Global prevalence of trichotillomania: a systematic review and meta-analysis and depression subtypes and their response to counselling for depression

Jonny Hartley

A thesis submitted in partial fulfilment of the requirements for the award of Doctor of
Clinical Psychology at the University of Sheffield

The University of Sheffield
Faculty of Science Clinical Psychology Unit
Department/School of Psychology

Submission Date: May 2022

Page left blank for Access to Thesis form

Page left blank for Access to Thesis form

Declaration
I confirm that the current thesis is my own work. I am aware of the University’s Guidance on the Use of Unfair Means (www.sheffield.ac.uk/ssid/unfair-means). This work has not previously been presented for an award at this, or any other, university.

Structure and Word Count 

Section One: Literature Review 
Excluding references and tables 						6453
Including references and tables 						11113

Section Two: Research Report 
Excluding references and tables 						5523 
Including references and tables 						7944 

Total Word Count 
Excluding references and tables 						11976 
Including references and tables 						19057

Lay Summary
Trichotillomania prevalence: Trichotillomania is a condition characterised by five core symptoms, a) recurrent hair pulling, resulting in hair loss, b) unsuccessful attempts to stop or reduce hair pulling, c) significant distress or impairment in social and occupational functioning because of hair pulling d) the hair pulling, or loss cannot be attributed to another medical condition or e) be attributable to another mental disorder. Trichotillomania is classified under Obsessive-Compulsive and Related Disorders in the Diagnostic and Statistical Manual 5 and the International Classification of Diseases 11th Revision after previously been classified an impulse control disorder. Uncertainties in trichotillomania’s prevalence remain, with existing study estimates varying considerably. These uncertainties can make inferences about the level of resource needed for assessment and treatment difficult. Therefore, the current systematic review and meta-analysis aimed to summarise and reliably estimate the point and lifetime prevalence of trichotillomania. A systematic literature search was conducted before studies were narratively synthesised and a meta-analysis conducted. Additional variables such as age, gender and sample size were explored to ascertain their impact on prevalence rates. The pooled estimated point prevalence for trichotillomania was 3% with a pooled estimated lifetime prevalence of 2%. Larger studies reported lower point prevalence rates with diagnostic interviews being associated with higher lifetime prevalence rates compared to studies using self-report assessments. The current study provides an up-to-date global prevalence rate for lifetime and point prevalence of trichotillomania and provides guidance on the methods of future studies and clinical implications for practice, classification, and service development.
Depression subtypes and their response to counselling: Depression is one of the world’s leading public health concerns with approximately 264 million individuals suffering worldwide. Despite this, existing psychological interventions are only moderately effective in its treatment. This includes counselling for depression (CfD) that is used in the Improving Access to Psychological Therapies (IAPT) initiative. Evidence has suggested that depression consists of several subtypes that respond differentially to treatment. The current study uses similar methodology to previous research to build on the evidence base by examining depression subtypes and their response to CfD. Data collected across eight National Health Service (NHS) trusts containing clinical records of 5646 was used.  Analysis identified depression subtypes and their response to counselling based on a questionnaire to assess depression. Five depression subtypes were found: somatic, cognitive, typical, severe and recovery with transitions occurring within rather than between subtypes. Typical subtype depression patients attended more sessions than somatic and cognitive subtypes. No significant differences were found between the three overarching depression subtypes regarding outcome nor acceptability. Unlike CBT, treatment outcomes in response to CfD appear to be comparable across subtypes. The results allow for clinically applicable insight in treating depression which includes evidence for subtyping based on routinely collected depression scores from new patients starting treatment. This could be used to classify patients according to their depression subtype and assign them to an intervention with the greatest likelihood of recovery. Furthermore, patients with depression subtypes that have an increased chance of dropout and lower chances of symptomatic improvement could be identified and prioritised for clinical supervision or more intense treatment.

Acknowledgements
I would like the thank Steve and Mel for their support and encouragement at all stages of the research process and my family for all their help with childcare and providing me with food and coffee throughout. 

Table of Contents
										Pages (s)
Declaration											4
Structure and Word Count									5
Lay Summary 											6
Acknowledgements 										8
Section One: Literature Review 								11
Title 												11
Abstract 											12
Practitioner points										13 
Introduction 										         14-18 
Methods 										         18-22 
Results 										         22-36 
Discussion 										         37-45 
References 										         46-60
Appendices 
         Appendix A - Table of search strategy 					                        61
         Appendix B - Quality assessment table						        62-64
Section Two: Research Report
 Title 												66 
Abstract 											67 
Introduction 										        69-72 
Methods 										        73-78 
Results 										        78-88 
Discussion 										        89-94
References 										       96-103 
Appendices 										     104-106
Appendix A –PHQ-9 									104 
Appendix B – GAD-7 								105
Appendix C – WSAS 									106

Global prevalence of trichotillomania: a systematic review and meta-analysis

Jonny Hartley1,2

1Department of Psychology, University of Sheffield, UK
2Sheffield Health and Social Care NHS Foundation Trust, UK

* Correspondence to Jonathan Hartley, Clinical Psychology Unit, Cathedral Court, University of Sheffield, S1 2LT, UK; Email: jhartley5@sheffield.ac.uk 

Abstract
Introduction: There is ambiguity concerning the prevalence of trichotillomania, which has implications for service commissioning and associated interventions.  Estimates have ranged from between 1.2-3.5 % in the general population, but uncertainties remain due to methodological issues in the evidence base.  Objectives: This systematic review and meta-analysis therefore aimed to summarise and reliably estimate the point and lifetime prevalence of trichotillomania.  Design: The review was preregistered (CRD42021238342) systematic review and meta-analysis. Methods: A systematic literature search was conducted to identify all relevant studies. Inclusion criteria were that studies had to have calculated a prevalence rate, used reliable form of assessment (self-report/diagnostic interviews) and reported appropriate data.  Studies were narratively synthesised and then a proportional random-effects meta-analysis conducted.  Moderator analyses explored whether prevalence rates were influenced by the type of sample used (clinical or community), study quality, sample size, method of assessment, age, gender and the continent on which the study was completed. Results: The pooled estimated point prevalence for trichotillomania was 3% (0.03; CI 0.02 to 0.05), with a pooled lifetime prevalence of 2% (0.02; CI 0.01 to 0.04). Sample size was a significant moderator for point prevalence, with larger studies reporting lower prevalence rates. Method of assessment was a significant moderator for lifetime prevalence, with diagnostic interviewing being associated with higher lifetime prevalence rates than self-report assessments. Conclusion: The current study provides an up-to-date global prevalence rate for lifetime and point prevalence of trichotillomania and provides guidance on the methods of future studies and clinical implications for practice, classification and service development.

Keywords: trichotillomania, hair-pulling, prevalence, meta-analysis

Practitioner points
•	Estimated point prevalence for trichotillomania was 3%
•	Estimated lifetime prevalence for trichotillomania was 2%
•	Method of assessment was a significant moderator of lifetime prevalence and sample size was a significant moderator of point prevalence
•	The current, up-to-date, global prevalence rate for lifetime and point prevalence of trichotillomania provides guidance on the methods that could be used in future research and provides information that could be used in trichotillomania assessment, classification, and service development. 

Introduction
Definition and classification
There are ambiguities in the clinical classification and diagnosis of trichotillomania. Trichotillomania is categorised under Obsessive-Compulsive and Related Disorders in the International Classification of Diseases 11th Revision (ICD-11, World Health Organisation, 2019) and the Diagnostic and Statistical Manual Version 5 (DSM-5, APA, 2013). The DSM-V states that trichotillomania comprises five core symptoms, a) recurrent hair pulling, resulting in hair loss, b) unsuccessful attempts to stop or reduce hair pulling, c) significant distress or impairment in social and occupational functioning because of hair pulling d) the hair pulling, or loss cannot be attributed to another medical condition or e) be attributable to another mental disorder (APA, 2013). The body sites from which hair is deliberately pulled (and at times ingested) are generally the frontal region of the scalp, eyebrows, eyelashes, beard and less frequently from the pubis (Huynh, Gavino & Magid,  2013). Trichotillomania was under the category of habit and impulse disorders in the International Classification of Diseases (ICD-10; World Health Organisation, 1993) and was also considered an impulse control disorder in DSM-IV-TR prior to its more recent transition to Obsessive-Compulsive and Related Disorders. This move coincided with the removal of some symptoms from the classification, namely tension prior to hair pulling and a sense of gratification whilst pulling (APA, 2000). 
However, there does remain continued debate as to the meta category under which trichotillomania should be classified. There are arguments for its continued inclusion alongside other impulse disorders by dint of the evidence concerning phenomenological similarities, pleasure and gratification following trichotillomania behaviours and findings that relatives are more likely to suffer from substance use disorders (Grant & Stein, 2014; Schlosser et al., 1994). There are also arguments for trichotillomania and other body focused repetitive behaviours (BFRBs) to be assigned their own meta category, although to do this additional research and neurobiological evidence would be required (Grant & Stein, 2014). In summary, the clinical classification of trichotillomania continues to be complex with diagnostic uncertainties and categorisation disputes remaining.  What is less in doubt is the evidence of the considerable emotional distress created by the disorder (Grant et al., 2020) and its associated significant impact on social functioning and academic performance (Franklin et al., 2008).  Management of emotions appears central therefore as a maintaining factor and theoretically the emotion regulation (ER) model has received most reseach attention (Arabatzoudis et al., 2017).
Existing prevalence research
The prevalence of trichotillomania concerns the proportion of a population whose deliberate hair pulling has resulted in hair loss at a given period of time, with prevalence estimates dependent on the timeframe of the estimate. Point prevalence would be the proportion of a population displaying trichotillomania at a specific point in time, period/cumulative prevalence is the proportion of a population displaying trichotillomania at any point during a stated time period (e.g., past 12-months) and lifetime prevalence is the proportion of a population who, at some point in their life has ever had trichotillomania (Munn, Moola, Riitano & Lisy, 2014).  Wide variations in estimates the point and lifetime prevalence of trichotillomania are apparent across studies and may have occurred due to methodological differences between studies. Estimates for lifetime prevalence vary from 1.2% (e.g., Leppink et al., 2016) to 3.5% (Grzesiak et al., 2017) and estimates of point prevalence have varied as widely as 0.6 % (Duke et al. 2010) to 13.3% (Siddiqui et al. 2012).  The point prevalence rate also changes when the rate is studied in the context of extant clinical samples. For example, Grant et al. (2010) observed a point prevalence rate of 13.5% among N=57 pathological skin pickers and Leibovici et al. (2014) reported a 0.8% point prevalence rate among N=2176 pathological skin pickers. 
These commonly cited studies of trichotillomania prevalence contain significant methodological limitations such as employing definitions that do not match the DSM-IV or V criteria (e.g., Lenane et al., 1992 used DSM-III), over reliance on self-report measures (e.g., Leibovici et al., 2014; Nunes-Neto et al., 2018) or such as the use of single item means of assessment (e.g., Grant et al., 2020a).  Sampling also is a source of bias, as samples may not be representative of the general population due to self-selection (e.g., Houghton et al., 2018), be comprised of only students (e.g., Duke et al., 2010), be small in size (e.g., Selles et al., 2015) or have unsatisfactory and low response rates (e.g., Odlaug & Grant, 2006). Such methodological design flaws creates the risk of either overestimation or underestimation of the true prevalence of trichotillomania (Higgins and Green, 2011). 
Regardless of risk of bias, it is also inappropriate to denote any one prevalence study as being the most accurate or representative of the general population (Barendregt et al.,2003) due to the context (e.g., country) in which the study is conducted (Munn, Moola, Lisy, Riitano & Tufanaru, 2015).  Collating prevalence rates across studies produces a more conservative and reliable estimate and can be used to estimate disease burden when done appropriately (Migliavaca et al., 2020b). Combining estimates from different regions of the world that have similar characteristics (e.g., westernised vs non-westernised, continent or country) can also identify otherwise hidden associations (Fiest et al., 2014). Bottesi et al. (2016) particularly argued for exploring the prevalence and phenomenology of trichotillomania according to different cultural contexts to determine if symptom and comorbidity patterns remain consistent. For example, Neal-Barnett & Stadulis (2006) and Neal-Barnett et al., (2010; 2011) observed phenomenological and functional impairment differences between African American and Latino cases when compared to Caucasians with the prevalence of trichotillomania still requiring clarification in European countries (Bottesi et al., 2016).
Study rationale 
There has been no previous attempt to synthesise studies of the prevalence of trichotillomania in order to derive a robust prevalence estimate or to assess how rates reported are moderated by methodological factors in the original studies. It has been suggested that a meta-analytical approach is appropriate and robust when exploring the prevalence of disease, irrespective of concerns about heterogeneity in existing studies (Jablensky et al., 2011; Munn et al., 2015). A proportional meta-analysis would therefore enable the calculation of a pooled, overall prevalence rate for trichomania to be calculated from the individual rates reported in the epidemiological evidence base (Barker et al., 2022).  Such a proportional meta-analytic approach has important healthcare implications, as it would be difficult to design and justify interventions for trichotillomania when the community burden is unreliable or unspecified (Mansfield et al., 2016). A reliable, synthesised estimate could therefore contribute to decision making in commissioning of services and the development of policies in existing services and appropriate treatment pathways and options are available (Kadandale et al., 2016; Munn et al., 2015).  The synthesising of the trichotillomania prevalence rate could also help encourage further research in its classification given its recent change but continued inconsistencies with the disorders it is categorised with.  Barker et al., (2022) also suggests that proportional meta-analytic approach enables estimates of covariates of the frequency of a disorder (e.g., whether trichotillomania varies by region of the world or differs according to gender).  
Considering the above, the current systematic review and proportional meta-analysis had three objectives. The first objective was to conduct a comprehensive, systematic literature search to identify all relevant studies reporting trichotillomania prevalence data. The second objective was to conduct two proportional meta-analyses in order to provide a more precise estimate of the point and lifetime prevalence. The third objective was to assess whether the exhibited variation in the trichotillomania prevalence estimate was associated with the following factors: type of sample used studied (clinical or community), the continent in which the participants lived, study quality, assessment method, age, gender and sample size. 
Method
Study Identification and Eligibility 
The review was preregistered on PROSPERO (CRD42021238342) prior to searches being conducted. This report is based around PRISMA reporting guidelines (Moher et al., 2009). Three electronic databases (Scopus, MEDLINE, PsycINFO) were used to enable a comprehensive literature search in order to identify relevant studies. See appendix A for a record of search terms. No date restrictions were implemented, and grey literature was not searched. The reference lists of eligible studies were also hand searched using forwards and backwards referencing to identify other relevant studies. The final searches were conducted in May 2021. Titles and abstracts were screened to determine eligibility and whether a full text review was required. A subset of the search results (36%) were dual-screened by a second reviewer to check the accuracy of screening and to determine the level of agreement. Any discrepancies were discussed between the researchers and an outcome decided upon. Inter-rater reliability was calculated using Cohen’s kappa (k, Cohen, 1960); almost perfect agreement (.81 - 1.0), substantial agreement (.61 - .80), moderate agreement (.41 - .60) and fair agreement (.21 - .40). There was substantial agreement regarding study selection (k = 0.80).  Inclusion criteria (see Table 1 for the PICOS framework) were any study that 1) calculated the prevalence of trichotillomania within the sample, 2) used self-report or diagnostic interviews to define and determine the presence of trichotillomania, and 3) reported appropriate data to determine trichotillomania prevalence. Studies were excluded when; 1) they were not available in English, 2) authors did not respond to requests for appropriate data to determine prevalence within a 3 week notice period, 3) prevalence data or data to determine prevalence was not available, 4) unpublished studies and 5) grey literature. 
Table 1 PICOS information regarding study inclusion/exclusion criteria
Parameter	Inclusion	Exclusion	
Participants	Humans, any age, race, gender, country. 	Non-human. Entire sample with trichotillomania.	
Interventions	Trichotillomania measured by interview or self-report	Trichotillomania not defined or measured	
Comparisons	Due to focus on prevalence, a comparator condition was not required. 
Comparisons of prevalence across age, gender, country, method of assessment & type of sample were included if reported	Not applicable	
Outcomes	Lifetime or point prevalence as a proportion of the total sample	No lifetime or point prevalence rate or data reported 	
Study Design	RCTs, cross sectional, longitudinal, cohort, census	Case studies, single case experimental design, not published in English, grey and unpublished studies	

