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Arrestee Drug Abuse Monitoring (ADAM) Program in the United States, 2001

Version
v0
Resource Type
Dataset : administrative records data, clinical data, medical records, survey data
Creator
  • United States Department of Justice. National Institute of Justice
Other Title
  • Archival Version (Subtitle)
Collective Title
  • Arrestee Drug Abuse Monitoring (ADAM) Program/Drug Use Forecasting (DUF) Series
Publication Date
2003-05-16
Funding Reference
  • United States Department of Justice. Office of Justice Programs. National Institute of Justice
Language
English
Free Keywords
ADAM/DUF Program; alcohol abuse; arrests; crime patterns; demographic characteristics; drug dependence; drug offenders; drug related crimes; drug testing; drug treatment; drug use; drugs; substance abuse; trends; urinalysis
Description
  • Abstract

    The goal of the Arrestee Drug Abuse Monitoring (ADAM) Program is to determine the extent and correlates of illicit drug use in the population of booked arrestees in local areas. Data were collected in 2001 at four separate times (quarterly) during the year in 33 metropolitan areas in the United States. The ADAM program adopted a new instrument in 2000 in adult booking facilities for male (Part 1) and female (Part 2) arrestees. Data from arrestees in juvenile detention facilities (Part 3) continued to use the juvenile instrument from previous years, extending back through the DRUG USE FORECASTING series (ICPSR 9477). The ADAM program in 2001 also continued the use of probability-based sampling for male arrestees in adult facilities, which was initiated in 2000. Therefore, the male adult sample includes weights, generated through post-sampling stratification of the data. For the adult files, variables fell into one of eight categories: (1) demographic data on each arrestee, (2) ADAM facesheet (records-based) data, (3) data on disposition of the case, including accession to a verbal consent script, (4) calendar of admissions to substance abuse and mental health treatment programs, (5) data on alcohol and drug use, abuse, and dependence (6) drug acquisition data covering the five most commonly used illicit drugs, (7) urine test results, and (8) weights. The juvenile file contains demographic variables and arrestee's self-reported past and continued use of 15 drugs, as well as other drug-related behaviors.
  • Abstract

    Beginning in 1996, the National Institute of Justice (NIJ) initiated a major redesign of its multi-site drug-monitoring program, the Drug Use Forecasting (DUF) system (DRUG USE FORECASTING IN 24 CITIES IN THE UNITED STATES, 1987-1997 [ICPSR 9477]). The program was retitled Arrestee Drug Abuse Monitoring (ADAM). ADAM extended DUF in the number of sites and improved the quality and generalizability of the data. The redesign was implemented in the first quarter of 2000. The original goal remained the same -- to determine the extent of drug use in the booked arrestee population (that is, arrestees brought to fixed booking facilities where digital or ink fingerprinting and other processing took place) in a defined area at specified points each year. However, the redesigned sampling protocol and instrument extended ADAM's goals in the following ways: (1) to provide a suitable probability-based sample of jails and arrestees to support prevalence estimates of drug use and related behaviors in each ADAM site, (2) to provide accurate estimates with confidence intervals that permit tests of the significance of drug use trends, (3) to create a standardized dataset on arrestees in multiple jurisdictions to allow cross-site comparisons, (4) to expand the scope of DUF data to include other areas of concern (treatment history, dependency/abuse assessment, drug markets), (5) to provide a platform for distinguishing between arrest and drug use practices and for drawing inferences about the total population of hardcore or heavy drug users, including those not in the current ADAM sample, (6) to provide data for policy responses to substance abuse issues both locally and nationally, (7) to investigate drug markets or purchases, including data on characteristics of the market, conditions of purchase or exchange, and prices paid, (8) to assess risk of alcohol and/or drug dependency, and drug and mental health treatment experiences, and (9) to use common definitions and, where possible, identical questions and response categories to allow meaningful links between ADAM and other national data systems.
