Depression and anxiety symptom change during psychological therapy for autistic clients: Evidence from national healthcare records in England

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Intervention development and adaptation is hampered by a lack of knowledge regarding whether subgroups of autistic people differ in their response to interventions, and what characterises subgroups of autistic people who respond differently. This study aimed to identify trajectories of depression and anxiety symptoms during routine psychological therapy for autistic people in general adult primary care mental health services and describe the characteristics of groups that followed particular symptom trajectories. Methods Sessional anxiety and depression symptom scores for N = 7,175 autistic individuals accessing NHS Talking Therapies, for anxiety and depression (NHS TTad) services between 2012-19 across England were drawn from a linked, national healthcare record dataset (MODIFY). Growth Mixture Models were estimated for depression and anxiety symptom severity change over eight sessions, and latent trajectory classes were described in relation to demographic and pre-treatment clinical variables. Findings Seven different anxiety trajectories, and five depression trajectories were observed. Groups showed stable, improving or deteriorating symptoms during treatment. Trajectories appeared distinct by the third treatment session. Identifying as belonging to an ethnically-minoritised group was associated with increased likelihood of deteriorating anxiety symptom trajectories compared with identifying as of White ethnicity. Lower pre-treatment difficulties in daily living were associated with greater likelihood of following trajectories defined by initially severe depression and anxiety symptoms which then improved during treatment, compared with trajectories that did not improve. Interpretation Findings highlight the value of examining early change in treatment response, and identifying individuals at risk of reduced intervention effectiveness before treatment has begun. After the third treatment session, clinicians could identify a possible change trajectory and consider adapted or augmented intervention if there is no improvement. Interventions should be adapted on the basis of individuals’ cultural backgrounds and neurodevelopmental conditions. Daily living, including social and private leisure (taking account of autism masking and burnout experiences) may be a promising focus for augmented treatment. Funding The funder of the study had no role in the study design, data collection, analysis, interpretation or writing of the report. Health sciences/Health care/Health services Health sciences/Medical research/Outcomes research Figures Figure 1 Figure 2 INTRODUCTION Autistic people are more likely to experience a range of mental health conditions including depression and anxiety compared to non-autistic people (Lai et al., 2019), and report negative experiences when they receive support for these (Brede et al., 2022). Evidence-based psychological therapies are recommended as first-line treatments for depression and anxiety, including for autistic people (Clark, 2011; Linden et al., 2023), but these interventions often require adaptation to better meet the needs of autistic service users (Linden et al., 2023; Pilling et al., 2012). In the largest study of its kind, using a national cohort of adults receiving psychological therapies in a general adult health care setting, it was demonstrated that autistic people experienced lower rates of improvement and recovery than a matched non-autistic comparison group (El Baou et al., 2023). However, that study and those preceding it have not yet been able to contribute to understanding which groups of autistic people may experience better or worse intervention outcomes, thus informing who might benefit from more personalised or more thoroughly-adapted intervention approaches. Statistical modelling approaches that can identify heterogeneous patterns of symptom change offer several advantages (Ram & Grimm, 2009). Firstly, they allow for an investigation of distinct trajectories of autistic people’s symptom change during psychological therapy, along with the pre-treatment characteristics associated with following those trajectories. This may help inform treatment planning, including the need for adapted, augmented or different treatment if the likely trajectory shows a slow or poor treatment response. Secondly, previous studies have used a similar approach to model treatment response in a majority non-autistic adult sample, and identified an association between symptom trajectories and pre-treatment difficulties with life tasks (i.e., impact on daily life across home, work, relationships and leisure activities). Greater pre-treatment life-task difficulties were associated with lower likelihood of following an improving trajectory (decreasing symptoms, benefitting from treatment) for anxiety or depression, relative to a stable one (i.e., non-response to treatment) (Skelton et al., 2023; Saunders et al., 2019). It is currently unclear as to whether difficulties with daily life tasks may affect outcomes for autistic people as they do for non-autistic people; however, given that these difficulties were previously observed to be higher for autistic people pre-treatment in a general adult mental healthcare setting (El Baou et al., 2023), and are also a known barrier to treatment (Brede et al., 2022), this warrants further investigation. The current study aims to identify trajectories of depression and anxiety symptom change for autistic people during psychological therapy delivered as part of routine care in a nationwide general adult psychological treatment programme, and to identify pre-treatment service user characteristics associated with different trajectories of treatment response. METHODS Study design Analyses were performed using the MODIFY dataset, which includes national data drawn from linked NHS Digital electronic healthcare records from across all healthcare regions in England (Bell et al., 2022). The linked databases forming MODIFY include psychological treatment service data drawn from NHS Talking Therapies for Anxiety and Depression (NHS TTad; formerly known as Improving Access to Psychological Therapies - IAPT) in England between 2012-19 (National Collaborating Centre for Mental Health [NCCMH], 2018). Further information on the TTad service model is provided in Supplement, Appendix A. TTad data were linked to i) Hospital Episode Statistics (HES; NHS Digital, 2019); ii) Office of National Statistics (ONS) mortality database (HES-ONS; NHS Digital, 2023); and iii) the Mental Health Services Dataset (MHSDS; NHS Digital, 2024). Further detail regarding MODIFY and linked data is also provided in Supplement, Appendix B. All data were fully anonymised, and linkage was achieved using anonymised subject identifiers provided by NHS Digital. Under the Governance Arrangements of Research Ethics Committees (REC) procedures, REC review was not required due to the anonymisation procedures followed by NHS Digital. Participants This study included all service users with an autism diagnosis identified in HES or MHSDS (ICD-10 diagnostic codes F84·0 (Childhood Autism), F84·1 (Atypical Autism) and F84·5 (Asperger Syndrome) as per prior research (Bell et al., 2022; El Baou et al., 2023). Inclusion criteria for this study were: (1) accessing a course of treatment with any TTad services between 2012-19; (2) meeting thresholds for ‘caseness’ on measures of either depression or anxiety symptoms at baseline; (3) being discharged from the services (so they were not still receiving treatment); and (4) receiving at least three assessment and treatment sessions (NCCMH, 2018). Service users whose diagnosis was recorded as a severe and enduring mental illness (e.g., schizophrenia) were excluded, because the services do not offer standardised treatment for these conditions (NCCMH, 2018). Measures Outcome Depression symptoms were measured using the Patient Health Questionnaire (PHQ)-9 (Kroenke, Spitzer, & Williams, 2001), and anxiety symptoms using the Generalized Anxiety Disorder scale (GAD)-7 (Spitzer et al., 2006). These measures have not been specifically validated in autistic populations, but are routinely used in NHS TTad settings. Clinical ranges (Table 1 ) were used to describe latent symptom trajectories. Table 1 PHQ-9 and GAD-7 clinical thresholds and ranges Measure Range Interpretation PHQ-9 0–4 No depressive symptoms 5–9 Mild depressive symptoms 10–14 Moderate depressive symptoms 15–19 Moderately-severe depressive symptoms 20–27 Severe depressive symptoms GAD-7 0–4 No anxiety symptoms 5–9 Mild anxiety symptoms 10–14 Moderate anxiety symptoms 15+ Severe anxiety symptoms Risk factors Difficulties with daily life tasks were measured using the Work and Social Adjustment Scale (WSAS; Mundt et al., 2002). In keeping with prior research (e.g., Barnett et al., 2023; Saunders et al., 2019; Skelton et al., 2023), home management, private leisure, social leisure and close relationship subscales were used in the primary analysis (WSAS-4; questions 2–5), and the employment subscale (question 1) removed due to a high number of “not applicable” answers (see Supplement, Appendix B). A further sensitivity analysis used all subscale scores (WSAS-5). Age, gender, ethnicity, employment status, use of psychotropic medication, presence of a long-term health condition and Intellectual Disability diagnosis were recorded in the NHS TTad, HES and MHSDS datasets (further detail provided in Supplement, Appendix B). Ethnicity was coded as a binary variable for this analysis, due to low cell counts in several categories (Appendix B; see Limitations). Detailed information on measurement is included in the Supplement, Appendix B. Procedure Initial data management was conducted in STATA 14·2. Subsequent analyses were conducted using MPlus 8·3. Missing PHQ-9 and GAD-7 data were handled using Maximum Likelihood Estimation with Robust Standard Errors in MPlus. The first eight sessions were modelled in this study. First, a single trajectory was estimated for all participants, separately for anxiety and depression, to obtain a mean trajectory. Two latent growth curve models (LGCM) were designed – one linear LGCM, and one in which six time scores were estimated as free parameters (with two fixed to allow model identification), allowing the shape of change to be determined by the data. Model fits were compared using the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and Standardised Root Mean Square Residual (SRMR) (see Supplement, Appendix C for further information on fit indices). To allow identification of distinct classes with reduced risk of convergence failure and computation time, we used Latent Class Growth Analysis (LCGA) to fit models that assume no within-class variance for growth factors (Ram & Grimm, 2009). Models were estimated up to eight classes to allow for the possibility of higher numbers of classes to be detected than in previous general population studies (Skelton et al., 2023; Saunders et al., 2019). Next, Growth Mixture Models (GMM) were fitted, which allow variances and covariances to be obtained around growth factors. Models up to eight classes were again specified for both anxiety and depression, and assessed using fit indices. Best-fitting models for each of anxiety and depression were selected for further analysis. LCGA and GMM models were compared using Bayesian Inference Criteria (BIC) and Sample Size-Adjusted BIC (SA-BIC), and three Likelihood Ratio Tests (LRT) of the ratio of log-likelihoods for the k class model with the k-1 class model: the Vuong-Lo-Mendell-Rubin (VLMR) LRT, Lo-Mendell-Rubin-Adjusted LRT (LMR-A), and Bootstrap LRT (BLRT) (see Supplement, Appendix C). All available fit information was reported and considered to select the best-fitting -model (Ram & Grimm, 2009), and conservatively when any k model LRTs were non-significant ( p >·05) the k-1 model was considered to be supported for selection. Modal class assignments and scores were exported into STATA, to calculate means and 95% Confidence Intervals. The “substantive meaningfulness” (i.e, external validity) (Muthén, 2003) of these modal classes was examined by characterising them in relation to reliable improvement, reliable recovery and reliable deterioration at the end of treatment. Classes were compared with those weighted by the probability of class assignment by MPlus, and reported in Appendix H. Multinomial regression models were fitted to investigate associations between pre-treatment patient characteristics and the probability of latent class membership using the R3Step procedure in MPlus. R3Step accounts for posterior probabilities of class assignment, correcting for classification error, and has been shown to be superior to approaches in which classification errors are not accounted for (Vermunt, 2010). Missing WSAS data were handled in MPlus using multiple imputation with 1,000 Bayesian iterations and 100 datasets. WSAS-4 scores were first entered into a multinomial regression along with ethnicity, gender, age, use of psychotropic medication, presence of a long-term health condition and presence of an Intellectual Disability diagnosis. Next, analyses were re-run in the same way using individual WSAS subscale scores instead of total WSAS-4 scores. A further sensitivity analysis was conducted by repeating all analyses, including employment subscale scores (WSAS-5). Reporting This study was reported using the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist (Supplement, Appendix J; van de Schoot et al., 2017) and the Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement (Supplement, Appendix K; Benchimol et al., 2015). Role of the funding source The funder of the study had no role in the study design, data collection, analysis, interpretation or writing of the report. RESULTS Data were available for 2,512,402 service users in the MODIFY dataset, and 11,198 had a linked autism diagnosis. After exclusion criteria were applied, a study sample of 7,175 was obtained (see Supplement, Appendix D for a study flow diagram). No predictor variables were associated with missing anxiety or depression data points at the initial time point, but ethnicity was related to missingness by the final timepoint. Data were handled as missing at random, and the relationship with ethnicity was accounted for in subsequent interpretation. Missing data and distributions are reported in full in the Supplement, Appendix E. Table 2 Sample characteristics Variable Mean (SD; range) or count (%) Demographic variables : Age (years) 30·68 (12·19; 18–82) Gender (female) 3,020 (42·09) Gender (missing) 34 (·47) Ethnicity (any ethnically minoritised background) 392 (5·55) Ethnicity (missing) 676 (9·42) Employment status (employed) 3,717 (51·80) Employment status (missing) 505 (7·04) Co-occurring conditions : Long-term condition 2041 (28·45) Long-term condition (missing) 1760 (24·53) Intellectual disability 305 (4·25) Baseline clinical variables : Baseline PHQ-9 16·75 (5·46, 0–27) Baseline GAD-7 14·79 (4·34, 0–21) Baseline WSAS 20·59 (9·31; 0–40) Primary diagnosis (depression) 2,042 (28·46) Primary diagnosis (generalised anxiety) 918 (12·80) Primary