typology of people with first-episode psychosis

Original Article

Typology of people with first-episode psychosisMythily Subramaniam,1 Huili Zheng,2 Pauline Soh,1 Lye Yin Poon,3 Janhavi A. Vaingankar,1

Siow Ann Chong1,3 and Swapna Verma3

1Research Division and 3Department ofEarly Psychosis Intervention, Institute ofMental Health, and 2Saw Swee HockSchool of Public Health, Singapore

Corresponding author: Dr MythilySubramaniam, Research Division, Instituteof Mental Health, Buangkok GreenMedical Park, 10 Buangkok View,Singapore 539747.Email: [email protected]

Received 17 May 2014; accepted 22 July2014

Abstract

Aims: The aim of the current studywas to create a typology of patientswith first-episode psychosis based onsociodemographic and clinical char-acteristics, service use and outcomesusing cluster analysis.

Methods: Data from all respondentswho were accepted into the EarlyPsychosis Intervention Programme(EPIP), Singapore from 2007 to 2011were analysed. A two-step clusteringmethod was carried out to classify thepatients into distinct clusters.

Results: Two clusters were identified.Cluster 1 comprised largely ofyounger people with mean age of 25.5(6.0) years at treatment contact, whowere predominantly male (55.3%),single (98.3%) and living with parents(86.3%). Cluster 1 had a higher pro-portion of people diagnosed with the

schizophrenia spectrum disorder(71.4%) and with a positive familyhistory of psychiatric illness. Patientsin cluster 2 were generally older with amean age of 33.6 (4.7) years and themajority were women (74.2%).Cluster 1 had people with higher Posi-tive and Negative Syndrome Scale(PANSS) scores at baseline as com-pared with cluster 2. After a 1-yearfollow up, their scores were stillpoorer than their counterparts incluster 2, especially for PANSS nega-tive score. The functioning level ofpeople in cluster 1 showed lessimprovement than the people incluster 2 after a year of treatment.

Conclusions: There is a compellingneed to develop new therapies andintensively treat young people pre-senting with psychosis as this grouptends to have poorer outcomes evenafter 1 year of treatment.

Key words: cluster analysis, first-episode psychosis, functioning, nega-tive symptoms, typology.

INTRODUCTION

Mental illnesses have largely been classified anddiagnosed using the Diagnostic and StatisticalManual of Mental Disorders, Fourth Edition (DSM-IV)1 criteria, wherein they are described in terms ofthe clinical manifestations of their illness withoutmuch regard to underlying neurobiological mecha-nisms. While the DSM-IV system provides a detailedclinical picture of signs and symptoms of variousdisorders; it is not as helpful for predicting the needfor or use of services2,3 or treatment response. Alter-natively, diagnosis-related groups (DRGs) are oftenused to classify patients based on illness episodes;however, DRGs are less useful in providing clinical

pictures or predicting the use of resources.4–6 Thus,there is increasing pressure to change the structureof psychiatric classification in order to acceleratebetter treatment, prevention and ultimately cure.7

The need for a new approach to diagnosis was per-ceived and included as a goal of the National Insti-tute of Mental Health leading to the launch of theResearch Domain Criteria project. This project aimsto create a framework to stimulate and organize theidentification of valid, reliable phenotypes (measur-able traits or characteristics) for mental disordersthat integrate biological and psychological compo-nents that are aimed at informing future classifica-tion schemes.8,9 Another approach for describingmental health-care users is in terms of clusters

Early Intervention in Psychiatry 2014; ••: ••–•• doi:10.1111/eip.12178

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based on characteristics otherwise known as typol-ogy. Typology refers to the study or systematic clas-sification of types that have characteristics or traitsin common, that is an abstract category or classcomprising characteristics organized around acommon principle relevant to a particular analysis.Categories or classes that are not inherentlytaxonomic are often formed by empiricallygrouping those who share features on severaldimensions using statistical methods such ascluster analysis.

