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Predicting quality of life for people living with HIV: international evidencefrom seven culturesS. M. Skevingtonab; S. Norwegabc; M. Standagead; The WHOQOL HIV Groupa WHO Centre for the Study of Quality of Life, University of Bath, Bath, UK b Department ofPsychology, University of Bath, Bath, UK c Department of Clinical Psychology, University of Konstanz,Konstanz, Germany d School for Health, University of Bath, Bath, UK

First published on: 14 May 2010

To cite this Article Skevington, S. M. , Norweg, S. , Standage, M. and The WHOQOL HIV Group(2010) 'Predicting qualityof life for people living with HIV: international evidence from seven cultures', AIDS Care, 22: 5, 614 — 622, Firstpublished on: 14 May 2010 (iFirst)To link to this Article: DOI: 10.1080/09540120903311466URL: http://dx.doi.org/10.1080/09540120903311466

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Predicting quality of life for people living with HIV: international evidence from seven cultures

S.M. Skevingtona,b*, S. Norwega,b,c, M. Standagea,d and The WHOQOL HIV Group1

aWHO Centre for the Study of Quality of Life, University of Bath, Bath, BA2 7AY, UK; bDepartment of Psychology, Universityof Bath, Bath, BA2 7AY, UK; cDepartment of Clinical Psychology, University of Konstanz, Konstanz, Germany; dSchool forHealth, University of Bath, Bath, BA2 7AY, UK

(Received 13 February 2009; final version received 4 September 2009)

The need for a validated quality of life (QOL) model focussing on people living with HIV/AIDS has led to aninternational re-evaluation and extension of the Chronic Illness Quality of Life model using complex latentmodelling techniques. After reoperationalising six model variables and including independence and sex-life, the

WHOQOL-HIV was administered to 1281 people with asymptomatic-HIV (42%), symptomatic-HIV (40%) orAIDS (18%; 34 years; 62% male) living in Australia, Brazil, India (north & south), Italy, Thailand and Ukraine.The overall model fit was acceptable. Social inclusion did not directly improve QOL, but increased positive

feelings, social support and perceived improvements of access to health and social care; all three improved QOL.Social inclusion increased perceived physical health indirectly through positive feelings. Better physical healthimproved sex-life and gave greater independence; both improved QOL. Gender and disease stage models were

acceptable, fitting best for men and asymptomatic-HIV. Similar aspects of QOL were depleted for women andsome disease stages. Increased social support did not consistently improve independence or positive feelings.Positive feelings improved the sex-life of men and those with asymptomatic-HIV. This cross-cultural approachcombining assessment with theory, could guide future international interventions and practice.

Keywords: quality of life; HIV; WHOQOL-HIV; CIQOL; cross-cultural; model

Despite growing research on the quality of life (QOL)

of people living with HIV/AIDS (PLWHA), few

models are available to guide disease management.

Where models exist, they are largely concerned with

HIV prevention. An exception is the Chronic Illness

Quality of Life (CIQOL) model for PLWHA

(Heckman, 2003) where life satisfaction (LS) was

explained by AIDS-related discrimination. The model

includes barriers to care, physical wellbeing, social

support and engagement coping, but accounts for

only one third of the variance in LS. Several

observations are made about this work.First, the CIQOL model is a misnomer because

only PLWHA were tested, so generalisation to other

illnesses is problematic. Furthermore, although the

model predicts LS, LS is just one component of QOL

(Camfield & Skevington, 2008; Felce & Perry, 1996;

Renwick, Brown, & Nagler, 1996). Therefore QOL

needs to be tested within the model, or the title

changed. Without evidence from other diagnoses, this

should be specified as an HIV/AIDS QOL model.Second, Heckman’s US sample (n�275) was

predominantly white (72%), so model cross-cultural

applicability is questionable. Furthermore, it was not

validated for different socio-demographic groups.

