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Predictors of child and adolescent mental health treatment outcome

Abstract

Background

To examine the predictors of treatment outcome or improvement in mental health difficulties for young people accessing child and adolescent mental health services.

Methods

We conducted a secondary analysis of routinely collected data from services in England using the Mental Health Services Data Set. We conducted multilevel regressions on N = 5907 episodes from 14 services (Mage = 13.76 years, SDage = 2.45, range = 8–25 years; 3540 or 59.93% female) with complete information on mental health difficulties at baseline. We conduct similar analyses on N = 1805 episodes from 10 services (Mage = 13.59 years, SDage = 2.33, range = 8–24 years; 1120 or 62.05% female) also with complete information on mental health difficulties at follow up.

Results

Girls had higher levels of mental health difficulties at baseline than boys (β = 0.28, 95% CI = 0.24–0.32). Young people with higher levels of mental health difficulties at baseline also had higher levels of deterioration in mental health difficulties at follow up (β = 0.72, 95% CI = 0.67–0.76), and girls had higher levels of deterioration in mental health difficulties at follow up than boys (β = 0.09, 95% CI = 0.03–0.16). Young people with social anxiety, panic disorder, low mood, or self-harm had higher levels of mental health difficulties at baseline and of deterioration in mental health difficulties at follow up compared to young people without these presenting problems.

Conclusions

Services seeing higher proportions of young people with higher levels of mental health difficulties at baseline, social anxiety, panic disorder, low mood, or self-harm may be expected to show lower levels of improvement in mental health difficulties at follow up.

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Background

It is known that levels of mental health difficulties in children and adolescents are increasing [12]. There is some evidence showing corresponding increases in levels of mental health difficulties for young people accessing child and adolescent mental health services [3, 4]. In general populations, young women in particular are experiencing increased levels of mental health difficulties [12]. There is a need for evidence about the characteristics that predict of treatment outcome (i.e., mental health difficulties at follow up) for young people accessing child and adolescent mental health services and, correspondingly, characteristics that account for variation in levels of mental health difficulties at baseline.

There has been increased attention on treatment outcome for young people accessing child and adolescent mental health services and what types of outcomes are most important [1, 10, 11]. Change in symptoms, functioning, and goals according to self- or parent/ carer-reported measures is the predominant metric of treatment outcome in child mental health. However, there is a need to go beyond describing treatment outcome to understand which factors are associated with different treatment outcomes. Levels of service use may be one such factor and, for example, young people with psychosis, substance use, or eating disorder have been shown to be particularly more likely to have higher levels of service use compared to young people with less severe difficulties (Edbrooke-Childs J, Rashid A, Ritchie B, Deighton J. Predictors of amoutns of child and adolescent mental health service use in admiistrative data, submitted). In addition, young people referred through certain pathways, such as social care/ youth justice, were more likely to have higher levels of service use. Moreover, evidence suggests that a range of intervention, clinician, service user, service delivery, organizational, and service system characteristics may be associated with treatment outcome [17]. In terms of service user characteristics, these may include clinician, demographic, and family characteristics [15]. Nevertheless, previous evidence has been inconsistent in the extent to which these factors were associated with treatment outcome for young people accessing child and adolescent mental health services.

One of the most consistent factors is levels of mental health difficulties at baseline. Generally, studies show that higher levels of difficulties at baseline are associated with higher levels of difficulties at follow up [14]. Therefore, to provide a complete understanding of predictors of child and adolescent treatment outcome, it is important to first examine characteristics that account for variation in levels of mental health difficulties at baseline. Notwithstanding, some studies have shown that higher levels of difficulties at baseline are associated with increased likelihood of improvement [13]. Other characteristics that have, somewhat less consistently, been shown to be associated with treatment outcome include diagnosis (e.g., psychosis, conduct disorder, hyperactivity, autism), levels of functional impairment, and an older age when accessing services [3, 4, 13,14,15]. It is known that structural inequalities mean that young people from minoritized ethnic groups are more likely to be referred to services through routes that are less likely to be voluntary, and it is therefore important to examine if there are similar differences in treatment outcome [5].

Aims of this study

There were two aims of the present study. The first aim was to examine characteristics in routine data from mental health services that account for variation in levels of baseline difficulties for children and adolescents. The second aim was to then examine predictors of treatment outcome for children and adolescents accessing mental health services. To address these aims, we conducted a secondary analysis of a large administrative dataset from child and adolescent mental health services in England.

