Background
Routinely collected patient information can be used to explain some of the variability in outcomes following psychological therapies for depression and anxiety (Delgadillo, Moreea, & Lutz, Reference Delgadillo, Moreea and Lutz2016; Goddard, Wingrove, & Moran, Reference Goddard, Wingrove and Moran2015; Kessler et al., Reference Kessler, van Loo, Wardenaar, Bossarte, Brenner, Ebert and Zaslavsky2017; Saunders, Buckman, & Pilling, Reference Saunders, Buckman and Pilling2020a). Most studies to date have focused on outcomes measured at the end of treatment, such as remission or recovery. Although clinically informative, reliance on endpoint outcomes may obscure interindividual differences in trajectories of symptoms occurring during treatment. Subgroups of patients may follow distinct trajectories, such as initially slow responders who nevertheless show clinically significant improvement by the end of treatment. Without a good understanding of these trajectories, treatments might be ended or altered early because patients, or their clinicians, consider the treatment unsuitable.
Person-centered structural equation modelling techniques can be used to reveal unobserved classes (groups or clusters) of individuals who exhibit similar longitudinal trajectories, which differ to those of the other classes. It is then possible to explore how baseline variables are associated with trajectory class membership. Relevant variables could be inspected at the start of treatment to identify a patient's most likely pattern of symptoms throughout therapy, and therefore inform patient and clinician expectations and decisions. Such information could also help monitor whether patients are doing less well than expected and thereby increase positive outcomes (de Jong et al., Reference de Jong, Conijn, Gallagher, Reshetnikova, Heij and Lutz2021; Delgadillo et al., Reference Delgadillo, de Jong, Lucock, Lutz, Rubel, Gilbody and McMillan2018; Lambert, Reference Lambert2007). Studies that have employed these methods with psychological treatment data include analyses of symptoms of depression (Lutz, Stulz, & Köck, Reference Lutz, Stulz and Köck2009), panic disorder (Lutz et al., Reference Lutz, Hofmann, Rubel, Boswell, Shear, Gorman and Barlow2014) and post-traumatic stress disorder (PTSD; Dewar, Paradis, & Fortin, Reference Dewar, Paradis and Fortin2020), which each reported between three and five trajectory classes and some associations with baseline variables such as comorbid anxiety and depression symptoms. However, most existing analyses are arguably underpowered to reliably identify trajectory classes and associations, with samples of fewer than 500 participants. One study investigated symptom trajectories at two North London National Health Service (NHS) Improving Access to Psychological Therapies (IAPT) sites, in a larger sample of 4394 patients (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019). As part of the NHS in England, the IAPT services provide psychological therapy for adults experiencing common mental health problems such as depression and anxiety. A stepped-care framework is followed, offering evidence based low- and high-intensity treatments that primarily differ in terms of therapist involvement and number of sessions. The North London study analysed data from patients who received high-intensity therapy between 2008 and 2013 (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019). Four classes of depression symptom trajectories were identified, and five classes of anxiety symptom trajectories. Compared with trajectory classes showing improvement, classes with initially moderate-to-severe symptoms and limited change over time were associated with higher baseline levels of functional impairment, depression, anxiety, and phobia symptoms. No associations were found with age, gender, ethnicity, or prescribed psychotropic medication. In the present study, we extended these findings in a larger and more ethnically diverse sample of IAPT patients and considered additional baseline variables to test for association. We also used more recent data which is likely of higher quality due to improvements in recording patient information in IAPT (HSCIC, 2014; Saunders et al., Reference Saunders, Cape, Leibowitz, Aguirre, Jena, Cirkovic and Buckman2020b).
