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Drug and Alcohol Dependence 130 (2013) 24– 29

Contents lists available at SciVerse ScienceDirect

Drug and Alcohol Dependence

j ourna l ho me p age: www.elsev ier .com/ locate /drugalcdep

rug spend and acquisitive offending by substance misusers

aren P. Hayhursta,∗, Andrew Jonesa, Tim Millara, Matthias Piercea, Linda Daviesb,amantha Westonc, Michael Donmalla

National Drug Evidence Centre (NDEC), University of Manchester, Manchester M13 9PL, UKHealth Economics Research, University of Manchester, Manchester M13 9PL, UKDepartment of Sociology and Criminology, Keele University, ST5 5BG, UK

r t i c l e i n f o

rticle history:eceived 2 February 2012eceived in revised form 1 October 2012ccepted 4 October 2012vailable online 3 November 2012

eywords:rimeegression analysisubstance abuse

a b s t r a c t

Aim: The need to generate income to fund drug misuse is assumed to be a driver of involvement in acquis-itive crime. We examined the influence of drug misuse expenditure, and other factors, on acquisitiveoffending.Methods: Clients (N = 1380) seeking drug treatment within 94 of 149 Drug Action Teams (DATs) acrossEngland completed a comprehensive survey, incorporating validated scales and self-report measures,such as levels of drug and alcohol use and offending.Results: Forty per cent (N = 554) had committed acquisitive crime in the previous month. Regressionanalysis showed that acquisitive offending was associated with the presence of problematic use ofcrack cocaine, poly-drug use, sharing injecting equipment, unsafe sex, overdose risk, higher drug spend,

unemployment, reduced mental wellbeing, and younger age.Conclusions: Rates of acquisitive crime among drug users are high. Drug using offenders can be distin-guished from drug using non-offenders by problematic crack cocaine use, younger age, income-relatedfactors, and indicators of a chaotic life style and complex needs. Behavioural and demographic factorswere associated more strongly with acquisitive crime than drug use expenditure, suggesting that the

is not

need to finance drug use

. Introduction

Estimates of the societal costs of problem drug use are high£15 billion economic and social costs of Class A drug use, Englandnd Wales, 2003/04; Gordon et al., 2006). It is reported that over onealf of all those arrested for acquisitive crime test positive for drugse in the UK (Boreham et al., 2007). Such evidence has been usedo infer a causal link between drug use and acquisitive crime. Thiss the primary focus, in the UK setting, as reflected in policy state-

ents over the past two decades (HM Government, 2008; Unitedingdom Anti Drugs Coordination Unit, 1998), in contrast to theS, where the focus is broader, and incorporates pharmacologicalffects or the effects of drug markets (Boyum and Kleiman, 2002;hite and Gorman, 2000).However, set against this, the evidence for a causal link is,

erhaps, weak. The literature suggests a more complex associa-ion between drug misuse and acquisitive offending than a simple

ausal relationship (Best et al., 2001; Buchanan, 2010; Hammersley,008; Seddon, 2000). Rather than drug use fuelling criminality, notll drug users commit acquisitive offences, and acquisitive crime

∗ Corresponding author. Tel.: +44 161 275 8365; fax: +44 161 275 1668.E-mail address: karen.hayhurst@manchester.ac.uk (K.P. Hayhurst).

376-8716/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.drugalcdep.2012.10.007

necessarily the main factor driving acquisitive offending by drug users.© 2012 Elsevier Ireland Ltd. All rights reserved.

often pre-dates problem drug use (Pudney, 2002; Stewart et al.,2000). Drug use and criminality may develop in parallel (Edmundset al., 1998), perhaps via a third factor such as socio-economicdeprivation (Seddon, 2000).

A number of previous studies suggest that drug treatmentimpacts favourably on levels of offending (Godfrey et al., 2002;Reuter and Stevens, 2008). Evidence from the US Drug AbuseTreatment Outcome Study (DATOS; Flynn et al., 1997) highlightsdecreased crime costs following drug treatment in both residentialand outpatient settings (Flynn et al., 1999). Drug treatment in theResearch Outcome Study in Ireland (ROSIE; Comiskey et al., 2009)was associated with a significant decrease in acquisitive offend-ing (Cox and Comiskey, 2011) and one-year follow-up in the UKNational Treatment Outcome Research Study (NTORS) observed atwo-thirds reduction in the level of acquisitive offences comparedto baseline (Gossop et al., 2000). Findings are consistent with theassumption that drug use fuels offending, but do not support acausal link.

