THE SOCIOSPATIAL DISTRIBUTION OF PSYCHOTIC SYMPTOMS AND DIMENSIONS

THE SOCIOSPATIAL DISTRIBUTION OF PSYCHOTIC SYMPTOMS AND DIMENSIONSA MULTILEVEL INVESTIGATION FROM THE AESOP STUDY

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 Skne 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 (Lkarprogrammet) as well as for a bachelors 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

ContentAbstract4Populrvetenskaplig sammanfattning51. Introduction61.1. Background61.2 Conceptualization of psychotic disorders71.3 Etiology81.4 Patophysiology and Possible Mechanisms101.5 Context and Significance of Thesis111.6 Objectives122. Methods142.1. Sample142.2. Outcome data (psychotic symptom dimensions and cluster142.3. Individual level sociodemographic & clinical data152.4. Neighbourhood level exposures and confounders162.5. Statistical analysis162.5.1. Symptom dimension overview and transformation162.5.2. Centre & Symptom dimension comparison172.5.3. Multilevel linear regression analysis approach172.5.4. Model Building172.5.5 Analytical approach for symptom dimensions182.5.6. Analytical approach for paranoia192.6. Sensitivity analysis193. Results203.1 Sample characteristics203.2 Distribution of symptom dimensions between centres203.3. Multilevel modeling of symptom dimensions203.4. Sensitivity analysis213.5. Multilevel modeling of paranoia213.6. Symptom dimension and paranoia differences by diagnostic category214. Discussion244.2 Methodological considerations244.3 Meaning of findings255. Appendix286. ReferencesError! Bookmark not defined.

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.

MethodsWith 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 (=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 (=0.22; 95%CI: 0.06, 0.37) while levels of disorganization where found to be higher in Nottingham (=-0.05; 95%CI: -0.09, -0.02). No differences were observed for mania or negative symptoms.

ConclusionThe 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 non-affective and affective disorders.

Populrvetenskaplig sammanfattning

Den sociala omgivningens pverkan p vr mentala hlsa r viktig att frska frst. Mycket forskning har gnats t att underska hur omgivningen pverkar antalet personer som insjuknar med olika typer av psykiatriska diagnoser. F har dock underskt hur omgivningen pverkar symptombilden inom psykiatriska tillstnd som till exempel psykotiska sjukdomar. Psykos r ett tillstnd som kan innefatta till exempel hallucinationer och vanfrestllningar, ofta i kombination med andra psykologiska symptom.

Med hjlp av data frn 469 personer med frstaepisods-psykos frn sydstra London och Nottingham kunde Demjaha et al. identifiera fem huvudsakliga symptomdimensioner inom det psykotiska sjukdomsspektrumet: mani, frvrngd verklighetsuppfattning, negativa symptom, depressiva symptom samt desorganisation. Med hjlp av olika statistiska metoder underskte vi sedan, fr samma urvalsgrupp, omgivningens pverkan p dessa fem symptomdimensioner.

Vi fann att niverna av frvrngd verklighetsuppfattning var hgre i London n i Nottingham. En utkad analys antydde att denna effekt var starkare fr de personer som diagnosticerats med en s kallad icke-affektiv psykotisk sjukdom (ssom schizofreni) jmfrt med s kallad affektiv psykotisk sjukdom (ssom depression med psykos). Det fanns ven tecken p kad niv av depressiva symptom i London och kad niv av desorganisation i Nottingham. Inga skillnader observerades fr mani eller negativa symptom.

Det har spekulerats att stimuli frn omgivningen kan pverka risken att utveckla s kallade positiva psykotiska symptom. Vrt fynd att hgre niver av frvrngd verklighetsuppfattning kunde identifieras i en mer urban milj (London jmfrt med Nottingham) passar vl med denna id. Resultaten i detta arbete indikerar att denna effekt frmst verkar glla fr icke-affektiva psykotiskta sjukdomstillstnd, vilket skulle kunna tyda p olika etiologiska bakgrunder (uppkomstorsaker) fr icke-affektiva och affektiva sjukdomstillstnd.

1. Introduction1.1. 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 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.

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 individuals 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 case-controlled 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.

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 confounders26.

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 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 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 Parkinsons 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 fire41.

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 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 Kapurs 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 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.

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 empirically-driven 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 subjects 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 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). Comment by Mats: Senare nmns CAS ward, som jag inte tror frklarats. Kanske introducera det hr?

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).

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 SOP 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 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:

where was the logarithmic transformation of the positive or negative value of our symptom dimension .

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 approachFor 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:

Yij = ij + rij

rij ~ N (0, 2)

where 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, 2. If the multilevel model would include n different variables, then would be modeled as:

ij= + 1xij1+ 2xi2 +nxijn+Si

Si~ N(0, 2)

where denotes the intercept and 1..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, 2. This is a simple random intercepts model, where the variable xi2 is a neighbourhood-level variable in this example, since the value of 2 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, :

= 2 .

(2+ 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 Akaikes 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).

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.

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