diagnosis (other anxiety condition) 870 (12·13) Primary diagnosis (mixed anxiety-depression) 1,422 (19·82) Primary diagnosis (missing) 1,923 (26·80) Taking psychotropic medication at baseline 3,634 (56·52) Treatment factors : Intervention received: Intensity known 3,210 (44·74) Intervention received: Low-intensity intervention 2,302 (71·71) Intervention received: High-intensity intervention 1,689 (52·62) Dropout 1,461 (20·36) Onward referral 446 (6·22) Treatment outcomes : Reliable improvement 4,563 (63·60) Reliable recovery 2,583 (36·00) Reliable deterioration 696 (9·70) Model description On the basis of superior RMSEA, CFI/TLI and SRMR indices, a base LGCM with free time scores was selected. Model specifications and output are available in Supplement, Appendix F. For this reason, the subsequent LCGA and GMM specifications were designed with free time scores. LCGA models converged, and fit indices suggested an eight-class depression model and a seven-class anxiety model had the best fit (see Supplement, Appendix G for specification and output). Subsequently, GMM models converged and showed superior fit (BIC, SABIC) compared with LCGA (specification and output in Supplement, Appendix H). A depression GMM with five classes (D1-D5) was selected for further analysis on the basis that a six-class model returned non-significant VLMR-LRT, LMR-A-LRT tests and a minimally improved BIC (>·005% reduction). An anxiety GMM with seven classes (A1-A7) was selected for further analyses on the basis that an eight-class model showed increased (poorer) BIC and non-significant VLMR-LRT and LMR-A-LRT tests. Entropy scores were .56 for the depression model and .61 for the anxiety model, suggesting limited confidence in the most probable class assignment – however the R3Step analysis would be robust to this as it accounts for class assignment probabilities. Model-estimated trajectories and sample means are reported in Supplement, Appendix H. Trajectories from the GMM models are shown below, using class modal assignment (Figs. 1 , 2 ). Means weighted by classification probability are shown and reported in Appendix H. Measure Class t1 (95% CI) t2 (95% CI) t3 (95% CI) t4 (95% CI) t5 (95% CI) t6 (95% CI) t7 (95% CI) t8 (95% CI) PHQ-9 D1 19.53 (19.29–19.78) 19.56 (19.31–19.80) 19.30 (19.06–19.56) 18.93 (18.68–19.19) 18.72 (18.46–18.99) 18.78 (18.52–19.04) 18.54 (18.28–18.80) 18.10 (17.82–18.38) D2 21.15 (19.89–22.40) 18.02 (16.35–19.70) 12.73 (10.46-15.00) 8.78 (6.78–10.78) 5.32 (3.72–6.91) 3.61 (2.55–4.66) 3.20 (2.29–4.11) 1.90 (1.29–2.61) D3 20.09 (19.79–20.39) 18.40 (18.04–18.75) 16.38 (15.96–16.80) 14.46 (14.03–14.88) 12.63 (12.21–13.04) 11.23 (10.85–11.61) 10.00 (9.62–10.38) 9.12 (8.72–9.51) D4 12.09 (11.81–12.37) 10.63 (10.35–10.91) 9.84 (9.56–10.12) 9.58 (9.28–9.87) 9.30 (9.00-9.60) 8.87 (8.57–9.17) 8.42 (8.11–8.72) 7.96 (7.66–8.26) D5 9.82 (8.70-10.93) 10.91 (9.43–12.39) 13.36 (11.61–15.11) 15.41 (13.84–16.97) 17.50 (16.26–18.73) 17.55 (16.03–19.06) 18.59 (17.30-19.88) 17.18 (15.53–18.83) Measure Class t1 (95% CI) t2 (95% CI) t3 (95% CI) t4 (95% CI) t5 (95% CI) t6 (95% CI) t7 (95% CI) t8 (95% CI) GAD-7 A1 17.61 (17.43–17.78) 17.61 (17.44–17.79) 17.57 (17.38–17.76) 17.45 (17.26–17.64) 17.05 (16.84–17.26) 16.97 (16.77–17.18) 16.60 (16.38–16.82) 16.29 (16.05–16.53) A2 18.00 (17.46–18.54) 15.67 (14.65–16.70) 12.57 (11.24–13.90) 8.74 (7.49–9.99) 5.83 (4.87–6.79) 3.76 (3.02–4.50) 3.09 (2.45–3.72) 2.14 (1.67–2.60) A3 17.33 (17.12–17.55) 15.92 (15.63–16.21) 14.42 (14.07–14.77) 12.99 (12.64–13.35) 11.36 (11.02–11.71) 10.07 (9.76–10.38) 9.21 (8.88–9.53) 8.13 (7.81–8.44) A4 11.89 (11.66–12.12) 11.05 (10.76–11.34) 10.70 (10.41–10.98) 10.68 (10.41–10.96) 10.53 (10.27–10.81) 10.31 (10.04–10.57) 10.26 (9.99–10.53) 9.48 (9.18–9.78) A5 12.81 (12.40-13.22) 9.75 (9.22–10.28) 6.98 (6.49–7.47) 6.08 (5.61–6.55) 4.83 (4.41–5.24) 4.65 (4.21–5.09) 3.75 (3.41–4.09) 3.62 (3.21–4.02) A6 9.94 (9.36–10.52) 10.92 (9.56–11.89) 13.77 (12.88–14.66) 13.86 (13.03–14.70) 15.03 (14.33–15.73) 15.53 (14.79–16.23) 15.82 (15.17–16.47) 15.26 (14.44–16.07) A7 6.42 (6.02–6.81) 6.00 (5.57–6.43) 5.76 (5.34–6.18) 5.46 (5.04–5.88) 5.60 (5.11–6.09) 5.29 (4.82–5.76) 4.90 (4.41–5.39) 4.97 (4.49–5.46) The five-class depression model is shown in Fig. 2 : ‘Moderately severe, not improving’ (D1; 39·0%) and ‘moderate, limited improvement’ (D4; 36·7%) trajectory classes accounted for the majority of the sample. Two classes showed improving trajectories from the ‘moderately severe’ range into ‘mild’ (D3; 18·6%) and ‘minimal’ (D2; 4·3%) ranges by session 8. A smaller class was composed of individuals who started in the moderate depression range but subsequently showed deterioration (D5; 1·5%). The seven-class anxiety model is shown in Fig. 3: Three classes representing around 60% of the sample showed trajectories with no or minimal improvement, in each of the severe (A1; 33·1%), moderate (A4; 20·5%) and mild (A7; 6·7%) ranges. Two classes showed improving trajectories from the severe range at baseline, one improving into the mild range by session 8 (A3; 18·4%) and one into the minimal range (A2; 4·9%). A further two classes began within the moderate anxiety range at baseline, showing each of an improving (A5; 13·9%) or deteriorating (A6; 2·6%) path. The trajectories obtained for anxiety were comparable to those shown for depression symptoms, with the addition of moderate improving, and mild classes. Both models showed 4–5% of the sample could be considered early responders, making rapid gains by the third session. By this point, trajectories could be distinguished with greater confidence than at baseline, and 95% Confidence Intervals between the groups starting in moderately-severe and severe ranges did not overlap by this point. Recovery and change indices Reliable change indices by modal group assignment are reported in full in the Supplement, Appendix I. When classes were considered by modal assignment, by the end of treatment almost all of the rapidly improving class members (D2, A2) had experienced a reliable improvement within both depression (99·7%) and anxiety (99·4%) models, with high rates (97·4%, 94·8%) of reliable recovery. By session 4, the rapidly improving depression group experienced a mean reduction of 12·45 points (double the PHQ-9 reliable change index of 6 points), and the anxiety group a mean of 10·2 points (2·5 times the GAD-7 reliable change index of 4 points). Gradually improving class members (D3, A3) showed lower, but still substantial, rates of reliable improvement (88·8%, 91·7%) and moderate rates of reliable recovery (57·5%, 48·6%%) by the end of treatment. All improving classes showed low rates of reliable deterioration (between 0% and ·38%). Conversely, moderate deteriorating groups showed high rates of reliable deterioration (62·6% and 74·6% on PHQ-9 and GAD-7 respectively) by the end of treatment. This suggested that when modal assignments were considered the patterns of recovery between groups were broadly consistent with the outcomes suggested by their mean trajectory. Associations of baseline risk factors with trajectory class membership As both the largest and most clinically concerning, the not-improving classes (D1 and A1) were chosen as the reference groups for depression and anxiety. Further comparisons were made using the moderate, limited improvement classes (D4 and A4) as reference categories. The moderate, deteriorating classes (D5 and A6) could not be used for this purpose due to the small proportions of patients following those trajectories. Odds ratios with 95% Confidence Intervals are reported in Table 3 (depression model) and Table 4 (anxiety model). To summarise the associations observed between risk factors and trajectory classes: For both anxiety and depression, lower pre-treatment total functional impairment (WSAS-4) scores (i.e., less functional difficulty) were associated with following rapidly improving trajectories from moderately severe or severe baseline ranges relative to the not-improving classes A1 and D1 [OR(95%CI) = ·91(·86 - ·96) for depression; and ·93(·89-·97) for anxiety]. Lower baseline WSAS social and private leisure subscale scores were associated with rapid improvement from the severe range on the GAD-7 (relative to A1) [OR(95%CI) = ·76(·69 - ·83) for social leisure; and ·80(·72-·89) for private leisure]. Lower pre-treatment total and social leisure WSAS scores were associated with gradually improving GAD-7 trajectories from the severe range (A2), relative to A1 [OR(95% CI) = ·94(·91-·97) for total scores; and ·85(·78-·93) for social leisure]. Each was also associated with improving GAD-7 trajectories from the moderate range (A5), relative to the moderate, limited improvement class A4 [OR(95% CI) = ·96(·93–98) for total scores; and ·88 (·79-·98) for social leisure). Overall, total WSAS-4 scores were associated with all but the moderately severe, gradually improving depression trajectory class (D3) relative to D1; and all but the moderate improving depression class (D5) relative to D4. WSAS-4 totals were associated with the likelihood of following any other trajectory relative to A1, and all but the severe, rapid improving (A2) and moderate, deteriorating (A6) classes relative to A4. A sensitivity analysis, including the employment subscale of the WSAS, replicated the same associations between WSAS-5 total and subscale scores as that observed in our main analysis (Supplement, Appendix J). For anxiety specifically, identifying as belonging to an ethnically-minoritised group was associated with increased likelihood of following a deteriorating GAD-7 trajectory (A6) relative to either reference class [OR(95% CI) = 2·66(1·24 − 5·68) versus A1; and 3·83(1·34 − 10·96) versus A4]. Having an Intellectual Disability diagnosis was associated with greater likelihood of belonging to the D4 class (relative to D1) [OR (95% CI) = 1·98(1·23 − 3·18)] and of following a moderate, improving GAD-7 trajectory (A5) relative to either A1 [OR (95% CI) = 3·07(2·07 − 4·53)] or A4 [OR (95% CI) = 4·62(2·89 − 9·33)] reference groups. Older age (continuous, years) was associated with following a deteriorating PHQ-9 trajectory (D5) relative to D4 [OR (95% CI) = 1·04(1·01–1·06)], but also conversely a consistently mild GAD-7 trajectory relative to A1 [OR (95% CI) = 1·03(1·01–1·05)]. Taking psychotropic medication pre-treatment was associated (relative to D1 and A1) with lower likelihood of following a moderate, limited improvement PHQ-9 trajectory (D4) [OR(95% CI) = ·46(·35-·59) for depression and a moderate improving GAD-7 trajectory (A5) [OR(95% CI) = ·53(·38 -·74). Female gender was associated with less likelihood of following a stable, mild anxiety trajectory relative to either severe, not improving [OR(95% CI) = ·44(·31-·63)] or moderate, limited improvement [OR(95% CI) = ·58(·37-·90)] reference classes. Table 3 Multinomial logistic regression output for depression model Comparison group: D1 D2 D3 D4 D5 OR 95% CI OR 95% CI OR 95% CI OR 95% CI Model 1 : Gender (female vs male) ·74 ·46–1·18 1·17 ·85–1·62 ·81 ·63–1·05 1·13 ·52–2·42 Age 1·02 1·00–1·04 ·98 ·96–1·00 1·02 1·01–1·03 1·04 1·01–1·06 Ethnicity (ethnically minoritised group vs White) ·57 ·16–2·03 ·72 ·36–1·43 1·03 ·60–1·77 2·25 ·64–7·85 Employment ·70 ·44–1·10 ·86 ·62–1·18 ·68 ·53 - ·88 ·74 ·33–1·65 Psychotropic medication ·88 ·53–1·46 ·62 ·45 - ·87 ·46 ·35 - ·59 ·95 ·40–2·26 Long-term health condition 1·12 ·67–1·85 1·18 ·83–1·68 ·97 ·73–1·29 ·51 ·19–1·41 Intellectual Disability (has diagnosis vs does not have diagnosis) 1·82 ·86–3·86 ·97 ·48–1·94 1·98 1·23–3·18 2·95 1·03–8·45 WSAS Total Score ·91 ·86 - ·96 ·98 ·94–1·01 ·79 ·76 - ·82 ·81 ·75 - ·88 Model 2 : WSAS: Home ·89 ·77–1·03 ·94 ·85–1·04 ·80 ·74 - ·86 ·78 ·65 - ·94 WSAS: Social leisure ·88 ·76–1·03 ·91 ·82–1·01 ·80 ·74 - ·87 ·88 ·73–1·05 WSAS: Private leisure ·88 ·77–1·02 1·02 ·93–1·12 ·73 ·68 - ·78 ·79 ·64 - ·98 WSAS: Close relationships ·99 ·84–1·15 1·05 ·94–1·16 ·83 ·78 - ·89 ·78 ·63 - ·95 Comparison group: D4 D1 D2 D3 D5 OR 95% CI OR 95% CI OR 95% CI OR 95% CI Model 1 : Gender (female vs male) - - ·91 ·56–1·50 1·45 1·00–2·09 1·39 ·66–2·91 Age - - 1·00 ·98–1·02 ·96 ·95 - ·98 1·02 1·00–1·04 Ethnicity (ethnically minoritised group vs White) - - ·56 ·15–2·01 ·70 ·31–1·58 2·18 ·68–6·98 Employment (in employment vs not in employment) - - 1·02 ·63–1·64 1·25 ·87–1·81 1·08 ·50–2·33 Psychotropic medication (taking vs not taking) - - 1·92 1·13–3·26 1·36 ·94–1·97 2·08 ·92–4·72 Long-term health condition (has diagnosed condition vs does not have diagnosed condition) - - 1·14 ·67–1·96 1·22 ·82–1·81 ·53 ·20–1·41 Intellectual Disability (has diagnosis vs does not have diagnosis) - - ·92 ·41–2·08 ·49 ·25 - ·96 1·49 ·59–3·77 WSAS Total Score - - 1·15 1·09–1·22 1·24 1·19–1·29 1·03 ·96–1·11 Model 2 : WSAS: Home - - 1·12 ·96–1·30 1·18 1·05–1·32 ·98 ·82–1·18 WSAS: Social leisure - - 1·10 ·93–1·29 1·13 1·01–1·26 1·09 ·92–1·30 WSAS: Private leisure - - 1·22 1·05–1·42 1·40 1·26–1·56 1·01 ·89–1·34 WSAS: Close relationships - - 1·19 1·01–1·40 1·26 1·13–1·41 ·94 ·77–1·14 Key D1 Moderately severe, not improving D2 Moderately severe, rapidly improving D3 Moderately severe, gradually improving D4 Moderate, limited improvement D5 Moderate, deteriorating Table 4 Multinomial logistic regression output for anxiety model Comparison group: A1 A2 A3 A4 A5 A6 A7 OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Model 1 : Gender (female vs male) ·86 ·59–1·27 ·90 ·68–1·20 ·76 ·58–1·00 ·79 ·57–1·09 ·72 ·40–1·32 ·44 ·31 - ·63 Age ·98 ·96–1·00 1·00 ·99–1·01 ·99 ·97–1·00 1·03 1·01–1·04 1·00 ·98–1·03 1·01 1·00–1·03 Ethnicity (ethnically minoritised group vs White) ·22 ·01–3·21 ·87 ·48–1·58 ·69 ·35–1·36 1·16 ·60–2·27 2·66 1·24–5·68 1·42 ·79–2·54 Employment (not in employment vs in employment) ·77 ·53–1·13 ·71 ·53 - ·95 ·81 ·62–1·06 1·00 ·72–1·39 1·27 ·71–2·27 ·77 ·55–1·09 Psychotropic medication (taking vs not taking) ·82 ·56–1·22 ·62 ·47 - ·83 ·87 ·66–1·15 ·53 ·38 - ·74 ·89 ·48–1·65 ·69 ·50 - ·97 Long-term health condition (has diagnosed condition vs does not have diagnosed condition) ·77 ·49–1·20 ·98 ·72–1·33 ·79 ·58–1·07 ·96 ·67–1·37 ·77 ·40–1·50 ·93 ·64–1·34 Intellectual Disability (has diagnosis vs does not have diagnosis) 1·37 ·73–2·57 ·93 ·52–1·65 ·66 ·35–1·25 3·07 2·07–4·53 1·27 ·48–3·36 ·56 ·24–1·28 WSAS Total Score ·93 ·89 - ·97 ·94 ·91 - ·97 ·89 ·87 - ·91 ·85 ·83 - ·88 ·87 ·83 - ·91 ·85 ·83 - ·88 Model 2 : WSAS: Home ·91 ·83–1·01 ·93 ·86–1·01 ·93 ·86–1·00 ·91 ·83–1·01 ·89 ·77–1·03 ·90 ·81–1·00 WSAS: Social leisure ·76 ·69 - ·83 ·85 ·78 - ·93 ·86 ·79 - ·93 ·76 ·69 - ·83 ·86 ·75–1·00 ·82 ·75 - ·90 WSAS: Private leisure ·80 ·72 - ·89 ·97 ·90 − 1·05 ·86 ·80 - ·93 ·80 ·72 - ·89 ·85 ·74 - ·98 ·80 ·72 - ·88 WSAS: Close relationships ·95 ·87–1·04 ·99 ·92–1·08 ·91 ·85 - ·98 ·95 ·87–1·04 ·87 ·76–1·00 ·89 ·82 - ·97 Comparison group: A4 A1 A2 A3 A5 A6 A7 OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CI Model 1 : Gender (female vs male) - - 1·14 ·75–1·72 1·18 ·83–1·68 1·03 ·68–1·57 ·95 ·48–1·87 ·58 ·37 - ·90 Age - - 1·00 ·98–1·02 1·01 ·99–1·03 1·04 1·02–1·06 1·02 ·99–1·04 1·03 1·01–1·05 Ethnicity (ethnically minoritised group vs White) - - ·31 ·02–4·50 1·26 ·54–2·96 1·68 ·62–4·52 3·83 1·34–10·96 2·04 ·88–4·76 Employment (in employment vs not in employment) - - ·96 ·64–1·44 ·88 ·62–1·26 1·24 ·81–1·89 1·57 ·81–3·03 ·96 ·63–1·46 Psychotropic medication (taking vs