In the UK, one in 10 children and young people (CYP) aged 5–16 years suffers from a psychiatric disorder; many more experience symptoms that, whilst not reaching the threshold of clinical disorder, cause significant distress for CYP and their families (Green et al., Reference Green, McGinnity, Meltzer, Ford and Goodman2005). Failure to address mental health difficulties (MHD) early in life affects individuals’ long-term functioning and wellbeing, and may also generate significant societal costs related to increased health care usage, unemployment, and antisocial behaviours (Joint Commissioning Panel for Mental Health, 2013).
Less than 35% of CYP with diagnosable MHD are identified (Burns et al., Reference Burns, Costello, Angold, Tweed, Stangl, Farmer and Erkanli1995), and only 25% of those with clinically impairing psychiatric disorder receive specialist care (Ford et al., Reference Ford, Hamilton, Meltzer and Goodman2007). A small number of studies suggest that parents of CYP with MHD often do not realise that their child may benefit from specialist support (Girio-Herrera et al., Reference Girio-Herrera, Owens and Langberg2013). Formal identification can highlight the severity of the child's MHD, and encourage parents to seek professional help. Well-designed programmes to identify CYP with MHD show promise for increasing access to supportive services, and may improve mental health (MH) outcomes if combined with evidence-based interventions (D'Souza et al., Reference D'Souza, Forman and Austin2005; Sayal et al., Reference Sayal, Owen, White, Merrell, Tymms and Taylor2010; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Mitchell et al., Reference Mitchell, Gryczynski, Gonzales, Moseley, Peterson, O'Grady and Schwartz2012).
There is strong international policy consensus that schools are well positioned to play a significant role in the early identification of CYP at risk of mental illness. Systematic school-based approaches detect a greater proportion of CYP with MHD compared with less formal processes (i.e. ah-hoc teacher or parent identification, or self-identification) (Garland, Reference Garland1995; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013; Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014). Students identified in school settings are more likely to receive parental and school support, as well as referral and access to MH services (D'Souza et al., Reference D'Souza, Forman and Austin2005; Nemeroff et al., Reference Nemeroff, Levitt, Faul, Wonpat-Borja, Bufferd, Setterberg and Jensen2008; Sayal et al., Reference Sayal, Owen, White, Merrell, Tymms and Taylor2010; Lyon et al., Reference Lyon, Maras, Pate, Igusa and Vander Stoep2015), and to achieve better long-term MH outcomes, compared with students with MHD identified in community healthcare settings (Ford et al., Reference Ford, Hamilton, Meltzer and Goodman2008; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Mitchell et al., Reference Mitchell, Gryczynski, Gonzales, Moseley, Peterson, O'Grady and Schwartz2012). However, teachers do not feel well equipped to perform this role and consistently under-identify early symptoms of various disorders (Caldarella et al., Reference Caldarella, Young, Richardson, Young and Young2008; Bruhn et al., Reference Bruhn, Woods-Groves and Huddle2014; Cunningham and Suldo, Reference Cunningham and Suldo2014).
The evidence-base on programmes to improve identification of MHD in school settings has not been synthesised. In this paper, we sought to synthesise evidence on the effectiveness and cost-effectiveness of school-based methods to identify CYP at risk of or experiencing MHD. This is a part of a larger systematic review of the effectiveness, harms, feasibility, and acceptability of school-based methods to identify CYP with MHD; findings on harms, feasibility, and acceptability will be published separately in due course. Given that we were specifically interested in the utility of the identification mechanism, effectiveness was defined as (i) rate of accurate identification (i.e. correct identification of cases) of CYP with MHD; (ii) rate of referrals to appropriate supportive services following identification; (iii) uptake of referrals to supportive services. Cost-effectiveness was defined broadly as the outcome of analysis comparing the resources required to deliver an intervention with the health, quality of life or other assumed outcomes achieved by an intervention (Knapp and Iemmi, Reference Knapp and Iemmi2013).
The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; https://www.crd.york.ac.uk/prospero), registration number: 42016053084 (amended version dated 18 January 2017).
