The association between social deprivation and the rate of identification of individuals at Ultra-High Risk for psychosis and transition to psychosis

Abstract

Background:

There is a higher incidence of psychotic disorders in neighbourhoods of greater social deprivation. However, it is not known whether this represents a causal relationship, as the stage at which social deprivation exerts its influence on the development of psychotic disorders is yet to be elucidated. We aimed to investigate the association between neighbourhood-level social deprivation and the rate of identification of individuals at Ultra-High Risk for psychosis (UHR), as well as the risk of transition to psychosis in UHR individuals.

Methods:

The cohort included all young people aged 15 to 24 identified as UHR attending an Early Intervention clinic in northwestern Melbourne over a 5-year period (2012–2016). Australian census data were used to obtain the at-risk population and social deprivation information according to the postcode of residence. Levels of social deprivation were arranged into quartiles. Poisson regression was used to calculate rate ratios and Cox regression analysis determined hazard ratios.

Results:

Of the 461 young people identified as UHR, 11.1% (n = 49) lived in the most affluent neighbourhoods (Quartile 1) compared to 36.7% (n = 162) in the most deprived neighbourhoods (Quartile 4). There was a 35% higher rate of identification of young people who were UHR from the most deprived neighbourhoods (aIRR = 1.35, 95% CI [0.98, 1.86]). Over a median follow-up of approximately 10 months (308 days (IQR: 188–557), 17.5% (n = 77) were known to have transitioned to a full-threshold psychotic disorder. Residing in a neighbourhood of above average deprivation had a hazard ratio of 2.05 (95% CI [0.88, 4.80]) for risk of transition, when controlling for age, sex and substance use.

Conclusions:

These findings provide more support that EI services should be funded as per the expected incidence of psychotic disorders.

Keywords

Social deprivation psychotic disorders Ultra-High Risk for psychosis transition to psychosis

Introduction

Over the last century, it has been established that there is spatial variation in the incidence of psychotic disorders between countries (El Saadi et al., 2004; Jongsma et al., 2018), as well as between regions within the same country (Kirkbride et al., 2012; Richardson et al., 2018). Subsequent investigation has sought to identify characteristics of the social environment that may underlie this spatial variation (March et al., 2008). This has uncovered associations between psychotic disorders and neighbourhood-level factors, such as social deprivation (Eaton et al., 2019; O’Donoghue, Roche, et al., 2016), population density (Kelly et al., 2010), urbanicity (Heinz et al., 2013; Kelly et al., 2010), social capital (Kirkbride et al., 2008) and social fragmentation (Ku et al., 2021). In particular, the relationship between the incidence of psychotic disorders and social deprivation, which refers to relative socio-economic disadvantage in the community, has garnered substantial attention (O’Donoghue, Roche, et al., 2016).

In a systematic review that examined the relationship between social deprivation and the risk of psychotic disorders, the majority of studies (17 of 23) demonstrated an association (O’Donoghue, Lyne, et al., 2016; O’Donoghue, Roche, et al., 2016). A higher incidence of psychotic disorders has been shown amongst individuals living in neighbourhoods of greater social deprivation at the time of presentation in Australia (Eaton et al., 2019), England (Dauncey et al., 1993; Hardoon et al., 2013; Kirkbride et al., 2014), Ireland (O’Donoghue, Lyne, et al., 2016; O’Donoghue, Roche, et al., 2016; Omer et al., 2014), the Netherlands (Van Os et al., 2000; Veling et al., 2015), Sweden (Sariaslan et al., 2015), Italy (Lasalvia et al., 2014) and Canada (Anderson et al., 2012). For example, a study conducted in Melbourne, Australia identified a two-fold increase in the incidence of non-affective psychotic disorders in the most socially deprived neighbourhoods, compared to that in the most affluent neighbourhoods (Eaton et al., 2019). In an urban and rural area of Ireland, a 3.4-fold increase in the incidence rate of First Episode Psychosis was demonstrated in neighbourhoods of highest social deprivation (O’Donoghue, Lyne, et al., 2016; O’Donoghue, Roche, et al., 2016). Even more striking is the finding from a large study in the UK of a near five-fold increase in the risk of receiving a diagnosis of schizophrenia associated with living in the most deprived areas (Hardoon et al., 2013). However, studies that have examined the association between social deprivation at the time of presentation and the rate of psychotic disorders have tended to be cross-sectional in nature and, therefore, unable to determine causality.