Cluster analysis of those with mental illnessresults in subgroups that can be correlated withclinical and sociodemographic variables and pat-terns of service use.10 Studies have identified clus-ters among patients with schizophrenia treated inthe community,11 patients with dual diagnoses ofserious mental disorders and substance use12 andthose with severe mental illness served by a psychi-atric hospital.13 By identifying subgroups who sharesimilar clinical and sociodemographic characteris-tics, it may be possible to tailor treatment plans anddeliver appropriate services according to the needsof the patients.10,14

Psychotic disorders are severe mental healthillnesses characterized by hallucinations, delusionsand disorganized thinking. In general, psychosisimposes a great burden on the patients in termsof suffering, economic cost and disrupted inter-personal relationships.15–18 Many studies havealso shown that patients with psychosis experiencea substantial delay in receiving treatment resul-ting in adverse outcomes.19–21 Hence, reducingthe duration of untreated psychosis (DUP) wasthe premise behind the establishment of earlyintervention services and it has garnered muchsupport from the international community as pro-vision of such services could improve outcomesfor those with psychosis. Few studies have exam-ined the typology of patients with first-episodepsychosis (FEP). Studies have shown that remis-sion and recovery do vary significantly amongthese patients and are influenced by varioussociodemographic and clinical characteristics.22–24

Identification of specific clusters in this populationwith distinct needs would enable the elucidationof within-group heterogeneity that would resultin a more holistic, person-focused classificationand treatment, taking into context not onlytheir diagnosis, but also their personal and socialbackground.25

The aim of the current study was to create a typol-ogy of patients with FEP based on sociodemo-graphic and clinical characteristics, service use andoutcomes using cluster analysis.

METHODS

Sample

The Singapore Early Psychosis Intervention Pro-gramme (EPIP) was initiated in April 2001 and is acomprehensive, integrated, multidisciplinary andpatient-centred programme with the followingaims: to raise awareness of and reduce stigma asso-ciated with psychosis, establish collaboration withprimary health-care providers in the early detec-tion, referral and management of those with psy-chosis; and improve the outcome of patients withFEP. The programme provides psychophar-macological management as well as case manage-ment for a period of two years, after which patientsare referred to other community-based treatmentprogrammes in Singapore. Psychopharmacologicalmanagement is based on a treatment algorithm thatemphasizes use of antipsychotic monotherapy(either first- or second-generation) at low-dose.Clozapine is generally used after failed trials of twoor three antipsychotic medications. Depot medica-tions (first-generation or long-acting risperidone)are given when medication non-adherence is iden-tified. Adjunct medications such as antidepressantsand mood stabilizers are used to treat comorbidanxiety and mood symptoms. Each patient has acase manager who provides supportive counselling,psychoeducation, as well as coordinates the variousservices and ensures the continuity of care throughthe different phases of the illness. If required,patients are also referred to a psychologist, an occu-pational therapist, a family therapist or for groupinterventions.

Data were used from the clinical database main-tained at the EPIP department. This registered clini-cal database is anonymized and data have beencaptured since the initiation of the programme in2001. Data on all patients were collected both bycase managers and other clinicians involved in thecare of the patients prospectively. The studyinvolved retrospective analysis of data from allrespondents who were accepted in the programmefrom 2007 to 2011. This was to ensure the uniformityof the data collected as data collection forms wererevised in 2007. The study has been approved by theNational Healthcare Group Domain Specific ReviewBoard, Singapore.

Assessments

Sociodemographic data on age, gender, ethnicity,educational level, marital status, occupation andliving situation were collected using a semi-structured questionnaire by trained case managers.

Typology of first-episode psychosis

2 © 2014 Wiley Publishing Asia Pty Ltd

The case managers also collected a detailed historyon the pathways to care (i.e. the sources of helpsought in chronological order till the patients werereferred to EPIP).

Diagnosis of patients was established using theStructured Clinical Interviews for DSM-IV clinicalversion26 at the first contact (baseline) with EPIP.DUP was operationalized as the time (in months)between onset of psychotic symptoms (delusions,hallucinations, disorganized behaviour) and thetime when a definitive diagnosis was made andtreatment initiated. Patients and the primary car-egivers were interviewed by the clinical team andasked to date the onset of psychotic symptoms, andthe DUP was estimated after combining informa-tion from the interviews and case records.

Severity of psychopathology was assessed usingthe Positive and Negative Syndrome Scale (PANSS)27

for schizophrenia. The PANSS assesses the levels ofpositive, negative and general psychopathologysymptoms (GPS) that are associated with psychosis.It consists of 30 items scored on a 1 (absent) to 7(extreme) scale and a higher score reflects greaterpsychopathology. The Global Assessment of Func-tioning (GAF) was used to assess level of function-ing.28 It is a numeric scale that ranges from 0 to 100and used to rate the social, occupational andpsychological functioning of adults. A higher scorereflects higher level of functioning. All raters partici-pated in periodic interrater reliability sessions toavoid rater drift. The interrater reliability wasassessed to be 0.94.