Risk of Bias
All studies determined to be eligible for inclusion were assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Studies Reporting Prevalence Data (Munn et al., 2014). This tool assesses studies on several domains including representativeness of the sample, if appropriate statistics were reported and how accurately the condition of interest was measured. The JBI checklist has been rigorously developed, with highly relevant and reliable items for assessing the methodological quality of prevalence studies (Migliavaca et al., 2020a; 2020b).  Studies can obtain a score out of 9 with higher ratings corresponding to less bias and hence higher quality. However, there is no specific cut off points corresponding to descriptive levels of quality. As a result, the researchers used arbitrary cut-offs, with studies scoring 0-2 (low quality), 3-6 (medium quality) and 7-9 (high quality). Half of the included studies (50%) were dual rated for quality by another reviewer, a trainee clinical psychologist. There was substantial agreement regarding ratings of study quality (k = 0.78; Cohen, 1960). 
Data Extraction
A bespoke tool was used to extract the relevant data from the included studies. Prevalence data were extracted as a proportion of the study’s sample, whether point or lifetime prevalence was being assessed and how trichotillomania was defined in the study. If a study reported point and lifetime prevalence rates both were extracted and entered into the spreadsheet separately to ensure no data points were lost. Additionally, if separate prevalence rates were reported for subgroups, these were both extracted and recorded as a proportion of the group size. For example, if a study reported prevalence rates for a clinical sample and then a community control group as a comparison, then both rates were extracted. The type of sample (clinical or community), mean age and SD, sample size, the female percentage of the sample, the country where the study was conducted (which was used to determine continent), how the condition was assessed (self-report or interview), response rate and any information on the difference in prevalence rates dependent on gender were also extracted. A sample was defined as clinical, when the study recruited participants with a diagnosed mental health condition or when participants were engaging with psychiatric support (e.g., inpatients in a psychiatric hospital or attending a community mental health service). Additionally, the sample was defined as clinical when participants were screened and reached caseness for specified mental health conditions (e.g., depression, pathological skin picking). A community sample was any other recruited sample, such as those from universities, census results or internet surveys. Data were extracted by one researcher. 
Data Synthesis and Analysis
Included studies were narratively synthesised including descriptions of the study characteristics and quality assessment.  Studies reporting sufficient data were synthesised quantitatively using R Studio (version 1.2.5019) using the metafor (Viechtbauer, 2010) and meta (Balduzzi et al., 2019) packages. Two separate meta-analytic comparisons were performed; (1) lifetime and (2) point prevalence rates. The pooled prevalence rate was calculated using a random effects model with studies weighted by the inverse of their variance. A random-effects model was used due to study heterogeneity as suggested by (Barker et al., 2021). Weightings of studies using the inverse variance method in proportional meta-analyses can lead to inflated study weights. This is when proportions are close to proportional limits (i.e. very low or very high prevalence), as the study variances are subsequently constrained and can appear smaller than they are (i.e. and therefore given a larger weight). To account for this the study proportions were transformed using a double arcsine transformation (so they are not constrained to fall between 0-1) prior to applying the weightings. The proportions were then back-transformed to aid interpretation of the overall weighted mean prevalence rate (see Barker et al., 2021). Study heterogeneity was assessed using the I² and Q statistics and publication bias was assessed using funnel plot asymmetry, rank correlation test and Egger's unweighted regression test.
Moderator analyses explored whether prevalence rates varied according to methodological (i.e., type of prevalence rate, quality rating, if trichotillomania was assessed using self-report or interview) and sample characteristics (i.e., age, population, % female, continent, sample size). Meta regression was used for continuous variables which included the percentage of the sample that was female, sample size, mean age and quality rating. Conversely categorical variables such as population type (community or clinical), how trichotillomania was assessed (self-report or interview) and continent were analysed using subgroup analysis. Sensitivity analyses were conducted for sample size to ascertain the robustness of the results. 
Results
Study Selection and Characteristics
The literature search provided 2,040 results (see Figure 1). Once duplicates were removed a total of 878 remained. Following the screening of titles and abstracts 646 records were excluded leaving 232 articles eligible for full text review. Of these, 200 articles were excluded (see reasons listed in Figure 1). An additional 14 articles were identified through backwards/forwards searching.  This left a total of 46 articles included and appropriate for the qualitative and quantitative synthesis. Study characteristics are summarised in Table 2.
Sample Characteristics
Most studies were conducted in America (k = 19; 40.4%), then Brazil (k = 7; 14.89%) and then Turkey (k = 3; 6.38%). Two studies were conducted in France, Germany and Japan respectively (k = 2; 4.26%), with the remaining countries (i.e. Canada, India, Israel, Italy, Lebanon, Pakistan, Poland, Salvador, Spain, Taiwan and the United Kingdom) contributing single studies (2.13%). Continentally, most studies (k = 21; 44.8%) originated in South America, with 10 from Asia (21.3%), 8 from Europe (17%) and 7 from South America (14.9%). Most studies used a clinical sample (k = 27; 58.7%), with fewer using a community sample (k = 19; 41.3%). 
Accounting for the N=46 studies included in the analysis, the overall sample size was 61, 019 with a mean of 1298 participants per study (SD = 2363.5, range = 11 – 10169). The average age of participants was 20.03 (SD = 11.32, range = 4 – 83). Females outnumbered males in the overall sample, with an average of 58.5%. This rate varied considerably however, with one study using only female participants (Eroğlu & Şahan, 2018) and no studies using only male participants.  Eighteen studies used diagnostic interviews to identify trichotillomania (k = 18; 38.3%) and these used the structured clinical interview for DSM-IV Axis I Disorders (SCID-I; First et al., 1995) and the Composite International Diagnostic Interview (CIDI). The next most common interview assessment approach was the use of the Minnesota Impulse Disorders Interview (MIDI) and this was used in 13 studies (k = 13; 27.66%). Other studies used other validated self-report questionnaires (e.g., Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease, QUIP, Weintraub et al., 2009; Repetitive Body-Focused Behavior Scale, RBBS, La Buissonnière-Ariza et al., 2021) and some used a bespoke measure for the purposes of the study (e.g., Bikram et al., 2019). Notably, although some used self-report measures, these measures were administered by a clinician or researcher (e.g., Grant et al., 2005). 

