Gendered patterns of severe and multiple disadvantage in England - Filip Sosenko, Glen Bramley & Sarah Johnsen (I-SPHERE, Heriot-Watt University) ...
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Gendered patterns of severe and multiple disadvantage in England Filip Sosenko, Glen Bramley & Sarah Johnsen (I-SPHERE, Heriot-Watt University)
Gendered patterns of
severe and multiple
disadvantage in England
Written by Glen Bramley
Filip Sosenko
Sarah Johnsen
Partners
2 3
Acroynms Acknowledgements
APMS IMD NHS We would like to express our sincere thanks • St Mungo’s for access to Combined
Adult Psychiatric Morbidity Index of Multiple Deprivation National Health Service to the many groups and individuals who have Homelessness and Information Network
Survey contributed in various ways to the production of (CHAIN) and Client Needs Survey data
LA OASys this report.
• Department for Education for access to
BME Local Authority Offender Assessment System
Children in Need data
Black and Minority Ethnic This project was undertaken as a collaboration
LCA PD between researchers at Heriot-Watt University • Public Health England for access to National
CHAIN Latent Class Analysis Primary Disadvantage domain and DMSS Research. DMSS led a series Drug Treatment Monitoring System (NDTMS)
Combined Homelessness and of consultations with a range of severely data.
Information Network LD SMD disadvantaged women - in order that this work
Learning Disability Severe and Multiple would be informed by their lived experiences
CIN Disadvantage and their perspectives on gender and multiple
Children in Need MEAM disadvantage - and produced a conceptual
Making Every Adult Matter SP report which has helped shape the analysis
DCLG Supporting People (McNeish and Scott, 2017). In addition we are
Department for Communities MEH grateful for the assistance of NatCen Social
and Local Government Multiple Exclusion TOP Research who ran specific analyses under their
Homelessness Treatment Outcome Profile Adult Psychiatric Morbidity Survey (APMS) data
DV use agreement with NHS Digital. The authors
Domestic Violence MH UC would like to acknowledge the support and Designed by Studio Rollmo
Mental Health Unitary County advice of Di McNeish, Sara Scott and Sally
DWP McManus in the production of the report. Photography by Henry/Bragg
Department for Work and MoJ VA
With support from:
Pensions Ministry of Justice Violence and Abuse Particular thanks are due to the advisory group
An Untold Story/
members, and to the six groups of women Voices Hull
NDTMS around the country who shared their varied
National Drug Treatment experience of multiple disadvantage with us and North Camden Zone
Monitoring System whose perspectives and advice have been so Likewise
important in shaping this study.
We would also like to extend our thanks to the The photography that is woven through this report emerged
in part, from a co-designed, participatory workshop
following organisations for granting permission
between people with lived experience of severe and
to use and/or facilitating access to data sources: multiple disadvantage, staff and volunteers in frontline
services and the photographers. We would like to extend
• NHS Digital for access to APMS data our warm thanks to everyone who participated so openly
and enthusiastically.
• Department for Communities and Local
Government for access to Supporting People
data
4 5
Glossary of terms
Adverse Childhood Experiences Index of Multiple Deprivation (IMD) PD3 Substance misuse
A widely used term referring to stressful events The official suite of measures of deprivation for Experiencing three out of four primary A broad definition is adopted, including not
occurring in childhood including being the local and small areas across England. disadvantage domains (e.g. ‘homelessness + only regular use of hard drugs but also ‘harmful’
victim of abuse, being the victim of neglect, poor mental health + substance misuse’). drinking of alcohol and dependence on cannabis.
being a witness of domestic violence, parental Latent Class Analysis (LCA)
abandonment, having a parent with a mental A form of cluster analysis, used in these analyses PD4 Supporting People (Client Record and
health condition, a member of the household to divide the population into different groups of Experiencing all four primary disadvantage Outcomes for Short-Term Services) (SP)
being in prison, and/or growing up in a people who share similar experiences. domains (e.g. ‘homelessness + poor mental A housing-related support services dataset
household in which there are adults experiencing health + substance misuse + violence and that includes most publicly-funded single
alcohol or drug use problems. Multiple Exclusion Homelessness (MEH) abuse’). homelessness services and covers most higher
A quantitative survey of people using ‘low tier (social services) authorities in England.
Adult Psychiatric Morbidity Survey (APMS) threshold’ homelessness, drug and other Poor mental health
A national household survey of mental health, services in seven UK cities conducted in 2010. A broad definition is adopted, including Violence and abuse
conducted every seven years. The questionnaire experiencing a common mental disorder (such Here defined as being a victim of interpersonal
also covers various adverse experiences, National Drug Treatment Monitoring System as depression, anxiety, phobia, obsessive- violence and abuse such as having been raped
including substance misuse, homelessness, (NDTMS) compulsive disorder or post-traumatic stress or sexually assaulted (by any perpetrator), or
experience of violence and abuse, having a A national dataset that monitors client journeys disorder), bipolar disorder, psychosis, or being suffering violence and coercive control by a
history of offending, and adverse childhood through substance misuse services. identified with a personality disorder. partner or ex-partner – where coercive control
experiences. includes behaviours which limit someone’s
Offending Primary domains of disadvantage freedom and diminish their self-worth such as
Cluster Analysis Having contact with the criminal justice system Here defined as including the four domains of threatening harm, denying access to money and
A statistical modelling approach that identifies (including being in trouble with the police homelessness, substance misuse, poor mental preventing them from seeing family or friends.
similar groups of people or topics in a dataset. involving court appearance). health, and violence and abuse.
Current disadvantage PD0 Secondary domains of disadvantage
Defined here as experiencing disadvantage in No experience of any of the four primary Here defined as living in poverty (material and/
the last 12 months. disadvantage (PD) domains. or financial), being a lone parent, being socially
isolated, living in poor quality accommodation,
‘Ever’ disadvantage PD1 being a migrant (particularly when compounded
Defined here as experiencing disadvantage ever Experiencing only one of the four primary by poor English skills), being a Gypsy/Traveller,
during adulthood (16+). disadvantage domains (e.g. ‘homelessness only’, having a physical disability, having a learning
‘poor mental health only’, or ‘substance misuse disability, being an offender, being involved in sex
Homelessness only’). work, having lost children to the care system.
