Toward a Conceptual and Social Indicator Framework for Constructing a Composite Drug Consequence Index

Eric L. SevignyDepartment of Criminology and Criminal Justice

University of South Carolina

Paper presented at the Fifth Annual Conference of the International Society for the Study of Drug Policy

Utrecht, NetherlandsMay 23-24, 2011

This work was supported through funding by the Office of National Drug Control Policy (ONDCP) and a Promising Investigator Research Award by the Office of Research and Graduate Education at the

University of South Carolina

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1. INTRODUCTION

When confronted with complex social problems that transect multiple domains and interests—such as the health, crime, economic, and quality of life burdens of illegal drug use—public agencies in the era of government accountability face the difficult task of effectively measuring policy and programmatic performance. Composite indexes (CIs), also known as composite indicators, have gained increasing acceptance as practical tools for performance assessment, benchmarking, policy analysis, and public communication (Organisation for Economic Co-operation and Development, 2008; Zhou, Ang, and Zhou, 2010). Formally, a CI is a mathematical aggregation of individual social indicators that captures a multidimensional concept in a single model-based number per year or region (OECD, 2008). CIs have therefore shown great promise both for parsimoniously monitoring multifaceted phenomena over time and for efficiently comparing complex social constructs across multiple jurisdictions (e.g., countries, states, cities). Their appeal to policymakers, analysts, and the general public has resulted in the development of literally hundreds of CIs in a wide array of substantive areas, including business and technology, sustainability, quality of life, governance, road safety, violence, and, yes, even drug policy (see e.g., Bandura, 2008; Brand et al., 2007; Brumbaugh-Smith et al., 2008; Hermans et al., 2009; Ritter, 2009).

CIs actually have a long history in drug policy, and have been employed in a variety of contexts. For example, in one of the first such applications, Person, Retka, and Woodward (1976) developed the Heroin Problem Index (HPI) from observable social indicators (e.g., treatment admissions, overdose deaths) to produce synthetic estimates of heroin prevalence across 23 U.S. cities. More recent efforts include the United Nations Illicit Drug Index (IDI), which was designed to measure regional and national variation in the global drug problem based on indicators drawn from the domains of production, trafficking, and abuse (UNODC, 2005), and the United Kingdom Drug Harm Index (UK-DHI), which aggregated drug harm indicators in the domains of health, community disorder, and crime to assess progress toward UK Drug Strategy objectives (MacDonald et al., 2005). On the policy side, Brand et al. (2007) developed the Alcohol Policy Index (API) by drawing on indicators from five regulatory domains to measure the relative strength of alcohol control policies across 30 developed countries. These examples underscore both the growing interest and potential usefulness of CIs in drug policy analysis.

Despite their ability to efficiently summarize complex phenomena, however, CIs can produce simplistic or misleading policy prescriptions and poor buy-in from stakeholders, especially if constructed in a technically unsound or nontransparent manner (Ritter, 2007; Saisana and Tarantola, 2002; Saltelli, 2007). Indeed, for these and related reasons, considerable debate surrounds the general utility of CIs for use in policy analysis and performance assessment (Saisana, Saltelli, and Tarantola, 2005). In the drug policy field, for instance, the stalled development of the IDI (Reuter et al., 2009:35) and criticisms of the UK-DHI (Newcombe, 2006; Stevens, 2008; Transform, 2009) highlight the salience of CIs as policy analytic tools, as

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well as the controversy they can engender. Still, well-developed composite drug indexes hold promise as tools for analyzing drug policy outcomes. As Ritter (2009:475) argues, “drug policy analysis would be substantially enhanced if we could develop an Index that can provide a summative measure of the impact of drug use and associated harms.” Toward that end, this paper describes initial efforts to develop a series of four drug-specific state-level indexes measuring interstate variations in U.S. heroin, cocaine, methamphetamine, and marijuana problems. This effort is part of a larger research project that also seeks to develop a U.S. Drug Consequences Index that will provide a summative time-series measure of the overall national drug problem and, envisioned for later, similar indexes describing the policy domain.

This paper proceeds as follows. First, an overview of previously developed composite drug indexes provides additional context for the study. The next two sections develop, in turn, the conceptual and social indicator frameworks guiding measurement and construction of the four drug-specific consequence indexes.

2. OVERVIEW OF COMPOSITE DRUG INDEXES

Composite indexes have fulfilled a variety purposes in drug policy, including indirect prevalence estimation, comparison of policy outcomes across jurisdictions, monitoring drug trends within a single jurisdiction, calculation of cost-savings related to specific governmental interventions, and policy analysis. This section provides an overview of previous work these areas.

CIs were first used in drug policy and planning for indirect estimation of drug abuse prevalence across cities or regions from a set of readily observable social indicators. In the first such application, Person, Retka, and Woodward (1976) developed the Heroin Problem Index (HPI) by extracting the first principal component from six social indicators (treatment admissions, price, purity, seizures, emergency room cases, and overdose deaths) to derive heroin prevalence rankings across 23 U.S. cities. A subsequent effort by the same authors regressed known regional heroin prevalence estimates (i.e., “anchor points”) on the HPI to obtain estimates for other regions without such prevalence information (Person, Retka, and Woodward, 1978), essentially solving a missing data problem through regression imputation (Smit, Van Laar, and Wiessing, 2006). Prevalence estimation methods have evolved considerably from these initial efforts, and related work (under the banner of synthetic estimation or the multivariate indicator method) has moved away from the principal component approach in favor of direct estimation methods that rely on the underlying social indicators themselves (Kraus et al., 2005).1 Nevertheless, data reduction methods are still used when the number of anchor points is small relative to the number of indicators, or when parsimony is sought in empirical applications. For example, to assess the social impacts of crack as it spread throughout the U.S. in the 1980s and 1990s, Fryer 1 Other unrelated methods such as the multiplier method, capture-recapture, and multiple imputation have also been employed in prevalence estimation.

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et al. (2005) developed city- and state-level indexes of the crack problem by extracting the first principal component from a range of indicators (local arrests, emergency room visits, deaths, crack mentions in newspapers, and DEA busts).