not taking) - - ·94 ·62–1·44 ·72 ·50–1·02 ·61 ·40 - ·94 1·02 ·50–2·05 ·80 ·53–1·21 Long-term health condition (has diagnosed condition vs does not have diagnosed condition) - - ·97 ·60–1·57 1·25 ·85–1·83 1·22 ·76–1·96 ·98 ·46–2·10 1·18 ·75–1·86 Intellectual Disability (has diagnosis vs does not have diagnosis) - - 2·06 ·95–4·48 1·40 ·61–3·19 4·62 2·89–9·33 1·91 ·55–6·64 ·84 ·30–2·38 WSAS Total Score - - 1·05 1·00–1·10 1·06 1·03–1·09 ·96 ·93 - ·98 ·98 ·94–1·03 ·96 ·94 - ·98 Model 2 : WSAS: Home - - 1·03 ·91–1·17 1·00 ·91–1·10 ·98 ·87–1·11 ·96 ·82–1·13 ·97 ·86–1·09 WSAS: Social leisure - - 1·01 ·90–1·14 1·00 ·91–1·09 ·88 ·79 - ·98 1·01 ·86–1·18 ·96 ·86–1·06 WSAS: Private leisure - - 1·07 ·94–1·22 1·13 1·03–1·25 ·93 ·81–1·07 ·99 ·84–1·17 ·93 ·82–1·05 WSAS: Close relationships - - 1·08 ·95–1·23 1·09 1·00–1·20 1·05 ·93–1·17 ·96 ·82–1·12 ·98 ·88–1·09 Key A1 Severe, not improving A2 Severe, rapidly improving A3 Severe, gradually improving A4 Moderate, limited improvement A5 Moderate, improving A6 Moderate, deteriorating A7 Mild DISCUSSION We conducted the first study to characterise trajectories of anxiety and depression change during psychological therapy for autistic people in any setting, to better understand the nature and drivers of symptom change. We identified subgroups of autistic clients who experienced no improvement, or worsening of symptoms, as well as those who experienced improvements. Identifying as a member of an ethnically-minoritised group was associated with greater odds of a deteriorating anxiety symptom trajectory. Ethnically-minoritised autistic people are underrepresented in the autism psychological intervention literature and may likely be affected by compounded disadvantage (Steinbrenner et al., 2022). Autistic clients with severe pre-treatment levels of depression and anxiety were more likely to follow an improving symptom trajectory if they had lower levels of difficulty in daily living pre-treatment. This result should be treated with some caution, as in places the strength of the association was small and 95% confidence intervals were close to the significance threshold (particularly when overall WSAS scores were used). Within the anxiety model however, the odds ratios comparing improving with non-improving social and private leisure trajectories showed a stronger effect and did not border on the threshold of significance. Overall the consistency of the pattern across classes and models lent some support to this not being a chance finding. This result is in keeping with comparable general population studies (Skelton et al., 2023; Saunders et al., 2019). However, autistic people were also shown in a previous study to have higher degrees of pre-treatment difficulty with daily life tasks on the WSAS compared with non-autistic adults (El Baou et al., 2023). As baseline WSAS scores were here associated with reduced odds of following improving trajectories from severe or moderately severe initial symptoms, difficulties in home, leisure and relationships may in turn perpetuate the health inequality experienced by autistic people. Lower initial impairment in private and social leisure activities was associated with the likelihood of improving from severe pre-treatment anxiety. Private leisure is defined in the WSAS as “done alone, such as reading, gardening, collecting, sewing, walking alone,” and social leisure as “with other people e.g. parties, bars, clubs, outings, visits, dating, home entertaining.” Intense and focused interests are a diagnostic feature of autism, and satisfaction with leisure activities may affect mental health outcomes more for autistic than non-autistic adults (Stacey et al., 2019). Participation in social recreation has been shown to buffer stress for autistic people (Bishop-Fitzpatrick et al., 2017). That autism masking (i.e., adopting learned communication behaviours to camouflage autistic traits) predicts psychological distress for autistic people (Cook, Hull, & Mandy, 2021), underlines that opportunities to engage with social contexts free from stigma and rejection are particularly important (Milton & Sims, 2016). Intellectual Disability was associated with membership of classes experiencing moderate baseline depression, and improvement from initial moderate anxiety. This was somewhat surprising, given that people with Intellectual Disabilities generally benefit less from psychological therapies than people without (Dagnan et al., 2022). This may reflect referral patterns (with those experiencing greater need referred to specialist services), or the relative disadvantage that autistic people without an Intellectual Disability also experience. Limitations The symptom and daily living impairment measures used here have not been specifically validated in an autistic population (Cassidy et al., 2018). However, measurement variance was found to be small or negligible between autistic and non-autistic college students on both the GAD-7 and PHQ-9 (supporting that the measures were capturing the same constructs in both groups) (Robeson et al., 2024). Recent work on autistic burnout as an experience distinct from depression, and reflecting a response to poorly-adapted environments highlights that current measures do not capture all aspects of autistic people’s experiences (Higgins et al., 2021). The meaning of “recovery” or “improvement” for this client group should therefore not be assumed to be the same as for non-autistic people, and more work is needed to clarify valued outcomes from lived experience expertise. Entropy scores observed within the final models did not suggest good confidence for most likely class allocation. However, when using modal class assignment the proportions of individuals within classes showing reliable improvement, recovery and deterioration by the end of treatment showed a pattern consistent with the mean trajectories of groups. In addition, R3Step analyses were used for the regression, which account for the uncertainty involved in class allocation and were therefore robust to lower entropy statistics. Due to underdiagnosis of autism there were likely many autistic service users within the dataset, whose outcomes could not be included. Only service users who engaged with treatment (and received at least three sessions) were included, and so this study cannot tell us about those who did not engage (as well as those who were not referred). While the sample was representative of service users with a known autism diagnosis who accessed NHS TTad, it is not fully representative of all autistic people with anxiety and depression in need of, or accessing, treatment. Finally, ethnically-minoritised participants were more likely to have missing endpoint data, and were underrepresented in this cohort. Furthermore, it was regrettable that we needed to combine ethnicity categories into those reflecting individuals identifying their ethnicity as White or as ethnically-minoritised. However, we were limited by small numbers in several ethnicity groups, and decided that this limited analysis would be preferable to excluding the data altogether. Our finding points to the need for finer-grained future analyses that investigate the intersection of autism and race/ethnicity in relation to health inequalities. Research and clinical implications and practice Clinicians can review progress at the third treatment session to distinguish likely change trajectories with greater confidence than at baseline (comparing pre-treatment and session three outcome measure scores). This allows an opportunity for early review to step-up sooner, consider combination or augmented treatment strategies, and/or identify for whom additional autism-specific and cultural adaptations might need to be made. Ethnically-minoritised autistic people appear to be especially in need of targeted support, and may benefit from cultural adaptations to therapy not just those made for autistic adults (Arundell et al., 2021). Work is needed to understand their experiences of treatment and improve care, and in future finer-grained analyses are needed to help understand the intersection of autism and race/ethnicity, in relation to mental health outcomes. Pre-treatment difficulties in daily living, including leisure activities may warrant further attention as a target for support prior to, or alongside, treatment as usual. Proposals for autism-informed social support (with appreciation for the complexities of autism masking and burnout) may be helpful (Charlton et al., 2020). Further research is needed to clarify how to adapt and improve psychological interventions for autistic people. Following the present study, research is recommended to clarify how adaptations to care can be implemented to improve outcomes, including whether i) pre-therapy support, ii) a third-session review, or iii) a combination of the two are able to improve outcomes for autistic people in NHS TTad services. Declarations All data were fully anonymised, and linkage was achieved using anonymised subject identifiers provided by NHS Digital. Under the Governance Arrangements of Research Ethics Committees (REC) procedures, REC review was not required due to the anonymisation procedures followed by NHS Digital. Ethical approval for the current study using the data included in MODIFY was granted by the UCL Clinical, Educational and Health Psychology departmental REC (ethics approval number: CEHP/2023/592B). Contributors RP, LC, WM, RS, AJ, JS and CEB conceptualised the study. RP, LC, WM and RS contributed to the methodology and analysis. CEB, AJ and JS assessed and verified the dataset. All authors contributed to the manuscript writing and review, and approved the final version. All authors had full access to data in the study, and responsibility for the decision to submit for publication. Data Sharing All data used for this study are available on successful application to NHS Digital via the Data Access Request Service: https://digital.nhs.uk/services/data-access-request-service-dars. Data fields can be accessed via the NHS Digital data dictionary: https://www.datadictionary.nhs.uk/ References Arundell, L. L., Barnett, P., Buckman, J. E., Saunders, R., & Pilling, S. (2021). The effectiveness of adapted psychological interventions for people from ethnic minority groups: A systematic review and conceptual typology. Clinical psychology review , 88 , 102063. Barnett, P., Saunders, R., Buckman, J. E., Naqvi, S. A., Singh, S., Stott, J., ... & Pilling, S. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5880564","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":421281609,"identity":"5f9e8fba-c3f9-4c15-892c-9c71bea5158f","order_by":0,"name":"Richard 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Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Crane","suffix":""},{"id":421281620,"identity":"880845cd-9138-4b33-b7fb-449a8ecc50b6","order_by":11,"name":"Will Mandy","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Will","middleName":"","lastName":"Mandy","suffix":""}],"badges":[],"createdAt":"2025-01-22 11:47:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5880564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5880564/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44220-025-00567-4","type":"published","date":"2026-01-22T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77591897,"identity":"0d5459c6-1383-424c-8ac2-fac4762b4d5f","added_by":"auto","created_at":"2025-03-03 11:28:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54654,"visible":true,"origin":"","legend":"\u003cp\u003eDepression trajectories using modal class assignment (GMM sample means)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5880564/v1/297bb809c0c388e675f4484c.png"},{"id":77591911,"identity":"65e0153b-acec-42b8-960b-7c50df6e43d8","added_by":"auto","created_at":"2025-03-03 11:28:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69394,"visible":true,"origin":"","legend":"\u003cp\u003eAnxiety trajectories using modal class assignment (GMM sample means)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5880564/v1/63ab61226a7c26b6730ea813.png"},{"id":100958637,"identity":"9f2ff266-0441-4472-b736-cfb602f2ccca","added_by":"auto","created_at":"2026-01-23 08:07:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2081641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5880564/v1/024bee00-42b9-4c26-97c8-5abc9436e8b9.pdf"},{"id":77591898,"identity":"b0f96d6e-5008-4f0e-b9af-cdee40d75d4b","added_by":"auto","created_at":"2025-03-03 11:28:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":484223,"visible":true,"origin":"","legend":"Supplementary materials to: Depression and anxiety symptom change during psychological therapy for autistic clients: Evidence from national healthcare records in England","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-5880564/v1/e8da7aa01e2c668adc2ff984.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Depression and anxiety symptom change during psychological therapy for autistic clients: Evidence from national healthcare records in England","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAutistic people are more likely to experience a range of mental health conditions including depression and anxiety compared to non-autistic people (Lai et al., 2019), and report negative experiences when they receive support for these (Brede et al., 2022).\u003c/p\u003e \u003cp\u003eEvidence-based psychological therapies are recommended as first-line treatments for depression and anxiety, including for autistic people (Clark, 2011; Linden et al., 2023), but these interventions often require adaptation to better meet the needs of autistic service users (Linden et al., 2023; Pilling et al., 2012). In the largest study of its kind, using a national cohort of adults receiving psychological therapies in a general adult health care setting, it was demonstrated that autistic people experienced lower rates of improvement and recovery than a matched non-autistic comparison group (El Baou et al., 2023). However, that study and those preceding it have not yet been able to contribute to understanding which groups of autistic people may experience better or worse intervention outcomes, thus informing who might benefit from more personalised or more thoroughly-adapted intervention approaches.\u003c/p\u003e \u003cp\u003eStatistical modelling approaches that can identify heterogeneous patterns of symptom change offer several advantages (Ram \u0026amp; Grimm, 2009). Firstly, they allow for an investigation of distinct trajectories of autistic people\u0026rsquo;s symptom change during psychological therapy, along with the pre-treatment characteristics associated with following those trajectories. This may help inform treatment planning, including the need for adapted, augmented or different treatment if the likely trajectory shows a slow or poor treatment response. Secondly, previous studies have used a similar approach to model treatment response in a majority non-autistic adult sample, and identified an association between symptom trajectories and pre-treatment difficulties with life tasks (i.e., impact on daily life across home, work, relationships and leisure activities). Greater pre-treatment life-task difficulties were associated with lower likelihood of following an improving trajectory (decreasing symptoms, benefitting from treatment) for anxiety or depression, relative to a stable one (i.e., non-response to treatment) (Skelton et al., 2023; Saunders et al., 2019). It is currently unclear as to whether difficulties with daily life tasks may affect outcomes for autistic people as they do for non-autistic people; however, given that these difficulties were previously observed to be higher for autistic people pre-treatment in a general adult mental healthcare setting (El Baou et al., 2023), and are also a known barrier to treatment (Brede et al., 2022), this warrants further investigation.