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Attention Deficit Hyperactivity Disorder (ADHD) in the United Kingdom: Regional and

socioeconomic variations in incidence rates (2004-2013)

Adrian J Hire1, Darren M Ashcroft1, David A Springate2 and Douglas T Steinke1

1Centre for Pharmacoepidemiology and Drug Safety, Manchester Pharmacy School, University of Manchester, Manchester, United Kingdom

2Institute of Population Health, Faculty of Medical and Human Sciences, University of Manchester, Manchester, United Kingdom

Corresponding Author: Adrian J Hire, Manchester Pharmacy School, Room 1.134 Stopford Building,

Oxford Road, Manchester, M13 9PT. Email: adrian.hire@postgrad.manchester.ac.uk

Author biographies

Adrian J Hire, MPharm, is a PhD student based within Manchester Pharmacy School at the University

of Manchester.

Darren M Ashcroft, PhD, is professor of pharmacoepidemiology at the University of Manchester and

director of the Centre for Pharmacoepidemiology and Drug Safety at Manchester Pharmacy School.

David A Springate, PhD, is a research fellow in the Institute of Population Health at Manchester

University. He uses electronic health records to answer a broad range of questions in primary

healthcare.

Douglas T Steinke, PhD, is a senior lecturer in pharmacoepidemiology at Manchester Pharmacy

School, University of Manchester. His research interest is in medicines use in chronic diseases, health

services and drug utilisation research.

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Abstract

Objective: To describe the incidence and distribution of ADHD within the United Kingdom, and to

examine whether there was any association between ADHD incidence and socioeconomic

deprivation.

Method: The study used data from the Clinical Practice Research Datalink (CPRD). Patients

diagnosed with ADHD before the age of 19 between January 1, 2004 and December 31, 2013 were

stratified according to the region in which their general practice was based. Practice Index of

Multiple Deprivation (IMD) score was used as a surrogate measure of patients’ deprivation status.

Results: ADHD incidence was relatively stable between 2004 and 2013, but peaked in the last 2 years

studied. Statistically significant (p £ .05) differences in incidence were observed between U.K.

regions. In almost every year studied, incidence rates were highest among the most deprived

patients and lowest among the least deprived patients.

Conclusion: In the United Kingdom, ADHD may be associated with socioeconomic deprivation.

Keywords

ADHD, incidence, variation, deprivation

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Introduction

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by

three core symptoms: hyperactivity, impulsivity and inattention (Bolea-Alamañac et al., 2014). In the

United Kingdom (UK) general practitioners (GPs) play a key role in the diagnosis, management and

treatment of the disorder. As gatekeepers to the UK healthcare system (Herrett et al., 2015; Murray

et al., 2014), GPs will generally be the first port of call for individuals concerned that they or their

child may have ADHD. After referring suspected cases to secondary care (such as paediatric or

psychiatry services) for a confirmation of the diagnosis, GPs may prescribe medications and

undertake monitoring measures as part of a shared care arrangement (National Institute for Health

and Care Excellence, 2008a). Four medications are currently licensed in the United Kingdom for the

treatment of ADHD (methylphenidate, dexamfetamine, lisdexamfetamine and atomoxetine) (Joint

Formulary Committee, 2015), though pharmacological intervention may not be required in all cases

(McCarthy et al., 2012).

McCarthy et al. (2012) and Holden et al. (2013) both detected increases in the incidence and

prevalence of ADHD in the UK during the first decade of the 21st century. However, there is some

evidence to suggest that the burden of the disorder is unevenly distributed. Rowlingson et al. (2013)

observed that primary care spending on methylphenidate varied significantly across England. In

addition, a UK cohort study by Russell et al. (2014) found that ADHD was particularly prevalent

amongst children living in circumstances of social and economic disadvantage.

The findings of Rowlingson et al. (2013) were based on national prescribing data from a

single month in 2011. Similarly the link between parentally-reported ADHD and socioeconomic

deprivation was based on a sample containing a relatively small number (n=187) of affected children

(Russell et al., 2014). The aim of this study was to establish if the regional prescribing variations

observed in the UK reflected regional variations in ADHD incidence, and to determine if ADHD

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incidence showed any association with socioeconomic deprivation on a national scale. The study also

sought to update the findings of earlier epidemiological studies, describing ADHD incidence rates

amongst children and adolescents in the UK between the years 2004 and 2013.

Method

Data source

A retrospective cohort study was performed using primary care consultation data from the Clinical

Practice Research Datalink (CPRD). This data consists of information routinely recorded by general

practitioners during their consultations with individual patients, including diagnoses made and

medications prescribed. CPRD has been collating anonymised patient-level data from UK general

practices since its inception (as the ‘General Practice Research Database’) in 1987 (Mansell, 2013).

General practices that contribute data to CPRD are required to meet certain data quality

requirements before they are declared ‘up to standard’ for research purposes (Bhaskaran, Forbes,

Douglas, Leon, & Smeeth, 2013). Only data from these ‘up to standard’ practices was included in the

study. The number of general practices sharing data with the CPRD has expanded steadily over time.

At the time the study was conducted CPRD held longitudinal, research-quality data for 684 UK

general practices (Clinical Practice Research Datalink, 2014). This equates to around 9% of the UK’s

general practices (Mansell, 2013) and records for approximately 13.5 million individuals (Clinical

Practice Research Datalink, 2014), a large sample that is broadly representative of the UK population

as a whole (Bushe, Wilson, Televantou, Belger, & Watson, 2015; Herrett et al., 2015; Holden et al.,

2013; Thomas, Mitchell, & Batstra, 2014; West, Fleming, Tata, Card, & Crooks, 2014).