Relatedly, CIs have also been used to make cross-jurisdictional comparisons across a broader set of drug-related constructs (e.g., strength of policy, drug-related problems). At the international level, for instance, Brand et al. (2007) developed the Alcohol Policy Index (API) by aggregating 16 policy indicators across five domains (physical availability, drinking context, prices, advertising, and driving laws) to measure the relative strength of alcohol control across 30 developed countries. Similarly, the United Nations Illicit Drug Index (IDI) was developed to provide country-specific estimates of the global drug problem across the domains of production, trafficking, and abuse (UNODC, 2005). CIs have also been utilized in subnational research, most often as a planning aid for substance abuse prevention and treatment services. For example, McAuliffe and colleagues developed and updated a number of simple indexes over a period of years to measure both interstate (McAuliffe et al., 1999; McAuliffe et al., 2003; McAuliffe and Dunn, 2004) and substate (Breer, McAuliffe, and Levine, 1996; McAuliffe et al., 1991; McAuliffe et al., 2002) variations in U.S. substance abuse problems and treatment needs. Similarly, Kim et al. (1998) and Kreiner et al. (2001) developed CIs for use in substate drug prevention planning and resource allocation.

Alternatively, CIs have been used to monitor drug-related trends within a single jurisdiction. The most prominent example of this type of CI is the UK Drug Harm Index (UK-DHI), which was developed to parsimoniously monitor progress toward the UK Drug Strategy objective of reducing the overall harm caused by illegal drugs (MacDonald et al., 2005). The UK-DHI was originally created by aggregating 19 generic drug harm indicators across the domains of health, community disorder, and crime; the base year of 1998 was then set to an index score of 100. Subsequent updates, which included measurement improvements and the addition of other relevant indicators, reveal a substantial decline in the index through 2006 to 68.8 (Goodwin, 2007; Home Office, 2009; MacDonald, Collingwood, and Gordon, 2006). Mounteney, Stoove, and Haugland (2010) provide another example of a CI that was developed to monitor emerging drug trends in Bergen, Norway.

CIs have also been employed to estimate drug-related cost-savings due to government interventions. In particular, the Australian Federal Police Drug Harm Index (AFP-DHI) (Australian Federal Police, 2004; McFadden, 2006; McFadden and Mwesigye, 2004) and the New Zealand Drug Harm Index (NZ-DHI) (Slack et al., 2008) measure the total drug harm potentially avoided due to reductions in drug availability from illegal drug seizures. Although the index construction methods differ somewhat, the basic approach involves (i) estimating the total social costs of drug abuse for a given year (e.g., in the areas of crime, labor costs, health care costs, road accidents, and intangible costs), (ii) normalizing these cost estimates by annualized estimates of total consumption and/or the number of drug users, and (iii) multiplying the

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normalized cost estimates by annual seizure totals. The resulting figures provide an estimate of the drug-related monetary costs averted from drug interdiction. Presumably, similar cost-savings could be estimated for other types of government interventions.

Lastly, CIs can be used in policy analysis. While many of the aforementioned indexes have proven useful for planning and performance monitoring, policy analysis makes a further key distinction between policy actions and policy outcomes (Dunn, 2008). Policy actions represent inputs (e.g., resources allocated) and processes (e.g., implementation), whereas policy outcomes reflect outputs (e.g., units of services delivered) and impacts (e.g., behavioral change). This distinction is made explicit in a number of existing drug policy assessment frameworks. For example, Longshore et al. (1998) proposed an analytic framework for identifying and measuring key aspects of drug policy and harm that, once scored or quantified, can be compared across jurisdictions to assess how specific policies and harms covary. Similarly, MacCoun and Reuter (2001:210-212) highlight this “policy-outcome link” in their analytic framework, which they developed to facilitate reasoned judgments about how changes in the drug policy environment might affect the resulting mix of drug-related harms (see Pacula et al., 2009, for an analogous example from the cost of drug abuse literature). In a CI context, a useful distinction can therefore be made between drug policy indexes and drug harm indexes. Development of one or both of these could support a variety of analyses. For example, Paschall, Grube, and Kypri’s (2009) study comparing alcohol control policies in 26 countries (as represented by the Alcohol Policy Index) with per capita alcohol consumption and adolescent drinking rates found an inverse association between API scores and alcohol outcomes. Notably, this study represents the only example to date of a CI used explicitly for policy analysis.

This overview highlighted the five broad purposes CIs have served in the drug policy field. The next section turns our attention to the current study.

2. METHODOLOGICAL APPROACH

The conceptual and technical challenges of constructing quality CIs are daunting. Major issues include the subjectivity of judgments about what to measure, the quantification of these concepts in the face of serious data limitations, and the intractability of combining available indicators into a common metric. In order to rigorously address these very real challenges, the current study followed an expert protocol on composite indicator construction developed in the Handbook on Constructing Composite Indicators: Methodology and User Guide (Organisation for Economic Co-operation and Development, 2008). The Handbook enumerates five basic tasks in CI construction: (1) develop a theoretical or conceptual framework, (2) identify relevant data sources and select individual indicators, (3) edit the data and assess multivariate structure of chosen indicators, (4) aggregate individual indicators into composite index, and (5) perform

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uncertainty and sensitivity analyses. Following this blueprint will help avoid common pitfalls in CI construction (OECD, 2008).

This paper describes progress on the first two of these research tasks. First, the conceptual framework is developed. A conceptual framework serves as starting point and roadmap for constructing composite indexes (OECD, 2008). In practice, this involves defining the purpose of the index, describing the phenomenon being studies, and mapping its multidimensional components. As noted above, CIs support a variety of analytic objectives in the field of drug policy. Because this objective drives subsequent measurement operations and aggregation methods, clarity of purpose is essential in index construction. The constructs of drug policy and drug harm are also multidimensional and highly variegated. Mapping this diversity is crucial both for guiding selection of the CI’s underlying indicators and for highlighting existing data gaps. The conceptual framework should also provide additional criteria for indicator selection, such as the level of aggregation (e.g., country, state, city) and drug-specificity (e.g., overall harms, legal vs. illegal drugs). Second, with the conceptual framework in place, a quality social indicator framework is developed and used to identify and assess potential indicators for inclusion in the indexes.

3. CONCEPTUAL FRAMEWORK

This section specifies the conceptual framework governing this project’s development of a U.S. Heroin Consequences Index (HerCI), Methamphetamine Consequences Index (MethCI), Cocaine Consequences Index (CocCI), and Marijuana Consequences Index (MarCI). First, the purpose of the indexes is elaborated. Second, the domains are mapped in a taxonomy of drug-related consequences.

3.1 Purpose of the Index

The purpose of the indexes is to provide standard measures of drug-related consequences by drug type that will enable direct comparisons and support policy analyses across states and over time.

A few clarifying points are in order. First, the CIs are classified as drug harm indexes as opposed to drug policy indexes. Thus, not only can they be used to make direct comparisons of harms across states, they can also be used in policy analysis as the outcome measure. For example, one could examine how state differences in medical marijuana laws relate to marijuana consequences as captured in the MarCI.