Statistical analysis

Cluster analysis was used to classify 900 early psy-chosis patients accepted into EPIP from April 2007–2011 into clusters. Cluster analysis was used as theaim of this exploratory study was to create a typologyof patients with FEP after identifying patients’groups sharing similar sociodemographic and clini-cal characteristics based on certain observed vari-ables. The following variables were used to identifythe specific clusters: age, gender, ethnicity, educa-tion level, living situation, marital status, familyhistory of psychiatric illness, DUP, first point ofcontact, number of contact points before admission,and baseline GAF total score, PANSS positive score,PANSS negative score and PANSS GPS score. Thesevariables were selected as we believed they wouldclassify the patients well based on literature reviewand experience. A two-step clustering method wascarried out to classify the patients into various dis-tinct clusters. This method was chosen as it is theonly clustering procedure that can take into account

different types of variables simultaneously.29 For thefirst step, we computed a data matrix for all the vari-ables involved in the clustering procedure using thelog likelihood as the distance measure. In the secondstep, a probabilistic hierarchical cluster analysis wasperformed to determine the optimal number of clus-ters across different clustering solutions based onBayesian information criteria.

After grouping the patients into clusters, differ-ences between the clusters were analysed usingindependent t-tests for quantitative variables thatwere normally distributed, Mann–Whitney U-testfor quantitative variables that were not normallydistributed, and Pearson chi-squared test for quali-tative variables. All analyses were performed usingthe Statistical Package for the Social Sciences (SPSS)software, version 18 for Windows (SPSS Inc.,Chicago, IL, USA).

RESULTS

The 900 patients were classified into two distinctgroups whereby there were 714 patients in onecluster, and 163 patients in the other cluster. Theremaining 23 patients were not included in eithercluster because of missing values among the vari-ables that were used to form the clusters. Table 1summarizes the characteristics of the sample of900 patients, while Tables 2 and 3 present thesociodemographic and clinical characteristics forthe patients in each cluster.

Cluster 1 comprised largely of younger peoplewith a mean age of 25.5 (6.0) years at treatmentcontact who were predominantly male (55.3%),single (98.3%) and living with parents (86.3%). Withregard to clinical aspects, cluster 1 had a higher pro-portion of people diagnosed with schizophreniaspectrum disorder (71.4%) and a positive familyhistory of psychiatric illness (27.6% as comparedwith 19.0% in cluster 2). Most patients (39.4%) inthis cluster were referred to EPIP by their family,relatives or friends as their first line of help. An inter-esting point to note is that the proportion ofpatients brought in by counsellors was higher forthis cluster (4.3%) than in cluster 2 (0.6%).

Patients in cluster 2 were generally older with amean age of 33.6 (4.7) years at contact with the pro-gramme and the majority were women (74.2%). Theproportions of people with primary, secondary ortertiary level of education as their highest educa-tional level were higher than those in cluster 1. Mostpeople in this cluster were married (85.9%) and livedwith their spouses (73.6%). With respect to clinicalcharacteristics, a significant proportion of people

M. Subramaniam et al.


TABLE 1. Sociodemographic and clinical characteristics of first episode psychosis patients (N = 900)

Variables N Percentage†

Age (years)Mean ± SD 27.1 ± 6.515–19 122 13.620–24 247 27.425–29 194 21.630–34 183 20.335–41 154 17.1

GenderMale 446 49.6Female 454 50.4

EthnicityChinese 678 75.3Malay 123 13.7Indian 69 7.7Others 30 3.3

Education level‡No education 8 0.9Primary 97 10.8Secondary 300 33.4Pre-university 70 7.8Tertiary 308 34.3ITE 107 11.9Others 9 1.0

Living situationAlone 33 3.7With husband/wife (± children) 123 13.7With a partner 12 1.3With parents 654 72.7With siblings 36 4.0With relatives 18 2.0Others 24 2.7

Marital status§Single/unmarried 717 80.3Married 142 15.9Separated 9 1.0Divorced 24 2.7Others 1 0.1