Method

Data preparation and procedure

Data were derived from routinely collected data extracted from the ‘community activity data package’ of the Mental Health Services Data Set by NHS Digital (years 2016–17 and 2017–18). From this extract, episodes of care were constructed, referring to periods of service use consisting of at least two care contacts and less than 180 days between care contacts (excluding text message, email, or unattended), using an approach adapted from a previous study [16]. To address the first research question, episodes of care were included in the present analysis if the age at episode start was 8–25 years, the age range that the included measures can be self-reported. Additionally, episodes of care were included if the case was closed, there was at least 50 episodes per service, and there was complete data on mental health difficulties at baseline (see Measures).

This resulted in a first dataset of N = 5907 episodes from 14 services with 65–1667 episodes per service (Mage = 13.76 years, SDage = 2.45, range = 8–25 years; 3540 or 59.93% female; Mnumber of services = 870, mediannumber of services = 769; SDnumber of services = 554; skewnumber of services = 0.37); please see Table 1 for descriptive statistics on all study variables. To address the second research question, episodes of care were additionally filtered based on complete data on mental health difficulties at follow up. This resulted in a second dataset of N = 1805 episodes from 10 services with 53–485 episodes per service (Mage = 13.59 years, SDage = 2.33, range = 8–24 years; 1120 or 62.05% female; Mnumber of services = 483, mediannumber of services = 443; SDnumber of services = 375; skewnumber of services = 0.88). The study was approved by the University College London Research Ethics Committee (12,689/001) and the NHS Digital Data Access Request Service (DARS-NIC-140981-R5N6Z).

Table 1 Descriptive statistics for all study variables

Measures

Deprivation

Deprivation was reported using quintiles of the Income Deprivation Affecting Children Index (IDACI) based on young people’s local area of residence (Lower Layer Super Output Area).

Demographic characteristics

Age, gender, and ethnicity were recorded by services as part of routine data recording. Ethnicity was captured using the categories from the 2001 Census. For the main analyses, to avoid including underpowered groups, ethnicity was grouped as follows: Any other White background, any other ethnic group, Asian, Black, mixed-race, not reported, and White British.

Referral source

Referral source was recorded by services using 44 categories that were grouped into nine study variables for the present analysis: primary care, self-referral, education, social care/ youth justice, child health, accident and emergency department, mental health, other, and not reported.

Presenting difficulties

Two sources were used to identify the presence or absence of 30 non-mutually exclusive presenting difficulties. First, the 30-item clinician-reported Current View questionnaire [8] on presenting problems were used. Second, clinician-reported ICD-10 free-text diagnoses were used, which were first mapped on to the 30 Current View presenting problems, thus creating one set of harmonised 30 presenting difficulties.

Mental health difficulties

Baseline and follow up mental health difficulties were assessed using five subscales using four self-reported measures summarized below. To ensure conceptual and operationalisation consistency across measures, we focussed only on those assessing depression and anxiety. To accommodate the completion of different measures, measures were transformed into z-scores, and when multiple measures were completed, the mean z-score of these measures was computed. Baseline measures were completed at the initial stages of treatment and follow up measures were completed 4-6 months later or at case closure.

  1. 1.

    Emotional difficulties subscale of the Strengths and Difficulties Questionnaire (SDQ) [6].

  2. 2.

    Depression and generalized anxiety subscales of the Revised Children’s Anxiety and Depression Scale (RCADS) [2].

  3. 3.

    Generalised Anxiety Disorder (GAD-7) [19], which is a 7-item questionnaire assessing symptoms of generalised anxiety.

  4. 4.

    Patient Health Questionnaire (PHQ-9) [18], which is a 9-item questionnaire assessing symptoms of depression.

Analytic strategy

To investigate characteristics that account for variation in levels of baseline difficulties (research question 1), two-level multilevel regressions were performed, with child as the level 1 group and service the level 2 group, in STATA 16 [20]. A null model without explanatory variables was computed with mental health difficulties at baseline as the criterion variable, and the intraclass correlation coefficient (ICC) was calculated. The ICC was 32.33% (95% Confidence Interval or CI = 15.96–48.71%) indicating that there was significant service-level variation and confirming that multilevel regression was the correct analytical approach. To examine the associations with individual-level characteristics, two models were tested. In Model 1, demographic characteristics were entered as level-1 explanatory variables: economic disadvantage (with the least deprived quintile coded as the reference category to facilitate interpretation), grand-mean centred age, female gender, and ethnicity (with White British as the reference category as it was the largest group). In Model 2, clinical characteristics were added as level-1 explanatory variables: referral source (with primary care as the reference category as it was the largest group) and the 26 presenting difficulty variables (to avoid including underpowered variables, four variables were not included as they had a frequency of < 5%: selective mutism, toilet problems, developmental difficulties, and gender identity difficulties). The likelihood ratio test was used to compare successive models, and both were significantly better fits to the preceding model; Model 1: χ2(12) = 641.136, p < 0.001 and Model 2: χ2(35) = 860.38, p < 0.001.

