This study aims to understand whether psychotropic prescribing practices for people with intellectual disabilities are in keeping with best practice guidelines.
This service evaluation project was a retrospective analysis of routinely collected data from the care records of all 36 people with intellectual disability discharged from an intellectual disability assessment and treatment unit during the first five years of the Stop Over medicating People with Intellectual Disabilities and/or autistic people (STOMP) initiative. Data were gathered at four time points (pre-admission, discharge, 6- and 12-month follow-up) before being analysed to understand whether psychotropic prescribing differed among people with different clinical characteristics/traits/diagnoses. Changes over time were also explored to ascertain whether and how prescribing altered from admission to discharge, and over the subsequent year of community living.
Most people with intellectual disabilities left the assessment and treatment unit on fewer regular psychotropic medications and at lower doses than at admission. These optimised regimes were still apparent 12 months post-discharge, suggesting effective discharge planning and community care packages. Inpatients with severe intellectual disabilities generally received more anxiolytics and hypnotics, at higher doses. Autistic people tended to receive more psychotropics in total and at higher cumulative doses, a pattern that persisted post discharge. A third of the sample were admitted on regular anti-psychotic medications despite having no corresponding psychotic diagnosis, a proportion that remained relatively stable through discharge and into the community.
This study highlights subsets of the intellectual disability population at particular risk of receiving high doses of psychotropics and a feasible template for providers intending to undertake STOMP-focused evaluations.
Painter, J., Chio, W., Black, L. and Newman, D. (2022), "A STOMP-focused evaluation of prescribing practices in one assessment and treatment unit for people with intellectual disabilities", Tizard Learning Disability Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TLDR-04-2022-0008
Emerald Publishing Limited
Copyright © 2022, Jon Painter, Winola Chio, Liam Black and David Newman
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Mental health (MH) problems are more common in people with intellectual disabilities than the general population (Buckles et al., 2013; Cooper et al., 2007; Hemmings et al., 2013). Medication can form a helpful aspect of MH care [National Institute for Health and Care Excellence (NICE), 2015, p. 23] however, even when prescribed according to best practice, psychotropic medications carry significant risks including cardiovascular disease (Mwebe and Roberts, 2019) and metabolic abnormalities (Mazereel et al., 2020). Given the already elevated prevalence of these physical comorbidities in people with intellectual disabilities (Cooper et al., 2015), this is cause for concern, particularly because these individuals are prescribed psychotropics more frequently, at higher doses, and for longer than the norm (Bowring et al., 2017; Glover and Williams, 2015; McMahon et al., 2020). Finally, in 2015, in England alone, 30,000–35,000 people with intellectual disabilities received these drugs each day, despite having no psychiatric diagnoses to warrant the prescription (Glover and Williams, 2015). There are a limited number of legitimate, short-term scenarios (NICE, 2015; de Kuijper and Lenderink, 2021); however, as people with intellectual disabilities are also more likely to be prescribed antipsychotics if they exhibit behaviours of concern (Bowring et al., 2017), it is likely that this is an inappropriate/off-licence form of behaviour management (Royal College of Psychiatrists, 2021; de Kuijper and Lenderink, 2021; Sheehan et al., 2015).
In 2015, responding to this situation, and the Winterbourne View scandal (Department of Health, 2012a, 2012b), NHS England launched a national campaign. Its aim was to raise awareness of the issue, promote non-pharmacological interventions and increase the number of people with intellectual disabilities engaged in regular medication reviews, to “Stop Over-Medicating People with a learning disability, autism or both with psychotropic medicines” (STOMP), ultimately improving the quality of life (NHS England, 2022). By 2019, Branford et al. argued national awareness of these issues had improved, however, overall patterns of prescribing remained unclear. More latterly, Rauf et al. (2021) indicated that the awareness-raising has led to tangible reductions in over-medicating people with intellectual disabilities. Reductions in prescribing are also noted by Mehta and Glover (2019) though with more caution about causality. However, identification of local exemplars (Branford et al., 2019), by definition, suggests variation and the continued existence of areas of poor prescribing practice (Kiernan et al.,1995). These national uncertainties necessitate ongoing local scrutiny of psychotropic medication use in people with intellectual disabilities to ensure quality of life is optimised (Bowring et al., 2017; da Costa et al., 2021).
