Social networks and functional status inpatients with psychosis

Howard L, Leese M, Thornicroft G. Social networks and functionalstatus in patients with psychosis.Acta Psychiatr Scand 2000: 102: 376±385. # Munksgaard 2000.

Objective: To investigate whether large social networks are associatedwith better functional outcome in psychotic disorders.Method: A prospective cohort study of epidemiologically representativepatients with psychosis was analysed to examine the relationshipbetween social networks and functional outcome. Standardized vali-dated measures were used for diagnosis, social networks and outcome.Results: There was strong evidence of cross-sectional associationsbetween the size of the social network and the number of activeinteractions, and functional outcome. Structural equation modellingfound weak evidence of an additional longitudinal association.Conclusion: There was consistent evidence of a positive correlationbetween the total size of patients' social networks and the number ofactive interactions, and functional outcome. The direction of causalitycannot be determined by this analysis, although it is suggested that theeffect of social networks on functioning is stronger than vice versa.Patients' functioning may improve with interventions that improvesocial networks.

Louise Howard, Morven Leese,Graham Thornicroft

Section of Community Psychiatry (PRiSM), Institute of

Psychiatry, De Crespigny Park, London, UK

Key words: disability evaluation; psychotic disorders;

social support

Louise Howard, MPhil, MRCP, MRCPsych, Wellcome

Trust Research Fellow, Section of Community

Psychiatry (PRiSM), Institute of Psychiatry, De

Crespigny Park, London, SE5 8AF, UK

Accepted for publication April 25, 2000

Introduction

Recent research has found that poor social supportis associated with psychological and physical mor-bidity and mortality (1). It is therefore plausible tohypothesize that social support may be associatedwith the functional status of patients with psy-chotic disorders. There are several differences bet-ween the social networks of patients with psychosiscompared with the general population. Patientswith schizophrenia have smaller social networks,with a larger proportion of relatives in theirnetworks, and patients may not ®nd their socialcontacts supportive or helpful (2). There is alsosome evidence that women patients have largerand richer social networks and that patients withaffective disorder describe larger networks thanpatients with schizophrenia (3).

Most research into social networks and severemental illness has focused on the relationshipbetween social networks and use of services (2, 4).There are few studies of the relationship betweensocial networks and functional outcome, but these

have found social isolation to be associated withpoorer outcome in schizophrenia (5). One follow-upstudy of schizophrenia has found that the presenceof a supportive relative is associated with goodoutcome (6) and Strauss and Carpenter (7) foundthat social support was associated with fewersymptoms and less hospitalization on follow-up,although social support was not measured in astandardized way. Good outcome in patients withbipolar affective disorder has also been associatedwith higher levels of social support (8). However,most of these studies have not used standardizedvalidated measures of diagnosis, social support orfunctional outcome. Study populations have beenof patients in contact with in-patient hospitalservices who may not be representative of allpatients with psychosis.

The aim of the present study was to investigatecross-sectional and longitudinal associationsbetween social networks and functional outcomein epidemiologically representative cases of psycho-sis over a 2-year follow-up period, with a main

Acta Psychiatr Scand 2000: 102: 376±385Printed in UK. All rights reserved

Copyright # Munksgaard 2000

ACTA PSYCHIATRICASCANDINAVICAISSN 0001-690X

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hypothesis that larger social networks are positivelyassociated with better functional outcome.

Material and methods

Study design

The PRiSM psychosis study was a non-randomized,controlled trial of two types of sectorized commu-nity mental health services in South London andcase identi®cation was designed to establish epide-miologically representative cases. The patientcohort was interviewed at time 1 (t1) (1992±4)and time 2 (t2) (2 years after t1 for each patient)with a number of standardized and validatedinstruments. The study areas were similar on anumber of population characteristics that are mostassociated with psychiatric morbidity (ethnicity,unemployment, age and Jarman score) (9). At thetime of the case identi®cation study, services weremainly hospital-based and community services weredeveloped through the course of the study (9).