Applicability to different sectors is essential to HIV

management e.g., women, in a field where men are

focal. Third, as social constructs, CIQOL variables

refer to subjective perceptions that cannot be directly

observed and assessed, so using observed variables

in structural equation modelling (SEM) (Structural

equation modelling, 2005) is inappropriate. In such

analyses, variables should be classified as latent; the

five degrees of freedom reported for Heckman’s (2003)

SEM indicate that latent constructs were not used. In

this research, we adopt a full-latent SEM approach.Many QOL instruments for PLWHA were devel-

oped in the USA, Europe and South Asia, being

translated from a source conceptualised by a different

culture. Consequently new language versions often

show lower semantic and conceptual equivalence, as

evidenced by poorer psychometric properties (e.g.,

Spanish MOS-HIV, Xhosa SF-36; Skevington &

O’Connell, 2003). Consequently, valid cross-cultural

comparisons are problematic (Bowden & Fox-

Rushby, 2003). International collaboration and new

processes for developing the World Health Organisa-

tion Quality of Life Assessment (WHOQOL) instru-

ments reduced equivalence problems by providing

global concepts, items and protocols (Skevington,

Sartorius, Amir, & the WHOQOL Group, 2004;

WHOQOL Group, 1994, 1995, 1998a, 1998b), and

*Corresponding author. Email: s.m.skevington@bath.ac.uk

AIDS CareVol. 22, No. 5, May 2010, 614�622

ISSN 0954-0121 print/ISSN 1360-0451 online

# 2010 Taylor & Francis

DOI: 10.1080/09540120903311466

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these were applied in the WHOQOL-HIV (WHO-

QOL-HIV Group, 2003a, 2003b).The current research re-examines the CIQOL

model using cross-cultural data from the

WHOQOL-HIV obtained in seven cultures. In addi-

tion to reoperationalising existing variables, two

QOL components relating to independence and sex-

life were added to the model following literature

reviews. Independence integrates mobility, daily ac-

tivities, medication dependence and working capacity

and is particularly salient when symptomatic-HIV

progresses to AIDS. Mobility is essential to working

capacity. As treatments improved, more PLWHA

have re-entered the workforce. Despite discrimina-

tion, work is a means to survive, providing identity

and status that improve QOL (Eller, 2001). Mobility

is needed to fulfil daily activities e.g., visiting others,

particularly for those living alone (Jacobson et al.,

1997). Dependence on medication and treatment

supports the maintenance of health and longevity

(Kalichman & Catz, 2000).Sex-life could directly affect QOL, but is infre-

quently assessed for PLWHA (Skevington &

O’Connell, 2003). Failure to assess QOL rather than

sexual behaviour means that information that might

have assisted adjustment to sexual difficulties remains

scarce. Silence on this important basis of identity

carries a potent message for those completing such

questionnaires. Furthermore, sex-life is a sensitive

indicator of wellbeing and health (Kalichman, 1998)

so inactivity signals deterioration.The summarised advantages to using the

WHOQOL-HIV permits the model proposed in this

work to be validated using equivalent cross-cultural

data. SEM analyses using a latent modelling ap-

proach was needed. Justified variables selected from a

comprehensive international range could be added to

enhance understanding and potentially improve

model utility. Model generalisation over different

populations needs validation for key socio-demo-

graphic and health status variables, and this is a

new departure. If this data fit the model, then the

convenient administration of a single, integrated

multi-dimensional instrument would be pragmatic,

promoting the dual use of model and measure by

practitioners in the field. The present study aimed to

test the fit of the CIOQOL model using cross-cultural

data, following the reoperationalisation of variables

and addition of two important WHOQOL-HIV

dimensions: independence and sex-life. We also aimed

to test the invariance of processes across gender

groups and disease status models (asymptomatic-

HIV, symptomatic-HIV and AIDS).

