| Literature DB >> 18709161 |
Ettore Zuccato1, Chiara Chiabrando, Sara Castiglioni, Renzo Bagnati, Roberto Fanelli.
Abstract
BACKGROUND: The social and medical problems of drug abuse are a matter of increasing global concern. To tackle drug abuse in changing scenarios, international drug agencies need fresh methods to monitor trends and patterns of illicit drug consumption.Entities:
Keywords: amphetamines; cannabis; cocaine; drug residues; illicit drugs; mass spectrometry; opiates; sewage epidemiology; urinary metabolites
Mesh:
Substances:
Year: 2008 PMID: 18709161 PMCID: PMC2516581 DOI: 10.1289/ehp.11022
Source DB: PubMed Journal: Environ Health Perspect ISSN: 0091-6765 Impact factor: 9.031
Analytical targets (DTR) selected for illicit drug monitoring in wastewater.
| Drug | DTR | Relation of DTR to parent drug | Percentage of drug dose excreted as DTR | Molar mass ratio (parent drug/DTR) | Correction factor |
|---|---|---|---|---|---|
| Cocaine | BE | Major metabolite | 45 | 1.05 | 2.33 |
| Cocaine | Parent drug (minor excretion product) | ||||
| Heroin | Morphine | Major but nonexclusive metabolite | 42 | 1.29 | 3.07 |
| 6-Acetylmorphine | Minor but exclusive metabolite | ||||
| Amphetamines | |||||
| Amphetamine | Amphetamine | Parent drug and major excretion product | 30 | 1.0 | 3.3 |
| Methamphetamine | Methamphetamine | Parent drug and major excretion product | 43 | 1.0 | 2.3 |
| Ecstasy | Ecstasy | Parent drug and major excretion product | 65 | 1.0 | 1.5 |
| Cannabis | THC-COOH | Major metabolite of THC (cannabis active principle) | 0.6 | 0.91 | 152 |
Levels of DTRs were used for back-calculating drug consumption; the correction factor takes into account the percentage of parent drug excreted as the chosen DTR, and the parent drug-to-DTR molar mass ratio.
Average for the most frequent route of intake.
Figure 1Average daily amounts (mean ± SD, g/day, n = 3) of illicit drug residues conveyed by wastewater to Milan’s STP (1.25 million people served). Levels of amphetamines were near or below the LOD based on available data (2-week period). To allow a rough comparison with the profiles of the other, more abundant drugs, undetectable levels were considered 50% of the limit of quantification (LOQ; typically around 1 ng/L in wastewater).
Amounts (mg/day/1,000 people) of major DTRs from illicit drug consumption conveyed daily in urban wastewater to STPs in Milan, Lugano, and London.
| DTR | Milan | Lugano | London (Mogden; Becton) |
|---|---|---|---|
| BE | 390 ± 63 | 267 ± 52 | 296 ± 18 (302; 290) |
| Cocaine | 157 ± 14 | 109 ± 23 | 140 ± 10 (141; 139) |
| Morphine | 32 ± 3 | 102 ± 15 | 173 ± 29 (196; 150) |
| THC-COOH | 20 ± 2 | 43 ± 10 | 50 ± 21 (56; 44) |
Values reflect collective DTR excretion rates. Data are mean ± SD of daily samplings for 1 week and 3 nonconsecutive weeks at Milan STP (n = 21), and for 1 week at Lugano STP (n = 7). Two London STPs were sampled for 2 days, on Thursday and Friday (n = 4).
Values in parentheses represent averages for Mogden and Beckton STPs, respectively.
Amounts (mg/day/1,000 people) of DTRs from amphetamine-type drugs conveyed daily in urban wastewater to STPs in Milan, Lugano, and London.
| DTR | Milan | Lugano | London |
|---|---|---|---|
| Amphetamine | 2.7 ± 2.8 (5/14) | ND (0/7) | 24 ± 5 (4/4) |
| Methamphetamine | 4.5 ± 1.6 (14/14) | ND (0/7) | 2.4 ± 0.3 (4/4) |
| Ecstasy | 4.2 ± 3.7 (12/14) | 7.3 ± 5.1 (7/7) | 3.4 ± 1.0 (4/4) |
ND, not detectable. Values in parentheses represent the number of positive/total samples. Data are mean ± SD, with values for negative samples averaged as half the LOQ.
Data for amphetamine-type drugs were available for 2 weeks from Milan.
Variation in collective excretion rates (mean ± SD, g/day) of major DTRs between days (RSD for the average of seven daily means) and between weeks (RSD for the average of three weekly means) in Milan.
| Collective excretion of DTR (g/day)
| |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| DTR | Monday ( | Tuesday ( | Wednesday ( | Thursday ( | Friday ( | Saturday ( | Sunday ( | Average of daily means ( | Average of weekly means ( | Between-days variation (RSD, %) | Between-weeks variation (RSD, %) |
| BE | 326 ± 47 | 334 ± 41 | 338 ± 35 | 407 ± 7 | 409 ± 80 | 522 ± 111 | 394 ± 60 | 390 ± 63 | 390 ± 39 | 16 | 10 |
| Cocaine | 153 ± 49 | 158 ± 104 | 157 ± 93 | 136 ± 51 | 155 ± 70 | 188 ± 94 | 155 ± 65 | 157 ± 14 | 157 ± 74 | 9 | 47 |
| Morphine | 31 ± 9 | 37 ± 11 | 31 ± 3 | 34 ± 5 | 29 ± 7 | 33 ± 10 | 31 ± 12 | 32 ± 2 | 32 ± 6 | 8 | 19 |
| THC-COOH | 21 ± 1 | 20 ± 4 | 18 ± 3 | 22 ± 3 | 19 ± 3 | 21 ± 5 | 18 ± 2 | 20 ± 1 | 20 ± 2 | 7 | 11 |
Figure 2Estimated consumption rates (mean ± SD, mg/day/1,000 people) of illicit drugs in Milan, Lugano, and London, back-calculated from DTR excretion rates after correction for the factors shown in Table 1. Estimates for amphetamine-type drugs are shown only where DTR levels were measurable (in > 85% samples). Estimates of heroin consumption were back-calculated after subtracting the fraction of wastewater morphine presumably excreted as a product of therapeutic morphine, as expected from the known morphine consumption in the three countries.