Employment statusPaid/self-employment 226 25.1Sheltered employment 2 0.2Unemployed 371 41.2Student 171 19.0Homemaker 52 5.8Others 78 8.7

DiagnosisSchizophrenia spectrum

(schizophrenia, schizophreniform,schizoaffective)

625 69.4

Variables N Percentage†

Bipolar disorder (± psychotic features) 68 7.6Delusional disorder 40 4.4Brief psychotic disorder 77 8.6Psychosis NOS 53 5.9Depression (with psychotic features) 37 4.1

Family history of psychiatric illness¶Yes 229 25.7No 663 74.3

DUP (months)Since onset of symptoms, median

and IQR5 (1, 12)

Since change in behaviour, medianand IQR

6 (2, 24)

First point of contactGP/polyclinic/other primary care

doctors160 17.8

Hospital 128 14.2Tradition/religious treatment 1 0.1Self 54 6Counsellors 33 3.7Police 111 12.3Court 4 0.4Family/relatives/friends 371 41.2EPIP 3 0.3IMH A&E 3 0.3Others 32 3.6

Number of contact points beforeadmission

Mean ± SD 3.2 ± 0.91 35 ± 3.92 97 ± 10.83 550 ± 61.14 147 ± 16.35 58 ± 6.46 11 ± 1.27 2 ± 0.2

GAF total score (mean ± SD) 40.0 ± 12.5GAF symptom score (mean ± SD) 40.9 ± 12.9GAF disability score (mean ± SD) 46.0 ± 12.6

PANSS total score (mean ± SD) 70.5 ± 18.3PANSS positive score (mean ± SD) 20.6 ± 6.2PANSS negative score (mean ± SD) 13.7 ± 7.4PANSS GPS score (mean ± SD) 36.4 ± 10.1

†Based on valid percentage.‡With one missing data.§With seven missing data.¶With eight missing data.DUP, Duration of Untreated Psychosis; EPIP, Early Psychosis Intervention Programme, Singapore; GAF, Global Assessment of Functioning Scale; GP, generalpractitioner; GPS, general psychopathology symptoms; IMH A&E, Accident and Emergency Department of the Institute of Mental Health; IQR, interquartilerange; ITE, Institute of Technical Education; NOS, Not Otherwise Specified; PANSS, Positive and Negative Syndrome Scale; SD, standard deviation.



were diagnosed with schizophrenia spectrum disor-ders (62.6%), delusional disorder (11.7%) or briefpsychotic disorder (11.0%). About half of thepatients (49.1%) in this cluster were referred to EPIPat the first instance by their family or friends.

Cluster 1 had people with higher PANSS scores atbaseline as compared with cluster 2. After 1-yearfollow up with EPIP, their scores were still poorerthan their counterparts in cluster 2, especially forPANSS negative score. The functioning level of

TABLE 2. Sociodemographic characteristics of first episode psychosis patients, stratified by cluster (N = 877)

Variables Cluster 1 (n = 714) Cluster 2 (n = 163) P-value

N Percentage† N Percentage†

Age (years)*Mean ± SD 25.5 ± 6.0 33.6 ± 4.7 <0.001

15–19 122 17.1 0 0.020–24 232 32.5 9 5.525–29 164 23.0 21 12.930–34 130 18.2 49 30.135–41 66 9.2 84 51.5 <0.001

Gender*Male 395 55.3 42 25.8Female 319 44.7 121 74.2 <0.001

Ethnicity*Chinese 548 76.8 110 67.5Malay 96 13.4 25 15.3Indian 50 7.0 19 11.7Others 20 2.8 9 5.5 0.037

Education level*‡No education 4 0.6 4 2.5Primary 70 9.8 26 16.0Secondary 238 33.3 56 34.4Pre-university 63 8.8 4 2.5Tertiary 239 33.5 58 35.6ITE 94 13.2 12 7.4Others 6 0.8 3 1.8 0.001

Living situation*Alone 29 4.1 2 1.2With husband/wife (± children) 0 0.0 120 73.6With a partner 4 0.6 8 4.9With parents 616 86.3 24 14.7With siblings 34 4.8 0 0.0With relatives 16 2.2 2 1.2Others 15 2.1 7 4.3 <0.001

Marital status*§Single/unmarried 702 98.3 2 1.2Married 0 0.0 140 85.9Separated 2 0.3 7 4.3Divorced 10 1.4 13 8.0Others 0 0.0 1 0.6 <0.001

Employment statusPaid/self-employment 162 22.7 56 34.4Sheltered employment 2 0.3 0 0.0Unemployed 305 42.7 53 32.5Student 168 23.5 1 0.6Homemaker 0 0.0 52 31.9Others 77 10.8 1 0.6 <0.001

*Variables used to compose the clusters.†Based on valid percentage for each cluster.‡With one missing data.§With seven missing data.ITE, Institute of Technical Education; SD, standard deviation.



