Pan-Dorset ADHD and Autism 2022

Both global and national research demonstrate a variance in prevalence estimates for both Autism and ADHD – however many show that there has been an increase over time. This may be due to a variety of factors such as improved awareness and identification of conditions, improved detection, and diagnoses and/or increased support availability. Research generally highlights higher prevalence among males however potential under diagnosis in females has been recognised and there is increasing presentation in this group.

Summaries of prevalence estimates based on national rates applied to local population estimates are shown below. It is important to consider the context that estimated rates vary quite considerably and the true rate of prevalence is difficult to determine. Estimates also assume that prevalence rates are similar across all age groups.

Neurodevelopmental conditions will also vary in presentation and severity of support needs – not all children or adults will have health, social care, or educational support needs.

Using prevalence data and estimates from literature we estimate across Dorset:

• approximately 19,200 people aged 5 and over with ADHD
• approximately 8,700 people of all ages with Autism

Children and young people

• approximately 6,500 children and young people aged 5 to 17 across Dorset estimated to have ADHD. Using estimates applied from USA data, approximately 900 children in Dorset may have severe ADHD. 4,100 children with ADHD are likely to have at least 1 co-existing condition
• approximately 2,500 children and young people aged 0 to 17 across Dorset estimated to have Autism. Around 800 of these are likely to have severe autism, and 1700 at least one co-existing condition
• prevalence numbers are forecasted to decline slightly to 2040, due to projected forecasts of population change. This assumes that prevalence remains the same over time

Adults

• approximately 12,700 adults aged 18 and over across Dorset are estimated to have ADHD
• approximately 6,200 adults aged 18 and over across Dorset are estimated to have Autism 2
• prevalence numbers are forecasted to increase to 2040 – this is due to projected increases in the population particularly in the older age groups. This assumes that prevalence remains the same over time

• in 2020/21 there were approximately 1000, 2 year olds who did not meet the expected development for communication skills and 900 who did not meet the expected level of development in the personal-social skills domain
• in 2020, there were 1730 school age children known to have Autism
• more recent SEN/D data shows approximately 2700 children and young people with SEN support or and EHCP known to have Autism
• both nationally and locally, the number of new suspected autism referrals is on an increasing trajectory, particularly since early in the pandemic in Dorset. Therefore, the number of open referrals has also been increasing steadily
• as of March 2022, there were 490 patients of all ages with an open referral for assessment for suspected autism to Mental Health Services (these are experimental statistics so may not provide a complete picture)
• in 2021/22 there were 344 new referrals to the Community Adult Asperger’s Service. As above, this service has also been seeing increasing new referrals over time
• in 2021/22 there has been a substantial increase in the number of ADHD referrals to Community Mental Health Teams. Looking at calendar year data shows this has been increasing over time
• of our current patient population with a recorded Learning Disability (5,301) just over a quarter have Autism recorded as a long-term condition – 1,428 people. Three quarters of these patients are Male (1066), and 81% are under the age of 35 (1157)

In-line with recommendations in the NHS 5-year autism strategy [1], it would be beneficial to look at information systems and how the collection of information about the health of autistic people and their use of / experience of health services can be improved. This enables us to build a clearer picture of local prevalence, needs, co-morbidities and evaluate outcomes and effectiveness of our interventions.

UK estimates suggest that around 1 in 10 people are neurodivergent – meaning that the brain works or interprets information differently [2] to what is considered typical of the majority of the population. Neurotypical describes much of the population who fall within our historic understanding of the parameters of intelligence [3]. The historical view of conditions being labelled as ‘disorders’ or ‘divergence’ is beginning to shift and it is important to recognise the benefits in the ability to think differently – as demonstrated in the strengths wheel produced by the ADHD foundation below.

Figure 1: Neurodiversity wheel produced by the ADHD Foundation for Neurodiversity Network report April 2022

This infographic presents 8 different neurodiverse condition and its associated strengths. The segments include: dyslexia (light purple), autism (medium blue), ADHD (dark purple), Tourette syndrome (orange), mental health (yellow/light green), dysgraphia (light blue), dyscalculia (dark blue) and dyspraxia (teal). 

