ANGELO.Psychosis characteristics (thesis)May25th2015 (final version) (1)

Discrimination, Race, Gender & APPS 1
Discrimination may contribute to a higher endorsement of Attenuated Positive Psychotic
Symptoms (APPS) in black compared to white college students irrespective of gender.
Angelo Laine
Mentor: Deidre Anglin, PhD
Department of Psychology
City College of New York
May 25th, 2015

Abstract
Background: Studies have shown that psychotic disorders are part of a spectrum,
meaning that their symptoms extend to the general population in attenuated forms. Both
clinically psychotic individuals and attenuated forms of the disorders are subject to the same risk
factors.
Currently, we know that the incidence of schizophrenia is gendered and that ethnic
minorities are at increased risk for psychosis. Ochoa et al. (2008) found a 1.4 rate ratio male to
female in the incidence of schizophrenia but studies are inconclusive at finding similar results in
the prodromal phase of psychosis. Ethnic minorities have been found to be at a higher risk for
psychosis and a higher endorsement of attenuated positive psychotic symptoms in the presence
of discrimination.
Objective: The study had two main aims: first, a women to men difference in APPS
should be present in blacks as long as it is sufficiently mediated by discrimination since
discrimination is a gendered experience; secondly, due to discrimination, such gender difference
in APPS in the black group should be significantly higher than the ones that may be found in
white individuals.
Methods & Results: A sample of 1633 young adults who self-identified as black or
whites were recruited from two US Northeast colleges. They completed self-report
questionnaires of APPS, experiences of discrimination. Our results showed that black individuals
irrespective of gender reported more discrimination, endorsed more APPS and distressing APPS
than white individuals. We also found a gender difference among black individuals in term of
APPS categories, and discrimination. However, discrimination may only partially mediate the
relation between race and gender in regard to APPS in the black sub-sample.

Psychosis characteristics
Psychotic disorders, including schizophrenia, are disorders exhibiting symptoms in at
least one or more of the following domains: “delusions, hallucinations, disorganized thinking
(speech), grossly disorganized or abnormal motor behavior (including catatonia), and negative
symptoms” (American Psychiatric Association [APA], 2013, p 87). Delusions and hallucinations
are also known as positive symptoms, which are considered to be excess in one’s normal
behavior (Sims, 2002). The definition of delusion is to firmly hold onto beliefs that are
contradictory to reality and to counterevidence (APA, 2013). Such beliefs may appear in
different forms. Persecutory delusions are the beliefs that one is under the constant threat of
harm, harassment and so on from individuals, organizations or other groups (APA, 2013).
Grandiose delusions are an exaggerated sense of self in regard to one’s abilities, wealth or fame
(APA, 2013). Somatic delusions are excessive preoccupations in regards to one’s health (APA,
2013). Referential delusions refer to the beliefs that gestures, comments made by people, objects
or even environmental cues are actually directed to oneself (APA, 2013). Hallucinations refer to
auditory or visuals experiences reported by psychotic individuals that happen in the absence of
stimulus from the environment.
Negatives symptoms, which are comprised of avolition, asociability, anhedonia, blunted
effect, and alogia, refer to deficit in normal behavior (Sims, 2002). Such symptoms are more
common in schizophrenia than other psychotic disorders (APA, 2013). Psychotic individuals
may for example regress from their daily hygiene routine, from work, or school work, also
known as avolition. They may speak very little or not speak at all for an extended amount of
time, a symptom known alogia. They may lose their interests in social activities and interactions,

such as losing contact with friends, which results in their own isolation, also known as
asociability. They experience less pleasure and express less expression emotion, known as
blunted effect and anhedonia.
Disorganized thinking refers to one’s incoherent and incomprehensible speech pattern.
Grossly disorganized or abnormal motor behavior refers to one’s inability to conform to
community standards. Those behaviors are varied. One group of such behaviors, catatonia, for
example is defined by a “marked decrease in reactivity to the environment” (APA, 2013, p 88).
Catatonic behaviors can be: a “resistance to instructions or negativism”, the “maintenance of a
rigid or inappropriate posture” known as mutism, the “complete lack of verbal and motor
responses” known as stupor, finally “purposeless and excessive motor activity without obvious
causes” also known as catatonic excitement (APA, 2013, p 88). But abnormal motor behaviors
can also be very “childlike to unpredictable agitation” that may interfere with their daily
activities (APA, 2013, p 88).
Schizophrenia and the psychotic spectrum
Schizophrenia is a brain disorder that “involves a range of emotional, cognitive and
behavioral dysfunctions” (APA, 2013, p 99). According to the 5th and latest edition of the
diagnostic statistical manual for mental disorders (APA, 2013), the diagnostic criteria belonging
to schizophrenia are as follow: the patient should exhibit symptoms belonging to two or more of
the psychotic domains for a significant amount of time during a 1 month period. At least 6
months of marked decrease in level of functioning in one or more major areas like work or
interpersonal relationships should be present. Finally, none of the symptoms can be explained by
any other disorders. Persecutory delusions and auditory hallucinations are the two most

commons schizophrenic symptoms (Sartorius, Shapiro, Jablonksy, 1974). Currently, in the
United States, about 0.3% to 0.7 % of the population suffers from schizophrenia (APA, 2013).
Schizophrenia is surprisingly a very costly disease. In the year 2002, in the United States,
Wu et al. (2005) estimated that schizophrenia’s financial impact to be at 62.7 billion US dollars
(USD) from data collected from privately and publicly insured patients. Similar results were
found in other countries. Phanthunane, Whiteford, Vos, Bertram (2012) estimated, from data
collected in 2008, that schizophrenia’s financial impact in Thailand was 925 million USD.
Magalore & Knapp (2007) estimated schizophrenia’s cost to be 6.7 billion pounds in England for
the 2004/2005 year. It should be noted that in all three studies, indirect costs consisted the
majority of the financial impact. Wu et al. (2005) only attributed 22.5 billon USD of direct costs
associated to the disease in term of medications, treatments and else while Magalore & Knapp
(2007) attributed 2 billion of the financial costs of the disease to direct cost . Phanthumane et al.
(2012) said that unemployment was the biggest culprit in the total financial costs in Thailand.
It is possible to predict someone’s risk. The most well-known way to predict the risk of
schizophrenia in individuals is through their family history. Indeed, it has been shown that
schizophrenia has a strong genetic component. While the general population lifetime risk to
develop schizophrenia is less than 1%, children whose at least one of their parents has
schizophrenia have a 13 % chance to develop it too (McGuffin, Gottesman, Farmer, 1987). The
risk is higher in fraternal and identical twins with a 17 % and 48 % risk respectively (Mc
Gruffin, et al. 1987).
Furthermore, schizophrenia and other psychotic disorders are part of the psychotic
spectrum. The psychotic spectrum refers to the continuum of distressing or non-distressing