Therefore, the aim of this study was to understand whether local prescribing adhered to best practice guidelines by examining:
the needs, characteristics, traits and diagnoses of the people with intellectual disabilities admitted to one assessment and treatment unit (ATU) and prescribed psychotropics;
how psychotropic prescribing altered between admission, discharge and the following 12 months of community living; and
whether psychotropic prescribing differed among individuals with different clinical characteristics/traits/diagnoses.
Data were gathered from the care records of all people with intellectual disabilities discharged from an ATU (n = 36) during the first five years of STOMP. Twenty-one (58.3%) were male; 30 (83.3%) were white British and half were aged 18–30. Pre-admission accommodation was recorded for 31 (86.1%) of individuals with 13 (36.1%) admitted from their family home; 8 (22.2%) from supported living; 6 (16.7%) from residential accommodation; 2 (5.6%) from other hospitals; 1 (2.8%) from their own house and 1 (2.8%) from a friend’s. The mean number of significant life events pre-admission as per the Mini PAS-ADD (Prosser et al., 1998) was 1.22 (SD 1.10); the most common being; contact with the police (n = 9), serious illness/injury (n = 7), bereavement (n = 7), moving residence (n = 5), and sexual abuse (n = 5). At admission, levels of intellectual disability (ID) were skewed towards mild/moderate levels and over 40% were autistic. Half had at least one psychiatric diagnosis of which psychosis was the most common (Table 1). Median length of stay was 147 days (range 19–754).
Pre-admission, social services funded the care packages of 18 individuals at a median annual rate of £33,101 (range £11,321–£177,280). At discharge, 29 individuals were funded at a median annual rate of £112,603 (range £10,579–£274,189), a level which remained relatively stable for 12 months.
With reference to the project’s goals, a bespoke data set was developed. At admission, this included demographic information, significant life events, diagnoses, community care packages and psychotropic medication. At discharge, this comprised diagnoses, care packages and psychotropics. At 6- and 12-months post-discharge, care packages and psychotropics were recorded. Staff gathered these data from the records of all ATU discharges during the first five years of STOMP (December 2015 to April 2020).
Following pseudonymisation and secure electronic transfer, data were exported into SPSS version-24 (IBM, 2016), during which, it was noted that one female had three admissions and three males each had two. The remaining 32 people with intellectual disabilities each had one admission. Consequently, to create a final data set of unique patient records (n = 36), only the earliest stays were retained. The impact of this data cleansing on participants’ characteristics (above) was negligible.
This project was registered by the relevant NHS Trust as service evaluation and ethically approved by Sheffield Hallam University (ID:ER25221337).
For each of the four timepoints, regularly prescribed psychotropics were grouped under four British National Formulary (BNF) [Joint Formulary Committee (JFC), 2021] categories. These were antipsychotics; mood stabilisers/anti-manics; anxiolytics and hypnotics; and antidepressants; plus, a final “other regular psychotropics” category (ultimately comprising procyclidine alone). For each category, the number of medications and total percentage of maximum BNF doses were calculated. Here, the method mirrored one typically used to define high-dose antipsychotic therapy (Royal College of psychiatrists, 2014), meaning figures over 100% were possible for individuals prescribed multiple medications from the same BNF group. These category totals were subsequently summed to create overall figures for regular psychotropics. Finally, although actual usage of Pro Re Nata (PRN) psychotropics was not captured, the number prescribed at each timepoint was recorded.