Neurodevelopmental conditions – referring to the brain’s development of pathways that influence performance or functioning - include attention-deficit hyperactivity disorder (ADHD), Autism (ASD), dyspraxia, dyslexia, dyscalculia, epilepsies and/or seizures and sensory processing disorders [4].

This paper focuses on the conditions of Autism (or ASD) and ADHD.

Autism, or Autism Spectrum Disorder (ASD) is a diverse group of conditions characterised by a degree of difficulty with communication and social interaction. Atypical patterns of activities and behaviours may be another characteristic. Abilities and needs can vary and evolve over time – some people with autism can live independently while others may have severe disabilities and life-long support needs [5]. Characteristics may be detected in early childhood but autism is often not diagnosed until later in life. A diagnosis of autism can be made as early as 18 to 24 months of age [6].

NICE guidance [7] defines ADHD as being characterised by the core symptoms of hyperactivity, impulsivity and inattention which are judged to be excessive for the person’s age or overall level of development. Two main diagnostic systems are in use (ICD-10 (Hyperkinetic Disorder) and DSM-5), and both require that symptoms are present in several settings - such as school / work, home life and leisure activities. Symptoms should be evident in early life, even if in retrospect.

There may be groups in our population who have increased prevalence of ADHD compared with the general population. These groups include:

• people born pre-term
• looked-after children and young people
• children and young people with mood disorders or diagnosed with ODD or conduct disorder
• people with a close family member with diagnosed ADHD
• people with epilepsy
• people with neurodevelopmental disorders
• adults with a mental health condition
• people with a history of substance misuse
• people known to the Youth Justice System or Adult Criminal Justice System
• people with acquired brain injury

Research states that ADHD is one of the most common neuro-behavioural conditions that presents for treatment in children and adolescents. Symptoms and impairment often span into adulthood [8].

The NHS 5-year Autism Research strategy [9] states that Autism is not a rare condition, and should not bar anyone from a ‘happy, healthy and long life’ – however variation is seen in outcomes. Compared to their non-autistic peers, people with autism can frequently experience more mental ill health; greater likelihood of poor physical health and/or disabilities, experience more risk factors for poor health, have greater difficulties accessing care and ultimately experience a shorter life. These inequalities exist despite increasing numbers of policies committing to improvement, therefore, future systems and policies must systematically work to reduce health inequalities.

In common with many other groups, research is highlighting that the COVID pandemic has exacerbated many of the inequalities people with autism face [10]. Research from the National Autistic Society during June and July 2020 found autistic people were 7 times more likely than the general public to be chronically lonely and 9 out of 10 were worried about their mental health [11]. Similarly, the pandemic is reported to have exacerbated difficulties for young people with ADHD and their families, with reports of low mood, isolation, and a decrease in general wellbeing [12]. However, it is notable that lockdown did not have a negative impact in all cases, with emerging evidence that some reported improvements – attributed by parents to less stress associated with school attendance and structure and/or increased time spent as a family.

A recent systematic review [13] found that estimates of Autism prevalence in international literature varied but that overall trends revealed an increase in measured autism prevalence. From the available literature the review estimates approximately 1 in 100 children are diagnosed with ASD globally with a 4:2 male-to-female ratio. This is likely reflecting factors such as increased awareness of Autism, changes and improvements to identification and definition and increased support capacity.

This theory can be further supported by looking back to past research – one example of a population prevalence study in 7 to 12-year-olds in a South Korean community in 2011 found that 2/3 of ASD cases identified by the study were undiagnosed pupils within mainstream schools. The study concluded the importance of better detection and assessment, suggesting a large proportion were yet to be diagnosed [14].

International literature also shows variation in prevalence estimates for ADHD, but again supporting the increase in measured prevalence over time. A U.S. health survey found the prevalence of having ever been diagnosed with ADHD increased by 42% between 2003 and 2011 [15]. A higher prevalence of ADHD in males than females has also been consistently found, with the median age of onset being 6.

More recently the American Academy of Child and Adolescent Psychiatry estimated ADHD national prevalence of 3.5% (when most stringent definitions used) - with up to 70% also reporting a comorbidity [16]. Adult estimates are more uncertain - the Global Health Epidemiology Reference Group, 2021 produced global estimates of persistent ADHD (childhood onset) at 2.58% and symptomatic adult ADHD at 6.76% but noted the need for a well-defined strategy for diagnosing adult ADHD and large-scale research of epidemiology.

