THE SOCIOSPATIAL DISTRIBUTION OF
PSYCHOTIC SYMPTOMS AND DIMENSIONS
A MULTILEVEL INVESTIGATION FROM THE AESOP STUDY
Author:
FREDRIK OHER
Supervisors:
JAMES KIRKBRIDE (main)
MATS BOGREN
ANNA LINDGREN
Preface
This thesis is a result of research pursued at the University of Cambridge, Department of Psychiatry,
during the summer of 2012. The analyses in this paper have been undertaken together with my main
supervisor Dr. James Kirkbride, Sir Henry Wellcome Research Fellow at the University of Cambridge. I
am grateful for all his invaluable feedback and continued support during the creation of this thesis. It
has been a genuine learning experience.
I am further grateful for the encouragement and valuable comments from my supervisors Dr. Mats
Bogren, senior psychiatric consultant at Skåne University Hospital Lund, and Dr. Anna Lindgren,
Associate Professor at the Department of Mathematical Statistics, Lund University.
I also want to thank Professor Peter B. Jones at the University of Cambridge, whose support has been
vital for my research in England, and Associate Professor Elisabet Holst at Lund University, whose
support for my somewhat atypical course has never wavered.
This thesis has been written for the Medical Programme (Läkarprogrammet) as well as for a
bachelor’s degree in Industrial Engineering and Management, both at Lund University. This
procedure has been communicated clearly to the Faculty of Medicine as well as the Department of
Mathematical Statistics. I believe that the reader will find that competences from and an interest in
both these fields have been fundamental for the creation of this thesis.
Sincerely,
Fredrik Oher, Dec 2012
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Content
Abstract ................................................................................................................................................... 4
Populärvetenskaplig sammanfattning..................................................................................................... 5
1. Introduction ......................................................................................................................................... 6
1.1. Background................................................................................................................................... 6
1.2 Conceptualization of psychotic disorders ..................................................................................... 7
1.3 Etiology .......................................................................................................................................... 8
1.4 Patophysiology and Possible Mechanisms .................................................................................. 10
1.5 Context and Significance of Thesis .............................................................................................. 11
1.6 Objectives .................................................................................................................................... 12
2. Methods ............................................................................................................................................ 14
2.1. Sample ........................................................................................................................................ 14
2.2. Outcome data (psychotic symptom dimensions and cluster ..................................................... 14
2.3. Individual level sociodemographic & clinical data ..................................................................... 15
2.4. Neighbourhood level exposures and confounders .................................................................... 16
2.5. Statistical analysis ....................................................................................................................... 16
2.5.1. Symptom dimension overview and transformation ........................................................... 16
2.5.2. Centre & Symptom dimension comparison ........................................................................ 17
2.5.3. Multilevel linear regression analysis approach ................................................................... 17
2.5.4. Model Building .................................................................................................................... 17
2.5.5 Analytical approach for symptom dimensions ..................................................................... 18
2.5.6. Analytical approach for paranoia ........................................................................................ 19
2.6. Sensitivity analysis ...................................................................................................................... 19
3. Results ............................................................................................................................................... 20
3.1 Sample characteristics ................................................................................................................. 20
3.2 Distribution of symptom dimensions between centres .............................................................. 20
3.3. Multilevel modeling of symptom dimensions ............................................................................ 20
3.4. Sensitivity analysis ...................................................................................................................... 21
3.5. Multilevel modeling of paranoia ................................................................................................ 21
3.6. Symptom dimension and paranoia differences by diagnostic category .................................... 21
4. Discussion .......................................................................................................................................... 24
4.2 Methodological considerations ................................................................................................... 24
4.3 Meaning of findings ..................................................................................................................... 25
2
5. Appendix............................................................................................................................................ 28
6. References ............................................................................................. Error! Bookmark not defined.
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Abstract
Background
The impact of our social environment on our mental health is important. Many have studied how the
environment influences the incidence and prevalence of different psychiatric disorders. Few have
investigated how our environment influences the symptom dimensions within psychiatric conditions
such as for example psychotic disorders.
Methods
With data from 469 subjects presenting with first episode psychosis in southeast London and
Nottingham, five main symptom dimensions were identified: mania, reality distortion, negative
symptoms, depressive symptoms and disorganization. Different statistical methods were pursued to
investigate the impact of the environment on these symptom dimensions.
Results
The levels of reality distortion were found to be higher in London than in Nottingham (𝛽=0.17;
95%CI: 0.07, 0.27). Further analysis indicated that this effect might have been stronger for subjects
diagnosed with non-affective psychosis. Levels of depressive symptoms were also found to be higher
in London (𝛽=0.22; 95%CI: 0.06, 0.37) while levels of disorganization where found to be higher in
Nottingham (𝛽=-0.05; 95%CI: -0.09, -0.02). No differences were observed for mania or negative
symptoms.
Conclusion
The notion of higher rates of reality distortion in a more urban environment is consistent with the
idea that environmental stimuli could influence our levels of positive psychotic symptoms. While
more work is needed, the results in this thesis indicate that this effect might be valid primarily for
non-affective psychotic disorders, suggesting possibly different etiological backgrounds for nonaffective and affective disorders.
4
Populärvetenskaplig sammanfattning
Den sociala omgivningens påverkan på vår mentala hälsa är viktig att försöka förstå. Mycket
forskning har ägnats åt att undersöka hur omgivningen påverkar antalet personer som insjuknar med
olika typer av psykiatriska diagnoser. Få har dock undersökt hur omgivningen påverkar
symptombilden inom psykiatriska tillstånd som till exempel psykotiska sjukdomar. Psykos är ett
tillstånd som kan innefatta till exempel hallucinationer och vanföreställningar, ofta i kombination
med andra psykologiska symptom.
Med hjälp av data från 469 personer med förstaepisods-psykos från sydöstra London och
Nottingham kunde Demjaha et al. identifiera fem huvudsakliga symptomdimensioner inom det
psykotiska sjukdomsspektrumet: mani, förvrängd verklighetsuppfattning, negativa symptom,
depressiva symptom samt desorganisation. Med hjälp av olika statistiska metoder undersökte vi
sedan, för samma urvalsgrupp, omgivningens påverkan på dessa fem symptomdimensioner.
Vi fann att nivåerna av förvrängd verklighetsuppfattning var högre i London än i Nottingham. En
utökad analys antydde att denna effekt var starkare för de personer som diagnosticerats med en så
kallad icke-affektiv psykotisk sjukdom (såsom schizofreni) jämfört med så kallad affektiv psykotisk
sjukdom (såsom depression med psykos). Det fanns även tecken på ökad nivå av depressiva symptom
i London och ökad nivå av desorganisation i Nottingham. Inga skillnader observerades för mani eller
negativa symptom.
Det har spekulerats att stimuli från omgivningen kan påverka risken att utveckla så kallade positiva
psykotiska symptom. Vårt fynd att högre nivåer av förvrängd verklighetsuppfattning kunde
identifieras i en mer urban miljö (London jämfört med Nottingham) passar väl med denna idé.
Resultaten i detta arbete indikerar att denna effekt främst verkar gälla för icke-affektiva psykotiskta
sjukdomstillstånd, vilket skulle kunna tyda på olika etiologiska bakgrunder (uppkomstorsaker) för
icke-affektiva och affektiva sjukdomstillstånd.
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1. Introduction
1.1. Background
Psychosis is a descriptive psychiatric term referring to an altered condition of the mind, where
delusions and divergent perceptions of reality are central features. The condition is characterized by
distorted interpretations of stimuli and experiences, which can influence attention, emotional state
and social life. Symptoms that might occur include hallucinations, confusion and behavioral
abnormalities, such as disorganized speech and grossly disorganized or catatonic behaviour1.
Psychosis can arise in several instances, including as a core feature of a number of mental illnesses,
as a result of neurological damage, through intoxication, or as a side effect to pharmaceuticals.
Psychotic disorders, i.e. syndromes where psychosis is a core feature, are generally divided into
affective and non-affective disorders, such as bipolar disorder and schizophrenia, respectively. In
schizophrenia and similar syndromes, psychosis generally occurs episodically2. In certain personality
disorders, psychosis can also occur as an accompanying, but non-defining, feature.
For individuals with schizophrenia and their families, the cost in terms of suffering and lower quality
of life can be substantial. Life expectancy is 12-15 years shorter in people with schizophrenia, due to
increased rate of suicides and decreased somatic health3. Costs are also considerable for society; In
Sweden, it has been estimated that the cost per affected individual and year is equivalent to roughly
50,000 British Pounds (500,000 Swedish kronor). The largest part is attributed welfare support to the
individual4, but medicine and hospitals costs are also considerable. In Europe, the total cost to health
services and society of psychotic disorders is only eclipsed by dementia and common mental
disorders,5 and few syndromes entail as much human suffering 6, 7. Understanding the causes, course
and treatment of such disorders is therefore of great importance.
As a phenomenon, schizophrenia might have a long history, but the first evident accounts of the
syndrome seem to have appeared as late as the beginning of the early nineteenth century. This
stands in contrast to depression and mania, which reportedly already appeared in ancient texts8.
During the end of the nineteenth century, the German psychiatrist Emil Kraepelin suggested a
distinction in the classification of mental disorders between what he termed dementia praecox and
mood disorders9. Dementia praecox, meaning “early dementia”, was an attempt to describe a
syndrome that appeared early in life, and resulted in reduced psychic functioning6. Kraeplin believed
that dementia praecox was a result of brain damage, an incurable process that with time would
totally alter the personality of the individual9. The distinction between “early dementia” and mood
disorders became the foundation underpinning modern psychiatry, broadly classifying subjects with
mood disorders (such as depression) or with psychotic disorders (such as schizophrenia). Today, we
note that such distinctions are not always clear and that features of the two sets of disorders can
occur together (see below).
At the turn of the twentieth century, the Swiss psychiatrist Eugen Bleuler concluded that the
syndrome described by Kraepelin was not a type of dementia, based upon the observation that some
of his patients seemed improved over time rather than deteriorated. He also believed that manic or
depressive symptoms could appear within this syndrome9. He decided to call this condition
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schizophrenia, meaning roughly “splitting of the mind”, intending to describe the separation of
function of thinking, memory, perception, emotion, and personality.