Figure 1
PRISMA diagram showing identification and screening of included articles

Records identified through database searching
(n = 2040)

Screening

Included

Eligibility

Identification

Records after duplicates removed
(n = 878)

Abstracts and titles screened
(n = 878)

Records excluded
(n =   646)

Full-text articles assessed for eligibility
(n = 232)

Full-text articles excluded, with reasons 
(n =200)
No trichotillomania data (n = 35)
No prevalence calculation (n = 103)
Full text not available (n = 5)
Full text not in English (n = 13)
Pooled prevalence calculation (n = 44)

Studies included in qualitative synthesis
(n = 46)

Studies included in quantitative synthesis
(n = 46)

Studies remaining after assessment for eligibility
(n =   32)

Additional studies from backwards/forwards referencing
(n =   14)

From:  Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097

Table 2. 
Table of included study characteristics
Study First Author
(Year)	Country
[continent]	Population type [assessment method]	Lifetime or point prevalence	Mean age (SD)[range]	Sex (% female)	Sample Size	N of sample with Trichotillomania
(percentage)	N of females in sample with Trichotillomania
(percentage)	N of males in sample with Trichotillomania
(percentage)	Risk of bias
	
Bezerra et al.
(2020)	Brazil
[South America]	Community
[Self-report]	Point	27.2 (7.9)	71.3	7639	110	89	21	6	

Bikram et al.
(2019)	Canada [North America]	Clinical
[Self-report]	Lifetime	10.2 (2.9)	17.3	739	18	0	0	4	
Bohne
(2010)	Germany [Europe]	Community
[Self-report]	Lifetime	27.1 (3.6) [17-48]	63.9	571	0	0	0	0	

Chien et al.
(2011)	Taiwan [Asia]	Community [Self-report]	Point	N/A	48	2731	41	29	12	7	

de Alvarenga et al.
(2012)	Brazil [South America] 	Clinical 	Lifetime	34.9 (0.54)	58.4	813	51	N/A	N/A	5	

de Mattos et al.
(2016)	Brazil [South America]	Clinical	Point	38.7 (10.6)	83.3	171	7	6	1	3	

Domingues-Castro et al. (2019)	Brazil [South America]	Clinical	Lifetime	35.8 (12.5)	58.1	995	60	N/A	N/A	7	
Duke et al.
(2010)	USA [North America]	Community [Self-report]	Point	20.28 (2.11) [17-43]	74.6	527	4	N/A	N/A	2	

Eroğlu et al.
(2018)	Turkey [Asia]	Community	Lifetime	28.4 (5.7)	100	152	1	1	N/A	2	

Garret et al.
(2019)	USA [North America]	Community	Point	N/A	31.6	8868	905	N/A	N/A	6	

Ghisi et al.
(2013)	Italy [Europe]	Community [Self-report]	Point	30 (13.3)	66.5	520	11	8	3	1	

Grant et al.
(2020a)	USA [North America]	Clinical [Self-report]	Point	38.8 (13.8)	55.4	213	27	N/A	N/A	1	

Grant et al.
(2020b)	USA [North America]	Community [Self-report]	Lifetime	[18-68]	50	10169	253	N/A	N/A	4	

Grant et al.
(2020b)	USA [North America]	Community [Self-report]	Point	[18-69]	50	10169	175	84	89	4	

Greenberg et al.
(2018)	Canada/USA [North America]	Clinical [Self-report]	Lifetime	18.8 (13.5)	23	811	29	52	24	4	

Grzesiak et al.
(2017)	Poland [Europe]	Community [Interview]	Lifetime	23.7 (1.4) [22-26]	61.4	339	8	6	2	2	
Houghton et al.
(2018)	USA [North America]	Community [Self-report]	Point	18.77 (1.13) [18-44]	69.3	4335	33	N/A	N/A	1	
İpçi et al.
(2020)	Turkey [Asia]	Clinical [Self-report]	Point	11.2 (2.42) [8-15]	26.1	326	4	4	0	1	

Kotyuk et al.
(2020)	Japan [Asia]	Clinical [Self-report]	Point	21 (2.8) [18-28]	57.4	1730	120	N/A	N/A	3	

La Buissonnière-Ariza et al. (2021)	USA [North America]	Clinical [Self-report]	Point	13.7 (2.4) [9-17]	41.8	67	4	N/A	N/A	2	

Lenane et al.
(1992)	USA [North America]	Community [Interview]	Lifetime	35.2 (9.66) 6-74	53.8	65	3	N/A	N/A	1	
Leppink et al.
(2016)	USA [North America]	Community [Self-report]	Lifetime	22.3 (3.72) [18-56]	58	1717	21	N/A	N/A	6	

Mikhael et al.
(2021)	Lebanon	Community [Self-report]	Point	21.5 (0.75)	52.9	489	11	9	2	5	
Muller et al.
(2011)	Germany [Europe]	Clinical [Interview]	Point	38.4 (13.4) [18-73]	62	234	2	N/A	N/A	1	
Muller et al.
(2011)	Germany [Europe]	Clinical [Interview]	Lifetime	38.4 (13.40) [18-74]	62	234	3	N/A	N/A	1	

Ocal et al.
(2019)	Turkey [Asia]	Clinical [Interview]	Point	27.4 (8.1) [18-52]	71.1	90	5	N/A	N/A	3	

Odlaug et al.
(2010)	USA [North America]	Community [Self-report]	Lifetime	20 (1.25) [17-24]	67.9	791	31	22	9	5	

Odlaug et al.
(2013)	USA [North America]	Community [Self-report]	Lifetime	22.7 (7.4)	58.1	1916	25	N/A	N/A	8	

Paul et al.
(2020)	India [Asia]	Clinical [Self-report]	Point	60.1 (9.7)	37.2	102	3	N/A	N/A	1	

Saez-Francas et al.
(2016)	Spain [Europe]	Clinical [Interview]	Point	63.16 (9.06)	36.5	115	3	0	3	3	

Siddiqui et al.
(2012)	Pakistan [Asia]	Community [Self-report]	Point	21.5 [18-27]	73.81	210	28	14	14	2	
Sulkowski et al.
(2011)	USA [North America]	Community [Self-report]	Point	19.45 (1.19) [17-27]	71.51	358	11	7	4	3	

Nunes-Neto et al.
(2018)	Brazil [South America]	Clinical [Self-report]	Lifetime	22.7 (7.4)	87.6	330	105	N/A	N/A	8	

Frank et al.
(2011)	

UK [Europe]	

Clinical [Interview]	

Point	

32.5 (15.4)	

35.5	

31	

3	

N/A	

N/A	

2	

Grant et al.
(2010a)	USA [North America]	Clinical [Interview]	Point	36.5 (11.65)	36.5	51	2	N/A	N/A	6	
Grant et al.
(2010a)
	USA [North America]	Clinical [Interview]	Lifetime	36.5 (11.65)	36.5	51	2	N/A	N/A	6	

Grant et al.
(2010b)	USA [North America]	Clinical [Interview]	Point	34.2 (13.1)	86.8	53	7	N/A	N/A	6	

Grant et al.
(2010b)	USA [North America]	Clinical [Interview]	Lifetime	34.2 (13.1)	86.8	53	9	N/A	N/A	6	

Leibovici et al.
(2014)	Isreal [Europe]	Clinical [Self-report]	Point	25.1 (4.8) [17-60]	64.3	2176	1	N/A	N/A	5	

Lovato et al.
(2012)	Brazil [South America]	Clinical [Interview]	Point	34.4 (12.7)	57.3	901	44	N/A	N/A	5	

Grant et al.
(2002)	USA [North America]	Clinical [Interview]	Lifetime	37 (15.6) [13-68]	63.6	22	2	N/A	N/A	2	

Bayle et al.
(2003)	France [Europe]	Clinical [Interview]	Lifetime	37.9 (7.9)	81.8	11	1	N/A	N/A	1	

Matsunaga et al.
(2005)	Japan [Asia]	Clinical [Interview]	Point	38.35 (7.5)	57.52	153	7	N/A	N/A	4	
Mueller et al.
(2010)	America / Germany [N/A]	Clinical [Interview]	Point	43.3 (10.9) [19-71	93	146	3	N/A	N/A	2	
Mueller et al.
(2010)	America /Germany [N/A]	Clinical [Interview]	Lifetime	43.3 (10.9) [19-72]	93	171	3	N/A	N/A	2	

Fontenelle et al.
(2005)
	Brazil [South America]	Clinical [Interview]	Point	33.4 (12.1)	44.28	45	3	0	3	2	
Grant et al.
(2005)	USA [North America]	Clinical [Interview]	Point	40.5 (13.2) [18-83]	54.9	204	7	N/A	N/A	4	
Grant et al.
(2005)	USA [North America]	Clinical [Interview]	Lifetime	40.5 (13.2) [18-83]	54.9	204	9	N/A	N/A	4	

Lejoyeux et al.
(2002)	

France [Europe]	

Clinical [Interview]	

Point	

41.3	

78	

107	

3	

3	

0	

1	
Duke et al.
(2009)	USA [North America]	Community [Self-report]	Point	20.28 (2.11) [17-43]	50.2	832	5	N/A	N/A	2	

McCarley et al.
(2002)	USA [North America]	Clinical [Self-report]	Point	19.54 (3.08)	60.47	635	13	N/A	N/A	1	

Selles et al.
(2015)	Salvador [South America]	Community [Self-report]	Point	8.39 (3.1) [4-17]	47.3	315	9	N/A	N/A	2	

Phillips et al.
(2005)	USA [North America]	Clinical [Interview]	Lifetime	32.35 (11.95)	68.5	200	5	N/A	N/A	3	

Note. Abbreviations: N/A: not applicable; N: number of participants, studies included twice if they provided prevalence rates for 2 different groups (e.g., clinical and community sample) or if provided lifetime and point prevalence rates
Risk of bias assessment
The table of quality ratings per study can be found at appendix B. The average JBI quality rating per study was 3.29 (SD=2.09; range=0 to 8) indicating a medium level of overall quality of the evidence base. In total, k = 4 studies were high quality, k = 21 medium quality and k = 21 as low quality. The most common elements increasing risk of bias was the sample frame used and how participants were recruited. There were few studies that used random sampling, with the majority obtaining participants from non-representative samples (e.g., via convenience sampling or using participants from one service or university; Bohne, 2010; Grant et al., 2005). There was also poor reporting of response rates and poor reporting of statistical analyses across the studies, with the majority particularly failing to provide confidence intervals.  Conversely, studies scored well and reduced their risk of bias concerning the manner in which trichotillomania was identified and measured, with participants and settings often described in detail. Finally, those studies with larger samples (>800) achieved a better-quality assessment rating with a mean of 4 (SD=2.16) compared to those studies with <800 participants which had a mean of 3 (SD=2.07). Studies with larger sample sizes tended to use self-report measures and those using smaller samples used clinical interviews.
Meta-analysis of trichotillomania prevalence rates
Point prevalence
Thirty-two studies reported point prevalence estimates (N = 44,543 participants).  The point prevalence rate for trichotillomania was 0.03 (95% confidence intervals [CI’s] 0.02 - 0.05). There was significant and large between study heterogeneity (I2  = 97.17%, 95% CI 95.23 - 98.47; Q = 1527.98, p < .001). Figure 2 reports the forest plot regarding the global point prevalence of trichotillomania. The funnel plot was asymmetrical (see Figure 3) and statistical tests of publication bias were significant (rank correlation Kendall’s Tau=0.39, p=.0014; Egger’s regression z = 2.79, p = 0.005), indicating an absence of smaller studies reporting lower prevalence rates. 
Figure 2
Forest plot for point prevalence

Figure 3
Funnel plot for the point prevalence of trichotillomania

Lifetime prevalence 
Twenty-one studies reported lifetime prevalence rates (N = 27,333 participants).  The lifetime prevalence rate for trichotillomania was 0.02 (95% confidence intervals [CI’s] 0.01 to 0.04). Figure 4 illustrates the forest plot regarding the global lifetime prevalence of trichotillomania. There was significant and large between study heterogeneity (I2  = 96.17%, 95% CI 93.04 - 98.63; Q = 544.34, p < .001). The funnel plot was asymmetrical (Figure 5) and statistical tests of publication bias were significant (rank correlation Kendall’s Tau=-0.03, p=0.882; Egger’s regression z = 2.85, p = 0.004), indicating an absence of smaller studies reporting lower prevalence rates. 

