BMC Public Health 2008, 8:149 http://www.biomedcentral.com/1471-2458/8/149 Methods Data collection The National Drug Treatment Monitoring System (NDTMS) records details of individuals in contact with structured drug treatment services in England. Such serv- ices include substitute prescribing, structured psychoso- cial interventions and abstinence-based services but exclude agencies such as needle exchange services [16]. It is mandatory for staff working in these services to report to their regional NDTMS key items of data relating to all drug users who present to their service for treatment. Data are collected and checked monthly and are reported on an annual basis (1st April to 31st March). To maintain client anonymity, data are collected in a pseudo-anonymous form with an individual identified by their 'attributor code' comprised of their initials, date of birth, sex and their D(A)AT of residence. The NDTMS database for the North West of England (includes the Cheshire and Merseyside, Cumbria and Lan- cashire and Greater Manchester geographies) was interro- gated to retrospectively identify all individuals who had a triage date, and thus began a new episode of treatment, within the 2005/06 reporting period. Measures of treat- ment retention were established in accordance with NTA guidance [12]. Firstly, all clients under the age of 18 were removed from the dataset because the majority of young people participate in short-term interventions not designed to last for 12 weeks. The triage and discharge dates were used to determine the length of each treatment episode. Where two episodes for the same person over- lapped (the treatment start date of the second episode being before the discharge date of the first episode), the individual was recorded as being continuous in the treat- ment system and the triage date of the first episode and discharge date of the second was used to identify the length of contact. An individual was also considered to be in continuous treatment if the start date of a second epi- sode of treatment was within three weeks of the discharge date of the first episode of treatment, even if they dropped out prematurely from the first agency (three weeks reflects the effectiveness target that an individual should be seen within three weeks of being referred to a service [12]). An individual is defined as being retained in treatment if their total treatment episode within the system was 12 weeks or longer. An individual was deemed to have dropped out prematurely if their total treatment episode lasted for less than 12 weeks and the reason for discharge was coded as 'dropped out/left'. Individuals who were not in the treat- ment system for 12 weeks who had other discharge rea- sons (such as 'died', 'sent to prison') were excluded from the analyses. To avoid correlations in the data and adhere to the NTA's methodology for retention analysis, attribu- tor codes were used to remove double counting of indi- viduals within the dataset. Drop out or retention status was defined using an individuals' most recent treatment episode. Other variables obtained from the NDTMS database were sex, age at triage assessment, ethnicity code, D(A)AT of residence, route of referral into treatment and up to three drugs. D(A)AT of residence was recoded to public health zone, and route of referral was recoded into criminal jus- tice or otherwise. North West NDTMS data for 2003/04 and 2004/05 were used to identify whether each individ- ual had been in contact with treatment within these two periods (used to define 'recent contact'). The variables included in the final dataset are detailed in Table 1. Dep- rivation was assessed using a population weighted Index of Multiple Deprivation (IMD) that was available for each D(A)AT from the North West Public Health Observatory. The IMD (2004) combines indicators collected from rou- tine data relating to the following seven domains: income, employment, health and disability, education, skills and training, barriers to housing and services, living environ- ment and crime [17]. A higher IMD score indicates a greater level of deprivation. Based upon their IMD scores, D(A)ATs were separated into deprivation quintiles (five D[A]ATs were allocated to quintiles one and five, and four each to quintiles two, three and four; Table 2). Each indi- vidual was assigned a deprivation score based upon their D(A)AT of residence. Data manipulation and matching was undertaken using Microsoft Access. Data analysis All variables were investigated for erroneous or missing values. Bivariate analyses were conducted on all data. Only records with complete data were included in multi- variate analyses to ensure that Likelihood Ratio Tests (LRT) compared nested models. For the purpose of this analysis, the quantitative variable 'age at triage' was cate- gorised as follows: 18–24, 25–34, 35–44, 45–54 and 55– 74. Bivariate analyses between outcome (retention or drop out) and all explanatory variables were assessed inde- pendently using odds ratios and chi-square analyses. For- ward stepwise logistic regression was then used to identify factors associated with drop out following simultaneous adjustment for other variables. Variables whose signifi- cance in the bivariate analyses was P ≤ 1.0 were entered into the model and their effects tested separately. Varia- bles were entered into the model in the following order: age, ethnicity, opiate user, alcohol user, recent treatment contact, public health zone and deprivation. Interactions between age and all other explanatory variables were tested because previous research in the UK showed drug treatment outcomes varied significantly with age [18]. Page 3 of 11 (page number not for citation purposes)