Study population and study period

The study population comprised patients diagnosed with ADHD before the age of 19, between

1/1/2004 and 31/12/2013. Data stored within CPRD is coded; terminology relating to patients’

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clinical management is encoded using a standardised set of codes termed ‘Read codes’ to promote

consistency and uniformity (Chisholm, 1990). Individuals with a diagnosis of ADHD were identified by

the presence of Read codes relating to the disorder in their CPRD record. To be eligible for inclusion

as an incident case of ADHD the earliest occurrence of a relevant code had to occur within the study

window, and following at least 365 days continuous registration with their general practice. A list of

Read codes denoting a diagnosis of ADHD and a further list of Read codes denoting drugs used in its

treatment were compiled (both lists available at clinicalcodes.org, an online repository for clinical

codes used in database research (Springate et al., 2014)). The drugs selected encompassed all agents

currently licensed for the treatment of ADHD in the UK - methylphenidate, dexamfetamine,

lisdexamfetamine and atomoxetine. All four drugs are licensed for use in patients between the ages

of six and eighteen years of age; atomoxetine is also approved for use in adults (Joint Formulary

Committee, 2015). With the exception of dexamfetamine (which is also licensed for the treatment of

narcolepsy), the drugs of interest examined by this study are solely licensed for the treatment of

ADHD (Joint Formulary Committee, 2015).

Assessment of geographical location

Every practice contributing data to CPRD has a unique identifying number. Associated with this

number is information about that practice’s geographical location within the UK. By looking at the

practice identifier associated with a particular patient, their location within the UK can be discerned.

CPRD subdivides the UK (a nation itself comprised of four ‘nations’ – England, Scotland, Wales and

Northern Ireland) into thirteen geographical regions. Scotland, Wales and Northern Ireland comprise

three of these regions; the remainder are regions situated within England (North West, North East,

Yorkshire and the Humber, East Midlands, West Midlands, East of England, South West, South

Central, London and the South East Coast) (West et al., 2014).

Assessment of deprivation: ‘Practice-level’ deprivation score

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England and Wales are divided up into approximately 35,000 defined geographical areas known as

Lower Layer Super Output Areas (LSOA) (Office of National Statistics, 2015). Generated for the

purposes of statistical research, these areas each contain populations of between 1000 and 3000

people (Office of National Statistics, 2015). Similar geographic divisions are applied to Northern

Ireland and Scotland, which is divided into smaller areas termed datazones (DZ) (UK Data Service,

2014). Measurements relating to seven key indicators of socioeconomic deprivation are routinely

compiled for each LSOA/DZ. These indicators examine household income, employment, health and

disability, education and training, barriers to housing and services, crime and the living environment

(UK Data Service, 2014). An amalgamation of this information is used to calculate an Index of

Multiple Deprivation (IMD) score for each LSOA/DZ, allowing each to be ranked in order of relative

deprivation.

Every general practice contributing data to CPRD has an Index of Multiple Deprivation score

based on the LSOA/DZ in which it is situated. These scores are available to CPRD researchers,

rounded to the nearest quintile. For the purposes of this study, ‘practice-level’ IMD scores were used

as a surrogate measure of patients’ deprivation status. This measure was deemed appropriate as

patients would be expected to reside in the locality of their general practice, within a geographically-

defined catchment area (NHS Choices, 2014).

Assessment of deprivation: ‘Patient-level’ deprivation score

For around 70% of English practices (covering just over 50% of all patients in CPRD) IMD scores can

be provided for individual patients based on the LSOA in which their home address is situated

(Thomas, 2014). This direct measure of deprivation status was requested for the subset of ADHD

patients for whom it was available. By comparing these individuals’ practice-level IMD score to their

patient-level IMD score, it could be established if practice-level deprivation scores provided an

accurate reflection of patient-level deprivation scores.

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Data Analysis

Incidence calculation: The earliest occurrence of an ADHD-related Read code in each patient’s

records was identified, and the calendar year in which this occurred was noted. The patient was then

counted as a newly-diagnosed incident case for that calendar year. The incidence denominator for

each year comprised of person-time contributed by individuals who were considered ‘at risk’ of

developing ADHD in that year.

Incidence rates were expressed as cases per 10,000 person years at risk (PYAR) and

presented with 95% confidence intervals (CI). Annual incidence rates were calculated and stratified

according to patient gender, nation (England/Scotland/Wales/Northern Ireland) and practice-level

deprivation (IMD) quintile. An overall incidence rate was calculated for the study period as a whole;

this was stratified according to gender, nation, CPRD region (in the case of English patients), age

group and deprivation quintile. Multivariable Poisson modelling was used to determine incidence

rate ratios (IRR) and accompanying 95% confidence intervals and p-values, adjusted for gender,

nation, age group and deprivation quintile. A regression was similarly conducted using only English

patients; this was adjusted for gender, age group, CPRD region and deprivation quintile. Statistical

significance was set at p≤0.05, and all statistical analyses were performed using STATA version 13

(Stata Statistical Software, College Station, TX, USA).

Results

Overall and annual incidence rates (UK)

Over the 10 year study period 10,284 new diagnoses of ADHD were recorded in under 19s in CPRD.