Method
Sample
The sample came from anonymised patient records of routinely collected data from the four IAPT services of the South London and Maudsley (SLaM) NHS Foundation Trust. Using the Clinical Record Interactive Search (Stewart et al., Reference Stewart, Soremekun, Perera, Broadbent, Callard, Denis and Lovestone2009), we extracted treatment records of patients who had started and ended psychological therapy for symptoms of depression or anxiety between July 2014 and September 2020, irrespective of the reason for ending. From the 115 304 patients covering 587 120 sessions extracted, we identified a high-intensity treatment sample of 16 258 patients and 110 773 sessions suitable for analysis (see Fig. 1 and online Supplementary Information S1). IAPT high-intensity therapies are formal psychological treatments consistent with National Institute for Health and Care Excellence guidelines (NICE, 2011) and include disorder-specific cognitive-behavioral therapy (CBT; Roth and Pilling, Reference Roth and Pilling2008), counselling for depression, and other types such as interpersonal therapy (see the IAPT manual for details of therapy protocols and techniques; NCCMH, 2021). We modelled up to 11 sessions, including a baseline assessment, to ensure sufficient complete data across time points for the analysis and as modelling more sessions than a substantial proportion of patients had data for (see online Supplementary Table S1) could make optimal solutions invalid and unstable (Lutz et al., Reference Lutz, Leach, Barkham, Lucock, Stiles, Evans and Iveson2005). To be representative of patients receiving treatment, we did not exclude patients scoring below clinical thresholds on depression and anxiety symptom scales.
Fig. 1. Flowchart detailing the exclusions applied to retain patient data suitable for analysis.
Measures
Outcomes were self-report questionnaires administered at each session. Current symptoms of depression and anxiety were measured respectively by the Patient Health Questionnaire 9-item version (PHQ9) (Kroenke, Spitzer, & Williams, Reference Kroenke, Spitzer and Williams2001; range 0-27; case threshold in IAPT ⩾10) and the Generalised Anxiety Disorder 7-item scale (GAD7) (Spitzer, Kroenke, Williams, & Löwe, Reference Spitzer, Kroenke, Williams and Löwe2006; range 0-21; IAPT case threshold ⩾8). Reliable improvement within trajectories was defined as a decrease of six or more points on the PHQ9, and at least four on the GAD7 (Gyani, Shafran, Layard, & Clark, Reference Gyani, Shafran, Layard and Clark2013; NCCMH, 2021). We selected baseline self-reported patient characteristics to investigate for associations with trajectory class membership based on the available data and previously reported associations with treatment outcomes (Buckman et al., Reference Buckman, Saunders, Stott, Cohen, Arundell, Eley and Pilling2022; Delgadillo et al., Reference Delgadillo, Moreea and Lutz2016; Delgadillo, Dawson, Gilbody, & Böhnke, Reference Delgadillo, Dawson, Gilbody and Böhnke2017a; Robinson, Kellett, & Delgadillo, Reference Robinson, Kellett and Delgadillo2020b; Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019). These were: total scores from each of the PHQ9 and GAD7 (when not the outcome modelled), Work and Social Adjustment Scale (WSAS; functional impairment measure; range 0–40; Marks, Reference Marks1986; Mundt, Marks, Shear, & Greist, Reference Mundt, Marks, Shear and Greist2002), age (in 10 year blocks), gender (female, male), ethnicity (White, Black, Asian, Mixed, other), employment (employed, unemployed, non-worker (e.g. retired, student)), psychotropic medication (not prescribed, prescribed) and disability (no, yes). In IAPT, ‘problem descriptors’ are used to indicate the disorder that is the agreed focus of treatment, in-line with NICE guidance (NICE, 2011). Problem descriptors are based on ICD-10 diagnostic codes, but their use does not mean that a patient necessarily met all diagnostic criteria for the disorder or that they would not meet criteria for other disorders. As such, comorbidity of disorders was unknown. Problem descriptor categories were depression, generalised anxiety disorder (GAD), PTSD, adjustment disorder, obsessive-compulsive disorder (OCD), mixed anxiety and depressive disorder, panic/phobia (panic disorder, agoraphobia, social phobia, specific phobia), and ‘other’ which included infrequent descriptors such as somatoform disorder. Despite showing strong associations in an existing study (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019), phobia scales were not included due to high, potentially non-random, missingness.
