Background
During the last two decades, an increasing number of individuals have received their first diagnosis of attention-deficit/hyperactivity disorder (ADHD) in young adult age, typically between 20 and 30 years of age (Edvinsson, Reference Edvinsson2017; Giacobini, Medin, Ahnemark, Russo, & Carlqvist, Reference Giacobini, Medin, Ahnemark, Russo and Carlqvist2018; Rydell, Lundström, Gillberg, Lichtenstein, & Larsson, Reference Rydell, Lundström, Gillberg, Lichtenstein and Larsson2018). Young adulthood is a crucial time for establishment on the labour market and ADHD can have a considerable detrimental effect on workability and may result in hardship to obtain and retain a job (Helgesson et al., Reference Helgesson, Björkenstam, Rahman, Gustafsson, Taipale, Tanskanen and Mittendorfer-Rutz2021; Helgesson, Tinghog, Niederkrotenthaler, Saboonchi, & Mittendorfer-Rutz, Reference Helgesson, Tinghog, Niederkrotenthaler, Saboonchi and Mittendorfer-Rutz2017; Hirvikoski, Lindström, Nordin, Jonsson, & Bölte, Reference Hirvikoski, Lindström, Nordin, Jonsson and Bölte2017; Johnson, Reference Johnson2001; Kupper et al., Reference Kupper, Haavik, Drexler, Ramos-Quiroga, Wermelskirchen, Prutz and Schauble2012). Studies on labour market integration among young adults with ADHD are scarce and existing studies are mostly performed on small samples and with a focus on reasons behind poor work performance, rather than assessing the rate of employment among individuals in this group (Kupper et al., Reference Kupper, Haavik, Drexler, Ramos-Quiroga, Wermelskirchen, Prutz and Schauble2012). Different concepts have been proposed to describe the varying degrees of labour market integration within a population (Gustafsson, Aronsson, Marklund, Wikman, & Floderus, Reference Gustafsson, Aronsson, Marklund, Wikman and Floderus2014; Muñoz-Bullón & Malo, Reference Muñoz-Bullón and Malo2003). One of them, the ‘core-peripheral’ model, gives a theoretical framework of labour market integration as a continuum from attachment (core) to marginalization (peripheral) (Atkinson, Reference Atkinson1984; Gustafsson et al., Reference Gustafsson, Aronsson, Marklund, Wikman and Floderus2014). This model can in a dynamic way outline to which extent young adults with ADHD have a connection to the labour market and can be of great importance for the choice of potential measures taken by healthcare and other work-related stakeholders.
At the time of diagnosis of ADHD, a battery of measures aimed at facilitating everyday life and increasing the ability to work are supposed to be introduced and is therefore a suitable time point to assess labour market integration. In epidemiological research, sequence analysis is a novel approach aiming to analyse the dynamic transitions of an outcome of interest within a defined population (Kjeldgård, Stigson, Alexanderson, & Friberg, Reference Kjeldgård, Stigson, Alexanderson and Friberg2020; Murley et al., Reference Murley, Tinghög, Karampampa, Hillert, Alexanderson and Friberg2020). Sequence analysis along with the ‘core-periphery’ model is therefore well-suited to visualize and categorize different patterns of labour market integration as well as the development over time regarding labour market integration among young adults with ADHD. This study can therefore by its focus on employment give new insights into the transition of work participation among young adults with ADHD. Also, sociodemographic factors such as sex, age at diagnosis, family composition, type of living area, country of birth as well as comorbid disorders may affect the relationship between ADHD and later labour market integration (Aduen et al., Reference Aduen, Kofler, Sarver, Wells, Soto and Cox2018; Bjorkenstam, Pierce, Bjorkenstam, Dalman, & Kosidou, Reference Bjorkenstam, Pierce, Bjorkenstam, Dalman and Kosidou2020; Chen, Lee, Yeh, & Lin, Reference Chen, Lee, Yeh and Lin2013; Cortese et al., Reference Cortese, Sun, Zhang, Sharma, Chang, Kuja-Halkola