The overall ADHD incidence rate for the study period was 11.67 cases per 10,000 person-years at risk

(95% CI 11.45 – 11.90). Incidence rates were at their lowest in 2008 [11.04 cases per 10,000 PYAR

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(95% CI 10.38 – 11.75)] and highest in 2012 [12.56 cases per 10,000 PYAR (95% CI 11.84 – 13.33)], as

shown in Figure 1.

Incidence by gender and age group

After adjustment for nation, deprivation quintile and age group, a large and statistically significant

difference (p≤0.001) in incidence rates was observed between males and females. Between 2004

and 2013 the overall incidence of ADHD amongst the male population at risk was 18.63 cases per

10,000 PYAR (95% CI 18.24 – 19.03). The overall incidence rate in females was much lower [4.37

cases per 10,000 PYAR (95% CI 4.18 – 4.57)]. As shown in Figure 1, female incidence rates were

relatively static from 2004 – 2010 but were notably higher in the last three years of the study period

[peaking at 5.45 cases per 10,000 PYAR (95% CI 4.80 – 6.21) in 2012].

Figure 2 shows the incidence of ADHD in males and females according to age group. In both

males and females, ADHD was most commonly diagnosed at age seven (1,057 new diagnoses in

males, 238 new diagnoses in females). Thirty five percent of all ADHD patients identified (n=3,606)

were diagnosed between the ages of seven and nine.

Incidence by nation (England, Scotland, Wales and Northern Ireland)

As shown in Table 1, Northern Ireland’s overall incidence rate was the highest of the four UK nations

[with 13.32 cases per 10,000 PYAR (95% CI 12.11 – 14.66)]. This was significantly higher than that of

Scotland (p≤0.001), England (p≤0.001) and Wales (p=0.015). Wales had the second highest incidence

rate across the study period, significantly higher than that of England (p=0.012) and Scotland

(p=0.010). Scotland’s overall incidence rate was the lowest of the four nations, though the difference

between Scottish and English rates was not statistically significant (p=0.359).

In England, annual fluctuations in incidence rates broadly corresponded to those of the UK

as a whole. Incidence rates were at their lowest in 2008 and highest in 2012 [peaking at 12.73 cases

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per 10,000 PYAR (95% CI 11.90 – 13.62)]. However, a decrease in incidence rates between 2007

[11.87 cases per 10,000 PYAR (95% CI 11.10 – 12.70)] and 2008 [10.27 cases per 10,000 PYAR (95% CI

9.55 – 11.05)] was observed in England but not observed in the UK data as a whole.

In Scotland, ADHD incidence was lowest in 2005 [7.60 cases per 10,000 PYAR (95% CI 6.00 –

9.62)] and highest in 2013 [14.80 cases per 10,000 PYAR (95% CI 12.52 – 17.48)]. In contrast to

England, 2008 saw a relatively high incidence of newly-diagnosed ADHD in Scotland and peak annual

incidence in Northern Ireland [15.46 cases per 10,000 PYAR (95% CI 11.62 – 20.58)]. In Wales, peak

ADHD incidence was observed in 2007 [15.63 cases per 10,000 PYAR (95% CI 12.98 – 18.83)].

Incidence by CPRD region (England)

Within each English region annual incidence rates fluctuated between the years 2004 and 2013

without any consistent pattern. However, the South East Coast region had both the highest number

of ADHD diagnoses during the study period (n=1,461, 18.3% of the England’s total cases) and the

highest overall incidence rate of ADHD in under 19s (see Table 2). This was significantly higher

(p≤0.001) than that of the Yorkshire and the Humber region, which had the lowest incidence rate of

England’s ten CPRD regions.

Incidence by deprivation (IMD) quintile

When stratified according to deprivation quintile, the UK’s diagnostic data suggested a significant

link between deprivation and ADHD incidence. In almost every year studied, incidence rates were

highest in the most deprived patients and lowest in the least deprived patients (see Figure 3).

Underlying this UK trend was England’s diagnostic data. Patients belonging to practices in the most

deprived areas of England (IMD quintile 5) had the highest incidence of ADHD overall [13.84 cases

per 10,000 PYAR (95% CI 13.23 – 14.47)]. This was significantly higher (p≤0.001) than the incidence

rates for quintiles 1, 2, 3 and 4. At the opposite end of the deprivation scale, patients belonging to

the least deprived quintile (1) had a significantly lower incidence (p≤0.001) of diagnosed ADHD than

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patients in any other quintile [9.24 cases per 10,000 PYAR (95% CI 8.72 – 9.80)]. Patient-level

deprivation data was accessible for 80.5% of English ADHD patients (n=6,424). In 4,476 of these

patients, their patient-level IMD quintile was either the same as their practice-level quintile or

higher. That is to say, in 69.7% of instances patients were either as deprived as their practice-level

IMD suggested, or more deprived.

In the other three nations of the UK, evidence for an association between deprivation and

ADHD was somewhat weaker (see Figure 4). In Scotland (as in England) patients belonging to

practices in the most deprived areas (IMD quintile 5) had the highest incidence of diagnosed ADHD;

rates were significantly higher (p≤0.001) than those in the less deprived quintiles (quintiles 1, 2, 3

and 4). In Wales, patients in the most deprived quintile had the highest incidence of ADHD,

significantly higher (p≤0.001) than that observed in the least deprived quintile. In Northern Ireland,

there was no clear association between ADHD and deprivation.

