** Categories not mutually exclusive.
The analysis cohort consisted of 18,965 opiate-positive cases and 78,838 test-negative controls. A quarter of both groups were female. Cases were older at their drug test (p < 0.001) and younger at their first recorded offence (p < 0.001). Cases were more likely to have a conviction for a serious acquisitive offence at this date (p < 0.001) and less likely to have a conviction for a violent offence (p < 0.001).
Sixty-seven per cent of opiate-positive cases had complete data on age-of-initiation. The majority of missing data were due to cases not having a linked treatment record (see Appendix A in the Supplementary material). The median age of initiation was similar for men and women.
Offending rates for four categories of offences.
All crimes | Non-serious acquisitive crimes | Serious acquisitive crimes | Violent crimes | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Gender | Category | person years follow-up | Number | Rate [95% CI] | Number | Rate [95% CI] | Number | Rate [95% CI] | Number | Rate [95% CI] |
Male | non-users | 923,663 | 837,019 | 0.91 [0.90, 0.91] | 176,783 | 0.19 [0.19, 0.19] | 150,177 | 0.16 [0.16, 0.16] | 61,730 | 0.07 [0.07, 0.07] |
Opiate users | 290,007 | 528,153 | 1.82 [1.82, 1.83] | 153,031 | 0.53 [0.53, 0.53] | 103,654 | 0.36 [0.36, 0.36] | 25,247 | 0.09 [0.09, 0.09] | |
Pre-initiation | 96,491 | 115,682 | 1.20 [1.19, 1.21] | 25,285 | 0.26 [0.26, 0.27] | 34,317 | 0.36 [0.35, 0.36] | 6672 | 0.07 [0.07, 0.07] | |
Post-initiation | 97,788 | 270,885 | 2.77 [2.76, 2.78] | 91,148 | 0.93 [0.93, 0.94] | 40,917 | 0.42 [0.41, 0.42] | 10,796 | 0.11 [0.11, 0.11] | |
Initiation missing | 95,728 | 141,586 | 1.48 [1.47, 1.49] | 36,598 | 0.38 [0.38, 0.39] | 28,420 | 0.30 [0.29, 0.30] | 7779 | 0.08 [0.08, 0.08] | |
Female | non-users | 304,612 | 100,525 | 0.33 [0.33, 0.33] | 51,518 | 0.17 [0.17, 0.17] | 4194 | 0.01 [0.01, 0.01] | 8192 | 0.03 [0.03, 0.03] |
Opiate users | 87,373 | 120,336 | 1.38 [1.37, 1.39] | 66,637 | 0.76 [0.76, 0.77] | 4509 | 0.05 [0.05, 0.05] | 4840 | 0.06 [0.05, 0.06] | |
Pre-initiation | 32,839 | 15,139 | 0.46 [0.45, 0.47] | 8335 | 0.25 [0.25, 0.26] | 1096 | 0.03 [0.03, 0.04] | 1149 | 0.03 [0.03, 0.04] | |
Post-initiation | 29,807 | 80,056 | 2.69 [2.67, 2.70] | 44,767 | 1.50 [1.49, 1.52] | 2451 | 0.08 [0.08, 0.09] | 2523 | 0.08 [0.08, 0.09] | |
Initiation missing | 24,727 | 25,141 | 1.02 [1.00, 1.03] | 13,535 | 0.55 [0.54, 0.56] | 962 | 0.04 [0.04, 0.04] | 1168 | 0.05 [0.04, 0.05] |
In total, the cohort had 1.6 million sanctioned offences. For men, the rate of historical offending for opiate-positive cases was almost double that for test-negative controls (rate per year, opiate users: 1.82; non-users: 0.91; p < 0.001); the rate for opiate-positive females was more than four times that for test-negative females (opiate users: 1.38; non-users: 0.33; p < 0.001). For both male and female opiate users, the rate of offending was lower prior to initiation of opiate use compared to post-initiation. For males and females, the rate of violent and serious acquisitive offending peaked during the late teens, whilst the rate of non-serious acquisitive offences had a later peak ( Fig. 1 a and b).