If acquisitive offending is undertaken to fund drug use, it followsthat drug use expenditure should be strongly associated (Bradford-

Hill, 1965) with such offending. Previous work has suggested thathigh levels of drug use are predictive of high rates of offending, buthas employed indirect indicators of the cost of drug use, such asfrequency of use, or quantity used (for example Gossop et al., 2000,

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002; Stewart et al., 2000). However, drugs may be obtained in aariety of ways that do not incur direct costs to the individual, suchs low-level supply to other users, or via a partner. Direct exam-nation, in an observational setting, of the contribution that drugse expenditure makes to the likelihood of committing acquisitiverime would provide further evidence of an association betweenhese factors, albeit falling short of a causal examination.

In addition to the contribution of expenditure on drugs, otheractors are also likely to distinguish between drug using acquisi-ive offenders and non-offenders. Evidently, the type of drugs useds likely to be an important factor; less costly patterns of use, e.g.,annabis and solvents, may be less likely to be drivers of acquisitiveffending (Boyum and Kleiman, 2002). Use of cocaine, in particularrack cocaine, has been linked to acquisitive crime. In ROSIE, amongpiate dependent clients, those using cocaine/crack were moreikely to report criminal activity than those not taking cocaine orrack (Cox and Comiskey, 2011). In NTORS, predictors of acquisitiveffending included regular use of cocaine (powder and/or crack);ith regular heroin use the main predictor (Stewart et al., 2000).ge may be predictive: two-thirds of a 2009 Class A drug usingffender cohort were aged less than 35 years (Home Office, 2010). Inoreham and colleagues’ arrestee survey, the likelihood of acquisi-ive crime declined with age (Boreham et al., 2007). Poly-substancese may also be important; poly-drug using offenders commit twices many offences as those not reporting multiple drug use (Bennettnd Holloway, 2005) and high levels of poly-drug use are recordedmongst drug using arrestees (Boreham et al., 2007).

The UK Drug Treatment Outcomes Research Study (DTORS;onmall et al., 2012; Jones et al., 2007, 2009) provides an opportu-ity to further examine factors associated with acquisitive crime in

cohort of treated drug users and to examine whether these factorsan reliably distinguish drug using acquisitive offenders from drugsing non-acquisitive offenders. In particular, the study gatheredata on actual expenditure on drug use, providing an opportunity toetter, and more directly, explore the relationship between offend-

ng and the need to generate income to support drug use. Our aimas not to propose a causal link but to investigate whether the

ssumed association between drug spend and acquisitive offendings observable or, indeed, weak, or even absent.

. Methods

.1. DTORS

The study was conducted as part of UK DTORS (Drug Treat-ent Outcomes Research Study; Donmall et al., 2012; Jones et al.,

007, 2009). DTORS was a longitudinal, observational, multi-site,ohort study, funded by the UK Home Office, examining drug treat-ent outcomes in adult drug users seeking treatment. Independent

nterviewers assessed participants at baseline and two follow-upime-points, scheduled for 3–5 months and one year. Baseline inter-iews were carried out between February 2006 and March 2007.ulti-site NHS Research Ethics approval was obtained.

.2. Participants

Participants (N = 1380) were recruited from 342 agencies within4 of 149 Drug Action Teams (DATs) across England. Eligibility crite-ia were: aged 18–65 years; seeking drug treatment; not engagedn treatment prior to the baseline interview. Every effort was madeo conduct client interviews as soon as possible following initial

ssessment for treatment but, as interviewers were not always sit-ated in treatment agencies, time to achieve baseline assessmentaried, meaning that a number of clients had received treatmentrior to their baseline interview; these cases have been excluded

Dependence 130 (2013) 24– 29 25

from this analysis. This sample was comparable, in terms of gen-der and ethnicity, but younger (32 years vs. 34 years) than thetotal study sample. Based on baseline interviews, participants werecategorised as involved/not involved in acquisitive crime in theprevious 4 weeks. Acquisitive crimes included shoplifting, sellingstolen goods, stealing a vehicle, stealing from a vehicle, house bur-glary, business burglary, violent theft, bag snatching, prostitution,drug dealing, other stealing, cheque/card fraud and benefit fraud.