subclinical psychotic symptoms experienced by the general population that may in turn progress
to the full blown clinical symptoms diagnosed in psychotic disorders ((Dhossche et al. 2002;
Hanssen et al. 2005; Wiles et al. 2006; Dominguez et al. 2011). In other words, the diagnosis of
prodromal symptoms in an individual does not mean he will be psychotic in the future. As a
matter of fact, subclinical psychotic symptoms are actually common in the general population. A
recent systematic meta-analysis found a median prevalence of 5% and a median incidence rate of
3% for prodromal psychotic symptoms and 75-90% of those symptoms disappeared over time
(van Os, Linscott , Myin-Germeys , Delespaul, and Krabbendam, 2009). To reflect the uncertain
transient nature of subclinical psychotic symptoms, a group of researchers proposed the term at
“risk for mental state” to imply that the individuals in the early phase of the disorder may never
fully develop the disorder. They preferred their term to the word “prodrome” which they stated
was a medical term that refers to the early signs of a disease and therefore alluded that the
disease will soon follow up (Yung et al, p286). Indeed, “false false positive” individuals are
people who “would have the same vulnerability markers as the true positives, who do progress
from an at-risk mental state to psychosis but, because of resilience or protective factors, do not
make the transition” ( Yung et al, 1996 p 288). Only a small amount of people with prodromal
symptoms makes the conversion to psychosis. Yung et al (1996) found that only 21.2 % of their
small sample (N=33) of at-risk for psychosis patients transitioned within 12 months. In another
study, about 40% of the high risk patients transitioned to clinical psychotic disorders within 12
months (Yung et al., 1998).
The development of subclinical psychosis and its transition to clinical psychosis is
dependent on many factors. After determining the amount of patients who transitioned to
psychosis, Yung et al. (2003) determined what were the highly predictors of psychosis of their

sample (N=49). They have found that individuals who have been experiencing the prodromal
symptoms for a long amount of time before diagnosis, who were functioning poorly at intake and
expressed severe psychotic symptoms, were at a significant risk to transition to psychosis.
Subsequently, the risk factors associated with the developing schizophrenia are the same as for
developing its milder symptoms. Van Os, et al., (2009) did a systematic review of 1558 papers
published in the past 5 years at the time of the publication of their article. They reported the same
risk of developing schizophrenia were also in its prodromal symptoms. We can infer from those
information that the development of subclinical psychosis is heavily dependent one’s level of
exposure to psychotic risk factors (van Os, et al, 2009) which in turn may transition to clinical
psychosis due to the length and severity of the symptoms experienced causing a decrease in the
level of functioning in the individual (Yung et al, 1998).
Diagnosis of Subclinical Psychotic Symptoms
There are many instruments that can be used to diagnose prodromal symptoms in
individuals and the Structured Interview for Prodromal Syndromes (SIPS) is one of those
instruments (McGlasan et al., 2001; Miller et al. 2003; Rosen et al. 2002; Miller et al., 1999). A
trained professional expecting to use the SIPS will find the following measures: the scale of
prodromal symptoms (SOPS), the schizotypal Personality Disorder Checklist based on the DSM
IV, the family history questionnaire and the global assessment of functioning scale (Miller et al.
2003). The family history section assesses the patient’s socio-demographic background.
Information regarding a personal or family history of psychosis, trauma and substance use is also
gathered there. The Schizotypal personality disorder assesses the presence of the disorder in the
patient. Five or more of the symptoms as listed in the DSM IV must be met for a full diagnosis.
The SOPS is a 19 item questionnaire used to assess the severity, onset and frequency of

psychotic symptoms in the patients. When investigating the presence or non-presence of
symptoms in positive, negative, disorganized and general symptoms, a trained professional rates
the items on a 0 to 6 point scale along with their frequency and date of onset. A score of 0-2
means that symptoms are normal or absent. A score of 3-5 means that the symptoms are
prodromal and a score of 6 means psychotic. Of course, a trained professional should always
note of the onset of the symptoms and indicate whether they have aggravated in the last 12
months (McGlashan,Walsh,Wood 2010). The Global Assessment of functioning assesses the
level of functioning of the patient over the past 12 months. The assessment is scored on 10
categories of 10 scores ranging from 1 to 100. The patient receives two score based on its present
and 12 month prior level of functioning (McGlashan,Walsh,Wood 2010).
After the interview, information gathered on the measures is analyzed based on two main
criteria: the presence of psychotic syndrome (POPS) and the Criteria of psychosis risk
syndromes (COPS) (Miller et al., 2003; Miller et al., 2010). On the one hand, the Information
meeting POPS criteria mean that the individual has been experiencing symptoms for a while and
currently meet the criteria for psychosis. On the other hand, information’s meeting COPS’
criteria can be categorized in one of the following three categories: brief intermittent psychotic
symptom (BIPS), attenuated positive symptom (APPS), and genetic risk and deterioration
(GRD). All three symptom categories were first identified by Yung et al (1996, 1998). Miller et
al. (2003) defines each COPS symptom as follow: BIPS is defined by the psychotic symptoms
have been experienced in the past three months but are too brief and too infrequent to be
considered at risk for a full psychosis syndrome; GRD is for individuals who experienced a 30%
decreased in GAF scored, meet the criteria for schizotypal personality disorder or have a first
relative who has the disorder; APPS is defined by the mild positive psychotic symptoms that