and Faraone2018; Gershon & Gershon, Reference Gershon and Gershon2002; Giacobini et al., Reference Giacobini, Medin, Ahnemark, Russo and Carlqvist2018; Haukenes, Gjesdal, Rortveit, Riise, & Maeland, Reference Haukenes, Gjesdal, Rortveit, Riise and Maeland2012; Helgesson et al., Reference Helgesson, Tinghog, Niederkrotenthaler, Saboonchi and Mittendorfer-Rutz2017; Kupper et al., Reference Kupper, Haavik, Drexler, Ramos-Quiroga, Wermelskirchen, Prutz and Schauble2012; Shaw et al., Reference Shaw, Kristman, Williams-Whitt, Soklaridis, Huang, Côté and Loisel2014; Virtanen, Liukkonen, Vahtera, Kivimäki, & Koskenvuo, Reference Virtanen, Liukkonen, Vahtera, Kivimäki and Koskenvuo2003; Waenerlund, Gustafsson, Virtanen, & Hammarström, Reference Waenerlund, Gustafsson, Virtanen and Hammarström2011; Zetterqvist, Asherson, Halldner, Långström, & Larsson, Reference Zetterqvist, Asherson, Halldner, Långström and Larsson2013). By also assessing these covariates, this study will contribute to an increased understanding of the dynamic processes of labour market integration among young adults diagnosed with ADHD.
The aims of the study were: (1) to investigate patterns of labour market integration before and following an incident diagnosis of adult ADHD and (2) to study the association between the patterns of labour market integration and sociodemographic factors and comorbid disorders.
Methods
Study population
Register data from the National Patient Register (NPR), held by the National Board of Health and Welfare, were used to identify young adults who were diagnosed with ADHD for the first time at the age of 20–29 from 1 January 2006 to 31 December 2011 (N = 11 026). The reason behind the choice of using an incident population diagnosed with ADHD was that we can thereby assess the outcome both before and after the diagnosis of ADHD. Hence, it allows us to study how a diagnosis, with associated interventions aimed to simplify everyday life and working life, may affect subsequent labour market integration.
ADHD was defined as having ICD-10 (International Classification of Diseases, the 10th edition) code F90 as the primary or secondary diagnosis from inpatient (from 1987) or specialized outpatient (from 2001) healthcare. The day of the first diagnosis of ADHD was chosen as the cohort entry date (CED) and all were residents in Sweden on 31 December two calendar years before CED. Those who had a record of prescribed ADHD medication in the Prescribed Drug Register (PDR), held by the National Board of Health and Welfare (from July 2005) for codes: N06BA01–N06BA13 and C02AC01–C02AC02 according to the Anatomic Therapeutic Chemical Classifications (ATC) before the CED (n = 2668) or emigrated (n = 133) or died (n = 180) during the follow-up period of 5 years were excluded. The final study population consisted of 8045 young adults. A control group of young adults without ADHD were matched on age, sex and educational level (n = 8018 due to missing data regarding the outcome for 27 individuals).
Variables
Outcome measures
The outcome measure in this study, labour market integration, was categorized according to the ‘core-peripheral’ scale (Atkinson, Reference Atkinson1984; Gustafsson et al., Reference Gustafsson, Aronsson, Marklund, Wikman and Floderus2014). All information was obtained annually from the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA), held by Statistics Sweden, 1 year before and 5 years after CED (2005–2016) and included the five following positions, from a strong (core) to a weak (peripheral) connection to the labour market:
• ‘Core’: income from work and where time with social benefits (i.e. disability pension, sickness absence, municipal support, parental leave, unemployment) or studies not exceeding 6 months during a year.
• ‘Close to core’: studying or on parental leave for more than 6 months during a year.
• ‘Middle’: more than 180 gross days of sickness absence or more than 180 days of unemployment during a year.