2.3. Measures

Baseline interviews gathered details of drug and alcohol use,including actual drug misuse expenditure, offending behaviour,physical and mental health, and variables such as employmentand accommodation. These were assessed via a comprehensivesurvey tool, incorporating bespoke measures and the followingvalidated scales: CMR (Circumstances, Motivation and ReadinessScale; De Leon et al., 1994); SDS (Severity of Dependence Scale;Gossop et al., 1995); SF12 (Ware et al., 1996); elements of MAP(Maudsley Addiction Profile; Marsden et al., 1998); and IRQ (Inject-ing Risk Questionnaire; Stimson et al., 1998). Measures relate to theprevious four week period, or current circumstances. Key assess-ments relate to stability of accommodation (stable being defined asowned or rented by the client, their family, or friends, or residen-tial drug treatment), whether participants self-defined as having aproblem with use of a particular substance, self-reported legitimateincome and self-reported spend on drugs. Full details are reportedelsewhere (Donmall et al., 2012; Jones et al., 2007, 2009).

2.4. Data analysis

The baseline DTORS sample had 89% power to detect a pre-determined between-group difference of £25 in weekly drug spendbetween CJS (Criminal Justice System) and non-CJS clients (Moodyet al., 2009). Instances where criminal justice personnel had directinput into the referral process were defined as CJS referrals (Joneset al., 2007). Clients were not recruited from prison settings.

Data from a number of cases (n = 15) were removed from theanalysis, as statistical outliers, on the basis of implausibly highincome or level of offending. Data were analysed using SPSS forWindows (version 19). Demographic data and measured variableswere compared between groups (acquisitive offenders vs. not)using Chi Squared, Mann–Whitney and t tests to identify possiblepredictors of acquisitive offending (see Tables 1 and 2).

In order to identify potential factors associated with acquisi-tive offending, whilst accounting for confounding, a multivariablemodel was constructed from 20 variables using a binary logis-tic regression model with acquisitive offending (in the previous 4weeks) as the outcome. Variables entered into the regression wererepresentative of demography, recent (previous 4 weeks) drug use,health and risk-taking, drug treatment and drug use history. Fromthis we identified variables as a priori statistical predictors anda parsimonious model was sought by eliminating the remainingvariables using a backwards stepwise procedure. The stepwise pro-cedure involved all a priori and potential predictor variables beingincluded in a multivariable logistic regression model; the potentialpredictor variable associated with the largest p-value was removed,whilst all a priori predictor variables remained, and the model wasrefit. All non a priori variables were removed until all potentialconfounding variables in the model had a p-value of less than 0.1.A backward elimination method was chosen as its use is associatedwith a lower risk of making a type II error, failing to identify an

outcome predictor (Field, 2005).

Demographic variables included were: age; finishing educa-tion before the age of 16 years (i.e., did not complete mandatorysecondary school education); gender; ethnicity; employment;

26 K.P. Hayhurst et al. / Drug and Alcohol Dependence 130 (2013) 24– 29

Table 1Key characteristics of groups.

Measure Acquisitive offenders p

Yes (N = 554) No (N = 826)

Age, yr: mean (SD) 31.5 (7.3) 33.1 (8.0) <0.001Male gender 407 (74) 605 (73) 0.927White ethnicity 494 (89) 712 (86) 0.103Finished education before age 16 yr 219 (40) 285 (36) 0.057Partner also uses drugsa 93 (48) 104 (32) 0.001Any unstable accommodation previous 4 wk 170 (31) 207 (25) 0.022In employment 33 (6) 102 (12) <0.001

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igures in tables are frequency counts with percentages unless stated otherwise.a Percentage of those with a current partner.

nstable accommodation in the previous month; having a drug-sing partner; and total legitimate income over the previous foureeks. Drug use variables included were: drugs reported as prob-

ematic at baseline; poly-drug use (not including alcohol); andotal drug spend in the previous month. Health and risk-takingehaviour variables were: SF-12 mental wellbeing score (Waret al., 1996); overdose in the previous three months; injectingrugs; sharing syringes or other drug use equipment; unsafe sex;nd poly-drug overdose risk (taking opiates together with otherpiates, alcohol or benzodiazepines). Drug treatment history wasepresented by the number of prior Tier 3 (community based spe-ialised and structured treatment) or Tier 4 (residential specialisednd structured treatment) episodes.