1.2 Conceptualization of psychotic disorders
There are two principal manuals widely used to diagnose psychiatric disorders. The first is the
Diagnostic and Statistical Manual for Mental Disorders (DSM) published by the American Psychiatric
Association; the other is the International Classification of Diseases [ICD], published by the World
Health Organization. The manuals have been revised several times since they first came out, with
new versions of each (DSM-V and ICD-11, respectively), due to be published in 201310 and 201511.
With regard to the diagnosis of schizophrenia, the approaches of DSM-IV and ICD-10 are quite
similar. The basis is derived from the identification of specific signs, symptoms and disabilities, as
well as the course of the illness. As a simplified summary, both DSM and ICD demand the
identification of at least two of the following symptoms for the duration of at least one month:
delusions, prominent hallucinations, disorganized speech, disorganized or catatonic behavior, and
negative symptoms. Furthermore, affective syndrome, organic etiology and drug abuse need to be
cancelled out as causing factors. The major difference between the manuals lies in the duration of
dysfunction. DSM demands six months of social of professional dysfunction out of which at least one
month includes psychosis features while ICD only requires one month of dysfunction including
psychosis features12. For an overview of a selection of psychotic disorders, see Table 1.
The distinction between schizophrenia and other psychotic conditions have changed with time and
varied between different countries and cultures. The ICD and DSM manuals are attempts to
standardize the diagnoses of these conditions. Nowadays, schizophrenia is regarded as the most
common syndrome in the psychotic disorder group, but is sometimes (somewhat confusingly) used
interchangeably with the term “psychosis”. Schizophrenia, however, is one particular constellation
of psychotic symptoms which meet certain above-mentioned criteria with regard to severity and
duration13.
In the beginning of the 1980s, Tim Crow and Nancy Coover Andreason separately proposed that two
distinct syndromes could be discerned within the schizophrenic symptom profile14, 15. The first
syndrome, referred to as the Type I or positive syndrome, included symptoms such as delusions,
hallucinations, and disorganized thinking. It was called positive, because these symptoms were
thought of as added to the personality. The other syndrome, called Type 2, or negative, was
characterized by deficits in affective, cognitive, and social functions16.
Crow speculated that although positive and negative symptom groups could both be a part of
schizophrenia, they had different etiological backgrounds, with positive symptoms thought to result
from hyperdopaminergia (excess of dopamine in the brain), and negative symptoms thought to result
from structural brain deficits16. Andreasen proposed that the positive and negative symptoms were
opposite sides of a continuum, after demonstrating a negative correlation between the prevalence of
the two symptom groups16. Today, it is generally believed that positive and negative symptoms can
co-occur, although it has been observed that the two symptom groups seem to follow independent
courses over time7.
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Ever since the days of Kraeplin and Bleuler, there has been an ongoing debate as to whether
schizophrenia represents a single disease entity or not. The DSM and ICD have traditionally followed
the categorical approach, attempting to create distinctions between different mental disorders. This
approach has consequently received a lot of attention during the last century. More recently,
however, there has been a growing opinion that grouping mental disorders into categories is, by
itself, not a satisfactory practice for effective prevention and treatment17.
As an adjunct to the categorical approach, an alternative dimensional approach has been proposed,
based upon the notion that symptoms for people with schizophrenia-like disorders vary greatly and
that symptoms often fit into different, replicable dimensions (as e.g. Crow and Andreason observed).
Proponents of this approach argue that identifying the presence and magnitude of these dimensions
within each patient is important for effective treatment, as well as the understanding of psychotic
disorders. It has further been argued that evidence of subclinical experiences of psychosis in the
general population support the validity of the dimensional approach17.
In 1987 Peter Liddle was the first person to use factor analysis to create symptom dimensions. Factor
analysis is a mathematical method by which variables (for example symptoms) can be grouped
together with other variables showing similar variance, creating a number of factors (for example
symptom dimensions). Liddle presented three dimensions: psychomotor poverty (including e.g.
poverty of speech and lack of spontaneous movement), disorganization (including e.g. disturbances
of the form of thought), and reality distortion (including certain types of hallucinations and
delusions)18. Since then, many other attempts at different divisions have been undertaken.
In a meta-analysis from 2012, researchers reviewed 39 papers that had examined the dimensional
structure in patients with a broad spectrum of psychotic disorders19. They found that the majority of
the studies had concluded that either four or five dimensions effectively describe the psychosis
profile. The most frequently reported dimensions were the positive, negative, disorganization, and
affective symptom dimensions. It is important to emphasize that dimensions does not describe
particular groups of patients, but can rather be seen as a tool to describe every individual’s unique
blend of symptoms.
1.3 Etiology
For schizophrenia, genes have been emphasized as a major factor in the development of the
condition20. Much early genetic research attempted to find a gene that by itself could more or less
explain the schizophrenia phenomenon. Such a gene has not been found, and it is today generally
believed that several genes act together in various combinations, contributing in different ways to
the incidence of psychosis21 .
Many studies have, nevertheless, shown that family history plays an important role. One casecontrolled family history study showed that the life-time risk of schizophrenia in the general
population is just below one percent, but over six percent for first degree relatives of patients22. Twin
studies have also shown that the risk increases to around 40 percent for monozygotic twins of
affected people23 and that the syndrome has general heritability estimates of around 80 percent
(compared to for example 30-50 percent for hypertension)7.
8
More recently, it has been suggested that if the high heritability of schizophrenia cannot be
attributed to specific genes, environmental effects moderated by genes (gene-environmental
interaction) are likely to explain a proportion of the heritability rates for psychotic disorders. The
observation that advanced paternal age is associated with increased schizophrenia risk might serve
as an example7. These factors are speculated to have an influence on, for example, twin heritability
estimates, i.e. the shared upbringing is adding to the effect of the shared genetic component.
Several environmental (i.e. non-genetic) risk factors for psychosis have been identified. These can
conceivably be biological in origin (i.e. obstetric complications, prenatal nutrition) or social (i.e.
childhood trauma, urban living) or even both (i.e. cannabis abuse). Regarding early biological events,
it has been shown that obstetric complications, such as premature birth, low birth weight, and
prenatal hypoxia, are associated with schizophrenia21. Several other factors relating to early life
stages, such as the level of maternal psychic stress during pregnancy, and the size of the head of the
newborn (people that later develop schizophrenia have smaller head sizes than average as a baby)
seem to influence the risk of developing schizophrenia24. Taken together, it seems like early
disturbances in neurodevelopment increases risk of schizophrenia later in life.
An example of a social factor is migrant status, which has been established as a risk factor for
schizophrenia and other psychotic disorders. It has been speculated that being a migrant produces
stress and therefore elevated rates of schizophrenia, and also that doctors more often diagnose
migrants with schizophrenia than the rest of the population, ceteris paribus25. The impact of cultural
bias has, however, not been found to have a major impact on the association with schizophrenia26.
Several studies have also shown that the risk for psychotic disorder decreases the greater proportion
of the own ethnic group in the area the person is living in26. This is known as the ethnic density
effect.
As a result, it has been argued that it is not being a migrant that increases the risk of developing
psychosis disorders per se, but rather the degree to which one belongs to a minority group in society.
Meta-analyses have demonstrated consistency in indicating an association between being in a
minority position and experiencing psychosis. This association was evident across a wide range of
“approaches, endpoints, settings and cultural group definitions, and after adjustment from range of
confounders”26.
A risk factor that has received a lot of attention is drug use, especially cannabis use. Stimulants like
cocaine and amphetamines are known to be able to induce a picture clinically similar or identical to
paranoid schizophrenia21. Meta-analytical work is also consistent in demonstrating an association
between cannabis use and psychosis27. One meta-analysis concluded that early cannabis use is
associated with a doubling of risk for schizophrenia28. This effect was still clear after controlling for
self-medication, indicating that cannabis use as an attempt to alleviate distress is not satisfactory as a
sole explanation for the increased risk. Another review article reported that cannabis use was
associated with an increased risk of psychotic disorder and symptoms with an odds ratio between 1.5
and 2.029. Although cannabis use seems to increase the risk of psychosis, it is important to note that
from a public health perspective only a small proportion of those who use it will go on to develop
psychotic disorder requiring clinical attention21.
A central risk factor for this thesis concerns the exposure to an urban environment on the individual.
In a meta analysis from 2008, Dana March et al. reviewed 20 studies where rates of psychosis were
9
examined according to urbanicity30. All but two of these studies indicated an association between
urban life and higher rates of psychosis. In the review, the studies were reported to define exposure
to urbanicity in primarily two different ways: as dichotomies of urban and rural, or by degrees, with
three or more categories of progressively larger population density30. In general, urbanicity was
associated with an approximate twofold increase in risk of psychosis.
The timing of urban exposure has been considered important. A few studies have reported an
association between psychosis and population density of residency at illness onset e.g. 31, 32 , and a
larger number have reported an increased risk of psychosis with urban exposure at birth or early life
e.g. 33, 34
. For studies assessing urbanicity exposure prior to illness onset, there is good evidence that
the increased risk of psychosis is not a simple effect of social drift30. In one of the only studies
examining duration of exposure to urbanicity, risk was reported to increase cumulatively in the most
urbanized areas35. The association between urbanicity and psychotic disorders appears to exist even
after adjustment for confounding factors such as age, sex and ethnicity26, 36.
The risk of experiencing non-affective psychotic disorders seems to follow a spatial pattern within a
specific city36. This effect might be caused by social factors, such as socioeconomic deprivation,
ethnic density and social stress. The documented effects of both urbanicity and minority status
position indicate that long-term experience of social disadvantage and isolation might be a defining
risk factor for the development of psychotic disorder.
Interestingly, the urban-psychosis association seems to manifest itself mainly for non-affective
psychotic disorders, and not for their affective counterparts30, 36. This was first observed by
researchers Faris and Dunham in 193937. As an example of this discrepancy, Pedersen and Mortensen
demonstrated in 2001 a dose-response relationship between an urban upbringing and
schizophrenia35, but reported two years later that they could not reproduce these results for bipolar
affective disorders38. This did not support the possibility that the association between urban
upbringing and schizophrenia was due to a lower “nosocomial” threshold, such as better access to
services, because such mechanisms would apply to bipolar affective disorder as well as
schizophrenia.
The reason for the difference in spatial distribution between non-affective and affective disorders is
so far unknown. One possibility could be that certain symptom dimensions within these groups might
be more influenced by the environment than others. If these specific symptom dimensions would be
more strongly correlated to non-affective psychotic disorders than the affective counterparts that
could, at least partially, explain the difference in spatial distribution between the two syndrome
groups.