occurred at least once a week in the past month and which severity have worsened in the past
year that put the patient at risk to form a fully diagnosable psychotic disorder. The SIPS was
modeled after Yung et al. criteria of prodromal psychotic symptoms (Miller et al. 1999). A
validity study found that 50 % of prodromal patients who were diagnosed with the SIPS
transitioned to clinical psychosis within a 12 month period of time (Miller et al. 2003).
Schizophrenia, Gender, and Prodromal phase
Extensive research on schizophrenia revealed some definitive gender differences in terms
of characteristics of the disorder such as age of onset, premorbid condition, symptomatology
characteristics, course of illness and comorbidity of substance use (Ochea, S., Usall, J., Labad,
X., Kulkarni, J., 2012). An earlier age of onset is seen in men but seems to disappear in the
presence of family history in schizophrenia (Goldstein, Tsuang, Faraone, 1989; Gureje, 1991;
Galderisi, bucci, Ucok, Peuskens, 2011; Ochoa et al., 2006; Albus et al., 1994; Perälä,
Kuoppasalami, Partonen, Kieseppä, 2007). For example, Galderisi et al. (2011) found the age of
onset in men was around 22 years and around 24 years in women. Men report a higher
comorbidity of cannabis, alcohol, cocaine and hallucinogen use than women (Galderisi, et
al,2011; Cotton et al., 2009; Wade, Harrigan, Edwards, Burgess, Whelan, McGorry, 2006; Foti,
Kotov, Guey, Bromet, 2010; Rodriguez-Jimenez et al., 2008). Also, while many studies did not
find gender differences in symptomatology (Addington, Addington, Patten, 1996; Lindstrom,
Knorring, 1994; Hayashi, Igarashi, Yamashina, Sunda, 1995), those that did find a difference
reported more negative and disorganization symptoms in men than women (Galderisi, et al.
2011; Morgan, Castle, Jablensky, 2008; Abel, Drake, Goldstein, 2010). Women are found to
have had better premorbid condition and course of illness than men (Shatasel, Gur, Gallacher,

Heimberg, Gur,1992; Morgan, Castle, Jablensky, 2008;Uggerby, Nielsen, Correll, Nielsen, 2011;
Usall, Araya, Ochoa, Busquets, gost, Marquez, 2001).
Yet, it is widely regarded that no gender difference exists in the prevalence of
schizophrenia. (Mc Grath, Saha, Chant, Welham, 2008; Saha, Chant, welham, Mc Grath, 2005).
Indeed, Mc Grath et al. (2008) conducted a meta-analysis of 383 studies in regard to prevalence,
incidence and mortality risks and found no gender difference in the prevalence of schizophrenia.
But he and his team (2008) found a 1.4 male to female rate ratio in the incidence of
schizophrenia. Such results were consistent with other research also demonstrating a higher
incidence of schizophrenia in men (Saha, Chant, welham, Mc Grath, 2005, Alerman, Kahn,
Selten, 2003, Abel, Drake, Goldstein, 2010). Furthermore, the number of research finding a
higher male incidence in schizophrenia vastly outnumber those studies that find the inverse (Mc
Grath, Saha, Welham, Saadi, MacCauley & Chant, 2004)
An attempt at understanding such discrepancy in the research could be made by looking
at the definition of the two words. According to Mc Grath et al. (2008), incidence measures the
amount of new cases of a disorder in a population during a specific period of time; while
prevalence relates to the measure of proportion “of surviving individuals who manifest a disorder
at a specific time or during a specific period” (p68). From the definition of the words, a higher
mortality risk in men seems to be the answer to the discrepancy. For example, Galderisi et al
(2012) found that males significantly abuse alcohol more than women. Rodriguez-Jimenez et al.
(2008) found a higher consumption of cocaine and hallucinogen and cannabis in men. While
women are more likely to attempt suicide (Austad, Joa, Johannessen, Larsen, 2013), men are
more likely to succeed in their suicide attempt (Test, Burke, Wallisch, 1990, Silvia, 2010). But
Mc Grath et al. (2008) seem to refute such hypothesis by reporting that they did not find a sex

difference in the mortality risk for all causes of mortality. More research is needed to determine
if health related issues in regard to substance use and suicide risk are enough in explaining the
discrepancy in the gender difference in incidence and in prevalence research.
In the prodromal phase, very few studies have looked at the gender difference in
incidence, premorbid condition, and so on. Of those studies, results found so far are mixed. As it
was explained previously, psychosis is part of a spectrum. In other words, gender difference
found in clinical psychotic disorders should also be found in subthreshold symptoms. It is with
this assumption that Barajas, Ochoa, Obiols, Lulucats-jo (2015) decided to conduct a literature
review on the gender difference in at-risk individuals. They were able to find only 12 studies.
Some of those studies results were consistent with gender differences in clinical psychosis, some
were not. Results that are consistent with current gender research in psychosis are that men
exhibit more severe negative symptoms, worst level of functioning than women (Barajas et al.
2015). While women receive more social support and exhibited more affective symptoms than
men (Barajas et al. 2015). In terms of incidence, Barajas et al. (2015) found some studies that
show a higher male to female incidence (e.g Von Os et al. 2009), others found the inverse or no
difference. Due to the low amount studies examined, Barajas et al. (2015) said that their results
could not be generalized.
Race & Psychosis
Many studies have shown that ethnic minorities, especially blacks, living in
predominantly white developed countries are at a higher psychosis risk (Fearon et al. 2006;
Bhugra et al., 1997; Mc Neil, 2005; Hutchinson et al., 1996; Spronton & Nazroo, 2002; Sohler &
Bromet, 2003; Bresnahan et al., 2007, Johns & Von OS, 2000). Three large sample incidence