TABLE 3. Clinical and admission related characteristics of first episode psychosis patients, stratified by clusters (N = 877)

Variables Cluster 1 (n = 714) Cluster 2 (n = 163) P-value

N Percentage† N Percentage†

DiagnosisSchizophrenia spectrum (schizophrenia,

schizophreniform, schizoaffective)510 71.4 102 62.6

Bipolar disorder (± psychotic features) 59 8.3 6 3.7Delusional disorder 20 2.8 19 11.7Brief psychotic disorder 57 8.0 18 11.0Psychosis NOS 41 5.7 10 6.1Depression (with psychotic features) 27 3.8 8 4.9 <0.001

Family history of psychiatric illness*Yes 197 27.6 31 19.0No 517 72.4 132 81.0 0.024

DUP (months)Since onset of symptoms* (median and IQR) 5 (1, 12) 4 (1, 12) 0.067Since change in behaviour (median and IQR) 6 (2, 24) 6 (1, 13) 0.035

First point of contact*GP/polyclinic/other primary care doctors 131 18.3 27 16.6Hospital 103 14.4 22 13.5Traditional/Religious Healers 1 0.1 0 0.0Self 42 5.9 9 5.5Counsellors 31 4.3 1 0.6Police 87 12.2 20 12.3Court 4 0.6 0 0.0Family/relatives/friends 281 39.4 80 49.1EPIP 3 0.4 0 0.0IMH A&E 2 0.3 1 0.6Others 29 4.1 3 1.8 0.253

Number of contact points before admission†Mean ± SD 3.2 ± 0.9 3.2 ± 0.91 25 ± 3.5 7 ± 4.32 85 ± 11.9 12 ± 7.43 431 ± 60.4 104 ± 63.84 120 ± 16.8 23 ± 14.15 42 ± 5.9 15 ± 9.26 9 ± 1.3 2 ± 1.27 2 ± 0.3 0 ± 0.0 0.394

Baseline GAF total score* (mean ± SD) 40.0 ± 12.7 40.0 ± 11.9 0.996Baseline GAF symptom score (mean ± SD) 40.9 ± 13.1 40.8 ± 12.3 0.925Baseline GAF disability score (mean ± SD) 45.9 ± 12.6 46.8 ± 12.7 0.374

Baseline PANSS total score (mean ± SD) 71.4 ± 18.6 66.9 ± 16.5 0.005Baseline PANSS positive score* (mean ± SD) 20.6 ± 6.2 20.7 ± 5.6 0.763Baseline PANSS negative score* (mean ± SD) 14.4 ± 7.5 11.0 ± 5.6 <0.001Baseline PANSS GPS score* (mean ± SD) 36.6 ± 10.3 35.2 ± 9.2 0.101

One-year GAF total score¶¶ (mean ± SD) 68.8 ± 14.1 70.9 ± 12.8 0.175One-year GAF symptom score¶¶ (mean ± SD) 70.9 ± 13.9 71.8 ± 13.0 0.561One-year GAF disability score¶¶ (mean ± SD) 69.7 ± 13.7 73.0 ± 12.2 0.034

One-year PANSS total score¶¶ (mean ± SD) 41.5 ± 14.1 39.9 ± 13.0 0.306One-year PANSS positive score¶¶ (mean ± SD) 9.5 ± 4.0 9.8 ± 4.6 0.438One-year PANSS negative score¶¶ (mean ± SD) 10.0 ± 5.0 8.8 ± 3.9 0.014One-year PANSS GPS score¶¶ (mean ± SD) 22.1 ± 7.2 21.3 ± 6.9 0.346

*Variables used to compose the clusters.†Based on valid percentage for each cluster.¶¶284 missing data in cluster 1, 72 missing data in cluster 2.DUP, Duration of Untreated Psychosis; EPIP, Early Psychosis Intervention Programme, Singapore; GAF, Global Assessment of Functioning Scale; GP, generalpractitioner; GPS, general psychopathology symptoms; IMH A&E, Accident and Emergency Department of the Institute of Mental Health; IQR, interquartilerange; NOS, Not Otherwise Specified; PANSS, Positive and Negative Syndrome Scale; SD, standard deviation.



people in cluster 1 showed less improvement thanthe people in cluster 2 after a year of treatment asreflected by their significantly lower GAF disabilityscores (Table 3).