1.4 Patophysiology and Possible Mechanisms
Traditionally, one of the most wide-spread patophysiologic theories to explain the manifestation of
psychotic symptoms has been the dopamine hypothesis of schizophrenia. Dopamine is a monoamine
neurotransmitter with several physiological roles in humans, for example as a mediator of desire,
creating “incentive salience” to reward-related stimuli39. The dopamine hypothesis is based upon the
belief that a disturbed or hyperactive dopamine signal transduction is responsible for some of the
principal psychotic symptoms which underpin schizophrenia and similar psychotic disorders. The
10
evidence is drawn from the effect of antipsychotic medicine, which mediates its effect by operating
as a dopamine-receptor antagonist40.
One argument for the dopamine hypothesis was based on the observation that drugs such as
amphetamines, which can cause psychosis-like symptoms, increases dopamine levels in the brain41.
Similarly, patients using dopamine-enhancing levodopa for Parkinson’s disease might experience
psychotic side effects of their treatment42. Finally, several studies are reported to have shown that
patients with schizophrenia experiencing psychosis have a higher synthesis of dopamine, a
heightened release of dopamine in response to an impulse, and a heightened level of synaptic
dopamine41.
There were, however, several problems with the original dopamine hypothesis. One issue is that
antipsychotic medication does not seem to completely extinguish delusions and hallucinations, but
rather just makes them less salient41. Another issue is that, although not instantaneous, symptoms
tend to return for patients whose treatment is stopped, suggesting that such medication does not
resolve underlying abnormalities, but serves mainly as symptomatic control41. Finally, the dopamine
hypothesis was in the beginning seen as a theory for schizophrenia as a whole, without clear
explanations of how or why the different symptoms associated with the syndrome occurred43.
In 2009, Oliver Howes and Shitij Kapur presented an updated version of the dopamine hypothesis, in
which they brought together updated evidence to suggest that dopaminergic function acted as the
‘last common pathway’ of schizophrenia43. They argued that dopamine dysregulation is linked to
psychosis rather than schizophrenia per se. Howes and Kapur emphasized that the disturbance of
dopamine is an effect of several factors, including genes, fronto-temporal dysfunction, social stress,
and drugs, and that the locus of dysregulation is situated at the presynaptic dopaminergic control
level43. Since dopamine is important in judging the salience of environmental cues, or stimuli, they
suggested that certain psychotic phenomena, such as hallucinations or delusions could be the
aberrant attempts of the mind to interpret ordinarily neutral stimuli, in a process referred to as
“aberrant salience” 43. In other words; ‘dopamine is the wind of the psychotic fire’41.
Howes and Kapur further argued that other aspects of schizophrenia, such as negative symptoms,
might be caused by other factors. Under such a hypothesis, it is therefore possible that social
exposures in the environment are more strongly related to certain psychotic symptoms – those
hypothesized to be driven by dopaminergic dysregulation, such as the positive symptoms of
psychosis – but not others, such as changes in affect (mania or depression). This idea is the focus of
this thesis.
1.5 Context and Significance of Thesis
In 1939, researchers Robert E. Lee Faris and Henry Warren Dunham were pioneers in observing
higher rates of schizophrenia “in the deteriorated regions in and surrounding the center of the city,
no matter what race or nationality inhabited that region”, in conjunction with no such pattern for
affective disorders37. Since then, numerous studies have investigated environmental effects on
patients diagnosed with schizophrenia, and/or other psychotic disorders, where some of the major
11
findings have been presented under the “etiology” section of this thesis. We still do not understand
why the differences between non-affective and affective psychoses with respect to place occur.
In a paper from 2006, Kaymaz et al. presented results that indicated that for bipolar disorder,
urbanicity was specifically associated with the level of psychosis, or so called positive symptoms,
within the disorder44. If this association holds true across the psychotic disorder spectrum, it would
mean that syndromes where psychosis is a more common feature (such as schizophrenia) would be
more strongly associated with urbanicity. Such a hypothesis would help explain at least some of the
difference in spatial distribution between non-affective and affective psychotic disorders.
In 2011, Lederbogen et al. demonstrated a stronger activation of amygdala-related brain areas when
exposed to stress tests for subjects with a more urban background45. An intriguing possibility is that
urbanicity increases sensitivity to stress, which might increase the risk of experiencing psychosis. In a
study from 2012, Mizrahi et al. presented findings that might support this hypothesis, demonstrating
a stress-induced dopamine sensitization for schizophrenia patients, suggesting a link between
urbanicity and psychosis through the dopamine hypothesis46.
It is thus possible that the social environment has particular effects on certain symptom dimensions
rather than at the level of disorder itself. No study has, to the best of my knowledge, addressed how
individual- and neighbourhood-level variables are associated with specific symptom dimensions.
Elucidation of such an issue might provide understanding of the geographical non-random variation
of non-affective psychotic disorders, as well as the value of using symptom dimensions in the
examination of etiology factors for psychotic disorders.
1.6 Objectives
In this thesis I sought to answer the following objectives:
1. To investigate, in incident cases of first-episode psychosis, whether any symptom dimension
differed significantly between two centers included in the AESOP study, using the dimensions
identified by Demjaha et al in the same dataset47.
2. To investigate, in the same sample, whether any symptom dimensions varied within centers
at the neighbourhood level.
3. To investigate whether any identified neighbourhood level variance in symptom dimensions
could be attributed to specific neighbourhood attributes.
The above objectives were performed on the five symptom dimensions previously identified by
Demjaha et al47: Mania, Reality distortion, Negative symptoms, Depressive symptoms, and
Disorganization. A specific sub-dimension related to paranoia was also investigated, given a strong a
priori hypothesis that this subgroup of the reality distortion dimension would show neighbourhood
level variance (see below).
Given Howes and Kapur’s theory that environmental cues will lead to psychotic symptoms associated
with dopaminergic activity, my primary hypothesis was that the reality distortion dimension would
be positively and significantly associated with urbanicity. In contrast, I hypothesized that the four
12
other dimensions would be much less strongly associated with dopaminergic activity and not show a
significant variation with urbanicity. With regard to possible neighbourhood level variance, I
speculated that factors related to social defeat or stress in the neighbourhood would be of
importance.
Within the reality distortion dimension, I posited that the paranoia cluster would be particularly
significantly associated with urbanicity. Urban environments may have a greater prevalence of cues
which can be interpreted as negative, threatening stimuli, fostering particularly paranoid thinking as
a result of aberrant salience resulting from dopaminergic dysregulation.
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2. Methods
2.1. Sample
We included all subjects who presented as an incidence case of first episode psychosis over a two
year period (1997-9) in the London and Nottingham centres of the Aetiology and Etnicity in
Schizophrenia and other Psychoses (AESOP) study. Diagnoses included were ICD-10 coded F20-29
and F30-33, derived from the Schedules for Clinical Assessment in Neuropsychiatry (SCAN; WHO,
1992), and determined by a panel of clinicians who were presented the information blind to the
ethnicity of the subject, by the lead clinician for care. Exclusion criteria were (i) the presence of a
disorder of the central nervous system and (ii) moderate or severe learning disabilities as defined by
the ICD-10.
2.2. Outcome data (psychotic symptom dimensions and cluster)
We investigated Centre- and neighbourhood-level variation in five theoretically- and empiricallydriven psychotic symptom dimensions and one a priori symptom cluster (paranoia). All dimensions
and paranoia were obtained from symptom data provided in the SCAN Item Group Checklist (IGC).
The SCAN itself covers a large number of symptoms and signs, determined by trained raters and
derived based on clinical interview, case-note review, and information from informants, such as
health professionals, close relatives or similar. The IGC algorithm combines scores on several SCAN
items into particular groups of symptoms to make the number of symptom groups more manageable
for statistical analyses. Item Group Checklists take the form of 0 if symptoms were absent, 1 if they
were moderate or 2 if they were severe. Inter-rater reliability on SCAN diagnoses in AESOP ranged
from 1.0 for all clinically relevant psychotic disorders (ICD-10 F10-33) to 0.63-0.75 for specific
diagnoses48.
A previous publication from the AESOP study47, which used principal axis factor analysis, has shown
that the SCAN IGC data load onto five theoretically-driven symptom dimensions: reality distortion,
negative symptoms, mania, depressive symptoms and disorganization. An overview of the items
which load onto these symptom dimensions is given in Table 2. These symptom dimensions fitted
well with underlying theoretical models of psychotic symptoms and other empirical research47. The
authors then investigated the association between individual level clinical factors (such as mode of
onset or DUP) and any particular symptom dimension. They did not inspect variation by place. In
that study, the researchers obtained subject dimension scores by summing each subject’s individual
scores (0,1,2) from each item which loaded significantly on that factor (dimension). This approach
led to ordinal factor scores for each dimension and ignored the contribution of items which had
smaller factor loadings on a given dimension.
To fully utilize the data available to us, and to overcome difficulties of fitting multilevel modeling on
ordinal outcome data, we chose to re-run the principal axis factor analysis to predict continuous
factor scores for each dimension for all subjects. We adopted the same initial approach as Demjaha
et al., using principal axis factor analysis on all IGC items except delusions about the body, other
speech abnormalities and catatonic behavior, all of which had very low endorsement in the AESOP
14
sample (9.5, 7.5, 2%, respectively). Five a priori factors were obtained and applied orthogonal
Varimax rotation to clarify the factor solution, as before. Fitted in Stata (v11), the five-factor solution
explained 48.5% of variance in the symptom items. This was similar to previously reported (47%).
Factor loadings (Table 2) were very similar to those previously reported by Demjaha et al. Unlike the
previous study, continuous factor scores for all individuals in the study was predicted from this
solution. Having obtained continuous factor scores, it was more computationally straightforward to
fit multilevel linear regression models when symptom dimensions were treated as continuous rather
than ordinal outcomes.
In addition to the five symptom dimensions we also included paranoia as an a priori symptom
cluster. This made it possible to test a prioir hypothesis that not only reality distortion, but
specifically items related to paranoia would show particular neighbourhood variation. The paranoia
cluster was created by summing up scores (0,1,2) for the two items in the SCAN IGC directly related
to paranoid thinking (delusions of persecution and delusions of reference). This gave rise to an
ordinal variable with minimum and maximum values in the range 0-4. Exchangeability of scores was
assumed, so that someone scoring a 1 and 1 on each paranoia item was equivalent to someone
scoring 2 and 0. Because this cluster was ordinal, different statistical methods were adopted to
handle the multilevel analysis of this cluster.