Historically, prevalence for both ASD and ADHD have been viewed as male-dominant, however the gender disparity may be smaller than thought. A 2017 systematic review and meta-analysis [17] of the male-to-female ratio found higher quality studies, or those who screened the general population instead of requiring an ASD diagnosis, found lower ratios closer to 3:1. It was identified that there appears to be a diagnostic gender bias – girls who meet the criteria for ASD are at disproportionate risk of not receiving a clinical diagnosis. Other studies suggest being less likely to be referred for assessment, more likely to receive an incorrect diagnosis of another mental health or neurodevelopmental condition [18] or be diagnosed later in life [19].

For example, some studies have indicated that girls may be up to twice as likely than boys to have inattentive form of ADHD and may experience more internalising symptoms and inattention in contrast to hyperactivity and behavioural difficulties sometimes shown in boys. However, this is not unanimously concluded as other research finds no evidence to suggest that the core symptomology of ADHD differs between genders [20].

The trend of increasing diagnosis over time has also been regularly reported in UK literature. Nationally the rate of children with Autism known to schools has steadily increased from 10.8 per 1,000 to 13.7 per 1,000 in 2018 [21]. Looking at deprivation – there is no apparent social gradient (figure 1) and the increase has been seen across all deciles (figure 2).

Figure 2: Children with Autism known to schools (2018) - England, County and UA deprivation deciles in England (IMD2019, 4/19 and 4/20 geog.)

This bar chart shows the rate of children with autism per 1,000 pupils across 10 deprivation deciles in England. The most deprived decile has the highest rate at 15.7 per 1,000, followed by the third most deprived (15.5) and fourth most deprived (15.4). The lowest rate is in the second least deprived decile at 10.6 per 1,000. The chart highlights variation in autism identification across socioeconomic groups.

Figure 3: Children with Autism known to schools for England

This line graph represents children with autism known to schools in England. The x-axis shows years from 2015 to 2018, and the y-axis shows the number of children with autism per 1,000 pupils, ranging from 8 to 16. Multiple lines represent different deprivation deciles based on the IMD2019, from most to least deprived. The graph shows a general increase in autism identification across all deciles over time, with higher rates and steeper increases in the more deprived deciles. The legend shows least deprived in teal, second least deprived in red, third less deprived in dark blue, fourth less deprived in yellow, fifth less deprived in pink, fifth more deprived in mid blue, fourth more deprived in orange, third more deprived in light green, second most deprived in purple and most deprived in light blue.

A recent study examined if this has been true growth in prevalence in the UK over a 20-year period. Findings demonstrated that there has been a 787% exponential increase in recorded incidence of diagnosis between 1998 and 2018. Greater increases in diagnostic rates were seen for females, and among adults over 19 years old. It was concluded that the increase was more likely due to increased reporting and application of diagnosis suggested by the rising diagnosis patterns seen among adults, females and higher functioning individuals [22].

The Nice guidance defines [23] ADHD diagnosis as meeting the criteria in DSM-5 or ICD-10 (hyperkinetic disorder) and cause at least moderate impairment, be pervasive and occur in 2 or more important settings. In the guidance it is estimates that prevalence rates under the ICD-10 criteria are 1 to 2% in childhood and 3 to 9% using the previous DSM-IV criteria (the new DSM-5 criteria may see these increase but there are currently no estimates available).

National data on trends in ADHD diagnosis are less clear [24] (minimal years presented) however estimate prevalence among 5 to 16 years olds at 1.5% in 2015. Interestingly, in contrast to the Autism data above, a social gradient is observed with incidence estimated to be higher in more deprived areas (Figure 3). The correlation with socioeconomic deprivation is also shown in literature [25].

Figure 4: Estimated prevalence of hyperkinetic disorders - % population aged 5 to 16 (2015) - England, County and UA deprivation deciles in England (IMD2015, pre 4/19 geog.)

This bar chart represents the estimated prevalence of hyperkinetic disorders in population aged 5 to 16 for England, county and UA. The x-axis shows percentages from 0 to 2%, and the y-axis lists deprivation deciles from most to least deprived. The chart shows a decreasing trend: the most deprived decile has the highest prevalence at 1.8%, while the least deprived decile has the lowest at 1.3%. The data indicates a higher prevalence of hyperkinetic disorders among children in more deprived areas.