  • Abstract

    The ADAM program in 2001 used an expanded adult instrument which was first implemented in 2000. This instrument was used in adult booking facilities for male (Part 1) and female (Part 2) arrestees. The juvenile data (Part 3) used the juvenile instrument from previous years. The ADAM program also continued to use probability-based sampling for the adult male population, a procedure initiated in 2000. Therefore, the adult male sample includes weights, generated through post-sampling stratification of the data. The shift to sampling of the adult male population in 2000 required that all sites move toward a common catchment area definition, generally a county. The 33 ADAM sites for the current year included Albany, New York (Capital Area), Albuquerque, New Mexico (Bernalillo County), Anchorage, Alaska (Anchorage Borough), Birmingham, Alabama (Jefferson County), Charlotte, North Carolina (Charlotte Metro), Chicago, Illinois (Cook County), Cleveland, Ohio (Cuyahoga County), Dallas, Texas (Dallas County), Denver, Colorado (Denver County), Des Moines, Iowa (Polk County), Detroit, Michigan (Wayne County), Honolulu, Hawaii (Oahu), Indianapolis, Indiana (Marion County), Kansas City, Missouri (Jackson County), Laredo, Texas (Webb County), Las Vegas, Nevada (Clark County), Minneapolis, Minnesota (Hennepin County), New Orleans, Louisiana (Orleans Parish), New York, New York (Manhattan Borough), Oklahoma City, Oklahoma (Oklahoma County), Omaha, Nebraska (Douglas County), Philadelphia, Pennsylvania (County of Philadelphia), Phoenix, Arizona (Maricopa County), Portland, Oregon (Multnomah County), Sacramento, California (Sacramento County), Salt Lake City, Utah (Salt Lake County), San Antonio, Texas (Bexar County), San Diego, California (San Diego County), San Jose, California (Santa Clara County), Seattle, Washington (King County), Spokane, Washington (Spokane County), Tucson, Arizona (Pima County), and Tulsa, Oklahoma (Tulsa County). The core instrument for the adult cases was supplemented by a facesheet, which was used to collect demographic and charge information from official records. Core instruments were used to collect self-report information from respondents. Both the adult and juvenile instruments were administered to persons arrested and booked on local or state charges relevant to the jurisdiction (i.e., not federal or out-of-county charges) within the past 48 hours. Trained interviewers used a paper and pencil instrument in a face-to-face setting in a secure and reasonably private area of the booking facility. The adult interview took a median of 20 minutes, with a slightly longer mean. The juvenile interview took an average of 5 minutes. Responses were recorded by the interviewer at the time of the interview. At the completion of the interview, the arrestee was asked to voluntarily provide a urine specimen. The adult male and female data reflect all the arrestees selected for an interview from the booking logs, including those for whom only facesheet information was collected. The final sample for each adult data file, however, is the subset of arrestees that accepted and completed an interview. An external lab used the Enzyme Multiplied Immunoassay Testing (EMIT) protocols to test for the presence of ten drugs or metabolites of the drug in the urine sample. All amphetamine positives were confirmed by gas chromatography/mass spectrometry (GC/MS) to determine whether methamphetamine was used. Local booking facilities provided a census of all adult males arrested in each facility collecting data for the time period of data collection in the target county. The census data are not in the public file but were used to develop sampling weights for the male data.