Figure 4
Forest plot of lifetime prevalence
Figure 5
Funnel plot for the lifetime prevalence of trichotillomania
Moderators and Sensitivity Analysis 
Tables 3 and 4 present the moderator analyses, with only two of the moderator analyses being statistically significance.  Sample size was statistically significant for point prevalence rates; studies using larger samples reported fewer cases of trichotillomania compared to those with smaller samples sizes. This suggests that as sample size increases, the prevalence of trichotillomania decreases. Sample size was not a significant moderator of lifetime prevalence. To further explore the impact of sample size on trichotillomania prevalence detected in studies a sensitivity analysis was performed. Using the suggestions of Hajian-Tilaki (2011) to determine the minimum sample size needed to be able to accurately detect a prevalence of 3%, as found in trichotillomania literature (e.g., Paul et al., 2020; Sulkowski et al., 2011), studies exceeding a total of 1987 participants were included. This resulted in 6 studies of point prevalence and 2 for lifetime being included in the sensitivity analysis. A 1.8% point prevalence rate (95% confidence intervals [CI’s] = 0.003 - 0.05) with significant and large between study heterogeneity (I2  = 99.57%, 95% CI 98.88 - 99.93; Q = 1306.16, p < .001) was found.  The lifetime prevalence rate was 0.9% (95% confidence intervals [CI’s] = 0.00 - 0.04), with significant and large between study heterogeneity (I2  = 99.66%, 95% CI 98.30 - 100; Q = 292.55, p < .001). This suggests that larger studies, when sampling error is likely reduced, resulted in a lower prevalence estimate. 
Assessment method was found to be statistically significant for lifetime prevalence rates; those studies using interviews were more likely to detect trichotillomania and so report higher prevalence rates.  Notably, assessment method for point prevalence approached, but did not quite reach significance, with studies using interviews identifying more cases of trichotillomania. Statistical significance was not found for any of the other moderators used.

Table 3 
Moderator Analyses for Categorical (Subgroups) and Continuous (Meta-regression) Moderators of Trichotillomania Point Prevalence Rates.
Categorical variable	Subgroup	k	Proportion	95% CI	I2(%)a	P (between subgroups)	R2 (%)	
Population type	Clinical	20	0.04	0.02 to 0.06	97.06	0.202	0.95	
	Community	12	0.02	0.01 to 0.04				
Continent	North America	13	0.04	0.02 to 0.06	97.05	0.987	0.00	
	South America	4	0.04	0.02 to 0.08				
	Asia	9	0.03	0.01 to 0.06				
	Europe	5	0.03	0.00 to 0.07				
Assessment method	Interview	13	0.04	0.02 to 0.07	96.01	0.052	14.17	
	Self-report	18	0.02	0.01 to 0.04				
Continuous variable		k	B-coefficientb	95% CI	SE	P	R2 (%)	
Quality	(rating  0-9)	32	0.00	-0.01 to 0.02	0.01	.664	0.00	
% female 	(0-100%)	32	-0.00	-0.00 to 0.00	0.00	.725	0.00	
Mean age	(6 – 74 years)	29	0.00	-0.00 to 0.01	0.00	.242	3.09	
Sample size	(11-10169)	32	-0.00	-0.01 to to -0.00	0.00	.011*	13.72	

Note. Abbreviations: k: number of comparisons, SMD: standardised mean difference; CI: confidence interval; R2: percentage of variation explained; SE: standard error; aPooled within-group estimates of between-study variance/heterogeneity, significance based on p value of associated Q statistic. b Positive coefficient indicates prevalence rates increase as value of moderator increases. * Statistically significant with Bonferroni correction for multiple comparisons (p≤0.0125)

Table 4 
Moderator Analyses for Categorical (Subgroups) and Continuous (Meta-regression) Moderators of Trichotillomania Lifetime Prevalence Rates.
Categorical variable	Subgroup	k	Proportion	95% CI	I2(%)a	P (between subgroups)	R2 (%)	
Population type	Clinical	13	0.01	0.00 to 0.03	94.68	0.084	7.34	
	Community	8	0.03	0.02 to 0.05				
Continent	North America	12	0.03	0.00 to 0.07	95.77	0.598	0.00	
	South America	3	0.03	0.02 to 0.05				
	Asia	1	0.01	0.00 to 0.04				
	Europe	4	0.03	0.02 to 0.05				
Assessment method	Interview	13	0.04	0.02 to 0.06	94.83	0.01*	25.84	
	Self-report	8	0.01	0.00 to 0.02				
Continuous variable		k	B-coefficientb	95% CI	SE	P	R2 (%)	
Quality	(rating  0-9)	21	-0.00	-0.01 to 0.02	0.01	.745	0.00	
% female 	(0-100%)	21	-0.00	-0.00 to 0.00	0.00	.856	0.00	
Mean age	(4 – 83 years)	20	0.01	-0.00 to 0.01	0.00	.129	4.92	
Sample Size	(11-10169)	21	-0.00	-0.00 to 0.00	0.00	.130	4.92	

Note. Abbreviations: k: number of comparisons, SMD: standardised mean difference; CI: confidence interval; R2: percentage of variation explained; SE: standard error; aPooled within-group estimates of between-study variance/heterogeneity, significance based on p value of associated Q statistic. b Positive coefficient indicates prevalence rates increase as value of moderator increases. * Statistically significant with Bonferroni correction for multiple comparisons (p≤0.01667)

Discussion
Given the previously reported disparities in prevalence rates for trichotillomania, the current proportional meta-analysis aimed to provide reliable pooled estimates of both lifetime and point prevalence rates and explored potential moderating variables. The global point prevalence of trichotillomania was observed to be 3%, with lifetime prevalence being slightly lower at 2%. The only moderator variable to reach statistical significance for point prevalence was sample size, which suggests studies with larger number of participants report lower prevalence rates. Conversely, the assessment method was a significant moderator for lifetime prevalence with those using interviews finding higher lifetime prevalence rates than those studies that used self-report measures. Assessment method for point prevalence almost reached statistical significance with those using interviews finding higher lifetime prevalence rates. All other moderators (age, gender, study quality, study sample and continent) failed to reach significance. It might be expected that a larger prevalence rate would be observed for lifetime prevalence rates, given that this considers the incidence of trichotillomania at any point in a participant’s life, but is something the current study fails to show. However, it is perhaps explainable by those studies assessing point prevalence being more likely to use clinical samples in their research.
The meta-analysed rates mirror the majority of extant trichotillomania prevalence evidence which reports prevalence rates between 1-3 %, and many of these studies were included in the current analysis. For instance, Grzesiak et al. (2017) reported a lifetime prevalence rate of 3%, whilst Leppink et al. (2016) found a more conservative 1%. Others have reported similar prevalence rates in the literature including Grant et al. (2020b) finding a rate of 2% and Muller et al. (2011) reporting 1%. Point prevalence rates in previous studies are also supported by the current research. Selles et al. (2015) reported the rate at 3% with Grant et al. (2005), Sulkowski et al. (2011) and Paul et al. (2020) reporting the same. The advantage of the current study was its systematic search strategy, study quality appraisal and robust proportional meta-analysis of the trichotillomania prevalence evidence base, which has allowed a much more reliable estimate of point and lifetime prevalence to be revealed. This proportional meta-analytical approach is an appropriate method in exploring the burden of a particular condition irrespective heterogeneity in included studies (Jablensky et al., 2011; Munn et al., 2015). Commissioners can now make more informed decisions in the planning and resourcing of assessment and intervention services for those with trichotillomania  (Kadandale et al., 2016; Munn et al., 2015). This is important, as designing and justifying interventions for trichotillomania when the community burden is unreliable or unspecified are impossible to achieve (Mansfield et al., 2016).
Contextualising the trichotillomania prevalence rates 
What remains uncertain is why some studies report much higher prevalence rates than the pooled prevalence rate in the current study. The possible reason for this is that some of these original rates appear to be the by-product of sampling from specific clinical samples, already reaching caseness for another mental health problem.  For example, Grant et al (2010; 2020) estimation of the prevalence of trichotillomania in pathological skin pickers reported rates as high as 10% (point) and 17% (lifetime) prevalence. This however also supports the argument that trichotillomania is closely related to, and shares common phenomenological factors, with other impulse control disorders and body focused repetitive behaviours (Grant & Stein, 2014; Schlosser et al., 1994). Similarly, Grant et al (2002) and Bayle et al (2003) found comparatively high (9%) trichotillomania lifetime prevalence rates among those with kleptomania, adding further support to its classification among the impulse control disorders. While not as pronounced as the high prevalence rates seen in studies using participants with impulse control disorders and BFRBs, there is some support among the included studies that trichotillomania is more prevalent in those with OCD. For example, Fontenelle et al. (2005) de Alvarenga et al. (2012) and Domingues-Castro et al. (2019) all report prevalence rates of around 6% when employing a sample of participants with OCD, again higher than the pooled prevalence rates seen in the current data. Similarly, this could be argued to support the evidence that phenomenological similarities between trichotillomania and OCD exist and overlap, and so also support trichotillomania to be classified in the Obsessive Compulsive and Related Disorders section of the DSM-5 (APA, 2013). These findings could be argued to highlight the need to consider the increased incidence of trichotillomania in those with impulse control disorders, BRFBs and OCD.      
When considering the prevalence rate of trichotillomania against other Obsessive-Compulsive and Related disorders there appears some inconsistencies. A recent proportional meta-analysis showed that the point and lifetime prevalence of OCD was 1.1% and 1.3% respectively, with gender being a significant moderator with women being more likely to experience OCD (Fawcett et al., 2020). Ferentinos et al. (2020) reported slightly higher point and lifetime prevalence rates of OCD at 1.6% and 2.5%. All moderators (sample type, diagnostic criteria, assessment method, gender, age, remission status) were non-significant, with a negative correlation identified between age and lifetime prevalence (Ferentinos et al., 2020). 
More variable results from the included studies come from Garret et al. (2019) and Siddiqui et al. (2012) who reported point prevalence rates of 10% and 13% respectively and also despite not using clinical samples. Garret et al. (2019) used participants from the United States armed forces personnel and showed that the incidence of impulse control disorders, including trichotillomania, was higher among younger recruits. This is in line with previous research that suggests ICDs are more likely to occur in younger individuals (Kessler et al., 2005; Odlaug & Grant, 2010). The use of interviews rather than self-report measures could also have increased the likelihood of identifying incidences of trichotillomania in that sample. Conversely, Siddiqui et al (2012) used a medical student sample in Karachi and found similarly high prevalence rates compared to previous studies and the currently observed pooled prevalence. This may have been caused by including hair manipulation in their assessment of trichotillomania and use of a not well validated self-report measure.  This highlights the need for studies to particularly concentrate on the valid and reliable assessment of trichotillomania and to the combine both diagnostic interviewing and also valid and reliable self-report measures.  Studies consistently using the same methods will aid in cross-study comparisons.
Sources of bias  
It is possible that publication bias influenced the findings of the current proportional meta-analysis. Barendregt, Doi, Lee, Norman, & Vos (2013) have highlighted that high study heterogeneity in proportional meta-analyses, using the average estimate of prevalence across many different populations that differ markedly in makeup may limited clinical value. The studies with smaller sample sizes in the current research were less likely to report lower prevalence rates and this might have been an artefact of the small sample size. This risk of bias could have created an artificial inflation of observed prevalence rates across studies sharing the same issue of small sample size. An additional explanation is that smaller studies were more likely to use clinical samples, and more so than larger studies. Given the suggestion that trichotillomania shares some phenomenological factors with other mental health disorders (Bayle et al., 2003; Grant & Stein, 2014), it is perhaps then more likely a higher prevalence rate will be observed in such studies. 
The use of clinical diagnostic interviews in smaller studies (i.e. rather than self-report measures of trichotillomania), may also have impacted with interviews potentially having higher sensitivity in the identification of trichotillomania, and as observed in the significant moderator analysis for lifetime prevalence. The sensitivity analysis completed goes someway to supporting this argument with lower prevalence rates observed in studies with higher sample sizes. Of the studies included in the sensitivity analysis, most used convenience sampling from a populace or purely student participants (e.g., Grant et al., 2020b; Nunes-Neto et al., 2018). Another factor worth considering is the impact of shame and embarrassment regarding deliberate hair pulling and associated loss on participation in studies. Those with trichotillomania suffer high levels of shame which could influence their likelihood of agreeing to participate in epidemiological studies, particularly when such studies create no added benefit to participants (Weingarden, & Renshaw, 2015; Noble et al., 2017). Resultantly, the prevalence rates provided in studies using community samples may be unrepresentative and underreported and may explain the lower prevalence rate seen in the sensitivity analysis. Future research needs to consider testing methods to reduce shame and embarrassment in trichotillomania sufferers in order that such people do not self-select out of studies due to fears of being judged.  
Moderator analyses 
Age, gender, continent, study quality and type of sample showed no significant differences in prevalence rates. This finding is somewhat explainable by the high levels of heterogeneity observed in the included studies. For example, there was a variety in how trichotillomania was assessed and diagnosed with some studies using self-report measures (Odlaug et al., 2019) while others used a full clinical interview (Domingues-Castro et al., 2019). Similarly, the sample type varied considerably with some studies using those with other mental health conditions such as OCD (de Alveranga et al., 2012) or another impulse control disorder (Mueller et al., 2010), while others used students (Chien et al., 2011) or a community sample (Selles et al., 2015). Further exploration of specific populations using similar diagnostic assessment would be of benefit in future research and allow a more valid comparison. This suggestion of more consistency across studies in the identification of trichotillomania is supported by assessment method being observed as a statistically significant moderator for lifetime prevalence rates.
The lack of statistical significance observed when using continent as a moderator was in contrast to the findings made by Neal-Barnett and colleagues in a series of studies (Neal-Barnett & Stadulis, 2006; Neal-Barnett et al., 2010; 2011). These studies found differences in the rate of trichotillomania between Caucasian participants and those who identified as African American or Latino.  However, the use of continent as a moderator could be argued to be too broad as a category, with future studies benefiting from being more country specific when exploring differences in trichotillomania across cultures. Botessi et al. (2016) suggested further research into the incidence of trichotillomania in Europe was needed, and the current meta-analysis finding only 8 studies using a European sample. No statistically significant intercontinental differences in prevalence rates were found suggesting that trichotillomania does not differ in terms of rate according to continental context. 
Limitations
The current research has some limitations that require acknowledgement. First, the choice and use of the quality assessment tool.  The JBI had no pre-determined cut off scores and so the authors resorted to defining the quality descriptors (low, medium, or high) dependent on study scores. Nevertheless, it can be argued that the JBI was the most appropriate tool because of its rigorous development and the prevalence-specific items (Migliavaca et al., 2020). Alternative tools could have been The Critical Appraisal Skills Programme (CASP) Checklist or the Measurement Tool to Assess Systematic Reviews (AMSTAR) (CASP, 2018; Shea et al. 2017). The low-quality ratings of some included studies could limit the reliability of the meta-analytic conclusions. Second, there was considerable variation in the types of samples used and methods of assessment which contributed to high levels of heterogeneity in pooled prevalence estimates. Despite attempts to explore sources of heterogeneity using moderator analyses, small sized sub-groups and low power may have hampered identification of significant effects. There was no inclusion of grey literature and this likely increased the possibility of publication bias.  
There is also the possibility that some study data used to calculate prevalence may have been reused among studies. Due to unclear reporting in some studies, it was not possible to be certain that each study used independent samples. There could exist some overlap in the samples, but this could not be inferred with the information provided. This could lead to a smaller sample size than suggested and should be held in mind when considering the current findings. The lack of double screening and quality assessment for some of the studies could also introduce bias among the findings.  The lack of studies reporting cumulative prevalence of trichotillomania could also be argued as a limitation. This could have complemented the existing findings of point and lifetime prevalence, by allowing an understanding the proportion of those who develop trichotillomania over a set period of time (Munn et al., 2018). 
Clinical and Research Implications
The findings in the current research support the need for continued well conducted and suitably sized epidemiological research into trichotillomania integrating valid and reliable forms of assessment that do not exclude those suffering from high levels of shame and embarrassment. Studies should always use valid and reliable forms of assessment and so single item questions need to be eliminated as a method.  A core battery of measures to enable effective cross-study comparison should be the norm.  True prevalence estimates need to be calculated from studies conducted in the community using random, robust and transparent sampling methods.  During the assessment process, comorbidities need always to be identified and reported.  While this proportional meta-analysis has identified a relatively low prevalence rate compared to other mental health conditions in the UK such as depression at 4.5% (WHO, 2017), and generalised anxiety disorder at 5.9% (McManus et al., 2014), it is still comparable to other such as bipolar disorder at 1.7% (Humpston et al., 2021). The findings suggest a considerable number of the population will experience trichotillomania, but the understanding of the condition could be argued to be still lagging behind many other mental health conditions. The pooled prevalence rates provided here could be used to support decision making in the funding and development of services that provide the identification and treatment of trichotillomania while highlighting the need for additional study.
The current research also supports the need for further study into trichotillomania amongst children and adolescents, as only 5 studies included them in their samples. This research amongst younger samples could benefit the understanding of trichotillomania’s development which in turn could support decisions around its classification.  Further research and meta-analyses into trichotillomania prevalence would benefit from the inclusion of grey literature to enable increased validity and to reduce the probability of publication bias.  Future reporting in prevalence studies should be clear about where the data originates to enable researchers to better assess for overlapping samples.  Future meta-analytic research would benefit from ensuring that all studies produced by the search terms are screened by an additional researcher and all studies selected for inclusion assessed for quality by more than one researcher.  
Conclusion
The current proportional meta-analysis has provided the first pooled prevalence estimate for point and lifetime prevalence of 3% and 2% respectively for trichotillomania. The advantage of the current study has been its systematic search, study appraisal and meta-analytic design that has allowed for a much more reliable estimate of point and lifetime prevalence to be produced than was previously possible from single studies. Clearly, the design of epidemiological studies of trichotillomania could be improved in terms of study quality.  More accurate estimates of prevalence could then allow governments, commissioners and service providers to make a more informed decision in the development of services, ensuring available resource is directed accordingly and evidenced-based trichotillomania treatment is available (Kadandale et al., 2016; Munn et al., 2015). This is particularly important considering the difficulties in designing and justifying interventions for trichotillomania when the community burden is unreliable or unspecified (Mansfield et al., 2016). Moderator analysis showed that sample size and assessment method may impact on the levels of prevalence observed in studies with greater consistency required in the future research of trichotillomania. To conclude, a small but significant number of the population deliberately pull their hair, suffer accordingly and are deserving of appropriate care.   