School-based methods of identification
The literature describes four main models of school-based identification of MHD (Whitney et al., Reference Whitney, Renner, Pate and Jacobs2011). Universal screening programs aim to systematically assess all students for risks of MHD using self-, parent-, or teacher-report measures (Whitney et al., Reference Whitney, Renner, Pate and Jacobs2011). Curriculum-based models, delivered to all students in a year group by a staff member or external person with relevant knowledge, are designed to increase students’ knowledge and recognition of common MH problems, and develop skills to address them. (Whitney et al., Reference Whitney, Renner, Pate and Jacobs2011). Staff in-service models rely on training all members of staff to recognise early signs of MHD and link students deemed to be at-risk with appropriate support. (Whitney et al., Reference Whitney, Renner, Pate and Jacobs2011). Teacher nomination involves asking a class teacher to identify students in their classroom who exhibit concerning behaviours or symptoms that may indicate the presence of MHD (Cunningham and Suldo, Reference Cunningham and Suldo2014). Additionally, we included traditional identification methods using office disciplinary referrals (ODRs), grade point average, attendance data, and teacher referral to identify students at risk of MHD.
Since the paper describes studies that evaluated the accuracy of identification of suicide risk, it is important to note there is a consensus that, although identification of suicide risk yields a high number of false positive results, the harm of these inaccurate identifications is outweighed by the benefit of prevention for future suicides among those whose risk is correctly identified (Carter et al., Reference Carter, Milner, McGill, Pirkis, Kapur and Spittal2017). The result of recent systematic review showed that pooled positive predictive value of clinical instruments used to assess suicide risk is around 5.5%, which suggests that majority of individuals who screen positive will, in fact, not attempt suicide (Carter et al., Reference Carter, Milner, McGill, Pirkis, Kapur and Spittal2017).
Inclusion and exclusion criteria
Comparative studies were included if they assessed the effectiveness or cost-effectiveness of strategies to identify students in formal education aged 3–18 years (1) with a MHD, (2) presenting symptoms of mental ill health, or (3) exposed to psychosocial risks that increase the likelihood of developing a MHD.
Studies that focused on the identification of global and specific learning disabilities were excluded.
We included studies published in any year comparing the effectiveness of different identification models within the same group, and studies in which the accuracy of identification was verified by a subsequent clinical evaluation, or compared with existing MH diagnoses.
Electronic bibliographic databases: MEDLINE and Embase via OvidSP; PsycINFO, ERIC, and British Education Index via EBSCOhost; and ASSIA via ProQuest were searched in May and June 2017 and again in July 2018. The search strategy combined terms for identification and school settings with terms for MH. Search terms were generated by examining the terminology used in key publications in the field, identifying synonyms, and discussing with experts in school-based MH research. The search terms were combined with standard MeSH terms for the MEDLINE database, Emtree terms for Embase, Thesaurus terms for ERIC, British Education Index and ASSIA, and Subject Headings for the PsycINFO database. Supplementary search methods included forward and backward citation search, and hand-searching CYP MH journals. The MEDLINE search strategy is shown in an online Supplementary Table S1.
Selection of studies
Two independent reviewers selected studies in three stages: (1) all titles were examined to remove obviously irrelevant reports; (2) abstracts of remaining studies were examined against inclusion/exclusion criteria; (3) full-texts of remaining reports were examined for compliance with inclusion/exclusion criteria. We resolved disagreements by referral to another research team member.
The fields of the extraction tables were piloted and refined using three randomly selected studies included in the review. Two researchers independently extracted data from included studies. We extracted the following information: first author, year of publication, and country where the study was conducted, study design, study aims, school level, informants, identification measures, description of an identification programme, characteristic of a sample, and findings. In a separate table, we listed programmes’ components (online Supplementary Table S4). Results were compared and disagreements were resolved by referral to another research team member.
We appraised the quality of included studies with the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool for Quantitative Studies (Armijo-Olivo et al., Reference Armijo Olivo, Stiles, Hagen, Biondo and Cummings2012), which has been deemed suitable to use systematic reviews of effectiveness (Deeks et al., Reference Deeks, Dinnes, D'Amico, Sowden, Sakarovitch, Song, Petticrew and Altman2003). The tool includes six quality components: selection bias, study design, confounders, blinding, data collection and drop-out rated against set criteria as strong, moderate, or weak. Two researchers independently conducted quality appraisal judging each study against criteria listed for each quality component; results were compared and disagreements were resolved by referral to another team member.