Method

Design

Participating centres were located in Victoria, Aus-tralia; Bangalore and New Delhi, India; Naples, Italy;Porto Alegre, Brazil; Bangkok, Thailand and Dni-propetrovsk, Ukraine. National statistics were notalways available to underpin representative sampling,so quotas guided collection: age (50%�30 years),gender (50% male) and HIV status (33% AIDS,HIV-symptomatic and HIV-asymptomatic). Mostdata were collected in primary care and hospitalout-patient clinics. Asymptomatic-HIV participantsknew they were infected and reported no symptoms.Symptomatic-HIV participants had minor symp-toms/signs of the disease. People with AIDS hadmajor signs e.g., weight loss, prolonged fever, Kaposisarcoma, meningitis and TB (O’Connell, Saxena, &Skevington, 2004).

Measure

The WHOQOL-HIV is a subjective self-assessment ofQOL, containing 120 items: 100 generic items (25facets) from the WHOQOL-100 and a 20 HIV-specific items (five facets): HIV symptoms, socialinclusion, death and dying, forgiveness, fear of thefuture (WHOQOL-HIV Group, 2003a, 2003b). Thefacets are scored in one of six domains: physicalhealth, psychological, level of independence, socialrelationships, environment and spiritual, religiousand personal beliefs. Psychometric properties showedgood factorial validity (e.g., Comparative Fit Index(CFI)�0.97), and valid discrimination between sickand well groups. The present study presents resultsfrom a secondary analysis of international field testdata (O’Connell et al., 2004).

The original model was reoperationalised usingmeasured dimensions from the WHOQOL-HIV.Some mapped closely onto CIQOL concepts: a socialinclusion facet assessed AIDS discrimination; a healthand social care facet addressed barriers to care, aphysical health domain represented physical wellbeingand a social support facet measured social support.Others required minor adjustments to concept and/ormeasurement: a general facet on overall QOL andhealth replaced LS. As engagement coping referred tothe process not outcomes of coping (unlike all otheroutcome variables in the CIQOL) it was replaced by apositive feelings facet. Although how people copeaffects decisions about outcomes, this differs fromhaving coped as a QOL outcome. Furthermore happi-ness is known as an outcome of good coping andemotional wellbeing (Fredrickson, 2002; Fredrickson &Joiner, 2002; Huppert & Whittington, 2003), so it

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was hypothesised that positive feelings woulddirectly affect general QOL, physical health and sex-life, and indirectly affect independence via physicalhealth.

Data analysis

Centre data were merged following assessment ofnormality; some variables showed acceptable positiveskewness. Univariate analysis of variance (ANOVA;pB0.01) investigated model variable differences be-tween gender and disease groups (Scheffe pB0.05).Pearson correlations (r) between predictor variablesand general QOL examined consistency across groups(pB0.01), then proposed models analysed usingAMOS (v 6.0). Non-normal distributions (Mardia’scoefficient) required amaximum likelihood estimationmethod with bootstrapping (Byrne, 2001). Indexes offit, and criteria for a well-specified model were CFI(�0.90 acceptable; �0.95 good); standardised rootmean square residual (SRMR; B0.08); root meansquare error of approximation (RMSEA; B0.06; Hu& Bentler, 1999). Squared multiple correlations(SMC) show the variance explained by independent(IV) variable(s) for the dependent (DV) variable(QOL). Indirect effects representing the mediatedeffect of a given variable in the IV�DV relationships,were explored. Multi-sample invariance analysistested for equality of constraints across gender anddisease stage models. Differences in absolute andincremental fit indices investigated invariance (Little,1997), and changes in CFI5�0.01 between increas-ingly more constrained models demonstrate this(Cheung & Rensvold, 2002).

Results

Sample

The total sample contained 1281 PLWHA; 62% men,mean age 33.6 years (SD�9.3; 17�71). Australiacontributed 253 (93% men); Brazil 244 (36% men);Italy 151 (70% men); Thailand 82 (60% men);Ukraine 300 (50% men), Bangalore, India 201(62% men) and New Delhi, India 50 (72% men).About 42% were asymptomatic (32% Italy � 60%New Delhi); 40% symptomatic (30% Australia &Italy � 60% Brazil); 18% had AIDS (3% Brazil �38% Italy).