\u003c/p\u003e \u003cp\u003eThe current study aims to identify trajectories of depression and anxiety symptom change for autistic people during psychological therapy delivered as part of routine care in a nationwide general adult psychological treatment programme, and to identify pre-treatment service user characteristics associated with different trajectories of treatment response.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eAnalyses were performed using the MODIFY dataset, which includes national data drawn from linked NHS Digital electronic healthcare records from across all healthcare regions in England (Bell et al., 2022). The linked databases forming MODIFY include psychological treatment service data drawn from NHS Talking Therapies for Anxiety and Depression (NHS TTad; formerly known as Improving Access to Psychological Therapies - IAPT) in England between 2012-19 (National Collaborating Centre for Mental Health [NCCMH], 2018). Further information on the TTad service model is provided in Supplement, Appendix A. TTad data were linked to i) Hospital Episode Statistics (HES; NHS Digital, 2019); ii) Office of National Statistics (ONS) mortality database (HES-ONS; NHS Digital, 2023); and iii) the Mental Health Services Dataset (MHSDS; NHS Digital, 2024). Further detail regarding MODIFY and linked data is also provided in Supplement, Appendix B. All data were fully anonymised, and linkage was achieved using anonymised subject identifiers provided by NHS Digital. Under the Governance Arrangements of Research Ethics Committees (REC) procedures, REC review was not required due to the anonymisation procedures followed by NHS Digital.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThis study included all service users with an autism diagnosis identified in HES or MHSDS (ICD-10 diagnostic codes F84\u0026middot;0 (Childhood Autism), F84\u0026middot;1 (Atypical Autism) and F84\u0026middot;5 (Asperger Syndrome) as per prior research (Bell et al., 2022; El Baou et al., 2023). Inclusion criteria for this study were: (1) accessing a course of treatment with any TTad services between 2012-19; (2) meeting thresholds for \u0026lsquo;caseness\u0026rsquo; on measures of either depression or anxiety symptoms at baseline; (3) being discharged from the services (so they were not still receiving treatment); and (4) receiving at least three assessment and treatment sessions (NCCMH, 2018). Service users whose diagnosis was recorded as a severe and enduring mental illness (e.g., schizophrenia) were excluded, because the services do not offer standardised treatment for these conditions (NCCMH, 2018).\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eDepression symptoms were measured using the Patient Health Questionnaire (PHQ)-9 (Kroenke, Spitzer, \u0026amp; Williams, 2001), and anxiety symptoms using the Generalized Anxiety Disorder scale (GAD)-7 (Spitzer et al., 2006). These measures have not been specifically validated in autistic populations, but are routinely used in NHS TTad settings. Clinical ranges (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were used to describe latent symptom trajectories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePHQ-9 and GAD-7 clinical thresholds and ranges\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately-severe depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere depressive symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo anxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild anxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate anxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere anxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk factors\u003c/h3\u003e\n\u003cp\u003eDifficulties with daily life tasks were measured using the Work and Social Adjustment Scale (WSAS; Mundt et al., 2002). In keeping with prior research (e.g., Barnett et al., 2023; Saunders et al., 2019; Skelton et al., 2023), home management, private leisure, social leisure and close relationship subscales were used in the primary analysis (WSAS-4; questions 2\u0026ndash;5), and the employment subscale (question 1) removed due to a high number of \u0026ldquo;not applicable\u0026rdquo; answers (see Supplement, Appendix B). A further sensitivity analysis used all subscale scores (WSAS-5).\u003c/p\u003e \u003cp\u003eAge, gender, ethnicity, employment status, use of psychotropic medication, presence of a long-term health condition and Intellectual Disability diagnosis were recorded in the NHS TTad, HES and MHSDS datasets (further detail provided in Supplement, Appendix B). Ethnicity was coded as a binary variable for this analysis, due to low cell counts in several categories (Appendix B; see Limitations).\u003c/p\u003e \u003cp\u003eDetailed information on measurement is included in the Supplement, Appendix B.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eInitial data management was conducted in STATA 14\u0026middot;2. Subsequent analyses were conducted using MPlus 8\u0026middot;3.\u003c/p\u003e \u003cp\u003eMissing PHQ-9 and GAD-7 data were handled using Maximum Likelihood Estimation with Robust Standard Errors in MPlus. The first eight sessions were modelled in this study. First, a single trajectory was estimated for all participants, separately for anxiety and depression, to obtain a mean trajectory. Two latent growth curve models (LGCM) were designed \u0026ndash; one linear LGCM, and one in which six time scores were estimated as free parameters (with two fixed to allow model identification), allowing the shape of change to be determined by the data. Model fits were compared using the Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and Standardised Root Mean Square Residual (SRMR) (see Supplement, Appendix C for further information on fit indices).\u003c/p\u003e \u003cp\u003eTo allow identification of distinct classes with reduced risk of convergence failure and computation time, we used Latent Class Growth Analysis (LCGA) to fit models that assume no within-class variance for growth factors (Ram \u0026amp; Grimm, 2009). Models were estimated up to eight classes to allow for the possibility of higher numbers of classes to be detected than in previous general population studies (Skelton et al., 2023; Saunders et al., 2019).\u003c/p\u003e \u003cp\u003eNext, Growth Mixture Models (GMM) were fitted, which allow variances and covariances to be obtained around growth factors. Models up to eight classes were again specified for both anxiety and depression, and assessed using fit indices. Best-fitting models for each of anxiety and depression were selected for further analysis. LCGA and GMM models were compared using Bayesian Inference Criteria (BIC) and Sample Size-Adjusted BIC (SA-BIC), and three Likelihood Ratio Tests (LRT) of the ratio of log-likelihoods for the \u003cem\u003ek\u003c/em\u003e class model with the \u003cem\u003ek-1\u003c/em\u003e class model: the Vuong-Lo-Mendell-Rubin (VLMR) LRT, Lo-Mendell-Rubin-Adjusted LRT (LMR-A), and Bootstrap LRT (BLRT) (see Supplement, Appendix C). All available fit information was reported and considered to select the best-fitting -model (Ram \u0026amp; Grimm, 2009), and conservatively when any \u003cem\u003ek\u003c/em\u003e model LRTs were non-significant (\u003cem\u003ep\u003c/em\u003e \u0026gt;\u0026middot;05) the \u003cem\u003ek-1\u003c/em\u003e model was considered to be supported for selection.\u003c/p\u003e \u003cp\u003eModal class assignments and scores were exported into STATA, to calculate means and 95% Confidence Intervals. The \u0026ldquo;substantive meaningfulness\u0026rdquo; (i.e, external validity) (Muth\u0026eacute;n, 2003) of these modal classes was examined by characterising them in relation to reliable improvement, reliable recovery and reliable deterioration at the end of treatment. Classes were compared with those weighted by the probability of class assignment by MPlus, and reported in Appendix H.\u003c/p\u003e \u003cp\u003eMultinomial regression models were fitted to investigate associations between pre-treatment patient characteristics and the probability of latent class membership using the R3Step procedure in MPlus. R3Step accounts for posterior probabilities of class assignment, correcting for classification error, and has been shown to be superior to approaches in which classification errors are not accounted for (Vermunt, 2010). Missing WSAS data were handled in MPlus using multiple imputation with 1,000 Bayesian iterations and 100 datasets. WSAS-4 scores were first entered into a multinomial regression along with ethnicity, gender, age, use of psychotropic medication, presence of a long-term health condition and presence of an Intellectual Disability diagnosis. Next, analyses were re-run in the same way using individual WSAS subscale scores instead of total WSAS-4 scores. A further sensitivity analysis was conducted by repeating all analyses, including employment subscale scores (WSAS-5).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReporting\u003c/h3\u003e\n\u003cp\u003e This study was reported using the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist (Supplement, Appendix J; van de Schoot et al., 2017) and the Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement (Supplement, Appendix K; Benchimol et al., 2015).\u003c/p\u003e\n\u003ch3\u003eRole of the funding source\u003c/h3\u003e\n\u003cp\u003eThe funder of the study had no role in the study design, data collection, analysis, interpretation or writing of the report.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eData were available for 2,512,402 service users in the MODIFY dataset, and 11,198 had a linked autism diagnosis. After exclusion criteria were applied, a study sample of 7,175 was obtained (see Supplement, Appendix D for a study flow diagram).\u003c/p\u003e \u003cp\u003eNo predictor variables were associated with missing anxiety or depression data points at the initial time point, but ethnicity was related to missingness by the final timepoint. Data were handled as missing at random, and the relationship with ethnicity was accounted for in subsequent interpretation. Missing data and distributions are reported in full in the Supplement, Appendix E.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD; range) or count (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDemographic variables\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026middot;68 (12\u0026middot;19; 18\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,020 (42\u0026middot;09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (\u0026middot;47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (any ethnically minoritised background)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392 (5\u0026middot;55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e676 (9\u0026middot;42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (employed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,717 (51\u0026middot;80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (7\u0026middot;04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCo-occurring conditions\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2041 (28\u0026middot;45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-term condition (missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1760 (24\u0026middot;53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305 (4\u0026middot;25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline clinical variables\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline PHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026middot;75 (5\u0026middot;46, 0\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline GAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026middot;79 (4\u0026middot;34, 0\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline WSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026middot;59 (9\u0026middot;31; 0\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (depression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,042 (28\u0026middot;46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (generalised anxiety)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e918 (12\u0026middot;80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (other anxiety condition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e870 (12\u0026middot;13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (mixed anxiety-depression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,422 (19\u0026middot;82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis (missing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,923 (26\u0026middot;80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaking psychotropic medication at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,634 (56\u0026middot;52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTreatment factors\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention received: Intensity known\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,210 (44\u0026middot;74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention received: Low-intensity intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,302 (71\u0026middot;71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention received: High-intensity intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,689 (52\u0026middot;62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDropout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,461 (20\u0026middot;36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnward referral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (6\u0026middot;22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTreatment outcomes\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliable improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,563 (63\u0026middot;60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliable recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,583 (36\u0026middot;00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliable deterioration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e696 (9\u0026middot;70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel description\u003c/h2\u003e \u003cp\u003eOn the basis of superior RMSEA, CFI/TLI and SRMR indices, a base LGCM with free time scores was selected. Model specifications and output are available in Supplement, Appendix F. For this reason, the subsequent LCGA and GMM specifications were designed with free time scores. LCGA models converged, and fit indices suggested an eight-class depression model and a seven-class anxiety model had the best fit (see Supplement, Appendix G for specification and output). Subsequently, GMM models converged and showed superior fit (BIC, SABIC) compared with LCGA (specification and output in Supplement, Appendix H).