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Psychological Medicine
cambridge.org/psm
Review Article
Cite this article: Edwards CJ, Garety P, Hardy
A (2019). The relationship between depressive
symptoms and negative symptoms in people
with non-affective psychosis: a meta-analysis.
Psychological Medicine 1–13. https://doi.org/
10.1017/S0033291719002381
Received: 11 January 2019
Revised: 12 August 2019
Accepted: 17 August 2019
Key words:
Depression; meta-analysis; negative
symptoms; psychosis
Author for correspondence:
Clementine Jane Edwards, E-mail: clementine.
edwards@kcl.ac.uk
© Cambridge University Press 2019
The relationship between depressive symptoms
and negative symptoms in people with non-
affective psychosis: a meta-analysis
Clementine Jane Edwards1,2 , Philippa Garety1,2 and Amy Hardy1,2
1
Department of Psychology, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De
Crespigny Park, SE5 8AF, UK and
2
South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital,
Monks Orchard Road, Beckenham, Kent, BR3 3BX, UK
Abstract
The negative symptoms of psychosis and depressive symptomatology share several features,
e.g. low motivation, apathy and reduced activity. Understanding the associations between
these two sets of symptoms will support improved assessment and the development of inter-
ventions targeting these difficulties in people with psychosis. This is the first large systematic
review and meta-analysis to quantify the relationship between these two categories of symp-
toms, as measured in studies to date. PsycInfo, Embase and Medline were systematically
searched to identify eligible studies. Inclusion criteria ensured the studies measured both
depression and negative symptoms using validated measures in a sample of over 8000 parti-
cipants with non-affective psychosis diagnoses. The search led to 2020 records being screened
and 56 included in the meta-analysis and review. Both meta-analyses and meta-regressions
were conducted to explore the main effect and potential moderating variables. A clear pattern
emerges showing that higher ratings of negative symptoms are associated with higher levels of
depressive symptoms, with a small effect [standardised effect size = 0.19, p< 0.05). This did
not vary greatly with the measures used (SES =0.19–0.26) and was not moderated by demo-
graphic variables or quality ratings. Interestingly, higher depressive symptoms predict a sig-
nificant relationship with co-occurring negative symptoms. However, higher negative
symptoms predict that it is less likely there will be a relationship with co-occurring depressive
symptoms. Heterogeneity was high across these analyses. The findings support the adoption
of a symptom-specific approach to understanding the interplay between negative and depres-
sive symptoms in psychosis, to improve assessment and intervention.
Introduction
The negative symptoms of psychosis include low motivation, anhedonia, alogia, social with-
drawal and blunted affect (Kirkpatrick et al., 2006). Research has shown that these symptoms
have a significant impact on functioning (Rocca et al., 2014; Robertson et al., 2014;
Menendez-Miranda et al., 2015), with some studies suggesting these difficulties are a bigger
barrier to recovery than other symptom domains (Berenbaum et al., 2008; Marchesi et al.,
2015). Negative symptoms were initially conceptualised as primary, a core feature of
schizophrenia-spectrum diagnoses, or secondary –present due to other factors such as sub-
stance misuse, medication side-effects, depression or as a response to the positive symptoms
(Peralta et al., 2000). This conceptualisation allows for the co-occurrence of depressive and
negative symptoms in non-affective psychosis. Recent research has focused on a further dis-
tinction within negative symptoms –experiential v. expressive (Messinger et al., 2011)
which enables more reliable measurement. Experiential symptoms include low motivation,
anhedonia and withdrawal, whereas expressive symptoms are identified as blunted affect
and alogia. Depression also includes a range of symptoms with similarities to experiential
negative symptoms, with loss of pleasure (anhedonia), low motivation and low mood high-
lighted as key in the diagnostic criteria (American Psychiatric Association, 2013). A narrative
review concluded that depressive symptoms are very common in people with a schizophrenia
diagnosis and worsen their prognosis; it has been reported that up to 50% would also meet
criteria for depression (Siris, 2003; Buckley et al., 2009).
The diagnostic conceptualisation of negative and depressive symptoms is that they relate to
distinct disorders which are driven by different organic processes and commonly occur
(Malaspina et al., 2014). It is important to consider that depression is defined by self-report
criteria (experiential), whereas psychosis is defined by clinician-rated (expressive) criteria.
Some attempt has also been made to identify people for whom low mood is a significant prob-
lem alongside psychosis and this has resulted in diagnoses such as ‘schizoaffective disorder’,
‘depression with psychotic features’and applies of course to bipolar disorder. The usefulness
of these diagnostic labels in clinical practice, particularly schizoaffective disorder, is still
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debated in the field (Siris, 2003). The DSM-V (American
Psychiatric Association, 2013) recommends the assessment of
eight domains in schizophrenia-spectrum disorders, including
depression, which represents a move towards dimensional as
well as categorical assessment. Kirschner et al.(2016) concluded
in their narrative review that the presence of depressive symptoms
in someone with psychosis may be missed because of the lack of
clarity regarding how to assess them reliably and this may nega-
tively impact on their treatment options. A more recent review
of the field highlights the continuing lack of clarity regarding
how to validly distinguish whether reported phenomenology are
reflective of psychotic or depressive disorder (Krynicki et al.,
2018).
