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Crime victimization following onset of mental illness: a Danish national registry study utilising

police data

Authors:

Kimberlie Dean, PhD1,2; Thomas Munk Laursen, PhD3,4; Carsten Bøcker Pedersen, DrMedSc3,4,5;

Roger T Webb, PhD6; Preben Bo Mortensen, DrMedSc3,4,5; Esben Agerbo, DrMedSc3,5

Affiliations:

1. School of Psychiatry, University of New South Wales, Australia

2. Justice Health & Forensic Mental Health Network, NSW, Australia

3. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark

4. The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH

5. CIRRAU – Centre for Integrated Register-based Research at Aarhus University, Denmark

6. Centre for Mental Health and Safety, The University of Manchester and Manchester Academic

Health Science Centre (MAHSC), Manchester, United Kingdom

Corresponding author:Kimberlie Dean, School of Psychiatry, University of New South WalesC/- Justice Health & Forensic Mental Health Network, Roundhouse, Long Bay Complex, PO Box 150, Matraville, 2036, NSW, Australia; Email: [email protected]; Tel: +61 9700 3000

Co-author addresses:Drs Agerbo, Laursen, Mortensen and Pedersen – National Centre for Register-based Research, Aarhus University, Fuglesangs Alle 4, Building 2641, 8210 Aarhus V, DenmarkDr Webb - The University of Manchester, Room 2.311, Jean McFarlane Building, The University of Manchester, Oxford Road, Manchester, UK M13 9PL

Word count: 3148

mailto:[email protected]

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KEY POINTS:

Question:

What is the incidence of police-reported victimization experiences following onset of mental illness and do persons with specific mental disorders have increased risk of crime victimization compared to those without illness?

Findings:

In this national cohort of over 2 million individuals, incidence of victimization by any crime and by violent crime was increased among those with mental illness. The association was seen across the diagnostic spectrum, in both men and women, with the strongest associations found for those with substance use and personality disorders.

Meaning:

Mental illness, across the diagnostic spectrum, is associated with increased risk of police-recorded violent and non-violent victimization.

3

ABSTRACT

Importance: People with mental illness are more likely to have contact with the criminal justice

system but research to date has focused on risk of offense perpetration, while less is known about

the risk of being a victim of crime and violence.

Objectives: To establish the incidence of victimization, by all types of crime and by violent crime

specifically, following the onset of mental illness across the full diagnostic spectrum, compared to

those in the population without mental illness.

Design: Longitudinal national cohort study utilising register data

Setting: Danish population-based study

Participants: A cohort of over 2 million persons born between 1965 and 1998 and followed from

2001 or from their 15th birthday until the end of 2013.

Exposures: Cohort members were followed for onset of mental illness recorded as a first contact

with outpatient or inpatient mental health services. Diagnoses across the full spectrum were

considered separately for men and women.

4

Main outcome: Incident rate ratios (IRRs) were estimated for first crime victimization event (any

and violent crime) reported to police following onset of mental illness. The IRRs were adjusted for

cohort member’s own criminal offending, in addition to a number of sociodemographic factors.

Results: In a total cohort of 2,058,063, the adjusted IRR for crime victimization associated with any

mental disorder was 1.49 (CI 1.46-1.51) for men and 1.64 (CI 1.61-1.66) for women. The IRRs were

higher for violent crime victimization (males with any mental disorder: 1.76, CI 1.72-1.80; females

with any mental disorder: 2.72, CI 2.65-2.79). The strongest associations were for persons

diagnosed with substance use disorders and personality disorders, but significant risk elevations

were found across almost all diagnostic groups examined.

Conclusions and relevance: Onset of mental illness is associated with an increased risk of

becoming a victim of crime, and of violent crime in particular. Elevated risk is not confined to

specific diagnostic groups. Women with mental illness are especially vulnerable to victimization.

An individual’s own offending account for some but not all of the increased vulnerability to

victimization.

5

Introduction

People who experience mental illness are more likely to come into contact with the criminal

justice system. Research to date has focussed more on the association between mental illness and

an elevated risk of crime perpetration,1,2 than on the heightened vulnerability to being victimised

by violent or nonviolent crime. However, evidence has emerged to indicate that victimization risk

may be at least as great, if not greater, than risk of crime perpetration,3 among persons with

mental illness. Studies of individuals with severe mental illnesses such as schizophrenia, have

reported strikingly high rates of self-reported victimization experiences.4-6 One survey of

individuals in contact with secondary mental health services in London, England, found rates as

high as 40% for self-reported past-year experience of victimization with any offense and 19% for

past-year violent crime victimization specifically, compared to 14% and 3%, respectively, for a

control sample obtained from a contemporaneous national crime survey.7 Whilst an increased risk

of victimization among those with mental illness is in itself worthy of a preventive focus, its

importance is further justified by evidence that victimization experiences may be associated with a

range of other adverse outcomes, including poor symptomatic and functional mental health

recovery.8 Victimization has also long been acknowledged to play an important aetiological role in

the development of mental illness, and particularly so early life trauma and abuse,9 but less

attention has been paid to the potential for mental illness to increase vulnerability to victimization.