Preliminary analyses

Physical QOL was better for women than men(Table 1), but disease status groups differed on allvariables: people with AIDS (PWD) and sympto-matic-HIV reported lower social inclusion than

asymptomatic-HIV. PWA perceived the poorestaccess to health and social care, physical QOL andsocial support. Symptomatic participants reportedpoorer physical QOL and social support thanasymptomatic. Asymptomatic participants reportedthe best QOL overall, positive feelings, independenceand sex-life. Across disease status and gendergroups, overall QOL was consistently, positivelyassociated with social inclusion, access to care,physical health, independence, sex-life, positive feel-ings and social support. Despite differences, predic-tor model variables were all highly, positivelycorrelated with overall QOL in different groups,allowing model testing for the total sample.

Structural equation modelling (SEM)

First, we tested the original model pathways(Heckman, 2003) using the reoperationalisedWHOQOL-HIV variables. Although all specifiedpaths were significant (z�1.96), the model onlyapproached an adequate fit to the data [(x2 (242)�1918.72; CFI�0.88; SRMR�0.09; RMSEA�0.074(90% CI�0.071�0.077)]. Fit indexes approachedan acceptable fit, but there was room for modelimprovement. After including two additionalvariables; sex-life and independence, the retestedmodel was an acceptable fit [(x2 (449)�2536.73;CFI�0.90; SRMR�0.06; RMSEA�0.061 (90%CI�0.058�0.063)] (Figure 1). Including theseadditional constructs within the model accountedfor an additional 4% of the variance in QOLscores.

Direct and indirect effects for the international sample

Tested pathways from the original CIQOL modelconfirmed that social inclusion did not directlypredict overall QOL, supporting Heckman’s (2003)findings. Nor did social inclusion predict betterphysical health, as expected. Social inclusion didhowever positively predict social support, and morestrongly with the present data. Furthermore, socialsupport predicted better overall QOL and health.Greater social inclusion predicted better access tohealth and social care and as access increased, so didoverall QOL. Also perceived access to care positivelypredicted social support, leading to better positivefeelings. Positive feelings predicted overall QOL.Moderately strong pathways explaining the effectof social inclusion on social support, access to careand positive feelings, provide new findings, asengagement coping was not previously significantin this relationship.

New findings relate to the addition of two novelvariables in the model, showing that a better sex-life

616 S.M. Skevington et al.

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and greater independence positively predicted overall

QOL. However, as expected, social inclusion did not

directly influence either. Better physical health

predicted independence, and a moderately better

sex-life. Furthermore, positive feelings positively

predicted sex-life. Adding these new components

provides a more complete and detailed picture of

how physical health relates to QOL.Two moderately strong pathways confirmed by

the present study were not confirmed by Heckman

(2003); social inclusion predicted positive feelings,

and positive feelings predicted physical health. In

contrast, the present data did not confirm Heckman’s

findings that social inclusion and access to care

independently predicted physical health, or that

physical health predicted QOL. Two pathways pre-

dicted but not confirmed by either study were that

physical health predicted social support, and access to

care predicted positive feelings.

An indirect relationshipbetween social inclusion and

overall QOL showed that this was primarily through

three distinctive pathways, namely through social sup-

port via perceived access to health and social care, and

through positive feelings (Table 2 and Figure 1). The

expected direct effect between social inclusion and

physical health was instead, primarily accounted for

by indirect effects through positive feelings. Social

inclusion indirectly affected each additional component:

it predicted independence levels through social support,

and sex-life both through improved positive feelings and

better subjective physical health (Figure 1). Positive

feelings influenced both sex-life and independence

through the same commonmediating effects of physical

health. Although it was expected that better subjective

physical health would predict QOL, this was only

indirect and primarily occurred through two pathways:

physical health strongly led to more independence, and

to a better sex-life.