Patient sample

Case identi®cation was established by obtainingstaff reports and case notes from multiple services(including psychiatric services, primary care, socialservices, chuches, police, prisons and homelesshostels (9)). Available records were screened usingthe Operational Criteria Checklist (OPCRIT, ver-sion 3.2) (10). Patients were included in the study iftheir notes generated an OPCRIT diagnosis of afunctional psychotic disorder, i.e. schizophrenia(ICD-10 (11) codes F20.0, F20.1, F20.3), delusionaldisorders (F22.0), affective psychoses (F25.0, F25.1,F30.2, F31.2, F31.7, F32.3, F33.3) and non-organicpsychoses of unknown aetiology (F29). The Sche-dule for Clinical Assessment in Neuropsychiatry(SCAN) (12) (lifetime) was administered at t1(1992±3) to supplement the OPCRIT data inestablishing patient diagnosis; patients wereexcluded if neither OPCRIT nor SCAN indicatedan ICD-10 diagnosis of psychotic illness. Where theschedules disagreed on whether there was apsychotic diagnosis, OPCRIT diagnoses took pre-cedence, so that patients with evidence of a historyof psychosis could be included even if they were notcurrently symptomatic. However, if there werediscrepancies between codes for psychosis SCANwas given preference, as it was based on moredetailed psychopathological information.

Five hundred and fourteen patients were identi-®ed with an ICD-10 psychotic diagnosis living inthe catchment areas in the index year. A randomsample was generated for each catchment area, andthe resulting group of 302 patients were allocated

for interview. An additional interview was carriedout with a carer or close relative and a clinician withcurrent knowledge of the patient. The patients whowere interviewed were representative of all thoseidenti®ed, in terms of a number of clinical, ethnicand social characteristics (9).

The interviews included:

(1) Schedules for Clinical Assessment in Neuro-psychiatry (SCAN) (12) (t1 only).

(2) Social Network Scale (SNS) (13) Ð this inter-view measure details total network size andnetwork subgroups (e.g. friends, relatives, non-friends (i.e. acquaintances, health staff who arenot friends, etc.), and the intensity of interac-tions (active, passive and intermediate (i.e.performance of a task/service with/for someonewithout any signi®cant verbal interchange)contacts) from the patient during the previousmonth.

(3) Global Assessment of Functioning (GAF) (14)Ð this staff interview measures general levelsof functioning in the previous month on a 1±90scale. Scores above 60 indicate the patient isgenerally functioning well. The two subscalesÐ the symptom scale and the disability scale Ðcan be used separately and are also scored from0±90. The disability scale was used here tomeasure functional outcome in social, occupa-tional and personal care domains.

(4) Social Behaviour Scale (SBS) (15) Ð this is adetailed measure of functioning describing 21problem areas which can limit rehabilitationpotential. In addition to a total score andindividual items a severe behaviour score (BSS)and mild and severe problems score (BSM) canbe generated from cut-offs on each of the 21behaviour ratings.

(5) Sociodemographic data including age, age at®rst illness, sex, ethnicity, social class andfamily history of mental disorder. Ethnicitywas recoded as a binary variable for structuralequation models. Information on substancemisuse was only available by collapsing theindividual variables from OPCRIT (lifetimediagnosis of alcohol or drug dependence,alcohol or drug abuse with psychopathology,alcohol or drug abuse in the index year) into asingle variable which was de®ned as any recordof substance misuse on any of these measures.

Information on informal and formal care inputsand on living situation over the follow-up periodwas obtained using interview data where available,local computerized databases, hospital case notes,discussions with key workers and contact withgeneral practitioners (GPs). Vigorous attempts were

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377

made to obtain follow-up data, including up toseven home visits to interview some people (16).

Data analysis

A descriptive analysis investigated the distributionof the variables under investigation. Most socialnetwork variables were positively skewed and weretherefore log-transformed to achieve approximatenormality where possible (1 was ®rst added to theraw scores, as some scores were 0). Variablestransformed were total size of network, numberof contacts with friends and con®dants and numberof active interactions. Social behaviour scores werelog-transformed to achieve approximate normality.Geometric means are given for the results ofanalyses using these transformed data. GAF dis-ability scores were squared.

Data analysis was carried out using Stata version5 (17) and EQS (18); 95% con®dence intervals (CIs)are quoted; P values are all two-sided. Comparisonsof responders and non-responders and subsequentunivariate analyses used t-tests for quantitativevariables that were approximately normally dis-tributed, non-parametric tests for other quantitativevariables, and chi-squared tests for proportions.Cross-sectional (t1 and t2) and longitudinal asso-ciations were initially investigated using linearregression. Beta coef®cients (standardized coef®-cients) are reported to allow comparison ofregression coef®cients within a model. Partial F-tests were used to examine the goodness-of-®t ofregression models for each additional variableadded. Where subjects had some missing valuesthese subjects were not included in multivariatemodels assessing the variables concerned, so thatgoodness-of-®t comparisons could be made on thebasis of nested models.