A broad definition of homelessness is adopted,
including not only rough sleeping, but also other PD2 Severe and multiple disadvantage (SMD)
forms of highly insecure and inappropriate Experiencing two out of four primary Here defined as experiencing at least two
accommodation, insofar as this is recorded in disadvantage domains (e.g. ‘homelessness + disadvantages focussed upon in this study,
the key datasets. substance misuse’; ‘substance misuse + violence with at least one of them being a ‘primary’ one
and abuse’; ‘substance misuse + poor mental (homelessness, substance misuse, violence and
health’). abuse, and poor mental health).
6 7
1. INTRODUCTION 10 7. GEOGRAPHY 90
Background, aims and objectives 12 Geographical patterns in the general 94
Definitions 16 household population
Report structure 18 Geographical patterns for homeless 96
people
Key points 100
2. METHOD 20
8. ADVERSE CHILDHOOD 102
3. SCALE AND PATTERN OF 28 EXPERIENCES
‘CURRENT’ EXPERIENCE Experiences of adversity during 106
Scale 32 childhood
Pattern 36 Being brought up by parents 110
Key points 38 experiencing severe and multiple
disadvantage
4. SCALE AND PATTERN 40 Key points 112
OF EXPERIENCE ‘EVER’ IN
ADULTHOOD 9. POVERTY, DISABILITY AND 114
Scale and overlap between primary 44 SOCIAL ISOLATION
domains Poverty 118
Patterns of offending 46 Disability 122
Key points 50 Social isolation 126
CONTENTS
Key points 128
5. CLUSTERS OF 52
DISADVANTAGE IN THE 10. CONCLUSION 130
GENERAL POPULATION Endnotes 140
Clusters of women 56 References 144
Clusters of men 60 Appendix 1: 146
Indicators of primary and secondary
Key points 66
domains of disadvantage
Appendix 2: 150
6. SOCIO-DEMOGRAPHIC 68 Cluster analysis results for women
PROFILE Appendix 3: 154
Age 72 Cluster analysis results for men
Ethnicity 74 Appendix 4: 158
Nationality and migration status 76 Age profile of Supporting People clients
Appendix 5: 161
Household composition 80
The relationship between severe and
Parenthood and child contact 82
multiple disadvantage and adverse
Housing tenure 85 childhood experiences
Educational qualifications 86
Key points 88
1 INTRODUCTION
BACKGROUND,
AIMS & OBJECTIVES
The subject of ‘severe and multiple
disadvantage’ has risen up the policy agenda
in recent years as the need to develop more
effective policy and practice has become
increasingly evident.
A key catalyst to associated debate was the disadvantage. That study also assessed the Although ultimately ‘about’ The analysis presented makes the best possible
publication of Hard Edges: mapping severe and feasibility of using an alternative conceptualisation use of existing administrative and survey data but
multiple disadvantage in England (Bramley et al, to produce a profile of those affected (McNeish et women, this report also casts is inevitably limited to the evidence that can be
2015), based on a study conducted by Heriot-Watt al, 2016). This exercise was conducted, in part, light on previously undocumented gleaned from these datasets. Like the Hard Edges
University for Lankelly Chase. Hard Edges England to ensure that the Hard Edges definition did not study which preceded it, this study is exploratory
analysed administrative (service-use) data to inadvertently become viewed as the only definition
manifestations of severe and rather than definitive, but offers the most robust
develop a statistical profile of people who were in of ‘severe and multiple disadvantage’, which itself multiple disadvantage affecting a account to date of the scale and overlap between
contact with homelessness, substance misuse was a newly coined term to describe a complex groups subject to the specific (gendered)
number of men.
and criminal justice services. The study indicated social phenomenon. combinations of disadvantage under investigation.
that the population with concurrent experience
In using this different definition of severe and
(within the same year) of all three of these This report builds on these earlier studies by
multiple disadvantage, we were particularly
particular disadvantages consisted predominantly documenting the findings of the quantitative
interested to find out:
of men. profiling exercise of women’s experiences of severe
and multiple disadvantage conducted thereafter
• how many, and what proportion of, women are
At the same time, a review commissioned by by Heriot-Watt University in collaboration with
affected
the cross-sector initiative Agenda (the alliance DMSS Research. The study’s central aim was to
for women and girls at risk) and conducted by develop a statistical profile of women affected • the socio-demographic profile of those affected
DMSS Research highlighted the importance of by severe and multiple disadvantage in England,
• how different domains of disadvantage overlap
understanding women’s experiences of severe and as defined by the alternative conceptualisation
multiple disadvantage as different from those of developed, in order to enhance understanding of • how severe and multiple disadvantage is
men (McNeish and Scott, 2014). Lankelly Chase their characteristics and circumstances (insofar as geographically distributed
subsequently commissioned DMSS Research and available data allowed). The conceptualisation was
• what existing data can tell us about associated
Heriot-Watt University to work together to consider developed specifically in relation to women, but
risk factors.
whether a different conceptualisation might bring comparable data pertaining to men is provided as
the lives of more women into view and shed light and where possible.
on other manifestations of severe and multiple
12 13
THE STUDY’S CENTRAL
AIM WAS TO DEVELOP A
STATISTICAL PROFILE OF
WOMEN AFFECTED BY SEVERE
AND MULTIPLE DISADVANTAGE
IN ENGLAND
14 15
DEFINITIONS
Consultations with groups of disadvantaged
women undertaken as part of the
conceptualisation and feasibility study made
clear that mental ill health and experience of
interpersonal violence and abuse were central
features of their experience which needed to be
taken into account.