To identify statistically significant differences between timepoints, descriptive statistics for each of these variables were calculated (Table 2), together with repeated measures ANOVAs with Greenhouse–Geisser correction. Where apparent, post hoc analyses with Bonferroni adjustment revealed more about the differences.
To examine prescribing differences between different diagnostic categories, one-way ANOVAs with Tukey post hoc tests were performed. Statistically significant differences in inpatient prescribing were identified on the basis of ID level, autism and psychiatric diagnoses recorded at admission. In light of improved data quality, and the dynamic nature of MH, analyses of differences in community prescribing used level of ID, autism and psychiatric diagnoses recorded at discharge. Finally, because of particular concerns regarding over-prescribing of antipsychotics, a cross-check for diagnostic indications was undertaken.
Prescribing patterns over time
Table 2 shows prescribing patterns for the seven medication groupings, across timepoints. Four statistically significant changes were identified.
The mean number of regularly prescribed mood-stabilisers decreased during admission, then remained constant post discharge. A repeated measures ANOVA with Greenhouse–Geisser correction determined the differences were statistically significantly between timepoints [F(1.928, 57.841) = 4.163, p = 0.022]. However, post hoc analysis did not identify any significantly different pairwise comparisons.
The mean number of regularly prescribed anxiolytics and hypnotics decreased over time. With the 12-month post-discharge mean being less than half the pre-admission mean. The mean differed significantly between timepoints [F(2.304, 69.107) = 3.697, p = 0.025]. Post hoc analysis with Bonferroni adjustment revealed differences were only statistically significant between admission and 12 months [0.452(95% CI, 0.001 to 0.902), p = 0.049].
The mean total percentage of maximum BNF doses of “other” psychotropics (i.e. procyclidine) halved during hospitalisation, before increasing slightly post discharge. These means differed significantly between timepoints [F(1.037, 29.044) = 19.822, p = <0.001]. Post hoc analysis revealed the 12-month mean differed significantly from admission: T1[85.817(95% CI, 30.80 to 140.834), p = 0.001]; discharge: T2[86.966(95% CI, 32.550 to 141.382), p = 0.001]; and 6 months: T3[86.966 (95% CI, 31.907 to 142.025), p = 0.001], respectively.
Finally, the mean number of PRN medications rose between admission and discharge, fell to six months post-discharge, then stabilised. These means differed significantly between timepoints [F(2.565, 74.392) = 3.604, p = 0.022]. Post hoc analysis revealed that the decrease was only significant from discharge to six months [0.367(95% CI, 0.049 to 0.685), p = 0.017].
Prescribing by diagnosis
The total number of anxiolytics and hypnotics prescribed to inpatients differed significantly by level of ID, as determined by a one-way ANOVA [F(3, 30) = 4.984, p = 0.006]. Tukey post hoc testing revealed the number of these medications prescribed at admission was higher for people with severe ID (1.67 ± 0.516) than mild (0.69 ± 0.855, p = 0.049) or moderate (0.40 ± 0.516, p = 0.010). Similarly, the total percentage of maximum BNF doses of anxiolytics and hypnotics prescribed at admission also differed significantly [F(3, 30) = 4.756, p = 0.008]. Post hoc testing revealed means were significantly higher for people with severe ID (87.97%±36.64%) than moderate (7.79%±12.23, p = 0.016).
The total percentage of maximum BNF doses of all psychotropics regularly prescribed to people with/without an autism diagnosis at admission also differed significantly [F(1, 32) = 5.432, p = 0.026]. Here, the autism mean was 175.68% versus 98.25% for those without. Post hoc tests were not possible.
The total number of regularly prescribed psychotropics at discharge also differed significantly by autism diagnosis [F(1, 33) = 9.247, p = 0.005]. The autism mean was 3.07 versus 1.71 for those without.