\u003c/p\u003e \u003cp\u003eA depression GMM with five classes (D1-D5) was selected for further analysis on the basis that a six-class model returned non-significant VLMR-LRT, LMR-A-LRT tests and a minimally improved BIC (\u0026gt;\u0026middot;005% reduction). An anxiety GMM with seven classes (A1-A7) was selected for further analyses on the basis that an eight-class model showed increased (poorer) BIC and non-significant VLMR-LRT and LMR-A-LRT tests. Entropy scores were .56 for the depression model and .61 for the anxiety model, suggesting limited confidence in the most probable class assignment \u0026ndash; however the R3Step analysis would be robust to this as it accounts for class assignment probabilities. Model-estimated trajectories and sample means are reported in Supplement, Appendix H. Trajectories from the GMM models are shown below, using class modal assignment (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Means weighted by classification probability are shown and reported in Appendix H.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et1 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et2 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et3 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et4 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et5 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003et6 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003et7 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003et8 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19.53\u003c/b\u003e (19.29\u0026ndash;19.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e19.56\u003c/b\u003e (19.31\u0026ndash;19.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e19.30\u003c/b\u003e (19.06\u0026ndash;19.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e18.93\u003c/b\u003e (18.68\u0026ndash;19.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e18.72\u003c/b\u003e (18.46\u0026ndash;18.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e18.78\u003c/b\u003e (18.52\u0026ndash;19.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.54\u003c/b\u003e (18.28\u0026ndash;18.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e18.10\u003c/b\u003e (17.82\u0026ndash;18.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21.15\u003c/b\u003e (19.89\u0026ndash;22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.02\u003c/b\u003e (16.35\u0026ndash;19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.73\u003c/b\u003e (10.46-15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8.78\u003c/b\u003e (6.78\u0026ndash;10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.32\u003c/b\u003e (3.72\u0026ndash;6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.61\u003c/b\u003e (2.55\u0026ndash;4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.20\u003c/b\u003e (2.29\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.90\u003c/b\u003e (1.29\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20.09\u003c/b\u003e (19.79\u0026ndash;20.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.40\u003c/b\u003e (18.04\u0026ndash;18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.38\u003c/b\u003e (15.96\u0026ndash;16.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e14.46\u003c/b\u003e (14.03\u0026ndash;14.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e12.63\u003c/b\u003e (12.21\u0026ndash;13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11.23\u003c/b\u003e (10.85\u0026ndash;11.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e10.00\u003c/b\u003e (9.62\u0026ndash;10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.12\u003c/b\u003e (8.72\u0026ndash;9.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.09\u003c/b\u003e (11.81\u0026ndash;12.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.63\u003c/b\u003e (10.35\u0026ndash;10.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.84\u003c/b\u003e (9.56\u0026ndash;10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e9.58\u003c/b\u003e (9.28\u0026ndash;9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9.30\u003c/b\u003e (9.00-9.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.87\u003c/b\u003e (8.57\u0026ndash;9.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e8.42\u003c/b\u003e (8.11\u0026ndash;8.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e7.96\u003c/b\u003e (7.66\u0026ndash;8.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.82\u003c/b\u003e (8.70-10.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.91\u003c/b\u003e (9.43\u0026ndash;12.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e13.36\u003c/b\u003e (11.61\u0026ndash;15.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e15.41\u003c/b\u003e (13.84\u0026ndash;16.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17.50\u003c/b\u003e (16.26\u0026ndash;18.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e17.55\u003c/b\u003e (16.03\u0026ndash;19.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.59\u003c/b\u003e (17.30-19.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e17.18\u003c/b\u003e (15.53\u0026ndash;18.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et1 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et2 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et3 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et4 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et5 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003et6 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003et7 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003et8 (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e17.61\u003c/b\u003e (17.43\u0026ndash;17.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e17.61\u003c/b\u003e (17.44\u0026ndash;17.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e17.57\u003c/b\u003e (17.38\u0026ndash;17.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17.45\u003c/b\u003e (17.26\u0026ndash;17.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17.05\u003c/b\u003e (16.84\u0026ndash;17.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e16.97\u003c/b\u003e (16.77\u0026ndash;17.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e16.60\u003c/b\u003e (16.38\u0026ndash;16.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e16.29\u003c/b\u003e (16.05\u0026ndash;16.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e18.00\u003c/b\u003e (17.46\u0026ndash;18.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e15.67\u003c/b\u003e (14.65\u0026ndash;16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.57\u003c/b\u003e (11.24\u0026ndash;13.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8.74\u003c/b\u003e (7.49\u0026ndash;9.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.83\u003c/b\u003e (4.87\u0026ndash;6.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.76\u003c/b\u003e (3.02\u0026ndash;4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.09\u003c/b\u003e (2.45\u0026ndash;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.14\u003c/b\u003e (1.67\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e17.33\u003c/b\u003e (17.12\u0026ndash;17.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e15.92\u003c/b\u003e (15.63\u0026ndash;16.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e14.42\u003c/b\u003e (14.07\u0026ndash;14.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12.99\u003c/b\u003e (12.64\u0026ndash;13.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11.36\u003c/b\u003e (11.02\u0026ndash;11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10.07\u003c/b\u003e (9.76\u0026ndash;10.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.21\u003c/b\u003e (8.88\u0026ndash;9.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e8.13\u003c/b\u003e (7.81\u0026ndash;8.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.89\u003c/b\u003e (11.66\u0026ndash;12.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e11.05\u003c/b\u003e (10.76\u0026ndash;11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10.70\u003c/b\u003e (10.41\u0026ndash;10.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e10.68\u003c/b\u003e (10.41\u0026ndash;10.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e10.53\u003c/b\u003e (10.27\u0026ndash;10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10.31\u003c/b\u003e (10.04\u0026ndash;10.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e10.26\u003c/b\u003e (9.99\u0026ndash;10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.48\u003c/b\u003e (9.18\u0026ndash;9.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.81\u003c/b\u003e (12.40-13.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.75\u003c/b\u003e (9.22\u0026ndash;10.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.98\u003c/b\u003e (6.49\u0026ndash;7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.08\u003c/b\u003e (5.61\u0026ndash;6.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.83\u003c/b\u003e (4.41\u0026ndash;5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.65\u003c/b\u003e (4.21\u0026ndash;5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.75\u003c/b\u003e (3.41\u0026ndash;4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e3.62\u003c/b\u003e (3.21\u0026ndash;4.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.94\u003c/b\u003e (9.36\u0026ndash;10.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.92\u003c/b\u003e (9.56\u0026ndash;11.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e13.77\u003c/b\u003e (12.88\u0026ndash;14.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13.86\u003c/b\u003e (13.03\u0026ndash;14.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e15.03\u003c/b\u003e (14.33\u0026ndash;15.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e15.53\u003c/b\u003e (14.79\u0026ndash;16.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e15.82\u003c/b\u003e (15.17\u0026ndash;16.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e15.26\u003c/b\u003e (14.44\u0026ndash;16.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.42\u003c/b\u003e (6.02\u0026ndash;6.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.00\u003c/b\u003e (5.57\u0026ndash;6.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.76\u003c/b\u003e (5.34\u0026ndash;6.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.46\u003c/b\u003e (5.04\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5.60\u003c/b\u003e (5.11\u0026ndash;6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.29\u003c/b\u003e (4.82\u0026ndash;5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.90\u003c/b\u003e (4.41\u0026ndash;5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e4.97\u003c/b\u003e (4.49\u0026ndash;5.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe five-class depression model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026lsquo;Moderately severe, not improving\u0026rsquo; (D1; 39\u0026middot;0%) and \u0026lsquo;moderate, limited improvement\u0026rsquo; (D4; 36\u0026middot;7%) trajectory classes accounted for the majority of the sample.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTwo classes showed improving trajectories from the \u0026lsquo;moderately severe\u0026rsquo; range into \u0026lsquo;mild\u0026rsquo; (D3; 18\u0026middot;6%) and \u0026lsquo;minimal\u0026rsquo; (D2; 4\u0026middot;3%) ranges by session 8.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA smaller class was composed of individuals who started in the moderate depression range but subsequently showed deterioration (D5; 1\u0026middot;5%).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe seven-class anxiety model is shown in Fig.\u0026nbsp;3:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThree classes representing around 60% of the sample showed trajectories with no or minimal improvement, in each of the severe (A1; 33\u0026middot;1%), moderate (A4; 20\u0026middot;5%) and mild (A7; 6\u0026middot;7%) ranges.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTwo classes showed improving trajectories from the severe range at baseline, one improving into the mild range by session 8 (A3; 18\u0026middot;4%) and one into the minimal range (A2; 4\u0026middot;9%).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA further two classes began within the moderate anxiety range at baseline, showing each of an improving (A5; 13\u0026middot;9%) or deteriorating (A6; 2\u0026middot;6%) path.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe trajectories obtained for anxiety were comparable to those shown for depression symptoms, with the addition of moderate improving, and mild classes. Both models showed 4\u0026ndash;5% of the sample could be considered early responders, making rapid gains by the third session. By this point, trajectories could be distinguished with greater confidence than at baseline, and 95% Confidence Intervals between the groups starting in moderately-severe and severe ranges did not overlap by this point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecovery and change indices\u003c/h2\u003e \u003cp\u003eReliable change indices by modal group assignment are reported in full in the Supplement, Appendix I. When classes were considered by modal assignment, by the end of treatment almost all of the rapidly improving class members (D2, A2) had experienced a reliable improvement within both depression (99\u0026middot;7%) and anxiety (99\u0026middot;4%) models, with high rates (97\u0026middot;4%, 94\u0026middot;8%) of reliable recovery. By session 4, the rapidly improving depression group experienced a mean reduction of 12\u0026middot;45 points (double the PHQ-9 reliable change index of 6 points), and the anxiety group a mean of 10\u0026middot;2 points (2\u0026middot;5 times the GAD-7 reliable change index of 4 points). Gradually improving class members (D3, A3) showed lower, but still substantial, rates of reliable improvement (88\u0026middot;8%, 91\u0026middot;7%) and moderate rates of reliable recovery (57\u0026middot;5%, 48\u0026middot;6%%) by the end of treatment. All improving classes showed low rates of reliable deterioration (between 0% and \u0026middot;38%). Conversely, moderate deteriorating groups showed high rates of reliable deterioration (62\u0026middot;6% and 74\u0026middot;6% on PHQ-9 and GAD-7 respectively) by the end of treatment. This suggested that when modal assignments were considered the patterns of recovery between groups were broadly consistent with the outcomes suggested by their mean trajectory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of baseline risk factors with trajectory class membership\u003c/h2\u003e \u003cp\u003eAs both the largest and most clinically concerning, the not-improving classes (D1 and A1) were chosen as the reference groups for depression and anxiety. Further comparisons were made using the moderate, limited improvement classes (D4 and A4) as reference categories. The moderate, deteriorating classes (D5 and A6) could not be used for this purpose due to the small proportions of patients following those trajectories. Odds ratios with 95% Confidence Intervals are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (depression model) and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (anxiety model).