Table 1. Model variable differences and correlations with overall QOL relating to HIV status and gender groups.

Variable

HIVasymptomatic

(n�533)

HIVsymptomatic(n�496)

AIDS(n�227) F

Male(n�793)

Female(n�481) F

Social inclusion

M (SD) 3.38 (0.76) 3.15 as (0.75) 3.07 as (0.85) 16.94** 3.28 (0.83) 3.17 (0.68) 6.35r 0.49* 0.49* 0.50* 0.56* 0.39*

Health & socialcareM (SD) 3.26 (0.71) 3.17 (0.76) 3.00 as, s (0.78) 9.62** 3.22 (0.77) 3.12 (0.72) 4.8

r 0.34* 0.35* 0.45* 0.47* 0.18*

Physical health

M (SD) 3.63 (0.66) 3.13 as (0.64) 2.87 as, s (0.66) 105.46** 3.22 D (0.75) 3.44 D (0.64) 19.22**r 0.57* 0.69* 0.68* 0.70* 0.63*

Social supportM (SD) 3.35 (0.78) 3.18 as (0.73) 2.96 as, s (0.76) 19.68** 3.22 (0.82) 3.20 (0.73) 0.15r 0.45* 0.46* 0.54* 0.52* 0.38*

Positive feelingM (SD) 3.04 (0.76) 2.66 as (0.81) 2.66 as (0.70) 38.93** 2.86 (0.79) 2.77 (0.72) 4.24

r 0.66* 0.60* 0.61* 0.68* 0.59*

Independence

M (SD) 3.66 (0.82) 3.03 as (0.76) 2.89 as (0.76) 111.86** 3.27 (0.86) 3.29 (0.85) 0.26r 0.59* 0.57* 0.69* 0.68* 0.58*

Sex lifeM (SD) 2.93 (0.85) 2.69 as (0.81) 2.68 as (0.82) 13.37** 2.75 (0.89) 2.87 (0.75) 6.31r 0.48* 0.45* 0.40* 0.50* 0.41*

General QOLM (SD) 3.24 (0.78) 2.81 as (0.76) 2.72 as (0.84) 54.07** 3.03 (0.87) 2.92 (0.71) 5.43

*pB0.01.

**pB0.001.

Note: r, Pearson’s correlation with QOL; SD, standard deviation; D, pairwise comparison differ at pB0.01; as, differs from asymptomatic at

B0.05; s, differs from symptomatic at B0.05. Only one variable is indicated in each case in order of appearance.

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Gender

Equivalence between gender groups was tested via

independent QOL models for men and women. The

baseline model showed an acceptable fit [(x2 (899)�3235.82; CFI�0.89; SRMR�0.05; RMSEA�0.045

(90% CI�0.044�0.047)] (Table 3). Baseline models

Table 2. Standardised parameter estimates of indirecteffects for total sample.

Parameter Effect

Indirect effectsSocial inclusion Social support 0.12

Social inclusion Positive feelings 0.12Social inclusion Physical health 0.38Social inclusion Sex life 0.27

Social inclusion Level of independence 0.41Social inclusion QoL 0.60Health & social care Positive feelings 0.04

Health & social care Physical health 0.03Health & social care Sex life 0.02Health & social care Level of independence 0.04

Health & social care QoL 0.07Social support Physical health 0.10Social support Sex life 0.07Social support Level of independence 0.10

Social support QoL 0.13Positive feelings Sex life 0.25Positive feelings Level of independence 0.59

Positive feelings QoL 0.26Physical health QoL 0.39

Note: All standardized indirect effects are significant (pB0.05).