To date, the association between mental illness and risk of victimization has been examined

mainly in cross-sectional surveys of selected samples of individuals with severe mental illnesses.4

Less is known about how risk might vary across the psychiatric diagnostic spectrum, specifically

6

following onset of mental illness, or at a population level. We previously undertook such an

approach to examining of the association between mental illness and crime perpetration, and

found risk following illness onset extended across the diagnostic spectrum2. Whilst victimization,

and violent crime victimization in particular, is increasingly regarded as an important but largely

neglected public health problem,10 the incidence of victimization experienced by those with

mental illness compared to the general population has not yet been established. Robust evidence

is therefore lacking to inform the development of preventive strategies, including initiatives aimed

at improving the experience of persons with mental illness who report victimization and

subsequently seek justice.11

In this population registry study, we examined, for the first time in a national cohort, the incidence

of victimization by all types of criminal offenses, and by violent crimes separately, following onset

of mental illness across the full diagnostic spectrum. We further explored the potential influence

of offense perpetration, given the known overlap between those who are victims and those who

perpetrate crime and violence,12 and the putative role of mental illness in determining the extent

of that overlap.13

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Method

The study cohort (N=2,058,063) consisted of all persons born in Denmark from 1965 to 1998, who

were alive at their 15th birthday (having excluded 650 who could not be linked to their mother in

the Civil Registration System, CRS), The CRS contains the personal identification number, sex, date

and place of birth, continuously updated vital status and parents’ personal identification numbers.

Each resident is assigned a unique personal identification number at birth or at point of first

address in Denmark, through which it is possible to link information within and between

registers.14

Crime victimization outcome measures

Data on victims of reported criminal offenses in Denmark were extracted from the Administrative

System of the National Police, available from 2001 and including data on all offenses reported to

police, including those not pursued following report. The dependent variables considered were

first crime victimization event (including all registered criminal offenses) and first violent crime

victimization (i.e. all violent - physical assault, aggravated acquisitive crimes, violent threats, sexual

offenses). The five most common crime victimization types – thefts, simple violence, threats,

robberies, and severe violence - were examined separately in relation to the presence or absence

of any mental disorder.

Mental disorder diagnostic categories

Information on mental illness was obtained from the Psychiatric Central Research Register, which

contains data relating to all admissions to psychiatric hospitals since 1969 and all outpatient

contacts and emergency room visits since 1995.15 Diagnoses were assigned based on International

8

Classification of Disease (ICD) coding, according to the 8th revision (ICD-8)16 until 1993 and the 10th

revision (ICD-10)17 from 1994. We classified diagnoses into 8 groups based on the broad ICD-10

categories (Table S1, supplementary materials).18 For each mental disorder, date of onset was

defined as the first day of the first psychiatric contact (inpatient, outpatient, psychiatric

emergency care unit) for the diagnosis of interest. Two approaches were taken to considering the

association between diagnostic category and victimization. Firstly, each of the 9 categories was

included alone in separate analyses (the ‘single diagnosis model’), then secondly, mutually

exclusive categories were created according to the hierarchical logic of the ICD-10 classification

system. Individuals with psychiatric contacts belonging to more than one diagnostic category

were allowed to move upward in the hierarchy as they accumulated new diagnoses over time, but

not downward.

Assessment of cohort member’s own offending and other covariates

From the National Crime Register, we extracted information on penal code violations.19 Only guilty

verdicts resulting in custodial sentences, suspended sentences, conditional withdrawal of charges,

fines, and sentences to psychiatric treatment were included.

To assess the potential confounding effect of parental socioeconomic status, we utilised

information on paternal income (in quartiles plus a ‘missing’ category) and maternal highest level

of educational attainment (coded as: primary, secondary, tertiary education) at each cohort

member’s 15th birthday. These covariate data were extracted from the Integrated Database for

Labour Market Research, containing information from the 1970 Population and Housing Census 20

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and annually updated information from 1980.21 We also included the following covariates in all

analyses: age in one-year bands, calendar year in one-year bands, and unknown paternal identity.