Second, drug-related consequences are defined to include those consequences and harms that derive from drug use and/or drug trafficking per se. Consequences that originate from the policy

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environment (i.e., enforcement, legal status) are not captured by this definition. In the present framework, these types of enforcement and legal factors are often more usefully conceptualized as policy measures or inputs. For example, the expanded incarceration of drug offenders, limitations on access to therapeutics (e.g., medical marijuana), and the use of no-knock drug raids are more usefully measured in terms of the punitiveness, restrictiveness, or intrusiveness of drug policy.

Third, the indexes focus on the four major illegal drugs of abuse. Both drug-related harms and installed drug control regimes vary greatly by drug type, which highlights the importance of treating drugs separately (MacCoun and Reuter, 2001). Still, if the purpose is simply to characterize the overall drug problem of a country, then an index that makes no differentiation by drug type might be worthwhile (Ritter, 2009). Indeed, that is the objective of a related U.S. Drug Consequences Index (DCI) that will also be developed out of this project.

Fourth, the level of analysis is U.S. states. Other levels of aggregation are possible (e.g., cities, counties), limited only by data quality and availability. Lastly, the indexes are designed to be updated annually to support comparisons and analyses over time.

3.2 Mapping the Domains of the Construct

Researchers in the drug policy community have developed a number of taxonomies that attempt to identify and logically organize relevant aspects of drug policy and harm. This section briefly reviews these prior efforts before describing the taxonomy guiding this project.

3.2.1 Previously Developed Drug Taxonomies

Researchers have developed a number of drug policy and drug harm taxonomies. It will prove instructive to provide a brief review of these prior efforts.

There have been several attempts to develop a unified typology of drug policy interventions. For example, Warner et al. (1990) developed a typology for legal and illegal drugs that differentiated between the type of intervention (information/education, economic incentives, regulation) and the point of intervention (user, supply chain, other intermediaries). Longshore et al. (1998)identified three domains of drug policy could be measured in a local context: accessibility of treatment, accessibility of other social services, and punitiveness of criminal sanctions. MacCoun and Reuter (2001:310-317; MacCoun, Reuter, and Schelling, 1996) differentiated more broadly between prohibitory, prescription, and regulatory regimes. Within these regimes, they specified eight drug control models—ranging from the most restrictive pure prohibition model (e.g., heroin) to the least restrictive free market model (e.g., caffeine)—each with a distinctive set of policy levers. Ritter and McDonald (2008) provide the most comprehensive mapping of individual drug policy interventions, identifying 108 specific policies or programs in a

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systematic review. More importantly, they coded and organized all 108 interventions into ten classification schemes (after identifying 21), finding that six “demonstrated good utility and promise as a way of coding drug policy interventions” (Ritter and McDonald, 2008:32). Among these, the four pillars framework of law enforcement, prevention, treatment, and harm reduction produced the most consistent results among coders and was judged to be a particularly effective classification scheme.

Other attempts to develop policy typologies have targeted specific policy areas, which helps to organize broader domains into subdomains. For example, Mazerolle and colleagues (2007)categorized enforcement interventions into four subgroups: international/national (e.g., crop eradication), reactive/directed (e.g., crackdowns), proactive/partnership (e.g., drug-free zones), and individualized (e.g., offender diversion). Likewise, Sweat (2008) introduces a framework for classifying HIV-prevention interventions.

Drug policy scholars have also developed a number of useful drug harm taxonomies. For example, Newcombe (1992) delineated between drug-related health, social, and economic harms (and benefits) at the individual, community, and societal levels. Longshore et al. (1998) offered an alternate conceptualization that included domains for measuring the prevalence of drug use, the severity of drug use, morbidity and mortality, crime and public disorder, and personal and social functioning. MacCoun and Reuter’s (2001; MacCoun, Reuter, and Schelling, 1996) taxonomy of drug-related harms is a conceptually sophisticated scheme that catalogues dozens of specific harms in the domains of health, social and economic functioning, safety and public order, and criminal justice. Their taxonomy also directs analytic attention to the bearer (e.g., users, employers, society) and source (e.g., use, illegal status, enforcement) of these harms, as well as to the different types of drugs under consideration.

Related drug harm classification schemes have also been developed in the areas of drug risk assessment and the economic costs of drug abuse. The drug risk assessment literature focuses on developing and testing empirically-based approaches for ranking the dangerousness of legal and illegal drugs (e.g., AMCD, 2010; Best et al., 2003; EMCDDA, 2009; Gable, 1993, 2004, 2006; Levitt, Nason, and Hallsworth, 2006; Morgan et al., 2010; Nutt et al., 2007; Nutt, King, and Phillips, 2010; van Amsterdam et al., 2010). Accordingly, drug type serves as the primary unit of analysis rather than the specific harms, which explains why concepts such as toxicity and dependence-producing potential figure more prominently in these schemes. Economic cost of drug abuse studies, in contrast, seek to monetarily value society’s overall drug-related burden by estimating and aggregating quantifiable costs across key domains such as healthcare, crime, and productivity (e.g., CASA, 2009; Collins and Lapsley, 2008; Godfrey et al., 2002; Miller et al., 2006; Nicosia et al., 2009; ONDCP, 2004; Rehm et al., 2006; Slack et al., 2009). Thus, for purposes of mapping the dimensions of drug-related harm, these bodies of work can contribute to the enumeration of specific harms and provide conceptual refinements and insights.

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3.2.2 Taxonomy of Drug-Related Consequences

Drawing on previous classification efforts, a taxonomy of drug-related consequences2 was developed to highlight important harm domains and guide indicator selection for the current project. Table 1 presents the taxonomy, which categorizes an array of drug-related consequences into three broad domains: Health, Social and Economic, and Crime and Disorder. Before describing each of these domains, it will be useful to characterize what the taxonomy does not seek to do. First, although it remains a useful heuristic, the taxonomy does not explicitly consider the bearer of the harm. As a practical matter, this kind of fine distinction is often impossible to make in a social indicator framework. Second, the taxonomy does not measure policy-related consequences. This circumscription derives from the stated purpose of the index and current goals of the project.3

The taxonomy presented in Table 1 categorizes drug-related consequences into three pillars or domains. Within each domain, three subdomains are highlighted that list potentially measurable drug consequence indicators. Each domain and subdomain is described in greater detail below.

3.2.2.1 Health Consequences.

Drug-related health consequences are divided into three subdomains: (1) mortality, (2) morbidity, and (3) drug-exposed infants.