and prevalence studies from UK and the US illustrate the high risk for psychosis in ethnic
minority groups. The Aetiology and Ethnicity of Schizophrenia and Other Psychosis (AESOP)
was a study conducted by Fearon et al. (2006) across three psychiatric centers in the UK for two
years. The study examined the psychotic incidence rate for different ethnic groups (N=568) with
an age range of 16 to 54. Potential participants could only participate to the study once and
should exhibit psychotic symptoms in delusions, hallucinations, thought disorders, and negative
symptoms that characterized them as at-risk individuals (Fearon et al. 2006). Participants also
identified themselves as African-Caribbean, Black African, Asian, Mixed, White British, and
white other. The results show that when adjusted for sex and age, all the categorical groups were
at an increased incidence rate ratio (IRR) for psychosis compared to white British. But the IRR
for psychosis in Black Caribbean (6.7; 95% confidence interval (CI) = 5.4-8.4) and Black
African ( 4.1; CI=3.2-5.3) were the highest in comparison to white British. Two things can be
inferred from this study. The first is that being an immigrant is linked to an increase risk of
developing schizophrenia. The second is that being a black immigrant makes you more likely
than the rest of the population to develop psychotic symptoms.
The Ethnic Minority Psychiatric Illness Rates in the Community (EMPIRIC) is a
community-based prevalence study in the UK that was conducted by Spronton & Nazroo (2002).
The different ethnic groups of the study were: Indian, Pakistani, Bangladeshi, Black Caribbean,
Irish, and White British. Researchers calculated the age-adjusted risk ratio of different ethnic
groups (N=4281). They found that African Caribbean were at a higher odds (1.84; CI= 1.11-
3.03) of expressing psychotic symptoms than whites. Similarly as AESOP, one’s race and
minority status matter in the development of psychosis. The difference to the AESOP study is
that the risk here is milder and only one ethnic black group was examined.

In the US, Breshnan et al. (2007) looked at the incidence rate of schizophrenia for
African American and white in birth cohort. To be eligible for the study, the participant’s mother
should have been enrolled during pregnancy at the Alemada County Kaiser Permanente Medical
Care Plan clinics between the years 1956-1966. Only one offspring per family was allowed to
participate in order to eliminate siblings’ correlated observations (Breshman et al., 2007).
Selected participants (N=6636) were followed during the years 1981-1997. The results found
that African-American were two times more likely to develop schizophrenia than white after
adjustment for socio-demographic variables (Breshman et al., 2007). Breshman et al. (2007) is
one the few incidence studies in psychosis conducted in the US. The fact that the participants and
their mothers are US-born put this study at a significant advantage over the other two. It allows
to look at the risk to develop schizophrenia based solely on race. From what we see, just like in
other two studies, being black constitutes a significant risk factor in relation to schizophrenia,
which further support that minorities are in increased risk of psychosis.
Unsurprisingly, the risk for psychosis decreases when an ethnicity is no longer a minority
(Bhugra et al., 2000; Hickling & Rodgers-Johnson, 1995; Mahy et al. 1999, Boydell et al., 2001).
First-onset studies for psychosis that were conducted in Trinidad (Bhurga et al, 1996) Jamaica
(Hickling & Robertss-Johnson, 1995) and Barbados (Mahy et al., 1999) found similar incidence
rate ratio across them (2.36 in Jamaica, 3.2 in Barbados and 2.2 in Trinidad). Those rates are
much lower than the ones found in African Caribbean living in the UK (Mahy et al. 1999; Fearon
et al., 2006). Veling,et al. (2008) found that immigrants living in low-ethnic density
neighborhood were at significantly increased adjusted rate ratio for psychosis (2.36) than those
who live in neighborhood with high-ethnic density neighborhood (1.36). Boydell et al (2001)

found that black individuals living in neighborhood where they were a minority were at a higher
risk of schizophrenia than those who lived in neighborhood where they were better represented.
Racial discrimination, psychosis & Black
According to Cater (2007), racial discrimination is a type of racism which role is to create
a distance, whether intentionally or not, between the dominant racial group members and the
minority racial groups through behaviors, thoughts, policies and strategies. Following this
definition, it is logical to infer that racial discrimination can be experienced in a wide range of
real life setting as long as it enables members of the dominant group to ostracize minorities. That
is why the self-report questionnaire, Experiences of Discrimination (EOD) (Krieger, Smith,
Naishadam, HartmanBadeau, 2005), ask both the place and the frequency where discrimination
is experienced. People can be discriminated at work, school, hospital, courts and other places
overtly or perniciously.
The discrimination that someone receives may be gender-specific. Sexism is the word
used for discrimination to the opposite-sex. Although, the word gives the impression of going
both ways, it is mostly expressed toward women. After all, discrimination is mostly expressed by
the dominant group and in the US, men are the dominant sex. For example, women may be
barred from specific position or income because of their gender, a process known as the glass
ceiling. The US census Bureau (2011) reported that women in general earn 23 percent less than
men in 2010.
Sexism is worse in ethnic minorities. Women from ethnic minorities have to deal with
negative perceptions related to their race/ethnicities along with negative perceptions related to

their gender, leaving them extremely disadvantaged. In the same income example presented
before, women typically earn less than their men counterpart within the same race/ethnicity.
African American women earn 11.1 percent less than African American men, Hispanic women
earn 22.3 percent less than Hispanic men, and Asian Women earn 8.7 percent less than Asian
men (US census Bureau, 2011).
Once experienced, racial discrimination is highly personal and painful (Delgado, 1982;
Landrine & Klonoff, 1996) and can lead to a series of negative physical, psychological
responses, and even worse health behaviors in the individual (Pascoe & Richman, 2009; William
& Mohammad, 2009; Brown et al., 2000, Krieger et al. 2013). Black individuals who
experienced discrimination are more likely to exhibit psychological distress, anxiety, depression
than those who do not experience discrimination (e.g, Brown et al, 2000). In turn, the
consequences of racial discrimination differ based on gender. Black women who experience
discrimination are more likely than men to express symptoms of depression and psychological
distress (e.g Brown et. al, 2000; Lambert, English & Ialongo, 2014).
In the literature, racial discrimination represents a major link between the risk of
psychosis and the ethnic minority status of an individual. For example, Anglin, Lighty,
Greenspoon, and Ellman (2014) conducted a series of self-report questionnaires based on
attenuated Psychotic Positive Syndromes (APPS), discrimination, depression and anxiety among
a US based young adults ethnic minorities population (N=650). Participants self-identified as
Hispanic, Asian, Black, White and other. They reported that ethnic minorities endorsed racial
discrimination more than white in the sample. They also found that racial discrimination was
associated with all of the domains of APPS and individuals exposed to racial discrimination were
more likely to endorsed eight or more distressing APPS symptoms, meaning that they are likely