As a result, there is debate as to whether it is causation or consequence that can be inferred from the association established between social deprivation and the incidence of psychotic disorders. The social causation theory asserts that exposure to social and environmental stressors, such as neighbourhood-level social deprivation, increases the risk of subsequent psychotic illness (Goldberg & Morrison, 1963; Heinz et al., 2013; Sariaslan et al., 2016). Conversely, the social selection or drift theory posits that psychotic symptoms and cognitive dysfunction inhibit socioeconomic attainment and thus may cause individuals to drift into more deprived neighbourhoods or prevent them from moving away from such neighbourhoods (Goldberg & Morrison, 1963; Sariaslan et al., 2016).

Disentanglement of the social causation and social drift hypotheses may be facilitated by examining how social deprivation contributes to the development of psychotic disorders. To assist in determining its aetiological role, it is valuable to first elucidate the stage in the illness trajectory that this social factor exerts its influence.

However, limited studies have investigated the effect of social deprivation at stages preceding the time of presentation of psychotic illness. For example, only a small number of studies have examined the association between social deprivation at the time of birth and the risk of developing a psychotic disorder. Importantly, these have mostly demonstrated that higher levels of neighbourhood deprivation at birth are associated with increased rates of psychotic illness, regardless of the level at the time of presentation (Castle et al., 1993; Selten & Cantor-Graae, 2007; Werner et al., 2007). This provides evidence in alignment with the social causation theory as it suggests the interaction between social deprivation and psychotic disorders commences prior to the onset of the disorder.

Another important stage to consider in the development of psychosis is the prodromal stage, which can be operationalised by examining the Ultra-High Risk for Psychosis (UHR) population. The UHR population comprises young individuals at increased risk for the development of a psychotic disorder compared to the general population. A meta-analysis identified that 18% of these UHR individuals transition to a full-threshold psychotic disorder within 6 months and 36% after 3 years, indicating the UHR state encompasses individuals in the prodromal phase of psychosis (Fusar-Poli et al., 2012). To be deemed UHR, an individual fulfils criteria demonstrating subthreshold or inconsistent psychotic symptoms. An example of an instrument used to identify UHR individuals is the Comprehensive Assessment of At-Risk Mental States (CAARMS). This is a semi-structured interview measurement tool that measures intensity, frequency, duration and recency of subthreshold psychotic symptoms (Yung et al., 1996, 2003). It has been shown to have good to excellent concurrent, discriminant and predictive validity (Yung et al., 2005). Such tools allow for the exploration of the association between social risk factors, such as social deprivation and those at UHR and thereby may provide important aetiological insights into the development of psychosis.

A small amount of research has been conducted that endeavoured to assess the association between social deprivation and UHR individuals. Bhavsar at al. (2018) found that, compared to the least deprived neighbourhoods, the most deprived neighbourhoods in South East London had a greater than two-fold increase in rate of identification of UHR individuals. In Melbourne, Australia, O’Donoghue, Yung, et al. (2015) found a trend association between a higher rate of identification in neighbourhoods with above average levels of social deprivation, whilst neighbourhoods with the highest and lowest levels of deprivation demonstrated similar rates of UHR identification.