Statistical analysis

All cohort members were followed from 2001 or from their 15 th birthday, whichever came last,

until their first victimization event, death, emigration or the end of follow-up on the 31st December

2013. The cohort study was analysed using Poisson regression with the GENMOD procedure in

SAS version 9.1.4 (SAS Institute Inc, Cary, NC). Poisson regression was used to conduct a time-to-

event survival analysis with the number of person years at risk used as an offset variable to enable

incidence rate ratio estimation.22 We calculated the incidence rate as the number of first

victimization events per 1000 person-years at risk. From the Poisson regression models, incidence

rate ratios (IRRs) were calculated for each diagnostic category group versus a reference category

of no recorded mental disorder. The 95% confidence intervals (CIs) for these IRRs were estimated

using maximum likelihood. We retained information on an individual’s first diagnosis within each

group occurring prior to date of first registered victimization for any criminal offense. Psychiatric

diagnostic category and cohort member’s own criminal offending were handled in the analyses as

being time-varying23,24, while all other covariates were fixed at the start of follow-up. The unit of

analysis for the study remained at the level of the individual for all analyses. Sex-specific analyses

were conducted for both outcomes (any crime and violent crime victimization), and models were

then fitted for the ICD-10 hierarchical classification controlling for cohort member’s own criminal

offending, along with other potential confounders. The incidence rates for any first crime

victimization and first violent victimization were examined for each of the covariates, among those

with and without any mental disorder.

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Results

Mental disorders and crime victimization

Among individuals in the cohort with any recorded mental disorder, rates of victimization were

considerably higher, even after adjustment for sociodemographic factors, than those experienced

by individuals without mental disorder (IRR 1.68, 95% CI 1.65-1.71 for men and 1.71, 95% CI 1.68-

1.73 for women). Variation in incidence rates across levels of each examined covariate was

identified (Table 1 for any offense; Table S2 for violent offenses in supplementary material).

Considering each diagnostic category separately (the ‘single diagnosis model’; Tables 2 and 3),

positive associations between specific categories of mental disorder and incidence of victimization

were found across the diagnostic spectrum for both men and women, with the exception of

developmental disorders (where a negative association was found) and mental retardation for

men (for which no statistically significant association was found). For both sexes, the strongest

associations were observed for substance abuse disorders (IRR 2.61, CI 2.53-2.69 for men; IRR

3.18, CI 3.06-3.32 for women) and personality disorders (IRR 2.23, CI 2.15-2.32 for men; IRR 2.00,

CI 1.95-2.06 for women). When the ICD-10 hierarchical classification was fitted, the pattern was

essentially unaltered for both sexes.

Mental disorders and violent crime victimization

Although a similar pattern of results was observed when violent crime victimization was

considered specifically, the magnitude of the associations was considerably greater, particularly

among women (Tables 4 and 5). The adjusted incidence rate ratios for violent victimization among

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those with any mental disorder were 2.10 (95% CI 2.05-2.15) for men and 2.99 (95% CI 2.92-3.06-

1.73) for women. Significant positive associations between having a specific category of mental

disorder and incidence of violent crime victimization were found across the diagnostic spectrum

(Tables 4 and 5, single diagnosis model), the only exception being developmental disorders for

men. The strongest associations were again found to be for those with substance abuse disorders

(IRR 3.45, CI 3.33-3.58 for men; IRR 7.01, CI 6.69-7.41 for women). When the ICD-10 hierarchical

classification was fitted, the pattern was again essentially unchanged for both men and women.

Adjustment for cohort member’s own criminal offending and type of crime victimization

Incidence rates for cohort member’s own criminal offending are presented in Tables 1 and S2

(supplementary materials), along with rates for each of the covariates previously considered. The

strength of the associations observed between mental disorders and victimization were all

attenuated by adjusting for cohort member’s own criminal offending (Tables 2-5), although the

apparent confounding influence varied by diagnostic category. The reduction in strength of

association for those with substance use disorders was the greatest (20-30% IRR reduction for any

offense; around 35% for violent crime victimization). The two sociodemographic covariates,

father’s income and mother’s education, made very little difference in attenuating the incidence

rate ratios over and above the far stronger potential confounding influence of cohort member’s

own criminal offending, as indicated in Table S3 (supplementary materials).

The five most common types of crime victimization obtained from the police data were the

following: thefts (1336xxx), simple violence (code 1252xxx), threats (1292xxx), robberies (1280xxx)

and severe violence (1255xxx). Incidence rates were raised for each of the crime victimization

12

types among those with any mental disorder compared to those with no disorder, with stronger

effects seen for women (Tables S4 and S5).

13

Discussion

Summary of main findings

This is the first study to systematically establish on a national basis the incidence of crime

victimization with any offense, and violent crime victimization specifically, following onset of

mental illness across the full spectrum of psychiatric diagnoses. In this study of over 2 million

persons, we identified an elevated incidence of crime victimization across multiple diagnostic

categories, compared to individuals without mental illness in the population. Incidence rate

ratios, adjusted for socio-demographic confounders, were particularly high for those with

substance use and personality disorders, but they were also raised for individuals with other

diagnoses including severe mental illnesses. When incidence of violent crime victimization was

examined specifically, similar patterns of elevated risk were found, but the magnitude of the

associations observed was consistently greater. The strongest associations overall were found for

women with substance use disorders; incidence rates of violent crime victimization were seven

times higher than for women without mental illness.