Moreover, they highlighted that disadvantages functioning being compromised by substance al, 2016). These are referred to as ‘secondary’ disadvantages (or at least those occurring
such as homelessness and substance consumption (including regular use of hard domains of disadvantage throughout this report within a single year) as was the case in Hard
dependence often resulted from different drugs but also ‘harmful’ drinking of alcohol and and include: living in poverty, being an offender, Edges, this study expands the focus to bring
difficulties in men’s and women’s lives, that dependence on cannabis); being a lone parent, being a migrant (particularly into view experience of disadvantage throughout
the experience of these disadvantages was when compounded by poor English skills), adulthood. The inclusion of disadvantage
gendered, and the ways in which services BEING A VICTIM OF INTERPERSONAL being a Gypsy/Traveller, being isolated, living in ‘ever’ experienced during adulthood was in
responded were often based on gendered VIOLENCE AND ABUSE poor quality accommodation, having a physical response to the emphasis that the women
expectations of how men and women ‘should’ Such as having been raped or sexually assaulted disability, having a learning disability, being we consulted placed on the cumulative
behave (McNeish et al, 2016). (by any perpetrator), or suffering violence and involved in sex work, and having lost children to impact of multiple disadvantage over the
coercive control by a partner or ex-partner - the care system. lifecourse – and in particular their insistence
In response, the current research develops a wherein coercive control includes behaviours that some disadvantages can be as harmful
profile of severe and multiple disadvantage which limit someone’s freedom and diminish This study’s definition of severe and multiple when they occur in a sequence as when they
defined, in part, in terms of four ‘primary’ their self-worth such as threatening harm, disadvantage therefore differs from that of its occur simultaneously (McNeish et al, 2016).
domains of disadvantage, which include the denying access to money and preventing them Hard Edges predecessor by including poor Insofar as data allows, the study also considers
following experiences during adulthood: from seeing family or friends; mental health and interpersonal violence adversity experienced during childhood in
and abuse, and omitting involvement with recognition of the cumulative impact of adversity
HOMELESSNESS HAVING POOR MENTAL HEALTH the criminal justice system, from primary over the entire lifecourse. In addition, this study
Not having a settled place to stay, such as Experiencing a common mental disorder (such disadvantage domains. In addition, it includes draws upon different data sources from Hard
sofa-surfing (staying with family or friends as depression, anxiety, phobia, obsessive- a range of secondary domains such as poverty, Edges, by including general household surveys
because the individual affected has no home compulsive disorder or post-traumatic stress disability, and social isolation, amongst others as well as administrative (service use) data. It
of their own), staying in temporary or refuge disorder), bipolar disorder, psychosis, or being (see above). thus illuminates the experiences of members
accommodation, or rough sleeping; identified with a personality disorder. of the private household population as well as
This study also departs from its Hard Edges homeless people and other groups using support
SUBSTANCE MISUSE A range of other forms of disadvantage were predecessor in two other ways. It employs a services that relate to the primary domains of
Consumption of drugs or alcohol above a certain highlighted by women in the consultations, albeit different timeframe, so rather than focusing disadvantage (see Chapter 2).
threshold, substance dependency, or daily with less frequency or emphasis (McNeish et almost exclusively on ‘current’ experience of
16 17
REPORT
STRUCTURE
The report consists of ten chapters. Chapter 2
outlines the methods employed in the collection
and analysis of data. Chapters 3 and 4 focus
on the scale and patterns of primary domains
of disadvantage affecting women ‘currently’
and ‘ever’ during adulthood. Chapter 5 focusses
on the ways that different combinations of
disadvantage tend to ‘cluster’ within the general
population, and includes consideration of both
primary and secondary domains. Chapter 6
summarises what is known about the socio-
demographic profile and housing status
of women affected by severe and multiple
disadvantage. Chapter 7 draws attention to
geographical patterns in its incidence. This is
followed, in Chapter 8, by analyses of childhood
adversity in the backgrounds of women and men
reporting severe and multiple disadvantage in
adulthood. Chapter 9 offers additional reflections
regarding key secondary disadvantages that
influence the quality of life of women affected
by severe and multiple disadvantage, such as
poverty, disability and social isolation. Chapter 10
draws together key conclusions from the study.
18 192
METHODMETHOD 3
MULTIPLE EXCLUSION HOMELESSNESS
(MEH)
A cross-sectional survey conducted in 2010
of people who had been homeless and had
experience of one or more of the following:
This study was preceded by a review of nearly institutional care, substance misuse, or
participation in 'street culture activities' (begging,
100 potential datasets, full details of which are street drinking, ‘survival’ shoplifting or sex
provided in the conceptualisation and feasibility work)2. It involved a census survey of users
of 'low threshold' support services in seven 4
study report (McNeish et al, 2016). cities throughout the UK (n=1,286), followed ST MUNGO’S CLIENT NEEDS SURVEY
by extended interviews with a sample of 452
individuals. The information is self-reported. A survey of clients of St Mungo’s, a charity
Seven datasets were subsequently selected for working with people who are sleeping rough, in
hostels and at risk of homelessness. To facilitate
detailed analysis and their key parameters are service planning, every year the organisation
as follows: surveys clients staying in its accommodation,
the majority of which is in London. This study
employs the 2016 database which contains
1,950 unique records. Data is generated by
support workers.
1
ADULT PSYCHIATRIC MORBIDITY SURVEY
(APMS) 5
COMBINED HOMELESSNESS AND
This cross-sectional survey collects data on INFORMATION NETWORK (CHAIN)
mental health among adults aged 16 and over
living in private households in England. APMS A multi-agency database recording information
also has a wealth of self-reported information 2 about people sleeping rough and the wider street 6
on other domains of disadvantage. Data from SUPPORTING PEOPLE (SP) CLIENT population in London. This study draws upon CHILDREN IN NEED (CIN)
the 2014 edition containing records for 7,546 RECORDS AND OUTCOMES FOR SHORT- aggregate figures for rough sleepers who had their
individuals was analysed and supplemented TERM SERVICES support needs assessed over 2015/16 (n=5,481). An administrative dataset that forms part of the
with data collected in the previous wave (2007). Data was generated by support workers. National Pupil Database. Data covers children
Permission to analyse the data was obtained This merged dataset provides information about referred to English Local Authorities children's
from the Data Request Service at NHS Digital. clients aged 16 and over who entered and left social services, and those who are assessed
housing support services that were in receipt of as in need of Local Authority social services
funding from the Supporting People programme support. Data was generated by social workers.
which ran from 2003 to 2011 1. Most were not
living in private households. We report primarily
7
on data from the last year of full participation
NATIONAL DRUG TREATMENT MONITORING
(2010/11), which contained 325,000 records.