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Appendix
Appendix A 
Table of search strategy 

Search database	Search strategy	
Scopus	
( TITLE-ABS-KEY ( “trichotillomania”  OR  "trichotillomanias"  OR  "hair pull*"  OR  "impulse control disorder" )  AND  TITLE-ABS-KEY ( “prevalence”  OR  “incidence” ) ) 	
PsycINFO	1.	Exp Epidemiology/ or prevalence.mp.
2.	incidence.mp. or exp At Risk Populations/
3.	1 or 2
4.	trichotillomania.mp. or exp Trichotillomania/
5.	hair pulling.mp. or exp Trichotillomania/
6.	exp Impulse Control Disorders/ or impulse control disorder.mp.
7.	Supportive counselling.mp.
8.	4 or 5 or 6
9.	3 and 7	
MEDLINE	1.	Exp Prevalence/
2.	exp Incidence/
3.	1 or 2
4.	Exp Trichotillomania [Epidemiology]
5.	“Disruptive, Impulse Control, and Conduct Disorders”/ or Trichotillomania/ or hair pulling.mp.
6.	Impulse control disorder.mp. or Disruptive, Impulse Control, and Conduct Disorders”/
7.	4 or 5 or 6 
8.	3 and 7	

Appendix B 
Quality assessment table

Author(s)	Year of Publication	JBI1	JBI2	JBI3	JBI4	JBI5	JBI6	JBI7	JBI8	JBI9	JBIOverall	
Bezerra et al.	2020	N	N	Y	Y	N	Y	Y	Y	Y	6	
Bikram et al.	2019	Y	N	Y	N	N	Y	N	Y	U	4	
Bohne	2010	N	U	N	N	N	U	U	U	U	0	
Chien et al.	2011	N	Y	Y	Y	Y	N	Y	Y	Y	7	
de Alvarenga et al.	2012	N	N	Y	Y	U	Y	Y	N	U	4	
de Mattos et al.	2016	N	N	N	Y	N	Y	U	Y	U	3		
Domingues-Castro et al.	2019	Y	N	Y	Y	U	Y	Y	Y	Y	7	
Duke et al.	2010	N	N	N	Y	U	N	N	N	Y	2	
Eroğlu et al.	2018	N	N	N	Y	U	Y	U	N	U	2	
Garret et al.	2019	Y	Y	Y	Y	Y	Y	N	N	U	6	
Ghisi et al.	2013	N	N	N	Y	U	N	N	N	U	1	
Grant et al.	2020a	Y	N	N	N	N	N	N	N	U	1	
Grant et al.	2020b	Y	N	Y	Y	Y	N	N	N	U	4	
Greenberg et al.	2018	Y	N	Y	N	N	Y	Y	N	U	4	
Grzesiak et al.	2017	N	N	N	N	U	Y	Y	N	N	2	
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Using Latent Class Analysis to Identify Depression Subtypes and Associated Responsivity to Counselling For Depression (CfD) in Improving Access to Psychological Therapies services.     

Jonny Hartley1,2

1Department of Psychology, University of Sheffield, UK
2Sheffield Health and Social Care NHS Foundation Trust, UK

* Correspondence to Jonathan Hartley, Clinical Psychology Unit, Cathedral Court, University of Sheffield, S1 2LT, UK; Email: jhartley5@sheffield.ac.uk 

Abstract 
Introduction: Psychological interventions are only moderately effective for depression. One proposed reason for poor outcome is that depression is a heterogenous psychological disorder containing differing subtypes with these subtypes responding differently to treatment. This study therefore sought to identify possible depression subtypes using latent transition analysis (LTA) and analyse how these subtypes respond to counselling for depression (CfD).  Design: Outcomes were drawn from item level PHQ-9 data sourced from eight Improving Access to Psychological Therapies (IAPT) services. This created a sample of N=5646 patients who had received CfD that was then analysed using LTA.   Results: Five depression subtypes were found: somatic, cognitive, typical, severe and recovery with transitions occurring within rather than between subtypes. Typical subtype depression patients attended more sessions than somatic and cognitive subtypes. No significant differences were found between the three overarching depression subtypes regarding outcome nor acceptability. Unlike CBT, treatment outcomes in response to CfD appear to be comparable across subtypes. Conclusion: The study supports the routine use of depression subtyping using assessment scores to match the type of depression to the treatment most likely to create a positive outcome. Methodological weaknesses are identified, and future research direction provided.  