Whilst previous studies have typically reported prevalence rather than incidence estimates and

have focussed on severe mental illnesses, the pattern of our findings is broadly in line with the

literature3-7, including one of the few previous studies utilising population linkage to police data.25

Our findings augment the existing literature by demonstrating that heightened vulnerability to

crime victimization with any offense, and violence crime victimization in particular, extends to

those with a wide range of mental illnesses, is true of those reporting to police, occurs following

illness onset, and is not confined to individuals with mental disorders in the population who are

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treated as inpatients. Our findings regarding the association between mental illness onset and

subsequent crime victimization are likely to be conservative, given that individuals victimised prior

to illness onset will have been censored from the analysis.

Sex differences

In the current study, relative risks were consistently higher for women with mental illnesses than

for men, particularly for violent crime victimization, a finding which has been noted in previous

studies,26 at least in the context of severe mental illnesses. In the general population, with the

exception of specific offenses (e.g. sexual crimes, domestic abuse/violence), women are less likely

to be victims than men.27 Whatever ordinarily protects some women from victimization appears to

be eroded by the presence of mental illness. The reasons for this are likely to be multifactorial 26

but it is reasonable to hypothesise that causal mechanisms arising directly from the onset and

impact of mental illness may be particularly relevant for women.

Adjustment for cohort member’s own criminal offending

We also found evidence that cohort member’s own criminal offending explained some of the

elevated risk of crime victimization observed following mental illness onset, even after further

adjustment was made for a range of covariates likely to be associated with both victimization and

offense perpetration. The degree of reduction in strength of association appeared to differ by

diagnosis (i.e. it was greatest for those with substance use disorders), and was slightly greater for

violent crime victimization, and also among men. Criminal offending is unlikely to be a

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straightforward confounder of the relationship between onset of mental illness and victimization,

but may instead lie on the causal pathway between the two phenomena (in either direction) for

some individuals. Few previous studies have considered risk of offending and victimization among

those with mental illness in the same study cohort,25,28 despite the overlap between perpetrators

and victims being well established in the general population.12 The overlap between perpetrators

and victims likely arises in part from the presence of shared risk factors for the two outcomes,

among individuals with and without mental illness (e.g. co-morbid substance misuse problems,29,30

psychiatric symptoms,13 co-morbid personality problems31, conflicted social relationships,32).

Strengths and limitations

The current study had a number of key strengths, including the large size of the national cohort,

long duration of follow-up, and the minimisation of selection, attrition and information biases

commonly encountered in other studies; advantages arising from the use of interlinked national

registers. The full spectrum of psychiatric diagnoses was examined, post-illness-onset incidence of

victimization rather than prevalence alone was estimated (thereby reducing potential reverse

causality bias), mental health data came from outpatient and emergency as well as inpatient

registers, and both violent and non-violent crime victimization experiences reported to police

were examined. The investigation was also strengthened by its consideration of the dual risks of

victimization and perpetration in the same study cohort. However, it did have some important

limitations.

While systematic studies validating all the diagnoses presented in this study are not available,

many of the key diagnoses (e.g. schizophrenia, dementia, affective disorders, depressive disorder,

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childhood disorders) have been validated with reassuring results.33,34 It should be noted that all

diagnoses were made by a treating clinician and often based on a period of clinical observation

rather than a single clinical or research interview. Reliance on such clinically-determined

diagnoses does enable results to be more readily generalised to clinical settings where structured

diagnostic interviews are used infrequently. However, we were unable to identify those individuals

with mental disorder in the population either not receiving any treatment or receiving treatment

only in primary or private care. For those disorder categories characterised by lower rates of

outpatient, emergency or inpatient contact (e.g. anxiety disorders), our findings likely reflect the

risk of victimization for a subgroup who may have more severe disorder, co-morbid problems or

other adversity which has contributed to their need for mental health care beyond primary care.

Also, although the cohort included individuals aged up to 45 years, we could not examine the

entire period of risk for onset of mental disorder, particularly for disorders with later onset such as

those in the organic disorders category. Whilst we could assess the potential confounding or

mediating influences of several sociodemographic factors and investigate the overlap between

victimization and perpetration events in the same individuals, we were unable to elucidate the

underlying mechanisms. A complex and multifactorial causal pathway is likely to explain the

associations observed between mental illness and victimization, operating at the following three

levels: 1) individual (e.g. specific symptoms, cognitive impairments, comorbidities); 2) familial (e.g.

interpersonal conflict, isolation); 3) neighbourhood (e.g. urban/rural, local crime density) levels.

We have previously proposed a longitudinal model of causation, considering the likely role of

distal early life as well as proximal factors31.