• ‘Close to peripheral’: municipal support or economic inactivity (which equals no registered income or social benefits in LISA) for more than 6 months during a year.
• ‘Peripheral’: more than 180 gross days of disability pension during a year.
Covariates
Information on covariates was linked from the following databases using the anonymized personal identity number (Ludvigsson et al., Reference Ludvigsson, Andersson, Ekbom, Feychting, Kim, Reuterwall and Olausson2011): (1) LISA (Ludvigsson, Svedberg, Olen, Bruze, & Neovius, Reference Ludvigsson, Svedberg, Olen, Bruze and Neovius2019): sex, age, educational level, family composition, type of living area and country of birth (all measured on 31 December in the year before CED). Those with missing data regarding educational level (229 individuals among those with ADHD and 203 in the control group) were included in the group with low educational level and those with missing data regarding country of birth were included in the group of those born outside Sweden (six individuals among those with ADHD and one in the control group); (2) NPR (Ludvigsson et al., Reference Ludvigsson, Andersson, Ekbom, Feychting, Kim, Reuterwall and Olausson2011): inpatient healthcare and specialized outpatient healthcare on comorbid disorders measured during the year before CED: mental disorders included depression/bipolar disorder (ICD-10: F30–F34), anxiety/stress-related disorders (ICD-10: F40–F48), autism-spectrum disorder (ICD-10: F84), substance use disorder (ICD-10: F10–F19), behavioural/emotional disorders (ICD-10: F91–F98), intellectual disabilities/developmental disorders (ICD-10: F70–F83, F85–F89), schizophrenia/psychoses (ICD-10: F20–F29), other mental disorders (ICD-10: remaining codes starting with F). Somatic disorders included musculoskeletal disorders (ICD-10: M01–M99), accidents (ICD-10: S00–S99) and other somatic disorders (ICD-10: remaining codes except O.80 and Z00–99); (3) the Cause of Death Register, provided by the National Board of Health and Welfare (Brooke et al., Reference Brooke, Talbäck, Hörnblad, Johansson, Ludvigsson, Druid and Ljung2017): date of death (during follow-up); and (4) PDR (2006–2016) (Wettermark et al., Reference Wettermark, Hammar, Fored, Leimanis, Otterblad Olausson, Bergman and Rosén2007): information of drug prescriptions for diabetes mellitus (ATC: A10, used for defining individuals with diabetes as a comorbid disorder as most patients are treated in primary care, and no information is available in national registers) and drugs used in addictive disorders (ATC: N07B, used for defining individuals with comorbid substance use disorder), measured during the year before CED.
Statistical analyses
Descriptive analyses were performed to illustrate the distributions of sociodemographic covariates and comorbidities in the study population. Sequences of annual labour market integration were estimated during the 7 years (from 1 calendar year before to 5 calendar years after the year of the diagnosis Y 0 (Y −1 to Y 5), during the period 2005–2016) with sequence analysis using TraMineR in R (Gabadinho, Ritschard, Muller, & Studer, Reference Gabadinho, Ritschard, Muller and Studer2011). First, the distance between sequences was calculated using optimal matching with transition matrix costs. Then, cluster analysis was performed to group similar sequences to identify clusters of labour market integration patterns. Hierarchical cluster analysis with Ward's linkage algorithm and the dissimilarity measures were used (Gabadinho et al., Reference Gabadinho, Ritschard, Muller and Studer2011). Thereafter, a sequence index plot of the population was constructed to visualize the heterogeneity in the individual sequences; the sequences were sorted by the state assigned in Y 5, i.e. the last year of the follow-up period. In addition, a density plot was constructed, illustrating the accumulation of each labour market position every year for all the clusters.
Multinomial logistic regression was used to analyse the association between sociodemographic factors, comorbid disorders and different clusters of labour market integration. Crude and mutually adjusted odds ratios (ORs), including their 95% confidence intervals (CIs) and Nagelkerke's R 2 values were calculated for these associations. All statistical analyses were performed by R (version 3.5.0).
