developing an annual estimate of community excretion of drugs- · developing an annual estimate of...
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Developing an annual
estimate of community excretion of drugs-
Preliminary findings from the Northwest region
of the U.S.
Caleb Banta-Green PhD MPH MSWResearch Scientist
Alcohol and Drug Abuse Institute
University of Washington
&
Jennifer Field PhDProfessor
Department of Environmental and Molecular Toxicology
Oregon State University
EMCDDA January 28, 2011
Outline
• Background- drug abuse epidemiology, place for WWTP testing• Study design• Annual sampling plan• Characteristics of WWTP
– Population estimates and possible variability – Composite sampling approaches of plants, sewer system
• Major data issues-– population measurement– Catchment area– error measurement– data distributions
• Preliminary data– Methadone
• Developing an annual estimate• Next steps
Drug use data sources e.g. MDMA
Data Name Population
Data
Type
# of
Events
Data
Interval Time Lag Place Terminology Major Strengths Major Limitations
Emergency Dept.,
Drug Abuse
Warning Network
E.D. patients #XXX/
X,XXXAnnual 6 months
3 County
Metro Area
Specific Drug Names
i.e. MDMA, GHB,
LSD
Population based
estimates
Hetero. Severity
Annual trend data.
Poly drug- can't assign
cause
Reporting biases
Public school
surveyStudents #
XX/
XXXXBi-annual 12 months City
MDMA,
Hallucinogens i.e..
LSD and other
psychedelics
Anonymous, self-
report survey, large
sample.
Out of school youth missing.
Inconsistent terminology.
Social desirability reporting
bias.
Drug treatment
admissions
Publicly funded
treatment #
X/
X,XXXOngoing 2 months 5 digit zip
Hallucinogens e.g.
LSD, mescaline,
peyote
Indication of
problematic use of
drugs.
Large population.
Annual trend data.
Club drugs rarely primary
drug.
Private pay missing.
Mortality-
Medical Examiner &
Toxicology Lab
All sudden,
unexpected and
unnatural
deaths
#X/
XXXOngoing 4 months 5 digit zip
Precise chemical
names.
Quantitative
chemistry.
Population based,
annual trend data.
Difficult to assign causation
to specific drug in multi-drug
cases.
Difficult to detect exogenous
GHB.
Community based
survey
Multiple sub-
groups# A
XXX/
XXXOne time 3 months Seattle Area
Specific drugs names-
detailed names &
slang terms for 11
club drugs
Patterns of use,
consequences
Convenience sample
One time survey.
Social desirability reporting
bias.
WWTP
Total
population
(on sewer)
#xxx/
xxxxx
variable/
flexiblenone Varies/City
Precise chemical
names.
Population based
Direct measure
Aggregated data
Precision ?
Accuracy ?
4
Methamphetamine- Labs and dump sites in Puget Sound Counties
0
100
200
300
400
500
600
700
1990 1992 1994 1996 1998 2000 2002 2004 2006
# o
f In
cid
en
ts (
lab
s a
nd
du
mp
sit
es) King (Seattle)
Pierce (Tacoma)
Snohomish (Everett)
5
Time and Place Displayed Together
6
Quantitative Drug Surveillance System Development
NIH National Institute on Drug Abuse R21 DA024800-01
• Small, exploratory grant
• ~54 samples in 20 Oregon and
Washington Cities in 2009
• Stratified random sample blocked on
season and day of week
• Cities vary in size, climate,
demographics
7
Aims
1. Develop and validate a sensitive and selective analytical method for quantifying the concentration of
drugs in 24 hr, [flow-normalized] composites of raw
influent entering WWTPs;
2. Develop procedures for obtaining samples from a
diverse set of WWTPs;
3. Determine the geographic and temporal (seasonal,
day of week) variability of drug excretion on a per capita and community basis in order to describe use
patterns and to develop sampling frames with optimal efficiency; and
4. Determine the correlation between measured drug
discharge estimates and other drug use indicator data.
Findings to date
• Analytic datasets for 9 cities to date
• Substances measured:– Illicit- Coke/BZE, Methamphetamine, MDMA
– Opioids- Methadone, hydrocodone, oxycodone
– Other compounds- Caffeine, nicotine, cotinine
• Data issues described using preliminary data
• Annual estimation plan described
9
Population covered by WWTP
WWTPs provide coverage to 85% of the population of King County, WA based upon place of residence: 1,482,427 of 1,737,034residents
10
Wastewater
Catchment Areas
for King County Area
•Multiple places•Moderate size
•Roughly align with cities
Catchment
switches
Accounting for population size/Estimating Per Capita Loads
•
=
personday
ng
population
1x
day
Linfluent flow x total
L
drug ng
• Drug concentration (analytical error varies ~5-10%)
• Total flow available from WWTP (variability 5-10%)
• Assumes constant population (not true)
• Missing error component (discussed later)
Population issues
• Measure
– via biomarkers- e.g. creatinine, caffeine,
nicotine- Validity/reliability not established
• Estimate
– Census- fixed value
– Census- estimated daytime population
Census-estimated daytime
population
• Intent- account for worker migration in and out
• Utility- account somewhat for mid-week vs
weekend population differences
• Imperfect if wwtp catchment area is not the
same as the political boundary for a city
• For our purposes, adjust mid-week estimates by
Half of the population estimate (awake 8 hours,
asleep 8 hours)…
14
Estimated Daytime Population (U.S. Census 2000 PHC-T-40)
Can create an estimated mid-week and weekend population with these data
Need updated data and match to actual geographic catchment area
WWTP
Total
resident
population
Estimated
daytime
population
Daytime population
change due to
commuting NotesNumber Percent
a 16,461 19,606 3,145 19.1
b 86,438 113,457 27,019 31.3 Regional capitol
c 18,397 21,006 2,609 14.2
d 83,259 71,447 -11,812 -14.2 2 WWTP, complicated geography
e 50,052 72,101 22,049 44.1 Multiple cities and changes based upon time of year
f 563,374 723,417 160,043 28.4 Catchment area changes seasonally
g 23,003 31,509 8,506 37.0
h 529,121 650,864 121,743 23.0
Impact of variable populationFor this city the increase in daytime population is
31%, assume mid-week and assume half of
waking hours, so multiply mid-week population
estimate by 1.155
.01
.02
.03
.04
.05
01jan2009 01apr2009 01jul2009 01oct2009 01jan2010date_n
Methadone_pop_adj Methadone_pop_fxd
WWTP & Sampling Characteristics
Population Sampling Drainage system
number source mode frequency separate or volume pumped
[T/V] [min] (av-max) combined sewer [S/C] into STP [%]
20,000 census T/V1
~60 S2
33
46,000 census V 18-43 S 18
72800* sewer plan V 20-60(?) na 95% (somewhere in the catchm.)