Quality of life (SMC = 0.81)

Physicalhealth

(SMC = 0.38)Sex life

(SMC = 0.29)

Socialinclusion

Health and social care

(SMC = 0.22)

Socialsupport

(SMC = 0.60)

Positive feelings (SMC = 0.39)

Level of independence(SMC = 0.96)

0.46 (0.03)

62 (0.04)

0.26 (0.04)

0.50 (0.06)

0.06 (0.02)

0.62 (0.03)

0.96 (0.01)

0.40 (0.04)

0.16 (0.07)

0.19 (0.04)

0.39 (0.04)

0.12 (0.03)

0.36 (0.03)0.14 (0.03)

0.17 (0.03)

0.63

0.78

0.840.88

0.21

0.44

0.79

Figure 1. Standardised solution for the revised model using the WHOQOL-HIV.

Note: For visual simplicity, factor indicators and their respective errors are not reported, but are available from thecorresponding author. All standardised estimates are significant (pB0.05). The bootstrap estimate of the standard error foreach parameter is shown in parenthesis, whereas the proportion of the variance explained for each dependent variable is

denoted in each endogenous variable by its squared multiple correlation (SMC) value.

Table 3. Standardised parameter estimates for men andwomen.

Path Men Women

Social inclusion to health & social care 0.58 0.21Social inclusion to social support 0.64 0.57Health & social care to social support 0.23 0.36

Social inclusion to positive feelings 0.56 0.37Social support to positive feelings 0.17 0.12*Positive feelings to physical health 0.67 0.52

Physical health to level of independence 0.95 0.99Social support to level of independence 0.09 �0.02*Physical health to sex life 0.38 0.41Positive feelings to sex life 0.25 0.11*

Positive feelings to quality of life 0.44 0.29Level of independence to quality of life 0.33 0.45Social support to quality of life 0.09 0.28

Health & social care to quality of life 0.16 0.16Sex life to quality of life 0.11 0.15

*Path not significant (zB1.96).

618 S.M. Skevington et al.

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for partial invariance of gender models did not

substantially depart from the total sample model

(Table 4). All significant paths in the total model were

significant for men; for women, three of these were

not significant (Table 3). In women, positive feelings

and independence did not predict increased social

support, although social support predicted overall

QOL. Positive feelings did not predict sex-life, but did

predict QOL. Health care access predicted more

social support for women than men (Table 3). Social

inclusion more strongly predicted access to care and

positive feelings for men than women.

HIV status

Differences between baseline models for three disease

status groups were compared with results approach-

ing an adequate fit to the data [(x2 (1347)�3704.99;

CFI�0.88; SRMR�0.07; RMSEA�0.037 (90%

CI�0.036�0.039)] (Table 5). Across samples, severalpathways were not significant, so were unconstrained

in the subsequent multi-sample invariance analysis

with partial invariance supported across groups

(Table 6).

Independent results from each disease stage model

supported all significant pathways between social

inclusion and social support, access to care and

positive feelings, plus all five pathways predicting

overall QOL in Figure 1. There were a few expected

differences between stages. Except during AIDS,

social support did not predict positive feelings, but

social support and sex-life predicted overall QOL, nor

did social support predict independence in sympto-

matic participants, like other stages. Positive feelings

only predicted sex-life in asymptomatic participants.

Discussion

With 33 million people living with HIV globally,

improving QOL is a priority for international organ-

isations (UNAIDS &WHO, 2007; WHO&UNAIDS,

2000). However few models exist to predict the QOL

of PLWHA, so the CIQOL (Heckman, 2003) is a

valuable addition to knowledge. The present study

evaluates internationally important factors in living

with HIV that affect QOL, by reoperationalising and

extending this model and improving the modelling.

Even with more complex latent modelling, an

Table 4. Results of the SEM multi-sample invariance analysis for the modified CIQL across gender groups.

Model tested x2 df Dx2 CFI SRMR RMSEA (90% CI)

Step 1 3235.82 899 0.89 0.05 0.045 (0.044�0.047)Step 2 3370.36 923 134.54* 0.89 0.06 0.046 (0.044�0.047)Step 3 3397.75 935 27.39* 0.89 0.07 0.046 (0.044�0.047)Step 4 3424.25 936 26.50* 0.89 0.07 0.046 (0.044�0.048)

*pB0.05.