With regard to the generalisability of our findings, overall crime rates in Denmark, as documented

by international crime victimization surveys35, are generally comparable to other industrialised

17

countries, although violent crime victimization rates may be lower than in some settings. The most

recently reported annual prevalence of violent crime victimization in Denmark (3.3%) is close to

the average rate (3.0%) but lower than in either the United States (4.3%) or the United Kingdom

(5.4%), for example.35 Finally, it is important to note that reliance on official police records of crime

victimization and perpetration ignores experiences and behaviours that are not reported and

there is some evidence that reporting rates may vary between countries, being higher in more

affluent settings.35 Also, persons with mental illness may be more reluctant or less able to report

victimization experiences to the police36 .

Implications

Following onset of mental illness, there is a heightened vulnerability to being a victim of crime and

violence. The focus in clinical practice and research on offending may have been at the expense of

neglecting risk of victimization. Risk assessment in clinical settings, for example, is dominated by

consideration of risks of suicide and violence, while risk of victimization is largely ignored.

Similarly, for those in contact with the criminal justice system, identification of mental health need

and provision of support and treatment is offered almost solely to offenders. At a policy level, our

findings have the potential to contribute to efforts to remedy public misconceptions about mental

illness, often fuelled by selective and pejorative media reporting, with the ultimate aim of reducing

stigma. Our findings highlight the need for further research to, firstly, determine more fully why

some people with mental illnesses are especially vulnerable to victimization (e.g. those with

substance use and personality disorders) and, secondly, to develop effective interventions to

reduce the elevated crime victimization risk among people with mental illnesses.

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Author Contributions:

KD, TML and EA were the principle investigators and contributed substantially to the study design

and interpretation of findings. TML and EA conducted the analysis; they had full access to all the

data in the study and take responsibility for the integrity of the data and the accuracy of the data

analysis. KD undertook the literature search. KD wrote all the drafts and the final version of the

paper. PBM contributed to the conception of the study, the study design and the interpretation of

findings. RW and CBP contributed to the interpretation of findings. All authors contributed to the

preparation of the report and approved the final version.

Declaration of interests:

We declare no competing interests.

Acknowledgements:

This study was supported by a grant from Justice Health & Forensic Mental Health Network, New

South Wales, Australia. Dr Dean is funded by Justice Health & Forensic Mental Health Network,

NSW, Australia. Drs Agerbo, Laursen, Mortensen and Pedersen are supported financially by the

Stanley Medical Research Institute and The Lundbeck Foundation Initiative for Integrative

Psychiatric Research, iPSYCH. Dr Webb is supported financially by the European Research Council.

The funders/sponsors had no role in the design and conduct of the study; collection, management,

analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or

decision to submit the manuscript for publication.

19

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21. Petersson F, Baadsgaard M, Thygesen LC. Danish registers on personal labour market affiliation. Scandinavian journal of public health 2011; 39(7_suppl): 95-8.22. Andersen PK, Keiding N. Multi-state models for event history analysis. Statistical methods in medical research 2002; 11(2): 91-115.23. Clayton D, Hills M, Pickles A. Statistical models in epidemiology: Oxford university press Oxford; 1993.24. Breslow N, Day N, Heseltine E. Statistical methods in cancer research. Vol. 2, The design and analysis of cohort studies/. by NE Breslow & NE Day; technical editor for IARC, E. Heseltine. 1987.25. Short TB, Thomas S, Luebbers S, Mullen P, Ogloff JR. A case-linkage study of crime victimisation in schizophrenia-spectrum disorders over a period of deinstitutionalisation. BMC psychiatry 2013; 13(1): 1.26. Khalifeh H, Dean K. Gender and violence against people with severe mental illness. Int Rev Psychiatry 2010; 22(5): 535-46.27. ONS. Overview of violent crime and sexual offences: year ending Mar 2016. Online: Office for National Statistics (UK), 2017.28. Johnson KL, Desmarais SL, Van Dorn RA, Grimm KJ. A typology of community violence perpetration and victimization among adults with mental illnesses. Journal of interpersonal violence 2015; 30(3): 522-40.29. Witt K, Van Dorn R, Fazel S. Risk factors for violence in psychosis: systematic review and meta-regression analysis of 110 studies. PloS one 2013; 8(2): e55942.30. Hiday VA, Swartz MS, Swanson JW, Borum R, Wagner HR. Criminal victimization of persons with severe mental illness. Psychiatric services (Washington, DC) 1999; 50(1): 62-8.31. Dean K, Moran P, Fahy T, et al. Predictors of violent victimization amongst those with psychosis. Acta Psychiatrica Scandinavica 2007; 116(5): 345-53.32. Silver E. Mental disorder and violent victimization: the mediating role of involvement in conflicted social relationships. Criminology 2002; 40(1): 191-212.33. Kessing L. Validity of diagnoses and other clinical register data in patients with affective disorder. European Psychiatry 1998; 13(8): 392-8.34. Jakobsen KD, Frederiksen JN, Hansen T, Jansson LB, Parnas J, Werge T. Reliability of clinical ICD-10 schizophrenia diagnoses. Nordic journal of psychiatry 2005; 59(3): 209-12.35. van Dijk J, van Kesteren J, Smit P. Criminal Victimisation in International Perspective: Key findings from the 2004-2005 ICVS and EU ICS. The Hague: United Nations Office on Drugs and Crime and United Nations Interregional Crime and Justice Research Institute, 2008.36. MIND. Another Assault: Mind's Campaign for Equal Access to Justice for People with Mental Health Problems: MIND; 2007.