To investigate predictors of treatment outcome (research question 2), two-level multilevel regressions were performed, with child as the level 1 group and service the level 2, with mental health difficulties at follow up as the criterion variable. In the null model, the ICC was 25.80% (CI = 8.70–42.90%). To determine treatment outcome, or mental health difficulties at follow up controlling for mental health difficulties at baseline, mental health difficulties at baseline was added as a level-1 explanatory variable in Model 1. The z-scores for mental health difficulties as baseline, and mean z-score, were computed again as this was a sub-sample of the overall sample. In Model 2, demographic characteristics were entered as level-1 explanatory variables: economic disadvantage, grand-mean centred age, female gender, and ethnicity. In Model 3, clinical characteristics were entered as level-1 explanatory variables: referral source, the 26 presenting difficulty variables, and grand mean centred number of care contacts. The likelihood ratio test was used to compare successive models, and all were significantly better fits to the preceding model; Model 1: χ2(1) = 641.13994.16, p < 0.001, Model 2: χ2(12) = 22.11, p = 0.0364, and Model 3: χ2(35) = 82.94, p < 0.001. It should be noted that using standardized criterion variables for both sets of analyses resulted in small coefficient estimates.

Results

Research question 1: what accounts for variation in baseline mental health difficulties?

Compared to children and adolescents from the least economically disadvantaged areas, children and adolescents from high (β = 0.09, 95% CI = 0.03–0.15) and the most (β = 0.10, 95% CI = 0.04–0.16) economically disadvantaged areas had higher levels of mental health difficulties at baseline. Compared to boys, girls had higher levels of mental health difficulties at baseline (β = 0.28, 95% CI = 0.24–0.32). Compared to younger children and adolescents, older children and adolescents had slightly higher levels of mental health difficulties at baseline (β = 0.02, 95% CI = 0.01–0.03). In terms of ethnicity, compared to White British young people, young people from mixed-race ethnic backgrounds had lower levels of mental health difficulties at baseline (β = − 0.15, 95% CI = – 0.26-–0.05). In terms of referral source, compared to young people referred through primary care, young people referred through social care/ youth justice (β = − 0.30, 95% CI = − 0.41-–0.20), mental health services (β = − 0.14, 95% CI = − 0.23-–0.05), or not known referral sources (β = − 0.09, 95% CI = − 0.17-0.00) had lower levels of mental health difficulties at baseline, although the CI for not known referral included 0 and therefore this finding should particularly be interpreted with caution. In terms of presenting difficulties, young people with social anxiety, generalized anxiety, panic disorder, agoraphobia, low mood, self-harm, or features of post-traumatic stress disorder, had higher levels of mental health difficulties at baseline than young people without these presenting difficulties (please see Table 2 for coefficients and CIs for presenting difficulties). In contrast, young people with specific phobia, conduct disorder, or risk management difficulties had lower levels of mental health difficulties at baseline than young people without these presenting difficulties.

Table 2 Multilevel regressions with demographic and clinical characteristics predicting baseline difficulties

Research question 2: what are the predictors of treatment outcome?

Young people with higher levels of mental health difficulties at baseline also had higher levels of deterioration in mental health difficulties at follow up (β = 0.72, 95% CI = 0.67–0.76). After controlling for levels of mental health difficulties at baseline, compared to boys, girls had higher levels of deterioration in mental health difficulties at follow up (β = 0.09, 95% CI = 0.03–0.16). In terms of referral source, compared to young people referred through primary care, young people referred through social care/ youth justice (β = − 0.20, 95% CI = − 0.37-–0.03) had higher levels of improvement in mental health difficulties at follow up. Young people with social anxiety, panic disorder, low mood, self-harm, or family relationship difficulties had higher levels of deterioration in mental health difficulties at follow up than young people without these presenting problems (please see Table 3 for coefficients and CIs for presenting difficulties). Finally, young people with a greater number of care contacts had slightly higher levels of deterioration in mental health difficulties at follow up compared to young people with a lesser number of care contacts (β = 0.00195, 95% CI = 0.0.00074–0.00316). It should be noted that the coefficient was very small, meaning this finding should be particularly interpreted with caution.