Structural equation modelling, i.e. modellingmultiple causal relations with multiple equations(EQS software (18)), was used to estimate a varietyof models describing the longitudinal dataset. Theadvantage of this technique is that direct andindirect effects can be modelled (e.g. social networks(t1) affecting GAF disability indirectly throughsocial networks at t2). EQS uses an observedcovariance matrix of all the variables, dependentand independent, speci®ed as input. A series oflinear equations for each dependent variable isspeci®ed by the user, and these imply a simpli®edcovariance structure, the parameters of which areestimated. These estimates and the residuals of theobservations from the ®tted values are standardizedso that they are independent of the scale ofmeasurement; they are thus similar in interpretationto beta coef®cients in ordinary regression.

Maximum likelihood techniques were used(assuming multivariate normality of the variables),and chi-square statistics were used to test overallgoodness-of-®t, where the null hypothesis is that thecovariance matrix is of the simpli®ed form predictedby the model. The Bentler±Bonett Normed Fit Indexwas also used (values >0.9 indicating a satisfactory®t). Likelihood ratio tests were carried out tocompare nested models by subtracting the secondmodel's goodness-of-®t chi-squared statistic fromthat of the ®rst with corresponding subtraction ofthe degrees of freedom (19).

Results

Data description

The sample. There were 143 (47%) men and 159(53%) women in the sample selected for interview.The median age was 40 (range 15±89, 25th quartile29, 75th quartile 55). One hundred and ninety-three (64%) patients were white, 74 (24%) wereblack Caribbean, 17 (6%) black African, 14 (5%)other and data were missing on ethnicity in fourcases. Two hundred and twenty-three (74%)patients had a diagnosis of functional psychosis,53 (17%) affective psychosis and 26 (9%) otherpsychosis. OPCRIT ratings of the course of thedisorder showed that 15 patients (5%) had had asingle episode, 87 (29%) had had multiple episodeswith good recovery, 67 (22%) had had multipleepisodes with partial recovery, 110 (36%) hadcontinuous chronic illness and four had chronicillness with deterioration; 19 (6%) patients were ofunknown status.

Comparison of responders and non-responders. Onehundred and eighty-eight (62%) subjects com-pleted the SNS schedule at t1 and 151 (50%) at t2.One hundred and twenty-nine (43%) patientscompleted the SNS at both time points. Twohundred and thirty (76%) GAF and SBS ratingswere made at t1 while 215 (71%) were made at t2;189 (63%) schedules were completed at t1 and t2.Table 1 compares the clinical and socialcharacteristics of responders to all SNS and GAF/SBS measures at t1 and t2 (n=108) with non-responders (i.e. patients who completed 0±3 of thefour measures) (n=194). There were no differencesbetween the two groups other than some weakevidence that non-responders were more likely tobe women (P=0.06). The responders were pre-dominantly in social classes III and IV. Pre-illnesssocial class could not be determined for the non-responders.

Patients who completed social network question-naires at both time points were compared with

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patients who completed the questionnaire at t1 only.Chi-squared tests indicated no evidence of differ-ences between these two groups on any of thecategorical variables listed in Table 1. However,there was some weak evidence that non-respondersat t2 were older with median age 46 (range 18±89)compared to responders at both time points, withmedian 39 (range 15±73) (Mann±Whitney testP=0.07).

Changes over time

Social networks. Wilcoxon matched pairs testswere used to compare social network data at t1and t2 (n=129) (Table 2). There was very strongevidence of increases in the total network size, andthe number of contacts with friends, non-friends,relatives, con®dants and the number of activeinteractions. There was also some weak evidenceof a change in the number of intermediateinteractions but no evidence of a change in thenumber of passive interactions.

Global assessment of functioning (GAF) scores.There was no evidence of overall changes in totalGAF scores over time (n=184). The mean totalGAF score at t1 was 58.6 (SD 17.1); at t2 it was60.5 (SD 18.1); t-test: t (182)=1.26; P=0.21. GAFscores at t1 and t2 were very strongly associated:

correlation coef®cient=0.40 between total GAFscores at t1 and t2; P<0.001. There was no evi-dence of a difference between GAF disabilityscores over time (the mean total disability scoreat t1 was 2.1 (SD 11.8) and at t2 it was 64.3(SD 12.5); P=0.12). The disability scores overtime were also strongly associated (correlationcoef®cient=0.32 for GAF disability scores at t1and t2; P<0.001).