Offending rates, per year by age, opiate users and non-users for: (a) male, non-serious acquisitive offences; (b) male, serious acquisitive offences; (c) male, violent offences; (d) female, non-serious acquisitive offences; (e) female, serious acquisitive offences; (f) female, violent offences.
Results of Generalised Estimating Equation analysis comparing historical offending rates of opiate users and non-users using whole sample (Model 1, N = 97,803) and those with complete data on age of initiation of opiate use (Model 2, N = 91,565), separately for males and females and for four categories of offences.
Male | Female | ||||||||
---|---|---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 1 | Model 2 | ||||||
Offence category | Variable | RR | 95% CI | RR | 95% CI | RR | 95% CI | RR | 95% CI |
All crimes | Opiate users vs. non-users | 1.99 | [1.96, 2.01] | – | – | 4.59 | [4.48, 4.69] | – | – |
Initiation of opiate use | – | – | 1.16 | [1.15, 1.17] | – | – | 2.00 | [1.95, 2.05] | |
Users (pre-onset) vs. non-users | – | – | 2.00 | [1.97, 2.03] | – | – | 2.80 | [2.71, 2.90] | |
Users (post-onset) vs. non-users | – | – | 2.32 | [2.29, 2.35] | – | – | 5.61 | [5.47, 5.75] | |
Age | 1.92 | [1.92, 1.93] | 1.90 | [1.90, 1.91] | 2.53 | [2.51, 2.55] | 2.32 | [2.30, 2.34] | |
Age-squared | 0.77 | [0.77, 0.78] | 0.77 | [0.77, 0.77] | 0.78 | [0.78, 0.78] | 0.79 | [0.79, 0.79] | |
Age-cohort | |||||||||
<1975 | 0.75 | [0.74, 0.76] | 0.74 | [0.73, 0.75] | 0.62 | [0.60, 0.64] | 0.68 | [0.66, 0.70] | |
1975–1979 | 0.86 | [0.85, 0.87] | 0.85 | [0.84, 0.86] | 0.78 | [0.76, 0.80] | 0.82 | [0.79, 0.84] | |
1980–1984 | Ref | Ref | Ref | Ref | |||||
1985+ | 1.32 | [1.30, 1.34] | 1.33 | [1.31, 1.35] | 1.76 | [1.71, 1.82] | 1.71 | [1.65, 1.76] | |
Non-serious acquisitive | Opiate users vs. non-users | 2.65 | [2.61, 2.69] | – | – | 4.79 | [4.66, 4.91] | – | – |
Initiation of opiate use | – | – | 1.72 | [1.69, 1.75] | – | – | 2.18 | [2.11, 2.25] | |
Users (pre-onset) vs. non-users | – | – | 1.97 | [1.92, 2.02] | – | – | 2.73 | [2.62, 2.85] | |
Users (post-onset) vs. non-users | – | – | 3.39 | [3.34, 3.45] | – | – | 5.95 | [5.78, 6.12] | |
Age | 1.85 | [1.84, 1.85] | 1.74 | [1.73, 1.75] | 2.46 | [2.43, 2.48] | 2.23 | [2.20, 2.25] | |
Age-squared | 0.83 | [0.83, 0.83] | 0.83 | [0.83, 0.83] | 0.76 | [0.76, 0.77] | 0.78 | [0.77, 0.78] | |
Age-cohort | |||||||||
<1975 | 0.87 | [0.85, 0.89] | 0.92 | [0.90, 0.93] | 0.80 | [0.78, 0.83] | 0.90 | [0.87, 0.93] | |
1975–1979 | 0.95 | [0.93, 0.97] | 0.96 | [0.94, 0.98] | 0.88 | [0.85, 0.91] | 0.93 | [0.89, 0.96] | |
1980–1984 | Ref | Ref | Ref | Ref | |||||