The symptom-specific conceptualisation views depressive
symptoms as part of the maintenance cycle of negative symptoms,
driven by psychological processes such as low self-efficacy beliefs
and reduced anticipatory pleasure (Sarkar et al., 2015). Indeed,
psychological models of psychosis, e.g. Garety et al.(2001), have
proposed a direct route from emotional changes to psychotic
symptoms. The phenomenological overlap between negative and
depressive symptomatology is more apparent with experiential
negative symptoms which include low motivation and anhedonia,
commonly seen in depression (American Psychiatric Association,
2013). Older measures of negative symptoms are in an interview
format and conceptualise negative symptoms as a single construct
including multiple symptoms, they do not make the distinction
between experiential and expressive negative symptoms. Newer
measures of negative symptoms include specific subscales of
experiential symptoms and there is some evidence that they
show good divergent validity from depressive measures (Forbes
et al., 2010; Llerena et al., 2013). This has been achieved by focus-
ing on low motivation across several areas of functioning (social,
employment, hobbies) rather than using terms such as ‘low
energy’or ‘low mood’which can measure affective and somatic
depressive symptoms. Experiential subscales do not assess cogni-
tive symptoms, specifically beliefs about self, world and the future,
and there is some indication in the literature that this may be
where distinctions can also be drawn from depressive symptoms
although findings are mixed (Kirkpatrick, 2014). Cognitions
which seem to be more specific to depression are those related
to guilt, hopelessness and suicidality. Some cognitions such as
defeatist beliefs appear to play a role in negative symptoms and
have been incorporated into the cognitive model of negative
symptoms (Grant and Beck, 2009). There is a clear need to inves-
tigate relationships between cognitive, somatic–affective and
behavioural phenomena associated with depressive and negative
symptoms to improve targeted treatments.
The evidence regarding the overlap between these symptoms
has been mixed with some studies finding an association between
depressive symptoms and negative symptoms and others report-
ing none (Blanchard et al., 2001; Pelizza and Ferrari, 2009; Amr
and Volpe, 2013; Edwards et al., 2015). Studies focusing on the
primary and secondary conceptualisation of negative symptoms
consistently report low levels of co-occurring depressive symp-
toms in people with psychosis identified as having primary nega-
tive symptoms (Kirkpatrick, 2014). The variation in findings may
also be due to the range of measures used to assess both depres-
sion and negative symptoms in people with psychosis. It has been
shown that in depression, measures have very little overlap with
one another, reflecting the heterogeneity of these symptoms
(Fried, 2017). Measures aim to have high divergent validity
between depressive and negative symptoms, adopting the
diagnostic rather than symptom-specific approach. However, a
recent review (Krynicki et al., 2018) showed that the domains
of anhedonia, avolition and anergia may be common to both
and used this to suggest an overlapping, dimensional model of
negative, positive and depressive symptoms. The findings of this
narrative review concluded that the symptom domains of pessim-
ism, low mood and suicidal ideation may be specific to depres-
sion, while alogia and blunted affect are specific to negative
symptoms. Hopelessness is an important factor in terms of the
relationship with suicidal intent and attempts; this has been
shown to be present in both depression and psychosis, although
hopelessness is more commonly seen in depression (Radomsky
et al., 1999; Warman et al., 2004). The time is therefore ripe for
a systematic meta-analysis of this field which aims to establish
whether there is a quantitative relationship between negative
and depressive symptoms in psychosis.
This method improves on previous systematic reviews by
applying rigorous meta-analytic techniques and will include stud-
ies which have assessed both negative and depressive symptoms.
Finally, this meta-analysis will be the first to look at the relation-
ships between depression measures and specific sub-domains of
negative symptoms as assessed by newer measures, which may
help to improve our understanding of how they interact.
The following research questions will be addressed in this
review and meta-analysis:
(1) Is there a significant relationship between negative symptoms
and depression in people with a diagnosis of non-affective
psychosis?
(2) Does the relationship between negative and depressive symp-
toms varies according to the measures or subscales used?
(3) Is this relationship moderated by depressive or negative
symptom severity?
(4) Is this relationship moderated by the diagnosis of the sample,
quality of the study or demographic factors?
Method
Literature search
PROSPERO was examined for reviews with an overlapping
research question, none were identified. This review was then
registered on the PROSPERO database (ID: CRD42017083440).
Relevant studies were identified through the systematic search
of the databases Medline, Embase and PsycINFO in February
2017 with no time period specified. These databases were selected
to fully capture the range of journals in this field. The following
search terms were used as heading or keyword searches:
(SCHIZOPHREN* OR SCHIZOAFFECT OR PSYCHOSIS OR
PSYCHOTIC) AND (NEGATIVE SYMPTOMS) AND
(DEPRESS*). The use of search terms targeting specific depressive
or negative symptoms (e.g. anergia, alogia, motivation) were con-
sidered but not included as the focus of this review is on the whole
range of depressive and negative symptomatology and including
individual symptoms may have biased the sample of papers iden-
tified. A recent narrative review (Krynicki et al., 2018) which did
include individual symptoms returned a similar number of papers
as the current review suggesting this strategy captured all relevant
papers.
The current review followed the flow of information as sug-
gested by the PRISMA statement (Moher et al., 2009).
Following the initial search, duplicate records were removed,
and the inclusion and exclusion criteria were applied. The search
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was conducted by CE and any studies where inclusion was unclear
were discussed with AH and PAG.
Inclusion criteria
Studies were included if they (i) include a sample with at least one
of the non-affective psychosis diagnoses, (ii) include a validated
measure of negative symptoms in psychosis, (iii) include a vali-
dated measure of depression in psychosis, (iv) have been pub-
lished in a peer-reviewed publication and (v) have been written
in English. Studies were included if the results reported a test of
a direct association between the negative symptom measure and
depression measure regardless of whether this was the primary
outcome of the study. Validated measures of depressive and nega-
tive symptoms were identified organically through the literature
search –if a validation paper was cited for the measure, then it
was considered eligible for inclusion.