Mortality. Drug users face heightened risks of mortality due to accidental overdose, disease, and trauma (Darke, Degenhardt, and Mattick, 2007). Recent evidence points to sharp increases in drug-induced deaths in the U.S. (Fingerhut and Cox, 1998; Shah et al., 2008), particularly related to prescription opioid misuse (Paulozzi, Budnitz, and Xi, 2006). While overdose and disease (e.g., HIV) are the leading causes of drug-induced mortality, many drug-related deaths are also the result of trauma that is either accidental (falls, vehicular crashes), self-inflicted (suicide), or interpersonal (homicide) in nature (Galea et al., 2002; Shane, Johan, and Michelle, 2009; Soderstrom et al., 2001).

Table 1. Taxonomy of Drug-Related ConsequencesHealth Social and Economic Crime and Disorder

2 The use of the word ‘consequences’ is a semantic change to distinguish this taxonomy from previous ones. It also is a more general term that is inclusive of both harms and benefits and therefore opens the door to future conceptual development in this area.3 Nevertheless, despite this circumscription, the taxonomy is general enough to also account for policy-driven consequences. For example, environmental degradation is a negative consequence of both the aerial spraying of illicit crops and illegal methamphetamine production. Likewise, morbidity can be caused by limited access to therapeutics as much as by their excess availability and use. Lastly, job loss and reduced earnings can follow from drug imprisonment just as well as dependent drug use. Incorporating policy-driven harms thus requires only that greater attention be given to the source of these harms.

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MortalityFatal OverdoseIllness & Disease (AIDS, etc.)Trauma (accident, suicide,

homicide)

MorbidityNonfatal OverdoseIllness & Disease (HIV,

cardiovascular, etc.)Trauma (accident, suicide,

homicide)Mental Illness (depression,

psychosis)Drug Use Disorders

Drug-Exposed InfantsBirth Complications STD InfectionCongenital DefectsLow Birth WeightDevelopmental Delays

Family Disruption and Child MaltreatmentDomestic ViolenceDivorce/SeparationFoster Care PlacementChild Abuse (physical, sexual,

psychological/emotional)MalnutritionDrug-exposed Children

Reduced Attainment and ProductivityDrop-outs/Reduced SchoolingAbsenteeismJob Loss/UnemploymentReduced WagesImpaired Work Relations

Stigmatization and MarginalizationImpoverishmentHomelessnessWelfare ParticipationDisenfranchisementLoss of Relationships

Road Safety and Occupational HazardsDrugged Driving/Road AccidentsTransportation AccidentsOn-the-job Accidents

Drug-related Crime and NuisanceViolence and Other Crime

(psychopharmacological, economic-compulsive, systemic)

Public Nuisance (drug marts, drug litter, drug tourism)Property Damage/DevaluationCommunity DisintegrationMoney Laundering/CorruptionPrecursor Diversion

Situational and Environmental HarmsChemical Dumping/Toxic

ExposureEnvironmental DegradationHome Fires/Structural Damage

Morbidity. Drug-related morbidity encompasses a wide range of injuries, illnesses, diseases, and disorders attributable to illegal drug use (Babor et al., 2010). Just as with deaths, drug-related injuries and physical complications often result from overdose and trauma (Vitale and Mheen, 2006; Warner-Smith et al., 2001). Both short- and long-term drug use is linked to an array of poor mental and physical health outcomes, including depression, psychosis, and cardiovascular and respiratory disease (Brandon and Daniel, 2010; Darke et al., 2008; Han, Gfroerer, and Colliver, 2010; Moore et al., 2007). Drug users, especially those who inject illegal drugs, have elevated risks of contracting infectious diseases such as tuberculosis, hepatitis, and HIV/AIDS (Edlin and Carden, 2006; Friedman, Pross, and Klein, 2006; Pevzner et al., 2010; Strathdee and Stockman, 2010). By definition, drug use disorders also contribute substantially to the overall burden of illegal drugs (Larkin et al., 2005).

Drug-exposed infants. Drug-exposed infants are a particularly vulnerable population with a unique set of negative health outcomes (Rayburn, 2007). Recent general population studies estimate that 3-5% of pregnant women used an illicit drug during their pregnancy (Ebrahim and Gfroerer, 2003; Havens et al., 2009). Drug use during pregnancy is related both to poor maternal and child outcomes, including labor complications and STD exposure for the mother and congenital defects, low birth weight, and developmental delays for the child (Bada et al., 2007; Bandstra et al., 2010; Kim and Krall, 2006; Kuczkowski, 2007; Shankaran et al., 2007; Wells, 2009).

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3.2.2.2 Social and Economic Consequences

Social and economic consequences are represented by three subdomains: (1) family disruption and child maltreatment, (2) reduced attainment and productivity, and (3) stigmatization and marginalization.

Family disruption and child maltreatment. Drug abuse is a common precursor to family disruption and child maltreatment (Barnard, 2007), and it is a contributing factor in domestic violence both in terms of perpetration and victimization (Fals-Stewart and Kennedy, 2005; Humphreys et al., 2005; Moore et al., 2008). Parental drug abuse also contributes to family break-ups, whether through divorce and separation (Amato and Rogers, 1997; Kaestner, 1997) or child placement into protective services and foster care (Brook and McDonald, 2009; Smith et al., 2007; Young, Boles, and Otero, 2007). Broken families, in turn, are associated with greater child and adolescent drug use (Barrett and Turner, 2006; Paxton, Valois, and Drane, 2007). Child maltreatment, whether sexual, physical, or psychological in nature, is also related to parental drug use. General population studies estimate that from 2-11% of caretakers of minor children are drug-involved (Office of Applied Studies, 2009; Simmons et al., 2009; Young, Boles, and Otero, 2007), with much higher rates for parents involved with child protective services (Jones, 2004). As a consequence of poor parenting and child-rearing practices, drug-exposed children are less likely to have their physical (e.g., food, shelter, clothing) and emotional needs met (Barnard and McKeganey, 2004; Leventhal et al., 1997; Magura and Laudet, 1996). Children in homes where illegal drugs are used or produced are also at greater risk of using or passively ingesting these substances themselves (e.g., through breastfeeding and second-hand inhalation) (Wells, 2009).

Reduced attainment and productivity. Illicit drug use can reduce educational/occupational attainment and economic productivity. For example, drug use during adolescence has been linked to greater high school drop-out rates and fewer years of postsecondary schooling (Chatterji, 2006; King et al., 2006; Townsend, Flisher, and King, 2007). Drug use—especially problematic use and dependence—has also been found to be related to greater absenteeism, job loss, and unemployment (Baldwin, Marcus, and De Simone, 2010; Bray et al., 2000; DeSimone, 2002; French, Roebuck, and Alexandre, 2001; Hoffmann, Dufur, and Huang, 2007; Huang et al., 2010; Macdonald and Pudney, 2000), reduced wages and lifetime earnings (Buchmueller and Zuvekas, 1998; Ringel, Ellickson, and Collins, 2006; Van Ours, 2007), and impaired work relations (Breen and Matusitz, 2009). The lure of making money in the drug trade can also sever people’s ties to the conventional economy.