As regards post-discharge prescribing by the specialist team, differences for autistic people continued. Firstly, the total percentage of maximum BNF doses of all regularly prescribed psychotropics at discharge differed significantly [F(1, 33) = 4.395, p = 0.044). The autism mean was 158.68% versus 91.57% for those without. Secondly, the total number of PRN psychotropics at six months also varied significantly [F(1, 32) = 4.163, p = 0.050]. Here, the autism mean was 0.64 versus 0.20 for those without. Finally, the total percentage of BNF maximum doses of all regularly prescribed psychotropics at 12 months was significantly different [F(1, 27) = 4.360, p = 0.046]. The mean autism percentage was 121.23% versus 52.78% for those without.
Table 3 shows antipsychotic prescribing with/without diagnostic indication.
At admission, 26 people with intellectual disabilities (72.25%) had no psychotic diagnoses. Nonetheless, 12 were prescribed antipsychotics. This represents almost half this subset and one-third of the study’s sample, a figure that remains reasonably constant across timepoints.
Considering MH more broadly, at admission, half the people with intellectual disabilities (n = 18) had no psychiatric diagnosis whatsoever but 11 (61.1%) were still prescribed antipsychotics. This equates to 30.55% of the sample. More positively, by discharge this had roughly halved (n = 6) before stabilising for the subsequent year.
Around half of individuals admitted on antipsychotics without diagnostic indication were autistic. This proportion was similar at discharge and 12 months but, at 6 months post-discharge it was notably higher.
Reassuringly, when considering change over time, most people with intellectual disabilities were discharged on fewer regular psychotropic medications and at lower doses than at admission. Arguably, this demonstrates the value of nurses, psychiatrists, psychologists, pharmacists, physician associates, speech and language therapists and occupational therapists working as a coordinated multi-disciplinary team to deliver positive behaviour support (PBS) interventions. Although we did not seek to definitively attribute reductions to PBS, in other studies (Gerrard et al., 2019) these interventions resulted in successful discontinuation of psychotropics in 60%–92% of participants. The government’s bed closure programme is undoubtedly laudable; however, progress has been slow and ATUs are likely to remain for some time (Devine, 2019; Painter et al., 2017). When these types of interventions are eventually routinely delivered in the community, it will be important to ensure this level of coordinated PBS is retained. It is also encouraging to see that, in general, medication reductions were maintained over the subsequent 12 months of community living. In conjunction with the increased number and costs of care packages instigated at discharge, this suggests collaborative discharge planning was effective. This is important as, community transitions that are effectively facilitated by professionals can positively impact people’s quality of life (Lennard et al., 2020).
Examining differences in prescribing patterns among sub-groups, inpatients with more severe ID tended to receive more anxiolytics and hypnotics, and at higher doses. Given that this group are less likely to be given a psychiatric diagnosis than the mild and moderate groups (Deutsch and Burket, 2021) this is somewhat counterintuitive; however, there are well-recognised complexities around diagnostic overshadowing that may be at play here. Additionally, autistic inpatients were prescribed more psychotropics in total and at higher cumulative doses. Given that no medications are marketed to “treat” autism (Murray et al., 2014) this again appears anomalous but, could, in part, be related to the higher rate of comorbid MH problems in autistic people (Mannion and Leader, 2013).
Post discharge, for these autistic individuals, the difference in the mean number of psychotropic medications disappeared by 12 months; however, they continued to receive higher cumulative doses than their neurotypical counterparts and (perhaps as a consequence) be prescribed more PRN psychotropics.
Finally, considering antipsychotic prescribing specifically, there was a slight increase across time which was not statistically significance but of course may still be clinically significant. Furthermore, mirroring other studies (Perry et al., 2018), a concerning proportion of people with intellectual disabilities were admitted on antipsychotics without diagnostic indication, thus being exposed to potentially serious and unnecessary side effects (Mwebe and Roberts, 2019). Post discharge, this picture improved for individuals with no psychiatric diagnoses but remained stubbornly constant for those with non-psychotic psychiatric diagnoses (a phenomena worthy of future investigation). Also of note was the six-month post-discharge spike in the proportion of non-psychotic individuals prescribed antipsychotics who were autistic. Individual reasons were not captured; however, this is clinically intuitive given autistic people struggle to adapt to change (Alhuzimi, 2021); often exhibit behaviours of concern when stressed (Bowring et al., 2019); and are typically prescribed antipsychotics ahead of PBS (Bowring et al., 2017). The service is, therefore, currently remodelling its community provision and seeking increased resources for enhanced post-discharge MH and PBS.