. Results

.1. Sample

The study sample was N = 1380 with 40% (N = 554) assignedo the acquisitive offender group. Table 1 shows the key char-cteristics of the two participant groups. Figures in Tables are

able 2roup differences: baseline measures.

Measure Ac

Ye

Drugs causing a problem at baselineHeroin 44Non-prescribed methadone 70Other opiates 68Crack cocaine 31Cocaine powder 94Non-prescribed amphetamines 70Cannabis 10Ecstasy 55Hallucinogens 40Alcohol 12Benzodiazepines 92Solvents 26

Poly-drug use previous 4 wk 48Drug spend previous 4 wk MLegitimate income previous 4 wk MHealth and risk-taking behaviour

SF12 mental health score: mean (SD) 33SF12 physical health score: mean (SD) 47Previous mental health referral 20Overdose in previous 3 months 61Injector in previous 4 wk 26Share equipment 15Poly-drug overdose riska 38Unsafe sex previous 3 months 29

Drug use and treatment historyNumber of prior Tier 3/Tier 4 treatment episodes, mean (SD) M

igures in tables are frequency counts with percentages unless stated otherwise.a Taking opiates together with other opiates, alcohol or benzodiazepines.

frequency counts with percentages unless stated otherwise. Com-pared to the non-offending group, acquisitive offenders had ayounger mean age (32 years vs. 33 years); were more likely tohave a drug-using partner; less likely to be in employment; andmore likely to have recently stayed in unstable accommodation.The two groups were comparable in terms of both gender andethnicity.

3.2. Group differences

Table 2 shows group differences on baseline measures. Acquis-itive offenders were more likely to report problem use of heroin,crack cocaine, or benzodiazepines than non-offenders. They weremore likely to be poly-drug users, to have a higher drug spend, anda lower legitimate income. They also had a lower mental healthscore on the SF-12 (but a similar physical health score) and weremore likely to have had a recent overdose, share drug using equip-

ment, inject, have a poly-drug overdose risk and have had recentunsafe sex. Offenders had experienced more episodes of Tier 3(community-based) or Tier 4 (residential) drug treatment thannon-offenders.

quisitive offenders p

s (N = 554) No (N = 826)

8 (81) 532 (64) <0.001 (13) 83 (10) 0.134

(12) 86 (10) 0.2817 (57) 314 (38) <0.001

(17) 140 (17) 0.993 (13) 103 (13) 0.9275 (19) 144 (17) 0.472

(10) 78 (9) 0.765 (7) 52 (6) 0.5006 (23) 178 (22) 0.600

(17) 90 (11) 0.002 (5) 25 (3) 0.1083 (87) 476 (58) <0.001edian = £910.5 Median = £240 <0.001edian = £240 Median = £280 0.022

.0 (11.4) 36.6 (12.8) <0.001

.1 (11.0) 47.5 (11.3) 0.5926 (37) 293 (36) 0.516

(11) 57 (7) 0.0077 (48) 209 (25) <0.0012 (27) 63 (8) <0.0016 (70) 325 (39) <0.0017 (54) 350 (42) <0.001

edian = 2 Median = 1 <0.001

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.3. Regression analysis

The model which predicted the most number of cases correctlysed a backward elimination method (using SPSS 19). The finalodel chose nine of the proposed predictive variables as reliably

istinguishing between clients self-reporting acquisitive offend-ng, and those not (�2 = 298.6, p < 0.001). The goodness-of-fit-2 Logikelihood statistic of the final model was 1330.0. The model wasignificant and passed the goodness-of-fit Hosmer and Lemeshowest (statistic = 6.74, p = 0.565). The proportion of variance in acquis-tive offending accounted for by the model was between 22% and0%, with 60% of acquisitive offenders correctly predicted. Theodel correctly predicted 81% of those clients not reporting acquis-

tive offending, giving an overall success rate of 72%.Table 3 shows unadjusted and adjusted odds ratios, p values and

5% confidence intervals for each of the nine predictors. Notably,nadjusted and adjusted odds ratios for the predictor ‘drug spend’re very similar. This means that the relationship between acquis-tive offending and drug spend is not affected by considering otherariables, indicating no confounding by these variables on this rela-ionship.