  • Abstract

    For the adult data (male and female, Parts 1 and 2), variables from the facesheet include arrest precinct, ZIP code of arrest location, ZIP code of respondent's address, respondent's gender and race, three most serious arrest charges, sample source (stock, flow, other), interview status (including reason an individual selected in the sample was not interviewed), language of instrument used, and the number of hours since arrest. Demographic information from the core instrument include respondent's age, ethnicity, residency, education, employment, health insurance coverage, marital status, and telephone access. Variables from the calendar provide information on inpatient and outpatient substance abuse treatment, inpatient mental health treatment, arrests and incarcerations, heavy alcohol use, use of marijuana, crack/rock cocaine, powder cocaine, heroin, methamphetamine, and other drug (ever and previous 12 months), age of first use of the above six drugs and heavy alcohol use, drug dependency in the previous 12 months, characteristics of drug transactions in past 30 days, use of marijuana, crack/rock cocaine, powder cocaine, heroin, and methamphetamine in past 30 days, seven days, and 72 hours, heavy alcohol use in past 30 days, and secondary drug use of 15 other drugs in the past 72 hours. Urine test results are provided for 11 drugs -- marijuana, cocaine, opiates, phencyclidine (PCP), benzodiazepines (Valium), proposyphene (Darvon), methadone, methaqualone, barbiturates, amphetamines, and methamphetamine. The adult data files include several derived variables. The male data also include four sampling weights, and stratum IDs and percents. For the juvenile data (Part 3), demographic variables include age, race, sex, educational attainment, employment status, and living circumstances. Other variables cover each arrestee's self-reported use of 15 drugs (alcohol, tobacco, marijuana, powder cocaine, crack, heroin, PCP, amphetamines, barbiturates, quaaludes, methadone, crystal methamphetamine, Valium, LSD, and inhalants). For each drug type, arrestees reported whether they had ever used the drug, age of first use, whether they had used the drug in the past 30 days and past 72 hours, number of days they used the drug in past month, whether they tried to cut down or quit using the drug, if they were successful, whether they felt dependent on the drug, whether they were receiving treatment for the drug, whether they had received treatment for the drug in the past, and whether they thought they could use treatment for that drug. Additional variables include whether the juveniles had ever injected drugs, whether they were influenced by drugs when they allegedly committed the crime for which they were arrested, whether they had been to an emergency room for drug-related incidents, and if so, if in the past 12 months, and arrests and charges in the past 12 months. As with the adult data, urine test results are also provided. Finally, variables on precinct (precinct of arrest) and law (penal law code associated with the crime for which the juvenile was arrested) are also provided for use by local law enforcement officials at each site.
  • Methods

    ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes..
  • Methods

    Presence of Common Scales: None.
  • Methods

    Response Rates: The ability to provide a true response rate will not be available until the 2002 data collection. However, the data do contain information on interview and urine specimen status. For the male data, 55.1 percent agreed to an interview, 12.2 percent declined, 22.5 percent were not available at the time of their selection, and 10.2 percent were not approached. Of the male arrestees who were interviewed, 90.5 percent provided a urine sample. For the female data, 58.4 percent agreed to be interviewed, 11.5 percent declined, 20.6 percent were not available, and 9.5 percent were not approached. Of the female arrestees who were interviewed, 91.4 percent provided a urine specimen. Response rates varied across sites but generally fall in the 80 percent to 95 percent range. This includes agreement to the interview and providing a urine sample.
  • Table of Contents

    Datasets:

    • DS0: Study-Level Files
    • DS1: Male Arrestee Data With Weights
    • DS2: Female Arrestee Data
    • DS3: Juvenile Arrestee Data
Temporal Coverage
  • 2001-01-01 / 2001-12-31
    Time period: 2001-01-01--2001-12-31
  • 2001-01-01 / 2001-12-31
    Collection date: 2001-01-01--2001-12-31
Geographic Coverage
  • Alabama
  • Alaska
  • Albany (New York)
  • Albuquerque
  • Anchorage
  • Arizona
  • Birmingham
  • California
  • Charlotte
  • Chicago
  • Cleveland
  • Colorado
  • Dallas
  • Denver
  • Des Moines
  • Detroit
  • Hawaii
  • Honolulu
  • Illinois
  • Indiana
  • Indianapolis
  • Iowa
  • Kansas City (Missouri)
  • Laredo
  • Las Vegas
  • Louisiana
  • Michigan
  • Minneapolis
  • Minnesota
  • Missouri
  • Nebraska
  • Nevada
  • New Mexico
  • New Orleans
  • New York (state)
  • New York City
  • North Carolina
  • Ohio
  • Oklahoma
  • Oklahoma City
  • Omaha
  • Oregon
  • Pennsylvania
  • Philadelphia
  • Phoenix
  • Portland (Oregon)
  • Sacramento
  • Salt Lake City
  • San Antonio
  • San Diego
  • San Jose
  • Seattle
  • Spokane
  • Texas
  • Tucson
  • Tulsa
  • United States
  • Utah
  • Washington
Sampled Universe
All persons arrested and booked on local and state charges (i.e., not federal and out-of-county charges) in any of the 33 ADAM counties in the United States during 2001.