Statistical analyses
Trajectory class models
We estimated separate growth mixture models (GMMs) for symptoms of depression and anxiety. For greater detail of the procedure, including all model selection criteria, see online Supplementary Information S2. To first determine the single trajectory form which best fit the observed data overall we compared linear (Bone, Delgadillo, & Barkham, Reference Bone, Delgadillo and Barkham2021), quadratic (Phelps et al., Reference Phelps, Steel, Metcalf, Alkemade, Kerr, O'Donnell and Forbes2018; Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019) and negative log-linear (base 10) (Lutz et al., Reference Lutz, Hofmann, Rubel, Boswell, Shear, Gorman and Barlow2014) trajectories (‘latent growth curves’). We then modelled up to six latent trajectory classes using GMM, each with the best-fitting form we had identified, to determine the optimal number of classes. This revealed whether the data was better explained by multiple latent growth curves than the single average one, indicating clusters of patients with similar trajectories rather than one group with homogeneous symptom change. The upper limit of six classes was informed by past studies of trajectories during psychological therapy, which suggest between three and five classes (Dewar et al., Reference Dewar, Paradis and Fortin2020; Lutz et al., Reference Lutz, Stulz and Köck2009; Reference Lutz, Hofmann, Rubel, Boswell, Shear, Gorman and Barlow2014; Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019), thus the unlikely six class model acted as a ceiling to test against. We compared each model to a model with one fewer class using several fit indices, with a preference for the Bayesian information criterion (BIC; online Supplementary Information S2). The BIC was interpreted with the aid of an elbow plot to identify the point of diminishing gains from the addition of an extra class. We favoured models that were interpretable in terms of clinical utility as well as past literature and theory. For statistical stability, we preferred models where each class contained more than 1% of the sample (Jung & Wickrama, Reference Jung and Wickrama2008). We described patients in each class and the overlap between depression and anxiety classes.
We followed recommendations (Jung & Wickrama, Reference Jung and Wickrama2008; Muthén & Muthén, Reference Muthén and Muthén2000; van de Schoot, Sijbrandij, Winter, Depaoli, & Vermunt, Reference van de Schoot, Sijbrandij, Winter, Depaoli and Vermunt2017) to first perform a version of GMM with zero variance (latent class growth analysis; LCGA). This is a simpler, less computationally demanding model that makes the often-unrealistic assumption that all individuals in a class follow exactly the same trajectory. This can result in trajectory classes that differ only in terms of starting score. We compared this simpler model to a GMM where the variance in each intercept was freed such that it could have a non-zero value but was constrained to be equal between classes, whilst the slope variance was fixed at zero. This allowed differences to be revealed in the patterns of change over time (slope of each class) rather than initial symptom levels (intercept of each class). This specification has successfully been used in previous symptom based GMMs (e.g. Lutz et al., Reference Lutz, Hofmann, Rubel, Boswell, Shear, Gorman and Barlow2014). Latent growth curve and LCGA results were similar between IAPT service-specific models (online Supplementary Figure S1) therefore analyses were performed across all four IAPT services. We used Mplus version 8.3 (Muthén & Muthén, Reference Muthén and Muthén2017), alongside R version 3.6.3 (R Core Team, 2020) and the MplusAutomation package (Hallquist & Wiley, Reference Hallquist and Wiley2018).
Missing data
Missing outcome data was handled using full information maximum likelihood with robust standard errors (Mplus option ‘MLR’) due to non-normal distributions of the outcomes (see online Supplementary Figure S2). If a patient had no observed data for the PHQ9 or GAD7 they were omitted from the model of that outcome. To determine support for generalisability of the results, patients included in the analytical sample were compared to excluded patients using suitable group difference tests. The large sample size could result in statistical significance of negligible differences, therefore we primarily considered effect sizes.
Associations of baseline variables with trajectory class
Associations between variables measured at the start of treatment and trajectory class membership were tested using multinomial regressions. To handle missing data in the baseline variables, we ran multiple imputation using the ‘mice’ package in R, specifying 20 imputed datasets and 20 iterations (van Buuren & Groothuis-Oudshoorn, Reference van Buuren and Groothuis-Oudshoorn2011). Explorations of missing and complete data between variables did now show any association patterns indicating non-random missingness. Patients were allocated to the trajectory class for which they had the highest probability of membership, and this ‘most likely’ class was the regression outcome. IAPT service was included as a covariate. We assessed statistical significance using a Bonferroni adjusted p value threshold of p < 0.025 to account for the two independent models.
Results
Sample descriptives
Descriptives for the sample of 16 258 patients are in Table 1. The majority had received at least one session of CBT (50.1%) or counselling (49.1%); a small proportion received other treatments (6.1%) such as interpersonal therapy. The average number of sessions received, after limiting to a maximum of 11 including the baseline assessment, was 6.81 (s.d. = 2.67), with 11.6% receiving only the minimum 3 sessions and 18.4% receiving 11 (online Supplementary Table S1). At each session, over 99% of the patients remaining in treatment had complete PHQ9 and GAD7 scores (online Supplementary Table S3). Patients in the analytical sample were similar to excluded patients (online Supplementary Table S4), besides differences that reflected inclusion criteria (e.g. number of sessions).