2.3. Individual level sociodemographic & clinical data
Data regarding ethnicity, place of birth, gender, age and social class was collected using the Medical
Research Council (MRC) Sociodemographic Schedule. Ethnicity was based on subject self-ascription
using 2001 census categories. From these we categorized subjects into seven ethnic groups (White
British, White other, Black Caribbean, Black African, Asian, Other, or Mixed, White & Black
Caribbean) in order to achieve a reasonable number of subjects in each group for meaningful
analyses. Place of birth was treated a dichotomous variable (UK born/not UK born).
We geocoded all subjects to their residential neighbourhood at the time of first contact for psychosis
to ascribe them to their study centre (categorical, London versus Nottingham) or their specific
neighbourhood (electoral wards). Electoral wards are used for census and electoral purposes in
England and have a mean population size of 6000 people. They have been widely used in previous
research in this area; see for example Kirkbride et al31. Subjects of no fixed abode or who could
otherwise not be geocoded were excluded from the present analyses (see results).
Mode of onset was determined using the WHO Personal and Psychiatric History Schedule, and
divided into two categories: acute (psychotic symptoms appeared incrementally within one month)
and insidious (psychotic symptoms appeared incrementally for longer than one month). The same
schedule was also used for determining duration of untreated psychosis (DUP), in conjunction with
interviews with the patient, and a close relative to the patient. Parental history of psychosis and
mental illness was collected by interviews and case-notes using the Family Interview for Genetic
Studies. Family history of mental illness and History of parental psychosis are dichotomous (yes/no),
Mode of onset is dichotomous (acute <1 month/insidious >1 month).
15
We obtained data on substance abuse prior to first contact using the Schedule for Drug Use
Assessment, which collated information on drug abuse from all available sources in the ÆSOP study,
including the SCAN, Personal & Psychiatric History Schedule [PPHS] and case note review. We derived
a variable to indicate lifetime poly-drug use, categorized as definite evidence of “no use”, “single
drug use” and “poly-drug use”. Participants with partially missing data were categorized into one of
these groups based on available information. Those with completely missing data were handled in
sensitivity analyses (see below).
2.4. Neighbourhood level exposures and confounders
Neighbourhood-level socio-economic risk factors were mainly obtained from nationally collected,
routine sources. The 2001 census was used to measure population density (people per hectare) and
ethnic density (black and minority ethnic (BME) population as part of total population). Deprivation
was obtained for each CAS ward using the Index of Multiple Deprivation (IMD) (2004), an aggregated
deprivation score composed of 37 indicators obtained from routinely-collected national surveys,
generally estimated concomitantly with the case ascertainment period 49. Since deprivation scores
were not published for wards directly, but at the nested ‘lower super output area’ [SOA] ward-level
deprivation was estimated as the mean of SOA deprivation in each statistical ward, weighted by SOA
population size. Voter turnout data, a proxy for social capital, was obtained at the 2002 local
elections for each ward within the study area. See Kirkbride et al50 for more information on the
factors above.
Inequality was also estimated from the 2004 Index of Multiple Deprivation, estimated as the disparity
in IMD scores across SOA in each electoral ward using the Gini coefficient [G]51. G gave the extent of
dispersion in income deprivation (at SOA-level) away from a theoretical distribution where income
deprivation took the same value for all SOA in each ward (i.e. perfect equality). When G equalled
zero there was perfect equality and when G was one, perfect inequality.
2.5. Statistical analysis
To detect possible spatial variation for the symptom dimensions and cluster we undertook multilevel
regression analyses in the statistical programme Stata (Version: MP 11.2) and adopted the following
procedures:
2.5.1. Symptom dimension overview and transformation
Basic kernel density plots (histograms) were inspected to get a handle of the distribution of symptom
dimensions in the sample. We also investigated the level of endorsement of items related to
paranoia in the sample.
To help potentially avoid violating the assumption of normality in the distribution of our residuals
following multilevel linear regression, we first transformed outcome data for all symptom
dimensions, except paranoia which was fitted using a non-linear approach. A certain type of
logarithmic transformation (zero-skewness logarithm using the lnskew0 command in Stata) was used
to handle this problem. The lnskew0 command determines the sign of the original values (+/-) and
16
subtracts a calculated constant k, so that the skewness of the logarithm of the original value minus k,
is zero. The formula can be written as:
𝑌𝑡 = ln⁡(±𝑌 − 𝑘)
where 𝑌𝑡 was the logarithmic transformation of the positive or negative value of our symptom
dimension 𝑌.
2.5.2. Centre & Symptom dimension comparison
We initially inspected whether values of the dimensions in our sample varied across neighbourhoods
in the London and Nottingham centres of the AESOP study. Given evidence that symptom
dimensions were highly skewed (see Results section), we inspected whether the median score on
each dimension and cluster varied significantly between London and Nottingham, using a Wilcoxon
rank-sum test. A skewness and kurtosis test for normality was also performed, using the sktest
command in Stata.
2.5.3. Multilevel linear regression analysis approach
For each symptom dimension, we then adopted a multilevel linear regression to account for the fact
that individuals were nested within neighbourhoods. A multilevel model, also called a hierarchical
model, is a model where a dependent variable (e.g. the transformed variable for a specific symptom)
is influenced by variables from different levels of causation, where the first level is usually (though
does not have to always be) nested in the higher order ones (e.g. individuals nested within
neighbourhoods or neighbourhoods nested in cities). The word “nested” is an attempt to illustrate
the fact that the first level is influenced by the second level (e.g. individuals are more influenced by
the neighbourhood they live in, than by other neighbourhoods). A multilevel approach is required in
the presence of nested data because the standard errors associated with variables included in the
model need to be adjusted; variances in individual level scores between and within our level-2 units
may be different. In other words, it may not be safe to assume the individuals in our data sample are
independent of each other (people living in the same community may be more similar than people in
different communities).
Multilevel models are characterized by fixed and random effects. With fixed effects, one is referring
to those variables whose level would stay the same if the experiment was replicated (i.e. if one
would create an experiment with the same number of subjects, the same distribution of age etc).
Random effects would not stay the same if the experiment were to be replicated, i.e. they cannot be
exactly controlled for.
The simplest two-level model is a random intercepts model, which allows for the value of the
intercept (i.e. the average symptom score) to vary between level-2 units (i.e. neighbourhoods). The
influence of fixed effects on predicted symptom scores are assumed to be constant across
neighbourhoods. A random slopes model relaxes this assumption, and allows for the influence of
fixed effects on symptom score to vary between neighbourhoods. For this thesis, random intercepts
linear regression models with individuals were nested within electoral wards (neighbourhoods).
2.5.4. Model Building
For our purposes we denote Yij as a transformed symptom dimension or cluster in the jth individual, in
the ith neighbourhood. Our basic multilevel model is the following:
17
Yij = βij + rij
rij ~ N (0, σ2)
where β denotes a regression equation, and can be expanded to represent all the available and
relevant predictors. The normally distributed residual term rij represents individual-level error
assumed to have a mean of zero and variance, σ2. If the multilevel model would include n different
variables, then β would be modeled as:
Βij = γ + δ1xij1 + δ2xi2 + δnxijn + Si
Si ~ N(0, τ2)
where γ denotes the intercept and δ1..n denotes the coefficient for the first, second and nth variables.
The x stands for the value of the variable for the jth individual and ith ward. The Si denotes the random
effect for the ith ward, assumed to be normally distributed with a mean of zero and variance, τ2. This
is a simple random intercepts model, where the variable xi2 is a neighbourhood-level variable in this
example, since the value of δ2 only varies across i wards. Any residual variance (in the outcome data)
would be represented as a significant random effect in the model. Therefore modeling a null, empty
model (without individual- or neighbourhood level covariates) allows one to partition the variance in
our outcome specifically due to neighbourhood level random effects. The proportion of variance in
the outcome attributable to the neighbourhood level is defined as the intra-class correlation
coefficient, ρ:
ρ=
τ2 .
(σ + τ2)
2
2.5.5 Analytical approach for symptom dimensions
Starting with an empty multilevel linear regression model, we investigated the proportion of variance
attributable to the neighbourhood level (Stata command: xtmixed symptom || id: , var). A binary
“centre” variable was put in to the model to examine the impact of living in London or Nottingham
on our symptom dimensions, and how this diminished variance attributable to the neighbourhood.
Individual variables were then added separately to investigate their contribution to their model.
After adding our a-priori variables of age, sex, and, ethnicity, we used a forward fitting approach to
identify the most parsimonious fit of the data to the model. From this model we sought to determine
whether any residual variation in neighbourhood level random effects remained.
We then sought to account for any significant residual neighbourhood variation in symptom
dimensions by including neighbourhood variables in our model. Goodness-of-fit for each model was
examined using Akaike’s information criterion (AIC). The AIC can be said to describe the tradeoff
between bias and variance, or more loosely, the tradeoff between accuracy and complexity. For this
measure, a simpler model that explains a certain amount of the variance might be more pertinent
than a very complex model that only explains a little more of the examined variance. For the AIC, a
lower score is an indication of a better model. In Stata, the command .estat ic. is used to request the
AIC (and also provides the Bayesian information criterion, BIC).
18
Ranksum tests were performed on all five dimensions to investigate whether subjects with missing
data (on symptoms of neighbourhood) differed from subjects where data was available.
2.5.6. Analytical approach for paranoia
Multilevel linear regression was inappropriate for modeling the symptom cluster of paranoia, since
this variable was ordinal. Instead we used multilevel ordinal logistic regression to inspect possible
neighbourhood-level variance in the distribution of paranoia scores in our study. Multilevel models
were fitted using the gllamm command in Stata using an ologit link function in the binomial family to
fit the model. Model fitting proceeded in the same way as described above for symptom dimensions.
2.6. Sensitivity analysis
A potential confounder on our analysis of the spatial influence on psychotic disorder was life-time
poly-drug use. Our data regarding this factor was not complete, and we therefore conducted a
sensitivity analysis to investigate the risk that poly-drug use (and two other factors with missing data:
mode of onset and parental history) might have confounded our results.