Exclusion criteria
Studies were excluded if they were (i) conference abstracts, (ii)
book chapters, (iii) theoretical or review articles, (iv) qualitative
data only were presented or (v) they were single case studies or
dissertations. Studies were also excluded if: the sample included
people with a diagnosis of bipolar affective disorder or depression
with psychotic features as low mood is primary in these diagno-
ses; they removed people who met criteria for depression from
their sample as we wished to analyse the relationship at all levels
of depressive symptoms; they only used a single item to assess
depressive symptoms as this was not considered sufficiently
robust. Studies were also excluded if insufficient statistical infor-
mation was provided for the paper to be included in the analyses,
e.g. only associations for change scores presented or authors did
not respond to request for additional data within the time
frame of the study (k= 3) (Dollfus and Petit, 1995; Forbes et al.,
2010; Schennach et al., 2015).
Quality assessment
Studies were assessed using an adapted version of the Quality
Assessment Tool for Quantitative Studies (Thomas et al., 2004);
see online Supplementary Material for rating scale and instruc-
tions. This was included for the purpose of characterising the stud-
ies included, and to analyse quality as a potential moderator of our
findings. The measure was adapted by removing sections C, D and
G which were relevant for randomised controlled trials only. One
additional item was added which assessed whether the analyses of
negative and depressive symptoms were outlined in the design of
the study or whether it was the result of secondary analyses. This
was identified as an important quality criterion in this group of
studies. All studies were rated by CE and a sample of 10% (k=6)
was rated by an independent assessor. One of these six papers
had a discrepancy >2 between raters which are specific to the selec-
tion bias item. This was discussed, and a consensus reached. The
ratings were shown to have excellent reliability [intraclass correl-
ation = 0.94, 95% confidence interval (CI) 0.76–0.99].
Data extraction and analytic procedure
Based on the inclusion criteria, 56 studies were considered eligible
for inclusion in the final meta-analyses. The following data were
extracted from each study by CE: sample size, age, gender,
ethnicity, diagnosis (% schizoaffective disorder), mean scores on
depression and negative symptoms measures, rstatistic and p
value for the correlation. To ensure each study was weighted
appropriately where multiple Pearson’srvalues were presented
for different subscales, these were averaged to combine them for
the main analysis, allowing all data points to be included without
introducing bias (Borenstein et al., 2009). Individual subscales
were reported in sub-group analyses. All scores were converted
to Fisher’szscores to represent the continuous nature of the
data and to minimise the risk of bias associated with Pearson’s
r(Borenstein et al., 2009). All analyses were conducted in Stata
(StataCorp, 2017) using the metan package for meta-analyses
and metareg for meta-regressions. We hypothesised that the
true effect sizes would vary with sample characteristics acting as
moderating variables. Therefore, random-effect models were cho-
sen for the meta-analyses of main effects as well as
meta-regressions and subgroup analyses (Borenstein et al.,
2010). The main analysis was conducted to assess the relationship
between depressive and negative symptoms and included all the
studies. Sub-group analyses were conducted to examine this rela-
tionship when different measures were used. Meta-regression ana-
lyses were carried out to examine whether the severity of
depressive or negative symptoms, age, gender, ethnicity, diagnosis
or quality score moderated the findings.
For all analyses, heterogeneity statistics (I
2
and τ
2
) are reported
to examine the amount of variance across studies. The I
2
statistic
was included as it has greater power to detect true heterogeneity
when analyses only include a small number of studies. The con-
vention is to consider an I
2
statistic higher than 25%, 50% or 75%
as representing low, moderate or high heterogeneity, respectively.
The τ
2
statistic measures the between-study variance in the
meta-analyses and a value >1 is suggestive of very high heterogen-
eity (Deeks et al., 2008). For the reporting of the main effect,
rather than the 95% CI, the more rigorous 95% prediction interval
was used, which takes into account the heterogeneity and
describes the range of values in which 95% of effect sizes in future
studies can be expected to fall (Borenstein et al., 2009).
Publication bias was assessed with the metabias package in
Stata which includes Egger’s test for asymmetry (Egger et al.,
1997) and Begg’s test (Begg and Mazumdar, 1994). A funnel
plot will also be produced to aid our assessment of bias. Egger’s
test is limited in its ability to detect bias in random-effects models
as it was designed for fixed-effects analyses. The analysis of qual-
ity ratings as a potential moderator is also a method of bias
analysis.
Results
Characteristics of studies
Fifty-six papers were included in the analyses, see PRISMA flow
diagram in Fig. 1. The included studies are summarised in
Table 1 below. Based on the data available, there were 8177 unique
participants in these studies and 66.79% were male. The mean age
reported for the samples ranged from 22.3 to 59.35 with a com-
posite mean age of 37.16 (SD = 9.58). Two studies selected people
aged over 40 years for inclusion in their sample (Zisook et al.,
1999; Mausbach et al., 2007). A further two studies did not report
mean age or gender for their samples (Addington et al., 1994;
Chemerinski et al., 2008) and one did not report mean age
(Norman et al., 2015). Only 10 studies reported the ethnicity of
the sample, with an average of 49.25% of participants identifying
as belonging to a Black and Minority Ethnic (BAME) group. This
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composite ethnicity categorisation was compared to a composite
category of ‘white’for the purposes of the meta-analysis to maxi-
mise power. Thirty-four of the studies included in the analyses
only included people with a diagnosis of schizophrenia. Of the
23 studies that did include people with schizoaffective disorder,
only 10 reported the percentage of their sample that had this diag-
nosis, with a mean of 16.12%. The majority of studies (k= 48)
reported findings from community samples, two studies included
mixed inpatient and outpatient participants and three studies
included people solely from an inpatient setting. Three studies
reported findings from participants experiencing their first or
second episode of psychosis.