Sampling
A probability sampling plan was used for adult male collection in all sites in 2001, which assured that the data truly represented the male arrestee population, not simply an unspecified proportion of that population. The goal of sampling was to represent with known probability the likelihood that a male arrestee was selected for an interview and to use that information to weight each sample case. Additionally, ADAM's goal was to represent all days of the week and all times of the day so as to avoid biasing the male sample against those types of arrests and arrestees who are brought in during the period interviewers were not collecting data (morning, after midnight, "slower" days of the week). The final sampling goal was to represent all the facilities in the target county -- small, large, suburban, urban, quick release, etc. -- again to represent all types of offenders arrested and booked on local and state charges within the past 48 hours. Each ADAM site adopted one of four designs for sampling jails. ADAM resource constraints, the number of jails in each county, and how male arrestees were processed through those jails dictated the resulting plan for each site. The single jail design applied to sites where all arrestees were booked into a single jail and were being held pending pretrial release or trial. In the single jail design, the site collected its entire male sample in the single booking facility in the county. For counties with a few booking facilities (typically six or fewer), a stratified jail design was used. ADAM interviewers sampled arrestees in each of those jails and were assigned to jails so that the site's male sample was distributed across all booking facilities in the county and was roughly proportionate to size based on bookings. For counties that had many jails, ADAM adopted a stratified cluster sample design through which facilities in the county were clustered by size into a small number of strata. The site's sample was distributed across one or two facilities in each cluster, proportionate to size. This design affords estimates for all jails even though only some jails were included in the sample. Finally, for situations where a large number of jails quickly transfer a selected group of arrestees to a central holding facility, ADAM adopted a feeder jail design wherein interviewers sampled arrestees as they were booked into the central facility. Interviewees selected at the central facility represented arrestees at each of the "feeder" jails. However, only a certain type of offender (typically those charged with serious crimes) were transferred. Therefore, interviewers also went to selected feeder jails to sample male arrestees who did not get transferred. ADAM created a process to sample male arrestees within a jail that were booked at any time of the day or any day of the week with a known probability of selection by splitting the booked population into two parts. The stock comprised males who had been booked before the interviewer arrived at the jail. Interviews were, in general, conducted from 4 p.m. to midnight. The flow comprised males who were booked while the interviewer was stationed at the jail. Flow data collection began the moment the data collection team entered a facility and represented the period of the day when bookings were at the highest point. Cases were selected throughout the period as they were available from booking, with the interviewer selecting the case booked closest to when his/her previous interview was completed. This method insured that the interviews moved throughout the shift and thus represented the full time period. For the same reason, when an interview target number was reached before the end of the shift, interviewing continued until the time period was over. Flow cases were selected from booking log or records data maintained by law enforcement in the facility. The booking log was also the source of the stock sampling. The interviewers in this case arrayed the male arrestees listed as booked during the non-interview times chronologically and took cases on an interval determined by the target number of stock cases for that day. Facesheets were filled out for all males who would be in the sample regardless of whether they were eventually interviewed. Arrestees selected in the sampling were not always still in the facility, making those remaining a potential biased estimate of the true male population characteristics. This bias was addressed in ADAM through weighting of cases. A convenience sample was used when collecting data from the adult female (32 sites) and juvenile populations (8 sites). The sample of sites was not a probability-based sample. In other words, both DUF and the subsequent ADAM sites were not sampled from a list of counties in the United States. They were selected through applications of sites that were interested in participating.
Collection Mode
  • Users are encouraged to review the "Methodology Guide for ADAM" and the "Analytic Guide for ADAM" available on the ADAM Web site at http://www.adam-nij.net/.

    The ADAM program changes implemented in 2000 continued during the 2001 collection. Because of the above changes to the ADAM program, analysts must be careful when comparing previous DUF and ADAM data to the 2000 or 2001 data, especially for male arrestees.