Over a 5-year follow-up period, it was found that the rate of transition to a full-threshold psychotic disorder in this UHR cohort was not associated with neighbourhood social deprivation (O’Donoghue, Nelson, et al., 2015; O’Donoghue, Yung, et al., 2015). Evidently, the interpretation of existing research examining social deprivation and UHR is challenging due to conflicting findings and an overall paucity of studies, particularly related to transition to psychosis.

As such, it is yet to be clearly elucidated how social deprivation is associated with the At-risk Mental State stage and transition phases of psychosis. Therefore, the aims of the present study were to investigate: (i) whether there is an association between neighbourhood-level social deprivation and the rate of identification of UHR individuals and (ii) whether there is an association between neighbourhood-level social deprivation and risk of transition to a full threshold psychotic disorder in those at high risk.

Methods

Setting

The Personal Assessment and Crisis Evaluation (PACE) service is a specialised outpatient clinic of Orygen Youth Health (OYH), a mental health service for young individuals aged between 15 and 24 years who reside in the north-western region of Melbourne. This region covers a catchment population of over one million people. The PACE clinic is attended by young people identified as UHR for psychosis, as operationalised using the CAARMS criteria. Referrals to the PACE service are made by general practitioners, counsellors and community health services, as well as by self-referrals. Typically, an individual identified as UHR will receive care over a period spanning between 9 and 12 months, but can receive care for a maximum of 2 years. An exception to this is if the individual is under the age of 18 after the 2 year period, in which case he or she can access treatment until they reach the age of 18. Appointments with a case manager or doctor are initially carried out weekly before extending to fortnightly following the acute presentation.

Design and participants

This cohort comprised of consecutive cases of young people meeting UHR criteria that attended the PACE service between 1 January 2012 and 31 December 2016. Relevant information was recorded prospectively in the clinical file by clinicians and extracted retrospectively by researchers for this study. Client files and electronic medical records were used to access demographic data including postcode of the suburb of residence at the time of first contact with PACE and clinical data, including whether transition to psychosis occurred.

Instruments

The CAARMS criteria stipulates that, to be identified as UHR for psychosis, an individual must be help-seeking and experience at least one of the following:

i. Attenuated Positive Psychotic Symptoms (APS): subthreshold symptom intensity or frequency present within the last year for at least a week.

ii. Brief Limited Intermittent Psychotic Symptoms (BLIPS): full-threshold psychotic symptoms that have lasted no longer than a week in the last year and spontaneously remitted (without treatment).

iii. Trait Vulnerability for a psychotic disorder: presence of risk factors of schizotypal personality disorder or a first-degree relative with a psychotic disorder.

Individuals must also have impaired functioning, defined as either a 30% drop in Social and Occupational Functioning Assessment Scale (SOFAS) score from their previous level of functioning and sustained for a month within the past year or a SOFAS score of 50 or less for the past 12 months or longer. The CAARMS was also used to determine if an individual had transitioned to full threshold psychosis, as it provides clear criteria for the presence of a psychotic disorder.

Definitions

Social deprivation describes relative socio-economic disadvantage within the community. The level of social deprivation of areas of residence were compared using the Index of Relative Socio-Economic Disadvantage, one of four indexes comprising the Socio-Economic Indexes for Areas (SEIFA). SEIFA is developed by the Australian Bureau of Statistics, derived from information obtained in the Australian National Census, which is completed every 5 years. It provides a numerical score that can be used to rank the relative socio-economic characteristics of specific postcode areas. The scores provided by the index of relative socio-economic disadvantage are a weighted combination of variables that indicate socio-economic disadvantage at the neighbourhood level including income, education level, employment, occupation, housing and other measures such as receipt of disability benefits. The scores have been standardised to a distribution with a mean of 1,000 and standard deviation of 100, such that a score of 1,000 indicates an area with all variables equal to the national average. Lower scores signify that an area is less disadvantaged than an area with a higher score. The areas were initially ordered into deciles on a continuum from least disadvantaged (lowest score) to most disadvantaged (highest score) within the catchment area of OYH.