People with Autism and ADHD will have a variety of needs, and not all may require health, care and education support. The Kings Fund highlighted that an estimated 52% of children with hyperkinetic disorders (such as ADHD) and 43% of children with Autistic Spectrum Disorders are receiving treatment – shown in figure 4 [26].

Table 1: National condition prevalence and % receiving treatment sourced from The Kings Fund mental health briefing

Source: Adult figures: Green H, McGinnity A, Meltzer H, Ford T, Goodman R (2005). Report. Mental health of children and young people in Great Britain. Crown Copyright. Basingstoke: Palgrave Macmillan. Children figures: McManus S, Meltzer H, Brugha T, Bebbington P, Jenkins R (2009). Research paper. Adult Psychiatry Morbidity in England, 2007: Results of a household survey Leeds: NHS Information Centre.

Most recently there has been a noted popularity in ADHD related content on social media platforms such as Twitter and TikTok – Content creators are often young people who identify as having ADHD, sharing posts and videos that are shared widely and videos tagged #ADHD on TikTok have been viewed more than 11 billion times [27]. This increasing interest and awareness of ADHD may be partly encouraging increased presentation – in an article in The Guardian, one creator reports receiving many messages from people who pursued assessment as a result of the content shared. Whilst the increased awareness of symptoms is likely beneficial for many, encouraging support seeking, helping to build community and destigmatize, there is a risk of people receiving misinformation around treatments and medications or encouragement of self-diagnosis [28].

Prevalence modelling is a technique to estimate the number of people with a particular condition in a population when direct evidence is not available. This may be because data collection has not been undertaken, is impractical, incomplete, or unreliable. In many cases routine service data is not able to directly measure the frequency and distribution of conditions in the population, and this is where estimation is the best alternative.

The following local estimates use the methodology of applying prevalence estimates from studies or trials to local populations. This is a commonly used and relatively straightforward methodology, however its usefulness depends on the size and representativeness of the study estimates and how much alignment there is between the variables used in the studies or surveys [29].

As the estimation methodology applies estimated prevalence rates for all ages to local population data, it is useful to understand how this is projected to change over time, as this will drive any changes in the estimated prevalence numbers. Figure 5 below shows population pyramids over time using the ONS 2018-based population projections (this is resident population estimates). This applies recent trends in births, deaths, and migration forward over time. It is anticipated that we are likely to see the population of children and young people reduce (reflecting falling birth rates) and our older population increase.

Figure 5: 2018-based resident population estimates

These 3 population pyramids show age and gender distribution in England for the years 2020, 2030, and 2040, the last two have been projected. Each pyramid displays male population on the left in orange, and female population on the right in blue, with age groups from 0 to 4 up to 90+ on the vertical axis. The 2020 pyramid shows a relatively broad base, while the 2030 and 2040 projections show a narrowing base and widening upper age bands, indicating an aging population over time.

Data due to be released from the 2021 Census data is likely to adjust the population bases and estimates – but as this may take some time to produce projections, the 2018-based estimates have been used in this paper.

The Nice guidance defines [30] ADHD diagnosis as meeting the criteria in DSM-5 or ICD-10 (hyperkinetic disorder) and cause at least moderate impairment, be pervasive and occur in 2 or more important settings. In the guidance it is estimates that prevalence rates under the ICD-10 criteria are 1 to 2% in childhood and 3 to 9% using the previous DSM-IV criteria (the new DSM-5 criteria may see these increase but there are currently no estimates).

Applying these to the ONS 2018-based population estimates (figure 6) for children and young people aged 5 to 17 produces an estimate of around 1,600 children using ICD-10 and 6,500 children at the mid-point of DSM-IV of 6%. Given the literature described previously in this paper, it seems more likely that prevalence is closer to DSM-IV. Estimates show the number of children and young people with ADHD is projected to fall – this is due to the population change described above.

Children aged 0 to 4 have not been included in these calculations, given that symptoms often become more noticeable when a child’s circumstances change such as starting school [31], and presentation is less likely in younger children – with previous research showing a median age of 6 at diagnosis.