Stigmatization and marginalization. Drug abuse and dependency is ingrained with deeper social stigmatization and marginalization (Room, 2005). Serious drug users face significant barriers to full participation in conventional society, with lives characterized by social inequality, exclusion,

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and deprivation (Ahern, Stuber, and Galea, 2007; Morgenstern et al., 2008). Moreover, drug use can exacerbate the impoverishment and social exclusion of already marginalized groups (Lichtenstein, 1997, 2007; Sunder, Grady, and Wu, 2007). Obvious markers of such marginalization include homelessness and welfare participation. U.S. mayors, for instance, regularly identify substance abuse as a top cause of individual and family homelessness (United States Conference of Mayors, 2009). Empirical research also supports this linkage between drug abuse and homelessness (Kemp, Neale, and Robertson, 2006; Neale, 2008; Shelton et al., 2009), although the association often manifests in complex ways (Johnson and Chamberlain, 2008; Vangeest and Johnson, 2002). Research also finds that rates of drug use tend to be higher among welfare recipients than the general population, and that drug use is linked to future welfare receipt (Kaestner, 1998; Pollack et al., 2002). Other, more exclusionary consequences of drug use such as eviction from public housing, denial of public and educational assistance, and voter disenfranchisement also contribute to stigmatization and marginalization.

3.2.2.3 Crime and Disorder Consequences

Crime and disorder consequences include an array of harms resulting from both drug use and drug distribution. These consequences are divided into (1) road safety and occupational hazards, (2) drug-related crime and nuisance, and (3) situational and environmental harms.

Road safety and occupational hazards. Drug use increases driving, occupational, and other risks of harm and property damage. The use of illegal drugs can impair driving performance and is a common factor in driving accidents (Bédard, Dubois, and Weaver, 2007; Gustavsen, Morland, and Bramness, 2006; Kelly, Darke, and Ross, 2004; Michael Walsh et al., 2005; Richer and Bergeron, 2009). Recent U.S. estimates show that 33% of fatally injured drivers with known drug test results tested positive for illegal drugs in 2009 (National Center for Statistics and Analysis, 2010) and 6% of daytime and 11% of nighttime drivers in a 2007 roadside survey tested positive for illegal drugs (Lacey et al., 2007). More serious drug involvement is also a factor in road accidents, as one recent U.S. study found that drug dependence was the strongest predictor of vehicular crash involvement (Hingson and Zha, 2009). Drug use is also implicated on increased accident rates for other common modes of transportation. For example, the use of illegal drugs by aviation employees, despite its low prevalence, was associated with an increased risk of aviation accidents during 1995-2005 (Li et al., 2011). Research finds that substance users also tend to have higher on-the-job accident and injury rates (Spicer, Miller, and Smith, 2003; Ramchand, Pomeroy, and Arkes, 2009).

Drug-related crime and nuisance. There is a strong link between drugs and crime (Bennett and Holloway, 2009; MacCoun, Kilmer, and Reuter, 2003; Stretesky, 2009). Goldstein’s (1985) classic typology divides this connection three ways. Psychopharmacological crime refers a drug’s action in promoting violent and aggressive tendencies—although evidence on this type of drug-crime connection is inconclusive at best (Kuhns and Clodfelter, 2009). Economic-

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compulsive crime refers to criminal activities committed to score drugs, such as theft, burglary, and robbery. Evidence suggests this association may be influenced by the intensity of drug addiction (DeBeck et al., 2007). Systemic crime occurs in connection with competition in the drug trade (e.g., turf battles, drug dealer robbery, corruption) as well as efforts to protect against enforcement (Reuter, 2009). Drug-related public nuisance is a broad concept that covers various activities related to drug use and dealing, such as drug litter, intimidation, noise, etc. (European Monitoring Centre for Drugs and Drug Addiction, 2005). Drug activity and drug markets are often placed-based, which can contribute to greater community disorganization (Martínez, Rosenfeld, and Mares, 2008; McCord and Ratcliffe, 2007) and opportunity to become involved with drugs (Storr, Chen, and Anthony, 2004).

Situational and environmental harms. Both drug use and production gives rise to a number of situational and environmental harms. For example, illegal methamphetamine production is positively related to domestic house fires and environmental release of chemicals and other reagents (Caldicott et al., 2005). Outdoor illegal cannabis production leads to degradation of public lands (Eth, 2008; Mallery, 2011), and indoor marijuana grow operations often makes homes uninhabitable due to structural hazards, fire risk from altered wiring, mold, etc. (National Collaborating Centre for Environmental Health, 2009).

4. SOCIAL INDICATOR FRAMEWORK

With the conceptual framework in place, attention now turns to the identification and selection of individual social indicators that will be used to construct the composite indexes. This process was guided by a quality social indicator framework that addresses six key considerations: (i) relevance to the overarching conceptual framework, (ii) accuracy of the data and credibility of the data source, (iii) timeliness and punctuality in availability, (iv) accessibility in terms of restrictions and cost, (v) interpretability in terms of documentation and metadata, and (vi) coherence in definition and format across jurisdictions and time (OECD, 2008).

To be relevant for the CI, an indicator had meet four criteria. First, the indicator had to measure a consequence of drug use or drug trafficking. Second, the indicator had to measure outcomes at the state level. Third, the indicator had to be drug-specific. Fourth, the indicator needed to be collected serially (preferably annually from 2000 to present). In short, the indicator had to be a drug-specific, serially-collected measure of harm at the state level of aggregation.

In order to populate the conceptual framework with real data, the next step was to identify all data sources and specific indicators that met these relevance criteria. Thus, this task began with identifying and documenting available public and private drug data sources that serially collect and disseminate state-level drug-specific information. Data on individuals, events, and other units of analysis were eligible. Initially, a catalogue of potential drug data sources was created

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from existing drug data inventories (Coffey et al., 2009; Collins and Zawitz, 1990; Ebener, Feldman, and Fitzgerald, 1993; Manski, Pepper, and Petrie, 2001; NIDA, 2006; ONDCP, 1990; 2003, 2009), data warehouses (e.g., ICPSR, TRACFED), web-based research, and other governmental and nongovernmental sources. A total of 104 U.S. data collection systems were inventoried through this process (see Appendix). Upon review, most of these data sources failed to meet the relevance criteria. Many were outdated, superseded, or one-time collections. Many could only provide national estimates, and still others did not collect drug-specific information. In terms of the drug-specificity of indicators, many data systems that reported information by drug type did so at a level of specificity that was broader than the four target illegal drugs considered here. For heroin and methamphetamine, in particular, it was often difficult to obtain any more specificity than the general class of opiates and stimulants, respectively. Accordingly, the net often had to be cast wider in terms of drug-specificity.