Table 3 Multilevel regressions with demographic and clinical characteristics predicting difficulties at follow up

Discussion

The aims of the present study were to examine characteristics that account for variation in levels of mental health difficulties at baseline and then predictors of treatment outcome. We conducted a secondary analysis of a large administrative dataset from child and adolescent mental health services in England.

Young people with higher levels of mental health difficulties at baseline also had higher levels of mental health difficulties at follow up. In terms of key characteristics that both accounted for variation in levels of mental health difficulties at baseline and were predictors of treatment outcome, girls had higher levels of mental health difficulties at baseline and of deterioration in mental health difficulties at follow up than boys. Compared to young people referred through primary care, young people referred through social care/ youth justice had lower levels of mental health difficulties at baseline and had higher levels of improvement in mental health difficulties at follow up. In terms of presenting problems, young people with social anxiety, panic disorder, low mood, or self-harm had higher levels of mental health difficulties at baseline, and higher levels of deterioration in mental health difficulties at follow up, compared to young people without these presenting problems. Although we found no evidence of association with levels of mental health difficulties at follow up, we did find that children and young people from areas with high, and the highest, levels of economic disadvantage had higher levels of mental health difficulties at baseline than children and young people from areas with the lowest levels of economic disadvantage. These findings may suggest that there is a need for children and young people from areas of higher levels of economic disadvantage to have earlier receipt of specialist mental health services. Currently, children and young people from such areas are receiving support when their difficulties have escalated to a higher level than children and young people from areas of lower levels of economic disadvantage.

The findings of the present research are consistent with previous research showing that higher levels of mental health difficulties at baseline are associated with higher levels of difficulties at follow up [14]. These findings also build on the troubling pattern in the literature that young women in particular are experiencing increased levels of mental health difficulties [12]. We found that social anxiety, panic disorder, low mood, and self-harm were associated with higher levels of mental health difficulties at baseline and at follow up. This is consistent with previous studies on common characteristics associated with lower treatment outcome and those that indicate high levels of clinical complexity [7, 13,14,15].

There is ongoing debate about how treatment outcome should be conceptualized and assessed [10, 11]. It is especially important to review how treatment outcomes are framed and measured with young people and particularly with those from minoritized groups who may be less likely to be represented in the evidence on which current treatment outcome approaches were developed. The findings of the present research did not show ethnic differences in treatment outcome, however such differences may have been masked due to the lack of data on structural inequalities in administrative datasets [9], especially as evidence shows structural inequalities in relation to accessing child and adolescent mental health services [5]. Future research should work with young people from minoritized ethnic groups and relevant community organizations so that administrative data can include information on inequalities that are meaningful to the experiences of individuals from minoritized ethnic groups.

Future research should examine the lack of significant association between economic disadvantage and mental health difficulties at follow up from the present research. This may possibly be accounted for by young people with the highest levels of economic disadvantage being more likely to have unmet needs and to be not known by child and adolescent mental health services, meaning they are not represented in administrative data. In the present research, young people from areas of higher economic disadvantage had higher levels of mental health difficulties at baseline compared to young people from areas of lower economic disadvantage, suggesting economic inequalities in receipt of specialist mental health support. Moreover, the findings of the present research suggest that young people referred through social care/ youth justice had lower levels of mental health difficulties at baseline and at follow up compared to young people referred through primary care. We are not able to explain why such differences were found in the present research. Future qualitative studies should examine if the types of outcomes measured in mental health services capture what young people and professionals think are important outcomes and reflect the reasons for which young people receive mental health services through these pathways.

Limitations of the present research include the relatively small sample sizes, meaning that the findings may not reflect all young people accessing child and adolescent mental health services. Although we restricted the analysis to only measures of depression and anxiety for conceptual and operationalisation consistency, future research is needed to examine the factor structure of the five subscales used to determine the extent to which items load onto the same factor. Similarly, using a subsample to examine mental health difficulties at follow up means the groups in the baseline and follow up analyses are not entirely comparable. More general limitations of using administrative data are also relevant to the present research [21]. Moreover, the use of complete case analysis to manage missing data, especially on mental health difficulties at follow up, may mean there are systematic differences in those with and without these data. Future research examining such patterns and differences is encouraged, working towards consistency in how missing data are handled in administrative child mental health records. We assessed presenting difficulties using two different types of clinician reports, Current View questionnaire [8] presenting problems and ICD-10 free-text diagnoses mapped on to the Current View presenting problems. Inaccuracies in ICD-10 recording and inconsistencies in mapping across the two sources are other potential limitations. Nevertheless, this approach resulted in a more comprehensive assessment of presenting difficulties than would have been possible with one source alone. Future research should examine different types of care provided, which was not available in the present dataset, to examine whether predictors of treatment outcome differ across treatment modalities.