Discussion

This study found that there were statistically significant differences in ADHD incidence rates

between the UK’s constituent nations, and between individual regions within England. The finding of

significant geographical differences within the UK is probably unsurprising. In the United States,

significant differences in diagnostic and treatment rates have been observed between states, and

between different communities within the same state (Fulton et al., 2009; McDonald & Jalbert,

2013). Furthermore, Rowlingson et al. (2013) had observed regional variations in methylphenidate

prescribing in England that had suggested such variations might be present. That study identified a

notable area in the South East of England where medical practices’ methylphenidate spending was

four times the national average; this study found that the CPRD’s South East Coast region had the

highest ADHD incidence rate of all CPRD regions.

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It may be the case that these differences in diagnostic rates are explicable by national and

regional differences in diagnostic and management procedures. All areas of the UK would be

expected to take the 2008 National Institute for Health and Care Excellence (NICE) guidance on the

diagnosis and management of ADHD as a primary resource. However, it is possible that a child

diagnosed with ADHD in one part of the UK may not have had the disorder recognised and

diagnosed had they lived in another part of the country. All four constituent nations of the UK have

distinct budgets for healthcare, and must prioritise spending according to national needs and

priorities (National Audit Office, 2012). Similarly, different regions within each nation have their own

allocated budgets which must be used to provide a well-rounded health service to the local

populace. It has been acknowledged that different areas of the UK provide inconsistent levels of

service provision for ADHD (National Institute for Health and Care Excellence, 2013), potentially

resulting in different levels of case recognition.

Alternatively, the regional and national differences in diagnostic rates may reflect genuine

differences in ADHD incidence across the UK. That is to say populations in some parts of the UK may

have a higher proportion of individuals with some genetic susceptibility to ADHD and/or higher

exposure to environmental risk factors that promote its onset. One environmental factor suggested

to play a role in the aetiology of ADHD is sunlight. In 2013, Arns, van der Heijden, Arnold, &

Kenemans reported an inverse association between regional solar intensity and ADHD prevalence

across 49 US states, and across several countries. That finding has been contested elsewhere

(Hoffmann et al., 2014), and this study’s findings did not appear to suggest an association between

ADHD and solar intensity in the UK. The South East Coast of England had the highest incidence of

ADHD of all English regions, despite its southerly latitude and its relatively high solar intensity (Met

Office, 2014). In addition, Scotland had the lowest ADHD incidence of all four UK nations despite

being the most northerly and having the lowest solar intensity overall (Met Office, 2014). This does

not rule out a relationship between ADHD and sunlight but does suggest that in a country the size of

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the UK, in the UK’s position geographically, regional differences in ADHD incidence do not appear to

be influenced by regional differences in solar irradiation.

Exposure to socioeconomic deprivation is another purported risk factor for ADHD, and this

study did observe a clear association between ADHD and socioeconomic deprivation. In England,

Scotland and Wales ADHD incidence rates were highest amongst patients belonging to practices in

the most deprived areas (IMD quintile 5). In all three nations, and in half of England’s 10 CPRD

regions, incidence rates amongst individuals in quintile 5 (the most deprived quintile) were

significantly higher (p ≤0.05) than those of individuals in quintile 1 (the least deprived quintile).

These findings lend support to the theory that an individual’s likelihood of being diagnosed with

ADHD may be increased by exposure to socioeconomic deprivation. This observation is in line with

the findings of several studies from around the world (Döpfner, Breuer, Wille, Erhart, & Ravens-

Sieberer, 2008; Froehlich et al., 2007; Hjern, Weitoft, & Lindblad, 2010; Nomura et al., 2012) and

from the UK specifically (Green, McGinnity, Meltzer, Ford, & Goodman, 2005; Russell et al., 2014). It

is beyond the capabilities of this study to identify an underlying reason for this apparent link

between deprivation and the development of ADHD. The measure of deprivation used by this study

(the Index of Multiple Deprivation 2010) assesses several distinct aspects of socioeconomic

deprivation, rather than just one specific characteristic or risk factor that could then be investigated

further. However, by identifying that local deprivation and ADHD often coexist, this study highlights

the need for adequate service provision in deprived areas of the United Kingdom.

In line with current consensus, this study found that ADHD incidence rates were significantly

higher in males than in females in every year studied and across the study period as a whole. The

overall incidence rate observed amongst males was approximately 4.3 times that of females. This

gender imbalance is not exceptional when compared to other studies in the literature.

Epidemiological studies have typically found ADHD to be two to four times more common in males

than in females (National Institute for Health and Care Excellence, 2008a). Though the association

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between ADHD and gender is long-standing, in recent years its underlying reasons have come under

increased scrutiny. Boys are more likely to have ADHD characterised by impulsivity and hyperactivity,

whilst inattentive symptoms tend to predominate in girls (Kooij et al., 2010). It has therefore been

hypothesised that ADHD in males is simply more visible and attention-grabbing than it is in females,

leading to higher rates of recognition and diagnosis.

In both males and females, ADHD diagnosis was most commonly observed during patients’

primary school years (especially the ages seven through to nine). This is broadly in line with the

findings of earlier UK studies. One study observed peak incidence rates in 6-17 year olds; the mean

age of diagnosis amongst this group was 9.8 years (standard deviation: 2.8) (Holden et al., 2013).

Another reported peak incidence rates in 6-12 year olds (McCarthy et al., 2012). Given that the

underlying causes of ADHD are yet to be fully understood, it is possible that the disorder commonly

develops or first manifests itself around this time in patients’ lives. However, it may be the case that

existing ADHD is particularly likely to be recognised as a problem during a child’s early years of

formal schooling. Whilst a child may have exhibited tendencies towards hyperactivity, impulsivity

and inattention at an earlier stage, its disruptive impacts on early schooling could be the catalyst for

seeking a medical assessment and subsequent diagnosis.