Table 2: Estimated population aged 5 to 17 with ADHD

Like other neurodevelopmental conditions, the level of symptoms and impairments that people with ADHD experience can vary. Severe symptoms are likely to result in marked impairments in school, work, or social settings [32].

In the USA, the 2016 National Survey of Children’s Health (NSCH) estimated that among children aged 2 to 17, 41.8% had mild ADHD, 43.7% moderate and 14.5% severe ADHD. Applying this estimated prevalence to 2020 estimates (DSM-IV @6%) suggests approximately 900 people could have severe ADHD therefore requiring greater support. An estimated 4600 people (of all levels of ADHD severity) are likely to have at least 1 co-existing condition. Note that there may be different definitions of severity in practice between the USA and UK, but these numbers are provided as an indicative estimate.

Research also suggests that around 67% of children with ADHD have at least 1 co-existing condition, such as anxiety, depression or Autism.

Figure 6: Estimated level of ADHD severity and co-existing conditions

This bar chart shows ADHD severity among approximately 6,500 children and young people with Autism. The chart displays 3 categories: Mild ADHD (2,700 individuals, 42%), Moderate ADHD (2,800 individuals, 43%), and Severe ADHD (900 individuals, 15%). A note indicates that 4,100 of these individuals are likely to have at least one co-existing condition.

Symptoms of ADHD often improve with age however many adults who were diagnosed in childhood will continue to experience issues or may have additional problems such as sleep or anxiety disorders. NHS estimates the prevalence of ADHD in adults at 2% [33]. There is no distinction between male and female prevalence, however this is likely given prevalence differences seen in conditions such as Autism.

Applying a 2% prevalence estimate to our 18 and over population gives an estimated 12,700 people with ADHD. This is projected to increase, again this will be reflecting the growing ageing population shown in the previous section – actual support needs are likely to vary in adulthood.

Table 3: Estimated prevalence of ADHD in adults aged 18 and over

Recent UK research estimated prevalence of Autism in children and young people at 2.81% in males and 0.65% in females [34]. The research also found differences in prevalence by ethnicity and social disadvantage – given the limitations of population data until the release of the 2021 Census the overall sex specific prevalence has been applied below. 0 to 4 year olds are included in this estimate as data was studied from ages 2 and over.

This produces an estimate of 2,600 children and young people, 2,100 males and 500 females. Both are projected to decrease over time, given the projected fall in our population of people in this age group.

Table 4: Estimated Autism Prevalence - males (2.81%), females (0.65%)

As Autism is often referred to as a spectrum disorder, some autistic people will need very little to no support during their everyday lives, whereas others may have very high levels of care and support needs [35]. There are various estimates of the prevalence of people with severe support needs, but this generally falls within 30 to 40% with a learning disability or intellectual needs [36, 37].

Alongside this, around 70% of people with Autism also meet the diagnostic criteria for at least one mental and/or behavioural disorder – most commonly anxiety and/or depression, ADHD and oppositional defiant disorder (ODD) [38].

Applying these estimates to the estimated 2020 prevalence figures, highlights that just over 800 children and young people may have severe support needs, and of all children and young people with Autism, around 1700 may have at least one co-existing condition (figure 10).

Figure 7: Estimated level of Autism severity and co-existing conditions

This single bar chart shows support needs among approximately 2,500 children and young people with Autism. It indicates that 1,700 individuals (67%) have high-functioning to moderate support needs, while 800 individuals (33%) have severe support needs. Additionally, 1,800 of the total are likely to have at least one co-existing condition.

The Projecting Adult Needs and Service Information tool [39] applies prevalence estimates of 1.8% for males and 0.2% females to our adult population. These prevalence estimates were calculated from the Adult Psychiatric Morbidity Survey 2007.

Table 5: Estimated prevalence of Autism age 18 and over - 1.8% males, 0.2% females

Given the underlying projected growth in our older population, the number of people with autism is therefore estimated to rise, with variation in the younger age groups. This is shown more clearly in the following figure, where the increase in the older population can be seen. It is worth reflecting if this is likely to have an impact on services, given the trends in presentation at much younger ages – there may be different support requirement as people age and increased awareness means that more people have a confirmed diagnosis.