SYSTEM (NDTMS)
The data was generated by support workers
who complete a structured questionnaire for
Contains records of people receiving treatment
each service user.
from a drug or alcohol misuse service in England.
Data was generated by support workers.
22 23A full list of the indicators used in relation to each for the time period under investigation. PD1 of primary disadvantages they topic by another reveals the composition of the
of the primary and secondary disadvantage refers to experience of one primary domain population. This strategy, however, becomes
domains is provided in Appendix 1. (e.g. ‘homelessness only’ or ‘violence and had experienced (0, 1, 2, 3, or 4) unfeasible when the number of topics one
abuse only’), PD2 to experience two out of the or the combinations of primary is interested in is large. For example, with 10
four primary domains (e.g. ‘homelessness + binary variables there are over 1,000 possible
It is important to note that in the disadvantages (e.g. ‘poor mental
substance misuse’ or ‘violence and abuse + poor combinations; it is simply too difficult for a
analysis a distinction is made mental health’), and so on. health only’, ‘homelessness + human to see the patterns.
between ‘current’ experience substance misuse + poor mental
of disadvantage (that is, things Two approaches were taken to data health but not violence and abuse’). LCA employs computer power to
experienced within the past twelve analysis. The first approach started identify combinations that are not
This approach was used on all datasets.
months) and disadvantages with the number and types of identical but ‘close enough’ and it
experienced ‘ever’ in adulthood. disadvantages. This is an ‘analyst- The second approach was used to analyse puts them together into one ‘class’
driven’ approach, where the analyst the APMS dataset only and is called ‘Latent or cluster. LCA also suggests how
Where used in tables and graphics, the Class Analysis’ (LCA). LCA is a way of dividing
short-hand term ‘PD0’ is used in reference
defined the groupings. Specifically, a population into groups. This is straightforward many classes or clusters there are
to individuals who have not experienced any the analyst combined people into when there are just a few topics (variables) overall.
of the primary domains of disadvantage that one is interested in: cross-tabulating one
groups depending on the number
24 25It shows what number of clusters strikes the of broad cluster groupings: five clusters for They may actively avoid services (due to shame, had three main purposes: to obtain participants’
best balance between having a picture of the women and five clusters for men. The overall stigma, fear of losing children or prior negative feedback on the main findings of the preliminary
population that is detailed and having a one that typology for women can be compared with the experiences, for example), and/or not appear data analysis; to test and flesh out interpretations
is simple and useable. LCA has been used on the overall typology for men. However, because in population surveys or feature only in such of the data in key areas; and to explore questions
APMS dataset but not on other datasets because different typologies emerged for women and small numbers that little or no useful analysis of arising from the data. Key findings from the
APMS has a larger number of variables of interest men, specific groups or clusters should not be their experiences can be conducted (McNeish consultations are reported in McNeish and Scott
than the other datasets analysed for this report. It considered comparable. and Scott, 2017). Furthermore, missing data (2017). These built upon the findings of five earlier
was carried out separately according to gender so in administrative records and potential under- consultations involving more than 100 women
that the different ways that disadvantage groups Datasets covering both the private household reporting of disadvantages in surveys (due to with lived experience and other key stakeholders
together in men and women could be captured in population and individuals using homelessness embarrassment or fear of negative consequences in England and Scotland conducted during
different typologies. The LCA was also restricted services were included to maximise coverage as of disclosure) means that estimates are likely to the conceptualisation and feasibility study (see
to people aged 16-64 because previous research far as possible. be conservative3. McNeish et al, 2016).
has shown that disadvantages tend to manifest
differently in older people. Our initial analysis was followed by a series of
However, it is inevitable that some consultations with women affected by severe and
The LCA identified a large number of distinct women may not be represented in multiple disadvantage. These were conducted
groups among women and among men. To either. in Hull, Dewsbury and London, and involved a
make the typologies easier to describe, some total of 30 women with lived experience and six
of these were combined into a smaller number support agency staff members. The consultations
26 273
SCALE
& PATTERN OF
‘CURRENT’
EXPERIENCEThis chapter focusses on the overall
scale and patterns of ‘current’
experience of primary disadvantage:
that is, experience of one or more of the
primary domains within the past year.
30 31SCALE
Table 3.1 presents the best available (albeit
conservative) estimate of the number of adults
in England experiencing some combination of
the four main primary domains of disadvantage
under investigation within a single year.
This data is drawn from two datasets, APMS primary domains at the same time. 2.3 million
and Supporting People, which are effectively adults (5.2%) experienced two or more of these
complementary and largely non-overlapping4. domains concurrently, while 9.6 million (21.6%)
Table 3.1 The figures should nevertheless be treated experienced one of them. This finding is strongly
Percent and number (scaled-up projection) of women, men as orders of magnitude rather than precise influenced by the inclusion of poor mental health
and all adults experiencing different numbers of current primary accounting – being based partly on a in the primary domains of disadvantage for this
disadvantage domains in England, c.2010-14. sample survey from 2014 and partly on an study (cf. the Hard Edges study, see Chapter 1).
administrative dataset from 2010/11. Poor mental health has a very high level of
current prevalence, affecting 21% of all adults
Count of primary Women Men Adults and 25% of adult women. Four in five (80%)
domains
Around 336,000 adults currently
cases experiencing one or more current primary
% N % N % N
affected by three or four primary domains of disadvantage are affected by poor
domains. Of these, there were mental health. This proportion rises to 87% of
all women currently experiencing at least one
PD0 71 16,239,000 75 16,427,000 73 32,667,000 approximately the same number primary domain of disadvantage.
PD1 24 5,422,000 19 4,230,000 22 9,652,000 of women and men (169,000 and
167,000 respectively). The number
PD2 4 976,000 5 998,000 4 1,973,000
experiencing the most complex
PD3 1 157,000 1 162,000 1 319,000
disadvantage (all four domains)
PD4336,000
adults were currently
affected by three
Homelessness
or four domains
of disadvantage.
Substance
Misuse
FOUR DOMAINS
2.3 million OF
adults (5.2%), in
the general population, DISADVANTAGE
experienced two
or more of these
domains concurrently.