Ethics

This project received ethical approval from the Melbourne Health HREC (QA2016141).

Statistical analysis

Poisson regression was used to compare the rate of identification of individuals at UHR for psychosis amongst each quartile of social deprivation, using quartile 1 as a reference. Incidence rate ratios were calculated. Transition to psychosis was analysed using survival analysis. Specifically, Cox regression analysis was used to calculate hazard ratios according to the level of social deprivation of individual’s area of residence at the time of presentation to PACE. Cox regression was used as there was variable time to follow-up. A further Cox regression analysis was performed controlling for age, sex and comorbid substance use, as substance use was found to be prevalent in those who transitioned.

Results

Description of participants

A total of 461 young people who were identified as being Ultra-High Risk for Psychosis attended the PACE clinic during the study period. Of these, 95.7% (n = 441) were known to be residing in the catchment area. A total of 56.2% (n = 248) were female and the mean age was 18.3 years (SD = 2.8). The majority of the cohort had never been married (n = 419, 95%) and were living with their parents (n = 308, 69.8%). Most were either unemployed (n = 117, 26.8%) or students (n = 241, 55.1%). A total of 96 (21.8%) young people had a family history of psychosis in first degree relatives.

The majority of young people met criteria for the Attenuated Positive Symptoms UHR subtype (n = 379, 91.3%), whilst 3.9% (n = 16) and 20.7% (n = 86) met criteria for the Brief Limited Intermittent Psychotic Symptoms (BLIPS) and Vulnerability subtypes, respectively (individuals could meet criteria for more than one sub-type). At the time of presentation, 11.1% (n = 49) individuals lived in areas within the quartile of lowest neighbourhood-level social deprivation (i.e. the most affluent areas), whilst 27.9% (n = 123) lived in quartile 2, 24.3% (n = 107) lived in quartile 3 and 36.7% (n = 162) lived in the most deprived quartile. The demographic and clinical characteristics of the cohort are displayed in Table 1.

Table 1.

Sociodemographic and clinical characteristics of the cohort (n = 441).

	N	%
Sex
Male	193	43.8
Female	248	56.2
Age at baseline (Mean ± SD)	18.3 (2.8)
Marital status
Never married	419	95
Widowed	1	0.2
Separated/divorced	2	0.5
Married/De facto	7	1.6
Not stated	12	2.7
Living status
Alone	18	4.1
Siblings	22	5.0
Friends	36	8.2
Parents	308	69.8
De facto husband or wife	55	12.5
Missing data	2	0.5
Employment status
Home duties	10	2.3
Unemployed	117	26.8
Employed	49	11.2
Student	241	55.1
Student not attending school	20	4.6
Missing data	4	0.9
Family history of psychosis
First degree relatives	96	21.8
Nil	345	78.2
Ultra-High Risk for psychosis (UHR) subtype^a
Attenuated positive symptoms	379	91.3
BLIPS	16	3.9
Vulnerability	86	20.7
Missing data	26	5.9
Social deprivation of area of residence
Quartiles
Quartile 1 (least deprived)	49	11.1
Quartile 2	123	27.9
Quartile 3	107	24.3
Quartile 4 (Most deprived)	162	36.7
Deciles
Decile 1 (least deprived)	76	17.2
Decile 2	44	44
Decile 3	73	73
Decile 4	17	17
Decile 5	60	60
Decile 6	77	77
Decile 7	43	43
Decile 8	19	19
Decile 9	32	32
Decile 10 (most deprived)	0	0

It is possible for participants to fulfil more than one of the Ultra-High Risk subtype criteria.

Deprivation and the rate of identification of UHR individuals

Using the most affluent neighbourhoods as the reference range, there was a trend for a higher rate of identification of UHR individuals in the most deprived neighbourhoods (aHR = 1.35, 95% [0.98, 1.86, p = .06), indicating that there were 35% more cases identified in the most deprived areas (although findings were above the threshold for a statistical significance of p < .05). The incidence rate ratios for each quartile of social deprivation are presented in Table 2.