    Local area estimates, national estimates, and inferences about the total population of hardcore or heavy drug users, including those not in the current ADAM sample, are possible with the new ADAM sampling design. The latter two items will be described in detail in forthcoming publications.

Note
2006-03-30 File UG3688.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-03-30 File CQ3688.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (OJP-98-C-001).
Availability
Delivery
One or more files in this study are not available for download due to special restrictions; consult the study documentation to learn more on how to obtain the data.
Alternative Identifiers
  • 3688 (Type: ICPSR Study Number)
Relations
  • Is previous version of
    DOI: 10.3886/ICPSR03688.v1
Publications
  • Caulkins, Jonathan P., Kilmer, Beau, Reuter, Peter H., Midgette, Greg. Cocaine's fall and marijuana's rise: Questions and insights based on new estimates of consumption and expenditures in US drug markets. Addiction.110, (5), 728-736.2015.
    • ID: 10.1111/add.12628 (DOI)
  • Fox, Andrew M., Rodriguez, Nancy. Using a criminally involved population to examine the relationship between race/ethnicity, structural disadvantage, and methamphetamine use. Crime and Delinquency.60, (6), 833-858.2014.
    • ID: 10.1177/0011128710364825 (DOI)
  • Galenianos, Manolis, Gavazza, Alessandro. A Quantitative Analysis of the Retail Market for Illicit Drugs. . 2014.
    • ID: http://www.cemfi.org/ftp/pdf/papers/Seminar/qdrugs.pdf (URL)
  • Kilmer, Beau, Everingham, Susan S., Caulkins, Jonathan P., Midgette, Gregory, Pacula, Rosalie Liccardo, Reuter, Peter H., Burns, Rachel M., Han, Bing, Lundberg, Russell. What America's Users Spend on Illegal Drugs: 2000-2010. Santa Monica, CA: RAND Corporation. 2014.
    • ID: http://www.whitehouse.gov/sites/default/files/ondcp/policy-and-research/wausid_results_report.pdf (URL)
  • Parsons, Nicholas L.. Meth Mania: A History of Methamphetamine. Boulder, CO: Lynne Rienner Publishers. 2014.
  • Golub, Andrew, Brownstein, Henry H.. Drug generations in the 2000s: An analysis of arrestee data. Journal of Drug Issues.43, (3), 335-356.2013.
    • ID: 10.1177/0022042613475599 (DOI)
  • Caulkins, Jonathan P., Bond, Brittany M.. Marijuana price gradients: Implications for exports and export-generated tax revenue for California after legalization. Journal of Drug Issues.42, (1), 28-45.2012.
    • ID: 10.1177/0022042612436650 (DOI)
  • Cooper, Jonathon A., Fox, Andrew M., Rodriguez, Nancy. Race, structural disadvantage, and illicit drug use among arrestees. Criminal Justice Policy Review.23, (1), 18-39.2012.
    • ID: 10.1177/0887403410390508 (DOI)
  • Miller, Riane N., Kuhns, Joseph B.. Exploring the impact of medical marijuana laws on the validity of self-reported marijuana use among juvenile arrestees over time. Criminal Justice Policy Review.23, (1), 40-66.2012.
    • ID: 10.1177/0887403410392026 (DOI)
  • Griffin, Marie L., Rodriguez, Nancy. The gendered nature of drug acquisition behavior within marijuana and crack drug markets. Crime and Delinquency.57, (3), 408-431.2011.
    • ID: 10.1177/0011128708327955 (DOI)
  • Katz, Charles M., Webb, Vincent J., Fox, Kate, Shaffer, Jennifer N.. Understanding the relationship between violent victimzation and gang membership. Journal of Criminal Justice.39, (1), 48-59.2011.
    • ID: 10.1016/j.jcrimjus.2010.10.004 (DOI)
  • Bhati, Avinash Singh, Roman, John K.. Simulated evidence on the prospects of treating more drug-involved offenders. Journal of Experimental Criminology.6, (1), 1-113.2010.
    • ID: 10.1007/s11292-010-9088-2 (DOI)
  • Bond, Brittany M., Caulkins, Jonathan P.. Potential for Legal Marijuana Sales in California to Supply Rest of U.S.. RAND Working Paper.765, Santa Monica, CA: RAND Corporation. 2010.