Keywords: depression, counselling for depression, depression subtypes, latent transition analysis
Practitioner Points
•	Clinicians should be aware that depression is a heterogeneous psychological disorder and may contain subtypes that respond differently to treatment.
•	LTA is an appropriate method for identifying disorder subtypes.    
•	There appear five subtypes of depression: somatic (20.5%), cognitive (26.2%), typical (39.5%), recovery (3.2%), and severe (10.6%). 
•	Matching of subtype to intervention would personalise and increase the precision of the psychological intervention being provided.  
•	Subtype transitions were more likely to occur within rather than between subtypes when patients are being treated with CfD

Introduction
Depression is a common mental health problem and globally is the second leading cause of burden in people’s lives (Disease and Injury Incidence and Prevalence Collaborators, 2017). Depression affects around 264 million individuals globally (James et al., 2018) with a global prevalence rate of 4.4% (WHO, 2017).  Whilst the label or diagnosis of depression implies the presence of a homogenous disorder, the possibility of over 100 different depression symptom combinations suggests it is a far more heterogeneous disorder (Zimmerman et al., 2015). Therefore, the common and heterogenous nature of depression also creates pressure on service providers to increase access to effective and efficient interventions that are matched to the needs of the patient (Fabbri et al., 2021; O’Connor et al., 2009). The National Institute for Care Excellence (NICE, 2009) currently recommends the use of medication and psychological interventions such as cognitive behavioural therapy (CBT), couple therapy, interpersonal therapy (IPT) and counselling in response to a diagnosis of depression (NICE, 2009).  Therefore, any practical methods that increase the likelihood of creating a positive treatment outcome from these recommended interventions are of huge practical and scientific interest.  
Counselling for depression (CfD) is one of the ‘high intensity’ NICE recommended psychological therapies offered at step 3 of Improving Access to Psychological Therapies (IAPT) services (Clark, 2011).  Counselling has been shown to be as equally effective as CBT delivered in routine practice (Barkham et al., 2021; Pybis et al., 2017). The high intensity psychotherapies offered in IAPT based on NICE guidelines include cognitive behaviour therapy (CBT), CfD, dynamic interpersonal therapy and psychodynamic interpersonal therapy for up to 20 sessions (NICE, 2011). Not all IAPT services however offer all the NICE recommended treatments for depression (British Association of for Counselling and Psychotherapy, BACP, 2016).  The high intensity IAPT interventions are standardized, protocol‐driven, and delivered by practitioners qualified to a post‐graduate level under regular supervision (National Collaborating Centre for Mental Health, 2018). CfD in IAPT consists of 6-10 sessions over 8-12 weeks for mild-to-moderate depression with up to 20 sessions for more severe depression. CfD is theoretically based in humanistic counselling (Proctor and Hayes, 2017; Rogers, 1959; Sanders and Hill, 2014) and a competency framework to direct the intervention has been developed (Roth et al., 2009).  Training in CfD requires completion of a training course that contains 80 hours of clinical practice assessed via recorded sessions where therapy adherence is measured (Pearce et al., 2012). 
Even when a person with depression is treated with one of the recommended interventions the outcome research suggests that this is only moderately effective on average (Cuijpers et al., 2013; 2020; Kolovos et al., 2016). For example, Hansen et al., (2002) showed that approximately 50% of individuals did not respond to treatment if offered 13-18 sessions with only 20% showing improvement if five or less sessions were attended. Ormel et al. (2012) noted that despite the increase in availability of evidenced-based psychological treatments there has been no drop in the prevalence of depression. Therefore, it has been suggested that personalised medicine approaches are better equipped to deal with the challenge of the heterogeneous nature of depression (Zimmerman et al., 2015). For example, Delgadillo and Gonzalez Salas Duhne (2020) showed that patients from deprived areas, from an ethnic minority and who had a disability would get better outcomes when allocated to CfD as compared to CBT.  Significantly higher reliable clinical change rates were found for those who were assigned to the model-indicated intervention in comparison to those who had suboptimal treatment (Delgadillo, and Gonzalez Salas Duhne, 2020). 
Existing attempts to differentiate depression subtypes has depended on clinical characteristics observed by clinicians and has been based on existing theoretical models or classifications (e.g., Musil et al., 2018) or reductionist biological differentiations (e.g., Stewart et al., 2009). However, these approaches do not accurately determine any subtype transitions over treatment time and could be argued to introduce bias through the consideration of existing models (Haslam & Beck, 1994). Attempts to match patients to treatments have also been based on viewing depression as a homogenous disorder (i.e. using overall depression severity) as a predictor of who will respond to treatment. However, the heterogenous nature of depression suggests that identifying different symptom profiles could help understand which subgroups of patients respond to depression treatments more favourably while reinforcing the support of treatment matching. 
More recently studies have used innovative data-driven methods to identify possible subtypes of depression. Latent class analysis (LCA) is one such approach which circumvents the influence of existing theoretical models and uses reliable, valid, and standardised questionnaires to derive subtypes from. However, the problem of static states without consideration of movement over treatment time remains. Latent transition analysis (LTA) has been introduced to be able to demonstrate differential treatment responses for different depression subtypes and how patients may transition between states/subtypes (Catarino et al., 2020; Simmonds-Buckley et al., 2021). Catarino et al. (2020) identified seven subtypes of depression during computer-delivered CBT drawn from the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001, appendix A) that broadly fitted within either a somatic (sleep, tiredness and appetite changes) or cognitive-affective (depression, tiredness and low self-esteem) subtypes. Simmonds-Buckley et al. (2021) then replicated Catarino et al.’s findings, identifying five overarching subtypes of depression; somatic, cognitive-affective, typical depression, mild and severe in patients accessing CBT in IAPT services. The somatic and cognitive-affective subtypes were characterised by peak intensity on somatic and cognitive focused symptoms respectively, the typical subtype exhibited similar intensity across all main symptoms. The typical subtype was further distinguished by different levels of risk (indexed by severity of PHQ-9 item nine concerning suicidal ideation), showing either low risk (minimal suicidal ideation) or high risk (regular thoughts about harming oneself). The severe state displayed close to maximum scores across all symptoms, whereas the mild state indicated a recovery state with minimal symptoms. Again, transitions between states demonstrated that the most likely movement was to a state of different severity of depression, rather than to a different subtype. Those scoring highly on severity were the most likely to transition to high or low-risk typical states. 
Combined, these two studies both based on CBT showed that somatic subtypes of depression (symptom profiles characterised by physical symptoms) did not respond as well to treatment as cognitive-affective depression subtypes (symptom profiles characterised by cognitive or affective symptoms). The results also showed that patients who were female, had co-morbid mental health conditions, a long-term physical health condition and took psychotropic medication within the somatic group were less likely to benefit from therapy (Catarino et al., 2020). High‐risk typical subtypes were also found to have lower dropout rates when compared to those in the low‐risk typical and high‐risk cognitive‐affective subtypes (Simmonds-Buckley et al., 2021). Now, treatment response of different depression subtypes to other recommended therapies for depression beyond CBT delivered in routine services needs to be investigated to identify additional treatment matching options.  Therefore, the current study utilised LTA to explore treatment responsivity of differing depression sub-types to CfD.  The study specifically sought to test whether 1) the same depression subtypes are apparent during CfD and 2) whether different treatment responses to CfD are apparent, including transitions within and across subtypes are evident.

Methods 
Design and Ethics 
The current research is a naturalistic, retrospective, observational cohort study. It analysed practice‐based data collected between 2014 and 2017 across N=8 National Health Service (NHS) Trusts in England. Ethical approval was granted by the London City & East NHS Research Ethics Committee (06/01/2016, Ref:15/LO/2200). University of Sheffield approval for the analysis of secondary data was also obtained (ref:190217910)). 
Sample Size Calculation
For latent transitional analyses uncertainty remains in how to establish estimates of effect for use in power analyses. Dziak et al. (2014) has provided guidance around sample size calculations using the bootstrap likelihood ratio test (BLRT) to empirically select the appropriate number of classes. Dziak et al. (2014) therefore provides sample size recommendations and comparison tables for benchmarked effect sizes based on an effect size w (Cohen, 1988) for measures with a range of different number of items. The current sample size calculation was based on power to detect a small effect size. Using the Patient Health Questionnaire‐9 (PHQ-9), recommendations would suggest in order to achieve 0.80 power to detect the smallest effect size (w=.1), this would require a minimum sample size of 3080 (Dziak et al., 2014). For a more conservative estimate, an additional step of multiplying the sample size by 1.15 is recommended. The current study therefore required a sample size of 3,542 to detect a small effect size in determining the optimal number of classes measured with the PHQ-9 based on the BLRT.
Population and sample selection
Anonymised archival data containing clinical records were accessed and assessed for eligibility and the study sample selected based on patients who had received CfD delivered at step 3 of an IAPT service (Clark, 2011). Participants were required to have presented with depression as their primary difficulty and completed the PHQ-9 during treatment. The final sample consisted of 5646 patients, figure 1 illustrates how cases were selected from the overall sample. The final sample description is presented in Table 1. 
Figure 1
Flow diagram showing selection of cases 

Enrollment

Total CfD cases in data source (n= 24069)

Excluded (n=15359)
•	No affective disorder diagnosis (n= 15359)

Allocation

CfD cases with affective disorder diagnosis = 8710

Excluded (n=765)
•	Less than 75% CfD sessions, and no valid first PHQ-9 score= (n= 765)

Session 1, with 75%+ sessions as CfD with valid first PHQ-9 score = 7945

Excluded (n= 774)
•	More than 10 sessions (n= 774)

Session 1, with 75%+ sessions as CfD with valid first PHQ-9 score and 10 or less sessions = 7171

Analysis

Excluded (n=1525)
•	Missing data for analysis (n= 1525)

After missing data removed during analysis in R = 5646

Abbreviations: PHQ‐9, Patient Health Questionnaire‐9; CfD, Counselling for Depression

IAPT Minimum Dataset Measures
1.	The Patient Health Questionnaire‐9 (PHQ‐9) is a self-report screening and case tracking measure of major depression. This was the primary outcome measure in this study as the patients were presenting with depression.  This nine‐item questionnaire assesses affective (items 1, 2), cognitive (items 6, 7, 9) and somatic domains (items 3, 

4, 5, 8).  Each item is rated on a 0–3 scale (0 = “not at all,” 3 = “nearly every day”), with a total score between 0-27 (Kroenke et al., 2001). A cut‐off point of ≥10 has been recommended to detect clinically significant depression symptoms (Kroenke et al., 2001). Cut off scores of 5, 10, 15 and 20 are suggested to correspond to mild, moderate, moderately severe, and severe levels of depression. 
2.	The Generalised Anxiety Disorder-7 (GAD‐7, appendix B) is a seven item, screening and case tracking measure of anxiety. Each item is also rated from 0 to 3, producing a total score between 0 and 21 (Spitzer et al., 2006). A cut‐off point of ≥8 has been recommended to identify clinically significant anxiety symptoms (Kroenke et al., 2007). Additionally scores of 5, 10 and 15 correspond to mild, moderate and severe levels of anxiety respectively (Spitzer et al., 2006). This was the secondary outcome measure in the current study.  
3.	Work and Social Adjustment Scale (WSAS; Mundt et al., 2002, appendix C) allows the collection of patients’ perspectives on how their condition impacts their functioning in day-to-day life. The WSAS has a maximum score of 40 with lower scores corresponding to better levels of functioning. This was a secondary outcome measure in this study.  
4.	Anonymised demographic and clinical data included age, gender, ethnicity, employment status, medication status, presence of a long‐term physical condition, and baseline impaired functioning were also accessed. 

Table 1
Demographic data and descriptive statistics of final sample
Characteristic	Descriptive Statistic 	
Age (mean, SD)	43.06 (14.86)	
Gender (% male)	30.1	
Ethnicity (% white british)	87.7	
Employment Status (% unemployed)	29.7	
Medication (% prescribed pharmacotherapy)	50.8	
Self-reported LTC (% with LTC)	23.7	
Baseline PHQ-9 (mean, SD, severity)	15.35 (6.38) - severe	
Baseline GAD-7 (mean, SD, severity)	12.77 (5.53) - severe	
Baseline WSAS (mean, SD)	18.31 (10.02)	

Abbreviations: LTC, long‐term health condition or illness; PHQ‐9, Patient Health Questionnaire‐9; GAD‐7, Generalized Anxiety Disorder‐7; WSAS, Work and Social Adjustment Scale.