Table 1. Baseline descriptives of patients who received high-intensity psychological therapy for symptoms of depression and anxiety (N = 16 258)
Note: Only the baseline values are presented for variables that were measured at multiple time points. The ‘Missing’ row was omitted if there was no missing data.
a Case thresholds were PHQ9 ⩾ 10, GAD7 ⩾ 8.
b Indicates the disorder treated: GAD = generalised anxiety disorder; PTSD = post-traumatic stress disorder; MADD = mixed anxiety and depressive disorder; panic/phobia = panic disorder, agoraphobia, social phobia, specific phobia; OCD = obsessive-compulsive disorder; ‘Other’ included somatoform disorder, severe mental illness.
c ‘Non-worker’ included homemaker, carer, retired, student.
d No negative responses were recorded; the absence of any value was counted as a negative response rather than missing.
e Only calculated for patients above case thresholds on the PHQ9 or GAD7 at baseline and with observed scores for their final session, otherwise coded as missing. Represents whether the patient reached recovery within the 11 sessions modelled; if they received more sessions and then recovered, they would appear unrecovered here.
Growth mixture models
For both depression and anxiety symptoms, the best-fitting latent growth curve form was quadratic (online Supplementary Information S3). On average, patients' symptoms were initially moderate and improved steadily from baseline (session 0) over approximately the first five treatment sessions, showing reliable improvement by session eight. For LCGA, a four-class model was selected as the optimum solution for each outcome (online Supplementary Information S4). The trajectories were largely uninformative as they primarily differed in terms of baseline severity (intercept). We therefore estimated GMMs with free but equal variance in the intercepts; these are the focus of our results.
Depression symptoms growth mixture model
For depression symptoms, a four-class GMM was selected based on fit indices alongside other considerations such as previous literature (online Supplementary Information S5). Figure 2a provides the mean trajectories of the four classes. Three classes showed moderate-severe symptoms at baseline. One of these showed no change (moderate-severe plateau; grey diamonds), the second had steady improvement (moderate-severe, gradual improvement; pink squares) and the third demonstrated fast improvement initially which plateaued after the sixth treatment session (moderate-severe, fast improvement; blue triangles). A fourth class showed mild symptoms and minimal improvement over the first four sessions which then plateaued (mild, small improvement; green circles). Approximately half the sample were assigned to this mild class and 14–18% in each of the other classes. Reliable improvement occurred on average by the seventh treatment session in the gradual class, and the second in the fast class, but was not observed in the plateau or mild class. At the final treatment session modelled, almost no (< 1%) moderate-severe plateau patients had ‘recovered’ to below PHQ9 and GAD7 case thresholds used in IAPT (< 10 and < 8, respectively). Recovery was also low in the gradual improvement class (18.7%), but higher in the fast improvement and mild classes (67.8% and 58.8%, respectively). Dropout was higher in the gradual improvement and plateau classes (20.3% and 18.9%) than fast improvement and mild classes (14.7% and 11.0%). The mean number of sessions was, however, similar across classes. The plateau class had the highest proportion referred to other services (11.5%). The full descriptives from the model are in online Supplementary Information S6.
Fig. 2. Four-class growth mixture model of (a) depression symptoms (PHQ9) and (b) anxiety symptoms (GAD7), during high-intensity psychological therapy (N = 16 258).
Note: This shows the model estimated mean and observed mean trajectories of each class. Patients had a likelihood of belonging to each trajectory class; counts and proportions (%) are based on their ‘most likely’ class membership.
Anxiety symptoms growth mixture model
For anxiety symptoms, the GMM also suggested a four-class model (Fig. 2b; model selection and descriptives in online Supplementary Information S7 and S8). The trajectory classes for anxiety were very similar to that of depression and as such we used the same labels. However, more patients in the anxiety GMM were assigned to the moderate-severe plateau and moderate-severe, fast improvement classes, and fewer to the moderate-severe, gradual improvement and mild, small improvement classes. Reliable improvement occurred on average by the sixth treatment session in the gradual class and the second in the fast class but not in the plateau or mild class. Most patients who followed a plateau or gradual improvement trajectory had not recovered by the tenth treatment session (1.0% and 34.9% recovered, respectively), whilst most patients in the fast improvement and mild classes had (69.1% and 64.0%). The plateau class had the highest proportion of dropout (22.7%) and referral to other services (9.2%).