  • Kposowa, Augustine Joseph, Adams, Michelle A., Tsunokai, Glenn T.. Citizenship status and arrest patterns in the United States: Evidence from the Arrestee Drug Abuse Monitoring program. Crime, Law and Social Change.53, (2), 159-181.2010.
    • ID: 10.1007/s10611-009-9224-y (DOI)
  • Kremling, Janine. An Analysis of the Influence of Sampling Methods on Estimation of Drug Use Prevalence and Patterns among Arrestees in the United States: Implications for Research and Policy. Dissertation, University of South Florida. 2010.
  • Pacula, Rosalie L., Kilmer, Beau, Grossman, Michael, Chaloupka, Frank J.. Risks and practices: The role of user sanctions in marijuana markets. B.E. Journal of Economic Analysis and Policy.10, (1), 1-36.2010.
  • Dave, Dhaval. Illicit drug use among arrestees, prices and policy. Journal of Urban Economics.63, (2), 694-714.2009.
    • ID: 10.1016/j.jue.2007.04.011 (DOI)
  • The White House. National Drug Control Strategy: Data Supplement 2009. NCJ 225448, Washington, DC: Office of National Drug Control Policy. 2009.
    • ID: http://www.whitehousedrugpolicy.gov/publications/policy/ndcs09/ndcs09_data_supl/09datasupplement.pdf (URL)
  • Zhang, Zhiwei. Modeling Nonresponse and Underreporting in Response Surveys of Arrestees. 2009 Joint Statistical Meeting.Washington, DC. 2009.
    • ID: http://www.amstat.org/sections/srms/proceedings/y2009/Files/305170.pdf (URL)
  • Decker, Scott H., Katz, Charles M., Webb, Vincent J.. Understanding the black box of gang organization: Implications for involvement in violent crime, drug sales, and violent victimization. Crime and Delinquency.54, (1), 153-172.2008.
    • ID: 10.1177/0011128706296664 (DOI)
  • The White House. National Drug Control Strategy. Data Supplement 2008.NCJ 221951, Washington, DC: Office of National Drug Control Policy. 2008.
    • ID: http://www.whitehousedrugpolicy.gov/publications/policy/ndcs08_data_supl/ndcs_suppl08.pdf (URL)
  • Wood, Darryl S.. Criterion validity of self-reported drug use among Alaska Native and non-Native arrestees in Anchorage, Alaska. Criminal Justice Studies.21, (1), 49-60.2008.
    • ID: 10.1080/14786010801972688 (DOI)
  • Brownstein, Henry H., Taylor, Bruce G.. Measuring the stability of illicit drug markets: Why does it matter?. Drug and Alcohol Dependence.90, (Supplement 1), 52-60.2007.
    • ID: 10.1016/j.drugalcdep.2006.11.010 (DOI)
  • Golub, Andrew, Johnson, Bruce D., Dunlap, Eloise. The race/ethnicity disparity in misdemeanor marijuana arrests in New York City. Criminology and Public Policy.6, (1), 131-164.2007.
  • Gorman, Dennis M., Huber, J. Charles, Jr.. Do medical cannabis laws encourage cannabis use?. International Journal of Drug Policy.18, (3), 160-167.2007.
    • ID: 10.1016/j.drugpo.2006.10.001 (DOI)
  • Harcourt, Bernard E., Ludwig, Jens. Reaction Essay. Reefer madness: Broken windows policing and misdemeanor marijuana arrests in New York City, 1989-2000. Criminology and Public Policy.6, (1), 165-181.2007.
  • Pacula, Rosalie Liccardo, Kilmer, Beau, Grossman, Chaloupka, Frank J.. Risks and Prices: The Role of User Sanctions in Marijuana Markets. NBER Working Paper Series.NCJ 13415, Cambridge, MA: National Bureau of Economic Research. 2007.