Quality ratings of studies
The quality scores are listed in Table 1. Studies generally scored
moderate–high for selection of the sample with the majority
recruiting from a wide pool of participants. Studies scored lower
in this area when they sampled from clinic, service or ward
only or their recruitment procedure was not described clearly.
Studies did not consistently report subscales for the negative
symptom measures used and this prevented them from achieving
the full score in this section. The lower scores in the analysis sec-
tion were given to studies which did not account for multiple cor-
relational analyses in their analysis or significance levels.
Fig. 1. PRISMA flow diagram.
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Table 1. Table of Included Studies
Study Authors and publication year N% Male Mean age (SD)
SZA disorder
included (Y/N, %)
Depression
scale(s)
Depression
severity mean (SD)
Negative
symptom scale (s)
Negative symptom
severity mean (SD)
Correlation
(rvalue) pValue
Quality
score (0–15)
1 Couture et al.(2011) 62 62.9 46.7 (8.4) Y (39.5) CDSS 2.85 (3.3) CAINS Expr = 5.6 (5.0)
MAP = 23.8 (12.9)
Expr = −0.08
MAP = 0.11
>0.05
>0.05
11
2 Grant and Beck (2009) 55 65 36.9 (9.9) Y (9) BDI 13 (11.5) SANS 23.7 (12.1) 0.20 >0.05 10
3 Uzenoff et al.(2010) 41 58.5 22.3 (3.5) Y CDSS 3.78 (4.26) PANSS-Neg 28.07 (7.10) 0.07 >0.05 10
4 Grant and Beck (2010) 123 65.8 38.6 (12.1) Y (17.9) BDI 17.1 (12.5) SANS 27.2 (11.9) 0.18 <0.05 10
5 Roseman et al.(2008) 144 80.87 52.33 (0.52) Y (36.42) CDSS NR PANSS-Neg NR 0.11 >0.05 10
6 Mausbach et al.(2007) 210 23 51.30 (7.54) Y (19) HAM-D 10.31 (7.26) PANSS-Neg 14.75 (4.74) 0.34 <0.05 9
7 Freeman et al.(2006) 187 72 37.5 (10.9) Y (11) BDI 21.6 (13.0) PANSS-Neg 21.0 (6.2) .28 <0.001 11
8 Todarello et al.(2005) 29 75.9 39.1 (10.9) Y MADRS 21.9 (8.3) PANSS-Neg 28.4 (10.5) 0.25 >0.05 10
9 Fitzgerald et al.(2002) 309 64.1 34.05 (10.6) Y MADRS 14.6 (9.07) PANSS-Neg 19.55 0.40 = 0.000 12
10 Muller et al.(2002) 57 63 42.9 (11.8) Y (7) CDSS 9 (6.3) PANSS-Neg 18.4 (7.4) 0.54 <0.001 8
11 Malla et al.(2002) 110 72 24.9 (7.8) Y (7.3) CDSS 3.3 (3.7) SANS 10.2 (2.5) Expr = 0.20
MAP = 0.42
>0.05
<0.001
13
12 Brebion et al.(2001) 40 70 34.1 (11.1) Y HAM-D 8 (5.2) SANS 8.4 (4.3) 0.34 <0.05 10
13 Peralta et al.(2000) 47 70 26.9 (9.1) Y (4) CDSS 2.4 (3.1) SANS 6.6 (5.3) 0.01 <0.05 9
14 Wolthaus et al.(
2000) 138 76.8 23.2 (5.26) Y (10.1) MADRS NR PANSS-Neg NR 0.51 <0.001 12
15 Zisook et al.(1999) 60 50 59.35 (10) N HAM-D 10.35 (5.73) SANS
BPRS-Neg
8.39 (4.91)
5.27 (2.77)
0.33
0.19
0.01
0.15
10
16 Peralta and Cuesta (1999) 45 63.6 31.6 (12.8) N CDSS 3.6 (4.8) PANSS-Neg 12.5 (5.8) 0.21 >0.05 9
17 Lancon et al.(2000) 95 62 33.9 (11.7) N CDSS
MADRS
HDRS
7.5 (5.1)
17.9 (9.1)
18.1 (6.5)
PANSS-Neg 24.7 (5.6) −0.01
0.12
0.02
>0.05
>0.05
>0.05
10
18 Brebion et al.(2000) 40 70 34.1 (11.1) N HDRS 7.98 (5.17) PANSS
SANS
16.3 (6.7)
8.39 (4.32)
0.19
0.35
>0.05
<0.05
10
19 Kontaxakis et al.(2000a,
2000b)
64 60.9 30.3 (8.9) N HDRS
CDSS
18.11 (5.46)
5.67 (5.13)
PANSS-Neg NR 0.19
0.09
>0.05
>0.05
9
20 Baynes et al.(2000) 120 76 39 (9.95) N BDI
HDRS
16.1(5.8)
12.65(6.7)
SANS 51.1(18.4) 0.11
0.35
>0.05
<0.001
13
21 Kilzeih et al.(2003) 43 97.7 43.05 (7.05) N HDRS 6.84 (4.25) SANS 62.23 (17.41) 0.19 >0.05 10
22 Bottlender et al.(2003) 33 66.67 32.15 (9.12) N MADRS 18.3 (8.8) SANS 55.5 (24.4) 0.15 0.41 10
23 Rocca et al.(2005) 78 59 36.13 (8.93) N CDSS 3.77(3.0) PANSS-Neg 17.1 (9.52) 0.42 <0.001 10
24 Chemerinski et al.(2008) 230 NR NR N BDI 11.5 (9.6) PANSS-Neg NR 0.14 0.03 9
25 Schennach-Wolff et al.(2011) 249 61 34.1 (11.09) N CDSS 6.97(2.49) PANSS-Neg 19.07(7.13) 0.29 NR 9
26 Rabany et al.(2011) 240 73.3 36.99 (12.21) N CDSS 3.16 (3.61) PANSS-Neg 27.38 (4.69) −0.184 0.012 11
(Continued)
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Table 1. (Continued.)