Synthesis of results
Due to high heterogeneity of study designs, interventions, and outcome measures, it was not appropriate to conduct a meta-analysis. We provided a numerical account of evidence and narrative synthesis of evidence-guided by the framework for systematic reviews developed by Popay et al. (Reference Popay, Roberts, Sowden, Petticrew, Arai, Rodgers, Britten, Roen and Duffy2006). This framework comprises four iterative stages: developing the theory of change, preliminarily synthesising of findings, exploring relationships in the data, and assessing the robustness of syntheses. We described findings separately for each research questions, as well as an overall summary and conclusions (Popay et al., Reference Popay, Roberts, Sowden, Petticrew, Arai, Rodgers, Britten, Roen and Duffy2006).
Twenty-seven studies were included in the final review Fig. 1 outlines the study selection process.
Characteristics of included studies are shown in Table 1. Studies covered a total of 44 unique identification programmes. Publication dates suggest increasing interest in this area over the last two decades, but it should be noted that nearly all evidence comes from the USA. Nearly half of the studies were cross-sectional (n = 13), followed by comparison group (n = 8) and cohort analytic studies (n = 4). There was only one case-control study and one randomised controlled trial (RCT). Most focussed on secondary school settings (n = 16) and identification of behavioural and emotional problems. Nine studies evaluated universal screening models; remaining studies compared universal screening with teacher nomination (n = 12), traditional identification methods (n = 3) and staff in-service training (n = 1). One cost-effectiveness study compared universal screening, staff in-service training, and curriculum-based models. Detailed characteristics of studies are presented in an online Supplementary Table S2.
ND, not defined
a In total 44 identification programmes are described in 24 studies. Some studies describe more than one programme thus some characteristics are reported multiple times for one study
b Some studies are conducted in multiple schools, at different school levels
Quality of included studies
As shown in Table 2, nearly a quarter of included studies were rated weak on selection bias, lacking sufficient description of recruitment procedures and representativeness of the sample. Nearly half of the studies failed to report withdrawals and attrition. All but one study was rated strong for data collection, having utilised standardised and validated measures.
Rates of accurate identification
Findings from all studies are described in an online Supplementary Table S3. Section ‘Universal screening programmes’ describes studies that evaluated the effectiveness of a single identification model (universal screening programmes); subsequent sections describe studies that compared the effectiveness of universal screening and other identification models.
(1) Universal screening programmes
Eight studies of universal screening programmes reported on rates of identification (Tisher, Reference Tisher1995; Jones et al., Reference Jones, Dodge, Foster and Nix2002; Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009; Robinson et al., Reference Robinson, Gook, Yuen, Hughes, Dodd, Bapat, Schwass, McGorry and Yung2010; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Morey et al., Reference Morey, Arora and Stark2015; Hilt et al., Reference Hilt, Tuschner, Salentine, Torcasso and Nelson2018). In six studies, positive screening results were verified by subsequent clinical interview conducted by MH professionals (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009; Robinson et al., Reference Robinson, Gook, Yuen, Hughes, Dodd, Bapat, Schwass, McGorry and Yung2010; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Morey et al., Reference Morey, Arora and Stark2015; Hilt et al., Reference Hilt, Tuschner, Salentine, Torcasso and Nelson2018), or an existing diagnosis of MHD (Tisher, Reference Tisher1995), giving a reliable rate of false positives.
Depression and anxiety: Studies that utilised student-report screening measures and subsequent clinical interview, found that 45–63% of secondary school students were identified as being at high-risk for depression (Robinson et al., Reference Robinson, Gook, Yuen, Hughes, Dodd, Bapat, Schwass, McGorry and Yung2010; Morey et al., Reference Morey, Arora and Stark2015). Teacher-completed universal screening accurately distinguished between students with and without clinical depression diagnoses; students currently treated for depression scored significantly higher compared with their non-diagnosed counterparts (p < 0.0001) (Tisher, Reference Tisher1995).
Behavioural and socioemotional problems: One study considered the utility of teacher- and parent-completed universal screening by the examination of long-term outcomes for children identified in kindergarten as at high-risk of behavioural and socioemotional problems (Jones et al., Reference Jones, Dodge, Foster and Nix2002). Children identified as high-risk were significantly more likely, in the next 6 years, to receive professional outpatient and inpatient MH services (p < 0.05), take medication for MH reasons (p < 0.01), and receive special education services or MH-related school counselling (p < 0.01), compared with low-risk children (Jones et al., Reference Jones, Dodge, Foster and Nix2002).