Table 1. Incidence of first crime victimization (any offense) by covariates, among those with and without mental disorder

No mental disorder Any mental disorder

Cases/person-years Incidence rate (per 1000

person years)


person years)

Total sample 233,851/18,932,458 12.4 31237/1,455,892 21.5

Sex Female 121,249 / 9,144,727 13.3 17993 / 788058 22.8

Male 112,602 / 9,787,731 11.5 13244 / 667835 19.8

Father Unknown 2,050 / 128,744 15.9 474 / 17951 26.4

Known 231,801 / 18,803,714 12.3 30763 / 1437941 21.4

Perpetrator Yes 17,511 / 935,042 18.7 6815 / 221515 30.8

No 216,340 / 17,997,416 12.0 24422 / 1234378 19.8

Paternal 1st quartile 49,873 / 5,036,019 9.9 10012 / 474094 21.1

income 2nd quartile 52,349 / 5,178,296 10.1 7388 / 381622 19.4

3rd quartile 61,664 / 4,463,528 13.8 6824 / 308121 22.1

4th quartile 61,165 / 3,651,994 16.7 5073 / 215436 23.5

Unknown 8,800 / 602,621 14.6 1940 / 76620 25.3

Maternal Primary 74,732 / 6,874,852 10.9 13291 / 606570 21.9

education Secondary 82,005 / 6,583,488 12.5 9629 / 458630 21.0

Tertiary 12,194 / 1,332,390 9.2 6053 / 274657 22.0

Unknown 64,920 / 4,141,729 15.7 2264 / 116034 19.5

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Table 2. Incidence rate ratios (IRR) for first crime victimization with any offense by mental disorder category (men)

Mental disorder

category

Cases/person-years Incidence rate (per

1000 person years)a

Single models

IRR (95% CI)b

ICD-10 hierarchical

classification

IRR (95% CI)c

ICD-10 hierarchical

classification adjusted

for ‘own criminality’

IRR (95% CI)d

Overall association

No mental disorder 112,602/9,787,731 11.50 1 .00 Reference 1 .00 Reference 1 .00 Reference

Any mental disorder 13,244/667,835 19.83 1.68 (1.65 – 1.71) 1.68 (1.65 – 1.71) 1.49 (1.46 – 1.51)

Diagnostic categories


1. Organic disorders 54/3,659 14.76 2.10 (1.61, 2.74) 2.23 (1.71, 2.92) 1.71 (1.31, 2.24)

2. Substance abuse 4,081/156,069 26.15 2.61 (2.53, 2.69) 2.69 (2.60, 2.77) 1.95 (1.89, 2.01)

3. Schizophrenia-

spectrum disorders

1,750/113,158 15.47 1.46 (1.40, 1.53) 1.18 (1.10, 1.26) 1.11 (1.04, 1.19)

4. Mood disorders 2,534/136,265 18.60 1.74 (1.67, 1.81) 1.62 (1.54, 1.71) 1.55 (1.48, 1.64)

5. Neurotic disorders 5,228/260,583 20.06 1.78 (1.73, 1.83) 1.56 (1.50, 1.62) 1.45 (1.39, 1.50)

6. Personality disorders 2,508/112,710 22.25 2.23 (2.15, 2.32) 1.92 (1.76, 2.08) 1.60 (1.47, 1.74)

7. Mental retardation 504/34,031 14.81 1.01 (0.92, 1.10) 0.85 (0.76, 0.96) 0.84 (0.74, 0.94)

8. Developmental

disorders

633/50,301 12.58 0.71 (0.66, 0.77) 0.72 (0.66, 0.80) 0.73 (0.66, 0.81)

a: Unadjusted rate per 1000 person-years; b: Each disorder in a separate model adjusted for age, calendar year, maternal education, paternal income, and paternal identity unknownc: All disorders in the same model (hierarchical with 1 highest and 8 lowest) adjusted for age, calendar year, maternal education, paternal income, and paternal identity unknownd: Same model as 3 but also adjusted for cohort member’s own criminal offending

23

Table 3. Incidence rate ratios (IRR) for first crime victimization with any offense by mental disorder category (women)

Mental disorder

category


person-years)a

Single models

IRR (95% CI)b

ICD-10 hierarchical

classification

IRR (95% CI)c

ICD-10 hierarchical



IRR (95% CI)d

Overall association


Any mental disorder 17,993/788,058 22.83 1.71 (1.68 – 1.73) 1.71 (1.68 – 1.73) 1.64 (1.61 -1.66)