\u003c/p\u003e \u003cp\u003eTo summarise the associations observed between risk factors and trajectory classes:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor both anxiety and depression, lower pre-treatment total functional impairment (WSAS-4) scores (i.e., less functional difficulty) were associated with following rapidly improving trajectories from moderately severe or severe baseline ranges relative to the not-improving classes A1 and D1 [OR(95%CI) = \u0026middot;91(\u0026middot;86 - \u0026middot;96) for depression; and \u0026middot;93(\u0026middot;89-\u0026middot;97) for anxiety].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLower baseline WSAS social and private leisure subscale scores were associated with rapid improvement from the severe range on the GAD-7 (relative to A1) [OR(95%CI) = \u0026middot;76(\u0026middot;69 - \u0026middot;83) for social leisure; and \u0026middot;80(\u0026middot;72-\u0026middot;89) for private leisure].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLower pre-treatment total and social leisure WSAS scores were associated with gradually improving GAD-7 trajectories from the severe range (A2), relative to A1 [OR(95% CI) = \u0026middot;94(\u0026middot;91-\u0026middot;97) for total scores; and \u0026middot;85(\u0026middot;78-\u0026middot;93) for social leisure]. Each was also associated with improving GAD-7 trajectories from the moderate range (A5), relative to the moderate, limited improvement class A4 [OR(95% CI) = \u0026middot;96(\u0026middot;93\u0026ndash;98) for total scores; and \u0026middot;88 (\u0026middot;79-\u0026middot;98) for social leisure).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOverall, total WSAS-4 scores were associated with all but the moderately severe, gradually improving depression trajectory class (D3) relative to D1; and all but the moderate improving depression class (D5) relative to D4. WSAS-4 totals were associated with the likelihood of following any other trajectory relative to A1, and all but the severe, rapid improving (A2) and moderate, deteriorating (A6) classes relative to A4.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA sensitivity analysis, including the employment subscale of the WSAS, replicated the same associations between WSAS-5 total and subscale scores as that observed in our main analysis (Supplement, Appendix J).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor anxiety specifically, identifying as belonging to an ethnically-minoritised group was associated with increased likelihood of following a deteriorating GAD-7 trajectory (A6) relative to either reference class [OR(95% CI)\u0026thinsp;=\u0026thinsp;2\u0026middot;66(1\u0026middot;24\u0026thinsp;\u0026minus;\u0026thinsp;5\u0026middot;68) versus A1; and 3\u0026middot;83(1\u0026middot;34\u0026thinsp;\u0026minus;\u0026thinsp;10\u0026middot;96) versus A4].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHaving an Intellectual Disability diagnosis was associated with greater likelihood of belonging to the D4 class (relative to D1) [OR (95% CI)\u0026thinsp;=\u0026thinsp;1\u0026middot;98(1\u0026middot;23\u0026thinsp;\u0026minus;\u0026thinsp;3\u0026middot;18)] and of following a moderate, improving GAD-7 trajectory (A5) relative to either A1 [OR (95% CI)\u0026thinsp;=\u0026thinsp;3\u0026middot;07(2\u0026middot;07\u0026thinsp;\u0026minus;\u0026thinsp;4\u0026middot;53)] or A4 [OR (95% CI)\u0026thinsp;=\u0026thinsp;4\u0026middot;62(2\u0026middot;89\u0026thinsp;\u0026minus;\u0026thinsp;9\u0026middot;33)] reference groups.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOlder age (continuous, years) was associated with following a deteriorating PHQ-9 trajectory (D5) relative to D4 [OR (95% CI)\u0026thinsp;=\u0026thinsp;1\u0026middot;04(1\u0026middot;01\u0026ndash;1\u0026middot;06)], but also conversely a consistently mild GAD-7 trajectory relative to A1 [OR (95% CI)\u0026thinsp;=\u0026thinsp;1\u0026middot;03(1\u0026middot;01\u0026ndash;1\u0026middot;05)].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTaking psychotropic medication pre-treatment was associated (relative to D1 and A1) with lower likelihood of following a moderate, limited improvement PHQ-9 trajectory (D4) [OR(95% CI) = \u0026middot;46(\u0026middot;35-\u0026middot;59) for depression and a moderate improving GAD-7 trajectory (A5) [OR(95% CI) = \u0026middot;53(\u0026middot;38 -\u0026middot;74).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFemale gender was associated with less likelihood of following a stable, mild anxiety trajectory relative to either severe, not improving [OR(95% CI) = \u0026middot;44(\u0026middot;31-\u0026middot;63)] or moderate, limited improvement [OR(95% CI) = \u0026middot;58(\u0026middot;37-\u0026middot;90)] reference classes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression output for depression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eComparison group: D1\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (female vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;46\u0026ndash;1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;85\u0026ndash;1\u0026middot;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;63\u0026ndash;1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;52\u0026ndash;2\u0026middot;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;96\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u0026middot;01\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;01\u0026ndash;1\u0026middot;06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity (ethnically minoritised group vs White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;16\u0026ndash;2\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;36\u0026ndash;1\u0026middot;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;60\u0026ndash;1\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;64\u0026ndash;7\u0026middot;85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;44\u0026ndash;1\u0026middot;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;62\u0026ndash;1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;53 - \u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;33\u0026ndash;1\u0026middot;65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychotropic medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;53\u0026ndash;1\u0026middot;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;45 - \u0026middot;87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;35 - \u0026middot;59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;40\u0026ndash;2\u0026middot;26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-term health condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;67\u0026ndash;1\u0026middot;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;83\u0026ndash;1\u0026middot;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;73\u0026ndash;1\u0026middot;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;19\u0026ndash;1\u0026middot;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntellectual Disability (has diagnosis vs does not have diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;3\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;48\u0026ndash;1\u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;23\u0026ndash;3\u0026middot;18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u0026middot;03\u0026ndash;8\u0026middot;45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;86 - \u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;94\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;76 - \u0026middot;82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;75 - \u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;77\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;85\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;74 - \u0026middot;86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;65 - \u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Social leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;76\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;74 - \u0026middot;87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;73\u0026ndash;1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Private leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;77\u0026ndash;1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;93\u0026ndash;1\u0026middot;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;68 - \u0026middot;78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;64 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Close relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026middot;84\u0026ndash;1\u0026middot;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;94\u0026ndash;1\u0026middot;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;78 - \u0026middot;89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;63 - \u0026middot;95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eComparison group: D4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eD1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eD2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003eD3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eD5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (female vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;56\u0026ndash;1\u0026middot;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;2\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;66\u0026ndash;2\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;98\u0026ndash;1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;95 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity (ethnically minoritised group vs White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;15\u0026ndash;2\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;31\u0026ndash;1\u0026middot;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;68\u0026ndash;6\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployment (in employment vs not in employment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;63\u0026ndash;1\u0026middot;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;87\u0026ndash;1\u0026middot;81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;50\u0026ndash;2\u0026middot;33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychotropic medication (taking vs not taking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;13\u0026ndash;3\u0026middot;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;94\u0026ndash;1\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;92\u0026ndash;4\u0026middot;72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-term health condition (has diagnosed condition vs does not have diagnosed condition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;67\u0026ndash;1\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;20\u0026ndash;1\u0026middot;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntellectual Disability (has diagnosis vs does not have diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026middot;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;41\u0026ndash;2\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;25 - \u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;59\u0026ndash;3\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;09\u0026ndash;1\u0026middot;22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;19\u0026ndash;1\u0026middot;29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;96\u0026ndash;1\u0026middot;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;96\u0026ndash;1\u0026middot;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;05\u0026ndash;1\u0026middot;32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Social leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026middot;93\u0026ndash;1\u0026middot;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;01\u0026ndash;1\u0026middot;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;92\u0026ndash;1\u0026middot;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Private leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;05\u0026ndash;1\u0026middot;42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;26\u0026ndash;1\u0026middot;56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;89\u0026ndash;1\u0026middot;34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Close relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;01\u0026ndash;1\u0026middot;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;13\u0026ndash;1\u0026middot;41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;77\u0026ndash;1\u0026middot;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerately severe, not improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerately severe, rapidly improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerately severe, gradually improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate, limited improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate, deteriorating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c14\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression output for anxiety model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eComparison group: A1\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (female vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;59\u0026ndash;1\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;68\u0026ndash;1\u0026middot;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;58\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;57\u0026ndash;1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;40\u0026ndash;1\u0026middot;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;31 - \u0026middot;63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;96\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;99\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;97\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u0026middot;01\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;98\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity (ethnically minoritised group vs White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;01\u0026ndash;3\u0026middot;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;48\u0026ndash;1\u0026middot;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;35\u0026ndash;1\u0026middot;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;60\u0026ndash;2\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;24\u0026ndash;5\u0026middot;68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u0026middot;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;79\u0026ndash;2\u0026middot;54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployment (not in employment vs in employment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;53\u0026ndash;1\u0026middot;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;53 - \u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;62\u0026ndash;1\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;72\u0026ndash;1\u0026middot;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;71\u0026ndash;2\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;55\u0026ndash;1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychotropic medication (taking vs not taking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;56\u0026ndash;1\u0026middot;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;47 - \u0026middot;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;66\u0026ndash;1\u0026middot;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;38 - \u0026middot;74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;48\u0026ndash;1\u0026middot;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;50 - \u0026middot;97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-term health condition (has diagnosed condition vs does not have diagnosed condition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;49\u0026ndash;1\u0026middot;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;72\u0026ndash;1\u0026middot;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;58\u0026ndash;1\u0026middot;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;67\u0026ndash;1\u0026middot;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;40\u0026ndash;1\u0026middot;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;64\u0026ndash;1\u0026middot;34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntellectual Disability (has diagnosis vs does not have diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;73\u0026ndash;2\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;52\u0026ndash;1\u0026middot;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;35\u0026ndash;1\u0026middot;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e3\u0026middot;07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;07\u0026ndash;4\u0026middot;53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;48\u0026ndash;3\u0026middot;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;24\u0026ndash;1\u0026middot;28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;89 - \u0026middot;97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;91 - \u0026middot;97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;87 - \u0026middot;91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;83 - \u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;83 - \u0026middot;91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;83 - \u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;83\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;83\u0026ndash;1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;77\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;81\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Social leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;69 - \u0026middot;83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;78 - \u0026middot;93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;79 - \u0026middot;93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;69 - \u0026middot;83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;75\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;75 - \u0026middot;90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Private leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;72 - \u0026middot;89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;90\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80 - \u0026middot;93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;72 - \u0026middot;89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;74 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;72 - \u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Close relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026middot;87\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;92\u0026ndash;1\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;85 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;87\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;76\u0026ndash;1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;82 - \u0026middot;97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eComparison group: A4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eA1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eA2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003eA3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eA5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e\u003cb\u003eA6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e\u003cb\u003eA7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender (female vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;75\u0026ndash;1\u0026middot;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;83\u0026ndash;1\u0026middot;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;68\u0026ndash;1\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;48\u0026ndash;1\u0026middot;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;37 - \u0026middot;90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;98\u0026ndash;1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;99\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u0026middot;02\u0026ndash;1\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;99\u0026ndash;1\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;01\u0026ndash;1\u0026middot;05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity (ethnically minoritised group vs White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;02\u0026ndash;4\u0026middot;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;54\u0026ndash;2\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;62\u0026ndash;4\u0026middot;52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e3\u0026middot;83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;34\u0026ndash;10\u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e2\u0026middot;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;88\u0026ndash;4\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEmployment (in employment vs not in employment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;64\u0026ndash;1\u0026middot;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;62\u0026ndash;1\u0026middot;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;81\u0026ndash;1\u0026middot;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;81\u0026ndash;3\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;63\u0026ndash;1\u0026middot;46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychotropic medication (taking vs not taking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;62\u0026ndash;1\u0026middot;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026middot;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;50\u0026ndash;1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;40 - \u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;50\u0026ndash;2\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;53\u0026ndash;1\u0026middot;21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-term health condition (has diagnosed condition vs does not have diagnosed condition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;60\u0026ndash;1\u0026middot;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;85\u0026ndash;1\u0026middot;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;76\u0026ndash;1\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;46\u0026ndash;2\u0026middot;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;75\u0026ndash;1\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntellectual Disability (has diagnosis vs does not have diagnosis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;95\u0026ndash;4\u0026middot;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;61\u0026ndash;3\u0026middot;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e4\u0026middot;62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;89\u0026ndash;9\u0026middot;33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;55\u0026ndash;6\u0026middot;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;30\u0026ndash;2\u0026middot;38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;1\u0026middot;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;03\u0026ndash;1\u0026middot;09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;93 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;94\u0026ndash;1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;94 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;91\u0026ndash;1\u0026middot;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;91\u0026ndash;1\u0026middot;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;87\u0026ndash;1\u0026middot;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Social leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;90\u0026ndash;1\u0026middot;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026middot;91\u0026ndash;1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026middot;79 - \u0026middot;98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u0026middot;01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;1\u0026middot;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;86\u0026ndash;1\u0026middot;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Private leisure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;94\u0026ndash;1\u0026middot;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;03\u0026ndash;1\u0026middot;25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;81\u0026ndash;1\u0026middot;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;84\u0026ndash;1\u0026middot;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWSAS: Close relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026middot;95\u0026ndash;1\u0026middot;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u0026middot;00\u0026ndash;1\u0026middot;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u0026middot;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026middot;93\u0026ndash;1\u0026middot;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026middot;96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026middot;82\u0026ndash;1\u0026middot;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026middot;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026middot;88\u0026ndash;1\u0026middot;09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKey\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSevere, not improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSevere, rapidly improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSevere, gradually improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eModerate, limited improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eModerate, improving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eModerate, deteriorating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe conducted the first study to characterise trajectories of anxiety and depression change during psychological therapy for autistic people in any setting, to better understand the nature and drivers of symptom change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified subgroups of autistic clients who experienced no improvement, or worsening of symptoms, as well as those who experienced improvements. Identifying as a member of an ethnically-minoritised group was associated with greater odds of a deteriorating anxiety symptom trajectory. Ethnically-minoritised autistic people are underrepresented in the autism psychological intervention literature and may likely be affected by compounded disadvantage\u0026nbsp;(Steinbrenner et al., 2022).\u003c/p\u003e\n\u003cp\u003eAutistic clients with severe pre-treatment levels of depression and anxiety were more likely to follow an improving symptom trajectory if they had lower levels of difficulty in daily living pre-treatment. This result should be treated with some caution, as in places the strength of the association was small and 95% confidence intervals were close to the significance threshold (particularly when overall WSAS scores were used). Within the anxiety model however, the odds ratios comparing improving with non-improving social and private leisure trajectories showed a stronger effect and did not border on the threshold of significance. Overall the consistency of the pattern across classes and models lent some support to this not being a chance finding.\u003c/p\u003e\n\u003cp\u003eThis result is in keeping with comparable general population studies\u0026nbsp;(Skelton et al., 2023; Saunders et al., 2019). However, autistic people were also shown in a previous study to have higher degrees of pre-treatment difficulty with daily life tasks on the WSAS compared with non-autistic adults\u0026nbsp;(El Baou et al., 2023). As baseline WSAS scores were here associated with reduced odds of following improving trajectories from severe or moderately severe initial symptoms, difficulties in home, leisure and relationships may in turn perpetuate the health inequality experienced by autistic people.\u003c/p\u003e\n\u003cp\u003eLower initial impairment in private and social leisure activities was associated with the likelihood of improving from severe pre-treatment anxiety. Private leisure is defined in the WSAS as \u0026ldquo;done alone, such as reading, gardening, collecting, sewing, walking alone,\u0026rdquo; and social leisure as \u0026ldquo;with other people e.g. parties, bars, clubs, outings, visits, dating, home entertaining.\u0026rdquo; Intense and focused interests are a diagnostic feature of autism, and satisfaction with leisure activities may affect mental health outcomes more for autistic than non-autistic adults\u0026nbsp;(Stacey et al., 2019). Participation in social recreation has been shown to buffer stress for autistic people\u0026nbsp;(Bishop-Fitzpatrick et al., 2017). That autism masking (i.e., adopting learned communication behaviours to camouflage autistic traits) predicts psychological distress for autistic people\u0026nbsp;(Cook, Hull, \u0026amp; Mandy, 2021), underlines that opportunities to engage with social contexts free from stigma and rejection are particularly important\u0026nbsp;(Milton \u0026amp; Sims, 2016).\u003c/p\u003e\n\u003cp\u003eIntellectual Disability was associated with membership of classes experiencing moderate baseline depression, and improvement from initial moderate anxiety. This was somewhat surprising, given that people with Intellectual Disabilities generally benefit less from psychological therapies than people without\u0026nbsp;(Dagnan et al., 2022). This may reflect referral patterns (with those experiencing greater need referred to specialist services), or the relative disadvantage that autistic people without an Intellectual Disability also experience.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe symptom and daily living impairment measures used here have not been specifically validated in an autistic population\u0026nbsp;(Cassidy et al., 2018). However, measurement variance was found to be small or negligible between autistic and non-autistic college students on both the GAD-7 and PHQ-9 (supporting that the measures were capturing the same constructs in both groups) (Robeson et al., 2024). Recent work on autistic burnout as an experience distinct from depression, and reflecting a response to poorly-adapted environments highlights that current measures do not capture all aspects of autistic people\u0026rsquo;s experiences\u0026nbsp;(Higgins et al., 2021). The meaning of \u0026ldquo;recovery\u0026rdquo; or \u0026ldquo;improvement\u0026rdquo; for this client group should therefore not be assumed to be the same as for non-autistic people, and more work is needed to clarify valued outcomes from lived experience expertise.