We re-ran the analysis including these three variables. In the first re-run, any missing data was coded
as the lowest possible value (baseline group). In the second re-run, missing data was coded to the
most extreme value (i.e. poly-drug use, insidious onset, positive family history). Changes in the
associations between neighbourhood-level or centre effects and symptom dimensions were reported
under each scenario.
19
3. Results
3.1 Sample characteristics
535 cases were documented within the AESOP study for London and Nottingham. Of these, there
was sufficient information to complete IGCs for 484 subjects (90 %). From this group, 15 subjects
were excluded because they were i) living outside the catchment areas at first referral, ii) had no
fixed abode, or iii) their address at first presentation could not be otherwise established. Excluded
subjects were more likely to be men (Chi2(1), p <0.01) and live in London (Chi2, p=0.06), but did
otherwise not differ significantly regarding age (Ranksum, p=0.31), ethnicity (Fisher’s exact, p=0.27),
dichotomous mode of onset (Chi2, p=0.40), social class (Fisher’s exact, p=0.27), or dimensional scores
[Ranksum for all five: mania (p=0.35), reality distortion (p=0.11), negative symptoms (P=0.37),
depressive symptoms (p=0.24), and disorganization (p=0.88)].
Out of the resulting total sample of 469 subjects included in our analysis, 309 (66 %) subjects came
from the South-East London area, and 160 (34 %) came from the Nottingham area. The mean and
median for age were 30.7 (Sd: 10.7) and 29 (IQR 22-36), respectively, and the male to female ratio
was 57:43. See Table 3 for more information. For further characterization of the AESOP sample,
please see Kirkbride et al31.
Factor analysis resulted in an identical structure to Demjaha et al47 (Table 2), with no significant
correlations between the five dimensions (see Table 4). The data for each dimension was highly
skewed, with most subjects having low symptom scores, and a few with more serious
psychopathology on each dimension. As a result of this skew, symptom dimensions were
transformed using a zero-skewness logarithmic transformation. Figure 1 illustrates examples of the
mania and reality distortion dimensions before and after transformation.
3.2 Distribution of symptom dimensions between centres
Initial inspection of the distribution of untransformed symptom dimensions suggested some
differences in severity between centres. Our findings indicate that subjects from London were
significantly more likely to have higher levels of reality distortion (Ranksum, p<0.01) and depressive
symptoms (p=0.054) and lower levels of disorganization (p<0.01) than subjects from Nottingham. A
summary of these findings is presented in Table 5. We next sought to understand whether these
differences persisted following multilevel modeling. (For results regarding paranoia, see section 3.5.)
3.3. Multilevel modeling of symptom dimensions
Initial null multilevel linear regression models suggested that a small but significant variance in reality
distortion (intraclass correlation coefficient [ICC]: 5.2%; Chi2 p=0.02) and disorganization (ICC: 3.3%;
p=0.04) could be attributed to the neighbourhood level. A priori variables of age, sex and ethnicity
were then added to each model, and a forward fitting approach was undertaken to identify
significant individual-level predictors of each symptom dimension.
20
The ICC variance diminished in the final models, with no ICC reaching significance for any dimension.
Insidious mode of onset was associated with lower scores for mania (effect size [ES]: -0.53, 95 % CI: 0.79,-0.23; p<0.01) and living in Nottingham was associated with lower reality distortion score (ES: 0.17, 95 % CI: -0.27,-0.07; p<0.01), lower depressive symptoms scores (ES: -0.22, 95 % CI: -0.37,-0.06;
p<0.01), and higher disorganization scores (ES: 0.05, 95% CI:0.02,0.09; p<0.01). Sex was associated
with negative symptoms (ES: -0.17, 95 % CI: -0.33,-0.02; p=0.03), i.e. more common for males, and
depressive symptoms (ES: 0.16, 95 % CI: 0.03,0.28; p=0.01), i.e. more common for females. The only
clearly significant effect for ethnicity was for depressive symptoms, where subjects within the Black
Caribbean category were associated with fewer symptoms (ES: -0.22, 95 % CI: -0.38,-0.05; p=0.01)
(Table 6, overleaf).
Next, we investigated whether neighbourhood variables (population density, ethnic density,
deprivation, voter turnout data as a proxy for social capital, and inequality) could account for any of
the centre effect for reality distortion, depressive symptoms, and disorganization, but could not find
evidence for any variable improving the models. A skewness and kurtosis test for normality was
performed of the residuals for each transformed dimension in our final models. These were nonsignificant for all dimensions except for the depressive dimension, where evidence remained that the
residuals were non-normally distributed (p<0.001). Specifically, the normality of the kurtosis could
not be ensured (kurtosis p<0.001, skewness p=0.74). See Figure 2 for a visualization of the residual
distribution.
3.4. Sensitivity analysis
A sensitivity analysis was conducted to investigate the potential confounding of poly-drug use, mode
of onset and parental history on the centre variable. The two scenarios analyses did not significantly
alter the centre effects for reality distortion, depressive symptoms and disorganization, see Table 7.
3.5. Multilevel modeling of paranoia
Multilevel ordinal logistic regression suggested that very little variation in paranoid symptoms was
attributable to the neighbourhood level in our null model (Variance <0.001). Nevertheless, there was
evidence that the odds of increased paranoia were greater in London than Nottingham (OR per
additional paranoia symptom: 1.85; 95% CI: 1.31, 2.61) after adjustment for age and sex, although
this effect disappeared after further control for ethnicity (OR: 1.36; 95% CI: 0.90, 2.06).
3.6. Symptom dimension and paranoia differences by diagnostic category
In a post-hoc analysis, the difference in centre effect between subjects with non-affective (n=320)
and affective (=128) psychotic disorder was investigated for the three dimensions identified as
showing centre differences in the full sample: reality distortion, depressive symptoms and
disorganization.
21
Prior to considering centre differences in these dimensions in the two samples, an inspection of the
distribution of these symptoms was undertaken in each set of disorders. Reality distortion was
normally distributed in both non-affective and affective psychosis samples (Figure 3), with no
evidence of skew or kurtosis in either group. However, a t-test revealed that the mean level of reality
distortion (on the transformed scale) was significantly greater for people with non-affective (mean:
0.68, s.d.: 0.43) than affective psychoses (mean: 0.44; s.d.: 0.37; t-test p<0.001). The distribution of
depressive symptoms within the non-affective sample was not skewed (left hand pane, Figure 4,
p=0.64), though there was evidence of significant kurtosis (p<0.001), suggestive of fewer depressive
symptoms in this sample.
By contrast, the affective sample displayed a bimodal distribution of depressive scores (right hand
pane, Figure 4), consistent with a sample containing people with both mania and depressive
psychotic disorders, as confirmed in Figure 5. The median level of depressive symptoms was
significantly lower in the non-affective sample than the mania sample (Ranksum test p<0.001), which
were in turn significantly lower than in the depressive psychoses sample (Ranksum test p<0.001).
Transformed disorganization symptoms were normally distributed for people with both non-affective
and affective psychoses (Figure 6), with no evidence of skew or kurtosis in either sample. A t-test
suggested that the mean level of disorganization was marginally higher in the non-affective sample
(mean: 1.59; s.d.: 0.16) compared with the affective sample (mean 1.55; s.d. 0.13; t-test p=0.03).
For people with non-affective psychosis, symptoms differences were stronger in London than
Nottingham for all three dimensions, after control for age, sex and ethnicity [reality distortion (ES: 0.20, 95 % CI: -0.32,-0.08; p=<0.01), depressive symptoms (ES: -0.27, 95 % CI: -0.46,-0.09; p<0.01),
disorganization (ES: 0.07, 95 % CI: 0.02, 0.11; p<0.01)]. By contrast, for the affective sample no centre
differences were observed for either reality distortion (ES: 0.04, 95 % CI: -0.13, 0.21; p=0.62) or
depressive symptoms (ES: -0.16, 95 % CI: -0.42, 0.10; p=0.23). A trend persisted for greater
disorganized symptoms in Nottingham compared with London in people with affective psychosis,
after control for age, sex and ethnicity (ES: 0.06, 95 % CI: -0.00, 0.12; p=0.051) (See Table 8).
Finally, we tested whether these centre by disorder interactions reached statistical significance for
any dimension in full multilevel model, restricted to the non-affective and affective samples (N=448).
Multilevel modeling suggested differences in reality distortion between London and Nottingham
were dependent on broad diagnosis (Wald p-value for interaction: p=0.06), though there was no
evidence of such an interaction for either depressive (p=0.13) or disorganized symptoms (p=0.58).
As for symptom dimensions, it was inspected whether the centre differences observed in regard to
paranoia differed amongst people who had been diagnosed with either a non-affective of affective
psychotic disorder. Initial inspection of paranoia in these two diagnostic groups suggested sufficient
individual-level variation was present for paranoia in both diagnostic groups to inspect the effect of
centre on paranoia separately for these disorders (see Figure 7).
For people with a non-affective psychosis, weak evidence of a higher odds of reporting paranoid
symptoms was observed in people from London compared with Nottingham, after controlling for
age, sex and ethnic group (OR: 1.63; 95% CI: 0.97, 2.73; p=0.07). No such association was found for
those people with affective psychoses (OR: 0.60; 95% CI: 0.27, 1.33). A full multilevel model fitted
with an interaction between centre and broad diagnosis confirmed the risk of paranoia in London
compared with Nottingham varied significantly by diagnosis (Wald p-value for interaction: p=0.02).
22
Further inspection of this effect for the non-affective psychosis group suggested that differences in
the odds of paranoia between London and Nottingham could be explained by increased population
density (OR: 1.01; 95% CI: 1.00, 1.02; p=0.01) and less socioeconomic deprivation (OR: 0.68; 95% CI:
0.50, 0.93), after control for age, sex and ethnic group.
23
4. Discussion
4.1 Principal Findings
In this study, two of five symptom dimensions commonly identified in psychotic disorders (reality
distortion and disorganization) presented with small, but significant variation at the neighbourhoodlevel in our initial models. This variance diminished to non-significance in our final models, and could
largely be attributed to centre differences in symptomatology between London and Nottingham,
entered into the models as fixed effects. Thus, living in London compared with Nottingham was
significantly associated with more reality distortion, more depressive symptoms, and less
disorganization, after control for sex, age and ethnicity.