Table 2.

Incident rate ratios for identification of individuals at Ultra-High Risk for psychosis according to level of social deprivation in the neighbourhood of residence.

	Adjusted incidence rate ratio	95% CI	p-Value
Quartile 1 (least deprived)	Ref	—	—
Quartile 2	1.09	[0.78, 1.52]	.61
Quartile 3	0.88	[0.63, 1.24]	.46
Quartile 4 (most deprived)	1.35	[0.98, 1.86]	.06

Demographic and clinical characteristics of individuals that transitioned to psychosis

Over a median follow-up time of 308 days (IQR: 188–557), 77 young people transitioned from UHR to a first episode psychosis, representing 17.5% of the total cohort. The median time to transition was 132 days (IQR: 43–264). Of those who transitioned, 36.4% (n = 28) were male and 63.6% (n = 49) were female. A total of 51.9% (n = 40) of individuals who transitioned were known to engage in comorbid substance abuse.

Regarding the level of social deprivation of participants’ areas of residence, 10.4% (n = 8) of UHR individuals who transitioned to psychosis lived in the most affluent neighbourhoods (Quartile 1), while 19.5% (n = 15) resided in neighbourhoods in Quartile 2. Furthermore, 36.4% (n = 28) of individuals that transitioned resided in areas of quartile 3 and 33.8% (n = 26) in quartile 4, comprising the most socially deprived areas. The sociodemographic and clinical characteristics of those who transitioned to psychosis and those that did not are presented in Table 3.

Table 3.

Sociodemographic and clinical characteristics of cohort according to transition status.

Age at transition (M ± SD)	Transitioned		Did not transition
	18.1 (2.8)
	N	%^a	N	%^b
Sex
Male	28	36.4	165	45.3
Female	49	63.3	199	54.7
Marital status
Never married	73	94.8	346	95.1
Widowed	0	0	1	0.3
Separated/divorced	0	0	2	0.5
Married/De Facto	1	1.3	6	1.6
Not stated	3	3.9	9	2.5
Living status^c
Alone	2	2.6	16	4.4
Siblings	2	2.6	20	5.5
Friends	7	9.1	29	8.0
Parents	54	70.1	254	70.2
De facto husband or wife	12	15.6	43	11.9
Employment status^d
Home duties	1	1.3	9	2.5
Unemployed	17	22.1	100	27.8
Employed	12	15.6	37	10.3
Student	45	58.4	196	54.4
Student not attending school	2	2.6	18	5.0
Family history of psychosis
First degree relatives	13	16.9	83	22.8
Nil	64	83.1	281	77.2
Migrant status
Australian-born	59	76.6	308	84.6
First or second-degree migrant	18	23.4	56	15.4
Comorbid substance abuse
Present	40	51.9	132	37.2
Absent	34	44.2	209	58.9
Not stated	3	3.9	14	3.9
Social deprivation of area of residence
Quartile 1 (least deprived)	8	10.4	41	11.3
Quartile 2	15	19.5	108	29.7
Quartile 3	28	36.4	79	21.7
Quartile 4 (most deprived)	26	33.8	136	37.4

Percentage of total number of individuals that transitioned to psychosis.

Percentage of total number of individuals that did not transition to psychosis.

Data available for 99.5% (n = 439) of the total cohort.

Data available for 99% (n = 437) of the total cohort.

Social deprivation and the rate of transition to psychosis

Using the most affluent neighbourhoods as a reference range in an unadjusted Cox regression model, the following risks for transition were found according to the level of social deprivation: Quartile 2 HR = 0.89, 95% CI [0.36, 2.19], p = .18, Quartile 3: HR = 1.92, 95% CI [0.83, 4.45], p = .13 and Quartile 4 (most deprived) HR = 1.17, 95% CI [0.50, 2.71], p = 0.72; Table 4).