\u003c/p\u003e\n\u003cp\u003eEntropy scores observed within the final models did not suggest good confidence for most likely class allocation. However, when using modal class assignment the proportions of individuals within classes showing reliable improvement, recovery and deterioration by the end of treatment showed a pattern consistent with the mean trajectories of groups. In addition, R3Step analyses were used for the regression, which account for the uncertainty involved in class allocation and were therefore robust to lower entropy statistics.\u003c/p\u003e\n\u003cp\u003eDue to underdiagnosis of autism there were likely many autistic service users within the dataset, whose outcomes could not be included. Only service users who engaged with treatment (and received at least three sessions) were included, and so this study cannot tell us about those who did not engage (as well as those who were not referred). While the sample was representative of service users with a known autism diagnosis who accessed NHS TTad, it is not fully representative of all autistic people with anxiety and depression in need of, or accessing, treatment. Finally, ethnically-minoritised participants were more likely to have missing endpoint data, and were underrepresented in this cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, it was regrettable that we needed to combine ethnicity categories into those reflecting individuals identifying their ethnicity as White or as ethnically-minoritised. However, we were limited by small numbers in several ethnicity groups, and decided that this limited analysis would be preferable to excluding the data altogether. Our finding points to the need for finer-grained future analyses that investigate the intersection of autism and race/ethnicity in relation to health inequalities.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResearch and clinical implications and practice\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinicians can review progress at the third treatment session to distinguish likely change trajectories with greater confidence than at baseline (comparing pre-treatment and session three outcome measure scores). This allows an opportunity for early review to step-up sooner, consider combination or augmented treatment strategies, and/or identify for whom additional autism-specific and cultural adaptations might need to be made.\u003c/p\u003e\n\u003cp\u003eEthnically-minoritised autistic people appear to be especially in need of targeted support, and may benefit from cultural adaptations to therapy not just those made for autistic adults\u0026nbsp;(Arundell et al., 2021). Work is needed to understand their experiences of treatment and improve care, and in future finer-grained analyses are needed to help understand the intersection of autism and race/ethnicity, in relation to mental health outcomes.\u003c/p\u003e\n\u003cp\u003ePre-treatment difficulties in daily living, including leisure activities may warrant further attention as a target for support prior to, or alongside, treatment as usual. Proposals for autism-informed social support (with appreciation for the complexities of autism masking and burnout) may be helpful (Charlton et al., 2020).\u003c/p\u003e\n\u003cp\u003eFurther research is needed to clarify how to adapt and improve psychological interventions for autistic people. Following the present study, research is recommended to clarify how adaptations to care can be implemented to improve outcomes, including whether i) pre-therapy support, ii) a third-session review, or iii) a combination of the two are able to improve outcomes for autistic people in NHS TTad services.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll data were fully anonymised, and linkage was achieved using anonymised subject identifiers provided by NHS Digital. Under the Governance Arrangements of Research Ethics Committees (REC) procedures, REC review was not required due to the anonymisation procedures followed by NHS Digital. Ethical approval for the current study using the data included in MODIFY was granted by the UCL Clinical, Educational and Health Psychology departmental REC (ethics approval number: CEHP/2023/592B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRP, LC, WM, RS, AJ, JS and CEB conceptualised the study. RP, LC, WM and RS contributed to the methodology and analysis. CEB, AJ and JS assessed and verified the dataset. All authors contributed to the manuscript writing and review, and approved the final version. All authors had full access to data in the study, and responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used for this study are available on successful application to NHS Digital via the Data Access Request Service: https://digital.nhs.uk/services/data-access-request-service-dars. Data fields can be accessed via the NHS Digital data dictionary: https://www.datadictionary.nhs.uk/\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eArundell, L. L., Barnett, P., Buckman, J. E., Saunders, R., \u0026amp; Pilling, S. (2021). The effectiveness of adapted psychological interventions for people from ethnic minority groups: A systematic review and conceptual typology. \u003cem\u003eClinical psychology review\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e, 102063.\u003c/li\u003e\n \u003cli\u003eBarnett, P., Saunders, R., Buckman, J. E., Naqvi, S. A., Singh, S., Stott, J., ... \u0026amp; Pilling, S. (2023). 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Defining autistic burnout through experts by lived experience: Grounded Delphi method investigating# AutisticBurnout. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(8), 2356-2369.\u003c/li\u003e\n \u003cli\u003eKroenke, K., Spitzer, R. L., \u0026amp; Williams, J. B. (2001). The PHQ‐9: validity of a brief depression severity measure. \u003cem\u003eJournal of general internal medicine\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(9), 606-613.\u003c/li\u003e\n \u003cli\u003eLai, M. C., Kassee, C., Besney, R., Bonato, S., Hull, L., Mandy, W., ... \u0026amp; Ameis, S. H. (2019). Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(10), 819-829.\u003c/li\u003e\n \u003cli\u003eLinden, A., Best, L., Elise, F., Roberts, D., Branagan, A., Tay, Y. B. E., ... \u0026amp; Gurusamy, K. (2023). Benefits and harms of interventions to improve anxiety, depression, and other mental health outcomes for autistic people: A systematic review and network meta-analysis of randomised controlled trials. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 7-30.\u003c/li\u003e\n \u003cli\u003eMilton, D., \u0026amp; Sims, T. (2016). How is a sense of well-being and belonging constructed in the accounts of autistic adults?. \u003cem\u003eDisability \u0026amp; Society\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 520-534.\u003c/li\u003e\n \u003cli\u003eMundt, J. C., Marks, I. M., Shear, M. K., \u0026amp; Greist, J. M. (2002). The Work and Social Adjustment Scale: a simple measure of impairment in functioning. \u003cem\u003eThe British Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e(5), 461-464.\u003c/li\u003e\n \u003cli\u003eMuth\u0026eacute;n, B. (2003). Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003).\u003c/li\u003e\n \u003cli\u003eNational Collaborating Centre for Mental Health. (2018). \u003cem\u003eThe improving access to psychological therapies manual.\u0026nbsp;\u003c/em\u003e https://www.england.nhs.uk/wp-content/uploads/2018/06/the-nhs-talking-therapies-manual-v6.pdf.\u003c/li\u003e\n \u003cli\u003eNHS Digital. (2019). \u003cem\u003eHospital Episode Statistics (HES) analysis guide\u003c/em\u003e. https://digital.nhs.uk/binaries/content/assets/website-assets/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics/hes_analysis_guide_december_2019-v1.0.pdf.\u003c/li\u003e\n \u003cli\u003eNHS Digital. (2023). \u003cem\u003eHospital Episode Statistics (HES) and Office for National Statistics (ONS) linked mortality data guide.\u0026nbsp;\u003c/em\u003ehttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/linked-hes-ons-mortality-data/hes-and-ons-linked-mortality-data-guide.\u003c/li\u003e\n \u003cli\u003eNHS Digital (2024). \u003cem\u003eUser guidance: Mental Health Services Data Set (MHSDS).\u003c/em\u003e https://digital.nhs.uk/binaries/content/assets/website-assets/data-and-information/datasets/mhsds/mhsdsv6.0_userguidance_v6.0.3_may2024.pdf.\u003c/li\u003e\n \u003cli\u003eRam, N., \u0026amp; Grimm, K. J. (2009). Methods and measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. \u003cem\u003eInternational journal of behavioral development\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(6), 565-576.\u003c/li\u003e\n \u003cli\u003eRobeson, M., Brasil, K. M., Adams, H. C., \u0026amp; Zlomke, K. R. (2024). Measuring depression and anxiety in autistic college students: A psychometric evaluation of the PHQ-9 and GAD-7. \u003cem\u003eAutism\u003c/em\u003e, 13623613241240183.\u003c/li\u003e\n \u003cli\u003eSaunders, R., Buckman, J. E., Cape, J., Fearon, P., Leibowitz, J., \u0026amp; Pilling, S. (2019). Trajectories of depression and anxiety symptom change during psychological therapy. \u003cem\u003eJournal of affective disorders\u003c/em\u003e, \u003cem\u003e249\u003c/em\u003e, 327-335.\u003c/li\u003e\n \u003cli\u003eSkelton, M., Carr, E., Buckman, J. E., Davies, M. R., Goldsmith, K. A., Hirsch, C. R., ... \u0026amp; Eley, T. C. (2023). Trajectories of depression and anxiety symptom severity during psychological therapy for common mental health problems. \u003cem\u003ePsychological medicine\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(13), 6183-6193.\u003c/li\u003e\n \u003cli\u003eSpitzer, R. L., Kroenke, K., Williams, J. B., \u0026amp; L\u0026ouml;we, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. \u003cem\u003eArchives of internal medicine\u003c/em\u003e, \u003cem\u003e166\u003c/em\u003e(10), 1092-1097.\u003c/li\u003e\n \u003cli\u003eStacey, T. L., Froude, E. H., Trollor, J., \u0026amp; Foley, K. R. (2019). Leisure participation and satisfaction in autistic adults and neurotypical adults. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 993-1004.\u003c/li\u003e\n \u003cli\u003eSteinbrenner, J. R., McIntyre, N., Rentschler, L. F., Pearson, J. N., Luelmo, P., Jaramillo, M. E., ... \u0026amp; Hume, K. A. (2022). Patterns in reporting and participant inclusion related to race and ethnicity in autism intervention literature: Data from a large-scale systematic review of evidence-based practices. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(8), 2026-2040.\u003c/li\u003e\n \u003cli\u003eVan De Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., \u0026amp; Vermunt, J. K. (2017). The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 451-467.\u003c/li\u003e\n \u003cli\u003eVermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. \u003cem\u003ePolitical analysis\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 450-469.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5880564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5880564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAutistic people appear less likely to benefit from currently-recommended mental health treatments. Intervention development and adaptation is hampered by a lack of knowledge regarding whether subgroups of autistic people differ in their response to interventions, and what characterises subgroups of autistic people who respond differently. This study aimed to identify trajectories of depression and anxiety symptoms during routine psychological therapy for autistic people in general adult primary care mental health services and describe the characteristics of groups that followed particular symptom trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSessional anxiety and depression symptom scores for N = 7,175 autistic individuals accessing NHS Talking Therapies, for anxiety and depression (NHS TTad) services between 2012-19 across England were drawn from a linked, national healthcare record dataset (MODIFY). Growth Mixture Models were estimated for depression and anxiety symptom severity change over eight sessions, and latent trajectory classes were described in relation to demographic and pre-treatment clinical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven different anxiety trajectories, and five depression trajectories were observed. Groups showed stable, improving or deteriorating symptoms during treatment. Trajectories appeared distinct by the third treatment session. Identifying as belonging to an ethnically-minoritised group was associated with increased likelihood of deteriorating anxiety symptom trajectories compared with identifying as of White ethnicity. Lower pre-treatment difficulties in daily living were associated with greater likelihood of following trajectories defined by initially severe depression and anxiety symptoms which then improved during treatment, compared with trajectories that did not improve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings highlight the value of examining early change in treatment response, and identifying individuals at risk of reduced intervention effectiveness before treatment has begun. After the third treatment session, clinicians could identify a possible change trajectory and consider adapted or augmented intervention if there is no improvement. Interventions should be adapted on the basis of individuals’ cultural backgrounds and neurodevelopmental conditions. Daily living, including social and private leisure (taking account of autism masking and burnout experiences) may be a promising focus for augmented treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder of the study had no role in the study design, data collection, analysis, interpretation or writing of the report.\u003c/p\u003e","manuscriptTitle":"Depression and anxiety symptom change during psychological therapy for autistic clients: Evidence from national healthcare records in England","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 11:27:56","doi":"10.21203/rs.3.rs-5880564/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-mental-health","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmentalhealth","sideBox":"Learn more about [Nature Mental Health](https://www.nature.com/natmentalhealth/)","snPcode":"44220","submissionUrl":"https://mts-natmentalhealth.nature.com/cgi-bin/main.plex","title":"Nature Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5cbae4f4-bf62-421f-9d46-b67582e5f6a6","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44905702,"name":"Health sciences/Health care/Health services"},{"id":44905703,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2026-01-23T08:07:19+00:00","versionOfRecord":{"articleIdentity":"rs-5880564","link":"https://doi.org/10.1038/s44220-025-00567-4","journal":{"identity":"nature-mental-health","isVorOnly":false,"title":"Nature Mental Health"},"publishedOn":"2026-01-22 05:00:00","publishedOnDateReadable":"January 22nd, 2026"},"versionCreatedAt":"2025-03-03 11:27:56","video":"","vorDoi":"10.1038/s44220-025-00567-4","vorDoiUrl":"https://doi.org/10.1038/s44220-025-00567-4","workflowStages":[]},"version":"v1","identity":"rs-5880564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5880564","identity":"rs-5880564","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}