Analysis
Latent transition analysis was used to identify depression subtypes and their longitudinal response to treatment based on PHQ-item level data. Treatment engagement was defined as per IAPT protocol with patients attending at least two sessions being deemed as engaged (The National Collaborating Centre for Mental Health, 2018). The two core symptom measures used by IAPT are the PHQ-9 and GAD-7 (appendix A and B). Reliable improvement in the current study was in line with the IAPT protocol and Simmonds-Buckley et al.’s (2021) study. A statistically reliable reduction in scores on either measure was needed (≥6 points on the PHQ‐9 and/or ≥4 on the GAD‐7) without an increase in the other measure (National Collaborating Centre for Mental Health, 2018,). Reliable deterioration was recorded when the PHQ-9 or GAD-7 showed a reliable increase in scores (≥6 points on the PHQ‐9, and/or ≥4 on the GAD‐7). Patients were classed by therapists as having dropped out if they unilaterally discontinued treatment and had an unplanned ending to their episode of care. Use of the same outcomes as Catarino et al. (2020) and Simmonds-Buckley et al. (2021) enabled comparisons with the reported subtype responses to CBT.
Analyses were conducted in R (version 3.6.1) using the package LMest (Bartolucci et al., 2017). Following the procedure reported in Catarino et al. (2020) and Simmonds-Buckley et al. (2021), a Hidden Markov Model (HMM) was applied to the longitudinal item‐level PHQ‐9 data for the entire sample to estimate latent depressive subtypes and the corresponding state‐to‐state transition probabilities. The model that has the best empirical support (smallest Bayesian Information Criterion value) was interpreted in line with Simmons-Buckley et al’s. (2021) results. This ensured complete objectivity in model selection and the ability to explore differences and/or similarities between states and transitions with greater granularity. After determining the optimal model, global decoding was performed to approximate a depressive state for each patient at every time point. Transition probabilities were extracted and plotted to explore between‐state transitions in response to treatment. 
To investigate treatment response, post-hoc comparisons used the starting state of each patient to then group them into overall depression subtypes. Chi-square analyses and analysis of variance (ANOVA) were used to compare treatment engagement (number of sessions attended, engagement and drop-out rates) and treatment outcomes (reliable improvement and reliable deterioration rates) of the three main subtype groups. Logistic regression was used to investigate demographic characteristics (age, ethnicity, existence of long-term health conditions, employment status and medication use) associated with different subtypes.
Results
Optimal depressive states model
Twelve sperate depressive states were identified using the HMM analysis. Each state showed varying levels of severity and different symptom profiles. The overall mean PHQ-9 scores and item level intensity for each state is shown in Figure 2. States 2, 4, and 9 showed similar symptom profiles on the PHQ-9 with notably higher scores on items 1 (loss of interest and pleasure), 2 (feelings of depression) and 6 (self-criticism). These states were deemed to represent a cognitive-affective subtype of depression of varying levels of severity with state 2 being the lowest severity, state 9 the highest and state 4 falling within the two. Conversely, state 3 and 5 share similar symptom profiles on items 3, 4 and 5 of the PHQ-9 (assessing sleep difficulties, tiredness, and appetite) which corresponds to somatic symptoms of depression and were representative of a somatic subtype with different severity levels. State 3 indicates a lower severity compared to state 5 which is of a higher level of severity. 
States 6, 7, 8, 10 and 11 demonstrated relatively even symptom profiles when considering PHQ-9 items 1-8 indicating a typical depression subtype. These states varied in levels of severity and risk with states 8 and 10 representing typical depression with high risk due to scores higher on item 9 of the PHQ-9. Conversely, states 7 and 11 correspond to the typical depression subtype with low risk when considering the observed average score on item 9. State 6 consists of a typical depression subtype which is subclinical in nature. Finally, state 1 is indicative of recovery with low scores across all PHQ-9 items whereas state 12 represents a severe depression presentation with high scores across all items.   

Figure 2
The 12 depressive state profiles identified through HMM analysis. 

Transitions between depression states during CfD
Transitions between states within subtypes were observed to be more likely than transitions across subtypes. This often consisted of those in a higher severity subtype state transitioning to a lower severity state of the same subtype (e.g., high severity typical state to a lower severity typical state). Figures 3 and 4 illustrate the array of transition probabilities most likely to be observed within and between states during CfD. There were no instances of transitions from those of a cognitive-affective subtype to either typical or somatic subtypes. The only transition from cognitive-affective subtypes to another subtype was to state 1, the recovery state identified during analysis. In comparison, while transition within somatic subtypes were more likely, there were some transitions across subtypes observed. This consisted of transitioning during CfD from somatic subtypes to cognitive-affective and typical subtypes of lower severity (e.g., state 5 to state 7 and state 3 to state 2). Transition from other subtypes to somatic subtype states were less likely with the exception of state 11 (typical low risk) to state 5 (somatic high severity).  However, transitions to higher severity states both within and across subtypes were observed to be unlikely with most transitions to states of a lower severity. Finally, those engaging with CfD and in state 12 (severe) at the start of therapy were most likely to transition to typical subtype states than either cognitive-affective or somatic subtype states. 

Figure 3
Transitions across states in response to CfD based on the 12 starting states identified. 

Figure 3 
The transition of depressive states in response CfD from the 12 starting states. 
Within and between‐state transition probabilities in response to CfD; the range of probabilities over the course of treatment are reported for each transition; p < .05 at more than half the time points are hidden; thickness of the arrow depicts the magnitude of the probability, thicker arrows represent transitions that are more likely; states are colour coded with green = somatic, yellow = cognitive, blue = typical, white = recovery, orange = severe, the figure is based on that of Simmonds-Bickley et al. (2021). 

Table 2
Treatment engagement, dropout, deterioration, improvement by starting states (1–12) and identified depression subtypes
Starting State	N	Baseline PHQ-9 (SD)	Mean number of sessions (SD)	Treatment Engagement (%)	Dropout (%)	Improvement (%)	Deterioration (%)	
1. Recovery	181	2.03 (1.55)	3.19 (2.48)	64.3	15.9	14.4	2.7	
2. Cognitive	285	4.55 (1.26)	4.88 (2.46)	88.8	12.6	28.8	3.5	
3. Somatic	387	8.51 (2.15)	4.43 (2.68)	83.5	20.7	44.7	2.8	
4. Cognitive	549	9.79 (2.25)	4.94 (2.55)	88.5	18.2	56.6	1.6	
5. Somatic	768	14.96 (2.34)	4.93 (2.57)	89.6	20.8	61.2	2.3	
6. Typical (subclinical, low risk)	110	7.93 (1.06)	4.64 (2.68)	86.4	16.4	39.1	5.5	
7. Typical (low risk)	403	11.59 (1.68)	5.38 (2.43)	92.8	16.1	57.1	4.0	
8. Typical (high risk)	382	16.32 (1.65)	5.20 (2.57)	90.6	21.2	62.3	2.1	
9. Cognitive	644	16.39 (2.73)	5.03 (2.66)	89.0	23.8	59.9	1.7	
10. Typical (high risk)	731	21.73 (1.91)	5.07 (2.64)	88.8	22.7	57.0	0.7	
11. Typical (low risk)	606	19.77 (1.96)	5.23 (2.66)	89.1	25.4	55.0	1.8	
12. Severe	599	24.58 (1.88)	4.82 (2.77)	82.8	26.5	43.1	0.2	
Somatic (3 & 5)	1155	12.81 (3.80)	4.76 (2.62)	87.5	20.8	55.7	2.5	
Cognitive (2, 4, & 9)	1478	11.68 (5.12)	4.97 (2.62)	88.8	19.6	52.7	2.0	
Typical (6, 7, 8, 10, & 11)	1909	17.74 (4.65)	5.17 (2.60)	89.8	17.9	63.8	2.1	
Typical high risk (8 & 10)	1113	19.87 (3.16)	5.12 (2.62)	89.4	22.2	58.8	1.2	
Typical low risk (7 &11)	1009	16.47 (4.42)	5.29 (2.57)	90.6	22.0	56.9	2.7	

Relationship between clinical outcomes, demographics, and depressive states
Table 2 presents the treatment engagement and response outcomes for each starting state (states 1-12) and the main overarching subtypes (somatic, cognitive-affective, typical [low and high risk]). On average rates of reliable improvement were 64% for the typical subgroup, 56% for the somatic subgroup and 53% for the cognitive subgroup.
Three-way comparison; somatic, cognitive-affective and typical subtypes
Outcomes were compared for patients who started treatment in one of the three main subtypes, somatic, cognitive-affective or typical and who had engaged in treatment (i.e. attended no less than two sessions of CfD) which constituted 4,327 patients (somatic n=1011, cognitive-affective n=1312, typical n=2004). The number of CfD sessions attended (F(2,4862) = 9.867, p=<.001) differed significantly across subtypes of depression; patients in the typical subtype attended more than those of in somatic (p=<.001) and cognitive-affective (p=.047) subtypes. There was no difference in CfD sessions attended between the somatic and cognitive-affective subtypes (p=.110). For patients who engaged with CfD, there were also no significant differences between somatic, cognitive-affective and typical depression subtypes in terms of rates of deterioration, (χ2 = 1.025, p =.599), reliable improvement (χ2 = 5.176, p = .075) or dropout (χ2 = 1.958, p < .376). 
Patients starting CfD in a somatic state (compared to a cognitive-affective or typical state) were more likely to be female (χ2 = 11.392, p <.003). Patients starting CfD in a cognitive-affective and typical state were less likely to be white British than those starting in a somatic subtype (χ2 = 10.414, p = .005). Those who began CfD in a typical state relative to the cognitive-affective or somatic subtypes were more likely to be unemployed (χ2 = 96.914, p < .001) be taking medication (χ2 = 53.526, p < .001), have LTC (χ2 = 7.749, p = .021) and have a disability (χ2 = 7.455, p < .024) compared to those starting CfD in a cognitive-affective or somatic subtype. 
Logistic regression investigating variables associated with starting CfD in a somatic state relative to a cognitive-affective subtype are reported in Table 3. Those starting CfD in a cognitive subtype are more likely to be older, male and unemployed. The three negative coefficients show those starting CfD in a cognitive subtype are more likely to have lower rates of white British patients and have a lower baseline depression and anxiety severity (χ2 = 62.07, p < .001, Nagelkerke R²= 0.057).
Comparison of high risk versus low risk typical subtypes
Patients in the low-risk typical states were more likely to deteriorate (χ2 = 8.170, p =.004), but less likely to drop out of CfD (χ2 = 4.747, p = .029) than those in the high-risk typical states. There were no significant differences in reliable improvement (χ2 = 1.26, p = .262), engagement (χ2 = .825, p = .364), and sessions attended (F(2,2120) = 1.815, p=<.120) when comparing low-risk typical states to high-risk typical states. Logistic regression results of the variables associated with starting CfD in the low-risk typical state relative to the high-risk typical state are reported in Table 4. Those starting in the high-risk typical state were younger, more likely to be male, have a LTC and have a higher PHQ-9 score at baseline compared to those in the low-risk typical group (χ2 = 235.24, p < .001, Nagelkerke R²= 0.24).  

Table 3 
Associations between participant demographics and start state for cognitive and somatic states analyses using logistic regression
Predictor variable	Mean (SD)/prevalence as %	b	SE	Wald	P	
	Cognitive	Somatic					
Age (years)	43.8 (15.82)	42.4 (15.32)	.006	.003	4.371	.037*	
Gender (% male)	31.8	25.8	.275	.122	5.63	.024*	
Ethnicity (% White British)	89.8	93.5	-.480	.205	5.47	.019*	
Employment Status (% unemployed)	22.8	19.0	.407	.140	8.48	.004**	
Medication (% prescribed pharmacotherapy)	49.0	51.8	-.053	.111	.226	.635	
Self-reported LTC (% with LTC)	26.7	28.8	-.058	.124	.223	.637	
Baseline PHQ-9 score	11.65 (5.13)	12.87 (3.82)	-.095	.017	32.83	<.001**	
Baseline GAD-7 score	10.56 (5.10)	10.70 (4.68)	-.044	.015	9.095	.003**	
Baseline WSAS score	14.12 (8.68)	15.22 (7.92)	-.004	.007	.367	.545	

Note: A positive relationship signifies that the variable is more likely to occur in patients entering
treatment in a cognitive‐affective state. GAD‐7, Generalized Anxiety Disorder‐7; LTC, long‐term health condition or illness; PHQ‐9, Patient Health Questionnaire‐9; WSAS, Work and Social Adjustment Scale.  
*p < .05; **p < .01*** p < .001.