Results
Among young adults with an incident diagnosis of ADHD in adult age, 55% were men and 45% were women (Table 1). A majority (56%) was diagnosed at the age of 19–24 and the vast majority had either elementary school (48%, <10 years of education) or high school (44%, 10–12 years of education) as the highest attained educational level. About 31% had some amount of work income during the calendar year before the diagnosis of ADHD. The most frequent comorbid mental disorders, occurring the year before the incident ADHD, were anxiety/stress-related disorders (25%), followed by depression/bipolar disorder (19%) and substance use (16%). About 3% had a comorbid autism-spectrum disorder and 2% had comorbid schizophrenia/psychoses. The prevalence of most mental disorders seems to be around 10 times lower in the control group of young adults without ADHD.
Table 1. Distribution of sociodemographic and medical characteristics among all young adults with an incident diagnosis of attention-deficit/hyperactivity disorder (ADHD) and a matched control group without ADHD during 2006–2011
a With or without children living at home.
b Type of living area: big cities – Stockholm, Gothenburg and Malmö; medium-sized cities – cities with more than 90 000 inhabitants within 30 km distance from the centre of the city; small cities/villages – remaining areas of living.
c All comorbid disorders were measured during the 1 year before the cohort entry date, which was the first diagnosis of ADHD in adult age, for depression and bipolar disorder [international classification of diseases, version 10 (ICD-10): F30–F34], anxiety and stress-related disorders (ICD-10: F40–F48), autism-spectrum disorder (ICD-10: F84), substance abuse (ICD-10: F10–F19 and ATC: N07B), behavioural and emotional disorders (ICD-10: F91–F98), intellectual disabilities/developmental disorders (ICD-10: F70–F83, F85–F89), schizophrenia/psychoses (ICD-10: F20–F29), other mental disorders (all the other codes left starting with F). Somatic disorders included musculoskeletal disorders (ICD-10: M01–M99), accidents (ICD-10: S00–S99) and other somatic disorders (all other ICD-10 codes except the above-mentioned, O.80 and Z00–99) measured year before CED.
Identified clusters
Using the sequence analysis (cluster analysis) and based on values from measures of cluster partition quality,Footnote †Footnote 1 eight clusters of outcome distribution in the direction from the ‘core’ to the ‘peripheral’ labour market position from 1 year before to 5 years after ADHD diagnosis were chosen for both young adults diagnosed with ADHD [Fig. 1, online Supplementary Fig. S1 (controls)]. The clusters found in the control group were similar to the clusters in the group that was diagnosed with ADHD. The differences in comparison to the control group were, however, that individuals diagnosed with ADHD had a peripheral labour market position to a much higher extent, 24% compared to 6% and a less extent had a ‘core’ labour market position, 17% compared to 54%. The sequence index plot made up of all individual sequences sorted on the position in the last period revealed a great heterogeneity among the individual sequences of labour market position among individuals diagnosed with ADHD [Fig. 2, online Supplementary Fig. S2 (controls)]. It is evident that young adults diagnosed with ADHD to a much lower extent had a ‘core’ labour market position compared to the control group. The distributions of characteristics, along with the adjusted associations between these characteristics and the distribution of the clusters compared to the reference cluster (cluster 1) for those diagnosed with ADHD are presented in Table 2 and in the results section (associations for the control group are presented as online Supplementary Table S2). The Nagelkerke's R 2 value for the model with all the included variables among those diagnosed with ADHD was 0.264, meaning that 26% of the variance was explained by the included covariatesFootnote 2 (Table 2). The educational level seemed to explain much of the variance among young adults diagnosed with ADHD.
Figure 1. Clusters of labour market integration among all young adults diagnosed with attention-deficit/hyperactivity disorder (ADHD) in adult age during 2006–2011 (N = 8045).
Figure 2. Sequence index plot of labour market integration, 1 year before until 5 years after an incident diagnosis of attention-deficit/hyperactivity disorder (ADHD), in all young adults, aged 19–29, diagnosed with ADHD in adult age during 2006–2011 (N = 8045).