Social behaviour schedule (SBS) scores. Therewas strong evidence of an improvement in totalSBS from t1 to t2 (n=190). The median at t1was 8 (range 0±47), compared to a median 5 att2 (range 0±40); Wilcoxon signed-rank testP=0.001. This appeared to be due to animprovement in milder behaviour problems inparticular, as the BSM scores improved from t1to t2 (P=0.003), whereas there was no evidenceof an improvement in BSS scores (P=0.14).

Association between functional outcome and exposure variables

Univariate analyses (Tables 3 and 4). There wasevidence that black patients, particularly blackAfrican patients, tended to have better functionaloutcome (GAF disability t2) and very weak evi-dence of an association between female sex andbeing in the intensive sector, and better GAF

Table 1. Characteristics of responders* and non-responders

Characteristics Responders (n=108) Non-responders (n=194) P value

Age, median (range); (interquartile range) 39 (15±71); (19±67) 41 (16±89); (18±81) P=0.45 (Mann±Whitney test)

Sex

Female 49 (45%) 110 (57%) x2=3.57; P=0.06

Male 59 (55%) 84 (43%)

Diagnosis

Functional psychosis 80 (74%) 144 (74%) x2=0.70; P=0.71

Affective psychosis 17 (15%) 35 (18%)

Other psychosis 11 (10%) 15 (8%)

Ethnicity

White 65 (60%) 128 (66%) x2=3.15; P=0.37

Black Caribbean 33 (31%) 41 (21%)

Black African 6 (6%) 11 (6%)

Other 4 (4%) 10 (5%)

Not known 0 (0%) 4 (2%)

Marital status

Married/cohabiting 21 (19%) 44 (223%) x2=0.63; P=0.43

Single/divorced/widowed/separated 87 (816%) 144 (74%)

Not known 0 (0%) 6 (3%)

Accommodation

Unsupported 84 (78%) 145 (75%) x2=1.31; P=0.25

Supported 21 (19%) 25 (139%)

Not known 3 (3%) 24 (12%)

GAF at time 1 (mean, SD) 61.1 (16.7) 58.4 (17.6) t=x1.19; P=0.23

Admissions per year (from ®rst contact: 1.4.92) 0 (0±3) 0 (0±2) P=0.17 (Mann±Whitney test)

(median, range) n=88 n=158

Substance misuse 23 (21%) 30 (16%) x2=1.63; P=0.20

* To SNS, GAF and SBS at time 1 and time 2.

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379

disability. The total size of the network and thenumber of active interactions were the only socialnetwork variables that were consistently associatedwith GAF disability (i.e. t1 and t2).

Ethnicity was not associated with total size ofnetwork at t1 (P=0.53) or number of active inter-actions (P=0.14). Sex was not associated with thesize of the network (P=0.11) but there was strongevidence that women had more interactions that wereactive (mean=9.87, SD=1.09) compared with men(mean=7.60, SD=1.09): P=0.03. As ethnicity andsex are very strong predictors of outcome in psychosisthey are included in the regression models below.

Analyses were repeated for the log-transformedSBS outcome measure and primary exposures(n=190). There was strong evidence of an associa-tion between better social behaviour scores andbeing in the intensive sector (Mann±Whitney test;P=0.001) but there was no evidence for associationsbetween SBS and other exposures. Using simplelinear regression, no social network variables wereconsistently associated with SBS and therefore nofurther analyses were carried out for SBS.

Regression analyses

Cross-sectional analysis at time 1 and time 2 (Table4). Total network size and the number of active

interactions, sex and ethnicity were included ininitial multivariate regression models, with GAFdisability as the outcome variable. This analysisshowed strong evidence for associations betweenthe social network variables (total size of networkand number of active interactions) and GAF dis-ability scores at both t1 and t2, when analysedseparately: P=0.01 and P=0.004 for total networksize and number of active interactions, respectively,at t1; P=0.03 and P=0.02 at t2. There was evi-dence of some small degree of confounding by sexand ethnicity in these relationships.