2. Substance abuse 2,312/57,269 40.37 3.18 (3.06, 3.32) 3.35 (3.21, 3.49) 2.65 (2.54, 2.77)

3. Schizophrenia-

spectrum disorders

1,708/78,125 21.86 1.68 (1.60, 1.76) 1.57 (1.49, 1.66) 1.52 (1.44, 1.61)

4. Mood disorders 5,466/244,244 22.38 1.68 (1.64, 1.73) 1.64 (1.59, 1.69) 1.61 (1.57, 1.67)




8. Developmental

disorders

218/14,575 14.96 0.73 (0.64, 0.83) 0.67 (0.54, 0.83) 0.66 (0.53, 0.82)


24

Table 4. Incidence rate ratios (IRR) for first violent crime victimization by mental disorder category (men)

Mental disorder

category


person years)a

Single models

IRR (95% CI)b

ICD-10 hierarchical

classification

IRR (95% CI)c

ICD-10 hierarchical



IRR (95% CI)d

Overall association


Any mental disorder 9,568/694,516 13.78 2.10 (2.05 -2.15) 2.10 (2.05 -2.15) 1.76 (1.72 -1.80)




2. Substance abuse 3,146/164,315 19.15 3.45 (3.33, 3.58) 3.63 (3.50, 3.76) 2.32 (2.23, 2.40)

3. Schizophrenia-

spectrum disorders

1,188/117,435 10.12 1.76 (1.66, 1.86) 1.35 (1.24, 1.47) 1.24 (1.14, 1.35)

4. Mood disorders 1,685/143,169 11.77 2.14 (2.04, 2.25) 2.00 (1.87, 2.13) 1.88 (1.76, 2.00)




8. Developmental

disorders

435/51,247 8.49 0.84 (0.76, 0.92) 0.83 (0.73, 0.94) 0.84 (0.75, 0.95)


25

Table 5. Incidence rate ratios (IRR) for first violent victimization by mental disorder category (women)

Mental disorder

category

Cases/person-years =

rate per 1000 person-

years1

Incidence rate (per 1000

person-years)a

Single models

IRR (95% CI)b

ICD-10 hierarchical

classification

IRR (95% CI)c

ICD-10 hierarchical



IRR (95% CI)d

Overall association


Any mental disorder 8,414/856,666 9.82 2.99 (2.92 -3.06) 2.99 (2.92 -3.06) 2.72 (2.65 -2.79)




2. Substance abuse 1,525/64,092 23.79 7.04 (6.69, 7.41) 7.98 (7.58, 8.40) 5.12 (4.84, 5.41)

3. Schizophrenia-

spectrum disorders

876/84,361 10.38 3.03 (2.84, 3.24) 2.83 (2.60, 3.07) 2.64 (2.43, 2.86)

4. Mood disorders 2,418/268,101 9.03 2.75 (2.64, 2.87) 2.65 (2.52, 2.78) 2.57 (2.45, 2.70)




8. Developmental

disorders

111/15,099 7.35 1.32 (1.10, 1.59) 1.11 (0.80, 1.54) 1.07 (0.77, 1.49)

a: Unadjusted rate per 1000 person-years; b: Each disorder in a separate model adjusted for age, calendar year, maternal education, paternal income, and parental identity unknownc: All disorders in the same model (hierarchical with 1 highest and 8 lowest) adjusted for age, calendar year, maternal education, paternal income, and paternal identity unknownd: Same model as 3 but also adjusted for cohort member’s own criminal offending

27

Table S1. Mental disorder diagnostic categories (ICD-10 and ICD-8 codes)

Diagnostic category ICD-10 codes Equivalent ICD-8 codes

1. Organic disorders

Organic, including symptomatic, mental disorders F00-F09 290.09, 290.10, 290.11, 290.18, 290.19, 292.x9, 293.x9, 294.x9, 309.x9

2. Substance use disorders

Mental and behavioral disorders due psychoactive substance use F10-F19 291.x9, 294.39, 303.x9, 303.20, 303.28, 303.90, 304.x9

3. Schizophrenia

Schizophrenia and related disorders F20-F29 295.x9, 296.89, 297.x9, 298.29-298.99, 299.04, 299.05, 299.09, 301.83

4. Mood disorder

Mood disorders F30-F39 296.x9 (excl 296.89), 298.09, 298.19, 300.49, 301.19

5. Neurotic disorders

Neurotic, stress-related, and somatoform disorders, and eating

disorders

F40-F48, F50 300.x9 (excl 300.49), 305.x9 305.68, 307.99, 305.60, 306.50, 306.58, 306.59