Across the UK as a whole, ADHD incidence peaked in 2012 and was higher in the last two

years of the study than it had been in any of the preceding eight years. This study’s results showed

some agreement with those presented by Holden et al. (2013), which examined the period 1998 to

2010. Both studies found that incidence fell between 2004 and 2005, before rising in 2006 and 2007

and then falling in 2008. However, the continued decline in incidence rates observed by the earlier

study in 2009 and 2010 was not observed by this study. Concordance with the findings of McCarthy

et al. (2012) was somewhat mixed. That study (covering the years 2003 – 2008) observed peak

incidence rates in 2006 and a slight decline in the following year; this study observed an increase in

incidence rates between 2006 and 2007. Though comparing incidence rates across the three UK

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studies is an interesting exercise, drawing firm conclusions from these comparisons is not possible.

Each study used slightly different case definitions, focussed on different study populations (treated

and untreated vs treated only, all ages vs under 19s) and used somewhat different sampling

populations. What is clear is that ADHD diagnostic rates amongst children and adolescents in the UK

have not been on a continual upward trend over the last decade, or even during the last five years. It

is unclear whether the increases observed in the last two years of this particular study are outliers,

or will be sustained in the coming years.

Strength and Weaknesses

The study’s main strength was its data source. CPRD is one of the largest primary care databases in

the world (Thomas et al., 2013). It provided a large sample of real-life patient data, allowing several

thousand real-world ADHD patients to be identified, their characteristics scrutinised and statistically

significant observations to be made. Its population is drawn from all four nations of the UK, and has

been evaluated as being representative of the UK’s general population (Bushe et al., 2015; Herrett et

al., 2015; Holden et al., 2013; Thomas et al., 2013; West et al., 2014). As such, this study’s findings

should be generalisable to the UK population as a whole (Thomas et al., 2013). Furthermore, the

validity of medical diagnoses in CPRD have been confirmed by several studies and for several

different conditions (Herrett et al., 2015; Herrett, Thomas, Schoonen, Smeeth, & Hall, 2010), though

not for ADHD specifically.

The reliable identification of valid ADHD cases represented the study’s biggest challenge.

Within CPRD, prescriptions for drugs are not directly linked with their indication for use. Therefore a

patient with Read code(s) referring to ADHD in their records, plus documented prescriptions for a

licensed ADHD medication may not conclusively represent a diagnosed, pharmaceutically-treated

ADHD patient (however suggestive this combination may be). As stated in NICE’s guidance on the

management of ADHD, diagnosed ADHD may not require pharmacological intervention in all cases

(National Institute for Health and Care Excellence, 2008b). Identifying these diagnosed, non-

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pharmaceutically treated patients posed further potential problems. Firstly, ADHD patients who

received no pharmaceutical treatment from their GPs may have received pharmaceutical treatment

elsewhere (for example, as a hospital outpatient). This would not necessarily have been detectable

using CPRD data. Secondly, the presence of a single relevant Read code in a patient’s CPRD record

may not conclusively denote a diagnosed, untreated ADHD patient. Holden et al. (2013) determined

that, in untreated patients, the presence of two ADHD-related diagnostic codes was required to

denote a diagnosis of ADHD. They hypothesised that, in cases of suspected ADHD, GPs would

document a provisional diagnosis of ADHD in a patient’s records before referring them for specialist

assessment. If specialist assessment confirmed a diagnosis of ADHD, this would be confirmed by the

presence of a second ADHD-related Read code in patients’ records. This ‘two code’ hypothesis

assumed that prescribers acted uniformly in their diagnostic and documentary practices, and the

authors conceded that it may have led to ‘fully diagnosed’ ADHD patients being overlooked. It is

unclear whether this or the earlier study’s approach to identifying untreated patients was best.

However, a sensitivity analysis revealed that the majority of untreated patients defined by this study

(82.4%) had at least two ADHD-related diagnostic codes in their CPRD record.

Estimating patients’ deprivation status using the IMD details of their general practice made

use of readily available data, but posed the risk of ecological fallacy. It was recognised that patients

would not always reside in the same LSOA/DZ as their general practice; in some cases they may live

in areas with a radically different level of socioeconomic deprivation. In these patients, their

practice-level IMD would not give an accurate impression of their exposure to deprivation. Despite

this, in the sample of English patients for whom data was available, practice-level IMD and patient-

level IMD showed relatively close correspondence.

Conclusions

Statistically significant differences in ADHD incidence were observed between the UK’s four

constituent nations, and between England’s ten CPRD regions. In addition, ADHD incidence showed a

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positive association with socioeconomic deprivation. Taking the UK as a whole, annual ADHD

incidence rates remained relatively stable between 2004 and 2013, but were highest in the last two

years studied.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship,

and/or publication of this article. This study is based on data from the Clinical Practice Research

Datalink obtained under licence from the UK Medicines and Healthcare products Regulatory Agency

(MHRA). However, the interpretation and conclusions contained in this paper are those of the

authors alone. The study protocol was approved by the independent scientific advisory committee

(ISAC) for CPRD research (reference number: 15_036R).

Funding:

This paper was produced as part of a PhD programme funded by the University of Manchester. The

author(s) received no other financial support for the research, authorship, and/or publication of this

article.