In our post-hoc analysis, we found some evidence to suggest that the strength of the centre effect
was stronger for people with non-affective psychoses than their affective counterparts for all three
dimensions. This difference, however, only approached statistical significance (p=0.06) for an
interaction between centre and diagnosis on reality distortion; this was the principal symptom
dimension which was hypothesized to show urban-rural differences in manifestation.
For paranoia, which included two of the ten symptom groups in the reality distortion dimension, we
observed centre differences consistent with findings from the reality distortion analysis, with
significantly higher odds of paranoia in London than Nottingham. As for reality distortion, differences
by diagnostic categories showed a similar pattern, with a strong statistical interaction between
centre and diagnosis on the odds of paranoia (p=0.02), such that rates of paranoia were only
elevated in London for the non-affective sample. We do not believe that this difference could be
attributed to a lack of variation in paranoia in the affective sample because the distribution of
paranoia was similar in both samples. Further inspection of this post-hoc finding suggested that the
increased odds of paranoid ideation in London compared with Nottingham could be attributed to
increased population density and, independently, lower levels of socioeconomic deprivation.
We noted that acute mode of onset was significantly associated with greater manic symptoms. Male
sex was also significantly associated with more negative symptoms, while depressive symptoms were
greater for women. The gender differences are consistent with some earlier studies, but not all; for
an overview, see Ochoa et al [12]55.
4.2 Methodological considerations
Our study has a number of limitations. First, while our analysis strategy ensured that the distribution
of residuals following model fit was normal for 4 symptom dimensions, and paranoia, our models did
not fit the data well for depressive symptoms, with residuals exhibiting substantial kurtosis following
modeling. For this symptom dimension, we recommend caution in interpreting our results. Another
limitation is that we did not have exposure data for any other levels than individual and
neighbourhood level, such as family level data. One further potential limitation, as mentioned in
Demjaha et al47, is that the sample in the AESOP study was not medication naïve. We could therefore
not exclude the possibility that medication could have influenced symptomatology and thereby the
24
dimensional scores included here. This could have biased our results in two ways. First, if the
proportion of medication naïve participants was a non-differential bias i.e. present to an equal extent
in our London and Nottingham centres or across neighbourhoods, this bias would have limited our
ability to detect differences in symptom severity between centres or neighbourhoods for some
dimensions. For example, treatment with anti-psychotic medication should reduce the level of
positive symptoms, including reality distortion and paranoid ideation. Since we observed centre
differences for such symptoms, we believe it is unlikely that this bias had a major effect on our
findings. A second, more problematic issue, would occur if this bias (in the probability of receiving
medication) was differential, and more likely to occur in one centre than another due to possible
differences in the organization, policy and delivery of mental health care in South London versus
Nottingham. If participants in Nottingham were more likely to receive antipsychotic medication prior
to symptom assessment in the AESOP study than their London counterparts, this could explain intercentre differences in reality distortion or paranoia.
Fifteen subjects were excluded from the original sample with sufficient information to complete the
IGC. The excluded subjects were more likely to be male and live in London, but did otherwise not
differ significantly from the included cases. Because the symptom dimension scores of the excluded
subjects did not differ significantly from the rest, the exclusion did likely not have a significant impact
on our results.
The AESOP study was cross-sectional, meaning that the potential individual change in dimensional
structure over time has not been accounted for. Information of residency was at time of first contact,
so the influence of social drift could also not be accounted for. Although the phenomenon cannot be
ruled out completely, earlier studies have shown that at least for urbanicity exposure prior to illness
the increased risk of psychosis is not just an effect of social drift30. This supports at least a partially
casual role for the environment in schizophrenia, indicating that it is unlikely that our results are
simply the results of drift. It might well be, however, that residency at earlier stages of life have
stronger effects on mental illness, working as some sort of primer or sensitization factor.
The comparison between London and Nottingham was not a classic urban-rural dichotomy. While
the London area studied was exclusively “urban”, the Nottingham area was a mix of urban, suburban,
and rural environments34. Our analysis of paranoia indicated, nevertheless, that population density
could explain part of the difference between London and Nottingham, lending support to using
centres as fixed effects.
Our study also has a number of strengths. It is, to the best of our knowledge, the first study to
investigate the role of urbanicity and neighbourhood-level factors on symptoms dimensions for a
subject sample with first-episode psychotic disorders. The AESOP study is a large, population-based
epidemiological study of first-episode psychosis study, and has produced reliable, valid findings on a
range of epidemiological and neuroscientific issues.
4.3 Meaning of findings
Our findings indicate that experiencing psychosis in a larger city is associated with more reality
distortion (Including paranoia), more depressive symptoms, and less disorganization. This supports
25
the possibility that where you live can have an influence on your mental health, and that there is
some unknown factor in the urban environment that plays a role in the development of psychotic
disorders.
The urban factor could be an effect of the increased stimuli presumably expected when living in a
larger city. If the sheer number of stimuli, and possibly the number of stressors, increases with the
size of the city, this might have an impact on our mental health, perhaps through the dopamine
system. Specifically, in more densely populated areas the opportunity to be exposed to a greater
number of social interactions may increase, and if these social interactions are interpreted more
negatively, they may give rise to greater levels of social stress, resulting in more reality
distortion/paranoia and depressive symptoms.
The epidemiology professor Michael Marmot has written that “How much control you have over
your life – and the opportunity you have for full social engagement and participation are crucial for
health, well-being, and longevity”56. One can speculate that within a small community, any person is
likely only a few steps away from the top of the social hierarchy ladder. In a much larger community,
the risk of being distanced from the top is vastly greater. In such a position, one’s opportunity for full
social engagement and participation might be diminished, resulting over time in social
marginalization and a mentally more vulnerable state, with a lower threshold for the development of
psychosis.
Interestingly, our post-hoc findings indicate a difference of geographical influence between nonaffective and affective psychotic disorders. Given that this finding is post-hoc it requires replication in
further studies, and only tentative conclusions should be drawn as to the potential meaning of these
findings. However, the pattern of our findings with respect to reality distortion, depressive symptoms
and paranoia are remarkably consistent; centre effects only appear to be present for those people
with a non-affective rather than affective psychosis. While we cannot rule out the possibility of TypeII error and encourage our findings to be replicated in larger samples, this finding supports the
possibility that non-affective and affective psychotic disorders have different aetiological pathways.
Van Os and Rutten have eloquently highlighted the importance of understanding the role of
cognition in the development of psychotic disorders following exposure to environmental factors29.
Interestingly, premorbid cognitive impairment is frequently seen amongst people with schizophrenia
and other non-affective psychoses, but not their counterparts who go on to develop affective
psychotic disorders57. Poorer social cognition in particular may be a relevant additional impediment
to successful non-aberrant interpretation of environmental cues, and premorbid childhood social
functioning seems to only be impaired in people with schizophrenia, but not their counterparts with
bipolar disorder.58 As such, subjects with impaired social cognition may be particularly vulnerable to
the effects of stressful urban environments. Although we were unable to inspect the role of
premorbid cognition directly in our data, the greater levels of reality distortion and paranoia
observed in London compared with Nottingham in our non-affective but not our affective sample are
consistent with this idea.
The findings in this thesis are consistent with non-affective disorders as disorders of aberrant
salience, where people in more urban environments have greater levels of reality distortion and
paranoia as a result of exposure to environmental cues that could lead to dopaminergic
dysregulation and misinterpretation. This possibility of a difference of geographical influence
26
between non-affective and affective psychotic disorders indicates that even though Howes and
Kapur might have demonstrated a valid model for the development of psychosis in schizophrenia,
their model might not be applicable for psychosis in affective syndromes.
The finding that paranoid ideation was significantly greater in less socioeconomically deprived (i.e.
more affluent) neighbourhoods, independent of population density is perhaps not very intuitive, but
could be consistent with a hypothesis such as social defeat or subordination.59 Thus, in more affluent
neighbourhoods some individuals – and particularly those within the community who fall below the
average level of affluence in the community – may feel particularly marginalized. Occupation of such
a subordinate position, may, over time, lead to greater paranoid ideation. We cannot draw any
definitive conclusions about this issue from our data, but suggest this possibility could become a
testable hypothesis for future studies investigating these associations.
Finally, our findings that certain symptom dimensions are associated with urbanicity, but that this
association is largely dependent on diagnostic category lends support to the notion that both the
categorical and the dimensional approach have their place in the research of psychotic disorders.
27
5. Appendix
Table 1: Simplified presentation of selected diagnostic groups of psychotic disorders.
Non-Affective
Schizophrenia
ICD code
F20
Schizotypal disorder
F21
Delusional Disorder
F22
Transient psychotic
disorder
F23
Schizoaffective
disorder
F25
Affective
Manic episode
F30
Bipolar disorder
F31
Depressive episode or
disorder
F32-33
Description
At least two of the following symptoms for the duration
of at least one month: delusions, hallucinations,
disorganized speech, disorganized or catatonic behavior,
and negative symptoms. Affective syndrome, organic
etiology and drug abuse cancelled out as causing factors.
Cold and inadequate affect, peculiar behavior, tendency
to be by him/herself and “aloofness”. Sometimes bizarre
ideas, suspiciousness, and vague language. Reminiscent
of schizophrenia but do not contain full-blown
schizophrenic symptoms60.
Continuous non-bizarre delusions (such as persecutory
delusions, delusions of reference, and hypochondriac
delusions), without other criterion for schizophrenia12.
Psychosis with a duration of maximum one month (three
months for certain subgroups) and at least one psychotic
symptom12. Psychotic symptoms may be stable or
variable and rapidly changing.
Prominent depression and/or mania concurrent with
schizophrenic symptoms as well as a duration of two
weeks or more where schizophrenic symptoms were
apparent without affective symptoms12. Criteria for this
diagnosis differs in part between DSM IV and ICD 10
Elevated, expansive or irritable mood for at least one
week, as well as (for psychotic mania) episodes of
hallucinations or delusions. Three or four of the following
symptoms: Hyperactivity, fleeing thoughts, reduced
inhibitions, reduced need for sleep, heightened selfesteem and grandiosity, reduced stringency of thoughts,
irresponsible behavior, and increased libido61.
Fluctuating periods of mania and depression, with
depression often having a larger share62, as well as (in
psychotic cases) episodes of hallucinations or delusions.