1985+ | 1.08 | [1.05, 1.10] | 1.05 | [1.02, 1.07] | 1.30 | [1.25, 1.35] | 1.26 | [1.21, 1.32] | |
Serious acquisitive | Opiate users vs. non-users | 1.84 | [1.81, 1.87] | – | – | 4.11 | [3.85, 4.38] | – | – |
Initiation of opiate use | – | – | 1.25 | [1.22, 1.27] | – | – | 1.76 | [1.62, 1.92] | |
Users (pre-onset) vs. non-users | – | – | 1.87 | [1.82, 1.91] | – | – | 3.16 | [2.88, 3.46] | |
Users (post-onset) vs. non-users | – | – | 2.33 | [2.27, 2.38] | – | – | 5.58 | [5.19, 6.00] | |
Age | 1.16 | [1.15, 1.16] | 1.11 | [1.11, 1.12] | 1.39 | [1.36, 1.42] | 1.27 | [1.23, 1.30] | |
Age-squared | 0.66 | [0.66, 0.66] | 0.65 | [0.64, 0.65] | 0.81 | [0.80, 0.82] | 0.81 | [0.80, 0.83] | |
Age-cohort | |||||||||
<1975 | 0.83 | [0.81, 0.84] | 0.73 | [0.71, 0.75] | 0.75 | [0.69, 0.82] | 0.84 | [0.77, 0.93] | |
1975–1979 | 1.40 | [1.37, 1.43] | 1.39 | [1.36, 1.42] | 0.83 | [0.76, 0.91] | 0.90 | [0.82, 0.99] | |
1980–1984 | Ref | Ref | Ref | Ref | |||||
1985+ | 1.05 | [1.02, 1.07] | 1.06 | [1.04, 1.09] | 1.44 | [1.31, 1.57] | 1.46 | [1.33, 1.61] | |
Violent offences | Opiate users vs. non-users | 1.39 | [1.36, 1.42] | – | – | 2.42 | [2.30, 2.55] | – | – |
Initiation of opiate use | – | – | 0.75 | [0.72, 0.77] | – | – | 1.04 | [0.96, 1.13] | |
Users (pre-onset) vs. non-users | – | – | 1.79 | [1.72, 1.85] | – | – | 2.51 | [2.31, 2.72] | |
Users (post-onset) vs. non-users | – | – | 1.34 | [1.30, 1.37] | – | – | 2.61 | [2.45, 2.77] | |
Age | 1.85 | [1.84, 1.87] | 1.91 | [1.89, 1.93] | 1.79 | [1.76, 1.83] | 1.80 | [1.75, 1.84] | |
Age-squared | 0.80 | [0.80, 0.81] | 0.80 | [0.80, 0.80] | 0.88 | [0.87, 0.89] | 0.88 | [0.87, 0.89] | |
Age-cohort | |||||||||
<1975 | 0.71 | [0.69, 0.73] | 0.67 | [0.65, 0.69] | 0.43 | [0.40, 0.47] | 0.44 | [0.41, 0.48] | |
1975–1979 | 0.71 | [0.69, 0.73] | 0.69 | [0.67, 0.71] | 0.60 | [0.56, 0.65] | 0.61 | [0.56, 0.65] | |
1980–1984 | Ref | Ref | Ref | Ref | |||||
1985+ | 1.87 | [1.82, 1.92] | 1.92 | [1.86, 1.97] | 2.53 | [2.38, 2.70] | 2.59 | [2.43, 2.78] |
See Appendix D (Supplementary material) for rate within years.
Controlling for age, age-squared and age-cohort, male opiate positive’s prior total offending rate was double that for test-negatives (Rate Ratio: 1.99, 95% CI: 1.96–2.01); for females, it was over four times greater (RR: 4.59, 95% CI: 4.48–4.69). There was a relative increase in all categories of offending associated with being opiate-positive, with a greater increase for females than for males. The greatest increase associated with being an opiate–positive was for females and for the category non-serious acquisitive offending (RR: 4.79, 95% CI: 4.66–4.91). The lowest increase was for males and for the violent offences category.