Table 4.

Cox regression analysis for the association between level of social deprivation at the time of presentation and transition to psychosis.

Level of social deprivation	HR	95% CI	p-Value
Quartile 1 (least deprived)	Ref	—	—
Quartile 2	0.89	[0.36, 2.19]	.80
Quartile 3	1.92	[0.83, 4.45]	.13
Quartile 4 (most deprived)	1.17	[0.50, 2.71]	.72

A further Cox regression analysis was also performed whilst controlling for age, sex and substance abuse. In this model, age and sex were not found to significantly influence the rate of transition, whilst comorbid substance abuse was demonstrated to significantly increase the rate of transition to psychosis (aHR 1.8, 95% CI [1.10, 2.93], p = .02; Table 5). After controlling for these variables, the association between the level of social deprivation and rate of transition to psychotic disorder overall remained similar, with the risk highest in quartile 3, although this was not statistically significant (aHR 2.05, 95% CI [0.88, 4.80], p = .10; Table 5). The Kaplan Meyer diagram is presented in Figure 1.

Table 5.

Cox regression analyses of the association between rate of transition to psychosis after adjusting for potential confounding factors.

	Adjusted hazard ratio	95% CI	p-Value
Sex	0.71	[0.42, 1.19]	.20
Age	0.99	[0.90, 1.08]	.75
Comorbid substance abuse	1.80	[1.10, 2.97]	.02
Level of social deprivation
Quartile 1 (least deprived)	Ref	—	—
Quartile 2	0.91	[0.37, 2.26]	.84
Quartile 3	2.05	[0.88, 4.79]	.10
Quartile 4 (most deprived)	1.16	[0.49, 2.72]	.74

Figure 1.

Cox regression analysis showing rate of transition to psychosis according to level of social deprivation.

Discussion

Summary of findings

This study identified trends for a higher rate of identification of UHR young people from more deprived neighbourhoods and also a trend for a higher rate of transition to psychosis in neighbourhoods with above average levels of social deprivation, when controlled for age, sex and substance use.

Comparison with previous literature

A multitude of studies have demonstrated an increased incidence of psychotic disorders in areas of higher social deprivation (O’Donoghue, Lyne, et al., 2016; O’Donoghue, Roche, et al., 2016). The study by Eaton et al., which was conducted in the same catchment area, at-risk population and study period as the current study found that the incidence of first episode of psychosis was higher in more deprived neighbourhoods (Eaton et al., 2019). However, such incidence studies cannot distinguish whether it is social causation or social drift underlying the increased rates in areas of higher social deprivation.

The present study examined the putative prodrome and transition stages of psychotic disorders as a means of investigating whether the influence of social deprivation precedes the onset of psychotic disorders, thus exploring the social causation theory. However, the results further contribute to the heterogeneity of previous findings regarding social deprivation in UHR populations. O’Donoghue, Yung, et al. (2015) reported a trend association between higher rates of UHR identification in neighbourhoods with above average levels of social deprivation and no association between deprivation and rate of transition in the same cohort. Despite the current study setting and catchment area being similar to that utilised by O’Donoghue et al., a trend-association was only found between above average levels of social deprivation and rate of transition and also UHR identification.

The findings should also be considered alongside previous studies demonstrating that greater neighbourhood-level social deprivation at the time of birth confers an increased risk of developing a psychotic disorder later in life (Castle et al., 1993; Selten & Cantor-Graae, 2007; Werner et al., 2007). This is important as it suggests social deprivation exerts its influence prior to the onset of psychosis, providing evidence for the social causation theory.