Risk of suicide: Student-report universal screening identified 317 students out of 2342 screened (13%) as being at risk of suicide. Subsequent clinical interview, however, indicated that 43% of these outcomes were false positives, (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009), which indicates over-identification as we anticipated. In contrast, following completion of a universal screening measure, all students in two other studies, regardless of risk-status, participated in a brief interview with a school counsellor. Interview outcomes indicated that screening yielded around 20% false negative results, which suggests that this method is likely to miss a significant number of students needing support (Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Hilt et al., Reference Hilt, Tuschner, Salentine, Torcasso and Nelson2018).
(2) Universal screening programmes v. teacher nomination
Twelve studies compared identification rates from universal screening and school staff nomination models (Tisher, Reference Tisher1995; Auger, Reference Auger2000; Reference Auger2004; Campbell, Reference Campbell2004; Dwyer et al., Reference Dwyer, Nicholson and Battistutta2006; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013; Cunningham and Suldo, Reference Cunningham and Suldo2014; Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014; Sweeney et al., Reference Sweeney, Warner, Brice, Stewart, Ryan, Loeb and McGrath2015; Kilgus et al., Reference Kilgus, Taylor, Van and Sims2018). In four studies positive identification outcomes were verified by subsequent clinical interview (Auger, Reference Auger2000; Reference Auger2004; Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009; Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014; Sweeney et al., Reference Sweeney, Warner, Brice, Stewart, Ryan, Loeb and McGrath2015), while remaining studies reported rates of overlap in identification between screening and staff nomination.
Depression and anxiety: Findings from a study that employed a multi-stage model of universal screening and a clinical interview to identify students with depression showed that this method produced a high number of false-positive results (up to 90%). By comparison, teacher nomination yielded a false positive rate of nearly 70% (Auger, Reference Auger2000; Reference Auger2004). Universal screening for social anxiety disorder (SAD) yielded fewer false-positives(20%), with only 12% of subsequently diagnosed students identified by teachers (Sweeney et al., Reference Sweeney, Warner, Brice, Stewart, Ryan, Loeb and McGrath2015); seven students with a final SAD diagnosis were identified solely by teacher nomination. Other evidence suggests that teachers correctly nominate 41–68% of students who screen positive for depression and/or anxiety, but since tested models did not include a clinical interview, the rates of false positive and negative results for each method cannot be determined (Campbell, Reference Campbell2004; Cunningham and Suldo, Reference Cunningham and Suldo2014).
Behavioural and socioemotional problems: Seven studies compared identification rates of students with behavioural and socioemotional problems that used universal screening and nomination models (Tyne and Flynn, Reference Tyne and Flynn1981; Garland, Reference Garland1995; Dwyer et al., Reference Dwyer, Nicholson and Battistutta2006; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013; Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014; Kilgus et al., Reference Kilgus, Taylor, Van and Sims2018). Only one study verified positive results of screening with subsequent clinical evaluation (Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014); remaining studies reported rates of overlaps between model outcomes. Evidence suggests that student-report universal screening identifies at least twice as many at-risk students as teacher nomination (Garland, Reference Garland1995; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013). Teachers identify 10–30% of students identified by a universal screener (Garland, Reference Garland1995; Dwyer et al., Reference Dwyer, Nicholson and Battistutta2006; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013). They are more likely to nominate students who have more severe difficulties (Garland, Reference Garland1995), and more ODRs (Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013). However, combining universal screening and nomination did not increase the accuracy of identification compared with universal screening alone (Kilgus et al., Reference Kilgus, Taylor, Van and Sims2018). One study found that a parent-completed universal screener more accurately identified students subsequently diagnosed with internalising disorders, compared with a teacher-completed measure (30–46% and 26–34%, respectively). In contrast, teachers’ positive global judgement about children's risk of developing MHD better predicts future externalising problems, compared with parent's judgement (Dwyer et al., Reference Dwyer, Nicholson and Battistutta2006). Findings from another study suggest the agreement between results of peer-report universal screening and teacher nomination increases with students’ age from 19% in 3rd grade (7–8 years old) to 55% in 5th grade (10–11 years old) (Tyne and Flynn, Reference Tyne and Flynn1981), perhaps because older students can more accurately judge others’ behaviours.
ADHD: Only one study focussed on the identification of children with ADHD. Identification results were verified by the full clinical assessment that suggested very low levels of agreement between teacher-completed screening and simple nomination (p < 0.0002) (Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014). Seventeen out of 18 children with clinically-confirmed ADHD diagnoses were identified by at least one screening measure (Kieling et al., Reference Kieling, Kieling, Aguiar, Costa, Dorneles and Rohde2014), while the agreement between nomination and the final diagnosis was significantly higher for negative cases than positive cases.