As ever, there are limitations to these findings. The convenience sample was relatively small, from a single ATU and with varying levels of data completeness/quality which limit the generalisability of findings. The dose calculation method did not capture nuanced prescribing decisions, e.g. medications given for multiple reasons or reasons other than their primary indication. Analyses were statistically robust, but some data (e.g. prescription duration, use of PRN psychotropics and individuals’ subjective experiences) were not captured. That said, all completed inpatient spells were included and findings chime with other studies.
STOMP has now been subsumed into the Department of Health’s (2012a, 2012b) Transforming Care Programme (Branford et al., 2019) with a range of deliverables. Unfortunately, progress with some aspects of this wider transformation initiative, e.g. bed closures have not matched the rhetoric (Devine, 2019; Painter et al., 2017). Continued uncertainty about STOMP’s national impact and geographical variations (Branford et al., 2019; Mehta and Glover, 2019) makes local initiatives, such as this project, valuable primarily to provide an accurate picture of local psychotropic prescribing practices with this high-risk group, but also as a template for other providers to use when evaluating their services. Finally, it provides foundations upon which to build local understanding of user (and staff) experience as it identifies areas that warrant qualitative investigation.
Levels of ID, presence of autism and psychiatric diagnoses
|Level of ID and presence of ASD|
|ID (level unspecified)||At admission||3||2||5|
|Missing data||At admission||0||1||1|
|At least one psychiatric diagnosis||At admission||18||18||36|
|Psychotic disorder||At admission||11||25||36|
|Anxiety disorder||At admission||5||31||36|
|Mood disorder||At admission||9||27||36|
|Psychiatric diagnoses other||At admission||4||32||36|
ASD: autism spectrum disorder
Prescribing patterns across four timepoints
|BNF category||Variable||Timepoint||N||Min.||Max.||Mean||SD||Statistically significant changes*||Statistically significant pairwise comparisons**|
|Total regularly prescribed medications||Number of medications||T1 (Admission)||36||0||7||2.6||1.9|
|T3 (6 months post-discharge)||36||0||5||1.9||1.3|
|T4 (12 months post-discharge)||32||0||4||1.7||1.3|
|Total % of BNF maximum doses||T1 (Admission)||35||0||378.3||127.1||98.9|
|T3 (6 months post-discharge)||35||0||307.1||101.4||80.2|
|T4 (12 months post-discharge)||30||0||350||90.1||102.9|
|Regularly prescribed antipsychotic medications||Number of medications||T1 (Admission)||35||0||3||0.7||0.8|
|T3 (6 months post-discharge)||35||0||2||0.8||0.6|
|T4 (12 months post-discharge)||32||0||2||0.8||0.7|
|Total % of BNF maximum doses||T1 (Admission)||35||0||168.8||27.1||41.8|
|T3 (6 months post-discharge)||35||0||141.7||33.5||37.3|
|T4 (12 months post-discharge)||32||0||140||31.7||37.9|
|Regularly prescribed mood stabilising medications||Number of medications||T1 (Admission)||35||0||2||0.3||0.6||F(1.928, 57.841) =
4.163, p = 0.022
|T3 (6 months post-discharge)||35||0||2||0.1||0.4|
|T4 (12 months post-discharge)||32||0||1||0.1||0.2|