Longitudinal analysis (Table 5). T2 GAF disabilitywas regressed on the t1 variables and t2 socialnetwork variables. There was no evidence thatGAF disability (t2) was associated with socialnetwork variables at t1 when GAF disability (t1)was included in the model, although there was stillevidence of a positive association between socialnetwork variables at t2 and GAF disability (t2).There was also strong evidence that black Africanshad a better functional outcome than whites,although this is based on a very small number ofblack African patients (n=6). There was no inter-action between ethnicity and total size of network(P=0.84) or number of active interactions(P=0.89). The inclusion of GAF disability at t1 in

Table 2. Changes in social networks over time, n=129

Social network variable

(numbers of contacts)

Time 1 (median, range)

(interquartile range)

Time 2 (median, range)

(interquartile range)

P-value

(Wilcoxon matched pairs test)

Total size of network 10 (1±56) (2±36) 15 (2±43) (4±40) <0.001

Con®dants 4 (0±26) (0±21) 5 (0±22) (0±21) <0.001

Friends 3 (0±35) (0±15) 4 (0±35) (0±22) 0.01

Non-friends 3 (0±30) (0±17) 4 (0±28) (0±23) 0.01

Number of relatives 3 (0±17) (0±13) 4 (0±26) (0±13) 0.01

Active contacts 7 (0±56) (1±32) 10 (0±35) (1±31) 0.001

Intermediate contacts 0 (0±13) (0±9) 1 (0±21) (0±13) 0.09

Passive contacts 0 (0±11) (0±5) 0 (0±16) (0±7) 0.46

Table 3. Associations between GAF disability at time 2 and potential confounders, n=210 unless otherwise stated

Coef®cient 95% CI P* Beta coef®cient

Sex (female vs. male) 4.88 x0.85, 10.62 0.10 0.12

Diagnosis

Functional psychosis (n=157) 0.91

Affective psychosis (n=32) 0.85 x7.28, 8.98 0.14

Other psychosis (n=21) 1.96 x7.78, 11.70 0.03

Age x0.13 x0.31, 0.05 0.16 x0.10

Family history (n=172) 1.59 x4.92, 8.09 0.63 0.04

Age ®rst ill (n=199) x0.12 x0.36, 0.12 0.32 x0.07

Ethnicity (n=209)

White (n=128) 0.002

Black Caribbean (n=59) 1.60 x4.77, 7.98 0.03

Black African (n=11) 24.96 12.22, 37.69 0.26

Other (n=11) 2.32 x10.41, 15.05 0.03

Sector (intensive vs. standard) 4.81 x0.99, 10.60 0.10 0.11

* Partial F-test (transformed GAF data used).

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the longitudinal analysis appears to suggest thatsocial networks at t1 are not associated with out-come, although the cross-sectional analyses sugg-ested that social networks are associated with GAFdisability at both time points.

However, these models do not take into accountthat the outcome may affect the exposure as well asbe affected by it over time (19) (i.e. GAF disabilitymay affect social networks in addition to GAFdisability being affected by social networks), andare unable to examine the possible pathwaysbetween the various variables over time. We there-fore used structural equation modelling to investi-gate causal pathways.

Structural equation modelling

The 107 patients with complete data were used (asin the ®nal regression models above). Two covar-

iance matrices were set up, one with total size ofnetwork and GAF disability, both at t1 and t2, theother with the number of active interactions andGAF disability at t1 and t2; in each case GAFdisability at t2 was the dependent variable of primeinterest. Ethnicity was also entered in later models,because the previous analyses had suggested that itwas associated with GAF disability at t2, but it didnot affect the conclusions, so these models are notpresented in detail here. The path diagrams belowillustrate the models set up, with the correspondingstandardized solutions obtained.