Possible explanation for findings

There are several possible explanations for the present findings that should be considered. Firstly, although individuals from neighbourhoods of higher social deprivation are more likely to develop a psychotic disorder, it is possible that environmental barriers to accessing early intervention services are more prevalent in these neighbourhoods. Many of the variables included in the SEIFA index of relative socio-economic disadvantage, which was used to measure social deprivation, are associated with reduced help-seeking and low health literacy (Bradford et al., 2008; He et al., 2019; Judge et al., 2005; McKee & Paasche-Orlow, 2012; Salaheddin & Mason, 2016). Examples of the variables used as indicators of deprivation include low income, education, employment and English proficiency levels, which may be barriers to care for UHR individuals.

Indeed, low income, education and employment are associated with a longer duration of untreated psychosis and delay in presenting to services for treatment (Craig et al., 2006; Hardy et al., 2018; Ku et al., 2020; Matthew et al., 2008; Qiu et al., 2019). Therefore, it is possible that individuals in socially deprived areas are more likely to present to mental health services after a full-threshold psychosis has developed. Additionally, the trend association found between the level of social deprivation and rate of transition may be attributable to a lack of power precluding it from reaching statistical significance. This is plausible as, although the sample size was relatively large compared to previous UHR and social deprivation studies, the overall transition rate was low.

Finally, it is important to consider that the present results represent a true finding that social deprivation does not exert its influence in the prodromal or transition stages of psychotic disorders. Thus, the findings cannot provide further clarity on the causality of increased incidence of psychotic disorders in areas of greater social deprivation. However, previous findings of higher levels of social deprivation at birth conferring an increased risk of developing a psychotic disorder later in life suggests that this environmental risk factor may impart vulnerability early in life. Interestingly, migrants are over-represented in first episode cohort however, it has been found that UHR migrants are not at an increased risk of transitioning to a full threshold psychotic disorder (Moore et al., 2021), which also indicates that there may be barriers to these high risk groups accessing UHR/At risk mental state clinics.

Strengths and limitations

A strength of this study was its inclusion of an epidemiological cohort of UHR individuals within a specified and diverse catchment area and study period. Additionally, fulfilment of strict UHR clinical criteria was required for inclusion in the study. This limited the potential for confounding cases of individuals experiencing ‘psychotic-like experiences’, which may have otherwise inaccurately inflated the identification of UHR individuals. However, as this cohort was comprised only of help-seeking individuals, it is likely that non help-seeking cases existed in the study’s catchment area and study period that were not included. This may have contributed to the limitation arising from the low overall rate of transition to psychosis, which may have precluded sufficient power to detect a difference between levels of social deprivation. Finally, unmeasured confounding factors and biases may have also influenced the study’s results.

Future research

To further investigate whether residing in more socially deprived areas is a cause or a consequence of psychotic disorders, future studies examining UHR identification and transition should ensure adequately powered samples, wider catchment areas and adjustment for confounders. Such potential confounders would include factors such as the social class at birth, as there have been mixed findings as to whether a lower social class at birth, defined by parental occupation, is a risk factor for a subsequent psychotic disorder (Kwok, 2014). Comprehensive information on all areas of residence since birth and a long period of follow-up could also provide valuable insights into the point or points at which this environmental factor exerts its influence. To this end, it would also be useful to examine whether there is an association between social deprivation and ‘psychotic-like experiences’, as this may provide additional information about whether its influence precedes the onset of the UHR state.

Furthermore, to ascertain the aetiological mechanisms of social deprivation, research is required to determine the factors mediating its interaction with psychotic disorders. This may be achieved by assessing the individual neighbourhood-level variables that comprise measures of social deprivation, such as education, income and employment, on rates of UHR identification and transition. This may also shed light on the barriers to accessing mental health services in socially deprived areas.

Conclusion

Considering social deprivation is a well-established risk factor for psychotic disorders, the findings may suggest that individuals in areas of higher deprivation are under-accessing mental health services despite experiencing psychotic symptoms or a decline in functioning. Further research is required to explore the barriers to utilising these services, and to confirm the relationship between neighbourhood-level social deprivation and UHR identification and transition using larger, more comprehensive studies.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Brian O’Donoghue

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