|Total % of BNF maximum doses||T1 (Admission)||35||0||100||8.1||21.9|
|T3 (6 months post-discharge)||35||0||136||7.5||26.6|
|T4 (12 months post-discharge)||32||0||80||3.3||14.7|
|Regularly prescribed antidepressant medications||Number of medications||T1 (Admission)||35||0||1||0.6||0.5|
|T3 (6 months post-discharge)||35||0||1||0.4||0.5|
|T4 (12 months post-discharge)||32||0||1||0.4||0.5|
|Total % of BNF maximum doses||T1 (Admission)||35||0||100||29.3||34.7|
|T3 (6 months post-discharge)||35||0||100||28.8||40.9|
|T4 (12 months post-discharge)||31||0||100||23.9||37.9|
|Regularly prescribed anxiolytic and hypnotic medications||Number of medications||T1 (Admission)||35||0||2||0.9||0.8||F(2.304, 69.107) =
3.697, p = 0.025
|T1-T4 0.452 (95% CI, 0.001 to 0.902), p = 0.049|
|T3 (6 months post-discharge)||35||0||2||0.6||0.7|
|T4 (12 months post-discharge)||32||0||2||0.4||0.7|
|Total % of BNF maximum doses||T1 (Admission)||35||0||150||51.9||56.1|
|T3 (6 months post-discharge)||35||0||150||28.7||44|
|T4 (12 months post-discharge)||32||0||200||27.9||55.8|
|Other regularly prescribed psychotropic medications||Number of medications||T1 (Admission)||35||0||1||0.2||0.4|
|T3 (6 months post-discharge)||35||0||1||0.1||0.3|
|T4 (12 months post-discharge)||32||0||1||0.1||0.3|
|Total % of BNF maximum doses||T1 (Admission)||35||0||50||5.2||12.6||F(1.037, 29.044) = 19.822,
p < 0.001
|T1-T4 85.817 (95% CI, 30.80 to 140.834), p = 0.001
T2-T4 86.966 (95% CI, 32.550 to 141.382), p = 0.001
T3-T4 86.966 (95% CI, 31.907 to 142.025), p = 0.001
|T3 (6 months post-discharge)||35||0||50||2.9||10.3|
|T4 (12 months post-discharge)||31||0||50||3.2||10.9|
|Pro Re Nate (PRN) medications||Number of medications||T1 (Admission)||35||0||2||0.6||0.7||F(2.565, 74.392) = 3.604,
p = 0.022
|T2-T3 0.367 (95% CI, 0.049 to 0.685), p = 0.017|
|T3 (6 months post-discharge)||35||0||2||0.4||0.7|
|T4 (12 months post-discharge)||32||0||2||0.4||0.7|
*Repeated measures ANOVA with Greenhouse–Geisser correction.
**Post hoc analysis with Bonferroni adjustment
Antipsychotic prescribing by diagnosis
|Diagnostic group||Antipsychotic medication status||Timepoint|
|Admission||Discharge||6 months post-discharge||12-months post-discharge|
|At least one psychotic diagnosis recorded||Prescribed||7||13||10||9|
|No psychotic diagnoses recorded||Prescribed||12(5)||13(7)||14(12)||12(6)|
|At least one psychiatric diagnosis recorded||Prescribed||8||20||17||15|
|No psychiatric diagnoses recorded||Prescribed||11(4)||6(3)||7(5)||6(4)|
NB Bracketed figures are individuals with an intellectual disability who are also autistic
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The authors would like to thank the Local STOMP Action Group and wider Learning Disability Team for their support with this project.
About the authors
Jon Painter is based at the Department of Nursing and Midwifery, Sheffield Hallam University – Collegiate Crescent Campus, Sheffield, UK.
Winola Chio is based at the Sheffield Health and Social Care NHS Foundation Trust, Sheffield, UK.
Liam Black is based at the Sheffield Health and Social Care NHS Foundation Trust, Sheffield, UK.
David Newman is based at the Sheffield Health and Social Care NHS Foundation Trust, Sheffield, UK.