Study Authors and publication year N% Male Mean age (SD)
SZA disorder
included (Y/N, %)
Depression
scale(s)
Depression
severity mean (SD)
Negative
symptom scale (s)
Negative symptom
severity mean (SD)
Correlation
(rvalue) pValue
Quality
score (0–15)
27 Addington et al.(1994) 150 NR NR N CDSS 4.1(4.28) PANSS-Neg 20.15(4.84) 0.27 <0.01 10
28 McAdams et al.(1996) 101 77 58.5 (9.7) N HDRS 9.6 (6.1) SANS 8.2 (4.8) 0.50 <0.05 10
29 Addington et al.(1996) 89 60 35.3 (10.3) N CDSS
HDRS
6.49 (3.31)
NR
PANSS-Neg 20.2 (9.6) −0.03
0.08
>0.05
>0.05
8
30 Collins et al.(1996) 37 75.6 32.33 (8.81) N HDRS
CDSS
NR PANSS-Neg NR 0.453
0.228
<0.005
>0.05
9
31 Nakaya et al. (1997) 89 45 31.19 (9.6) N HDRS 16.5 (7.3) PANSS 23.9 (4.7) 0.20 >0.05 13
32 Collins et al.(1996) 58 77.6 34.10 (8.01) N CDSS 5.40 (4.32) PANSS-Neg 18.74 (7.37) 0.178 >0.05 8
33 Norman et al.(1998) 60 68.3 38.8 N BDI
HRSD
12.37
6.00
SANS 34.87 0.15
0.15
>0.05
p> 0.05
10
34 Haug et al.(2016) 55 51 25.2 (7.3) Y CDSS 9.1 (6.0) PANSS-Neg 14.1 (6.7) −0.289 0.032 10
35 Norman et al.(2015) 127 78.7 NR Y CDSS NR SANS NR Expr: 0.30
MAP: 0.37
<0.01
<0.01
10
36 Fervaha et al.(2015) 62 67.7 26.3 (3.9) N CDSS 1.8 (2.7) SANS 11.5 (6.7) 0.21 >0.05 10
37 Bozikas et al.(2016) 48 62.5 32.81 (7.74) Y CDSS 5.21 (4.26) PANSS-Neg 15.38 (6.76) 0.404 <0.01 12
38 Kjelby et al.(2014) 124 68.5 37.2 (13.1) Y CDSS 5.44 (4.8) PANSS-Neg 20.6 (7.95) 0.15 <0.05 10
39 Alessandrini et al.(2016) 271 70.8 36.1 (11.9) N CDSS 4.2 (4.4) PANSS-Neg 20 (8.0) 0.17 >0.05 11
40 Best et al.(2014) 136 73.5 56.08 (9.23) Y BDI NR PANSS-Neg NR 0.21 0.019 10
41 DeRosse et al.(2014) 184 69.02 40.98 (11.07) Y HRSD 11.59 (7.65) SANS 29.01 (12.54) 0.32 <0.001 8
42 Fervaha et al.(2014) 1427 74.2 40.6 (11.1) N CDSS 4.6 (4.4) PANSS-Neg 19.3 (6.7) 0.18 <0.001 11
43 Ricarte et al.(2014) 31 80.6 38.5 (10.6) N BDI 13.03 (8.39) PANSS-Neg 13.41 (3.83) 0.15 >0.05 9
44 Rabany et al.(2013) 184 74.5 36.37 (12.58) N CDSS 3.17 (3.61) PANSS-Neg 27.30 (4.52) −0.189 0.01 9
45 Lin et al.(2013) 302 61.3 38.17 (9.48) N HDRS 5.89 (4.20) SANS 50.42 (15.97) 0.265 <0.001 11
46 Tapp et al.(2001) 104 65 30 (9) N HDRS 13.5 (4.14) SANS NR 0.47 <0.0001 10
47 Roche et al.(2010) 67 70.1 25 (9.78) N CDSS 2.16 (3.07) PANSS-Neg NR 0.005 >0.05 9
48 Kring et al.(2013) 162 57 46.8 (9.5) Y CDSS 2.7 (3.0) CAINS
SANS
BPRS-Neg
NR Expr:0.15
MAP: 0.13
0.25
0.05
>0.05
>0.05
<0.01
>0.05
11
49 Llerena et al.(2013) 37 64.9 50.16 (5.12) Y CDSS 1.11 (1.88) MAP-SR NR 0.13 >0.05 10
50 Kontaxakis et al.(2000b) 64 61 30.3 (8.9) N CDSS 5.67 (5.13) PANSS-Neg 20.22 (8.84) 0.123 >0.05 10
51 Sarro et al.(2004) 93 60.2 37.2 (10.4) N CDSS 4.1 (4.4) PANSS-Neg 19.8 (8.9) 0.239 <0.01 10
52 Polat Nazli et al.(2016) 65 76 34.6 (8.3) N CDSS 2.5 (3.8) BNSS 29.4 (17.6) −0.013 0.91 11
(Continued)
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Measures of negative symptoms
Four measures of negative symptoms were used in the studies
included in the analysis; these are detailed in Table 1. The most
commonly used assessment was the negative symptom subscale
of the Positive and Negative Syndrome Scale (PANSS) (Kay
et al., 1987) with 34 studies using this measure. The second
most common was also an older measure of negative symptoms –
the Scale for the Assessment of Negative Symptoms (SANS)
(Andreasen, 1989) with 17 studies using this measure. These mea-
sures are the most widely used which reflect the historical concep-
tualisation of primary and secondary negative symptoms. The newer
measures –the Clinical Assessment Interview for Negative
Symptoms (CAINS) (Forbes et al., 2010) (k = 5) and the Brief
Negative Symptom Scale (BNSS) (Kirkpatrick et al., 2011)(k=2)
were used far less often in these studies. The most important differ-
ences in the newer measures are that they draw a distinction
between expressive and experiential symptoms. Where these data
were reported, expressive and experiential subscales from the