to be at a high risk for psychosis (Anglin et al., 2014). In a follow up study to the EMPIRIC
study, they found that discriminatory exposure to racist insults (odd ration (OR=2.3; 95%
CI=1.4- 3.4; p<0.001) and unfair treatment at work (OD=2.1, CI=1.3-3.4, P=0.004) put someone
at the highest risk of mental disorders. Black individuals, in particular were at increased risk of
mental disorders when they were unfairly treated at work (OD=2.9, CI=1.2-7.3, p=0.02) (Bhui et
al, 2005).
In addition, the frequency with which someone is exposed to discriminatory situations is
also an important link to psychosis. Veling, Selten, Susser, Laan, Mackenbach, Hoek (2007)
conducted a study in the Netherlands on different ethnic minorities. They found that the risk for
psychosis was positively linked to the level (very low, low, medium, high) of perceived
discrimination reported among ethnic minorities (Veling et al., 2007). For example, a very low
report of discrimination was associated with an incidence rate ratio (IRR) of psychosis equals to
1.2 (CI=0.81-1.90) after adjustment for age and gender. While a high report of discrimination
was associated with an IRR = 4.00 (CI=3.00-5.35).
Current Study
We know so far that psychosis is a spectrum that shares the same risk factors regardless if
it’s clinically diagnosed or still in its sub-threshold stage (Von Os et al., 2009). At least in the
level of clinical psychosis, convincing gender differences were found in serval aspects of the
disorder. The rate ratio of men to women was 1.4, meaning that close to three men compared to
two women are newly clinically diagnosed for psychosis at any point in time (McGrath et al.
2008). Ethnic minorities, especially black individuals (Fearon et al. 2006), are a population at a
higher risk to psychosis associated to the type and frequency of discrimination they are exposed

to (Velin et al., 2007; Bhui et al., 2005). At the subthreshold level, discrimination was associated
to APPS, a category of prodromal symptoms, for ethnic minorities (Anglin et al., 2014).
Subsequently, it can be inferred from the information presented above, that first, a women to
men difference in APPS should be present in blacks as long as it is sufficiently mediated by
discrimination since discrimination is a gendered experience; secondly, due to discrimination,
such gender difference in APPS in the black group should be significantly higher than the ones
that may be found in white individuals.
To test those assumptions, we tested the following hypotheses:
1- Black individuals will be more discriminated against than white individuals.
2. We will find a gender difference in the discrimination reported by black individuals.
3- A gender difference will be found in black individuals in the report of APPS.
4- APPS will be higher in black than white individuals.
5- Black individuals will be more likely to endorse high level of distressing positive
symptoms than white individuals
6- Discrimination will mediate the relation between APPS and gender in the black
population.
Methods
Participants and Procedure

Our study sample was drawn from data collected from two large Northeast Universities
(N= 2539). Data collected from one of the universities was composed of primarily emerging
young adults (19 to 29 years old) from ethnic minorities. Participants from this particular
university had to self-identify as African-American/Black/ African Descent or as an immigrant
(first or second generation) in an online subject pool to maximize the number of young ethnic
minorities possessing characteristics of high psychosis risk (Anglin, Lee, Yang, & Opler, 2010).
Data collected from the other university was non-selective of the undergraduate students
recruited from another online subject pool across various interdisciplinary courses. All
Participants completed a series of self-report questionnaires in the laboratory computer of their
respective universities. The protocol was approved by the institutional review board of each
university. Participants signed written consent forms and received course credit for their
participation in the study. For the purpose of the study, only data from black and white
individuals were analyzed (N= 1633).
Measure
APPS. To assess the Attenuated Positive Psychotic Symptoms (APPS), each participant
was required to complete the Prodromal Questionnaire-Likert ( PQ Likert) ( Loewy, Bearden,
Johnson,Raine, & Cannon, 2005; Loewy, Bearden, & Cannon, 2007), which is a 92-item self-
report measure of subclinical psychotic symptoms experienced in the absence of alcohol, drugs,
and other medications. Participants mentioned whether they had experienced the PQ items in the
past month and whether they found them distressing or not. The PQ Likert is composed of four
subscales scores of which the positive symptoms (APPS) (45 items; e.g., unusual thought content
and perceptual abnormalities) have been found to have the strongest predictive value (Loewy et
al, 2005).

The PQ scale is not a clinical assessment tool. However, Loewy et al (2007) noted that
self-report questionnaires are reliable and valid. Endorsing 8 or more of positive PQ items
predicted a SIPS diagnosis (prodromal or clinical) with 90% sensitivity and 49% specificity
(Loewy et al., 2005). The endorsement of 8 or more distressing APPS in the PQ scale suggest
that the individual may be at a high risk of psychosis (Loewy et al., 2007). For the purpose of
the study, a dichotomous variable that compared High (8 or more positive symptoms) vs Low (4
or less positive symptoms) levels of endorsement of Distressing Positive PQ was created. We
also created a dimensional variable to look at the score of number of APPS endorsed for each
participant (0-45).
Self-reported Experiences of Discrimination (EOD) were determined using the EOD
Instrument (Kriegeret al., 2005), a questionnaire which assess the location of the experienced of
the discrimination and its frequency. The nine locations assessed are as follow: school, getting a
job, work, getting medical care, getting service in a store, getting credit or a loan, on the street,
and from the police or the courts. The frequency of occurrence of each situation was endorsed on
a three point scale by each participant (1-3), where 1 equates to once and 3 equates to four or
more times (Krieger et al, 2005). Two new variables were created for our study. First, a
dimensional variable, called “EODfreq”, was created to get, for each participant, the sum all of
the frequencies of occurrence of experienced discrimination (0-27). The second variable was also
a dimensional variable, called “EODscale”, to get into a singular number the sum of all the
different discriminatory items domains endorsed by each participant (0-9).
Sociodemographic data. Sociodemographic data collected from the participants were
their age (years), gender (female, male), immigration status, race/ethnicity, proficiency in
English. Race/ethnicity was determined in the question “choose one category that best captures