Being a 80%
victim of (four in five) cases
interpersonal experiencing one or more
violence and
abuse current primary domains
Having poor of disadvantage are
mental health
affected by poor mental
health.
34 35PATTERN MOST COMMON
DOMAIN COMBINATIONS
Figure 3.1 gives an overall picture of the
combinations of disadvantage that are most
common among those currently experiencing
any of the domains under investigation.
Three domains
Being a
victim of
Having poor Substance
interpersonal
mental health Misuse
violence and
abuse
Figure 3.1
Proportions of adult population All 4 domains
currently experiencing specific
Hless + MH + subst
combinations of primary
disadvantage domains by gender, VA + MH + subst
England, c.2010-14 VA +Hless + subst
— VA + Hless + MH
Sources: Authors’ analyses of MH + subst
Adult Psychiatric Morbidity
Survey (APMS) 2007/2014 and
Supporting People (SP) 2010/11.
Hless + subst
Hless + MH
Two domains:
Female
VA + subst
VA + MH
Mainly affecting Being a
Male VA + Hless women Having poor victim of
Substance only
mental health interpersonal
MH only
Hless only
violence and
VA only abuse
0 5 10 15 20 25
Among the single domains, poor mental health
is the most prevalent, and within that women
The most common combination of three
domains is experience of violence and abuse,
Two domains:
are more commonly affected. Next in prevalence, with poor mental health and substance misuse. Higher proportion
but a lot less common, is substance misuse, and
here men are much more commonly affected.
Combinations of two domains that are most
common involve either being a victim of of men Having poor Substance
Having been a victim of violence and abuse violence/abuse and poor mental health (mainly mental health Misuse
comes next, with a degree of balance between affecting women), or substance misuse and
genders. Homelessness appears relatively rare poor mental health (affecting a higher proportion
as a single experience, suggesting that it is of men).
most likely to be combined with other primary
domains of disadvantage, amongst users of SP
services at least6.
36 37KEY POINTS
It is possible to estimate that in England in
a typical year in the period 2010-2014, at
least 336,000 adults experienced more
complex combinations of disadvantage
(three or four primary domains), of whom
there are approximately the same number of
women as men. The number experiencing
all four primary domains at a point in time Experience of less complex combinations
was approximately 17,000, of whom around of primary domains was widespread.
70% were female. A total of 2.3 million adults (5.2%)
experienced two or more of these domains
currently, while about 9.6 million (21.6%)
experienced one of them. The numbers here
are largely accounted for by the inclusion
of poor mental health within the four
primary domains, and this also increases
the proportion of women represented in
the totals.
38 394
SCALE & PATTERN
OF EXPERIENCE
‘EVER’ IN ADULTHOOD
40 41This chapter also focusses on the
primary domains of disadvantage,
but explores experience of these at
any point (‘ever’) during adulthood.
In contrast to the preceding chapter
which focussed on ‘current’
experience, this takes account of
experiences that may not have
occurred contemporaneously
but at some point since the age
of 16. Asking about experiences
longer ago is more likely to be
subject to recall problems, and
so rates produced are likely to be
underestimates.
42 43SCALE AND OVERLAP
BETWEEN PRIMARY DOMAINS
Having poor
mental health
Figure 4.1 presents the estimated number
of adults reporting each main combination
of primary domains of disadvantage ever The combination of poor mental health
experienced, while Table 4.1 reports the and substance misuse accounts for
0.9 million adults, with three-quarters
percentages, in both cases distinguishing of these being men.
between men and women. Substance
Misuse
Figure 4.1
Table 4.1
Number of adults by combinations Hless + Subst + (MH or VA) Percent and number (scaled-up projection) of women, men and all
of primary disadvantage domains VA + MH + (Hless or Subst)
‘ever’ experienced during adults ‘ever’ experiencing combinations of primary disadvantage
MH + Subst
adulthood by gender, England domains during adulthood, England 2014
Hless + Subst
2014 (millions)
Hless + MH
— Ever PD Combination Women Men All Adults Women Men All Adults
Source: Authors’ analysis of VA + Subst
APMS data, 2014 VA + MH % Number (scaled-up projection)
Note: The second-top category VA + Hless None 45.8 58.3 51.9 10,386,120 12,652,029 23,038,149
‘VA+MH+(Hless or Subst)’
Subst only
includes MH+VA+Subst VA only 7.2 5.1 6.1 1,621,414 1,098,101 2,719,515
MH only
or MH+VA+Hless. PD4 is
captured by the top category Hless only Hless only 0.4 0.7 0.5 85,039 156,686 241,725
‘Hless+Subst+(MH and/or VA)’. VA only
MH only 26.6 20.5 23.6 6,032,113 4,448,827 10,480,940
None
Female Subst only 0.3 2.4 1.4 75,061 523,008 598,070
Male 0 2 4 6 8 10 12 14
VA + Hless 0.3 0.1 0.2 62,816 19,727 82,542
Million
VA + MH 14.3 5.0 9.8 3,242,828 1,080,739 4,323,567
From Figure 4.1 it can be seen that the most mental health with either of these accounts for
common experience is poor mental health only, 1.1 million adults, again with a majority being VA + Subst 0.3 0.7 0.5 64,857 141,494 206,351
affecting over 10 million adults with the majority female. The combination of poor mental health Hless + MH 0.3 0.9 0.6 75,288 205,731 281,019
being female. The second most common is the and substance misuse accounts for 0.9 million, Hless + Subst 0.0 0.1 0.0 0 13,086 13,086
combination of violence and abuse and poor but in this case three-quarters are men.
MH + Subst 1.0 3.2 2.1 222,689 696,621 919,310
mental health, which affects over 4 million adults, a
large majority of whom are women. The third most Table 4.1 shows that over half of adult women VA+MH+(Hless or Subst) 2.9 2.1 2.5 653,101 451,393 1,104,494
common category is violence and abuse only, report experiences in at least one of these Hless+Subst+(MH or VA) 0.7 1.0 0.9 158,967 225,697 384,663
again affecting more women. domains, whereas this is only true of a minority
Total 100 100 100
of men. Higher proportions of women are
Combinations involving one or two domains particularly strongly represented in the violence Base 4,488 3,058 7,546 22,677,117 21,701,594 44,378,712
involving homelessness or substance misuse are and abuse plus poor mental health combination
less common, implying that these experiences (with or without other domains), but also in Source: Authors’ analysis of APMS data, 2014.
are rarer and tend to coalesce with others. The violence and abuse only, poor mental health only, Note: The second-bottom category ‘MH+VA+(Hless or Subst)’ includes MH+VA+Subst or
combination of violence and abuse and poor and in violence and abuse plus homelessness. MH+VA+Hless. PD4 is captured by the bottom category ‘Hless+Subst+(MH or VA)’.