Online Supplementary Table S5 shows the overlap of trajectory classes between the depression and anxiety models. For example, 39.5% of patients in the depression gradual improvement class were in the anxiety moderate-severe plateau class, compared with 6.6% of the depression fast improvement class. In the anxiety model, 4.5% of the gradual and 0.8% of the fast improvement class were in the depression plateau class. Of the depression plateau class, 88.8% were in the anxiety plateau class, whilst 48.4% of the anxiety plateau class were in the depression plateau class.
Associations of baseline variables with trajectory class
Conditional growth mixture model of depression symptoms
Higher baseline anxiety was associated with lower odds of belonging to the mild, small improvement class, compared with the reference class moderate-severe plateau (Fig. 3, online Supplementary Table S6). Patients who had higher functional impairment, were prescribed psychotropic medication, or were not working (e.g. retired) compared with employed were less likely to be in the mild or the moderate-severe, fast improvement classes. Unemployed individuals or those reporting a disability had lower odds of being in the fast or gradual improvement or mild classes (i.e. the more favourable classes). Compared with White ethnicity patients, patients who identified as Black, Asian, multi-ethnic (‘mixed’), or ‘other’ ethnicity were less likely to follow the mild trajectory compared with moderate-severe plateau. Patients identifying as Asian also had lower odds of belonging to the fast improvement class. Compared with patients with a depression problem descriptor, patients with GAD, panic/phobia, adjustment, OCD or ‘other’ disorder were more likely to be in the mild class. However, OCD and PTSD were associated, respectively, with decreased odds of the fast improvement, and fast or gradual improvement trajectories. Age and gender had no notable associations.
Fig. 3. Multinomial regressions of trajectory class membership using baseline variables for a four-class growth mixture model of depression symptoms (PHQ9) and a four-class growth mixture model of anxiety symptoms (GAD7), during high-intensity psychological therapy (N = 16 258).
Note: The reference class in each model was moderate-severe plateau. Service, indicating where the patient received treatment, was included as a covariate with four categories. Error bars represent 95% confidence intervals. PHQ9 = Patient Health Questionnaire 9-item version; GAD7 = Generalised Anxiety Disorder 7-item scale. Comorbid symptoms refers to baseline GAD7 score in the depression model, and baseline PHQ9 score in the anxiety model. Functional impairment was measured by the Work and Social Adjustment Scale. Employment status ‘Non-worker’ included homemaker, carer, retired and student. GAD = generalised anxiety disorder; Panic/phobia = panic disorder, agoraphobia, social phobia, specific phobia; MADD = mixed anxiety and depressive disorder; PTSD = post-traumatic stress disorder; OCD = obsessive-compulsive disorder; ‘Other’ included somatoform disorder and severe mental illness.
Conditional growth mixture model of anxiety symptoms
Higher baseline depression or impairment scores and non-worker status were associated with decreased odds of being in the moderate-severe, fast improvement or mild, small improvement trajectory classes than the reference, moderate-severe plateau (Fig. 3, online Supplementary Table S7). Patients who were unemployed or prescribed psychotropic medication were less likely to belong to any favourable trajectory class. Reporting a disability was associated with decreased odds of the fast or gradual improvement classes. Patients identifying as Black, compared with White, were less likely to be in the gradual improvement class. Asian ethnicity was associated with lower odds of the fast improvement or mild class. Compared with a depression problem descriptor, all descriptors besides ‘other’ were associated with lower odds of the mild class. Additionally, patients with PTSD or OCD were less likely to be in the fast improvement class. Patients reporting male gender were more likely to be in the mild class than females.
Discussion
Overview
We identified patterns of change in depression and anxiety symptoms during high-intensity psychological therapy among 16 258 patients. For both outcomes a quadratic slope was optimal and the data were best explained by four classes of trajectories whereby patients within a class exhibited similar trajectories. Baseline variables were associated with trajectory class membership.
Growth mixture models
Both the depression and anxiety models had three classes with moderate-severe symptom scores at baseline. Using only intake symptom severity would therefore not allow a clinician to distinguish between three very different trajectories. One of these trajectories showed no change, labelled moderate-severe plateau. The other two classes reliably improved: moderate-severe, gradual improvement changing more steadily than moderate-severe, fast improvement. The fourth class, mild, small improvement, had average baseline symptoms below clinical thresholds, and thus less capacity for large improvements. These trajectory classes were broadly consistent in number and shape with a previous IAPT study (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019), despite several sample and methodological differences in our study, including a greater proportion of patients receiving counselling and fewer receiving CBT.