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Tables and FiguresTable 1: ADHD incidence

ADHD cases

Person-years at risk (nearest whole year)

Incidence rate 2004-2013 per 10,000 person-years

(95% CI)

Adjusted incidence rate

ratio*(95% CI)

UK (total) 10,284 8,808,590 11.67 (11.45 – 11.90) N/AGender

Male 8,407 4,512,611 18.63 (18.24 – 19.03) 1.00 (ref)Female 1,877 4,295,979 4.37 (4.18 – 4.57) 0.23 (0.22 – 0.25)Nation

England 7,984 6,851,691 11.65 (11.40 – 11.91) 1.00 (ref)Scotland 976 917,860 10.63 (9.99 – 11.32) 0.97 (0.91 – 1.04)

Wales 903 723,038 12.49 (11.70 – 13.33) 1.09 (1.02 – 1.17)Northern Ireland 421 316,002 13.32 (12.11 – 14.66) 1.26 (1.14 – 1.39)

Deprivation quintile

(practice-level)(least deprived)

1 1,539 1,646,181 9.35 (8.89 – 9.82) 1.00 (ref)2 1,864 1,706,407 10.92 (10.45 – 11.43) 1.16 (1.08 – 1.24)3 2,029 1,786,384 11.36 (10.87 – 11.86) 1.19 (1.12 – 1.27)4 2,359 1,904,857 12.38 (11.89 – 12.89) 1.30 (1.22 – 1.39)5

(most deprived)2,493 1,764,762 14.13 (13.58 – 14.69) 1.49 (1.39 – 1.58)

Age group1-3 years old 420 305,937 13.73 (12.48 – 15.11) 1.00 (ref)4-6 years old 1,984 847,226 23.42 (22.41 – 24.47) 1.71 (1.54 – 1.89)7-9 years old 3,606 1,191,796 30.26 (29.29 – 31.26) 2.20 (1.99 – 2.44)

10-12 years old 2,222 1,390,343 15.98 (15.33 – 16.66) 1.17 (1.06 – 1.30)13-15 years old 1,592 1,494,459 10.65 (10.14 – 11.19) 0.78 (0.70 – 0.87)16-18 years old 460 3,578,831 1.29 (1.17 – 1.41) 0.09 (0.08 – 0.11)

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Table 2: ADHD incidence by English region

ADHD cases

Person years at risk (nearest whole year)

Incidence rate 2004-2013 per 10,000 person years

(95% CI)

Adjusted incidence rate

ratio*(95% CI)

England (total) 7,984 6,851,691 11.65 (11.40 – 11.91) N/AEnglish region

North West 1,078 1,043,531 10.33 (9.73 – 10.97) 1.00 (ref)North East 168 155,931 10.77 (9.26 – 12.53) 0.98 (0.83 – 1.15)

Yorkshire and the Humber

209 258,378 8.09 (7.06 – 9.26) 0.73 (0.63 – 0.85)

East Midlands 335 281,963 11.88 (10.67 – 13.22) 1.14 (1.00 – 1.28)West Midlands 791 769,010 10.29 (9.59 – 11.02) 0.96 (0.87 – 1.05)East of England 1,174 795,373 14.76 (13.94 – 15.63) 1.53 (1.40 – 1.66)

South West 673 683,147 9.85 (9.13 – 10.62) 0.99 (0.90 – 1.09)South Central 1,175 1,001,399 11.73 (11.08 – 12.42) 1.30 (1.19 – 1.42)

London 920 942,061 9.77 (9.15 – 10.42) 0.92 (0.84 – 1.01)South East Coast 1,461 920,899 15.86 (15.07 – 16.70) 1.67 (1.54 – 1.81)

*Adjusted for gender, deprivation quintile, age group

510

1520

AD

HD

inci

denc

e (p

er 1

0,00

0 pe

rson

yea

rs a

t ris

k)

2004 2006 2008 2010 20122005 2007 2009 2011 2013

Year

Overall MaleFemale

Figure 1: UK incidence rate (2004-2013)

19

010

2030

4050

AD

HD

inci

denc

e (p

er 1

0,00

0 pe

rson

yea

rs a

t ris

k)

1-3 4-6 7-9 10-12 13-15 16-18

Age (years)

Figure 2: ADHD incidence by age of diagnosis

Male Female

810

1214

16A

DH

D in

cide

nce

(per

10,0

00 p

erso

n ye

ars

at ri

sk)

2004 2006 2008 2010 20122005 2007 2009 2011 2013Year

IMD quintile 1 (least deprived)IMD2IMD3IMD4IMD quintile 5 (most deprived)

Figure 3: Annual incidence rate (2004-2013) by deprivation quintile

20

510

1520

AD

HD

inci

denc

e (p

er 1

0,00

0 pe

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t ris

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1 2 3 4 5

IMD quintile (practice-level)

England Northern IrelandWales Scotland

Figure 4: ADHD incidence by deprivation quintile

21

References

Arns, M., van der Heijden, K. B., Arnold, L. E., & Kenemans, J. L. (2013). Geographic variation in the prevalence of attention-deficit/hyperactivity disorder: the sunny perspective. Biological Psychiatry, 74(8), 585–90. http://doi.org/10.1016/j.biopsych.2013.02.010

Bhaskaran, K., Forbes, H. J., Douglas, I., Leon, D. A., & Smeeth, L. (2013). Representativeness and optimal use of body mass index (BMI) in the UK Clinical Practice Research Datalink (CPRD). BMJ Open, 3(9), e003389. http://doi.org/10.1136/bmjopen-2013-003389