Table 2. Distribution and adjusted odds ratio (OR) for different factors in each of the eight clusters of labour market marginalization status/year from 1 year before to 5 years after a diagnosis of ADHD (Y −1 to Y +5) among 8045 individuals aged 20–29 years (cluster 1 is reference)
a Cluster 1 was the reference cluster.
b Nagelkerke R 2 and the difference between R 2 for the full model (0.264) and R 2 in a model where the covariate was excluded from the analysis.
c With or without children living at home.
d Type of living area: big cities – Stockholm, Gothenburg and Malmö; medium-sized cities – cities with more than 90 000 inhabitants within 30 km distance from the centre of the city; small cities/villages – remaining areas of living.
e All comorbid disorders were measured 1 year before the cohort entry date, which was the first diagnosis of ADHD in adult age, for depression and bipolar disorder [international classification of diseases, version 10 (ICD-10): F30–F34], anxiety and stress-related disorders (ICD-10: F40–F48), autism-spectrum disorder (ICD-10: F84), substance abuse (ICD-10: F10–F19 and ATC: N07B), behavioural and emotional disorders (ICD-10: F91–F98), intellectual disabilities/developmental disorders (ICD-10: F70–F83, F85–F89), schizophrenia/psychoses (ICD-10: F20–F29), other mental disorders (all the other codes left starting with F). Somatic disorders included musculoskeletal disorders (ICD-10: M01–M99), accidents (ICD-10: S00–S99) and other somatic disorders (all other ICD-10 codes except the above-mentioned, O.80 and Z00–99) measured 1 year before CED.
f Due to ethical reasons, estimates are shown only when there are 8 or more individuals in a group.
Individuals in cluster 1 (17%, reference cluster) had mainly a ‘core’ position in the labour market throughout the study period (Fig. 1). Individuals in cluster 1 were more likely to be men, had attained college/university educational level, were born in Sweden, were living in a single household without children and few had mental comorbidities at baseline (Table 1).
Individuals in both clusters 2 (8%) and 4 (11%) had a gradually increased ‘core’ position after the diagnosis of ADHD. In cluster 2, about 70% of the individuals had a ‘core’ position, while only about 40% in cluster 4 had a ‘core’ position at the end of the study period (Table 2). Individuals in cluster 4 had also to a high extent ‘middle’ position. Individuals in cluster 2 were less likely to be married/in partnership, were aged 25–29 or were men, while individuals in cluster 4 were more likely to be singles with children. Individuals in both clusters 2 and 4 were less likely to have attained high school or university/college education.
Individuals in cluster 3 (5%) had mainly a ‘close to core’ position during the first part of the follow-up period, and an increasingly ‘middle’ or ‘close to peripheral’ position towards the end of the study period. Individuals in cluster 3 were less likely to be men, aged 25–29 or had high school or university/college education but more likely to be living in medium-sized cities.
Individuals in cluster 5 (11%) had a ‘middle’, but increasingly ‘close to peripheral’ position towards the end of the study period. Individuals in cluster 5 were more likely to be men, living in medium-sized cities or small cities/villages or had a comorbid diagnosis of schizophrenia but less likely to have high school or university/college education.
Individuals in cluster 6 (17%) had mainly a ‘close to peripheral’ position, with increasing ‘middle’ and ‘peripheral’ positions towards the end of the study period. Individuals in cluster 6 were more likely to be men, living in medium-sized cities or small cities/villages, having substance use disorder but less likely to have attained high school or university/college education.
Most individuals in clusters 7 (6%) and 8 (24%) had a ‘middle’ to ‘peripheral’ position. In cluster 7, about half of the individuals remained in a ‘middle’ position throughout the study period, while most individuals in cluster 8 had a ‘peripheral’ position during most of the study period. Individuals in both clusters 7 and 8 were more likely to have a comorbid autism-spectrum disorder or schizophrenia/psychoses, living in medium-sized cities or small cities/villages but less likely to have attained high school or university/college education or being married/in partnership.