    • ID: http://www.nber.org.proxy.lib.umich.edu/papers/w13415.pdf?new_window=1 (URL)
  • Rhodes, William, Hunt, Dana, Chapman, Meg, Kling, Ryan, Dyous, Christina, Fuller, Doug. Using ADAM Data to Investigate the Effectiveness of Law Enforcement. NCJ 221073, Cambridge, MA: Abt Associates Inc.. 2007.
    • ID: http://www.ncjrs.gov/pdffiles1/nij/grants/221073.pdf (URL)
  • Rhodes, William, Kling, Ryan, Johnston, Patrick. Using booking data to model drug user arrest rates: A preliminary to estimating the prevalence of chronic drug use. Journal of Quantitative Criminology.23, (1), 1-22.2007.
    • ID: 10.1007/s10940-006-9016-9 (DOI)
  • Decker, Scott H., Katz, Charles M., Webb, Vincent J.. Assessing the validity of self-reports by gang members: Results from the Arrestee Drug Abuse Monitoring Program. Crime and Delinquency.52, (2), 232-252.2006.
  • Johnson, Bruce D., Golub, Andrew. Dependence on and treatment for street drugs among Manhattan arrestees. New Research on Street Drugs.Hauppauge, NY: Nova Science Publishers. 2006.
  • Ross, Michael W., Risser, Jan, Peters, Ronald J.. Cocaine use and syphilis trends: Findings from the Arrestee Drug Abuse Monitoring (ADAM) program and syphilis epidemiology in Houston. American Journal on Addictions.15, (6), 473-477.2006.
    • ID: 10.1080/10550490601000462 (DOI)
  • Beckett, Katherine, Nyrop, Kris, Pfingst, Lori, Bowen, Melissa. Drug use, drug possession arrests, and the question of race: Lessons from Seattle. Social Problems.52, (3), 419-441.2005.
    • ID: 10.1525/sp.2005.52.3.419 (DOI)
  • Golub, Andrew, Liberty, Hillary James, Johnson, Bruce D.. Inaccuracies in self-reports and urinalysis tests: Impacts on monitoring marijuana use trends among arrestees. Journal of Drug Issues.35, (4), 941-965.2005.
    • ID: 10.1177/002204260503500413 (DOI)
  • Golub, Andrew, Liberty, Hillary James, Johnson, Bruce D.. The variation in arrestees' disclosure of recent drug use across locations, drugs, and demographic characteristics. Journal of Drug Issues.35, (4), 917-940.2005.
    • ID: 10.1177/002204260503500412 (DOI)
  • Hendrickson, James, Gerstein, Dean. Criminal involvement among young male ecstasy users. Substance Use and Misuse.40, (9-10), 1557-1575.2005.
    • ID: 10.1081/JA-200066893 (DOI)
  • Jones, Peter R.. Drug use trends across the DUF/ADAM divide: 1988-2002. Boston, MA. 2005.
  • Katz, Charles M., Webb, Vincent J., Decker, Scott H.. Using the Arrestee Drug Abuse Monitoring (ADAM) Program to further understand the relationship between drug use and gang membership. Justice Quarterly.22, (1), 58-88.2005.
    • ID: 10.1080/0741882042000333645 (DOI)
  • Lord, Vivian B., Friday, Paul C., Brennan, Pauline K.. The effects of interviewer characteristics on arrestees' responses to drug-related questions. Applied Psychology in Criminal Justice.1, (1), 36-55.2005.
  • Rodriguez, Nancy, Griffin, Marie L.. Gender Differences in Drug Market Activities: A Comparative Assessment of Men and Women’s Participation in the Drug Market. NCJ 211974, . 2005.
    • ID: https://www.ncjrs.gov/pdffiles1/nij/grants/211974.pdf (URL)
  • Rodriguez, Nancy, Katz, Charles, Webb, Vincent J., Schaefer, David R.. Examining the impact of individual, community, and market factors on methamphetamine use: A tale of two cities. Journal of Drug Issues.35, (4), 665-693.2005.
    • ID: 10.1177/002204260503500402 (DOI)
  • Yacoubian, George S.. Estimating the prevalence of alcohol and drug abuse and dependence among New Orleans arrestees. American Journal on Addictions.14, (5), 471-477.2005.