Risk of suicide: One study compared the results of student-report universal screening for suicide risk and school staff nomination, with outcomes verified by subsequent clinical interview (Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009). MH professionals correctly nominated twice as many students as did administrative staff, with an accurate nomination rate of 36%, whereas screening correctly identified 63% of at-risk students. Screening yielded a 9% false-positive rate compared with 24% produced by staff nomination. Both methods combined produced only 5% false positives.
(3) Universal screening programmes v. traditional school identification methods
Three studies compared the accuracy of universal screening and traditional identification methods used by schools (i.e. ODRs, grade point average, attendance data, and teacher referral) (Hallfors et al., Reference Hallfors, Cho, Brodish, Flewelling and Khatapoush2006; Eklund and Dowdy, Reference Eklund and Dowdy2014; Naser, Reference Naser2014). Neither study verified outcomes by clinical assessment, reporting only rates of overlaps between methods.
Behavioural and socioemotional problems: Traditional identification based on teacher-referral and academic performance identified less than 40% of children who screened positive for internalising or externalising disorders (Eklund and Dowdy, Reference Eklund and Dowdy2014). Of kindergarten children identified by teacher-completed universal screening as being high-risk, less than 40% were identified by traditional methods during the first year of primary school (Forness et al., Reference Forness, Cluett, Ramey, Ramey, Zima, Hsu, Kavale and MacMillan1998). However, a substantial number of children identified by schools’ normal procedures were assigned to a different diagnostic category than the one indicated by the results of screening using validated, standardised measures (Forness et al., Reference Forness, Cluett, Ramey, Ramey, Zima, Hsu, Kavale and MacMillan1998).
Substance abuse: A study that compared the outcomes of universal screening and traditional methods of identifying substance-abusing students based on student's GPA, attendance record, and teacher referrals yielded equivocal results (Hallfors et al., Reference Hallfors, Cho, Brodish, Flewelling and Khatapoush2006). In one sample of students, high-risk of substance abuse indicated by student-report screening was associated with low GPA, while in other sample, low attendance and teacher referral, but not GPA, were strong predictors of substance abuse.
(4) Universal screening programmes v. staff in-service programmes
Substance abuse: Results of one study suggest that attending in-service training improves teachers’ ability to correctly nominate students identified by student-report universal screening as being at-risk of substance abuse (McLaughlin et al., Reference McLaughlin, Holcomb, Jibaja-Rusth and Webb1993). Teachers who completed the training more accurately identified students who were experimenting with, and regularly using drugs and alcohol, compared with their colleagues who had not attended the training, thereby reducing the gap in identification rates between the two methods (p < 0.001).
Rates of referrals and service uptake
Although a number of studies indicated that a referral was made for students identified as being at-risk, only three studies reported numbers and uptake of referrals to specialist support (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011; Hilt et al., Reference Hilt, Tuschner, Salentine, Torcasso and Nelson2018). All three studies evaluated universal screening for risk of suicide, although referral processes and services offered varied by programme. Of 317 students identified as at-risk of suicide by student-report screening in the Gould et al. (Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009) study, 182 (57%) were deemed to require additional support following a second clinical stage interview (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009). Referrals were made for 147 students (of whom 29 were already receiving MH services) reporting severe suicidality; the remaining 35 were given a list of local providers without a specific referral. The uptake of follow-up recommendations was 70.3%. Uptake did not differ between students who received a specialist referral or list of providers; those who were not currently receiving services were significantly more likely to follow-up with the referral compared with those already in treatment. Overall, 24% of the new service users had their first appointment within a month of the screening. Within 6 months, 52% attended their first appointment, and within a year, 70% had successfully accessed a MH care provider (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009). Of 2022 students participating in a universal screening for suicide risk programme, 444 students were determined to be in need of MH services following screening and clinical interview (Hilt et al., Reference Hilt, Tuschner, Salentine, Torcasso and Nelson2018). Of those identified as being at-risk, 77% were not currently in treatment. The majority (89%) were referred to community services, and those remaining received referrals to school services. Case-management confirmed that 50.2% of referred students attended one or more appointments; 22.5% completed three or more appointments.