Figure 8: Estimated Autism prevalence by age group

This stacked bar chart shows estimates for Autism prevalence by age group for Dorset. The x-axis shows years from 2020 to 2040 in five-year intervals, and the y-axis represents the number of individuals. Each bar is divided into segments for age groups: 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 64, 65 to 74, and 75+, all in different shades of blue to teal (young ages to older ages. The chart shows a general increase in autism prevalence over time, especially in older age groups. 

From 2015 all children in England became eligible for a development review around their second birthday. The review uses the Ages and Stages questionnaire (ASQ-3) providing an objective measure of development. The dimensions of development that are tested include communication, gross motor skills, fine motor skills, problem solving and personal-social skills [40].

Figure 13 shows annual data for the percentage of children who received a review and who were at or above the expected level of development in the 5 tested dimensions (note the axis begins at 50%). Across the 2 local authorities, regionally and nationally communication skills consistently have the lowest percentage of children who are at or above expected development, and this has shown a downward trend. In 2020/21 there were approximately 306 2 year olds in BCP and 700 2 year olds in Dorset who had a review and did not meet the expected development for communication skills. In the personal-social skills dimension there were 242 in BCP and 658 2 year olds in Dorset who did not meet the expected level of development.

Figure 9: ASQ-3 Annual Data - % of children reviewed who are at or above the expected level of development 

This line charts present ASQ-3 annual data in percentages of children reviewed who are at or above the expected level of development for BCP, Dorset, England and South West. The x-axis shows the years 2020 and 2021 and y-axis shows the percentage. The coloured lines represent communication skills (blue), fine motor skills (mid blue), gross motor skills (orange), personal-social skills (light blue) and problem solving (green).

Figures 14 and 15 show the rate of school-age children per 1000 enrolled in state-funded primary, secondary and special schools for whom Autistic Spectrum Disorder was recorded as the primary reason for Special Educational Needs support. The rate has been increasing over the last few years for England as a whole, and as can be seen in figure 16 all areas whose trends could be calculated had seen an increase. In BCP 789 children with Autism were known to schools (lower than national), and 941 children in Dorset (higher than national).

Figure 10: Children with Autism known to schools for Bournemouth, Christchurch and Poole

This line graph shows the number of children with Autism known to schools for Bournemouth, Christchurch and Poole. The x-axis spans from 2015 to 2020, and the y-axis shows the number of children per 1,000. The graph shows a steady increase in England’s rate from around 10 per 1,000 in 2015 to about 20 per 1,000 in 2020. A single blue data point for Bournemouth, Christchurch and Poole is shown for 2020, labelled as 'Lower' to the national rate.

Figure 11: Children with Autism known to schools for Dorset

This line chart shows the number of children with Autism known to schools for Dorset. The x-axis spans from 2015 to 2020, and the y-axis shows the number of children per 1,000. Two lines are plotted: Dorset (blue) and England (black). Dorset’s rate increases from approximately 13 per 1,000 in 2015 to about 20 per 1,000 in 2020. England’s rate also rises, from around 10 to 15 per 1,000 over the same period. Data points are marked with coloured dots indicating whether Dorset’s rate is lower (blue), similar (yellow), higher (light blue), or not applicable (white) compared to England.

Table 6: Children with Autism known to schools 2020

Source: Office for Health Improvement and Disparities 

This data suggests a total of 1,730 children known to schools in 2020, which compares quite closely to the estimated prevalence number of 1900 in figure 9 above.

Bournemouth, Christchurch and Poole (BCP)

The rate of EHCP’s and school pupils with SEN support has been increasing. In January 2022, 6,698 children and young people in BCP schools required SEN support. 56% were aged 5 to 11 and 36% 12 to 16. 393 SEN support pupils had Autistic Spectrum disorder and 1,368 had social, emotional and mental health needs. Of children with EHCP’s (3063) 30% (approx. 907) had Autism Spectrum Disorder.

Dorset

In January 2022, 6,798 children and young people in Dorset schools required SEN support. 47% were aged 5 to 11 and 44% 12 to 16. Approximately 476 SEN support pupils had Autistic Spectrum disorder and 1,224 had social, emotional, and mental health needs. Of children with EHCP’s (3233) 28% (approx. 905) had Autism Spectrum Disorder. Across both areas is an approximate total of 2700 children and young people with ASD – prevalence estimates from for a similar age range using 2020 estimates is 3,100, slightly higher which suggests there may continue to be further presentation from undiagnosed people (if prevalence rate estimates are correct for our area).