how you see yourself” and given several options. Responses were grouped into several
categories: Black, white, Hispanic, Asian. Participants also say whether they were born in the
US or not. A “Whitevsblack” binary was created for the purposes of our analyses where the
white group would serve as the reference group. Black and white individuals were put in the
same categorical variable since only those two groups are of interest for our study. Two other
variables combining gender and race were created. A 4 level variable, called “racegender” was
created and contained the different combination of gender and race possible, such as white male,
white female, black male, black female. Finally, a “sexXrace interaction” variable was created
by multiplying the variables whitevsblack and gender in order to look at the possible interaction
of sex and race on the endorsement of APPS.
Statistical analyses
Mean groups differences in gender, race and in gender in combination with each race
separately, in the endorsement of PQ positive symptoms, frequency and type of discrimination
were assessed using one factor and two factors ANOVA. A crosstab Chi square of PQ levels
High vs Low by “racegender” was performed in order to determine if there was a difference in
the endorsement of PQ positive distressing symptoms in the whole sample. Lastly, we did a two-
step linear regression analysis of APPS in terms of gender and EODfreq for the black population
of the sample to assess whether discrimination mediated the relation between race and gender in
regard to endorsement of APPS. Gender was in in the first step while EODfreq was added in the
second step.

Results
Sample characteristics
The overall demographic sample is described in Table 1. The majority of the sample was
female (72.9%), white (68.6%) and US born (87.5%). The average age was 20.21 (SD=2.04)
years old with a range of 18 to 29 years old. Mean APPS was 4.1 (SD=5.66) with the majority
of the sample (71.9%) endorsed one or more positive symptoms. But mean APPS-distress
(M=1.72 SD=3.08) was lower with a majority (58.3%) endorsing no distressing symptoms.
In term of discrimination, the mean of domain of discrimination reported was very low
(M=1.20 SD1.50) with 55.1% of our sample reporting one or more domains. The mean of
frequency of discrimination was much higher (M=2.26 SD=3.19) with 54.8% of the sample
reporting to have been discriminated against at least once.
APPS analyses
A two factor (2X2) ANOVA of race by gender in regard to endorsement of APPS
showed several significant results. First, there was a significant main effect of gender on the
amount of APPS endorsed, F(1,1627)= 20.51, p<0.001. Males reported more APPS than
females, Mdiff=1.47 95 % confidence Interval (CI) (0.83 – 2.11), p<0.001. There was also a
significant main effect of race on the amount of the APPS endorsed, F (1, 1627) = 180.32,
p<0.001. Black endorsed more APPS than whites, Mdiff= 4.36 95% Confidence Interval (CI)
(3.72 - 5.00), p<0.001. There was a significant interaction between gender and race on the
amount of APPS endorsed, F (1,1627)=8.03, p<0.001. In order to interpret the significant

interaction, 4 groups captured in the raceXgender interaction term were analyzed using one way
ANOVA
The One way ANOVA revealed a significant difference in APPS across the 4 race/gender
groupings. As expected from the two factor ANOVA, there was main effect in the race/gender
grouping (racegender) variable to the amount of APPS endorsed F(3,1627)=69.505, p<0.001.All
the mean APPS to the racegender variable are reported in figure 1. Tukey post hoc test revealed
that Black males endorsed significantly more APPS than Black females (Mdiff=2.39, CI(0.98 –
3.8), p<0.001), White males (Mdiff=5.28, CI (3.83 – 6.72), p<0.001)), and White females
(Mdiff=5.83 CI (4.51 – 7.13), p<0.001). It also revealed that Black females endorsed more APPS
than White males (Mdiff= 2.89 CI (1.86 – 3.91), p<0.001) and White females.( Mdiff= 3.44 CI
(2.60 – 4.28), p<0.001). Finally, White male endorsed slightly more APPS than white female,
but it was not significant Mdiff= 0.55 95% CI (-3.496-1.45), p>0.05.
Race/gender and High vs Low endorsement of distressing APPS
A chi-square of “racegender” by High vs low risk of psychosis shows a significant
relationship between the two, X2(3, N=1520) =44.8, p<0.001. Black females and males were
more likely to be in the high risk category (11% and 18% respectively) than white males and
females (2.7% and 4.8%). See figure 2 for percentage of high and low endorsement of distressful
APPS for every combination of race/gender.
Racial Discrimination
See figure 3 for the complete report of the mean of frequency of discrimination for each
combination of racegender. One way ANOVA of the racegender variable and frequency of
discrimination reported a main effect of the racegender variable on the frequency of

