Developing an annual estimate of community excretion of drugs- Preliminary findings from the Northwest region of the U.S.

Transcription

1 Developing an annual estimate of community excretion of drugs- Preliminary findings from the Northwest region of the U.S. Caleb Banta-Green PhD MPH MSW Research Scientist Alcohol and Drug Abuse Institute University of Washington & Jennifer Field PhD Professor Department of Environmental and Molecular Toxicology Oregon State University EMCDDA January 28, 2011

2 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

3 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,XXX Annual 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 survey Students # XX/ XXXX Bi-annual 12 months City MDMA, Hallucinogens i.e.. LSD and other psychedelics Anonymous, selfreport survey, large sample. Out of school youth missing. Inconsistent terminology. Social desirability reporting bias. Drug treatment admissions Publicly funded treatment # Hallucinogens e.g. X/ X,XXX Ongoing 2 months 5 digit zip LSD, mescaline, peyote Indication of problematic use of drugs. Large population. Club drugs rarely primary drug. Private pay missing. Annual trend data. Mortality- Medical Examiner & Toxicology Lab All sudden, unexpected and unnatural deaths # X/ XXX Ongoing 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 subgroups # A XXX/ XXX One time 3 months Seattle Area Specific drugs namesdetailed names & Patterns of use, slang terms for 11 consequences club drugs Convenience sample One time survey. Social desirability reporting bias. WWTP Total population (on sewer) # xxx/ xxxxx variable/ flexible none Varies/City Precise chemical names. Population based Direct measure Aggregated data Precision? Accuracy?

4 Methamphetamine- Labs and dump sites in Puget Sound Counties 700 # of Incidents (labs and dump sites) King (Seattle) Pierce (Tacoma) Snohomish (Everett)

5 Time and Place Displayed Together 5

6 Quantitative Drug Surveillance System Development NIH National Institute on Drug Abuse R21 DA 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 6

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. 7

8 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 1,737,034 residents 9

10 Wastewater Catchment Catchment switches Areas for King County Area Multiple places Moderate size Roughly align with cities 10

11 Accounting for population size/ Estimating Per Capita Loads ng drug x total flow influent L L day x 1 population = ng day person 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)

12 Population issues Measure via biomarkers- e.g. creatinine, caffeine, nicotine- Validity/reliability not established Estimate Census- fixed value Census- estimated daytime population

13 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) WWTP Total resident population Estimated daytime population Daytime population change due to commuting Number Percent Notes a 16,461 19,606 3, b 86, ,457 27, Regional capitol c 18,397 21,006 2, d 83,259 71,447-11, WWTP, complicated geography e 50,052 72,101 22, Multiple cities and changes based upon time of year f 563, , , Catchment area changes seasonally g 23,003 31,509 8, h 529, , , Can create an estimated mid-week and weekend population with these data Need updated data and match to actual geographic catchment area 14

15 Impact of variable population For 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 jan apr jul oct jan2010 date_n Methadone_pop_adj Methadone_pop_fxd

16 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/V 1 ~60 S ,000 census V S * sewer plan V 20-60(?) na 95% (somewhere in the catchm.) 98,000 connections V 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 C many interm. and cont. op. pump st. Time/Volume None flow prop.

17 Sample data methadone Total Samples <LD <LOQ Data problem >LOQ Average daily excretion mg/per capita* SD Min Max Range % Relative SD Summary statistics are NOT correct as they do not incorporate <loq and <ld data

18 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

19 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

20 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 :419A-423A.

21 Number and proportion of single-day drug index loads by urbanicity ORDERED CATEGORICAL DATA BZE (Cocaine metabolite) Level by Urbanicity 100% 80% 60% 40% 20% 0% Urban Large Rural City/Tow n Upper Tertile Middle Tertile Low est Tertile Below quantification Not Detected # of Municipalities Smalll Rural Tow n Equivalency across RUCA* Trend across RUCA** Substance df chi-square p-value df chi-square p-value Benzoylecgonine (cocaine metabolite) Methamphetamine MDMA

22 MDMA Loads in 2 Cities NIDA Study Censored quantitative data City S ND ND ND ND ND ND ND ND City O 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 City S City O Median Load % ND 16% 58% The greatest information is in the proportion ND Cannot calculate median if more than half of the data are missing!

23 Censored data methods

24 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

25 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