The association between total size of network andGAF disability. Total size of network (att1=SNS1; at t2=SNS2) and GAF disability (att1=GAF1; at t2=GAF2) were investigated in aseries of models as below:

Table 5. Models for GAF disability at time 2, controlling for GAF disability at time 1, n=107

Coef®cient (B) 95% CI P value* Beta

GAF disability (time 1) 0.38 0.18, 0.57 <0.001 0.39

Total size of network (time 2) 6.00 x0.35, 12.36 0.06 0.14

Ethnicity

White (n=65)

Black Caribbean (n=32) x1.90 x9.76, 5.96 0.63 x0.02

Black African (n=6) 21.37 5.90, 36.84 0.01 0.24

Other (n=4) 10.17 x8.71, 29.05 0.29 0.10

GAF disability (time 1) 0.37 0.18, 0.57 <0.001 0.39

Active interactions (time 2) 5.33 0.12, 10.54 0.05 0.16

Ethnicity

White (n=65)

Black Caribbean (n=32) x2.49 x10.34, 5.36 0.53 x0.03

Black African (n=6) 20.88 5.43, 36.32 0.01 0.23

Other (n=4) 8.82 x10.02, 27.67 0.36 0.09

* Partial F-test (transformed GAF and SNS data used).

Table 4. Cross-sectional regressions of GAF disability scores on social networks (total and active interactions)

Coef®cient 95% CI P value* Beta

Time 1 GAF disability (n=135)

Total network size

Total network (time 1) 6.54 1.46, 11.62 0.01 0.22

Total network+sex 6.17 1.03, 11.31 0.02 0.20

Total network (time 1)+ethnicity 6.62 1.50, 11.74 0.01 0.22

Active interactions

Active interactions (time 1) 6.23 2.05, 10.40 0.004 0.25

Active interactions (time 1)+sex 5.89 1.62, 10.15 0.007 0.23

Active interactions (time 1)+ethnicity 6.43 2.19, 10.67 0.003 0.25

Time 2 GAF disability 2 (n=130)

Total network size

Total network (time 2) 6.81 0.73, 12.89 0.03 0.19

Total network (time 2)+sex 6.08 x0.019, 12.19 0.05 0.71

Total network (time 2)+ethnicity 6.84 0.81, 12.86 0.03 0.19

Active interactions

Active interactions (time 2) 6.25 1.17, 11.34 0.02 0.210

Active interactions (time 2)+sex 5.59 0.47, 10.71 0.03 0.19

Active interactions (time 2)+ethnicity 5.95 0.89, 11.00 0.02 0.20

* Partial F-test (transformed GAF and SNS data used).

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Model 1. Path diagram and standardized solution:SNS1pSNS2

Q QGAF1pGAF2

SNS2=0.38*SNS1+0.92E2GAF1=0.19*SNS1+0.98E3GAF2=0.17*SNS2+0.34*GAF1+0.92E4

chi-square (2)=1.05; Bentler±Bonett Normed FitIndex=0.97; Akaike's Information Criterion(AIC)=x2.95.

This model suggests that GAF disability at t2is associated with GAF disability at t1 (coef®c-ient 0.34) with less of the variance in GAF2explained by the size of the total network att2 (coef®cient 0.17). T1 total network sizesimilarly explains some of the variance in t1GAF disability (coef®cient 0.19) but accounts formore of the variance in t2 total network size(coef®cient 0.38).

Model 2.SNS1pSNS2

Q: QGAF1pGAF2

SNS2=0.38*SNS1+0.92E2GAF1=0.19*SNS1+0.98E3GAF2=0.14*SNS2+0.32*GAF1+0.10*SNS1

+0.92E4

chi-square (1)=0.10; Bentler±Bonett Normed FitIndex=0.997; AIC=x1.90 likelihood ratio testcomparing model 2 and model 1: chi-square(1)=1.05±0.10=0.95; P>0.25.

This model investigates the (cross-lagged) tem-poral relationship between SNS1 and GAF2, whichcould provide more evidence of a causal relation-ship between social networks and GAF disability.The size of the network at t1 explains only a smallamount of the variance of GAF disability at t2(coef®cient 0.10). The likelihood ratio test providesno evidence that the second model provides a better®t than model 1 and model 1 is also moreparsimonious. Nevertheless, while a weak effect,this coef®cient was signi®cant at P=0.04.

As ethnicity was included in the linear model,ethnicity was included in model 1 by adding ethnic-ity as a binary explanatory variable for GAF2. Forthis model, chi-square (5)=1.51 and Bentler±BonettNormed Fit Index=0.96. Comparing model 1 withthis model on the basis of the likelihood ratio testgives a chi-square (3)=1.51±1.05=0.35; P>0.5.There is therefore no evidence that ethnicity isnecessary in this model.

Alternative models were also examined investi-gating the pathway from GAF to social networks.