    • ID: 10.1080/10550490500247172 (DOI)
  • Burke, Cynthia. Drug Use Among Adult Arrestees in San Diego County. CJ Bulletin.San Diego, CA: SANDAG, Criminal Justice Research Division. 2004.
    • ID: http://sandiegohealth.org/disease/drug/publicationid_1109_3506.pdf (URL)
  • Golub, Andrew, Johnson, Bruce D.. How Much Do Manhattan-Arrestees Spend on Drugs?. Final Report to the National Institute of Justice Regarding Monitoring Drug Markets in Manhattan with ADAM.NIJ 207147, New York, NY: National Development and Research Institutes, Inc.. 2004.
    • ID: http://www.ncjrs.gov/pdffiles1/nij/grants/207147.pdf (URL)
  • National Drug Intelligence Center. National Drug Threat Assessment 2004. Washington, DC: United States Department of Justice, National Drug Intelligence Center. 2004.
    • ID: http://www.usdoj.gov/ndic/pubs8/8731/8731p.pdf (URL)
  • Webb, Vincent J., Katz, Charles M., Decker, Scott H.. Assessing the Validity of Self-Reports by Gang Members: Results From the Arrestee Drug Abuse Monitoring Program. Conference of the Academy of Criminal Justice Sciences.Las Vegas, NV. 2004.
  • Yacoubian, George S., Jr., Peters, Ronald J.. Exploring the prevalence and correlates of methamphetamine use: Findings from Sacramento's ADAM program. Journal of Drug Education.34, (3), 281-294.2004.
    • ID: 10.2190/QBHY-ADHA-MYMW-HCMR (DOI)
  • Yang, Y. Michael. Survey errors and survey costs: Experience from surveys of arrestees. Joint Statistical Methods.Toronto. 2004.
  • Falkowski, Carol L.. Drug Abuse Trends. Center City, MN: Hazelden Foundation. 2003.
  • Myrstol, Brad. Alcohol use among Anchorage Arrestees. Alaska Justice Forum.19, (4), 3-4.2003.
    • ID: http://justice.uaa.alaska.edu/forum/19/4winter2003/194.winter2003.pdf (URL)
  • Myrstol, Brad. Drug use trends among Anchorage arrestees: 1999-2001. Alaska Justice Forum.19, (4), 1, 10-12.2003.
    • ID: http://justice.uaa.alaska.edu/forum/19/4winter2003/194.winter2003.pdf (URL)
  • United States Department of Justice, National Institute of Justice. Annualized Site Reports 2001: Adult Male Weighted Data, Adult Female Unweighted Data, Juvenile Unweighted Data. ADAM Arrestee Drug Abuse Monitoring Program.NCJ 200761, Washington, DC: United States Department of Justice, National Institute of Justice. 2003.
    • ID: http://www.ojp.usdoj.gov/nij/adam/adam_2001AnnualizedReports.pdf (URL)
  • Yang, Y. Michael, Gerstein, Dean R.. A comparison of two arrestee drug use estimation methods. Joint Statistical Meetings.San Francisco, CA. 2003.
  • Zhang, Zhiwei, et al. Drug Use and Related Matters among Adult Arrestees, 2001. NCJ 212901, Washington, DC: United States Department of Justice, National Institute of Justice. 2002.
    • ID: http://www.ncjrs.gov/nij/adam/ADAM2001.pdf (URL)
  • Heliotis, Joanna, Kuck, Sarah, Hunt, Dana. Analytic Guide for ADAM. Arrestee Drug Abuse Monitoring (ADAM) Program.Washington, DC: United States Department of Justice, National Institute of Justice. 2001.
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Update Metadata: 2015-08-05 | Issue Number: 6 | Registration Date: 2015-06-15

United States Department of Justice. National Institute of Justice (2003): Arrestee Drug Abuse Monitoring (ADAM) Program in the United States, 2001. Archival Version. Arrestee Drug Abuse Monitoring (ADAM) Program/Drug Use Forecasting (DUF) Series. Version: v0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR03688