Compared to those not reporting acquisitive crime, acquisi-ive offenders were more likely to: share syringes or other drugquipment (adjusted OR 2.4, 95% CI 1.7–3.5); report problematicse of crack cocaine (aOR 1.6 [1.3–2.1]); be a poly-drug user (aOR.4 [1.7–3.4]); engage in unsafe sex (aOR 1.4 [1.05–1.8]); have aoly-drug overdose risk (aOR 1.7 [1.2–2.3]); have a higher drugpend (aOR 1.03 [1.02–1.04] £100 increments); be younger (aOR.7 [0.6–0.8] per additional decade of age); and have a lower ratingf mental health (aOR 0.8 [0.7–0.91] per 10 point increase in SF-12ental Health score). Acquisitive offenders were also less likely to

e in employment (aOR 0.6 [0.4–1.02]).Although a predictive variable in the univariable analysis, there

as borderline evidence of an effect of employment in the finalultivariable model (Table 3). The strongest univariable relation-

hip, using the R2 statistic, was between acquisitive offending andoly-drug use (see Table 3). Actual differences between groups areet out in Tables 1 and 2.

.4. Supplementary analysis

Multi-collinearity was not present in the dataset (tolerance ofbove 0.5 and VIF less than 2.0 for each predictor). Interactionerms between two continuous variables (age, drug spend) and theog of these variables were significant, suggesting non-linearity ofhese continuous predictors. Drug spend was not a significant pre-ictor of acquisitive offending in the oldest age group (55+ years,

= 0.535). Problematic use of crack cocaine was a significant pre-ictor of acquisitive offending in the group reporting up to £500p = 0.038) drug spend, and the group reporting between £500 and1000 (p < 0.001), drug spend per month; but, perhaps surprisingly,

able 3ogistic regression analysis of acquisitive offending as a function of drug treatment client

Variables Unadjusted OR CI

Age (decades) 0.76 0.660 0.Employment 0.44 0.299 0.Problematic crack cocaine use 2.15 1.724 2.Poly-drug use 5.00 3.762 6.Total drug spend previous 4 wk (£100 multiples) 1.04 1.033 1.SF12 Mental Health score (10 point increments) 0.79 0.719 0.Sharing syringes/other equipment 4.56 3.320 6.Unsafe sex in previous 3 months 1.51 1.211 1.Poly-drug use overdose riskb 3.54 2.818 4.

a Adjusted for the presence of other variables in the model.b Taking opiates together with other opiates, alcohol or benzodiazepines.

Dependence 130 (2013) 24– 29 27

problematic crack use was not a significant predictor of offendingin those groups reporting high drug spend (over £1000 per month,p = 0.087 and over £3000 per month, p = 0.095).

The type of acquisitive offences more likely to be carried outby crack users was explored. Acquisitive offenders who used crack(use in the previous 4 weeks and/or self-defined problematic use:N = 410, 74%) were significantly more likely than acquisitive off-enders not using crack to report carrying out shoplifting; bagsnatching; and cheque/card fraud.

4. Discussion

Sixty per cent of the study sample had not committed acquisitivecrime in the month prior to study entry, highlighting that acquis-itive offending is, by no means, inevitable in a group of clientsseeking drug treatment. Use of a logistic regression model indi-cated that a variety of factors, including demographics, drug useand health and risk taking behaviour, together with expenditureon drugs, were associated with recent involvement in acquisitivecrime.

As noted earlier, there is a common perception that the linkbetween drug use and crime is causal, fuelled by the necessityto generate income to purchase drugs. If correct, it follows thatthe level of expenditure on drugs, and thus the need for income,should be strongly associated with involvement in acquisitivecrime. Previous studies have considered the association with ref-erence to measures such as frequency, regularity, or quantity used(Gossop et al., 2000, 2002; Stewart et al., 2000). Whilst indicative,their relationship to expenditure is indirect: even quantity useddoes not provide a direct indicator, because there may be tem-poral or geographic variation in market values and opportunitiesto reduce cost via bulk-purchasing. Eight percent of the samplereported drug expenditure that was lower than the value of thedrugs they consumed and a further three percent reported thattheir drug use incurred no cost to them. Thus expenditure, ratherthan frequency or quantity, should better explain the need to obtainincome.