Discussion
Main findings
By using sequence analysis, eight clusters of labour market integration were found and revealed a considerable heterogeneity of labour market integration during 5 years following a first diagnosis in specialized health care of ADHD in adult age. Labour market integration was in this study defined according to the core-peripheral model as a continuum from a strong (core) to a weak (peripheral) connection to the labour market. A mainly ‘core’ labour market position was found in just about one-sixth (17%) of those diagnosed with ADHD, whereas about one-fourth (24%) belonged to a cluster with a transition to a ‘peripheral’ labour market position. In the control group, more than half (54%) had a ‘core’ labour market position, while only 6% had a ‘peripheral’ labour market position. One-fourth of those diagnosed with ADHD belonged to clusters with increasing ‘middle’ and ‘close to peripheral’ labour market positions. It was foremost educational level that explained different clusters and individuals with elementary school as the highest attained educational level were more likely to belong to a cluster with a transition towards a mainly ‘peripheral’ labour market position. Also, those living outside of big cities, women and those with comorbid autism-spectrum disorders, substance use or schizophrenia/psychoses were more likely to belong to a cluster with a transition towards a ‘peripheral’ labour market position.
Identified clusters
About one-fourth of the individuals diagnosed with ADHD were found to have a transition to a mainly ‘peripheral’ position in the labour market. This is a rather high proportion and in addition, one-fourth had a ‘middle’ or ‘close to a peripheral’ position across the study period. Only about one-sixth of individuals diagnosed with ADHD belonged to a cluster with a ‘core’ position on the labour market, indicating a favourable labour market integration, throughout the study period. Compared with young adults of the same age without ADHD, where 54% had a strong connection to the labour market, this is a concerningly low share. This shows that young adults diagnosed with ADHD for the first time in young adulthood are in a very vulnerable position in the labour market. This tendency of low occupational attainment among adults with ADHD has also been shown by other studies (Helgesson et al., Reference Helgesson, Tinghog, Niederkrotenthaler, Saboonchi and Mittendorfer-Rutz2017, Reference Helgesson, Björkenstam, Rahman, Gustafsson, Taipale, Tanskanen and Mittendorfer-Rutz2021; Kupper et al., Reference Kupper, Haavik, Drexler, Ramos-Quiroga, Wermelskirchen, Prutz and Schauble2012), mostly in individuals diagnosed with ADHD during childhood or adolescence. The present study can add that there seems to be a dynamic process towards a ‘peripheral’ labour market position also among those who are diagnosed with ADHD in adult age. The time of the diagnosis of ADHD seems therefore not to be a turning point; the weak link to the labour market tends to worsen for many after the diagnosis instead.
Of the included covariates, educational level had by far the greatest explaining value of the distribution of the clusters. Those with attained college/university education were most likely to have a ‘core’ labour market position, while those with only elementary school as the highest attained education had a high probability of belonging to the ‘peripheral’ labour market. This is, however, not a new finding (Chen et al., Reference Chen, Mittendorfer-Rutz, Björkenstam, Rahman, Gustafsson, Taipale and Helgesson2021; Helgesson et al., Reference Helgesson, Björkenstam, Rahman, Gustafsson, Taipale, Tanskanen and Mittendorfer-Rutz2021), while the magnitude of the explanatory value of educational attainment was far much higher among young adults with ADHD compared to studies with young adults of the same age with depression, anxiety- or stress disorders (Helgesson et al., Reference Helgesson, Tinghog, Wang, Rahman, Saboonchi and Mittendorfer-Rutz2018). Also, a very high proportion (48%) of those diagnosed with ADHD had only elementary school education as the highest attained education at baseline. In a study of individuals of the same age diagnosed with depression, anxiety- or stress disorders, only about 26% had just elementary school as the highest attained education (Helgesson et al., Reference Helgesson, Tinghog, Wang, Rahman, Saboonchi and Mittendorfer-Rutz2018). As at least upper secondary education is required for most jobs in Sweden, the high share with only elementary school education among young adults with ADHD might therefore explain the high rate of individuals in a ‘peripheral’ labour market position. It is much more likely that the severity of ADHD has made it hard to attain a higher educational level.