44 45PATTERNS OF OFFENDING
Our consultations with groups of women
affected by the disadvantages discussed
in this report highlighted contact with the
criminal justice system as a particularly
gendered experience.
While it plays a significant role in the pattern of point in adulthood, although few in number,
severe and multiple disadvantage experienced are much more likely than men who have
by many men (see also Bramley et al, 2015) done so to report experience of other primary
it does the same in the lives of comparatively disadvantage domains. The sharpest difference
few women. Therefore, although contact with is in experience of violence and abuse, but these
the criminal justice system was included in the women are also much more likely than men
Hard Edges definition of severe and multiple who have had contact with the criminal justice
disadvantage it is not treated as one of the four system to report experience of homelessness
primary domains in this study. Some reflection and poor mental health7.
on its prevalence and relationship with other
domains is nevertheless warranted.
Table 4.2
APMS provides details regarding contact with Experience of primary disadvantages among
the criminal justice system, as indicated by women and men reporting contact with criminal
having ‘spent time in prison on remand or justice in the general household population,
serving a sentence’, or ‘being in trouble with the APMS 2014 (percent)
police involving court appearance’. The number
of respondents having spent time in prison is
‘Ever’ primary Women Men
much lower than those having been in trouble domain
with the police. This data confirms that amongst
members of the private household population, Ever VA 66 24
having contact with the criminal justice system
is much less common among women than men, Ever homeless 21 12
with only 1.2% of female APMS respondents
ever having done so, compared with 5.9% of
Ever MH 76 54
men. These figures also confirm that experience
of offending is far less prevalent amongst
both women and men than is experience of Ever 26 20
substance
poor mental health, or violence and abuse, for
example (see Table 4.1 above). Base 115 388
Table 4.2 shows that women who have had
contact with the criminal justice system at some Source: authors’ analysis of APMS data, 2014.
46 47Table 4.3
Current/recent offending status by type of substance misuse and gender,
for those receiving treatment for drugs or alcohol, 2015/16
Women Men
Not Offender Total N Not Offender Total N
offender offender
Alcohol 97 3 100% 19,574 92 8 100% 31,410
only
Drugs and 88 12 100% 6,070 81 19 100% 18,813
alcohol
Drugs only 85 15 100% 12,346 76 24 100% 37,509
Source: Authors’ analysis of National Drug Treatment Monitoring System data.
It is also worth noting that the prevalence dependent children, for example).
of each of the primary disadvantages in Although offending rates generally rise with
adulthood is much higher among those who more complex combinations of primary domains,
‘spent time in prison on remand or serving a statistical modelling suggests that this appears
sentence’ than among those who reported to be driven mostly by the presence of substance
only being ‘in trouble with the police involving misuse. This link between substance misuse and
court appearance’. For example, the prevalence offending is slightly stronger for women than for
of ever having experienced homelessness during men.
adulthood is 31% among men with the former
experience and 7% among men in the latter The NDTMS dataset allows us to explore
group. whether alcohol dependency has a different
relationship with offending than drug
Amongst the population using homelessness and dependency. As Table 4.3 shows, those who
housing-related support services, more men are are dependent on drugs are more likely to be
current/recent offenders than women. A very high current/recent offenders than those dependent
proportion of men in Supporting People data – on alcohol, across both genders. This pattern is
more than half – are offenders. However, a third unchanged when homelessness is controlled for:
of women using Supporting People services are those who are drug users are more likely to be
current/recent offenders. Within the homeless homeless than those who are alcohol users, and
population, it is single homeless people who are those who are homeless are more likely to be
more likely to be offenders (as compared with current/recent offenders.
women experiencing homelessness who have
48 49KEY POINTS Women who have had contact with the
criminal justice system during adulthood,
although relatively few in number, are much
more likely than men who have done so to
report experience of primary disadvantage
domains at any point during adulthood.
Many of the single homeless people using
homelessness and housing support
services are offenders, and this is true for
one third of female service users (cf. half
of male users). There is a clear association
Just over half of adult women report between substance (particularly drug)
experience of at least one of the four primary misuse and offending, and this is slightly
domains of disadvantage at some point stronger for women than for men.
(‘ever’) during adulthood, whereas this
is only true of a minority of men. Higher
proportions of women are particularly
strongly represented in the violence and
abuse plus poor mental health combination
(with or without other domains), but also
amongst those experiencing violence and
abuse only, poor mental health only, and
violence and abuse plus homelessness.
50 515
CLUSTERS
OF DISADVANTAGE
IN THE
GENERAL
52
POPULATION
53This chapter expands the focus of the
research to encompass both primary
and secondary domains of disadvantage.
While the previous chapters present the
proportion of women and men to have
experienced different numbers and types
of disadvantages, this chapter presents
the proportions of women and men found
to be in different disadvantage groups.
The groups were identified using cluster
analysis. Each contains women or men
experiencing a similar pattern of primary
and secondary disadvantages, reflecting
how these tend to coalesce in the
population.
As noted in Chapter 1, this data was
obtained from the APMS and analysis was
restricted to individuals of working age
within the private household population.
The analysis focusses on experience of
primary domains of disadvantage ‘ever’
during adulthood, and ‘current’ experience
of secondary domains. The first section
of this chapter reports findings relating to
clusters of women, and the second refers
to those of men.