As far as we are aware, the current study is the first large-scale drug treatment outcome study to have considered drug useexpenditure as a potential explanation for acquisitive offending.Therefore, it is striking that our findings in respect of expenditurewere equivocal: although expenditure was a statistically significantpredictor (adjusted OR 1.03 [1.02–1.04] £100 multiples), it was aweak substantive predictor, with a 3% increase in the likelihood ofacquisitive offending for each £100 increase in drug spend. Analysistook into account the level of legitimate income, which might plau-

sibly mitigate the need to obtain income via offending; this variabledid not progress into the final model. This resonates with the lit-erature challenging the direct causal relationship between druguse and acquisitive offending, insofar as other demographic and

predictor variables.

p R2 Adjusteda OR CI p

878 <0.001 0.0078 0.68 0.566 0.813 <0.001676 <0.001 0.0088 0.62 0.383 1.016 0.058674 <0.001 0.0256 1.64 1.258 2.148 <0.001650 <0.001 0.0794 2.37 1.652 3.398 <0.001053 <0.001 0.0569 1.03 1.021 1.044 <0.001860 <0.001 0.0154 0.82 0.734 0.913 <0.001266 <0.001 0.0524 2.41 1.651 3.523 <0.001871 <0.001 0.0074 1.36 1.046 1.772 0.022452 <0.001 0.0671 1.68 1.240 2.275 0.001

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ehavioural factors were more strongly associated with acquisitiveffending than was the level of income required to purchase drugs.

Factors relating to the type of drug used were able to dis-riminate between the two groups, although it is notable thatroblematic use of heroin was not retained in the final regressionodel. Use of relatively inexpensive substances, such as cannabis

nd solvents, did not differ between groups. As in other UK andnternational work (Comiskey et al., 2012; Gossop et al., 2002),roblematic use of crack cocaine was associated with a higher like-

ihood of recent acquisitive criminal involvement (aOR 1.6, 95%I 1.3–2.1). Almost half (47%) of the study sample reported usingrack cocaine in the month prior to baseline, a much higher propor-ion than that (17%) observed during the 1990s in NTORS (Gossopt al., 1998). This is consistent with increasing rates of cocainese, including crack, reported elsewhere (European Monitoringentre for Drugs and Drug Addiction, 2010; Hoare and Moon, 2010;chifano and Corkery, 2008) and illustrates that crack use hasecome more commonplace among the drug treatment population.

The prominent association of crack cocaine use with acquisi-ive offending is cause for concern. Drug treatment may not be asuccessful in addressing crack use compared with other forms ofrug use. In NTORS the use of crack did not differ at the 4–5 yearollow-up compared with intake (Gossop et al., 2002). The authorsuggest that this could be explained by participants initiating crackse during treatment; as also suggested elsewhere (Grella et al.,997). More recent findings from DTORS (Jones et al., 2009) indicatehat rates of crack cocaine use reduced from 46% at baseline to 26%t first follow-up, suggesting that responses to crack use may haveecome more successful. Psychosocial treatment can be effectiveMarsden et al., 2009; National Treatment Agency for Substance

isuse, 2002), although further work is needed both to design ser-ices to better attract crack users into drug treatment and to derivehe effective ingredients of such treatment (Reuter and Stevens,008; Seddon, 2000). This need is especially acute in view of the

evels of particularly risky health behaviour, psychological prob-ems and serious health consequences associated with crack useCox and Comiskey, 2011; Gossop et al., 1998; Grella et al., 1995;ongshore and Hsieh, 1998).

Poly-drug use also showed a strong association with acquisi-ive crime (aOR 2.4 [1.7–3.4]). Drug using arrestees may exhibitigh levels of poly-drug use (Boreham et al., 2007) and poly-drugse may be associated with more severe psychiatric problemsMarsden et al., 2000); it is notable that better mental health wasssociated with lower odds of acquisitive offending among DTORSarticipants (aOR 0.8 [0.7–0.91] per 10-point increment). Other fac-ors indicative of high-risk behaviour were also associated withcquisitive offending; engaging in unsafe sex (aOR 1.4 [1.05–1.8]),yringe sharing (aOR 2.4 [1.7–3.5]), and having an overdose risk dueo poly-drug use (aOR 1.7 [1.2–2.3]).