Bolea-Alamañac, B., Nutt, D. J., Adamou, M., Asherson, P., Bazire, S., Coghill, D., … Young, S. J. (2014). Evidence-based guidelines for the pharmacological management of attention deficit hyperactivity disorder: update on recommendations from the British Association for Psychopharmacology. Journal of Psychopharmacology (Oxford, England), 28(3), 179–203. http://doi.org/10.1177/0269881113519509

Bushe, C., Wilson, B., Televantou, F., Belger, M., & Watson, L. (2015). Understanding the treatment of attention deficit hyperactivity disorder in newly diagnosed adult patients in general practice: a UK database study. Pragmatic and Observational Research, Volume 6, 1. http://doi.org/10.2147/POR.S74161

Chisholm, J. (1990). The Read clinical classification. BMJ (Clinical Research Ed.), 300(6732), 1092. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1662793&tool=pmcentrez&rendertype=abstract

Clinical Practice Research Datalink. (2014). Release Notes - CPRD GOLD December 2014.

Döpfner, M., Breuer, D., Wille, N., Erhart, M., & Ravens-Sieberer, U. (2008). How often do children meet ICD-10/DSM-IV criteria of attention deficit-/hyperactivity disorder and hyperkinetic disorder? Parent-based prevalence rates in a national sample--results of the BELLA study. European Child & Adolescent Psychiatry, 17 Suppl 1, 59–70. http://doi.org/10.1007/s00787-008-1007-y

Froehlich, T. E., Lanphear, B. P., Epstein, J. N., Barbaresi, W. J., Katusic, S. K., & Kahn, R. S. (2007). Prevalence, recognition, and treatment of attention-deficit/hyperactivity disorder in a national sample of US children. Archives of Pediatrics & Adolescent Medicine, 161(9), 857–64. http://doi.org/10.1001/archpedi.161.9.857

Fulton, B. D., Scheffler, R. M., Hinshaw, S. P., Levine, P., Stone, S., Brown, T. T., & Modrek, S. (2009). National variation of ADHD diagnostic prevalence and medication use: health care providers and education policies. Psychiatric Services (Washington, D.C.), 60(8), 1075–83. http://doi.org/10.1176/appi.ps.60.8.1075

Green, H., McGinnity, Á., Meltzer, H., Ford, T., & Goodman, R. (2005). Mental health of children and young people in Great Britain, 2004. Retrieved June 8, 2015, from http://www.esds.ac.uk/doc/5269/mrdoc/pdf/5269technicalreport.pdf

Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology. http://doi.org/10.1093/ije/dyv098

22

Herrett, E., Thomas, S. L., Schoonen, W. M., Smeeth, L., & Hall, A. J. (2010). Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British Journal of Clinical Pharmacology, 69(1), 4–14. http://doi.org/10.1111/j.1365-2125.2009.03537.x

Hjern, A., Weitoft, G. R., & Lindblad, F. (2010). Social adversity predicts ADHD-medication in school children--a national cohort study. Acta Paediatrica (Oslo, Norway : 1992), 99(6), 920–4. http://doi.org/10.1111/j.1651-2227.2009.01638.x

Hoffmann, M. S., Polanczyk, G. V, Kieling, C., Dos Santos, I. P., Willcutt, E. G., Rohde, L. A., & Salum, G. A. (2014). Attention-deficit/hyperactivity disorder and solar irradiance: a cloudy perspective. Biological Psychiatry, 76(8), e19–20. http://doi.org/10.1016/j.biopsych.2013.07.044

Holden, S. E., Jenkins-Jones, S., Poole, C. D., Morgan, C. L., Coghill, D., & Currie, C. J. (2013). The prevalence and incidence, resource use and financial costs of treating people with attention deficit/hyperactivity disorder (ADHD) in the United Kingdom (1998 to 2010). Child and Adolescent Psychiatry and Mental Health, 7(1), 34. http://doi.org/10.1186/1753-2000-7-34

Joint Formulary Committee. (2015). British National Formulary (online). Retrieved September 8, 2015, from http://www.medicinescomplete.com

Kooij, S. J. J., Bejerot, S., Blackwell, A., Caci, H., Casas-Brugué, M., Carpentier, P. J., … Asherson, P. (2010). European consensus statement on diagnosis and treatment of adult ADHD: The European Network Adult ADHD. BMC Psychiatry, 10, 67.

Mansell, P. (2013). HRA approvals clear CPRD for broad data linkage. Retrieved June 19, 2014, from http://www.pharmatimes.com/article/13-10-01/HRA_approvals_clear_CPRD_for_broad_data_linkage.aspx

McCarthy, S., Wilton, L., Murray, M. L., Hodgkins, P., Asherson, P., & Wong, I. C. K. (2012). The epidemiology of pharmacologically treated attention deficit hyperactivity disorder (ADHD) in children, adolescents and adults in UK primary care. BMC Pediatrics, 12(1), 78. http://doi.org/10.1186/1471-2431-12-78

McDonald, D. C., & Jalbert, S. K. (2013). Geographic Variation and Disparity in Stimulant Treatment of Adults and Children in the United States in 2008. Psychiatric Services, 64(11), 1079–1086. http://doi.org/10.1176/appi.ps.004442012

Met Office. (2014). Solar energy. Retrieved September 8, 2015, from http://www.metoffice.gov.uk/renewables/solar