Table 2 presents the mapping of specific indicators to the conceptual framework’s taxonomy of drug-related consequences. A key issue becomes immediately apparent upon examining the table. While some subdomains have multiple indicators, others have none. In fact, the subdomains of family disruption and child maltreatment and stigmatization and marginalization are not populated with any drug-specific consequence indicators that can be measured by state and over time. These types of indicators are rare, which highlights one of the central challenges in this kind of work: the quantification of concepts in the face of serious data limitations.4 Indeed, out of the more than 100 data sources originally inventoried, only 13 were able to provide indicators meeting the stated relevance criteria—and not all of these met the other quality criteria.

The Methamphetamine Consequences Index (MethCI) is examined in more detail to define the indicators and demonstrate the assessment of their fitness along the indicator framework’s quality dimensions. A total of 13 indicators were identified for the MethCI, and each is briefly discussed in turn.

Table 2. Mapping of Social Indicators to Taxonomy of Drug-Related ConsequencesConsequence Domains and Subdomains

Heroin Methamphetamine Cocaine Marijuana

Mortality Heroin/opiate-related deaths [1]

Stimulant-related deaths[1]

Cocaine-related deaths[1]

Morbidity Heroin treatment admissions[2]

Inpatient hospital discharges for heroin poisoning[3]

Inpatient hospital discharges for heroin/opiate dependence/abuse[3]

Human exposure poison center calls for

Meth/amphetamine treatment admissions[2]

Inpatient hospital discharges for stimulant poisoning[3]

Inpatient hospital discharges for stimulant dependence/abuse[3]

Human exposure poison center calls for

Cocaine treatment admissions[2]

Inpatient hospital discharges for cocaine poisoning[3]

Inpatient hospital discharges for cocaine dependence/abuse[3]

Human exposure poison center calls for cocaine[12]

Mean marijuana potency[4]

Marijuana treatment admissions[2]

Inpatient hospital discharges for marijuana dependence/abuse[3]

Human exposure poison center calls for marijuana[12]

4 Although not discussed in this paper, this limitation is less drastic for the national index because the requirements for drug-specificity and state-level aggregation are dropped.

14

Health

heroin[12] methamphetamine[12]

Drug-Exposed Infants Inpatient hospital discharges for narcotic affecting fetus or newborn[3]

Inpatient hospital discharges for cocaine affecting fetus or newborn[3]

Soci

al &

Eco

nom

ic

Family Disruption & Child MaltreatmentReduced Attainment & Productivity

Opiate positivity rate among the general U.S. workforce[5]

Lifetime heroin prevalence among high school students[6]

Methamphetamine positivity rate among the general U.S. workforce[5]

Lifetime methamphetamine prevalence among high school students[6]

Cocaine positivity rate among the general U.S. workforce[5]

Lifetime cocaine prevalence among high school students[6]

Marijuana positivity rate among the general U.S. workforce[5]

Current marijuana prevalence among high school students[6]

Prevalence of recent marijuana use on school property among high school students[6]

Stigmatization & Marginalization

Crim

e &

Dis

orde

r

Road Safety & Occupational Hazards

Heroin/opiate-involved drivers in fatal vehicle crashes[7]

Amphetamine-involved drivers in fatal vehicle crashes[7]

Cocaine-involved drivers in fatal vehicle crashes[7]

Marijuana-involved drivers in fatal vehicle crashes[7]

Drug-related Crime & Nuisance

Percent of police agencies reporting violent crime most connected to heroin[8]

Percent of police agencies reporting property crime most connected to heroin[8]

Federal heroin arrests[13]

Percent of police agencies reporting violent crime most connected to methamphetamine[8]

Percent of police agencies reporting property crime most connected to methamphetamine[8]

Percent of police agencies reporting methamphetamine production in area[8]

Federal meth-amphetamine arrests[13]

Percent of police agencies reporting violent crime most connected to cocaine[8]

Percent of police agencies reporting property crime most connected to cocaine[8]

Federal cocaine arrests[13]

Marijuana grow operation weapons seized[9]

Percent of police agencies reporting violent crime most connected to marijuana[8]

Percent of police agencies reporting property crime most connected to marijuana[8]

Percent of police agencies reporting marijuana growing in area[8]

State marijuana arrests[11]

Federal marijuana arrests[13]

Situational & Environmental Harms

Meth lab incidents[10] Marijuana grow operations[9]

Data Sources:[1] Multiple Cause of Death Data, CDC[2] Treatment Episode Data Set, SAMHSA[3] Healthcare Cost and Utilization Project—State Inpatient Database,

AHRQ[4] Marijuana Potency Monitoring Program, University of Mississippi[5] Quest Diagnostics Drug Testing Index[6] Youth Risk Behavior Survey, CDC[7] Fatality Analysis reporting System, NHSTA

[8] National Drug Threat Survey, NDIC[9] Domestic Cannabis Eradication/Suppression Program, DEA[10] National Seizure System, EPIC[11] Uniform Crime Reports, FBI[12] National Poison Data System, AAPCC[13] U.S. Marshall Prisoner Tracking System, FJSRP

Stimulant-related deaths. This indicator comes from the CDC’s Multiple Cause of Death Files, which are based on U.S. resident death certificates collected by state registries and provided to the National Vital Statistics System. Deaths were defined as the total number of deaths with any mention of the following conditions (and ICD-10 codes): ‘mental and behavioral disorders due to use of stimulants’ (F15.0-F15.9) and ‘poisoning by psychostimulants with abuse potential’ (T43.6). Thus, this variable includes deaths in which stimulants were listed as a contributing, not just underlying, cause. Relevance is challenged by an ICD-10 coding system that gets only as specific as stimulants—which encompasses a wider class of drugs including methamphetamine.

15

Accuracy is also challenged by differences in death reporting practices across states and by small state-year death counts. CDC cautions that death rates based on counts of twenty or less are statistically unreliable, and this occurs for 67% of the state-years between 2000 and 2006. The indicator is also lacking in terms of timeliness. For the reference period 2000-2010, these data are available only through 2006—a serious time lag. Importantly, however, the data are freely accessible online through CDC WONDER and well-documented, and the data elements of interest have been consistently recorded in this system across time.