CAINS, BNSS and SANS were analysed separately in the sub-group
meta-analyses. Three is the minimum number of studies needed to
conduct a robust sub-group analysis (Borenstein et al., 2010)and
therefore the studies which solely used the BNSS were not analysed
separately.
Measures of depression
Four measures of depression were used in the sample of studies
included in the analyses; these are also detailed in Table 1. The
most commonly used measure was the Calgary Depression
Scale for Schizophrenia (CDSS, k= 34) (Addington et al., 1990).
This measure was designed specifically for use in this population
and the scale was developed not to include items which overlap
with negative symptoms and has been shown to reliably distin-
guish these two symptom clusters (Lako et al., 2012). The second
most common measure was the Hamilton Depression Rating
Scale (HDRS, k= 16) (Hamilton, 1960) which is a more general
measure used in many different populations and includes many
of the physical symptoms of depression. The other two measures
used, the Beck Depression Inventory (BDI, k= 9) (Beck et al.,
1988) and the Montgomery–Asberg Depression Rating Scale
(MADRS, k= 5) (Williams and Kobak, 2008), were developed ini-
tially for the assessment of people with mood disorders and
include the full range of depressive symptoms, including cognitive
features such as hopelessness and low self-esteem.
Meta-analysis findings
(1) Is there a relationship between negative symptoms and
depression in people with psychosis?
The meta-analysis testing the relationship between nega-
tive symptoms and depression showed a small but significant
association between increased levels of reported negative
symptoms and depressive symptoms in people with non-
affective psychosis [k= 56, pooled standardised effect size
(SES) = 0.194, 95% CI 0.141–0.247, z= 7.20, p< 0.001] (see
Fig. 2).
(2) Does this relationship vary according to depression or nega-
tive symptom measures or subscales used?
The relationship was consistently present across the sub-
group analyses looking at each depression and negative symp-
toms measure. When the most common combination –
PANSS Neg and CDSS –was examined, the effect size was
also small but significant (k= 23, pooled ES = 0.135, 95% CI
Table 1. (Continued.)
Study Authors and publication year N% Male Mean age (SD)
SZA disorder
included (Y/N, %)
Depression
scale(s)
Depression
severity mean (SD)
Negative
symptom scale (s)
Negative symptom
severity mean (SD)
Correlation
(rvalue) pValue
Quality
score (0–15)
53 Engel and Lincoln (2016) 50 56 35.7 (10.36) Y BDI 16.37 (7.30) MAP-SR 25.93 (10.39) 0.39 <0.001 11
54 Valiente-Gomez et al.(2015) 100 74 40.98 (12.5) N CDSS 3.12 (3.91) CAINS-MAP
CAINS-Expr
CAINS Total
17.88 (8.69)
6.70 (3.60)
24.58 (11.1)
0.34
0.27
0.35
<0.01
<0.01
<0.01
11
55 Mucci et al.(2015) 912 69.8 40.1 (10.7) N CDSS 4.0 (4.0) BNSS NR 0.28 <0.00001 11
56 Kim et al.(2016) 139 54.7 38.9 (11.1) N CDSS 4.9 (4.9) MAP-SR NR 0.09 >0.05 11
Psychological Medicine 7
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Fig. 2. Forest Plot of the relationship between negative and depressive symptoms.
Main effect (95% CIs). Lines around main effect represents 95% prediction interval (−0.15 to 0.54) based on effect sizes included in the meta-analysis.
8 Clementine Jane Edwards et al.
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0.055–0.216, z= 3.29, p= 0.001). The expressive (k= 6, pooled
ES = 0.189, 95% CI 0.090–0.288, z= 3.75, p < 0.001) and
experiential (k= 12, pooled ES = 0.263, 95% CI 0.185–0.341,
z= 6.58, p< 0.001) subscales also had small but significant
relationships with measures of depression which was numer-
ically larger for experiential subscales. However, the CIs for
the pooled ESs slightly overlap, and so it is not possible to
conclude whether there is a stronger relationship between
depressive and experiential symptoms than alogia and
blunted affect.