Note: Step 1, baseline; Step 2, measurement weights constrained; Step 3, structural weights constrained; Step 4, structural covariances

constrained.

Table 5. Standardised parameter estimates for the disease status samples.

Path HIV no symptoms HIV symptoms AIDS

Social inclusion to health & social care 0.49 0.42 0.49Social inclusion to social support 0.63 0.55 0.72Health & social care to social support 0.22 0.30 0.19

Social inclusion to positive feelings 0.46 0.58 0.37Social support to positive feelings 0.17* 0.00* 0.38Positive feelings to physical health 0.53 0.62 0.61

Physical health to level of independence 0.95 0.99 0.87Social support to level of independence 0.06 0.00* 0.17Physical health to sex life 0.27 0.55 0.41Positive feelings to sex life 0.30 0.06* 0.13*

Positive feelings to quality of life 0.40 0.39 0.29Level of independence to quality of life 0.32 0.38 0.47Social support to quality of life 0.18 0.18 0.07*

Health & social care to quality of life 0.12 0.20 0.20Sex life to quality of life 0.16 0.09 0.09*

*Non-significant path (zB1.96).

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acceptable fit was found providing a theoretical

underpinning for developing QOL interventions and

a means to test them. Testing a ‘‘universal’’ model

was possible due to data available from seven diverse

cultures from a quality cross-cultural measure with

good equivalence between languages. Furthermore,

the WHOQOL-HIV manual can be used to derive

further language versions to investigate the impact of

HIV on QOL in infected communities where this is

unknown.Introducing two important QOL dimensions of

sex-life and independence, and replacing LS with

QOL per se, contributed to the model fit being

acceptable. Although social inclusion did not predict

QOL, social inclusion predicted more positive feel-

ings, which in turn, predicted better overall QOL and

health. The present model supports the view that

happiness buffers the negative effects of social exclu-

sion on QOL, although Heckman (2003) did not find

that AIDS discrimination affected coping engage-

ment. Positive coaching techniques e.g., expressing

gratitude (Seligman, 2008) offer new strategies to

defray the negative impact of exclusion.Twin mechanisms represent buffers against the

negative impact of social exclusion on QOL. Social

inclusion reduced barriers to accessing quality care

and also improved social support; both mechanisms

subsequently improved QOL. Furthermore, there

were strong mutually reinforcing positive interrela-

tionships between all these variables. Interventions to

counter stigma may therefore need to actively and

jointly harness resources from informal support and

formal health care to enhance life quality.There were several unexpected findings, but these

departures may be as much connected with the

international perspective adopted here, as with sam-

pling issues, as most published studies have adopted a

mono-cultural and usually Western approach. This

cross-cultural data showed that physical QOLplayed a

moreminor role in explaining the relationship between

social inclusion and QOL, and physical health percep-

tions were only indirectly affected by social inclusion.

Instead, inclusion enhanced positive feelings, which

improved perceived physical health (see Carver et al.,

1993; Fredrickson, 2002; Taylor, Kemeny, Reed, &

Aspinwall, 1991). Unexpectedly, physical QOLdid not

directly improve overall QOL, but facilitated greater

independence and a better sex-life. Taking account of

both additional dimensions improvedQOLprediction.