Meth/amphetamine treatment admissions. This indicator comes from the Treatment Episode Data Set (TEDS), maintained by the Office of Applied Studies, Substance Abuse and Mental Health Services Administration (SAMHSA). The indicator reflects the number of admissions (not individuals) to treatment programs that receive public funds where methamphetamine or amphetamine was reported as the primary, secondary, or tertiary substance of abuse at the time of admission. In terms of relevance, the indicator measures both methamphetamine and amphetamine admissions since four states (Arkansas, Connecticut, Oregon, and Texas) do not distinguish between these two drugs. Nevertheless, SAMHSA reports that methamphetamine represents about 95% percent of combined methamphetamine/amphetamine admissions in those states that do make this distinction. Accuracy can be challenged by state variations in admission criteria, treatment availability, and data reporting practices. The data are somewhat timely, with 2008 the most recent year of microdata available (although limited online access runs through 2010). For users whose university has an institutional license, the microdata are freely accessible through the Inter-university Consortium for Political and Social Research (ICPSR). Finally, reporting was fairly consistent, as only a handful of state-years had missing data on this indicator in the 2000-2008 time period.

Inpatient hospital discharges for stimulant poisoning/Inpatient hospital discharges for stimulant dependence/abuse. These indicators are derived from the Healthcare Cost and Utilization Project—State Inpatient Databases (HCUP-SID), Agency for Healthcare Research and Quality, Department of Health and Human Services. The HCUP-SID currently captures about 90% of all U.S. community hospital discharges. Poisoning was defined as ‘poisoning by psychostimulants’ (ICD-9-CM codes 969.7, 969.70-969.73; 969.79, E854.2); dependence/abuse was defined as either ‘amphetamine and other psychostimulant dependence’ (ICD-9-CM codes 304.40-304.43) or ‘amphetamine or related acting sympathomimetic abuse’ (ICD-9-CM codes 305.70-305.73). Relevance issues arise because the ICD-9 coding system gets no more specific than ‘psychostimulants,’ although data refinements introduced in the ICD-9 coding system in 2009 allow stimulant poisonings to be further disaggregated down to ‘amphetamine.’ The data are timely, currently released through 2009. However, accessibility to the state-level microdata is limited by the costs; obtaining all available state data from 2000-2009, for instance, would cost in excess of $20,000. Fortunately, an online data system allows advanced queries of the data; unfortunately, not all states participate (15 in 2000, 35 in 2009).

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Human exposure poison center calls for methamphetamine. This indicator is available from the National Poison Data System (NPDS) managed by the American Association of Poison Control Centers. Unfortunately, accessibility is severely limited by exorbitant cost. Obtaining state-level data from 2000 forward, for example, would run on the order of tens of thousands of dollars. Accordingly, state-level poison center data were not available for the current project.

Methamphetamine positivity rate among the general U.S. workforce. This indicator comes from the Quest Diagnostics Drug Testing Index, made available by data agreement from Quest Diagnostics Incorporated. The positivity rate is defined as the proportion of positive results for methamphetamine relative to all methamphetamine tests performed. About 7 million tests were performed for all drugs in 2009. The data are timely; the company releases reports on national data with about a one-year lag. The microdata used for this study were available through 2009. Methamphetamine was reported separately beginning 2003; prior to that a general amphetamine positivity rate was reported. One limitation in terms of interpretability is that documentation of drug testing methods is relatively sparse in publically available reports.

Lifetime methamphetamine prevalence among high school students. This indicator comes from CDC’s Youth Risk Behavior Survey. The data were freely avialable through the CDC Youth Online system, which provides data on weighted state-level estimates for odd years 2001-2009. Not all states participated in each survey year, and states that did not obtain a sufficient weighted response rate of 60% were excluded from officially reported results. In these cases, unweighted estimates were used if they could be obtained from other CDC publications or state reports.

Amphetamine-involved drivers in fatal vehicle crashes. This indicator comes from the Fatality Analysis Reporting System (FARS), National Highway Traffic Safety Administration. The indicator measures both methamphetamine and amphetamine drug testing results for all drivers involved in fatal vehicle crashes. Due to high rates of missing data, a multiple imputation procedure was implemented based on a similar NHSTA model for imputing alcohol testing results. State differences in drug testing and reporting procedures pose some limitations for cross-state comparability. However, the data are freely available through the agency website for 2000-2009 with excellent documentation of data collection methods.

Percent of police agencies reporting violent crime most connected to methamphetamine/Percent of police agencies reporting property crime most connected to methamphetamine/Percent of police agencies reporting methamphetamine production in area. These indicators come from the National Drug Threat Survey, National Drug Intelligence Center—made available through a data use agreement. The data are based on a representative survey of state law enforcement agencies. State-level data are available for the period 2003-2010. Online documentation of the questionnaire and survey administration is fairly sparse, although questionnaires were made available as part of the data agreement. There were some changes in questionnaire wording over time, but were fairly consistent for these particular indicators.

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Federal methamphetamine arrests. This indicator comes from the U.S. Marshall Prisoner Tracking System, made available through the Federal Justice Statistics Reporting Program. The data are freely downloadable and cover the period 2000-2009. Perhaps the biggest concern with these data regard the relevance to state-level drug problems, since federal enforcement activity is often driven by considerations other than the underlying level of harm in the state.

Meth lab incidents. This indicator comes from the National Seizure System, El Paso Intelligence Center. State-level data for 2000-2009 were obtained through ONDCP. The indicator measures all recorded seizure events involving labs, chemicals/glassware/equipment, and dumpsites. Despite being widely reported, there is very little publically available documentation of these data. Anecdotally, however, there is some concern with variations in reporting practices across states.

5. SUMMARY AND NEXT STEPS

In summary, an overall assessment of the indicators identified and reviewed for inclusion in the MethCI might lead to a very dim view of the feasibility of the indicators to adequately measure the underlying construct of methamphetamine-related consequences. Not only were there significant data gaps in populating the conceptual framework, but the available indicators suffered from any number of quality issues. More generally, these issues might lead one to conclude that CIs are simply not worth pursuing as policy tools. This author does not subscribe to that position. With the appropriate level of transparency about what the CIs actually measure and in full recognition of the attendant measurement and quality limitations, a parsimonious representation of state-level drug problems, however circumscribed, promises some degree of utility for making interstate comparisons and assessing policy outcomes. If nothing else, the type of effort pursued here can provide policymakers and other stakeholders with information on drug-specific data gaps and measurement limitations in order to improve future data collection efforts. Next steps in this effort will involve dealing with remaining data quality issues (e.g., missing data), choosing a final list of indicators, assessing their multivariate structure, aggregating the indicators, and performing uncertainty and sensitivity analyses. Considerable challenges remain in these tasks, especially concerning the weighting of individual indicators and the specific method(s) for combining them into a single, standardized measure.