Heterogeneity analyses
The full sample included in the main effect analyses showed high
levels of heterogeneity ( p< 0.001, I
2
= 79.5%, τ
2
= 0.0283) as
expected given the wide range of different measures used. The
95% prediction interval (−0.15 to 0.54) is displayed around the
main effect size in the Forest Plot (see Fig. 2).
In line with this, the heterogeneity was lower in the sub-group
analyses (see online Supplementary Material for full results), and
for expressive ( p= 0.216, I
2
= 29.3%, τ
2
= 0.0308) and experiential
(p= 0.263, I
2
= 25.3%, τ
2
= 0.007) subscales, the heterogeneity
was even lower and non-significant.
Publication bias
Visual inspection of the funnel plots showed publication bias to
be unlikely. This was confirmed by the Egger’s and Begg’s tests
conducted which found no evidence of publication bias in the
main effect analyses (Egger’sp= 0.962, Begg’sp= 0.772). This
was consistent across the negative symptom (Egger’sp= 0.138–
0.932, Begg’sp= 0.621–1.0) and depression measures used
(Egger’sp= 0.224–0.687, Begg’sp= 0.419–0.917).
(3) Is this relationship moderated by depressive or negative
symptom severity?
Meta-regression analyses using the subset of the full sam-
ple that reported severity scores showed that the severity of
depressive symptoms positively predicted a relationship
with negative symptoms (k= 51, t= 2.08, p= 0.044).
Negative symptom severity also predicted the association
with depressive symptoms but in the opposite direction
(k= 43, t=−2.45, p= 0.019). As these analyses included the
whole sample, the heterogeneity was high (I
2
res = 78.13%,
73.84%, τ
2
= 0.02579, 0.02569) and thus the results should
be considered with caution. This analysis was not repeated
by specific measure sub-groups as the overall relationship
was consistent across all measures when analysed separately.
(4) Is this relationship moderated by the diagnosis of the sample,
quality of the study or demographic factors?
To investigate whether variables which differed between
samples accounted for heterogeneity in findings, meta-
regression analyses were conducted for demographic data
and study characteristics including those studies which
reported these data (see Table 1). No significant results
were found for age, gender or ethnicity (ts = 0.10–0.85, ps=
0.418–0.924). The proportion of the sample with schizoaffect-
ive disorder also did not significantly moderate the findings
(t= 0.22, p= 0.829). The quality ratings for each study were
also examined to assess whether they moderated the presence
of an association between the measures, this analysis was
non-significant (t= 0.51, p= 0.61).
Discussion
The findings confirm that there is a relationship between negative
symptoms and depressive symptoms in people with non-affective
psychosis. In the first large meta-analysis to examine this, with
data from 56 studies and over 8000 unique participants, and
across a range of measures, a clear pattern emerges showing
that overall there is a small, significant relationship between
depressive and negative symptoms. The relationship was consist-
ent across measures, so it does not appear to be the result of meas-
urement artefacts. The effect size did vary with the measure used,
but not greatly. There were no significant moderating effects of
demographic or quality variables suggesting it is robust and gen-
eralisable. A non-reciprocal relationship was highlighted in the
findings –higher depression severity was linked to higher negative
symptom severity but there was an inverse relationship in the
other direction whereby higher negative symptom severity was
linked to lower depression severity. All these findings support
the hypothesis that this relationship is consistent with a
symptom-specific approach and highlights the phenomenological
overlap in the dimensions of depression and negative symptoms.
These findings support the model proposed in the recent
review by Krynicki et al.(2018) which suggests that an overlap-
ping, symptom-specific approach to these symptom categories
may best represent their relationships. This approach allows the
co-occurrence of specific symptoms in the dimensions, as sug-
gested by the evidence. Depression may act as a driver of negative
symptoms as proposed in cognitive models, which highlight the
role of emotion in psychosis, e.g. Garety et al.(2001). This is
also consistent with the secondary negative symptom conceptual-
isation, where depression drives the presentation of negative
symptoms (Kirkpatrick, 2014). Indeed, the inverse reciprocal rela-
tionship found in this study supports the existence of primary
negative symptoms which do not predict co-occurring depressive
symptoms as highlighted in the work of Kirkpatrick and
Carpenter (Kirkpatrick et al., 2006; Kirkpatrick and Galderisi,
2008). A recent factor analysis concluded that a five-factor not
two-factor solution is more appropriate within the category of
negative symptoms, providing further evidence supporting a
symptom-specific approach (Strauss et al., 2018).
The sub-group analyses of negative symptom sub-domains
and depression suggested that, as expected, the experiential nega-
tive symptoms have phenomenological overlap with depression,
with expressive symptoms appearing more distinct from depres-
sion. These symptoms of low motivation, apathy and anhedonia
are present in the majority of both the negative and depressive
symptom measures used in the studies in this meta-analysis.
However, an important difference in anhedonia in depression
and psychosis is not commonly assessed in these measures. A
recent review highlights that people with psychosis do not experi-
ence a reduction in their capacity to experience pleasure (Strauss
and Cohen, 2018), whereas this is commonly seen in people with
depression and described as anhedonia. Unfortunately, the sub-
scales reported in the depression measures included are not
detailed enough to analyse this difference in our findings, but it
should be considered in future research. Measures such as the
CDSS have attempted to reduce phenomenological overlap by
excluding experiential symptoms in their assessment of depres-
sion, but this may result in false negatives and could therefore
lack validity. It seems from recent reviews of the area that suicidal
ideation, pessimism and guilt are a more common characteristic
of depression (Krynicki et al., 2018). Expressive symptoms, with
Psychological Medicine 9
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poorer verbal and emotional expression, are more uniquely found
in people experiencing negative symptoms (Kirkpatrick, 2014;
Krynicki et al., 2018).