Involving elements such as: depressed mood, lack of
interest, reduced energy, lowered self-confidence and
self-esteem, recurrent thoughts of death and suicide,
reduced cognitive ability, sleep disturbance, reduced or
increased appetite63.
28
Table 2: Psychopathological dimensions in the AESOP study following principal-axis factor analysis1
Dimensions (% variance)
Factors
1
2
3
4
5
Manic (17%)
Heightened subjective functioning
Rapid subjective tempo
Expansive mood
Expansive delusions & hallucinations
Overactivity
Socially embarrassing behaviour
0.86
0.83
0.82
0.63
0.62
0.40
-0.08
-0.04
-0.07
0.13
-0.01
0.16
-0.08
-0.06
-0.07
-0.07
0.01
0.11
-0.06
-0.03
-0.06
-0.07
-0.08
0.12
-0.04
0.01
-0.04
0.07
0.24
0.22
Reality distortion (10%)
Altered perception
Non-specific auditory hallucinations
Non-specific psychotic experiences
Non-affective auditory hallucinations
Experience of disordered form of thoughts
Delusions of control
Bizarre delusions & interpretations
Miscellaneous delusions
Delusions of reference
Delusions of persecutions
0.12
-0.09
0.01
-0.08
0.05
-0.06
-0.09
0.08
0.11
-0.08
0.31
0.55
0.34
0.61
0.48
0.37
0.41
0.38
0.46
0.43
-0.01
0.10
-0.02
0.11
0.03
<0.01
-0.04
-0.08
>-0.01
-0.07
-0.06
0.06
0.01
-0.14
-0.08
-0.03
-0.15
0.08
0.21
0.03
0.03
-0.33
-0.01
-0.30
0.12
0.14
0.23
0.07
0.09
0.18
Negative (9%)
Flat & incongruous affect
Poverty of speech
Non-verbal communication
Self neglect
Motor retardation
-0.11
-0.07
0.09
-0.01
-0.09
0.02
-0.11
-0.03
0.09
0.01
0.65
0.69
0.73
0.37
0.61
0.01
-0.01
0.17
0.02
0.36
0.12
-0.04
-0.04
0.19
-0.18
Depressive (9%)
Special features of depressed mood
Depressed mood
Depressive delusions & hallucinations
-0.06
-0.17
-0.09
0.04
-0.06
0.01
0.13
0.04
0.13
0.84
0.82
0.58
-0.06
-0.05
-0.09
0.16
0.10
0.12
0.06
0.23
<0.01
-0.02
-0.12
0.39
0.46
Disorganisation (4%)
Emotional turmoil
Incoherent speech
1All
item group checklist items were included in the factor analysis and contributed to factor scores. For clarity of
presentation and comparability with Demjaha et al., only items which had high factor loadings on at least one factor are
reported here. High factor loadings (≥±0.30) are presented in bold.
29
Table 3: Characteristics for included subjects from the AESOP study (no:469)
Characteristics
Number (percent)
Age
- Median
- IQR
29
22-36
Gender
- Male
- Female
266 (56.7)
203 (43.3)
Ethnicity
- White British
- White other
- Black Caribbean
- Black African
- Asian
- Other
- Mixed, White and Black Carib.
223 (43.1)
35 (6.7)
137 (26.5)
64 (12.4)
19 (3.7)
29 (5.6)
10 (1.9)
Diagnosis
- Drug-induced psychosis
- Non-affective psychosis
- Affective psychosis
21 (4.5)
320 (68.2)
128 (27.3)
Centre
- London
- Nottingham
309 (65.9)
160 (34.1)
Mode of onset
- Acute
- Insidious
- NA
211 (45.0)
222 (47.3)
36 (7.7)
30
Table 4: Correlation between dimensions
Mania
Mania
Reality
Distortion
Negative
symptoms
Depressive
symptoms
Disorganization
1.000
Reality
Distortion
Negative
symptoms
Depressive
symptoms
Disorganization
-0.012
(p: 0.78)
-0.014
(p: 0.76)
-0.019
(p: 0.68)
0.025
(p: 0.62)
1.000
0.010
(p : 0.84)
-0.010
(p: 0.82)
-0.004
(p: 0.93)
1.000
0.047
(p: 0.30)
-0.002
(p: 0.97)
1.000
-0.053
(p: 0.24)
1.000
Figure 1: Mania and Reality distortion before and after transformation
Distribution of scores for mania (left column) and reality distortion (right column),
before (top row) and after transformation (bottom row).
31
Table 5: Centre differences for symptom dimensions
Mean (Sd)
Median (IQR)
Ranksum
(Wilcoxon)
London
Nottingham
London
Nottingham
Mania
0.03
(1.00)
-0.06
(0.82)
-0.37
(-0.50,-0.18)
-0.36
(-0.47,-0.14)
0.53
Real.Dist
0.13
(0.90)
-0.27
(0.75)
-0.04
(-0.52,0.68)
-0.50
(-0.78,0.13)
<0.01
Negative
-0.03
(0.90)
0.05
(0.88)
-0.37
(-0.59,0.18)
-0.27
(-0.57,0.38)
0.13
Depressive
0.05
(0.94)
-0.10
(0.86)
-0.40
(-0.62,0.50)
-0.49
(-0.66,0.25)
0.05
Disorg.
-0.09
(0.77)
0.18
(0.70)
-0.13
(-0.62,0.34)
0.04
(-0.30,0.60)
<0.01
Figure 2: Distribution of residuals for depressive symptoms
Residuals of depressive symptoms plotted against normality, using the qnorm command in Stata
32
Table 6: Results of empty and final model for symptom dimensions
Mania
Reality
Distortion
Negative
Depressive
Disorganization
0.00 %
1.00
944
3.30 %
0.04
-415
Empty model
ICC
χ2 p-value
AIC
0.88 %
0.31
1615
5.20 %
0.02
545
0.91 %
0.32
1165
Final model
ICC
χ2 p-value
AIC
Variables
Age
Sex
Insidious
onset of
psychosis
Centre
Ethnicity○
White
Other
Black
Caribbean
Black
African
Asian
Other
Mixed,
White, &
Black.Carib
.
0.25 %
0.44
1625
0.01 %
0.50
570
0.29 %
0.44
1192
0.00 %
1.00
974
0.06 %
0.37
-357
-0.01
(-0.02,0.05)
0.22
(-0.03,0.47)
-0.53**
(-0.79,-0.28)
0.00
(-0.00,0.00)
-0.03
(-0.11,0.05)
NA
0.00
(-0.01,0.01)
-0.17*
(-0.33,-0.02)
NA
0.00
(-0.00,0.01)
0.16*
(0.03,0.28)
NA
0.00
(-0.00,0.00)
-0.02
(-0.05,0.00)
NA
NA
-0.17**
(-0.27,-0.07)
NA
-0.22**
(-0.37,-0.06)
0.05**
(0.02,0.09)
-0.25
(-0.73,0.23)
-0.08
(-0.39,0.21)
0.23
(-0.15,0.60)
-0.31
(-1.02,0.41)
0.07
(-0.46,0.61)
0.29
(-0.55,0.12)
0.11
(-0.05,0.27)
0.04
(-0.07,0.15)
0.12
(-0.01,0.26)
-0.10
(-0.33,0.13)
0.01
(-0.16,0.19)
-0.20
(-0.46,0.07)
0.05
(-0.25,0.35)
-0.10
(-0.28,0.09)
0.03
(-0.21,0.27)
-0.23
(-0.67,0.22)
0.06
(-0.28,0.39)
0.52
(-0.00,1.05)
-0.07
(-0.31,0.18)
-0.22*
(-0.38,-0.05)
-0.15
(-0.36,0.06)
0.05
(-0.30,0.40)
0.01
(-0.26,0.28)
-0.03
(-0.44,0.39)
0.01
(-0.05,0.07)
0.00
(-0.04,0.04)
-0.01
(-0.06,0.03)
-0.01
(-0.09,0.07)
0.00
(-0.07,0.06)
0.04
(-0.06,0.13)
Variables included in final model of each dimension are displayed.
ICC: Intraclass Correlation Coefficient | AIC: Akaike Information Criterion
* P <0.05, ** P <0.01 |○ Baseline: White British
33
Table 7: Centre effect – sensitivity analysis
Original
Scenario 1○
Scenario 2○
Reality
distortion
-0.17
(-0.27,-0.07)
-0.15
(-0.25,-0.05)
-0.15
(-0.26.0.05)
Depressive
symptoms
-0.22
(-0.37,-0.06)
-0.23
(-0.38,-0.09)
-0.27
(-0.42,-0.12)
Disorganization
0.05
(0.02,0.09)
0.06
(0.02,0.10)
0.06
(0.03,0.10)
○
Scenario 1: Missing values coded to baseline group (no drug use, acute onset,
no parental history). Scenario 2: Missing values coded to highest exposure category
(poly-drug use, insidious onset, positive parental history of psychosis)
Figure 3: Distribution of reality distortion by diagnostic category
ICD 20-29: Non-affective psychotic disorder, ICD 30-33: Affective psychotic disorder
Figure 4: Distribution of depressive symptoms by diagnostic category
34
Figure 5: Distribution of depressive symptoms - bipolar and depressive psychotic disorder
Figure 6: Distribution of disorganization by diagnostic category
35
Table 8: Centre effect (living in Nottingham) on dimensions where centre was part of final model.
Centre effect
Full sample
(No: 469)
Non-affective
(No: 320*)
Affective
(No: 128*)
Reality Dist.
Depressive
Disorganization
-0.17
(-0.27,-0.07)
-0.20
(-0.32,-0.08)
0.04
(-0.13,0.21)
-0.22
(-0.37,-0.06)
-0.27
(-0.46,-0.09)
-0.16
(-0.42,0.10)
0.05
(0.02,0.09)
0.07
(0.02,0.11)
0.06
(-0.00,0.12)
Numbers indicating coefficient value and 95 % confidence interval.
* 21 subjects with drug-induced psychosis (ICD10-19) were not included in this analysis
Figure 7: Distribution of paranoia by ICD-10 diagnostic group
36
6. References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
Cullberg J. Psykoser - Ett integrerat perspektiv (Psychoses - An integrative perspective) 2nd
ed: Natur och Kultur 2005.
Läkemedelsverket TSPD. Läkemedelsboken (The Swedish Pharmaceuticals Book), page 1057.
2011-2012.