DISCUSSION

The study was designed to develop a typology of theFEP patients, which would contribute towardsimproving mental health-care service planning inthe future. Results identified predominantly twoclusters that differed significantly in terms ofsociodemographic and clinical characteristics.

Cluster 1 comprised mostly younger men with anearly onset of schizophrenia spectrum disorders,while cluster 2 comprised mainly of women whowere older and married when they first presentedfor treatment. This is consistent with past literature,which strongly suggests a gender difference in menhaving an earlier age at onset of schizophrenia.30

While, in our study ‘age at onset’ refers to ‘age at firstcontact with treatment service’, there was no signifi-cant difference in the DUP among the two clusters.This is however different from results that emergedfrom the Danish OPUS study31 that did not find sig-nificant differences in the mean age of onset or firstcontact between male and female patients. Reasonsfor the early age of onset in men may be due toeither biological or cultural/environmental factors.As in some cultures, social interaction of womenlargely tend to occur within the family group, whilemen’s roles involve more external interactions, it ispossible that women’s symptoms might be con-tained within the family and are less likely to beperceived as the manifestation of mental illness. It isalso possible that men present with more positivesymptoms and/or aggression, while women presentwith social withdrawal or functional decline, andthus symptoms in men are likely to come to atten-tion earlier. Gender differences may also existbecause of differential ability to conceal symptomsand the differential societal perception of ‘normalbehaviours’ among men and women. Thus, a delayin the recognition of the early symptoms of schizo-phrenia in women might result in a systematic biasin reporting onset in women as occurring later thanmen. There are other factors that could influenceage of onset of psychiatric illnesses. Jablensky andCole32 emphasized that marriage may be a con-founding factor. They postulated that marriagecould work both through a ‘selection’ and a ‘protec-tion’ mechanism. In our study, there was a higherproportion (27.6%) of people with family history ofpsychiatric illness in cluster 1. Esterberg and

Compton33 examined the impact of family historyon age of onset of patients with FEP and found thatFEP patients with a family history of psychosis had ayounger age at onset of the prodrome as well as ayounger age at onset of psychosis. These findingsare in line with much of the extant literature,34–36

which found that patients with a family history ofpsychosis have a significantly younger age at onsetrelative to patients with no family history. Interest-ingly, gender differences in age at onset are notobserved in samples with a family history,36 whichcould partially account for the early age of onset insome of the women in cluster 1.

Cluster 2, comprising mainly of women in their30s, is a smaller cluster and raises the possibility thatFEP programmes by limiting the age group forinclusion in their programmes, may be excludingpeople with a later age of onset who could benefitfrom the specialized services. It would not only beimportant to include this group for early interven-tion, but it would be vital to understand the riskfactors, disease progression and outcomes in thisgroup.

The baseline PANSS total scores in particular thePANSS negative scores were higher among those incluster 1 than those in cluster 2, and these changespersisted at the 1-year follow up. While the GAFscores were comparable in both clusters at baseline,the functioning level of the people in cluster 1showed less improvement than the people in cluster2 after a year of treatment. Studies of family loadingfor schizophrenia have shown that positive familyhistory of psychosis not only influences illness lia-bility, but also clinical outcome.37 Some studies havealso shown a moderate relationship between familyhistory of psychosis and gender with respect tonegative symptoms.36 Men, as opposed to women,have been shown to have higher rates of corticalatrophy and increased ventricle–brain ratios, whichhas been associated with cognitive impairment,poor premorbid adjustment and more negativesymptomatology.38 It is thus possible that the higherproportion of men and patients with family historyof mental illness in cluster 1 could have resulted inmore negative symptoms and poorer outcomes.

