 364 0.338−0.122 0.006
d ks
n health an South Yor (1)Standard controls 1.740− 388 0.0416 0.008− 366 0.443− 387 0.461−
o
s
Table 6 Marginal effect Variables EQ-VAS Sibling with ADHD PC ADHD screen Own SDQ-hyperactive Own SDQ-conduct PC partner at home PC no job SC ADHD screen N 2R Adjusted CHU-9D Sibling with ADHD PC ADHD screen Own SDQ-hyperactive Own SDQ-conduct PC partner at home PC no job SC ADHD screen N Happy with family Sibling with ADHD PC ADHD screen Own SDQ-hyperactive Own SDQ-conduct PC partner at home PC no job SC ADHD screen N 2R Adjusted Happy with life Sibling with ADHD PC ADHD screen Own SDQ-hyperactive
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