The figures in this section refer to suspected autism referrals of all ages within Mental Health Services for England and NHS Dorset – both all open referrals and the number of new referrals per month.

Both nationally and locally, the number of new referrals is on an increasing trajectory, particularly since early in the pandemic in Dorset. Therefore, the number of open referrals has also been increasing steadily.

Waiting times data suggests that nationally patients are waiting longer, with the proportion receiving a first appointment within 13 weeks slowly reducing. There has been some fluctuation locally, but it is worth noting that while there have been increases in waiting times, due to the increase in the new referrals, the actual numbers seen within 13 weeks at the lowest point in November 2021 are similar to those in September 2019.

The latest years data for England (March 2021 to February 2022) shows that for the patients who received a diagnosis in the month, 67% received a diagnosis of Autism. Of the remaining patients, 22% were diagnoses with a mental or behavioural condition that was not Autism, and 11% received a non-mental or behavioural diagnosis.

Data quality – it is worth noting that in England data the number of patients with more than one open referrals in the month has been increasing, suggesting some data quality issues. In Dorset this number is low and has been fairly stable. These are experimental statistics with known data quality limitations in terms of completeness so contribute to understanding of ASD diagnostic pathways but unlikely to provide a complete picture.

Figure 12: New suspected Autism referrals for England

This line graph from NHS digital shows the number of new 'suspected autism' referrals (ASD12) per month from April 2019 to March 2022. The x-axis represents time in monthly intervals, and the y-axis shows the number of referrals, ranging from 2,000 to 8,000. The graph begins at 3,983 referrals in April 2019 and shows an overall increasing trend, with fluctuations and notable peaks, such as around August 2019 with 5,775 referrals.

Figure 13: New suspected Autism referrals for NHS Dorset

This line graph from NHS Digital shows monthly new 'suspected autism' referrals (ASD12) for NHS Dorset CCG from April 1 2019, to March 1 2022. The x-axis represents time in monthly intervals, and the y-axis shows the number of referrals, ranging from 0 to 100. The graph shows an overall upward trend in referrals, with fluctuations and several noticeable peaks and dips throughout the period.

Figure 14: Number of open suspected Autism referrals for England

This line chart from NHS Digital shows the number of open 'suspected autism' referrals in England from April 1 2019 to December 1 2021. Below the graph, December 2021 statistics are listed: 5,964 new referrals (ASD12), 4,351 closed referrals (ASD13), 87,956 open referrals (ASD16), 3,225 patients with at least one contact in the referral pathway (ASD02), 34 autism diagnoses (ASD21), and 34 mental health or behavioural disorder diagnoses excluding learning disabilities (ASD22).

Figure 15: Number of open suspected Autism referrals for NHS Dorset

This line graph from NHS Digital show the number of patients with an open 'suspected autism' referral in NHS Dorset CCG from April 1 2019, to March 1 2022. The x-axis represents monthly reporting periods, and the y-axis shows patient numbers from 0 to 500. The graph depicts a steady increase in referrals over time, starting at approximately 40 patients in April 2019 and rising to around 490 patients by March 2022.

Figure 16: Proportion of open referrals receiving an appointment in 13 weeks or less, England

This line graph from the NHS Digital shows the proportion of patients with an open 'suspected autism' referral for at least 13 weeks who received a first appointment within 13 weeks. The data covers England from April 1, 2019, to March 1, 2022. The y-axis shows percentages ranging from about 9% to just over 10%. The trend shows a slight decline over time, from approximately 10.35% in April 2019 to around 9.18% in March 2022.

Figure 17: Proportion of open referrals receiving an appointment in 13 weeks or less, NHS Dorset

This line graph from NHS Digital shows the proportion of patients with an open 'suspected autism' referral for at least 13 weeks who received a first appointment within 13 weeks. The data covers NHS Dorset CCG from April 1, 2019, to March 1, 2022. The y-axis represents percentages from 0 to 80, and the x-axis shows monthly intervals. The graph starts at 64% in April 2019, fluctuates over time with a peak around mid-2020, then generally declines before a slight rise again by March 2022.