6. Personality disorders

Personality disorders F60 301.x9 (excl 301.19), 301.80, 301.81, 301.82, 301.84

7. Mental retardation

Mental retardation F70-F79 311.xx, 312.xx, 313.xx, 314.xx, 315.xx

8. Developmental disorders

Pervasive developmental disorders and behavioural/emotional

disorders with onset usually occurring in childhood and adolescence

F84, F90-F98 299.00, 299.01, 299.02, 299.03, 306.x9, 308.0x

28

9. Any mental disorder

F00-F99 290-315

29

Table S2. Incidence of first crime victimization (violent offense) by covariates, among those with and without mental disorder

No mental disorder Any mental disorder


person years)


person years)

Total sample 98,250/19,656,536 5.00 17982/1551182 11.59

Sex Female 33,059/9,618,531 3.44 8414/856666 9.82

Male 65,191/10,038,005 6.49 9568/694516 13.78

Father Unknown 981/134,314 7.30 307/19262 15.94

Known 97,269/19,522,222 4.98 17675/1531920 11.54

Perpetrator Yes 12,503/971,045 12.88 5355/235531 22.74

No 85,747/18,685,491 4.59 12627/1315651 9.60

Paternal 1st quartile 24,619/5,181,046 4.75 6370/501390 12.70

income 2nd quartile 23,289/5,350,391 4.35 4376/405389 10.79

3rd quartile 25,565/4,659,771 5.49 3726/329928 11.29

4th quartile 20,602/3,837,905 5.37 2297/232410 9.88

Unknown 4,175/627,423 6.65 1213/82064 14.78

Maternal Primary 38,560/708,696 5.44 8756/642104 13.64

education Secondary 34,056/6,837,349 4.98 5351/488604 10.95

30

Tertiary 20,168/4,362,468 4.62 2494/2977917 8.37

Unknown 5,466/1,370,623 3.99 1381/122557 11.27

31

Table S3. Incidence rate ratio of first victimization (any crime and violent offense) with adjustment by covariate combinations,

among those with mental disorder compared to those without mental disorder

Any crime victimization Violent victimization

Covariates included in each model* Incident rate ratio (95% CI) Incident rate ratio (95% CI)

Basic model Men 1.69 (1.66-1.72) 2.25 (2.20-2.30)

Women 1.72 (1.69-1.74) 3.14 (3.06-3.21)

Adjusted for own criminality Men 1.49 (1.46-1.52) 1.83 (1.79-1.87)

Women 1.64 (1.61-1.66) 2.81 (2.74-2.88)

Adjusted for own criminality, father’s income Men 1.48 (1.46-1.51) 1.77 (1.74-1.81)

Women 1.63 (1.61-1.66) 2.74 (2.67-2.81)

Adjusted for own criminality, mother’s education Men 1.49 (1.47-1.52) 1.80 (1.76-1.84)

Women 1.64 (1.62-1.67) 2.77 (2.70-2.84)

Adjust for own criminality and both father’s income

and mother’s education

Men 1.49 (1.46-1.51) 1.76 (1.72-1.80)

Women 1.64 (1.61-1.66) 2.72 (2.65-2.79)

32

*Each model (including the basic model) is adjusted for age, calendar period and having an unknown father

33

Table S4. Incidence rate ratios (IRR) for first crime victimization, by type of crime, for those with and without any mental disorder

(men)

Crime victimization categorya Cases/person-years Incidence rate (per 1000 person

years)b

Adjusted IRR (95% CI)c

Thefts

No mental disorder 55,027/10,210,663 5.39 1.00 Reference

Any mental disorder 5,305/751,210 7.06 1.20 (1.17,1.23)

Simple violence

No mental disorder 40,001/10,210,952 3.92 1 .00 Reference

Any mental disorder 5,640/731,987 7.71 1.68 (1.63 – 1.73)

Threats


Any mental disorder 2,154/768,884 2.80 2.13 (2.03, 2.24)

Robberies



Severe violence



34

a: Five most frequent types of victimization

b: Unadjusted rate per 1000 person-years

c: Adjusted for age, calendar year, maternal education, paternal income, paternal identity unknown, and cohort member’s own criminal offending

35

Table S5. Incidence rate ratios (IRR) for first crime victimization, by type of crime, for those with and without any mental disorder

(women)

Crime victimization categorya Cases/person-years Incidence rate (per 1000 person

years)b

Adjusted IRR (95% CI)c

Thefts


Any mental disorder 12,037/860,395 13.99 1.27 (1.25,1.30)

Simple violence



Threats



Robberies


Any mental disorder 565/937,347 0.60 2.47 (2.24, 2.72)

Severe violence


Any mental disorder 703/936,818 0.75 3.61 (3.28, 3.97)

36

a: Five most frequent types of victimization

b: Unadjusted rate per 1000 person-years

c: Adjusted for age, calendar year, maternal education, paternal income, paternal identity unknown, and cohort member’s own criminal offending

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