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APPENDIX

Table A1. Inventory of Drug Data SourcesData Source Main Sponsor(s)Adoption and Foster Care Analysis and Reporting System ACF/NDACANAdverse Events Reporting System FDAAlcohol and Drug Services Study SAMHSAAlcohol Policy Information System NIAAAAnnual Survey of Jails BJSAnnual Survey of Jails in Indian Country BJSArrestee Drug Abuse Monitoring Program NIJAutomation of Reports and Consolidated Orders System DEABehavior Risk Factor Surveillance System CDCBuprenorphine Physician and Treatment Program Locator SAMHSACampus Safety and Security Statistics EDCannabis Potency Monitoring Project University of Mississippi/NIDACensus of Adult Correctional Facilities BJSCensus of Fatal Occupation Injuries BLSCensus of Jails BJSClandestine Laboratory Seizure System DEAClient-Oriented Data Acquisition Process NIDACommunity Epidemiology Work Group NIDACore Alcohol and Drug Use Surveys Core InstituteCrash Outcome Data Evaluation System NHSTADomestic Monitoring Programs DEADomestic Cannabis Cultivation Assessment NDICDomestic Cannabis Eradication and Suppression Program DEADrug Abuse Warning Network NIDADrug and Alcohol Services Information System SAMHSADrug Services Research Survey NIDAEcstasy Data Testing Project MAPS/ErowidExpenditure and Employment Data for the Criminal Justice System BJSFatality Analysis Reporting System NHSTAFederal Justice Statistics Resource Center Urban InstituteFederal-wide Drug Seizure System DEAFinancial Crimes Enforcement Network Department of TreasuryFirearms Trace Data ATFGeneral Estimates System NHSTAHazardous Substances Emergency Events Surveillance ATSDRHealth Behavior in School-aged Children NICCHDHealthcare Cost and Utilization Project AHRQHispanic Health and Nutrition Exam Survey III NCHS/CDCHIV/AIDS Surveillance CDCJustice Expenditure and Employment Statistics BJSJuvenile Court Statistics OJJDP

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Table A1. Inventory of Drug Data SourcesData Source Main Sponsor(s)Juveniles Taken Into Custody OJJDPLaw Enforcement Management and Administrative Statistics BJSMedical Expenditure Panel Survey AHRQMonitoring Files USSCMonitoring the Future University of MichiganNational Ambulatory Medical Care Survey NCHSNational Child Abuse and Neglect Data System ACF/NDACANNational Corrections Reporting Program BJSNational Crime Victimization Survey BJSNational Drug and Alcoholism Treatment Unit Survey NIDANational Drug Threat Survey NDICNational Forensic Laboratory Information System DEANational Health and Nutrition Exam Survey III NCHS/CDCNational HIV Behavioral Surveillance System CDCNational Hospital Ambulatory Medical Care Survey CDCNational Hospital Discharge Survey NCHSNational Incident-Based Reporting System FBINational Incidence Study of Child Abuse and Neglect DHHSNational Jail Census BJSNational Judicial Reporting Program BJSNational Justice Agency List BJSNational Longitudinal Survey of Labor Market Experience Department of LaborNational Maternal and Infant Health Survey NCHS/NIDA/NIAAANational Motor Vehicle Crash Causation Survey NHSTANational Poisoning Data System AAPCCNational Roadside Survey of Alcohol and Drug Use by Drivers NHSTANational Seizure System EPICNational Survey of American Attitudes on Substance Abuse CASANational Survey of Parents and Youth NIDA/NIHNational Survey of Prosecutors BJSNational Survey on Drug Use and Health NIDANational Survey on Substance Abuse Treatment Services SAMHSANational Toxic Substance Incidents Program ATSDRNational Vital Statistics System NCHSNational Youth Survey NIMHOffender Based Transaction Statistics BJSOnline Tuberculosis Information System CDCOpioid Treatment Program Directory SAMHSAPartnership Attitude Tracking Study PDFAPregnancy Risk Assessment Monitoring System CDCPRIDE Surveys ISAPulse Check ONDCPQuest Diagnostics Drug Testing Index Quest Diagnostics

20

Table A1. Inventory of Drug Data SourcesData Source Main Sponsor(s)School Crime Supplement to the National Crime Victimization Survey BJSSchool Survey on Crime and Safety EDState Court Processing Statistics BJSSubstance Abuse Treatment Facility Locator SAMHSASurvey of Adults on Probation BJSSurvey of Inmates in Federal Correctional Facilities BJSSurvey of Inmates in Local Jails BJSSurvey of Inmates in State Correctional Facilities BJSSurvey of Youth in Custody BJSSystem to Retrieve Information From Drug Evidence II DEATheft or Loss of Controlled Substances DEAToxic Exposure Surveillance System DHHSTransactional Records Access Clearinghouse Syracuse UniversityTreatment Episodes Data Set SAMHSATreatment Outcomes Prospective Study NIDA/NIJUniform Crime Reports FBIUniform Facility Data Set SAMHSAWorldwide Survey of Substance Abuse and Health Behaviors Among Military Personnel

DOD

Youth Risk Behavior Survey CDCAcronyms: Administration for Children and Families (ACF), Agency for Healthcare Research and Quality (AHRQ), Agency for Toxic Substances and Disease Registry (ATSDR), American Association of Poison Control Centers (AAPCC), Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF), Bureau of Justice Statistics (BJS), Bureau of Labor Statistics (BLS), Centers for Disease Control and Prevention (CDC), Department of Defense (DOD), Department of Education (ED), Department of Health and Human Services (DHHS), Drug Enforcement Administration (DEA), El Paso Intelligence Center (EPIC), Federal Bureau of Investigation (FBI), Food and Drug Administration (FDA), International Survey Associates (ISA), Multidisciplinary Association for Psychedelic Studies (MAPS), National Center on Addiction and Substance Abuse (CASA), National Center for Health Statistics (NCHS), National Data Archive on Child Abuse and Neglect (NDACAN), National Drug Intelligence Center (NDIC), National Highway Traffic Safety Administration (NHSTA), National Institute of Child Health and Human Development (NICCHD), National Institute of Justice (NIJ), National Institute of Mental Health (NIMH), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA), Office of Juvenile Justice and Delinquency Prevention (OJJDP), Office of National Drug Control Policy (ONDCP), Partnership for a Drug-Free America (PDFA), Substance Abuse and Mental Health Services Administration (SAMHSA), United States Sentencing Commission (USSC)

21

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