54 55CLUSTERS OF WOMEN CLUSTER 5 CLUSTER 9
VA only, no/low disadvantage on secondary PD 2-3, low disadvantage on secondary
domains. Approximately 8% of women. All domains. Approximately 3% of women. Women
of the women in this cluster have experienced in this cluster have a very high chance of having
interpersonal violence and abuse during ever experienced poor mental health (84%) and
adulthood but not other primary domains of violence and abuse (80%), while a majority have
The cluster analysis identified ten different disadvantage. In terms of economic position,
health and social isolation, this cluster is on
experienced substance misuse (68% chance)
and a substantial proportion have experienced
groups of women with broadly similar average only slightly worse than Cluster 1. homelessness (34% chance). The chances of
experiences as regards the type and CLUSTER 6
having experienced two, three or four primary
domains of disadvantage are 42%, 49% and
combination of disadvantages experienced. 9% respectively. Half of women who have
VA and MH, no/low disadvantage on experienced three primary domains belong to this
These are described below. A detailed secondary domains. Approximately 8% of cluster, while the other half belong to Cluster 10.
breakdown of all relevant statistics is provided women. Slightly over half of all women with the Their chance of having a disability is low, as is the
experience of ‘violence and abuse plus poor chance of being unemployed or inactive. Their
in Appendix 2. mental health only’ belong to this cluster. In chance of being materially deprived is also low,
terms of economic position, health and social although not as low as in the least disadvantaged
isolation, this cluster is on average only slightly clusters.
worse than Cluster 1.
CLUSTER 10
CLUSTER 7
PD 2-4, very high disadvantage on secondary
VA and MH, high chance of health issues. domains. Approximately 3% of women. There
Approximately 2% of women. One in six of all is a very high chance of having ever experienced
CLUSTER 1 CLUSTER 3 women with experience of ‘violence and abuse poor mental health (94%) and violence and
plus poor mental health only’ belong to this abuse (85%), a clear majority have experienced
No primary domains, no/low disadvantage MH only, no/low disadvantage on secondary
cluster. On average, their material situation is homelessness (70% chance), and nearly half have
on secondary domains. Approximately 35% domains. Approximately 20% of women.
good and the chance of social isolation is small, experienced substance misuse (42% chance).
of all women fall into this group. Women in this Women in this cluster have experienced poor
but the chance of poor health is high. There The chances of having experienced two, three or
cluster have never experienced any primary mental health but no other primary severe and
is also a high chance of being a carer. Half of four primary domains of disadvantage are 24%,
severe and multiple disadvantage domain. On multiple disadvantage domain. Their economic
women in this cluster are aged 55-64. 59% and 17% respectively. Half of women who
average, they are better-off economically, have position is very similar to that of women in
have experienced three domains belong to this
better health, and are less socially isolated than Cluster 1. The chance of having a disability is
CLUSTER 8 cluster, as do 61% of those who have experienced
women in other clusters. slightly higher than in Cluster 1, as is the chance
all four. Women in this cluster are on average in
of being a carer.
VA and MH, high disadvantage on secondary the worst socio-economic situation. For example,
CLUSTER 2 domains. Approximately 4% of women. Slightly over half are in serious debt or arrears (54%
CLUSTER 4 over a quarter of all women who have ever chance), a substantial proportion live in material
No primary domains, high disadvantage
experienced ‘violence and abuse plus poor deprivation (37% chance), the probability of being
on secondary domains. Approximately 11% Mainly MH only, high disadvantage on
mental health only’ belong to this cluster. Their in the lowest income quintile is 59%, and the vast
of women may be classified in this group. secondary domains. Approximately 6% of
economic situation is on average worse than majority are unemployed or economically inactive
As in Cluster 1, women in this cluster have women. Nearly all (93%) women in this cluster
in any other preceding cluster. For example, (80% chance). There is also a very high chance
never experienced any primary domain of have experienced poor mental health but not
the chance of being in serious debt or arrears of having a disability (66%) and a high chance
disadvantage. However, their position in terms other primary domains; the rest experienced
is 40% (vs. 15% in Cluster 2); the chance of of being a carer (24%). Around one in six is a
of poverty, health and social isolation is on ‘homelessness only’. Their economic situation,
being in the lowest income quintile is 56%; and lone parent (16% chance). In terms of household
average considerably worse than that of women health and isolation are strikingly worse than
the chance of having mould at home is 44%. composition, the chance of being a single person
in Cluster 1. The chance8 of unemployment or women in Cluster 3. While the majority are
Home ownership is very low (13% chance), household is higher than in other clusters (26%).
economic inactivity is particularly high (70%). White British, women from Asian / Asian British
social isolation is very high (45% chance) and The majority live in social housing (67% chance).
Cluster 2 has relatively more women from a or Black / Black British ethnic background are
the probability of poor health is high as well (e.g. Nearly half are socially isolated (48% chance). The
BME background than Cluster 1. significantly over-represented in this cluster.
40% chance of having a disability). Women in probability of having a history of offending is also
this cluster have the highest probability of being very high at 22%, as is the chance of having ever
a lone parent (21%). sold sex as compared with other clusters (7%).
56 57Figure 5.1
Cluster groupings for women aged 16 to 64
These ten clusters may be consolidated into
five broader groupings, depicted by the colour
coding in Figure 5.1, and used in sub-group
analysis later in Chapters 6-9. These broad
groupings are as follows:
1. No primary disadvantage, no/low secondary disadvantage
46%
No primary
1 CLUSTER 1 & 2 2 CLUSTER 3 & 5 3 CLUSTER 6, 7 & 9 disadvantage
Characterised by women who Including women who have Including those women 2. No primary disadvantage, high secondary
have never experienced any experienced one of the primary who have experienced disadvantage
of the primary disadvantage disadvantages (either poor combinations of two or even
domains, and together mental health or experience three primary domains but are
make up a total of 46% of violence and abuse) but are not highly disadvantaged in
of women in the private not multiply disadvantaged socio-economic terms, and
household population. These in socio-economic terms (i.e. comprise 13% of women in the 3. MH only, not deprived
are described in graphics they have no/low disadvantage private household population. 28% MH or VA, no/low
in following chapters by the on secondary domains), These are described in secondary disadvantage
short-hand term ‘No PD’. comprising a total of 28% of graphics as ‘PD2-3, fair’.
the female private household 5. VA only, not deprived
population. These are described
in graphics as ‘MH/VA only, fair’.