Notwithstanding the above limitations, the present research identified predictors of treatment outcome in a large and recent administrative dataset from child and adolescent mental health services in England. Based on the findings presented in this paper, services seeing higher proportions of young people with higher levels of mental health difficulties at baseline, social anxiety, panic disorder, low mood, or self-harm may be expected to show lower levels of improvement in mental health difficulties at follow up.

Availability of data and materials

Requests to access the data from which the data for this paper were derived can be made to NHS Digital through the Data Access Request Service.

Abbreviations

GAD-7:

Generalised Anxiety Disorder 7

ICC:

Intraclass Correlation Coefficient

IDACI:

Income Deprivation Affecting Children Index

PHQ-9:

Patient Health Questionnaire 9

RCADS:

Revised Children’s Anxiety and Depression Scales

SDQ:

Strengths and Difficulties Questionnaire

References

  1. Bear HA, Edbrooke-Childs J, Norton S, Krause KR, Wolpert M. Systematic review and Meta-analysis: outcomes of routine specialist mental health Care for Young People with Depression and/or anxiety. J Am Acad Child Adolesc Psychiatry. 2020;59(7):810–41. https://doi.org/10.1016/j.jaac.2019.12.002.

    Article  PubMed  Google Scholar 

  2. Chorpita BF, Yim L, Moffitt C, Umemoto LA, Francis SE. Assessment of symptoms of DSM-IV anxiety and depression in children: a revised child anxiety and depression scale. Behav Res Ther. 2000;38(8):835–55. https://doi.org/10.1016/s0005-7967(99)00130-8.

    Article  CAS  PubMed  Google Scholar 

  3. Edbrooke-Childs J, Deighton J, Wolpert M. Changes in severity of psychosocial difficulties in adolescents accessing specialist mental healthcare in England (2009–2014). J Adolesc. 2017a;60:47–52. https://doi.org/10.1016/j.adolescence.2017.07.006.

    Article  CAS  PubMed  Google Scholar 

  4. Edbrooke-Childs J, Macdougall A, Hayes D, Jacob J, Wolpert M, Deighton J. Service-level variation, patient-level factors, and treatment outcome in those seen by child mental health services. Eur Child Adolesc Psychiatry. 2017b;26(6):715–22. https://doi.org/10.1007/s00787-016-0939-x.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Edbrooke-Childs J, Patalay P. Ethnic differences in referral routes to youth mental health services. J Am Acad Child Adolesc Psychiatry. 2019;58(3):368–75.e361. https://doi.org/10.1016/j.jaac.2018.07.906.

    Article  PubMed  Google Scholar 

  6. Goodman R. Psychometric properties of the strengths and difficulties questionnaire. J Am Acad Child Adolesc Psychiatry. 2001;40(11):1337–45. https://doi.org/10.1097/00004583-200111000-00015.

    Article  CAS  PubMed  Google Scholar 

  7. Hawton K, Saunders KEA, O'Connor RC. Self-harm and suicide in adolescents. Lancet. 2012;379(9834):2373–82. https://doi.org/10.1016/S0140-6736(12)60322-5.

    Article  PubMed  Google Scholar 

  8. Jones M, Hopkins K, Kyrke-Smith R, Davies R, Vostanis P, Wolpert M. Current view tool: completion guide: London: CAMHS Press; n.d. https://www.ucl.ac.uk/evidence-based-practice-unit/sites/evidence-based-practice-unit/files/pub_and_resources_resources_for_profs_current_view.pdf

  9. Knight HE, Deeny SR, Dreyer K, Engmann J, Mackintosh M, Raza S, et al. Challenging racism in the use of health data. Lancet Digit Health. 2021. https://doi.org/10.1016/S2589-7500(21)00019-4.

  10. Krause K, Midgley N, Edbrooke-Childs J, Wolpert M. A comprehensive mapping of outcomes following psychotherapy for adolescent depression: the perspectives of young people, their parents and therapists. Eur Child Adolesc Psychiatry. 2020. https://doi.org/10.1007/s00787-020-01648-8.