Murray, M. L., Hsia, Y., Glaser, K., Simonoff, E., Murphy, D. G. M., Asherson, P. J., … Wong, I. C. K. (2014). Pharmacological treatments prescribed to people with autism spectrum disorder (ASD) in primary health care. Psychopharmacology, 231(6), 1011–21. http://doi.org/10.1007/s00213-013-3140-7

National Audit Office. (2012). Healthcare across the UK: A comparison of the NHS in England, Scotland, Wales and Northern Ireland. Retrieved June 11, 2015, from http://www.nao.org.uk/wp-content/uploads/2012/06/1213192es.pdf

National Institute for Health and Care Excellence. (2008a). Attention deficit hyperactivity disorder (ADHD): full guideline. Retrieved June 11, 2015, from

23

https://www.nice.org.uk/guidance/cg72/resources/cg72-attention-deficit-hyperactivity-disorder-adhd-full-guideline-2

National Institute for Health and Care Excellence. (2008b). Attention deficit hyperactivity disorder: diagnosis and management of ADHD in children, young people and adults. Retrieved June 11, 2015, from http://publications.nice.org.uk/attention-deficit-hyperactivity-disorder-cg72/guidance

National Institute for Health and Care Excellence. (2013). NICE support for commissioning for attention deficit hyperactivity disorder. Retrieved from http://www.nice.org.uk/guidance/qs39/resources/qs39-attention-deficit-hyperactivity-disorder-nice-support-for-commissioning2

NHS Choices. (2014). Tips on how to choose a GP - Patient choice - NHS Choices. Retrieved from http://www.nhs.uk/choiceintheNHS/Yourchoices/GPchoice/Pages/ChoosingaGP.aspx

Nomura, Y., Marks, D. J., Grossman, B., Yoon, M., Loudon, H., Stone, J., & Halperin, J. M. (2012). Exposure to gestational diabetes mellitus and low socioeconomic status: effects on neurocognitive development and risk of attention-deficit/hyperactivity disorder in offspring. Archives of Pediatrics & Adolescent Medicine, 166(4), 337–43. http://doi.org/10.1001/archpediatrics.2011.784

Office of National Statistics. Super Output Area (SOA). Retrieved March 20, 2015, from http://www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/census/super-output-areas--soas-/index.html

Rowlingson, B., Lawson, E., Taylor, B., & Diggle, P. J. (2013). Mapping English GP prescribing data: a tool for monitoring health-service inequalities. BMJ Open, 3(1). http://doi.org/10.1136/bmjopen-2012-001363

Russell, G., Ford, T., Rosenberg, R., & Kelly, S. (2014). The association of attention deficit hyperactivity disorder with socioeconomic disadvantage: alternative explanations and evidence. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 55(5), 436–45. http://doi.org/10.1111/jcpp.12170

Springate, D. A., Kontopantelis, E., Ashcroft, D. M., Olier, I., Parisi, R., Chamapiwa, E., & Reeves, D. (2014). ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records. PloS One, 9(6), e99825. http://doi.org/10.1371/journal.pone.0099825

StataCorp. (2013). Stata. College Station; TX: StataCorp.

Thomas, K. H., Martin, R. M., Davies, N. M., Metcalfe, C., Windmeijer, F., & Gunnell, D. (2013). Smoking cessation treatment and risk of depression, suicide, and self harm in the Clinical Practice Research Datalink: prospective cohort study. BMJ (Clinical Research Ed.), 347(oct11_1), f5704. http://doi.org/10.1136/bmj.f5704

Thomas, R., Mitchell, G. K., & Batstra, L. (2014). Attention‐deficit/hyperactivity disorder: are we helping or harming? British Medical Journal, 6172(November), 1–7. http://doi.org/10.1136/bmj.f6172

24

Thomas, T. M. (2014). Personal Correspondence (CPRD Knowledge Centre). CPRD Knowledge Centre.

UK Data Service. (2014). UK Data Service Census » Deprivation data. Retrieved June 22, 2014, from http://census.ukdataservice.ac.uk/get-data/related/deprivation.aspx

West, J., Fleming, K. M., Tata, L. J., Card, T. R., & Crooks, C. J. (2014). Incidence and prevalence of celiac disease and dermatitis herpetiformis in the UK over two decades: population-based study. The American Journal of Gastroenterology, 109(5), 757–68. http://doi.org/10.1038/ajg.2014.55

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Incidence calculation

The incidence of ADHD amongst the CPRD population during a given year was calculated using the

following equation:

Incidence of ADHD = Number of incident cases in year X (numerator)

in year X Person years at risk in CPRD in year X (denominator)

(per 10,000 person years)

The incidence denominator for each year comprised of person-time contributed by individuals who were considered ‘at risk’ of developing ADHD in that year.

Patients without a prior diagnosis of ADHD contributed person-time from the latest point of the following:

1. The date their practice became ‘up to standard’.

2. The date at which point they had been registered with their practice for 365 consecutive days.

3. The study start date (1/1/2004).

Patients then contributed person-time until the earliest point of the following:

1. The date they received a diagnosis of ADHD (if applicable)

2. The date at which they transferred out of their CPRD-registered GP practice (if applicable).

3. The date of their death (if applicable).

4. The last date on which data was transferred from their practice to CPRD.

5. January 1st of the year they turned 19 (patients’ exact day and month of birth was not available; patients were therefore assumed to have been born on January 1st of each year).

6. The study end date (31/12/2013).