discrimination endorsed, F (3, 1627) = 160.03, p<0.001. Tukey post hoc test showed where the
significant differences lie. Black males significantly endorsed a higher frequency of
discrimination than black females (Mdiff= 0.92 CI(0.21 – 1.65), p=0.005), white female (Mdiff=
3.84 CI(3.16 – 4.51), p<0.001, white male (Mdiff= 3.81 CI(3.07 – 4.55), p<0.001). Black female
significantly endorsed a higher frequency of discrimination than white female (Mdiff= 2.91 CI(
2.48 – 3.34), p<0.001) and white male (Mdiff= 2.89 CI(2.36 – 3.42), p<0.001). White male did
not significantly endorse more frequency of discrimination than white female (Mdiff= 0.020 CI(-
0.44 – 0.48), p>0.05).
Just like for frequency of discrimination, the One way ANOVA analysis of racegender
and EODscale showed a main effect of racegender combination on the domain of
discrimination endorsed, F (3, 1627)= 202.85, p<0.001. Tukey post hoc test further clarified
where the difference lied. Black males did not significantly endorse more domains of
discrimination than black female (Mdiff= 0.31 CI(-0.17 – 0.64), p=0.07) but did endorse more
domain of discrimination than white female (Mdiff= 1.86 CI(1.56 – 2.17), p<0.001) and white
male (Mdiff= 1.86 CI(1.52 – 2.19), p<0.001. It also showed that black female significantly
endorsed more domain of discrimination than white female (Mdiff= 1.55 CI (1.35 – 1.75),
p<0.001) and white male (Mdiff= 1.54 CI(1.30 – 1.78), p<0.001). White males had a negligible
and insignificant rise in endorsed domain of discrimination than white female (Mdiff= 0.008 CI(-
0.22 – 0.20), p>0.05). The complete mean of endorsed domain of discrimination for each
gender/race combination can be seen in figure 4.

Frequency of Discrimination, Black individuals and APPS.
Two linear regression models were conducted to determine whether the effect of gender
on APPS in the Black subsample may be partly explained by exposure to racial discrimination.
The first unadjusted model compared gender’s relation to APPS and found a significant relation
(b=2.39 standard error(se)= (0.75), p=0.001). In the second model, mean frequency of racial
discrimination was added to the unadjusted model and was significant (b=.21 se (0.077),
p=0.005). Notably, the beta coefficient for gender in this adjusted model was slightly decreased
but remained significant, b=2.19 (se (0.077), P=0.003). Those results and more are reported in
table 2.
.
Discussion
Aims
The study had two main aims: first, a women to men difference in APPS should be
present in blacks as long as it is sufficiently mediated by discrimination since discrimination is a
gendered experienced; secondly, due to discrimination, such gender difference in APPS in the
black group should be significantly higher than the ones that may be found in white individuals.
The first aim was partially supported by our results. Black man reported a higher frequency of
discrimination, more endorsement of APPS, and more endorsement of distressing APPS than
black females (see fig 1, 2, 3 &4). The two models of linear regression lines of frequency of
discrimination, gender in the endorsement of APPS found a significant relation between
frequency of discrimination and the level of APPS reported. However, discrimination did not
mediate the relation of gender and race in the black population (see table 2).

Our second aim was also partially supported. Black participants, especially black males,
endorsed more discrimination than white participants regardless of their gender (see fig 3 & 4).
Subsequently, Black participants, especially black males, reported a higher endorsement of
APPS and distressing APPS than white males and females participants. However, as reported in
the paragraph above, discrimination is not the main link explaining those differences across race.
Study implications
The gender difference in perceived discrimination reported by our black sample is in line
with the literature. One might have expected black females to have reported more instance of
discrimination due to the dual effect of sexism and racism. Black women have to deal with
certain discrimination in regard to their sexuality, level of work, intelligence, femininity that
black men do not experience (Shorter-Gooden & Jones, 2003). But there are two main reasons
for our current results: first, discrimination needs to be recognized in order to be reported by an
individual; second, black men are more overtly discriminated against in the US. Shorter-Gooden
& Washington (1996) reported that race is more salient than gender in black individuals. For
example, in a series of survey that they conducted (Shorter-Gooden, Jones, 2003) among US
black women (N=333), only found 69% of them who reported at least one instance of
experienced discrimination. Surprisingly, when interviewed later, many of the ones who did not
report an instance of discrimination shared personal discriminatory stories related to unequal
pay, harassment, sexual violence. Shorter & jones (2003) concluded that black women are not
sensitized enough to recognize those forms of discrimination. Furthermore, studies have shown
that black men are more frequently the target of racial stereotypes than females that picture them
as more threatening and violent (Krieger & Sidney, 1996; Williams & Mohammed, 2009).

Our results support the idea that there is a gender difference in risk of psychosis,
especially in ethnic minorities (Ochoa et al., 2008; Ochoa et al, 2004, Alerman et al., 2005:
Veilin et al, 2006). Ochoa et al (2008) found a higher incidence of schizophrenia in men than
women. Our results showed men generally endorsed more APPS than women (Mdiff=1.47 95 %
confidence Interval (CI) (0.83 – 2.11), p<0.001). Further analyses showed that race modified the
relationship between gender and APPS which is in line with Veling et al (2006) study in the
Netherlands. In term of ethnicity, Veling et al. (2006) reported that Moroccan men, second and
first generation immigrants, were at an increased risk for schizophrenic disorders compared to
both Moroccan woman, second and second generation, and white Dutch irrespective of the
gender. Similarly, our results showed that black men endorsed more APPS than black female
(p<0.001), white males (p<0.001) and white female (P<001) (see fig 1). Even white men
endorsed more APPS than females, even though it was not significant (p>0.05) (fig 1). Black
men were also more likely to endorse more distressing APPS than everyone else (see fig 2).
Veling et al. (2006) reported Moroccan encounter great difficulties in their process of
acculturation, such as more perceived discrimination, social defeat, and else. Likewise, our study
showed that black men are more discriminated against than anyone else in our sample (see fig 3).
Extraneous factors
Other factors must be working in sync with discrimination to produce the results found in
the study since it may only partially mediate the relation between race and gender in the black
subgroup. A look at table 1 shows that a higher proportion of black (24.2%) participants were
first generation immigrants than the white sample (7.2%). Studies have shown that immigration
is an important risk factor in the development of psychotic disorders (eg. Fearon et al, 2006).
Both the AESOP and EMPIRIC studies showed that black immigrant groups were at higher