Table 4 
Associations between participant demographics and start state for low-risk typical and high-risk typical states analyses using logistic regression
Predictor variable	Mean (SD)/prevalence as %	b	SE	Wald	P	
	Low-risk typical	High-risk typical					
Age (years)	43.63 (14.1)	41.75 (14.31)	-.010	.005	4.38	.036*	
Gender (% male)	25.4	36.0	.605	.143	17.92	<.001***	
Ethnicity (% White British)	87.7	88.2	.101	.218	.212	.645	
Employment Status (% unemployed)	29.5	38.9	.133	.144	.855	.355	
Medication (% prescribed pharmacotherapy)	51.2	59.5	.095	.140	.459	.498	
Self-reported LTC (% with LTC)	26.0	23.4	-.288	.146	3.92	.048*	
Baseline PHQ-9 score	16.47 (4.42)	19.87 (3.16)	.234	.023	106.44	<.001**	
Baseline GAD-7 score	13.81 (4.54)	15.73 (3.65)	-.014	.020	.504	.478	
Baseline WSAS score	19.96 (8.74	21.52 (8.73)	-.005	.009	.317	.573	
Note: A positive relationship signifies that the variable is more likely to occur in patients entering
treatment in a cognitive‐affective state. GAD‐7, Generalized Anxiety Disorder‐7; LTC, long‐term health condition or illness; PHQ‐9, Patient Health Questionnaire‐9; WSAS, Work and Social Adjustment Scale.  
*p < .05; **p < .01*** p < .001.
Discussion
Summary of findings
This pragmatic study sought to use LTA to analyse whether differing depression subtypes were evident for patients attending for CfD in IAPT and what relationship there was to clinical outcome. This was conducted in order to broaden the evidence base for the heterogeneity of depression affecting responsivity to treatment beyond that of CBT.  A total of 5646 patients from a multicentre data set engaging with CfD to treat a major depressive disorder were analysed using LTA. This identified five distinct subtypes of depression based on PHQ-9 item level scores. These five subtypes and the percentage of patients in each state consisted of somatic (20.5%), cognitive-affective (26.2%), typical (39.5%), recovery (3.2%), and severe (10.6%). It was also found that transitions during engagement with CfD occurred mostly within subtypes and from a higher to lower severity. Between subtype transitions that did occur on occasion included those in the severe subtype transitioning to high-risk typical states and from somatic states to lower severity typical and cognitive-affective states with transitions in the opposite direction rarely observed. There were also observed differences in clinical outcomes dependent on the subtype which could benefit application to clinical practice. Patients of a typical subtype attended more sessions than those in either a cognitive-affective or somatic state. Furthermore, those in a low-risk typical state were more likely to deteriorate and drop out of therapy, compared to those in a typical high-risk state. 
Current findings in relation to previous research
The current results resemble findings of previous research and so reinforce the need for recognition of the heterogeneity of depression and the clinical application this could offer. Most notably are the similarities with Simmonds-Buckley et al’s., (2021) sample receiving CBT within the IAPT programme, with the five overarching subtypes being largely replicated for patients receiving CfD. Specifically, the differences between cognitive-affective, somatic and typical subtypes and the varying severity found within each subtype. The severe (state 12) and recovery (state 1) subtypes strongly resemble state 14 and state 1 found by Simmonds-Buckley et al., (2021). The increased likelihood of transition within subtypes rather than across was an additional replication, as was the finding that when between transitions occurred, they were to typical subtypes, rather than cognitive-affective or somatic subtypes. Finally, Simmonds-Buckley et al. (2021) also found, as is the case in the current study, that high-risk typical subtypes had a lower likelihood of dropout when compared to low-risk typical states. 
Despite the many similarities, the post-hoc analyses revealed a different pattern of subtype treatment responses. Catarino et al. (2020) and Simmonds-Buckley et al. (2021) found significant differences between subtypes in the level of engagement and reliable improvement after CBT, namely those in the cognitive-affective subtype showing greater levels of recovery with those in the typical subtype groups having lower engagement and attending fewer sessions. In the current study, although reliable improvement rates after CfD were instead observed to be larger for the somatic subtype relative to the cognitive-affective subtype, no statistically significant differential treatment responses were found according to subtype. The comparability of outcomes for somatic and cognitive-affective subtypes after CfD, but not CBT suggests patients with the somatic subtype (i.e., presenting with symptoms such as tiredness and sleep disturbance) may be better served by allocation to CfD. The increased attendance for typical subtypes also indicates CfD fosters higher levels of attendance for those with typical subtypes of depression. It is unclear as to why these contradictions were observed but could be due to sample heterogeneity or differential effects of CfD compared to CBT whereby the former fosters higher levels of attendance for those with typical and somatic subtypes of depression. It could be that the different mechanisms of change between CfD and CBT play an important role. For example, CBT could be argued to be more change focused and is often reliant on between session tasks being completed, conversely CfD is less prescriptive and does not require between session tasks and takes a less directive stance (Cuijpers et al., 2012). As such, this could enable those of a somatic subtype, who score higher on symptoms such as tiredness and sleep difficulties to be less disadvantaged against other subtypes compared to if they were to engage with CBT. Conversely, given the nature of CBT and its focus on cognitions it is perhaps unsurprising that research has found it to be more effective when treating the cognitive-affective subtypes compared to either somatic or typical subtypes. Nevertheless, such results require further investigation through direct comparisons before implementing indicated treatment selection.
The current findings also support other research that has explored depression subtypes using LTA despite using different measures and populations. Ni et al., (2017), for example, identified subtypes of varying levels of severity with a sample of older adults in China when using the Center for Epidemiological Studies Depression Scale (CES-D; Andresen et al., 1994) as did Ulbritch et al’s. (2016; 2018b) when using the Quick Inventory of Depressive Symptomatology (QIDS-SR; Rush et al., 2003) with women treated with citalopram and men with depression. While the above studies do not overtly distinguish between cognitive and somatic subtypes, the symptom clusters corresponding to some of the states could be argued to indicate this difference. For example, Ni et al., (2017) identified a lack of positive affect subtype that could correspond to a cognitive-affective subtype profile, with Ulbritch et al., (2016) identifying subtypes that included observable differences in appetite and psychomotor disturbances that too could be argued to differentiate between somatic and cognitive symptoms. Further support for the current findings come from Catarino et al., (2020) who observing cognitive and somatic subtypes using LTA based on PHQ-9 items with those undergoing internet-based CBT. They also found that females were most likely to present in the somatic subtype with transition occurring within rather than between subtypes, both findings replicated in the current research. Finally, Lamers et al., (2012) reinforced the lack of transition across subtypes using a 2 year, longitudinal follow up of working age adults in the Netherlands. They observed a 76% stability across the two years the study spanned. While some differences are evident, particularly in studies that used different populations and measures, the similarities undoubtedly reinforce LTA’s effectiveness in depression research and the clinical applicability of such findings. 
Strengths and limitations
In terms of study strengths, the following are of note.  The use of a power calculation to determine the sample size required for a small effect is a strength of the current study. The conservative approach used and based on Dziak et al. (2014) adds confidence to the reliability of the current results. The use of LTA itself is also a strength and provides evidence of its usefulness in meeting the aims of the research. Replication of the methods used by Catarino et al. (2020) and Simmonds-Buckley et al. (2021) allowed cross-study comparisons to be made. A large, sufficiently powered sample originating from multiple routine services is an additional strength, as is the novel focus on CfD.   In terms of study weaknesses, the following are of note.  The lack of any diagnostic interviewing means that there cannot be certainty that the patients were actually depressed. The use of the PHQ-9 constrained the analysis to the items of the PHQ-9 and meant the states identified are only applicable to this cohort and the items of the PHQ-9. The analysis only used up to 10 sessions per patient of CfD and thus may have missed differences in those that engaged in CfD for longer. Finally, there was no measure of treatment fidelity and therefore although the therapists reported that CfD was delivered, this may not have actually been the case in practice (i.e. the study did not assess therapist drift).  
Clinical implications and future research
NICE recommendations do suggest further research into matching patients with treatment is required to improve outcomes in routine practice (NICE, 2011). The algorithm used for depression subtyping could be prospectively applied to routinely collected depression scores at screening to dictate the form of psychological treatment. This could then be used to classify them according to their latent depression profiles/subtypes and then assigned to an intervention with greatest likelihood of recovery. Patients with depression subtypes that have an increased chance of dropout and lower chances of symptomatic improvement could be identified and prioritised for targeted clinical supervision. The current finding that CfD could facilitate higher levels of engagement in typical subtypes suggests its use with such subtypes might be preferential. This is further supported by the dose response other studies have found (e.g., Hansen et al., 2002; Robinson et al., 2020) with more sessions attended increasing the likelihood of recovery. More specifically in relation to the current study, this adds weight to suggestions that those of a typical subtype may be more suited to CfD given the significant finding in relation to sessions attended compared to other subtypes. In addition, somatic subtypes were not disadvantaged compared to cognitive-affective subtypes as was seen in the treatment response to CBT in Catarino et a.ls., (2020) and Simmonds-Buckley et als. (2021) research. This suggests somatic subtypes could be allocated to CfD rather than CBT, however direct comparisons are needed to establish if there are relative benefits of CfD over CBT for individuals with somatic presentations. The current study highlights the potential for precision (Delgadillo & Gonzalez Salas Duhne, 2020) highlighting more favourable outcomes in patients matched to optimal treatment dependent on presentation.  In this respect, the treatment indicated by any algorithm also needs to be balanced with informed patient choice.  
The findings of the current study offer notable suggestions for research. Firstly, the successful use of LTA show support for its use when exploring subtypes of mental health conditions and their corresponding transitions and response to treatment. LTA needs now to be applied to patients presenting with anxiety disorders in routine practice and also used for analysing responsivity to NICE indicated treatments for anxiety.  Follow-up data would be a valuable addition to future research and would assess the subtype at short and long-term follow-up compared to treatment.  Diagnostic interviewing would add to the internal validity of any future LTA study.  Further research could also employ the use of other measures including those that are observer rated to explore whether similar results are obtained. 
Conclusion
The current study has used LTA, a novel and data driven approach, to determine depression subtypes, transitions in response to CfD and demographic properties of patients within each subtype in a large, multicentre and sufficiently powered sample. Five subtypes were identified, cognitive-affective, somatic, typical, recovery and severe which broadly replicate previous research findings. Transition between subtypes during CfD was observed as unlikely and specific demographic factors appeared significantly related to different subtypes. Unlike CBT, treatment outcomes in response to CfD appear to be comparable across subtypes, with greater patterns of attendance for typical depression presentations. To conclude, the current study reinforces considering depression as a heterogenous psychological disorder and that psychological treatments do not tend to shift patients between subtypes. The different patterns of treatment response to CfD and CBT interventions for the somatic, cognitive-affective and typical depression subtypes indicates potential opportunity for treatment matching according to subtype. Further research on disorders other than depression is now indicated as it other psychological therapies. Balancing data driven and patient preference approaches to treatment matching would appear to be optimal.

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Appendix

Appendix A
Patient Health Questionnaire 9 (PHQ-9) 

Appendix B 
The Generalised Anxiety Disorder Assessment (GAD-7)

Appendix C 
Work and Social Adjustment Scale (WSAS)