In other studies, the difference between work disability and unemployment between men and women diagnosed with ADHD has been shown to be rather small (Chen et al., Reference Chen, Mittendorfer-Rutz, Björkenstam, Rahman, Gustafsson, Taipale and Helgesson2021; Helgesson et al., Reference Helgesson, Björkenstam, Rahman, Gustafsson, Taipale, Tanskanen and Mittendorfer-Rutz2021). In this study, sex had a higher explanatory value. Specifically, men had lower odds of belonging to a ‘peripheral’ labour market position but higher odds of belonging to a ‘close to peripheral’ or a ‘middle’ labour market position, i.e. clusters with a high probability for economic inactivity, municipal support or both unemployment and sickness absence. With a more dynamic measure of labour market integration, there seem to be more obvious differences between men and women. This novel information can be vital for the creation of gender-based treatment strategies.
Individuals with comorbid mental disorders, especially autism-spectrum disorders and schizophrenia/psychoses, had a high probability to have a transition towards a ‘peripheral’ labour market position. Comorbidity with other mental disorders, both depression, anxiety, and bipolar disorders as well as autism-spectrum disorders or schizophrenia/psychoses, are reported to be very common among adults with ADHD (Bjorkenstam et al., Reference Bjorkenstam, Pierce, Bjorkenstam, Dalman and Kosidou2020; Katzman, Bilkey, Chokka, Fallu, & Klassen, Reference Katzman, Bilkey, Chokka, Fallu and Klassen2017). Symptoms of many comorbid mental disorders may overlap the symptoms of ADHD, and this can create challenges both for diagnostics and treatment strategies (Katzman et al., Reference Katzman, Bilkey, Chokka, Fallu and Klassen2017). In general, when ADHD coexists with other psychiatric pathologies, the more severe disorder should be treated first according to evidence-based guidelines and the authors of a review on the treatment of ADHD and comorbid mental disorders concludes that: ‘Early and optimal treatment of ADHD has the potential to change the trajectory of psychiatric morbidity later in life and to substantially improve functional outcomes across the spectrum of psychiatric comorbidities’ (Katzman et al., Reference Katzman, Bilkey, Chokka, Fallu and Klassen2017). It is hence important to have a strategy to treat both the symptoms of ADHD and the comorbid disorder(s) to prevent a weak connection to the labour market (Katzman et al., Reference Katzman, Bilkey, Chokka, Fallu and Klassen2017). Among individuals diagnosed with ADHD and simultaneous comorbid mental disorders, it is important to give those with remaining work ability a chance to stay in the labour market, either in the regular or a sheltered labour market. Many with ADHD have the motivation to work, and with the right prerequisites, they can be as productive as others (Robbins, Reference Robbins2017).
A high proportion of individuals belonged to clusters with a mainly ‘middle’ labour market position, indicating long periods of either unemployment or sickness absence. As many of those individuals will be granted disability pension (Helgesson et al., Reference Helgesson, Tinghog, Wang, Rahman, Saboonchi and Mittendorfer-Rutz2018), it is very important to address these groups at an early stage, to prevent a permanent exclusion from the labour market among them. Individuals that are living outside big cities are more likely to be in these clusters. The labour market has in Sweden been harsh outside of the big cities during the last decades, and unemployment has been higher among individuals living in small towns or rural areas (Helgesson et al., Reference Helgesson, Tinghog, Wang, Rahman, Saboonchi and Mittendorfer-Rutz2018). Support from municipalities, authorities and health care might therefore be especially needed among those who are living in medium-sized towns and small towns/villages.