incidence rate ratio to develop schizophrenia than the white natives (Fearon et al, 2006; Spronton
& Nazroo, 2002). Immigrants, whether new or not, have to handle a lot of stressful situation,
such adapting to a new culture, learning a new language, lack of financial and social support and
even xenophobia. The degree to which they adapt to their new environment is link to psychosis
(Vega, Sribney, Miskimen, Escobar, and Aguilar-Gaxiola 2006). In the same train of thought,
children of first generation immigrants, also known as second-generation immigrants, have been
found to have higher risk of developing a psychotic disorder than their parents (Veiling, et al.
2006, Cantor-Graae &Selten, 2005). Further analysis is needed to confirm the extent of the effect
of immigration has on the endorsement of APPS, on top of discrimination, in our sample.
Socio-demographic variables, such as income, neighborhood one’s living in, and
traumatic life experiences are also important factors linked to psychosis (eg. Von Os et al., 2009,
Boydell et al., 2001; Beards et al. 2013,Fisher et al., 2009). Beards et al. (2013) found that adults
who were exposed to traumatic life events were several times more likely to develop psychotic
experiences than those who do not. Anglin, Polanco-Roman & Lui (2015) found that
dissociation mediated the relation between traumatic life experiences and APPS. But such
relation was dependent upon one’s ethnicity. Notably, full mediation was only present in the
black subgroup, partially present in Hispanics and not found in Asians. In term of gender
difference, Fisher et al (2009) found that severe physical or sexual abuse in childhood is linked
to later psychosis in women more than men. Boydell et al. (2007) found individuals living in
neighborhood where they were ethnically well-represented were at lower risk for psychosis than
those who live in neighborhoods where they were minorities. Furthermore, people of lower
socio-demographic who see society’s continuously hinder their efforts to move up the socio-
economic ladder may fall into social defeat (Cantor-Graae, Selton, 2005). Selton & Cantor-Graae

(2005) hypothesized that social defeat, which they defined as a positon of submission or be an
outsider, may be a link to schizophrenia. As evidence, they cited a series of animal experiments
which showed that constant exposure to social defeat leads to a hyperactivity of the dopamine
mesolimbic system. In humans, the dopamine mesolimbic system is oversensitive in
schizophrenic individuals. Therefore, they suggested that just like in animals, socially defeated
humans may have sensitive mesolimbic dopamine system which may heighten their descent to
psychosis. Further research could indicate the level of social defeat, trauma and the type of
neighborhood associated to the black sample of our study and how they work with discrimination
to explain their high endorsement of APPS and APPS distress.
Limitations
There are several limitations in our study. The first of our limitations is our reliance to APPS
through the PQ scale. Loewy et al. (2005, 2007) accentuated that the PQ is not a clinical
assessment tool. Instead, it should be taken as a tool that pinpoints those who may be found at
risk of psychosis when assess through clinically valid interviews. The second of our limitations
is that endorsing APPS alone through the PQ scale is not indicative of risk of psychosis. People’s
hunger, fatigue, concentration, culture, religion may all affect the way they respond to the
questionnaire. For example, people who are hungry and tired may not pay as much attention to
the questionnaire and falsely answer a series of items. Alternatively, their understanding of
hallucinations or other positive psychotic symptoms may be skewed through their cultural and
religious understanding. People also could have other disorders, which influence their responses.
David et al. (1999) showed that Latinos with PTSD were more likely to endorse psychotic
symptoms in self-report questionnaires. The third of our limitations is the sample used. All of our
participants are college based. The results may lack the external validity needed to extend the

findings to the general population. Lastly, data was collected on a single point in time for each
participant making this study not as strong as a longitudinal study which could have shown the
progression of the symptoms in the participants.
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Table 1- descriptive information of the sample on gender, race and endorsement of APPS
and discrimination
Overall Sample (n =
1633)
Black ( n=512) White (n=1121)
Demographics
Female, n (%) 1189 (72.9) 387 (75.6) 802 (71.5)
Age (years), mean (SD)
[range] 20.21 (2.04) [18-29]
20.5 (2.4) [18-
29]
20.07 (1.83)
[18-29]
Race, n (%)
Black 512 (31.4) --- ----
White 1121 (68.6) --- ----
Immigrant Status, n (%)
US Born
1426 (87.3) 386 (75.8) 1040 (92.8)
US foreign 204 (12.5) 123 (24.2) 81 (7.2)
APPS, mean (SD) [range]
4.1 (5.66)[0-40] 6.75(7.3) [0-31] 2.89 (3.96) [0-
40]
APPS-distress, mean (SD) [range] 1.72 (3.08) [0-23] 2.48 (3.82)[0-23] 1.35 (2.54)[0-19]
Frequency of Discrimination,
mean (SD) [range]
2.26(3.19) [0-25] 4.04(4.16)[0-23] 0.92 (1.68)[0-14]
Domain of Discrimination, mean,
(SD) [range]
1.19 (1.50) [0-9] 2.1 (1.88)[0-9] 0.48 (0.8)[0-5]
APPS = Attenuated Positive Psychotic Symptoms

Table 2- Unadjusted regression lines of EODfreq, gender to APPS for the black
subsample.
Figure1- APPS means by Race/gender
8.57
6.18
3.86
4.76
0
1
2
3
4
5
6
7
8
9
Black male Black female white female white male
MeanendorsementofAPPS
APPS b(se)[p-value]
Step 1 Step 2
Gender 2.39 (0.75) [p=0.001] 2.19 (0.75)[p=0.003]
EODfreq 0.215 (0.077) [p=0.005]
Constant 6.18 (0.368)[p<0/001] 5.36 (0.469) [p<0.001]
Unadjusted R2 0.20 [p=0.001] 0.35[p=0.005]

Figure2- % high vs low endorsement of APPS-distress by race/gender
18 11.1 2.7
4.8
82
88.9
97.2 95.2
0
20
40
60
80
100
120
black male black female white man white female
%highvslowendoersemtnofAPPS-distress
combination of race/gender
High
low

Figure 3- Mean frequency of discrimination by race/gender combination
4.74
3.82
0.91 0.93
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
black male black female white male white female
meanfrequncyofdiscrimination
combination of race and gender
frequency of discrimination

Figure 4- Mean of endorsed domain of discrimination by race/gender combination
2.34
2.03
0.478 0.486
0
0.5
1
1.5
2
2.5
Black male Black female White female White male
Mean domain of discrimination
endorsed

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