Figure 3Comparison of local profiles of illicit drug use (mean ± SD, doses/day/1,000 people) obtained from drug residues in wastewater and national profiles of drug use (defined as the percentage of users among persons 15–64 years of age) based on annual prevalence data in the countries under study. (A) Data derived from estimated drug consumption rates (Figure 2) divided by the amount of the active drug in a typical dose. Values for amphetamine/ecstasy that are barely visible are 0.42 ± 0.18, 0.11 ± 0.08, and 2.9 ± 0.2 doses/day/1,000 people in Milan, Lugano, and London, respectively. (B) Data from the UNODC (2006).
Back-calculation of heroin consumption (mg/day/1,000 people) in Milan, Lugano, and London after correcting for the contribution of therapeutic morphine to the overall amount of wastewater morphine.
| Milan | Lugano | London | |
|---|---|---|---|
| Therapeutic morphine consumption | 11 | 82 | 123 |
| Estimated excretion of Ther-M | 9 | 70 | 105 |
| Total-M | 32 | 102 | 173 |
| Heroin-derived morphine | 23 | 32 | 68 |
| Back-calculated heroin consumption | 70 | 100 | 210 |
Total-M, total morphine measured in wastewater. Heroin-derived morphine = Total-M – Ther-M.
Based on yearly consumption of morphine in Italy, Switzerland, and United Kingdom of 4, 30, and 45 mg per capita per year (Zuccaro et al. 2006).
Back-calculated from consumption rates, taking into account the DTR fractional excretion (85%).
Characteristics, advantages, and potential limitations of the “sewage approach” for monitoring illicit drug consumption.
| Possible bias
| |||||||
|---|---|---|---|---|---|---|---|
| Measurements | Type of data | Reliability of data | Source of bias | Probability of occurrence | Estimated inaccuracy (this study) | Action to improve accuracy (future large-scale studies) | |
| Excretion of DTR (mg/day/1,000 people) | Concentration of DTR in wastewater (ng/L) | Potentially very reliable (if validated, highly specific analytical methods are used) | Possible adsorption of some DTR to particulate | Low | Probably negligible | Monitoring multiple DTR for each drug
| |
| Wastewater flow into STP (L/day) | Normally well controlled (in modern STP) | Leakage from sewers of substantial wastewater | Low | Probably low | Wastewater flow strictly controlled at STP, sewer leakage checked by dilution tests | ||
| Population size (no. of people served by STP) | Likely reliable (variations reflected by water consumption changes) | Fluctuating number of people in the catchment area (inhabitants, commuters, tourists, etc.) | Low to medium (depending on type of community) | Probably low (as proven by low variation over time of collective excretion rates for some DTR) | Actual number of people at any time in the catchment area monitored/controlled by various indicators (e.g., other human by-products in wastewater, energy consumption) | ||
| Possible bias
| |||||||
| Estimates | Type of information | Related assumptions | Source of information | Source of bias | Probability of occurrence | Estimated inaccuracy (this study) | Action to improve accuracy (future large-scale studies) |
| Drug consumption rate (mg/day/1,000 people) | Total fraction (%) of a drug dose excreted as DTR (used to back-calculate amount of drug consumed from amount of excreted DTR) | Definition of correction factors ( Wastewater used as a surrogate pooled urine sample from local population Wastewater (24-hr sample) reflects near steady-state excretion rate of drug residues when STP serves large population (> 105 people No significant fluctuation in drug use simultaneously in a large proportion of users | a. Current literature on human drug metabolism/kinetics b–c. Previous studies on therapeutic drugs showing correspondence between known drug consumption and back-calculated consumption (from wastewater DTRs) | Limited number of subjects in most studies | Low to medium (depending on drug under study) | Probably low for absolute rate of consumption if main specific metabolite is chosen No effect on comparability of drug use profile between locations over time at given location | Further studies on metabolism/kinetics for drugs of abuse (larger number of subjects, different consumption routes and use patterns). Meta-analysis of all available metabolism/kinetics studies |
| Number of doses consumed (no. of doses/day/1,000 people) | Amount of active drug in a typical dose (used to back-calculate number of doses from amounts consumed) | Definition of best approximated typical dose from available data | National/international drug agencies, official reports on drug abuse, scientific literature | Local differences in drug market Differences in drug intake route (intranasal, smoke, ingestion, injection) Different habits (light vs. heavy; regular vs. occasional use) | Variable Variable Variable | Can affect overall estimates of number of doses but not consumption rates Can affect estimated size of typical dose and fraction of drug dose excreted as DTR Can affect estimated size of typical dose | Local differences in drug market controllable by analysis of drugs seized Mathematical modeling to account for different patterns of drug intake (from consumer interviews) Mathematical modeling to account for drug use habits (from consumer interviews) |
Tentative estimate to be investigated in ad hoc studies.