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BMC Public Health 2007
Validating estimates of problematic drug use in EnglandAbstract: Questionnaire study, in which the 149 English Drug Action Teams were asked to evaluate the MIM estimates for their DAT.The response rate was 60% and there were no indications of selection bias. Of responding DATs, 64% thought the PDU estimates were about right or did not dispute them, while 27% had estimates that were too low and 9% were too high. The figures for the IDU estimates were 52% (about right), 44% (too low) and 3% (too high).This is the first UK study to determine the validity estimates of problematic and injecting drug misuse. The results of this paper highlight the need to consider criterion and face validity when evaluating estimates of the number of drug users.The UK Government's direct annual expenditure on combating illicit drug use is £1.48 billion [1] (2005/2006). By 2007/08, expenditure on treatment will increase to £478 million from £253 million in 2004 [2]. By 2008, the Government's target is to have the capacity to treat 200,000 drug users [3]. However, direct methods of ascertaining the number of problematic drug users in the country are unlikely to yield accurate figures. This is because of multiple response bias affecting the likelihood of problem drug users a) living in a household that would be included in a sample survey, b) agreeing to participate in a sample survey and c) reporting recent problem drug use [4,5]. One alternative method which has been recommended for country wide prevalence estimates is the Multiple Indicator Method (MIM) [6]. This method combines information on prevalence that is available only in a few areas (the 'anchor points') and drug indicators (e.g. number of crimes, seizures) that are available in all areas [7]. We previously reported a MIM analysis for all Drug Action Team (DAT) areas in England for 2001 [8]. The MIM involved a three stage process: a) factor analysis of the drug indicators, b) regression analysis linking factor scores to "known" prevalence estimates and c) imputation of estimates in all other D
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