98,000 connections V 48-75 C 78% (best guess for all pump st.)
19,000 census T/V 30 (29-35) C 100% (probably infl. WWTP)
575,930 census V na 99.9% (somewhere in the catchm.)
35200* sewer plan V 20-60(?) na 95% (somewhere in the catchm.)
650,000 V 10-15min S(90%) all pump stations 85%
1,350,000 na V 45-60 C many interm. and cont. op. pump st.
Time/Volume
None flow prop.
Sample data methadone
Summary statistics are NOT correct as they do not
incorporate <loq and <ld data
Total Samples <LD <LOQ
Data
problem >LOQ
Average daily
excretion
mg/per capita* SD Min Max Range
% Relative
SD
54 … … 1 53 0.0516 0.0179 0.015 0.107 0.09 34.6
52 … … 1 51 0.0222 0.0095 0.006 0.049 0.04 42.7
55 … 1 2 52 0.1047 0.0333 0.020 0.208 0.19 31.9
53 … … … 53 0.0152 0.0055 0.007 0.028 0.02 36.3
48 … … 1 47 0.0299 0.0106 0.0105 0.0558 0.05 35.3
44 … … 1 43 0.0352 0.0153 0.007 0.085 0.078 43.4
41 … … 2 39 0.0310 0.0137 0.0081 0.0625 0.054 44.2
46 … 1 … 45 0.0250 0.0113 0.0070 0.0550 0.048 45.2
50 … … … 50 0.0183 0.0079 0.0043 0.0485 0.0441 43.5
Sample data methadone cont.
• %RSD similar to other WWTP derived estimates
• Fairly large %RSD,
– some recommend not using survey data w/
%RSD >30%
• However, a valid direct measure with a large RSD is more useful than an invalid indirect measure with a small %RSD
Variability
Without error bounds point estimates cannot be compared within or across places
√ (Analytical error)2 + (Flow error)2 + (Sampling error)2
Easy Easy Hard
How use these complicated data?
• Substantial
– < level of detection
– < level of quantification
• Cannot ignore
• Should not do simple substitutions
• Must report actual data distributions
• Consider censored data techniques
• Excretion estimates to start with
More than obvious: better methods for interpreting nondetect data.
Helsel DR. Environ Sci Technol. 2005 39:419A-423A.
Number and proportion of single-day drug index loads by urbanicity
ORDERED CATEGORICAL DATA
BZE (Cocaine metabolite) Level by Urbanicity
0%
20%
40%
60%
80%
100%
# of Municipalities
Upper Tertile 17 3 6
Middle Tertile 6 13 6
Low est Tertile 9 6 11
Below quantification 3 3 3
Not Detected 1 1 8
UrbanLarge Rural
City/Tow nSmalll Rural Tow n
Equivalency across RUCA* Trend across RUCA**
Substance df chi-square p-value df chi-square p-value
Benzoylecgonine
(cocaine metabolite)8 26.1 0.001 2 10.97 0.004
Methamphetamine 4 3.51 0.477 2 0.894 0.640
MDMA 8 8.88 0.353 2 6.16 0.046
MDMA Loads in 2 Cities
NIDA StudyCensored quantitative data
City S City O
Median Load 0.0056 0.0072
% ND 16% 58%
City S 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.004 0.004 0.004 0.004 0.004 0.004 0.005 0.005 0.005 0.006 0.006
0.006 0.006 0.006 0.007 0.007 0.008 0.008 0.008 0.008 0.010 0.010 0.010 0.011 0.014 0.014 0.015 0.015 0.017 0.017 0.020 ND ND ND ND ND ND ND ND
City O 0.004 0.004 0.004 0.005 0.005 0.005 0.006 0.006 0.006 0.006 0.007 0.008 0.008 0.008 0.008 0.009 0.009 0.009 0.011 0.011 0.018 0.023
ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND
The greatest information is in the proportion ND
Cannot calculate median if more than half of the data are missing!
Censored data methods
To do:
Generating Annual Estimates• Explore different population estimate
approaches – E.g. reduce variability of expected constant substances
• Determine weekend v mid-week differences by
drug accounting for population, representative
days?
• Explore the impact of reduced sample sizes on
% RSD for different
– Days of week, periods e.g. mid-week
– WWTP sampling approaches and systems
– City characteristics- demographics, migration, events
– Substances
Summary
• Collecting reliable and valid data over time will require careful attention to:
– Compositing approach
– Population size- actual, flexible
– Catchment changes
– Always include error component
– Appropriate statistical summaries and tests