Läkemedelsverket TSPD. Läkemedelsboken (The Swedish Pharmaceuticals Book), page 1059.
2011-2012.
Läkemedelsverket TSPD. Läkemedelsboken (The Swedish Pharmaceuticals Book), page 1067.
2011-2012.
Gustavsson A, Svensson M, Jacobi F, et al. Cost of disorders of the brain in Europe 2010. Eur
Neuropsychopharmacol Oct 2011;21(10):718-779.
Herlofson J. Psykiatri, page 285. 1st ed: Studentlitteratur 2009.
van Os J, Kapur S. Schizophrenia. Lancet Aug 22 2009;374(9690):635-645.
Heinrichs RW. Historical origins of schizophrenia: two early madmen and their illness. J Hist
Behav Sci Fall 2003;39(4):349-363.
Stotz-Ingenlath G. Epistemological aspects of Eugen Bleuler's conception of schizophrenia in
1911. Med Health Care Philos 2000;3(2):153-159.
American Psychiatry Association: http://www.psychiatry.org/practice/dsm/dsm-iv-tr (Data
last confirmed January 14th 2013).
World Health Organization: http://www.who.int/classifications/icd/revision/en/index.html
(Data last confirmed January 14th 2013).
Läkemedelsverket TSPD. Läkemedelsboken (The Swedish Pharmaceuticals Book), page 1061.
2011-2012.
Web page: www.internetmedicin.se, search word: "Schizofreni", author: Eva Lindström (Data
last confirmed January 14th 2013).
Crow TJ. Positive and negative schizophrenic symptoms and the role of dopamine. The British
journal of psychiatry : the journal of mental science Oct 1980;137:383-386.
Crow TJ. Molecular pathology of schizophrenia: more than one disease process? British
medical journal Jan 12 1980;280(6207):66-68.
Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for
schizophrenia. Schizophrenia bulletin 1987;13(2):261-276.
van Os J. Is there a continuum of psychotic experiences in the general population?
Epidemiologia e psichiatria sociale Oct-Dec 2003;12(4):242-252.
Liddle PF. The symptoms of chronic schizophrenia. A re-examination of the positive-negative
dichotomy. The British journal of psychiatry : the journal of mental science Aug
1987;151:145-151.
Potuzak M, Ravichandran C, Lewandowski KE, Ongur D, Cohen BM. Categorical vs
dimensional classifications of psychotic disorders. Comprehensive psychiatry Jun 7 2012.
Cullberg J. Psykoser - Ett integrerat perspektiv (Psychoses - An integrative perspective), page
64. 2nd ed: Natur och Kultur; 2005.
Picchioni MM, Murray RM. Schizophrenia. BMJ Jul 14 2007;335(7610):91-95.
Kendler KS, McGuire M, Gruenberg AM, O'Hare A, Spellman M, Walsh D. The Roscommon
Family Study. I. Methods, diagnosis of probands, and risk of schizophrenia in relatives.
Archives of general psychiatry Jul 1993;50(7):527-540.
Cardno AG, Marshall EJ, Coid B, et al. Heritability estimates for psychotic disorders: the
Maudsley twin psychosis series. Archives of general psychiatry Feb 1999;56(2):162-168.
Ottosson J-O. Psykiatri. 7th ed: Liber; 2009.
Bhugra D. Migration and schizophrenia. Acta psychiatrica Scandinavica Supplementum
2000(407):68-73.
37
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
van Os J, Kenis G, Rutten BP. The environment and schizophrenia. Nature Nov 11
2010;468(7321):203-212.
Minozzi S, Davoli M, Bargagli AM, Amato L, Vecchi S, Perucci CA. An overview of systematic
reviews on cannabis and psychosis: discussing apparently conflicting results. Drug and
alcohol review May 2010;29(3):304-317.
Henquet C, Murray R, Linszen D, van Os J. The environment and schizophrenia: the role of
cannabis use. Schizophrenia bulletin Jul 2005;31(3):608-612.
Moore TH, Zammit S, Lingford-Hughes A, Barnes TR, Jones PB, Burke M, Lewis G. Cannabis
use and risk of psychotic or affective mental health outcomes: a systematic review. Lancet Jul
28 2007;370(9584):319-328.
March D, Hatch SL, Morgan C, Kirkbride JB, Bresnahan M, Fearon P, Susser E. Psychosis and
place. Epidemiologic reviews 2008;30:84-100.
Kirkbride JB, Fearon P, Morgan C, et al. Heterogeneity in incidence rates of schizophrenia and
other psychotic syndromes: findings from the 3-center AeSOP study. Archives of general
psychiatry Mar 2006;63(3):250-258.
Sundquist K, Frank G, Sundquist J. Urbanisation and incidence of psychosis and depression:
follow-up study of 4.4 million women and men in Sweden. The British journal of psychiatry :
the journal of mental science Apr 2004;184:293-298.
Mortensen PB, Pedersen CB, Westergaard T, Wohlfahrt J, Ewald H, Mors O, Andersen PK,
Melbye M. Effects of family history and place and season of birth on the risk of
schizophrenia. The New England journal of medicine Feb 25 1999;340(8):603-608.
van Os J, Pedersen CB, Mortensen PB. Confirmation of synergy between urbanicity and
familial liability in the causation of psychosis. The American journal of psychiatry Dec
2004;161(12):2312-2314.
Pedersen CB, Mortensen PB. Evidence of a dose-response relationship between urbanicity
during upbringing and schizophrenia risk. Archives of general psychiatry Nov
2001;58(11):1039-1046.
Kirkbride JB, Fearon P, Morgan C, Dazzan P, Morgan K, Murray RM, Jones PB. Neighbourhood
variation in the incidence of psychotic disorders in Southeast London. Social psychiatry and
psychiatric epidemiology Jun 2007;42(6):438-445.
Silver E, Mulvey EP, Swanson JW. Neighborhood structural characteristics and mental
disorder: Faris and Dunham revisited. Soc Sci Med Oct 2002;55(8):1457-1470.
Mortensen PB, Pedersen CB, Melbye M, Mors O, Ewald H. Individual and familial risk factors
for bipolar affective disorders in Denmark. Archives of general psychiatry Dec
2003;60(12):1209-1215.
Berridge KC. The debate over dopamine's role in reward: the case for incentive salience.
Psychopharmacology Apr 2007;191(3):391-431.
Ottosson J-O. Psykiatri, page 180. 7th ed: Liber; 2009.
Kapur S. Psychosis as a state of aberrant salience: a framework linking biology,
phenomenology, and pharmacology in schizophrenia. The American journal of psychiatry Jan
2003;160(1):13-23.
Moskovitz C, Moses H, 3rd, Klawans HL. Levodopa-induced psychosis: a kindling
phenomenon. The American journal of psychiatry Jun 1978;135(6):669-675.
Howes OD, Kapur S. The dopamine hypothesis of schizophrenia: version III--the final common
pathway. Schizophrenia bulletin May 2009;35(3):549-562.
Kaymaz N, Krabbendam L, de Graaf R, Nolen W, Ten Have M, van Os J. Evidence that the
urban environment specifically impacts on the psychotic but not the affective dimension of
bipolar disorder. Social psychiatry and psychiatric epidemiology Sep 2006;41(9):679-685.
Lederbogen F, Kirsch P, Haddad L, et al. City living and urban upbringing affect neural social
stress processing in humans. Nature Jun 23 2011;474(7352):498-501.
Mizrahi R, Addington J, Rusjan PM, et al. Increased stress-induced dopamine release in
psychosis. Biological psychiatry Mar 15 2012;71(6):561-567.
38
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
Demjaha A, Morgan K, Morgan C, et al. Combining dimensional and categorical
representation of psychosis: the way forward for DSM-V and ICD-11? Psychological medicine
Dec 2009;39(12):1943-1955.
Fearon P, Kirkbride JB, Morgan C, et al. Incidence of schizophrenia and other psychoses in
ethnic minority groups: results from the MRC AESOP Study. Psychological medicine Nov
2006;36(11):1541-1550.
Noble M, Wright G, Dibben C, et al. Indices of Deprivation 2004 London: ODPM; 2004.
Kirkbride JB, Morgan C, Fearon P, Dazzan P, Murray RM, Jones PB. Neighbourhood-level
effects on psychoses: re-examining the role of context. Psychological medicine Oct
2007;37(10):1413-1425.
Kawachi I, Kennedy BP. The relationship of income inequality to mortality: Does the choice of
indicator matter? Social Science &amp; Medicine 1997;45(7):1121-1127.
Addington D, Addington J, Patten S. Gender and affect in schizophrenia. Canadian journal of
psychiatry Revue canadienne de psychiatrie Jun 1996;41(5):265-268.
Larsen TK, McGlashan TH, Moe LC. First-episode schizophrenia: I. Early course parameters.
Schizophrenia bulletin 1996;22(2):241-256.
Szymanski S, Lieberman JA, Alvir JM, et al. Gender differences in onset of illness, treatment
response, course, and biologic indexes in first-episode schizophrenic patients. The American
journal of psychiatry May 1995;152(5):698-703.
Ochoa S, Usall J, Cobo J, Labad X, Kulkarni J. Gender differences in schizophrenia and firstepisode psychosis: a comprehensive literature review. Schizophrenia research and treatment
2012;2012:916198.
Marmot M. The Status Syndrome: Holt McDougal 2005.
Koenen KC, Moffitt TE, Roberts AL, Martin LT, Kubzansky L, Harrington H, Poulton R, Caspi A.
Childhood IQ and adult mental disorders: a test of the cognitive reserve hypothesis. Am J
Psychiatry Jan 2009;166(1):50-57.
Rietschel M, Georgi A, Schmael C, et al. Premorbid adjustment: a phenotype highlighting a
distinction rather than an overlap between schizophrenia and bipolar disorder. Schizophr Res
May 2009;110(1-3):33-39.
Selten JP, Cantor-Graae E. Social defeat: risk factor for schizophrenia? Br J Psychiatry August
1, 2005 2005;187(2):101-102.
Ottosson J-O. Psykiatri, page 169. 7th ed: Liber; 2009.
Ottosson J-O. Psykiatri, page 235. 7th ed: Liber; 2009.
Läkemedelsverket TSPD. Läkemedelsboken (The Swedish Pharmaceuticals Book), page 1037.
2011-2012.
Ottosson J-O. Psykiatri, page 225. 7th ed: Liber; 2009.
39