In line with Thomas et al. (2005), our findings indicate

the importance of addressing a diversity of QOL

domains in stigma interventions, not physical health

exclusively.Models of gender and disease stage showed only

minor departures from the total sample, and for men,

QOL was almost identical. However, gender makes a

difference (Green, 1996) as three predominantly

psychosocial pathways were not significant for wo-

men. Although often reported elsewhere (e.g.,

Patrick, Cottrell, & Barnes, 2001), social support

did not improve women’s QOL; nor did women

derive independence and positive feelings from social

support, or happiness from their sex-lives. Without

longitudinal data it is not known whether several key

QOL resources are shut down or withdrawn, deplet-

ing the richness of women’s lives. As large female

samples were recruited in Brazil and Ukraine, this

suggests that previous findings may be more culture-

bound than formerly appreciated.A similar pattern was found when disease stages

were compared, where asymptomatic-HIV best fit the

model. While cross-sectional data prevents reliable

causal conclusions, psychosocial issues appeared to

be adjusted with disease progression. In pre-AIDS

stages, social support does not predict positive

feelings, but does improve QOL. However those

with AIDS derived positive mood from social sup-

port, even though support no longer improved QOL

and health. Here the models underscore previous

findings about the importance of social support

throughout the illness. However provision is complex

as PLWHA often have disproportionately fewer

significant others than usual, as the providers of

front-line support may be dying from HIV themselves

(Vedhara & Folkman, 2000). However social support

seems to disrupt independence once symptoms

Table 6. Results of the SEM multi-sample invariance analysis for the CIQOL across disease status samples.

Model tested x2 df Dx2 CFI SRMR RMSEA (90% CI)

Step 1 3704.99 1347 � 0.88 0.07 0.037 (0.036�0.039)Step 2 3805.25 1395 100.27* 0.88 0.08 0.037 (0.036�0.039)Step 3 3841.69 1415 36.44* 0.88 0.08 0.037 (0.036�0.038)Step 4 3849.95 1417 8.26* 0.88 0.08 0.037 (0.036�0.038)

*pB0.05.

Note: Step 1, baseline; Step 2, measurement weights constrained; Step 3, structural weights constrained; Step 4, structural covariances

constrained.

620 S.M. Skevington et al.

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appear. Social interventions designed to connect

PLWHA to empathic sources of informational,

instrumental and emotional support through ‘‘bud-

dying’’ programmes and internet communities

(Burrage & Demi, 2003) could incorporate this.

Furthermore, greater happiness only lead to a better

sex-life when asymptomatic, suggesting a disrupted

mechanism as symptoms develop.The current study model fit the data well support-

ing most direct effects that were medium to large.

Including two additional facets improved the model

fit. However there are limitations. Two pathways

were predicted but unconfirmed by either study

suggesting redundancy in the model. Insufficient

data from Thailand and Delhi precluded a reliable

cross-cultural test of this complex model, and a

culture�gender interaction. The effect of substitut-

ing engagement coping is unknown and deserves

further investigation. Quota rather than representa-

tive sampling, and small proportions of AIDS and

women participants were problems. Model general-

isation may be limited by increased availability of

ART since 2004, affecting health care access e.g.,

Brazil (UNAIDS & WHO, 2003). New centres could

cross-validate this international model; sub-Saharan

Africa is a priority, and the WHOQOL-HIV is in

development in Zimbabwe and Zambia.

Note

1. The WHOQOL-HIV group comprises a coordinating

group of collaborating investigators in the field sites, and

a panel of consultants. Dr. S. Saxena directed the project

which was initiated by Dr. R. Billington and Dr. J.

Orley. Technical assistance was given to the project by

Ms M. Lotfy and Dr. K. O’Connell. The field work

reported here was carried out in the following field

centres: Mr M. Bartos, Centre for the Study of Sexually

Transmissible Diseases, La Trobe University, Victoria,

Australia; Dr. P. Chandra, National Institute of Mental

Health and Neuroscience (NIMHANS), Bangalore,

India; Dr. R. Bhargava, Department of Psychiatry, All

India Institute of Medical Sciences, New Delhi, India;

Prof. F. Starace, Consultation and Behavioural Epide-

miology Service, Naples, Italy; Dr. M. Fleck, Depart-

ment of Psychiatry and Legal Medicine, University of

the State of Rio Grande do Sul, Porto Alegre, Brazil; Dr.

K. Meesapya, Branch of Preventive Mental Health,

Department of Mental Health, Ministry of Public

Health, Bangkok, Thailand and Dr. S. Pkhidenko,

Dniepropetrovsk State Medical Academy, Dniepropa-

trovsk, Ukraine.

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