Acquisitive offending was negatively associated with being inmployment (aOR 0.6 [0.4–1.02]; a relationship that achieved bor-erline significance in the final model. Accessibility of employmentas been identified as a factor that mitigates levels of drug crimet a community level (Ihlanfeldt, 2007). Older participants werelightly less likely to be involved in acquisitive offending (aOR 0.70.6–0.8] per decade), mirroring findings elsewhere (Boreham et al.,007; Gossop et al., 2000). As in NTORS (Stewart et al., 2000) genderas not related to acquisitive offending.

The decision to consider acquisitive crime involvement as ainary outcome, and to subject this to a logistic regression analysis,as prompted by concerns around combining information about

ffending frequency across crime types that have different levels

f seriousness. As in other cohorts (Home Office, 2010; Gossopt al., 1998; Stewart et al., 2000), shoplifting accounted for much ofhe offending, but participants engaged in a range of other moreerious criminal activities, including minor fraud, burglary, and

Dependence 130 (2013) 24– 29

violent theft, each of which might generate different levels ofincome. Although a compound estimate of offending volumemight have been produced on the basis of, for example, pub-lished estimates of specific crime costs (Brand and Price, 2000),these do not cover the full range of acquisitive crime types rep-resented in the DTORS dataset. Our approach would be expectedto be less likely to find differences between groups. However,the chosen method of analysis was able to statistically predict60% of the acquisitive offending group. The use of self-reportdata as an indicator of participants’ behaviour may be open toquestion; however, it does have reasonable concordance withobjective measures (Darke, 1998; Neale and Robertson, 2003). Afurther limitation is that the proportion of variance in acquisitiveoffending accounted for by the model was 30%, suggesting thatthere are other, unmeasured, factors associated with acquisitiveoffending. For example, risk factors such as impulsivity, hyperac-tivity, or quality of early family relationships (White and Gorman,2000) were beyond the scope of the current study. In addition,the generalisability of these findings may be restricted to drugusers seeking treatment in UK settings. Our sample may dif-fer from drug users who do not seek treatment and may differfrom treatment samples in non-UK settings (Reuter and Stevens,2008).

4.1. Conclusions

Factors associated with acquisitive offending in drug users seek-ing treatment include: income-related variables, including higherdrug spend and unemployment; problem use of crack cocaine;and a number of risky behaviours, such as poly-drug use, unsafesex and sharing injecting equipment. As expected, acquisitive off-enders experience more health problems; in addition to riskyhealth behaviours they also have lower levels of mental well-being, indicating areas of unmet need. These findings support theneed for interventions to meet the health and social needs of drugusing acquisitive offenders, in order to impact more favourably ondrug-related offending outcomes. Most important, behavioural anddemographic factors were more strongly associated with acquisi-tive crime involvement than drug use expenditure, suggesting thatthe need to finance drug use is not necessarily the major factorunderpinning drug user offending.

Role of funding source

DTORS was commissioned and funded by the UK Home Officein 2005. The funding source had no involvement in study design,data collection, manuscript preparation or the decision to submitthis paper for publication.

Contributors

Karen P. Hayhurst – data analysis, literature search, drafting themanuscript.

Andrew Jones – study design, obtaining grant funding, helpeddraft the manuscript, helped with data analysis.

Tim Millar – study design, obtaining grant funding, superviseddrafting of the manuscript.

Matthias Pierce – helped with data analysis, helped draft themanuscript.

Linda Davies – study design, helped draft the manuscript.

Samantha Weston – study design, helped draft the manuscript.Michael Donmall – Principal Investigator, obtaining grant fund-

ing, helped draft the manuscript.All authors contributed to and approved the final manuscript.

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onflict of interest

None of the authors has a conflict of interest to declare.

cknowledgements

We would like to thank the large number of individuals andrganisations who made the study possible, specifically drugervice clients; drug service staff; DATs; NHS Trusts; interviewersnd operations staff; the Project Advisory Group; Andrew Shaw,lison Moody, Tracy Anderson and Tom Anderson at NatCen; Dannioole at NDEC; Graham Dunn, Matt Gittins, Chris Roberts and Islayemmell at The University of Manchester; and Anna Richardson,icola Singleton, May El Komy and Sara Skodbo from the Homeffice.

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