The gradual improvement class may be particularly clinically relevant as the apparent lack of early response could lead to premature alteration or termination of treatment. Symptom change was initially indistinguishable from the moderate-severe plateau trajectory and only showed reliable improvement after six or seven sessions. ‘Recovery’ represents both depression and anxiety symptom scores falling below clinical thresholds by the final session. Recovery was lower in the gradual than the fast improvement class, especially for the depression model. Reflecting this, patients in the gradual class were less likely to experience improvement in the other outcome. This can be seen in the higher proportion of the depression gradual class than fast improvement class in the anxiety plateau class. The gradual improvement class might represent patients who require a high number of treatment sessions to show recovery. Supporting this, a study of high-intensity CBT found that most patients who significantly improved did so within 14 sessions (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b) and a systematic review of therapy dose recommended up to 26 sessions for patients who have improved but not recovered before this point (Robinson, Delgadillo, & Kellett, Reference Robinson, Delgadillo and Kellett2020a).
The moderate-severe plateau class may also represent patients who would benefit from more treatment, although some ‘non-responders’ would probably not recover even after a very high number of sessions (Howard, Kopta, Krause, & Orlinsky, Reference Howard, Kopta, Krause and Orlinsky1986). Notably, the frequency of onward referrals in this class suggested the presence of co-occurring problems that required specialist services. That said, despite limited symptom change, some patients may have experienced other, unmeasured, benefits such as prevention of deterioration or hospitalisation. More patients belonged to the plateau class for the anxiety than the depression model. This may be driven by disorders such as OCD and PTSD, which on average require a greater dose of therapy to observe improvement than most depression and anxiety disorders (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b), and are better detected by the GAD7 than the PHQ9 (Kroenke, Spitzer, Williams, Monahan, & Löwe, Reference Kroenke, Spitzer, Williams, Monahan and Löwe2007). However, slightly over half of the anxiety plateau class showed mild or improving depression symptoms, whilst most patients in the depression plateau class showed no improvement on either measure as were also in the anxiety plateau class.
Patients in the fast improvement class generally appeared to require fewer than ten treatment sessions. They were below case thresholds after an average of four treatment sessions and had the highest recovery rate, in-line with meta-analytic evidence that reliable symptom improvement by the fourth session predicts recovery (Beard & Delgadillo, Reference Beard and Delgadillo2019). It is important to continue treatment for several sessions after remission to help prevent false positives or relapse (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b), however, this finding warrants further investigation as it could have implications for service efficiency.
The mean initial symptom score estimated by the model for the mild, small improvement trajectory class, for both depression and anxiety models, was below case threshold, yet all patients had received high-intensity therapy. This was consistent with the observed baseline means. However, it would be inappropriate to conclude that high-intensity treatment was unwarranted for these patients. Scores varied around these means and many patients met the case threshold on the other symptom measure; the majority of mild class patients were an observed case on one or both measures. These patients may also have had additional symptoms not assessed by the PHQ9 or GAD7.
Associations of baseline variables with trajectory class
The most consistent associations with trajectory class membership were prescribed medication, reporting a disability, and not being in employment, which were generally associated with lower odds of being in any of the more favourable classes compared with moderate-severe plateau. These associations are especially informative as they discriminated between individuals with similarly high baseline scores who improved or not. The association with employment status was consistent with findings from endpoint treatment outcome studies (Buckman et al., Reference Buckman, Saunders, Stott, Cohen, Arundell, Eley and Pilling2022; Delgadillo et al., Reference Delgadillo, Moreea and Lutz2016; Delgadillo, Huey, Bennett, & McMillan, Reference Delgadillo, Huey, Bennett and McMillan2017b). The prior IAPT trajectory study found no association with medication (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019) despite associations with treatment outcomes in endpoint studies (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b). Neither employment or disability were included in the prior trajectory study, and the present findings highlight the importance of routinely recording them. These factors may negatively impact therapy response and acknowledging and supporting patients to navigate them might reduce the likelihood of a moderate-severe plateau trajectory. This supports the argument for psychological services working closely with employment advisors, as per the IAPT manual (NCCMH, 2021).
