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Using wastewater-based epidemiology to estimate drug consumption—Statistical analyses and data presentation.

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Title: Using wastewater-based epidemiology to estimate drug consumption—Statistical analyses and data presentation.
Authors: Banta-Green, Caleb J.1 calebbg@uw.edu, Brewer, Alex J.2,3, Ort, Christoph4, Helsel, Dennis R.5, Williams, Jason R.1, Field, Jennifer A.2
Source: Science of the Total Environment. Oct2016, Vol. 568, p856-863. 8p.
Subjects: Epidemiology methodology, Drug utilization, Benzoylecgonine, Ecstasy (Drug), Water sampling, Data modeling
Abstract: Aim Analysis of wastewater samples can be used to assess population drug use, but reporting and statistical issues have limited the utility of the approach for epidemiology due to analytical results that are below the limit of quantification or detection. Unobserved or non-quantifiable—censored—data are common and likely to persist as the methodology is applied to more municipalities and a broader array of substances. We demonstrate the use of censored data techniques and account for measurement errors to explore distributions and annual estimates of the daily mean level of drugs excreted per capita. Measurements Daily 24-hour composite wastewater samples for 56 days in 2009 were obtained using a random sample stratified by day of week and season for 19 municipalities in the Northwest region of the U.S. Methods Methamphetamine, benzoylecgonine (cocaine metabolite), 3,4 methylenedioxymethamphetamine (MDMA), methadone, oxycodone and hydrocodone were identified and quantified in wastewater samples. Four statistical approaches (reporting censoring, Maximum Likelihood Estimation, Kaplan-Meier estimates, or complete data calculations) were used to estimate an annual average, including confidence bounds where appropriate, dependent upon the amount of censoring in the data. Findings The proportion of days within a year with censored data varied greatly by drug across the 19 municipalities, with MDMA varying the most (4% to 94% of observations censored). The different statistical approaches each needed to be used given the levels of censoring of measured drug concentrations. Figures incorporating confidence bounds allow visualization of the data that facilitates appropriate comparisons across municipalities. Conclusions Results from wastewater sampling that are below detection or quantification limits contain important information and can be incorporated to create a more complete and valid estimate of drug excretion. [ABSTRACT FROM AUTHOR]
Copyright of Science of the Total Environment is the property of Elsevier B.V. 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: Engineering Source
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DbLabel: Engineering Source
An: 117734338
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Using wastewater-based epidemiology to estimate drug consumption—Statistical analyses and data presentation.
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  Data: <searchLink fieldCode="JN" term="%22Science+of+the+Total+Environment%22">Science of the Total Environment</searchLink>. Oct2016, Vol. 568, p856-863. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Epidemiology+methodology%22">Epidemiology methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+utilization%22">Drug utilization</searchLink><br /><searchLink fieldCode="DE" term="%22Benzoylecgonine%22">Benzoylecgonine</searchLink><br /><searchLink fieldCode="DE" term="%22Ecstasy+%28Drug%29%22">Ecstasy (Drug)</searchLink><br /><searchLink fieldCode="DE" term="%22Water+sampling%22">Water sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink>
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  Data: Aim Analysis of wastewater samples can be used to assess population drug use, but reporting and statistical issues have limited the utility of the approach for epidemiology due to analytical results that are below the limit of quantification or detection. Unobserved or non-quantifiable—censored—data are common and likely to persist as the methodology is applied to more municipalities and a broader array of substances. We demonstrate the use of censored data techniques and account for measurement errors to explore distributions and annual estimates of the daily mean level of drugs excreted per capita. Measurements Daily 24-hour composite wastewater samples for 56 days in 2009 were obtained using a random sample stratified by day of week and season for 19 municipalities in the Northwest region of the U.S. Methods Methamphetamine, benzoylecgonine (cocaine metabolite), 3,4 methylenedioxymethamphetamine (MDMA), methadone, oxycodone and hydrocodone were identified and quantified in wastewater samples. Four statistical approaches (reporting censoring, Maximum Likelihood Estimation, Kaplan-Meier estimates, or complete data calculations) were used to estimate an annual average, including confidence bounds where appropriate, dependent upon the amount of censoring in the data. Findings The proportion of days within a year with censored data varied greatly by drug across the 19 municipalities, with MDMA varying the most (4% to 94% of observations censored). The different statistical approaches each needed to be used given the levels of censoring of measured drug concentrations. Figures incorporating confidence bounds allow visualization of the data that facilitates appropriate comparisons across municipalities. Conclusions Results from wastewater sampling that are below detection or quantification limits contain important information and can be incorporated to create a more complete and valid estimate of drug excretion. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science of the Total Environment is the property of Elsevier B.V. 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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              Text: Oct2016
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