Of the 2488 students included in the Husky et al. (Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011) study, universal screening and subsequent clinical interview identified 299 (12%) students as at-risk of suicide (Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011). Based on current suicidal ideation as assessed by clinical interview, past suicide attempts, and current MH treatment status, 128 (43% of those identified) students received a referral to school-based MH services only, 78 (26%) to community-based MH services only, and 93 (31%) to both school and community-based services. Of those referred, 76% had at least one appointment with a MH provider and 56% received minimally adequate treatment defined as three or more appointments or any number if terminated by provider's recommendation. Among the 221 students referred to school-based services, 80% attended at least one appointment, 71.3% of whom received minimally adequate treatment. Of 171 students referred to community-based services, 42% received at least one visit, 68% of whom received minimally adequate treatment.
Another study reported the uptake of a clinical interview following a positive student-report screen for suicide risk (Cotter et al., Reference Cotter, Kaess, Corcoran, Parzer, Brunner, Keeley, Carli, Wasserman, Hoven, Sarchiapone, Apter, Balazs, Bobes, Cosman, Haring, Kahn, Resch, Postuvan, Varnik and Wasserman2015). Of 516 students invited for a follow-up assessment, 37% attended. Recent suicide attempt, high levels of depressive, anxiety or emotional symptoms, hyperactivity/inattention, peer relationship problems, and functional impairment increased the likelihood of attending a follow-up interview.
Only one study compared the cost-effectiveness of different methods of identifying suicide risk (Burke et al., Reference Burke, Wasserman, Carli, Corcoran, Keeley, Balazs, Bobes, Apter, Brunner, Cosman, Haring, Pierre Kahn, Marusic, Postuvan, Saiz and Varik2013), and concluded that universal screening is more cost-effective (in terms of improving quality-adjusted life years – i.e. function of quality and length of life), than curriculum-based or in-service training programmes. The study utilised data from a sample of 11 100 adolescents from 168 schools across 10 European Union countries, so although the findings may accurately represent average cost-effectiveness of suicide screening across the EU countries, results may differ by country and world region. This represents a gap in the research literature.
Summary of findings
We identified 27 studies with a total of 26 256 participants that analysed the effectiveness of school-based MHD identification programmes. None of the studies was UK-based. Only one study used a randomised design. Most studies evaluated the utility of universal screening but programmes differed in format and outcomes; where comparison of identification rates was made, the comparator test varied across studies. Whilst the purported aim of many programmes was to increase the rate of MH support among children and young people, only two studies reported referral and uptake data.
Overall, the heterogeneity of studies, the absence of randomised studies and poor outcome reporting make for a weak evidence-base that only generate tentative conclusions about the effectiveness of school-based identification programmes.
Summary of effects of interventions
Some evidence suggests that overall, universal screening may be the most effective method of identification; however, the rate of false-positive results yielded by this method is high (Auger, Reference Auger2000; Reference Auger2004; Husky et al., Reference Husky, Kaplan, McGuire, Flynn, Chrostowski and Olfson2011), so the expectations of teachers, pupils, and parents would need to be managed accordingly. Some findings indicate that multistage models are more accurate (Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009; Morey et al., Reference Morey, Arora and Stark2015; Sweeney et al., Reference Sweeney, Warner, Brice, Stewart, Ryan, Loeb and McGrath2015); however two studies reported that a single assessment with a universal screening measure is sufficient to accurately identify high-risk individuals, and additional assessments and informants do not improve accuracy (Dowdy et al., Reference Dowdy, Dever, Raines and Moffa2016; Kilgus et al., Reference Kilgus, Taylor, Van and Sims2018). Teacher nomination yields a higher number of false negative results than universal screening (Campbell, Reference Campbell2004; Dwyer et al., Reference Dwyer, Nicholson and Battistutta2006; Eklund et al., Reference Eklund, Renshaw, Dowdy, Jimerson, Hart, Jones and Earhart2009; Dowdy et al., Reference Dowdy, Doane, Eklund and Dever2013; Cunningham and Suldo, Reference Cunningham and Suldo2014). Teachers are most likely to nominate high-risk students, while those who are at-risk but without obvious signs of MHD are often overlooked in ad-hoc identification procedures (Ollendick et al., Reference Ollendick, Greene, Weist and Oswald1990; Auger, Reference Auger2004). Limited evidence suggests that staff in-service training and curriculum-based programmes improve identification of MHD (McLaughlin et al., Reference McLaughlin, Holcomb, Jibaja-Rusth and Webb1993; Robinson et al., Reference Robinson, Gook, Yuen, Hughes, Dodd, Bapat, Schwass, McGorry and Yung2010); however, costs associated with programme delivery make them less feasible than universal screening (Burke et al., Reference Burke, Wasserman, Carli, Corcoran, Keeley, Balazs, Bobes, Apter, Brunner, Cosman, Haring, Pierre Kahn, Marusic, Postuvan, Saiz and Varik2013). Combining universal screening and staff nomination shows promise for increasing accuracy of identification (Gould et al., Reference Gould, Marrocco, Hoagwood, Kleinman, Amakawa and Altschuler2009; Scott et al., Reference Scott, Wilcox, Schonfeld, Davies, Hicks, Turner and Shaffer2009), although this proposition requires testing using randomised designs.