Higher functional impairment was associated with lower odds of the fast improvement or mild class than the plateau class, but not the gradual improvement class. This variable may therefore be useful for discriminating between fast and gradual improvement for patients with similar baseline symptoms. The effect sizes for functional impairment were smaller than those for symptom scores, but still notable given they reflected the association for a one-unit difference on the WSAS, which has a wide scoring range. In the previous IAPT trajectory study, higher functional impairment and symptom scores similarly showed greater likelihood of the corresponding moderate-severe plateau class (Saunders et al., Reference Saunders, Buckman, Cape, Fearon, Leibowitz and Pilling2019). Our results were also consistent with a study reporting that patients with higher baseline scores on the PHQ9, GAD7, WSAS, prescribed antidepressants, a disability or unemployment were less likely to improve with an increasing number of sessions, similar to our plateau class (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b).
The associations between mild trajectory class and problem descriptor in each model were ecologically valid. For example, patients were more likely to be in the mild trajectory class of the depression model if they had an anxiety descriptor compared with depression. Additionally, PTSD and OCD were associated with lower odds of a more favourable trajectory class. Improvement may have been observed after a greater number of sessions than modelled here, consistent with existing literature (Robinson et al., Reference Robinson, Kellett and Delgadillo2020b), or using measures more sensitive to symptoms of PTSD and OCD.
Patients who identified as one of the minoritised ethnic groups rather than White ethnicity were less likely to follow a favourable trajectory, especially the mild depression class, indicating that they had more severe symptoms at intake. These findings are consistent with lower recovery rates in national IAPT data and indicate that some patients from minoritised ethnic groups may benefit from culturally-adapted treatment (Arundell, Barnett, Buckman, Saunders, & Pilling, Reference Arundell, Barnett, Buckman, Saunders and Pilling2021; Beck, Naz, Brooks, & Jankowska, Reference Beck, Naz, Brooks and Jankowska2019). The ethnic diversity of the sample allowed us to study ethnicity in more detail than the binary variable used in similar studies and therefore identify that this pattern was most pronounced for Asian patients. That notwithstanding, there was insufficient power to analyse associations with ethnicity at an even greater level of granularity and this will require further research.
Overall, some variables were generally associated with greater likelihood of the plateau trajectory class than any more favourable class. These included reporting a disability, unemployment or prescribed medication. Other baseline characteristics differentiated between the more favourable classes, including non-worker status, functional impairment, specific problem descriptors and ethnicity.
Strengths and limitations
A large sample size enabled us to produce more accurate estimates and include a broader range of ethnicity categories and baseline variables, such as employment and disability, compared with previous studies. These important and easily recorded factors could help identify likely symptom trajectories at the start of treatment. However, the results may be confounded by the presence of additional variables indicative of treatment prognosis, such as chronicity (Kessler et al., Reference Kessler, van Loo, Wardenaar, Bossarte, Brenner, Ebert and Zaslavsky2017; Lorenzo-Luaces, Rodriguez-Quintana, & Bailey, Reference Lorenzo-Luaces, Rodriguez-Quintana and Bailey2020). Whilst routinely collected data may generalise to clinical settings more readily than clinical trial data, the provision of different interventions, dependent on problem descriptor, makes IAPT data more complex to analyse. Investigations of treatment and descriptor-specific trajectories were beyond the scope of this study. The inclusion of patients with a depression descriptor in the anxiety symptoms model, and vice versa, may therefore have contributed to the high proportion in the mild trajectory classes. Although comorbidity is common between depression and anxiety, the extent of comorbidity was unknown in the current sample as the IAPT problem descriptor only represents the condition being treated. That notwithstanding, the assessment of transdiagnostic symptoms appears particularly important for treatment outcomes (O'Driscoll et al., Reference O'Driscoll, Buckman, Fried, Saunders, Cohen, Ambler and Pilling2021) so we chose to include all patients in both models. Our findings therefore offer a broad representation of what we expect to see in routine clinical settings. The reliability and validity of IAPT problem descriptors has not been established. However, clinicians are trained to use ICD-10 to determine them, and in services with a higher frequency and accuracy of providing problem descriptors, patients are more likely to recover or reliably improve (Clark et al., Reference Clark, Canvin, Green, Layard, Pilling and Janecka2018; Saunders et al., Reference Saunders, Cape, Leibowitz, Aguirre, Jena, Cirkovic and Buckman2020b).