Importantly, the findings were not moderated by demographic
variables such as age, ethnicity and diagnosis suggesting the
depression and negative symptom relationship is present across
the population of people with schizophrenia-spectrum diagnoses.
The quality ratings did not moderate the findings, although there
was a limited range of scores because of the measure used and
inclusion criteria applied to the studies. The lack of moderation
by schizoaffective disorder is perhaps surprising as people with
this diagnosis might be expected to report more symptoms related
to mood. It therefore tentatively suggests that the overlap between
depressive and negative symptoms is consistent across the diagno-
ses included.
The findings of the meta-regressions showed a non-reciprocal
relationship between negative and depressive symptoms. As the
severity of depressive symptoms increases, the more likely they
are to demonstrate a positive association with negative symptoms.
However, if a person reports more severe negative symptoms, the
less likely they are to be related to depressive symptoms. This is a
cross-sectional finding and hypotheses regarding a directional
relationship are therefore speculative at this stage. As negative
symptom severity increases, the person is more likely to experi-
ence expressive deficits and greater apathy or numbing of emo-
tion. This may either limit their ability to report depressive
symptoms or be protective against them. It is important to con-
sider that depressive symptoms are more often self-reported,
whereas negative symptoms are always interviewer-rated. This
may explain this non-reciprocal relationship in terms of how
symptoms are expressed in an interview –which may be more
challenging for someone with severe negative symptoms.
Negative symptoms may also be a less potent bridge to
co-occurring depressive symptoms (Borsboom, 2017). The role
of depressive symptoms in driving psychosis has been discussed
previously (Garety et al., 2001; Sarkar et al., 2015) and it may
be that this is a more potent route to co-occurring negative symp-
toms. A true symptom-specific approach would explore the phe-
nomena associated with the concepts of ‘depressive’and ‘negative’
symptoms across a broad population. Such an approach will assist
with determining the factors contributing to the presenting symp-
toms, and specifically whether apparent negative symptoms are
primary or secondary to depressive symptoms.
The main analysis and some of the sub-group analyses had
high heterogeneity in the included studies which is a limitation
of including different measures in the analysis, although this
did increase power. Only two studies were excluded due to miss-
ing data; however, many studies did not report the sample demo-
graphics, with ethnicity data particularly lacking. Meta-analyses
that consider symptoms are only as good as the measures of
those symptoms used. Several studies did not report the measure
total scores and so they could not be included in the
meta-regressions, which limits these findings. More robust
conclusions would have been possible with a greater number of
studies in the sub-group analyses considering subscales of both
negative (i.e. expressive and experiential) and depressive symp-
toms (e.g. behavioural, cognitive and somatic–affective symp-
toms). The role of positive and cognitive symptoms cannot be
elucidated from the data available; future analyses may wish to
include these data if possible to examine whether these difficulties
play a moderating role in the relationship between depressive and
negative symptoms. The narrow range of quality ratings provided
by the scale used may have limited the power of the moderation
analysis. Future meta-analyses addressing these questions may
wish to include a wider range of bibliographical sources, although
this may increase heterogeneity.
These important findings tell us that depressive and negative
symptoms can both be present in people with non-affective
psychosis. This means both should be assessed using the most
current and robust measures, and care should be taken to ensure
the measure selected captures the full range of symptoms the per-
son is experiencing. It follows that treatment for both depressive
and negative symptoms might be indicated, although further
research is required to explore whether this requires targeting
the same or different causal mechanisms.
The findings highlight the importance of mood across the
psychosis spectrum as proposed in several cognitive models of
psychosis (Chadwick et al., 1996; Garety et al., 2001; Freeman
et al., 2002; Birchwood, 2003). A symptom-specific approach to
considering these difficulties in the context of fuzzy boundaries
between diagnostic categories may have the greatest clinical utility
(van Os and Reininghaus, 2016). Indeed, the findings of a recent
factor-analysis suggest that negative symptoms are best concep-
tualised as five factors: blunted affect, alogia, anhedonia, avolition
and asociality rather than the two expressive and experiential fac-
tors discussed previously (Strauss et al., 2018). Thus, it seems
there is increasing evidence that each of these symptoms is best
considered as a unique entity and subsequently each can be
expected to have a different relationship with depressive symp-
toms. Although the findings of the review suggest that depressive
and negative symptoms mirror each other, we are aware that there
is a phenomenological complexity behind this and research
focused on gaining a deeper understanding of these symptoms
is required. This further work is needed to develop our theoretical
understanding of the causes and maintenance factors underlying
specific symptoms in order to improve therapeutic outcomes.
Assessment of these individual symptoms is important, as the
diagnostic and conceptual lines we have drawn so far appear to
be more complex than we anticipated. The impact of these symp-
toms is at least as, if not more significant than any other group of
symptoms and they are a priority for service users (Rose, 2014).
Supplementary material. The supplementary material for this article can
be found at https://doi.org/10.1017/S0033291719002381.
Acknowledgements. PAG acknowledges the support from the National
Institute for Health Research (NIHR) Biomedical Research Centre of the
South London and Maudsley NHS Foundation Trust and King’s College
London. The views expressed are those of the author(s) and not necessarily
those of the NHS, the NIHR, the Department of Health.
Conflict of interest. None.
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