6. VA, MH, not deprived
13% 2-4 primary
disadvantages, no/low
4 CLUSTER 4 5 CLUSTER 8 & 10 VA, MH, poor health 7.
secondary disadvantage
VA, MH, Subst, not deprived 9.
Consisting of women who Consisting of women who
have experienced one primary have experienced between 6% MH only, high secondary
MH only, deprived 4. disadvantage
disadvantage (predominantly two and four primary domains
poor mental health) and as well as being affected by VA, MH, deprived 8. 7% 2-4 primary
who are highly deprived serious current economic, disadvantages, high
in socio-economic terms social and health-related 2-4 PDs, deprived 10. secondary disadvantage
(i.e. experience a range of disadvantages. They comprise
secondary domains). This a total of 7% of the female
grouping comprises 6% private household population.
of women in the private These are described in
household population and is graphics as ‘PD2-4, depriv’.
described in graphics as ‘MH
only, depriv’. Source: authors’ analysis of APMS 2014
58 59CLUSTERS OF MEN
Men aged 16-64 in the private household
population can be classified into six
clusters based on the extent and nature of
their experiences of severe and multiple
disadvantage (see Figure 5.2 and Appendix 3).
CLUSTER 1 CLUSTER 3 CLUSTER 5 CLUSTER 6
(No primary disadvantage). Approximately (MH only, high disadvantage on secondary (PD2-3 inc MH, no/low disadvantage on (PD2-4, multiply deprived on secondary
56% of men may be classified in this cluster. domains). Approximately 6% of men. Men in this secondary domains). Approximately 10% of domains). Approximately 5% of men. This
They have not experienced any primary domain cluster have experienced poor mental health but men. This cluster is dominated by men who cluster contains all men who have experienced
of disadvantage. On average, this and the next not other primary domains. However, they have have experienced two primary domains (86% four primary domains, nearly two-thirds of
cluster (Cluster 2) are the least disadvantaged a much higher chance of being in a negative chance); the rest have experienced three. Three- men who experienced three primary domains,
clusters in terms of material situation, health, material situation, to suffer from poor health quarters of men affected by two primary domains and one in six of men who have experienced
isolation and other secondary domains. and/or social isolation than men in Cluster 2. of disadvantage at any point in adulthood are two primary domains. In particular, those who
For example, the chance of being unemployed in this cluster; the rest are in cluster 6. Nearly all have experienced homelessness as one of two
or economically inactive is 75%, the chance of members of this cluster have experienced poor domains are relatively more likely to be in Cluster
CLUSTER 2
being in serious debt or arrears is 24%, and the mental health (93% chance). There is also a 6 than those with other combinations. This
(MH only, no/low disadvantage on secondary chance of being disabled is 60%. Around half high risk of having been a victim of violence and is the most disadvantaged cluster by a large
domains). Approximately 13% of men. Men in are social renters (52% chance). abuse (61% chance) and substance misuse (51% margin: the risk of having a history of offending
this cluster have experienced poor mental health chance). Men in this cluster have on average is 43%, half (50% chance) are in serious debt
but not other primary domains, and have a low CLUSTER 4 a similar economic situation to men in Cluster or arrears, the majority are unemployed or
chance of socio-economic deprivation. 65% of 4, but have a higher risk of disability and social economically inactive (62% chance), over a third
men who have ever experienced ‘MH only’ fall (PD1, no/low disadvantage on secondary isolation - although this risk is still lower than the have no qualifications (34% chance), over half
into this cluster, while the remaining 35% fall domains). Approximately 10% of men. Almost all equivalent in cluster 3. are disabled (52% chance), there is a high risk of
into Cluster 3. men in this cluster have experienced one primary having a learning difficulty (21%), half are socially
domain of disadvantage; the largest group is isolated (50% chance), nearly all are renters
those who experienced ‘violence and abuse only’ (60% chance social housing, 34% chance
(59% chance) followed by ‘substance misuse only’ private rented) and a substantial proportion are
(30% chance). The remainder have experienced in single person households (38% chance). They
‘homelessness only’ (9% chance) and ‘violence are more likely than men in other clusters to be in
and abuse plus poor mental health’ (2% chance). the 25-44 age bracket.
With regards to economic position and health,
men in this cluster are on average only slightly
more disadvantaged than men in Clusters 1 and 2.
60 61Figure 5.2
Cluster groupings for men aged 16 to 64
As was the case for women, the clusters for
men may be consolidated into broader groups
for subsequent sub-group analysis, based
on observable patterns and commonalities
in their characteristics9. They have been
amalgamated into five groups, depicted by the
colour coding in Figure 5.2, as follows:
1. No primary disadvantage
56%
No primary
1 CLUSTER 1 2 CLUSTER 2 & 4 3 CLUSTER 5 disadvantage
This includes those men who Comprising those men Characterised by men who
have not been affected by who have experienced have experienced two or three
any primary disadvantage one primary disadvantage primary domains but show no
domain, and comprises (predominantly either poor or low levels of disadvantage
56% of the male private mental health or having on secondary domains. This
household population. These been a victim of violence and group comprises 10% of 2. MH, not deprived
are described in the graphics abuse) and making up a total the male private household
in following chapters by the of 23% of men in the private population and are described 23% MH or VA, not deprived
short-hand term ‘No PD’. household population. These in graphics as ‘PD 2-3, not
are described in graphics deprived’. 4. VA or substance, not deprived
as ‘MH/VA only, fair’ for
simplicity although a small
minority of those men have 10% 2-3 primary
5. 2-3 primary disadvantages, not deprived
experienced ‘substance only’
disadvantages,
or ‘homelessness only’.
not deprived
4 CLUSTER 3 5 CLUSTER 6
MH, deprived 3. 6% MH, deprived
Consisting of men who have Comprising men who are
experienced poor mental highly disadvantaged across
2-4 primary
health and no other primary a range of 2-4 primary 6. 5% 2-4 primary
disadvantages, deprived
domain, but who are highly domains, as well as secondary disadvantages, deprived
disadvantaged across domains, and making up a
secondary domains, and total of 5% of the male private
making up 6% of those in the household population. These
private household population. are described in graphics as
These are described in ‘PD2-4, depriv’.
graphics as ‘MH only, depriv’.
Source: authors’ analysis of APMS 2014
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