The pre-initiation offending rate for male opiate-positive cases was double the rate for test-negative controls (RR = 2.00, 95% CI: 1.97–2.03), whilst the equivalent increased rate for females was 2.80 times (95% CI: 2.71–2.90). Initiation of opiate use increased the RR by 16% for males and 100% for females. Thus, the post-initiation rate was 2.32 times greater for cases than controls among males (95% CI: 2.29–2.35) and 5.61 times greater for females (95% CI: 5.47–5.75).
Both male and female cases had higher historical rates of non-serious and serious acquisitive offences prior to, and subsequent to, initiation of opiate use. For both serious and non-serious acquisitive offending categories and for both genders, initiation of opiate use increased the difference between cases and controls. Additionally, for both genders, there was a greater increase in the RR associated with initiation of opiate use for non-serious acquisitive crimes than serious crimes. In the case of violent offences, for females, the comparison between cases and controls was similar pre, and post, opiate-use initiation (RR: 2.51 and 2.61 respectively); the effect of opiate-use initiation in males was to reduce the RR (RR: 1.79 and 1.34).
We observed cohort effects; for example, controlling for age and drug-test status, later birth cohorts had higher rates of overall historical offending than earlier birth cohorts. However, this did not hold for the sub-categories of non-serious acquisitive crime, where each birth cohort had a similar rate of offending, or for serious acquisitive crime where, for men, earlier birth cohorts had a higher rate of offending.
A sensitivity analysis which separated the opiate-positive group into those that tested positive for opiates only and those that tested positive for opiates and cocaine, showed that the effect of opiate initiation was similar for both (see Appendix C in the Supplementary material).
4.1. summary of main findings.
Those testing positive for opiates had substantially higher rates of prior sanctioned offending over their life-course than those testing negative for opiates and cocaine. This finding held for both males and females, whilst controlling for age and birth cohort. Findings support our four a priori hypotheses regarding offending prior to, and post, opiate-use initiation: 1) opiate–positives had higher rates of offending than test-negative controls prior to their opiate-use onset; 2) initiation of opiate use exacerbates existing levels of offending compared to controls; 3) initiation of opiate use was associated with a larger increase in the rate ratio (RR) for female than male users; 4) the effect of opiate-use initiation on historical offending differs by crime type as well as by gender.
Of particular interest is the RR reduction in violent offending associated with opiate use initiation observed in male users; while for female users, the RR was relatively unchanged. Opiate-use initiation was associated with greater elevation in non-serious (e.g., shop-lifting) than serious (e.g., burglary) acquisitive crime for both male and female users.
Our previous work demonstrated the association between opiate use and recent offending, whilst highlighting that the strength of the association varies by gender and offence type ( Pierce et al., 2015 ). The present study expands on this analysis to investigate the longitudinal relationship between opiate-use initiation and crime. The majority of research carried out to examine the association between opiate use and crime has used a single cohort, pre/post design ( Hayhurst et al., 2017 ), rather than a separate control group. Our use of offending records over the life-course, together with a suitable control group of non-using offenders, whilst also controlling for age and birth cohort, are all important design strengths. Additionally, we use a large sample size (n = 18,965 cases; n = 78,838 controls) to supply the necessary statistical power needed to detect differences differentiated by gender and sub-category of offending.
The current study has some weaknesses. First, the use of a retrospective design limits the inferences that can be made – for instance, we cannot assess the influence that prior offending has on the likelihood of future opiate use. We are unable to hypothesise the extent to which offending prior to opiate-use initiation is associated with use of other substances, such as cannabis or alcohol, which may precede opiate use initiation ( Lessem et al., 2006 , Lynskey, 2003 ). Also, the opiate-using cohort may not be representative of opiate users in general. The cohort is sampled from individuals who received a drug test on arrest and were subsequently sanctioned; therefore, it is of greater relevance to opiate-using offenders.
