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Fatal opioid poisoning: a counterfactual model to estimate the preventive effect of treatment for opioid use disorder in England.

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Title: Fatal opioid poisoning: a counterfactual model to estimate the preventive effect of treatment for opioid use disorder in England.
Authors: White, Martin, Burton, Robyn, Darke, Shane, Eastwood, Brian, Knight, Jon, Millar, Tim, Musto, Virginia, Marsden, John
Source: Addiction. Aug2015, Vol. 110 Issue 8, p1321-1329. 9p. 5 Charts.
Subjects: Substance abuse treatment, Confidence intervals, Drug overdose, Narcotics, Research funding, Data analysis software, Descriptive statistics, Odds ratio, Prevention
Geographic Terms: England
Abstract: Aim A counterfactual model was used to estimate the number of fatal opioid-related poisonings prevented by public treatment services for opioid use disorder (OUD) in England between April 2008 and March 2011. Methods Patient OUD treatment episode data recorded by the English National Drug Treatment Monitoring System were linked to data on opioid deaths recorded by the Office for National Statistics. The source population was the official estimate of non-medical opioid users (aged 15-64 years; approximately 260 000 each year). The target population was all individuals (aged 15-64 years) treated for OUD in the study period ( n = 220 665). The outcome measure was fatal opioid-related poisoning (opioid death). The opioid death rate [per 100 person-years (PY)] and mortality rate ratios (MRR) were computed for study year, age group (15-24, 25-34, 35-64 years) and for three treatment-related states: time spent 'prior to treatment', 'during treatment' and 'after treatment'. Results Between April 2008 and March 2011, there were 3731 opioid deaths in the study: 741 during treatment (0.20 per 100 PY; referent category); 2722 prior to treatment [0.77 per 100 PY; MRR = 3.76, 95% confidence interval (CI) = 3.18-4.44]; and 268 after treatment (0.41 per 100 PY; MRR = 1.99, 95% CI = 1.64-2.41). By counterfactual estimation, national OUD treatment services prevented an average of 880 opioid deaths each year (95% CI = 702-1084). Conclusions Between April 2008 and March 2011, a counterfactual model shows that the English public treatment system for opioid use disorder prevented an average of 880 deaths each year from opioid-related poisoning. Counterfactual models of mortality prevention can be used for outcome and performance monitoring of substance use disorder treatment systems. [ABSTRACT FROM AUTHOR]
Copyright of Addiction is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="AR" term="%22White%2C+Martin%22">White, Martin</searchLink><br /><searchLink fieldCode="AR" term="%22Burton%2C+Robyn%22">Burton, Robyn</searchLink><br /><searchLink fieldCode="AR" term="%22Darke%2C+Shane%22">Darke, Shane</searchLink><br /><searchLink fieldCode="AR" term="%22Eastwood%2C+Brian%22">Eastwood, Brian</searchLink><br /><searchLink fieldCode="AR" term="%22Knight%2C+Jon%22">Knight, Jon</searchLink><br /><searchLink fieldCode="AR" term="%22Millar%2C+Tim%22">Millar, Tim</searchLink><br /><searchLink fieldCode="AR" term="%22Musto%2C+Virginia%22">Musto, Virginia</searchLink><br /><searchLink fieldCode="AR" term="%22Marsden%2C+John%22">Marsden, John</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Addiction%22">Addiction</searchLink>. Aug2015, Vol. 110 Issue 8, p1321-1329. 9p. 5 Charts.
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  Data: <searchLink fieldCode="DE" term="%22England%22">England</searchLink>
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  Data: Aim A counterfactual model was used to estimate the number of fatal opioid-related poisonings prevented by public treatment services for opioid use disorder (OUD) in England between April 2008 and March 2011. Methods Patient OUD treatment episode data recorded by the English National Drug Treatment Monitoring System were linked to data on opioid deaths recorded by the Office for National Statistics. The source population was the official estimate of non-medical opioid users (aged 15-64 years; approximately 260 000 each year). The target population was all individuals (aged 15-64 years) treated for OUD in the study period ( n = 220 665). The outcome measure was fatal opioid-related poisoning (opioid death). The opioid death rate [per 100 person-years (PY)] and mortality rate ratios (MRR) were computed for study year, age group (15-24, 25-34, 35-64 years) and for three treatment-related states: time spent 'prior to treatment', 'during treatment' and 'after treatment'. Results Between April 2008 and March 2011, there were 3731 opioid deaths in the study: 741 during treatment (0.20 per 100 PY; referent category); 2722 prior to treatment [0.77 per 100 PY; MRR = 3.76, 95% confidence interval (CI) = 3.18-4.44]; and 268 after treatment (0.41 per 100 PY; MRR = 1.99, 95% CI = 1.64-2.41). By counterfactual estimation, national OUD treatment services prevented an average of 880 opioid deaths each year (95% CI = 702-1084). Conclusions Between April 2008 and March 2011, a counterfactual model shows that the English public treatment system for opioid use disorder prevented an average of 880 deaths each year from opioid-related poisoning. Counterfactual models of mortality prevention can be used for outcome and performance monitoring of substance use disorder treatment systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Addiction is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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