Dorset’s CAAS Service offers a wide range of services to people over the age of 18 with a diagnosis of an Autism Spectrum Condition who don’t have a learning disability. Part of the services offered includes diagnostic assessment, including second opinions [41].

Over the last 2 years, the number of referrals have been increasing on the previous 6 months – and most recently the referrals for females has overtaken that for males. Predominantly new referrals fall within the 18 to 25 age range, however in the most recent 6 months the service has seen increasing referrals in the 26 to 40 age range.

Figure 18: New referrals to the CAAS Service, 2020 and 2021

This bar chart shows the number of males and females with suspected autism referrals across 4 time periods: 1 April to 30 September 2020, 1 October 2020 to 31 March 2021, 1 April to 30 September 2021, and 1 October 2021 to 31 March 2022. Each bar is divided into orange (males) and blue (females). The chart shows that in the first period, male and female numbers are roughly equal. In the subsequent three periods, the number of females exceeds the number of males, with the total number of individuals increasing over time, peaking in the final period.

Note: The service was impacted at the beginning of the COVID-19 pandemic so the number of referrals in the first 6-month period of 2020 may be lower than typically expected.

Data on ADHD referrals has been collated by Community Mental Health Teams – this may be under-reported due to data recording variance however show a substantial increase in referrals in the 2021/22 financial year. Looking at referrals by calendar year further highlights the increasing trend in numbers year on year is seen (note 2022 data is partial year).

Figure 19: ADHD referrals to CMHT per financial year

This line graph represents ADHD referrals to CMHT per financial year. The x-axis shows financial years from 2018/19 to 2021/22, and the y-axis represents the number of referrals, ranging from 0 to 1200. 

Figure 20: ADHD referrals to CMHTs per calendar year

This line graph represents ADHD referrals to CMHTs per calendar year. The x-axis spans from 2018 to 2022, and the y-axis shows the number of referrals, ranging from 0 to 900. A solid blue line represents the actual number of ADHD referrals per year, while a dotted blue line shows the trend. 

Analysis of primary care record data from Dorset GP surgeries shows as of early 2022 3,621 adults aged 18 and over had a diagnosis of ADHD recorded. This is 0.54% of the patient population so falls below prevalence estimates suggesting some underdiagnosis. However, further work is investigating whether there are additional read codes that would identify a diagnosis, which would therefore increase figures.

Of our current population with a recorded learning disability (5,301) just over a quarter have Autism recorded as a long-term condition – 1,428 people. Three quarters of these patients are male (1066), and 81% are under the age of 35 (1157). As these patients have a recorded learning disability, they are likely to have moderate to high support needs.

The number of prescriptions for Central Nervous System (CNS) Stimulants and drugs used for ADHD has increased gradually over time from 3,922 in quarter 1 for 2013/14 to 4808 in quarter 4 for 2017/18.

Table 7: Prescription data for CNS stimulants and drugs used for ADHD, NHS Dorset

Source: Dorset CCG

Figure 21: Items prescribed over time

Bar chart showing quarterly data from Q1 2013/14 to Q4 2017/18. The x-axis lists each quarter chronologically, while the y-axis represents numerical values ranging from 0 to 5,000. Each bar corresponds to a quarter, illustrating fluctuations in the measured values over time. The chart highlights trends and changes across the five-year period.

• projections data used to calculate prevalence estimates are based on the ONS 2018 based projections, the latest available at the time of writing. A specific timeline for an update to the projections based on the 2021 Census is not available at the time of writing. These are likely to be much more reflective of the current population, so care should be taken in interpreting exact numbers, and it’s recommended to update these forecasts when 2021 population data is available
• age-specific prevalence estimates are limited, but it is likely that diagnosis rates vary by age. The projections in this paper do not take this variation into account
• specific diagnosis of Autism or ADHD not recorded consistently or at all by many non-specialist services – e.g. data may be recorded on service users disability status or and speech, language or communication needs
• most research literature focuses on children and young people – through school related information or surveys of children and parents. This makes it more difficult to understand how support needs and presentation might change through the life course

In-line with recommendations in the NHS 5-Year Autism Strategy [42], it would be beneficial to look at information systems and how the collection of information about the health of autistic people and their use of / experience of health services can be improved. This enables us to build a clearer picture of local prevalence, needs, co-morbidities and also evaluate outcomes and effectiveness of our interventions.

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