Few studies focused on identification of pre- and primary school children. It is vital to identify children with MHD as early as possible since evidence shows that presence MHD in children as young as three years old can impact future outcomes across multiple domains including education, employment, substance use, criminal activity, and physical and MH (Jones et al., Reference Jones, Greenberg and Crowley2015). Half of MHD is evident by the age of 14, with the even earlier onset of anxiety and impulse control disorders. There is, therefore, a strong case for developing methods of identification for use in primary school settings (Jones, Reference Jones2013).
Very few studies reported rates of service referral and uptake following identification. Given that MH services are already overwhelmed, commissioners, and service providers may be concerned that school-based identification, and universal screening programmes in particular (which yield a significant number of false positive results), will add unwarranted pressure to already struggling services. Conversely, evidence suggests some children have subclinical levels of psychopathology and will benefit from specialist support (Ford T et al., Reference Ford, Sayal, Meltzer and Goodman2005).
Few studies explicitly set out to assess adverse events or harms associated with identification. Since it is recognised that the identification process may cause distress, especially in high-risk students (Robinson et al., Reference Robinson, Yuen, Martin, Hughes, Baksheev, Dodd, Bapat, Schwass, McGorry and Yung2011), all studies should assess negative consequences associated with identification.
In general, the description of programmes was poor, with key details such as methods for obtaining consent omitted. Poor reporting of interventions is ubiquitous, and is in part explained by the word limits imposed on authors for papers published in peer-reviewed journals (Hoffmann et al., Reference Hoffmann, Glasziou, Boutron, Milne, Perera, Moher, Altman, Barbour, Macdonald, Johnston and Lamb2014; Maggin and Johnson, Reference Maggin and Johnson2015). Nevertheless, without adequate description, it is difficult, if not impossible, to compare trials. The mechanisms by which interventions were hypothesised to lead to change were also rarely reported. This, in combination with poor programme description, means we were unable to identify and define the role of programme components in the causal pathway leading to benefit, no effect or harm. We also note that there was poor attention to broader contextual factors that may influence programme implementation and outcomes, Intervention development, modelling, feasibility and pilot studies, along with trials of effectiveness, need to theorise and evaluate the contextual conditions necessary for intervention mechanisms to be activated. If there is to be any hope of identifying and scaling promising programmes, then concerted effort is needed to articulate, test and refine programme theories underpinning these complex interventions so as to make explicit how individual study components and contextual factors interact to generate desired outcomes (Wells et al., Reference Wells, Williams, Treweek, Coyle and Taylor2012; Fletcher et al., Reference Fletcher, Jamal, Moore, Evans, Murphy and Bonell2016; Howarth et al., Reference Howarth, Devers, Moore, O'Cathain and Dixon-Woods2016).
Commissioners and practitioners call for more interventions to be tested real-life settings, since the focus on internal validity and creating optimal conditions can significantly limit external relevance and impede dissemination (Bowen et al., Reference Bowen, Kreuter, Spring, Cofta-Woerpel, Linnan, Weiner, Bakken, Kaplan, Squiers, Fabrizio and Fernandez2009). More economic evaluations of identification programmes are required to inform the resource allocation to achieve the best value for money.