Despite advances made in treating the positivesymptoms of schizophrenia, treatment of negativesymptoms remains an unmet therapeutic need.Studies suggest that it is mainly the negative symp-toms that mediate the influence of neurocognitionand social cognition on functional outcome ofschizophrenia.39 Thus, there is a compelling need tonot only develop new drugs that specifically targetnegative symptoms, but also to explore other inter-ventions like cognitive remediation therapy (CRT)



to improve impaired social and occupational func-tioning of patients with schizophrenia. Severalstudies have shown positive results of CRT amongpeople with schizophrenia. Tan and King40 reportedsignificantly greater improvement in all neuro-cognitive domains among those receiving CRT andgreater attainment of vocational or independentliving skills and better functional outcomes at post-intervention follow-up. Garrido et al.41 reportedimprovements in neuropsychological performanceand in quality of life and self-esteem measurementsamong people with schizophrenia who underwentCRT. Other approaches to the treatment of schizo-phrenia have evolved from research on cognitivebiases and distortions evident in psychosis, includ-ing metacognitive therapy. Studies have demon-strated both improvements in PANSS scores as wellas in metacognitive beliefs after therapy.42,43 Thesemust be explored in larger randomized controlledtrials among those with psychosis.

We found that in both clusters, majority of thepatients turned to their family as their first point ofhelp. This was even more evident in cluster 2, wherealmost half of the patients were brought in for help bytheir family.The next most common source of help inboth clusters were the general practitioners, and thepatients in cluster 1 also sought help from theirschool counsellors. These data would be beneficialin planning early detection and outreach pro-grammes where we would have to develop strategiestargeting these three important groups. While plan-ning such programmes, it would be imperative torecognize that patients belonging to cluster 2 have adifferent profile from those in cluster 1. Since itsinception, EPIP has championed an ongoing publiceducation campaign, which comprises a multi-pronged approach with different modes of commu-nication and education. The aim of providing thepublic with information on the manifestation of psy-chosis and the importance of early treatment was toencourage help-seeking behaviour. As part of theoutreach EPIP also educates and actively engagesprimary health-care providers who are often the firstpoint of contact for those with mental illness. Edu-cation is provided through regular talks, forums andworkshops, as well as a bimonthly newsletter tothese groups of professionals.44 This, outreachshould not be limited exclusively to young peopleand training the teachers and counsellors in schoolsand tertiary educational institutions. It would beequally, if not more important, to reach out to thegeneral population and families through the use ofmass media and public educational campaigns. Astudy from the Treatment and Intervention in Psy-chosis found that when their awareness campaign

was stopped, there was a clear regressive change inhelp-seeking behaviour with a consequent increasein DUP45 and hence ongoing public awarenesscampaigns are essential and necessary for earlydetection. These outreach programmes must beconstantly assessed for their effectiveness, and ifneeded, changes must be made such that theyremain relevant to those seeking help.

The study has certain limitations. As this was anaturalistic study, there was considerable missingdata especially in the outcome-related variables.Some patients were ‘unclassified’, that is they did notbelong to either cluster. Family history was not cap-tured using a structured instrument, but was basedon patient’s/family’s self-report. Data like ‘familyhistory of mental illness’, DUP, pathways to helpseeking, are all based on self- or family report, andare subject to recall bias and social desirability.Cluster 2 was a relatively small cluster and there is apossibility that our conclusions are not valid. Lastly,our study was conducted in a predominantlyChinese population of early psychosis patients, andthus our results may not be generalizable to otherpopulations. The strengths of the study are the largesample size, recruited from a single site and assessedsystematically using structured instruments by atrained clinical team. The use of a hierarchicalcluster analysis, which is an exploratory approach(vs. a more hypothesis-driven one), has revealedsome unique findings as it groups observations orvariables without preconceived divisions and thus isable to identify naturally occurring groups in thedata beyond the narrow focus of categorical diagno-sis of schizophrenia or other psychotic disorders.

In conclusion, our study has shown two ratherdistinct clusters of patients presenting with FEP to aspecialized treatment programme in a multi-ethnicAsian country. While some of the differences in thetwo clusters may be accounted for by gender differ-ences between the clusters, family history and out-comes of the patients are also significantly differentbetween the clusters. There is a compelling need todevelop new therapies and intensively treat youngpeople presenting with negative symptoms at theirfirst contact as this group tends to have poorer out-comes even after 1 year of treatment.

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typology of people with first-episode psychosis

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