A group of young adults who have received increased attention during the last decades are called ‘NEET’, an acronym for ‘Not in Education, Employment or Training’ (OECD, 2020; Sellström, Bremberg, & O'campo, Reference Sellström, Bremberg and O'campo2010). An increasing group of young adults with ADHD and other neuropsychiatric disorders have been reported to withdraw from social activities and work and several projects are targeting this group of young adults (Husu & Välimäki, Reference Husu and Välimäki2017). About one-sixth of the study population in this study have a ‘close to peripheral’ labour market position and is a cluster that resembles ‘NEET’. Individuals in this cluster may hence require more support than their peers, and sometimes be targeted by outreach efforts (Husu & Välimäki, Reference Husu and Välimäki2017; Umeda et al., Reference Umeda, Shimoda, Miyamoto, Ishikawa, Tachimori, Takeshima and Kawakami2019). Comorbidity with foremost substance use disorders seems to be of importance for belonging to this cluster. This group might be self-medicating for ADHD and may also need treatment for addiction (Van de Glind et al., Reference Van de Glind, Brynte, Skutle, Kaye, Konstenius, Levin and Franck2020). Here, different stakeholders as employers, the social insurance administration and the healthcare sector can play an even more active role in customizing a working life according to the individual needs of those diagnosed with ADHD.
Strengths and limitations
The major strength of this study was the use of large population-based longitudinal register data linked from five nationwide Swedish registers. The population-based register data made the sequence analysis more powerful, allowing robust clusters and categorizations. Further, many different covariates, both sociodemographic factors and several disorders are known to be comorbid to ADHD, were analysed for their impact on work attachment, providing more information for work rehabilitation programmes in young adults with ADHD diagnosis. Still, some limitations need to be mentioned. The diagnosis of ADHD was based on data from specialized health care and individuals who had previously received ADHD-related medication were excluded. As ADHD is a disorder with typical onset in childhood and due to the lack of retrospective data in childhood and full coverage of the outpatient care register (with 70–75% of coverage in 2001–2004, later the coverage increased to 97%) (National Board of Health and Welfare, 2020), some individuals with an earlier date of a first diagnosis might have been missed. Similarly, the onset of the disorder might have occurred long before the date of the first diagnosis, but the lack of data on disease onset prevented us from including such information, which is also true for information on symptom severity. Moreover, the information on sickness absence was restricted to spells over 14 days. Still, sickness absence related to an ADHD diagnosis can be anticipated to be considerably longer than 14 days. Finally, as there were very few individuals in some of the strata when introducing diagnoses, it was not possible to include information on that in the matching process. However, the influence of comorbid disorders regarding cluster affiliation has been taken care of in all estimates shown for both individuals with ADHD and the matched control group.
Conclusions
Many young adults with ADHD have challenges regarding labour market integration after the first diagnosis of ADHD at an adult age. This gives an alarming picture of sustainable labour market participation for those diagnosed with ADHD during young adulthood. Those with low educational level, those living outside big cities and those with comorbid severe mental disorders may require extra support to be able to participate in the labour market.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S003329172300096X
Data
These data cannot be made publicly available due to privacy regulations. According to the General Data Protection Regulation, the Swedish law SFS 2018:218, the Swedish Data Protection Act, the Swedish Ethical Review Act and the Public Access to Information and Secrecy Act, these types of sensitive data can only be made available for specific purposes, including research, that meets the criteria for access to this type of sensitive and confidential data as determined by a legal review. Readers may contact Professor Kristina Alexanderson (kristina.alexanderson@ki.se) regarding the data.
Acknowledgements
This work was funded by AFA Insurance Agency (AFA Försäkring), Sweden (grant number180295).
Financial support
This work was supported by AFA Insurance Agency (grant number180295).
Competing interests
None.
Ethical standards
This study was performed in line with the principles of the Declaration of Helsinki. Ethics approval was obtained from the Regional Ethical Review Board of Stockholm, Sweden.
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