In terms of ethnicity, although generally representative of the local population that SLaM IAPT serves (Office for National Statistics, 2012), the patient sample was more ethnically diverse than IAPT nationally. The two most frequently reported ethnicities in the sample were White (60%) and Black (18%), and in IAPT national data are White (80%) and Asian (6%) (NHS Digital, 2021). It is critical that mental health and treatment outcome research is applicable for all patients, and oversampling from minoritised groups is one way of achieving this. In terms of ethnicity, this overrepresentation occurred naturally within our study, and we view the ethnic diversity of this sample as a strength. Additionally, although caution is recommended when using the IAPT measures across cultures (Beck et al., Reference Beck, Naz, Brooks and Jankowska2019), the PHQ9 and GAD7 have demonstrated good reliability and validity across a range of communities (see Beck et al., Reference Beck, Naz, Brooks and Jankowska2019 for a summary).
Another issue was dropout. Dropout is also observed in clinical trials and can occur due to different reasons, including dissatisfaction with treatment or recovery before the clinically recommended number of sessions. Regardless, and despite the use of missing data methods, scores at later sessions were based on remaining individuals. Replication in a sample receiving a fixed treatment length is therefore required.
Finally, the GAD7 does not cover core symptoms for disorders such as OCD and PTSD. However, it does show reasonably high specificity and sensitivity for several disorders, including these (Kroenke et al., Reference Kroenke, Spitzer, Williams, Monahan and Löwe2007), and therefore provides some indication of likely trajectories for them. Future studies should aim to model trajectories of disorder specific symptoms, and also functional impairment, which is particularly important to patients and potentially improves later in treatment than symptoms (Howard, Lueger, Maling, & Martinovich, Reference Howard, Lueger, Maling and Martinovich1993). This was not possible in the present sample due to the extent of missingness on these variables. Modelling a higher number of sessions would also be beneficial, to reveal more about patients who require a greater dose of therapy.
Implications and conclusions
Using a person-centered analysis allowed us to identify substantial heterogeneity in depression and anxiety symptom change during psychological therapy which could be explained by four classes of trajectories. The identified trajectory classes were differentially associated with baseline variables such as employment and ethnic group. To be implemented clinically, these findings need to be extended into a predictive tool that combines baseline variables and outputs a patient's most likely symptom trajectory. Once validated in an independent dataset, this could be used to inform treatment plans at the start of therapy and also monitor whether patients are ‘on-track’ according to their predicted trajectory, which has been shown to improve patient outcomes (Delgadillo et al., Reference Delgadillo, de Jong, Lucock, Lutz, Rubel, Gilbody and McMillan2018). This would be especially relevant for patients in the gradual improvement class who on average did not show reliable improvement until session six or seven. This study is therefore a crucial first step towards clinical use of trajectory classes to ultimately improve patient outcomes and service efficiency.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291722003403
Acknowledgments
This study represents independent research funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at SLaM NHS Foundation Trust and King's College London, and the Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or King's College London. The authors acknowledge the valuable work of the NIHR Maudsley Biomedical Research Centre Clinical Record Interactive Search team, especially Matthew Broadbent, who performed the data extraction.
Author contribution
All authors were involved in conceptualising the study. MS performed the data pre-processing and analysis with technical input from EC, JEJB, KAG, and RS. Clinical expertise was provided by JEJB, CRH, KAR, RS and JW. MS drafted the manuscript and all other authors provided critical feedback. All authors approved the final version prior to submission. TCE and GB supervised this work.
Ethical standards
We assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The use of anonymised data in this study was approved by the South Central - Oxford C Research Ethics Committee (15/SC/0586) and CRIS Oversight Committee (16010).
Data availability
This study used anonymised patient data from the SLaM NHS Foundation Trust that is not publicly available. The data can be accessed within a secure firewall via the Clinical Record Interactive Search (CRIS) tool by persons with the appropriate permissions. For more information please contact: cris.administrator@slam.nhs.uk.
Grant numbers and disclosures
MS was funded by an NIHR Maudsley Biomedical Research Centre studentship. JEJB was funded on a fellowship from the Wellcome Trust (201292/Z/16/Z). CR was supported by a grant from Fondation Peters to TCE and GB. AJP was supported by an ESRC studentship. GB has sat on advisory boards for Otsuka and Compass Pathways. The authors declare no conflict of interest.
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