The measures used are imperfect. Drug-using offenders may be more likely than non-users to be apprehended ( Bond and Sheridan, 2007 , Stevens, 2008 ) due, for example, to intoxication leading to easier identification. This may account for some of the differences detected in the current analysis, and, potentially, for differences in the period prior to initiation of opiate use, during which the likelihood of arrest may be affected by misuse of other substances, but this explanation is unlikely to account for the strength of the association observed here. Our work corresponds with previous research highlighting high levels of offending in opiate users prior to opiate-use onset ( Shaffer et al., 1987 ); suggestive of common factors underlying both behaviours. Additionally, misclassification of non-cases was evident: 7% of negative testers were linked to an NDTMS record confirming drug-user status. Cases were identified via a saliva test which, despite having high sensitivity and specificity ( Kacinko et al., 2004 ), only detects opiates used up to 24 h prior to testing( Verstraete, 2004 ) and so may not have identified less-problematic users. Any such misclassification would mean that the opiate-user and non-user group identified in this study are more similar than they would be under any ‘gold-standard’ testing procedure, meaning that the results presented are likely to be overly conservative, therefore not disputing our conclusions.
There was missing information on age of initiation for 33% of opiate positive testers; the majority because they did not have a treatment record over the data collection period. Secondary analysis of those with missing data (see Appendix A in the Supplementary material) showed that those who were not linked to NDTMS were less likely to test positive for both opiates and cocaine and were more likely to be male. Inspection of the graphs of offending rate by age group shows that those with missing linkage to NDTMS records had lower rates of offending over the life-course than those with complete information (see Appendix E in the Supplementary material). This could be because individuals who had not sought treatment were a shorter time into their using careers and not caught in a cycle of addiction and offending seen among those in this analysis. Therefore, the generalisability of these results might be affected by our focus on those individuals with a linked treatment record (75% of our cohort).
The findings of the present study are subject to unmeasured confounding. Information on important social factors, such as substance use or criminal behaviour among family members, was not available; neither was socio-economic status ( Gauffin et al., 2013 ). However, even if suitable data were available, it may be difficult to establish the temporal ordering of change in socio-economic status and drug-use initiation.
Our findings are directly relevant to Government drug policy as they are derived from individuals who have persisted in both their opiate use and offending. The findings confirm the relationship between opiate use and offending observed by others ( Bennett et al., 2008 , Bukten et al., 2011 ). We were also able to demonstrate that opiate-use onset is associated with crime escalation, independent of changes which occur with age. Therefore, initiation of opiate use appears to be a crucial driver of offending; measures to reduce offending should include drug-use prevention.
Others have highlighted that onset substance use in offenders impedes the process of “maturing” out of crime described by the age-crime curve ( Hussong et al., 2004 , Ouimet and Le Blanc, 1996 , Schroeder et al., 2007 ). Greater escalation of offending, compared to controls, post-opiate initiation, was seen in female than male users. This confirms the findings of a recent review, which indicated lower offence rates pre-opiate use in females than males but a greater escalation of crime subsequent to opiate-use onset in females ( Hayhurst et al., 2017 ).
The absence of a relationship between violent crime and onset-opiate use in this study is of significance. Our previous work found a strong association between women testing positive for opiate use and recent violent offending, although such offences were only recorded in 8% of women ( Pierce et al., 2015 ). The current study indicates no apparent increase in violent offending by women associated with opiate initiation, and a relative reduction in violent crime for men. This finding tallies with previous research indicating no confirmed relationship between violent crime and onset-substance use ( Parker and Auerhahn, 1998 , White and Gorman, 2000 ).
The large impact of opiate-use initiation on non-serious acquisitive crime mirrors that of our previous work, which demonstrated a rate of shoplifting in opiate users that was between 3.5 (males) and 4.7 (females) times that of non-using offenders ( Pierce et al., 2015 ). These findings could be explained by opiate users focussing on criminal activity that generates sufficient income to support current drug use and that is within the skill set of the individual user ( James et al., 1979 ).
Previous research indicated greater increases in offending levels post-opiate use in individuals with onset of opiate use at an earlier age ( Hayhurst et al., 2017 ). This corresponds with key offending theories in demonstrating that early antisocial or delinquent behaviour is associated with a more pronounced offending trajectory ( Moffitt, 1993 ). It would be informative to examine this interaction further with the use of a control cohort. It would also be advantageous to analyse prospective, longitudinal cohorts so that information could be incorporated on those who desist in their offending and opiate use.