Model 3.SNS1pSNS2

q qGAF1pGAF2

SNS2=0.36*SNS1+0.11*GAF2+0.92E2GAF2=0.34*GAF1+0.94E4SNS1=0.20*GAF1+0.98E1

chi-square (2)=3.18; Bentler±Bonett Normed FitIndex=0.92; AIC=x0.82.

It appears that model 3 also ®ts the data well.This model suggests that GAF disability in¯uencessocial networks, although more of the variance isexplained by t1 social network data affecting t2social networks. The chi-square value is too small toproduce a signi®cant likelihood ratio test with afurther nested model (e.g. by the addition ofSNS1pGAF2). Model 1 and model 3 ®t the dataadequately, but of the two models, 1 has the smallerAIC and a lower chi-square (with the same numberof parameters) and so is preferable.

The same series of models was ®tted to thecovariance matrix containing active interactions,rather than total network size, and this showed verysimilar results.

To summarize the conclusions from the structuralequations: while there is insuf®cient evidence toprove that the direction of the effect is from socialnetworks to disability (model 1) rather than viceversa, this is the best-®tting model. There is someweak evidence for a cross-lagged effect in model 2.This would be interpreted as a direct effect of socialnetworks at t1 affecting GAF at t2, in addition toany indirect effect through the cross-sectionalrelationships included in model 1.

Discussion

Main ®ndings

This study shows that there is evidence of anassociation between social networks and functionaloutcome, with larger social networks being asso-ciated with higher levels of functioning. Cross-sectional analyses at t1 and t2 both demonstratedthis association for the total size of the network andthe number of active interactions, and disability asmeasured by the disability subscale of the GAF.However, longitudinal analyses did not demon-strate a temporal association between either socialnetwork variable at t1 and GAF disability at t2. It istherefore unclear from these analyses as to whethersuch associations can be explained by the level of

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functioning affecting the size of the social network,the size of the social network affecting the level offunctional outcome, or both. The suggestion fromthe structural equations is that social networksaffect disability and that this could operate over atime period of 2 years (the length of the follow-up).However, the power to detect such effects is nothigh, with a sample size of 107. Furthermore, thereare also a number of speci®c methodological issuesthat need to be considered when interpreting theseresults.

Limitations

Selection bias. It is impossible to be sure that allpatients with psychotic disorders were identi®ed.Some people may never have had contact withany agencies of any kind, and such patients mayhave different characteristics to those identi®ed Ðfor example, they may be particularly isolated andhave smaller social networks. However, wherewell-developed services exist missed patientsshould be low in number (20). This is one of thefew population-based studies in this area andshould therefore be more representative of thepopulation of patients with psychotic disordersthan studies which use patients in contact withservices only.

Potentially more concerning is the bias that couldoccur if patients who agreed to complete ques-tionnaires have different exposures compared withnon-responders. There was some evidence that non-responders were more likely to be women, and dataat t1 were more likely to be available for patientswith schizophrenia (non-affective psychosis) andpatients who were more disabled as measured by theGAF. Although other proxy measures of disabilityshowed no differences between responders andnon-responders interpretation of the t1 data islimited by this response bias. There is little evidenceof response bias at t2, although it is possible thatother exposures that were not measured in the studydo differ between the two groups.

Information bias. Misclassi®cation of exposures ispossible for a number of reasons. The SNS tries toavoid the subjective bias of the subject but thiscannot be entirely avoided in self-report assess-ments. However, there is no particular reason toexpect that this would differ by level of disability.The SNS has been validated using observations oflong-stay hospital patients (16) but it may not bean accurate measure of social networks of patientsin the community. Observer bias is unlikely, asobservers were trained and had regular checks ontheir use of instruments.

Confounding. It is recognized that black Africanand black Caribbean patients have a more favour-able functional outcome in terms of risk of self-harm and likelihood of having a continuous illnesscompared with white patients (21), at least early intheir illnesses, although these ethnic groups havemore involuntary admissions (22, 23). There areproblems in ratings of ethnicity, as they often failto reveal the heterogeneity of the groups (24),although the patients themselves generally agreedwith the classi®cation used (23). In this study therewas a much greater predicted improvement infunctional outcome in black Africans comparedwith other groups, although the numbers of suchsubjects were small. The relationship betweensocial networks and disability remained whenethnicity was included in the ®nal linear regressionmodel, and the results of the structural equationswere not greatly affected.