Quality of the evidence
More than half of included studies were rated weak in terms of study design, and documentation of withdrawals and drop-outs. Only one RCTs was identified, despite recommendations for trials that focus on both outcomes and processes (Oakley et al., Reference Oakley, Strange, Bonell, Allen and Stephenson2006). Nearly a quarter had not sufficiently described sample selection and recruitment procedures, which raises questions about the generalisability of results. Most included studies compared outcomes of two different identification models in the same sample of students. Authors draw conclusions about models’ accuracy based on overlaps between their results, thereby assuming that if students are identified by two independent models, then the outcome is most likely correct. Few studies that verified outcomes with subsequent clinical interview further assessed students initially identified as being at-risk, thereby failing to account for false negative results. Studies evaluating the effectiveness of identification models need to include an established, reliable method of verifying both positive and negative results, to minimise the risk of harms that may result from the failure to identify children who have MHD, as well as the over-diagnosis of MHD among children without MH problems (Cohen et al., Reference Cohen, Korevaar, Altman, Bruns, Gatsonis, Hooft, Irwig, Levine, Reitsma, De Vet and Bossuyt2016).
Strengths and limitations
To our knowledge, this is the first attempt to synthesise evidence for the effectiveness of school-based identification models. The inclusion criteria were designed to encompass all existing identification models. The broad scope in terms of study design offered a more comprehensive and realistic understanding of the state of school-based identification than if we excluded ad-hoc identification methods. Second, the review included all age groups, from pre-school to secondary school, which allowed for comparison across school levels. Third, the review did not place any restriction on the type of MH condition, which allowed for cross-condition comparisons. Finally, in addition to exploring the effectiveness of the different models, we also examined referral and uptake rates. This is important because school-based identification models do not end at screening, and understanding the subsequent pathways to care is essential.
Notwithstanding, we acknowledge several limitations. First, the review only included studies published in English. Second, while we generally view our broad inclusion criteria as positive, the lack of exclusion based on study design led to the inclusion of several methodologically weak studies. While we kept a broad scope in terms of MHD, we did not include neurodevelopmental conditions or learning disabilities, which may be closely linked to MH problems. Finally, the quality and heterogeneity of included studies precluded meta-analysis or any other statistical summary. Future reviewers may seek to broaden the aims of the present review through the inclusion of these conditions.
This first comprehensive systematic review of the effectiveness of school-based models of identifying children at risk of, or experiencing MHD shows that the current evidence-base is very limited and does not support the recommendation of any particular model. Well-designed pragmatic trials that include the evaluation of cost-effectiveness and detailed process evaluations are necessary to establish the accuracy of different models, as well as effectiveness in connecting pupils to appropriate support in real-world settings.
(1) Precise rates of false positive and false negative results yielded by different identification methods need to be established through a reliable method of outcome verification (i.e. clinical assessment by MH professionals or standardised diagnostic assessment with or without clinical review)
(2) Research to establish which identification models work for younger children, including those under 5 years of age, are particularly needed.
(3) Studies are needed to evaluate and report the uptake of supportive services following positive identification to estimate additional demand on MH services.
(4) Detailed descriptions of evaluated identification programmes highlighting their ‘core components’ should be an essential part of every study, to ensure effective implementation and optimal outcomes once a programme is rolled-out.
(5) Effectiveness trials including process evaluation components (from identification to treatment) are needed to establish which models most accurately identify which conditions, and which external factors may influence programme outcomes. Identification models need to be tested in real life conditions to ensure that they are sustainable beyond the duration of a research project. Cost-effectiveness is an essential and currently under-studied component of this work of effectiveness trials.
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291718002490
J. Anderson led the systematic review including the development of methodology, literature searches, selection of studies for inclusion, data extraction, quality rating, synthesis of findings and writing manuscript drafts. E. Howarth and E. Soneson acted as second reviewers for selection of studies and data extraction, reviewed all manuscript versions, and made substantial revisions. E. Howarth made substantial contributions to writing introduction and discussion, particularly sections regarding implications for practice and directions for future research. E. Soneson acted as the second rater for the quality of studies appraisal. T. Ford advised on the scope of the review, particularly defining the search terms and specifying the inclusion/exclusion criteria, reviewed, and reviewed all manuscript versions. J. Thompson Coon and M. Rogers advised on methodology, particularly the development of search strategy, inclusion/exclusion criteria, and data extraction, and reviewed all manuscript versions, with a particular focus on methods. A. Humphrey reviewed all manuscript versions and contributed to writing the discussion. D. Moore and P. Jones reviewed all manuscript versions. E. Clarke, as a second reviewer, contributed to a screening of titles and abstracts. All authors read and approved the final version of the manuscript.
This paper presents independent research funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care (CLAHRC) East of England, at Cambridgeshire and Peterborough NHS Foundation Trust, and CLAHRC South West. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
Conflict of interests