We have previously highlighted a surprising lack of high-quality research with which to delineate the nature of the relationship between drug use, in general, and opiate use, in particular, and crime. This is one of a handful of studies to employ a control group to account for the well-known relationship between age, drug use and crime. Findings indicate a more complex drugs-crime relationship than that espoused by current drug policy ( Home Office, 2010 ) with already higher than expected levels of offending in those who go on to use drugs, such as opiates, problematically and whose offending behaviour then escalates. Having a more nuanced understanding of the nature of the drugs-crime relationship is crucial to the development of policy responses underpinning decisions about how best to intervene to interrupt the pathway from onset crime to onset substance use ( Hayhurst et al., 2017 ). Findings suggest that complex interventions that target young, particularly female, offenders are required. Indeed, our findings align with the conclusions of others who have suggested that it is quite viable to identify future problematic substance users by patterns of early-life delinquent and offending behaviour, allowing for targeted intervention ( Macleod et al., 2013 ).
This research was funded as part of the Insights study by the UK Medical Research Council (MR/J013560/1). The MRC had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The Home Office have been provided with a pre-submission version of this manuscript but have not exerted any editorial control over, or commented on, its content. Sheila Bird is funded by Medical Research Council programme number MC_U105260794.
Millar , Pierce and Hayhurst conceived of the study. Pierce with input from Bird wrote the analysis plan. Pierce analysed the data and wrote a first draft of the manuscript. Millar , Bird and Dunn supervised data analysis. All interpreted the data, edited, and approved of the manuscript.
Millar has received research funding from the UK National Treatment Agency for Substance Misuse and the Home Office. He has been a member of the organising committee for conferences supported by unrestricted educational grants from Reckitt Benckiser, Lundbeck, Martindale Pharma, and Britannia Pharmaceuticals Ltd, for which he received no personal remuneration. He is a member of the Advisory Council on the Misuse of Drugs. Bird holds GSK shares. She is formerly an MRC programme leader and has been elected to Honorary Professorship at Edinburgh University. She chaired Home Office’s Surveys, Design and Statistics Subcommittee (SDSSC) when SDSSC published its report on 21st Century Drugs and Statistical Science. She has previously served as UK representative on the Scientific Committee for European Monitoring Centre for Drugs and Drug Addiction. She is co-principal investigator for MRC-funded, prison-based N-ALIVE pilot Trial. Seddon has received research funding from the UK National Treatment Agency for Substance Misuse and the Home Office. Hayhurst has received grant research funding from Change, Grow, Live (CGL), a 3rd-sector provider of substance misuse services.
A number of organisations and individuals enabled access to data to support this research, including: The Home Office, The Ministry of Justice, Dr Sara Skodbo, Maryam Ahmad, Anna Richardson, Hannah Whitehead, and Nick Manton.
Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.drugalcdep.2017.07.024 .
The following is Supplementary data to this article:
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Telegram, a widely used messaging platform, has raised concerns regarding its potential to facilitate criminal activity. Existing research suggests that public groups and channels within Telegram ...
Terms including cybercrime, cyber-crime, computer crime, cloud-crime and computer misuse are often used interchangeably and can refer to any internet- or computer-related criminal activity (Goodman and Brenner, 2002). Throughout this paper, any criminal behaviour utilising the Internet will be termed 'cybercrime' unless referring to ...
The ASI was used for assessment of severity, treatment allocation, and for follow-up and research purposes. Since the present database was blinded and delivered to our research group in 2006, a number of papers have been published based on this data material, including predictions of drug-related mortality Citation 9, Citation 10 and criminal ...
The Criminal Organisations Control Amendment Bill 2024 was introduced to the Legislative Assembly on 28 August, containing a number of measures seeking to increase the effectiveness of Victoria's Criminal Organisations Control Act 2012, Victoria's principal Act for curtailing organised crime in Victoria.. This Bill Brief provides an overview of the Bill as well as key background and ...
In the next five years, it is anticipated that more than 2.5 million devices will be fully online. This paper focuses on artificial intelligence (AI), crime prediction and crime prevention. A ...