  11. Krause KR, Chung S, Adewuya AO, Albano AM, Babins-Wagner R, Birkinshaw L, et al. International consensus on a standard set of outcome measures for child and youth anxiety, depression, obsessive-compulsive disorder, and post-traumatic stress disorder. Lancet Psychiatry. 2021;8(1):76–86. https://doi.org/10.1016/s2215-0366(20)30356-4.

    Article  PubMed  Google Scholar 

  12. NHS Digital. (2020). Mental Health of Children and Young People in England, 2020: Wave 1 follow up to the 2017 survey. Retrieved 22 April 2021 from https://digital.nhs.uk/data-and-information/publications/statistical/mental-health-of-children-and-young-people-in-england/2020-wave-1-follow-up

    Google Scholar 

  13. Ogles BM, Carlson B, Hatfield D, Karpenko V. Models of case mix adjustment for Ohio mental health consumer outcomes among children and adolescents. Admin Pol Ment Health. 2008;35(4):295–304. https://doi.org/10.1007/s10488-008-0171-1.

    Article  Google Scholar 

  14. Phillips SD, Hargis MB, Kramer TL, Lensing SY, Taylor JL, Burns BJ, et al. Toward a level playing field: predictive factors for the outcomes of mental health treatment for adolescents. J Am Acad Child Adolesc Psychiatry. 2000;39(12):1485–95. https://doi.org/10.1097/00004583-200012000-00008.

    Article  CAS  PubMed  Google Scholar 

  15. Phillips SD, Kramer TL, Compton SN, Burns BJ, Robbins JM. Case-mix adjustment of adolescent mental health treatment outcomes. J Behav Health Serv Res. 2003;30(1):125–36. https://doi.org/10.1007/bf02287818.

    Article  PubMed  Google Scholar 

  16. Reid G, Stewart SL, Zaric GS, Carter JR, Neufeld RW, Tobon JI, et al. Defining episodes of Care in Children's mental health using administrative data. Admin Pol Ment Health. 2015;42(6):737–47. https://doi.org/10.1007/s10488-014-0609-6.

    Article  Google Scholar 

  17. Schoenwald SK, Hoagwood K. Effectiveness, transportability, and dissemination of interventions: what matters when? Psychiatr Serv. 2001;52(9):1190–7. https://doi.org/10.1176/appi.ps.52.9.1190.

    Article  CAS  PubMed  Google Scholar 

  18. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders Patient Health Questionnaire. JAMA. 1999;282(18):1737–44. https://doi.org/10.1001/jama.282.18.1737.

    Article  CAS  Google Scholar 

  19. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. https://doi.org/10.1001/archinte.166.10.1092.

    Article  Google Scholar 

  20. StataCorp. Stata statistical software: release 16. College Station; StataCorp LLC; 2019.

    Google Scholar 

  21. Wolpert M, Rutter H. Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health. BMC Med. 2018;16(1):82. https://doi.org/10.1186/s12916-018-1079-6.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would also like to thank NHS Digital for supplying data through the Data Access Request Service. The manuscript does not necessarily reflect the views of MQ or NHS Digital. We would also like to thank the members of the CORC Board (Kate Dalzell, Isobel Fleming, Beth Ingram, Kate Martin, Ann York, Ashley Wyatt, Mick Atkinson, Amy Marie Rose Herring, Duncan Law, and Rebecca Lewis) and team including JEC, BR, and AR (Martha Reily, Anja Teichert, Nicholas Tait, Florence Ruby, Luís Costa da Silva, Jenna Jacob, Rachel Piper, Sally Marriott, Lee Atkins, and Kate Dalzell).

Funding

The research was funded by MQ: Transforming Mental Health (MQD16\59). The funder had no role in the design of the study; collation, analysis, and interpretation of data; or in writing the manuscript.

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Contributions

JEC, BR, and JD conceived of the study. BR and AR obtained, collated, and cleaned the data. JEC analysed the data with support from BR and AR, under the supervision of JD. JEC lead the drafting of the manuscript, with input from BR, AR, and JD. All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Julian Edbrooke-Childs.

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Ethics approval and consent to participate

The study was approved by the University College London Research Ethics Committee (12689/001) and the NHS Digital Data Access Request Service (DARS-NIC-140981-R5N6Z). It is a secondary analysis of anonymised administrative data, and individual consent was obtained as part of primary data capture.

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There is no information relating to an individual person.

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Edbrooke-Childs, J., Rashid, A., Ritchie, B. et al. Predictors of child and adolescent mental health treatment outcome. BMC Psychiatry 22, 229 (2022). https://doi.org/10.1186/s12888-022-03837-y

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