Length of illness may be an important confounderbut was not measured in the original study.Psychotic symptoms could confound the relation-ship between social networks and function; orsymptomatology may be part of the causal pathway.However, when the analysis was repeated using totalGAF scores, which incorporate symptoms into theoutcome score, the positive cross-sectional associa-tions were still found.

Interpretation of ®ndings

While there appears to be a relationship betweensocial networks and disability in psychotic patients,the direction of effect is unclear from this analysis.The total size of the network and the number ofactive interactions appear to be more importantthan other social network variables included in thesocial network scale. Causality in social networkresearch is dif®cult to elucidate without long-termprospective studies or intervention studies. Certaincharacteristics of psychotic patients may lead to aweakening of their social networks, sometimes aspart of the prodrome or even the premorbidpersonality (2), while better social networks maylead to a better outcome. It is likely that acombination of the two acts over time and it isdif®cult to test such a reciprocal model. Structuralequation modelling helps examine this problem, butthe models of social networks and functionaloutcome used here simplify a complex area whereother unmeasured variables ranging from life eventsto medication are likely to in¯uence all variablesinvolved in these models. This study includedpatients at many different stages in their illness,which complicates analysis further; more informa-tion on the chronicity of illness would have enabledthis to be examined as a potential confounder.

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A temporal association was investigatedthrough regression models and structural equationmodelling but neither method was able todemonstrate conclusive evidence of a longitudinalassociation between social networks and disabil-ity, although a weak t1 social networks effect ondisability at t2 was noted. This may re¯ect lack ofpower, bias as discussed above or confounding byvariables not measured in the study. In addition,the GAF disability scale may not be sensitiveenough to measure changes in outcome, particu-larly over the relatively short time period of 2years' follow-up in this study. Moreover as theGAF is a global rating, areas of difference may bemissed. Alternatively, the social network scalemay not measure aspects of social relationshipsthat may be more important in outcome, i.e. thecontent of the relationship and its meaning to thesubject (this is only partly measured by asking forthe number of con®dants). Negative socialexperiences may also have a greater signi®cance.More detailed qualitative social network datacould be more helpful in future studies of socialnetworks.

It is not clear how social support variablescould in¯uence outcome. It has been hypothesizedthat social networks in¯uence functional outcomethrough a generalized bene®cial effect becauseeffective social networks provide the individualwith regular positive experiences, and/or socialsupport could buffer the individual from poten-tially pathogenic in¯uences of stress (25). Aprevious report of the links between socialnetworks and quality of life suggested thatincreases in the size of the social network arerelated to average quality of life up to a certainlevel, with a tailing-off at higher network sizes(26). However, other interventions which improveoutcomes, such as the development of new betterantipsychotic drugs, could help enhance the socialnetworks of patients with psychotic disorders,which may in turn lead to further improvementsin their functional outcome and quality of life.

Future investigations into the role of socialnetworks in the functional outcome of patientswith psychosis could be part of studies of ®rstepisode patients with psychosis in order to remove,as far as possible, the effect of the psychosis itself onsocial networks. Randomized controlled trialsinvestigating interventions, such as assertive com-munity treatment programmes, could focus onimproving social networks and measuring anychanges in functional outcome for these patients,which would allow stronger conclusions to bedrawn about whether social networks can affectfunctional outcome.

In conclusion, this study provides evidence ofconsistent cross-sectional associations betweensocial networks and functional status, althoughthe directions of effect are not clear. The studypopulation consisted of an epidemiologically repre-sentative population so that the ®ndings should begeneralizable Ð at least to other English inner-citydeprived populations of people with psychosis. It isplausible that function and social support arereciprocally related but this needs further investiga-tion. Recommendations about policy on interven-tions to change social networks based on this studywould be premature, but this study adds to agrowing body of evidence based on observationaland quasi-experimental studies that suggest thatcertain aspects of social networks in patients withpsychotic disorders can be of bene®t (5). This studysuggests that active interactions appear to be moreimportant for functional status than passive inter-actions, or interactions with particular groups suchas con®dants or relatives, although the overall sizeof the social network is also of importance.

Acknowledgements

L.H. was funded by the Wellcome Trust as part of a WellcomeTrust research training fellowship in health services research.This study is also part of a thesis for an MSc in epidemiology,University of London. We would like to thank Dr Martin Princeand Professor Thomas Becker for their advice and comments onan earlier draft of this manuscript.

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