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Salvatore S1 Røislien J2 Baz-Lomba JA3 Bramness JG4

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Presentation on theme: "Salvatore S1 Røislien J2 Baz-Lomba JA3 Bramness JG4"— Presentation transcript:

1 Salvatore S1 Røislien J2 Baz-Lomba JA3 Bramness JG4
Lisbon Addictions (WWBE) Wednesday, October 25th :00-12:30 Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data. Salvatore S1 Røislien J2 Baz-Lomba JA3 Bramness JG4 1) Department of Informatics, University of Oslo, Oslo, Norway 2) Department of Health Studies, University of Stavanger, Stavanger, Norway. 3) Norwegian Institute of Water Research, Oslo, Norway 4) Norwegian National Advisory Unit on Concurrent Substance Abuse and Mental Health Disorders

2 Background The abuse of prescription drugs/medicinal drugs is an increasing problem Definitely in US Maybe also in Europe Monitoring: all data sources have their problems Reporting/recall bias (illegal, amnesia) Selection bias (who answers, black market) Monitoring: could waste water data be an option? Spatial differences (where are drugs used in excess?) What could we get out of temporal differences?

3 What's been done in WWBE? Salvatore and Bramness 2015 (unpublished data)

4 Spatial variation (EMCDDA web-site)

5 Temporal variation Zuccato et al 2008
Different drugs are abused differently and behave differently in the sewer Are there more sophisticated methods for investigating weekend peak?

6 AIM Can the known weekly temporal variations for drugs of abuse be used to characterise prescription drugs of abuse? Methadone Methylphenidate Can more advanced statistical methods be used? Methylphenidate and methadone were chosen because They were measured They have less well known black market than e.g. benzodiazepines They are not metabolically related to other drugs

7 Materials and methods Sewage samples February 2014 Oslo, Norway.
The weekly pattern of each drug Extracted by fitting of generalized additive models (GAM), Trigonometric functions to model the cyclic behavior The weekly component: main temporal features extracted by functional principal component (FPC) analysis We get FPCs and corresponding FPC scores.

8 MEDICINAL DRUGS OF ABUSE?
Oxazepam Methadone Methylphenidate KNOWN DRUGS OF ABUSE (pos. control) Methamphetamine Cocaine (BE) Heroin (morphine) Amphetamine MEDICINAL DRUGS WITHOUT AN ABUSE POTENTIAL (neg. control) Paracetamol (acetaminophen) Carbamazepine Atenolol Metoprolol Citalopram (R- and S-)

9 Functional principal components (FPC) Non-normalized data
«Level of drug in WW» «Height of weekend peak» «Timing of weekend peak»

10

11 Findings and conclusion
Letting the data define “weekend” we can extract more information from weekly temporal variations Methylphenidate, but not methadone seems to have a “weekend peak” This indicates recreational use of methylphenidate Published in Pharmacoepidemiology & Drug Safety 2017; 26:

12 Thank you for your attention!
Salvatore S, Røislien J, Baz-Lomba JA, Bramness JG


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