Measuring Interstate Variations in the Consequences of Illegal Drugs: A Composite Indicator Approach

Social Indicators Research volume 128, pages501–529(2016)Cite this article

Abstract

This study develops a series of Drug Consequences Indices (DCIs) measuring interstate variations in the harmful consequences of heroin, methamphetamine, cocaine, and marijuana in the US from 2000 to 2009. Indicators measuring drug-related health, social and economic, and crime and disorder consequences were selected from key drug data systems. Using weights derived from an Analytic Hierarchy Process conducted with addiction and drug policy experts, indicators were normalized by min–max scaling and aggregated using geometric means to produce each drug-specific DCI. Index scores were generated on a best–worst scale of 0–100 for all 50 states across 10 years. The Heroin Consequences Index reveals a general uptick in heroin-related problems, but the most severely impacted states fall in the Northeast. The Methamphetamine Consequences Index reveals that the worst affected states lay west of the Mississippi River and confirms the methamphetamine problem peaked about mid-decade. The Cocaine Consequences Index also shows a general decline beginning mid-decade, but in contrast to methamphetamine the most impacted states lay along the Gulf and East Coasts. The Marijuana Consequences Index in contrast is neither highly regionalized nor consistently trending. The DCIs provide a parsimonious yet comprehensive snapshot of geographic and temporal variations in the nature and extent of illicit drug problems. They have a number of practical applications, including the ability to succinctly communicate drug policy needs, objectives, and progress at the national, regional, and state levels. Other policy applications include aiding benchmarking, performance assessment, and resource allocation decisions.

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Notes

  1. 1.

    We contacted 36 individuals representing a cross-section of backgrounds and expertise: 19 agreed to participate, 3 agreed to participate but were unable to fulfill their commitment, 3 declined, and 11 could not be reached or did not respond to the invitation.

  2. 2.

    The choice of the geometric mean, as opposed to the classical arithmetic average, is driven by both conceptual and methodological purposes. Conceptually, perfect substitutability among the index components (as occurs with the arithmetic average) is not desirable. Substitutability (or compensability) is understood here as the undesirable offsetting of poor performance on some indicators with good performance on others. Methodologically, the use of arithmetic average would have been problematic because it would have implied that the level of priority given to a dimension of drug consequences is invariant to the level of attainment. Instead, the geometric mean gives more incentives for improvement to low values (concave function). This explains the orientation of normalized values in Eq. (1) First, in order to derive participatory-driven weights for use in constructing the DCIs, we geometric mean with a double change of orientation was preferred over a simple generalized mean of power 2 for two reasons that are specific to our context: (a) consistency between the nominal weights assigned to the three main dimensions and their “importance” or main effects (see Sect. 3.6) and (b) greater variation in the index scores across the four drug types.

  3. 3.

    For example, in the Crime and Disorder domain for the Cocaine Consequences Index, there are no indicators for ‘community and environmental harms.’ Thus, the weights for the ‘drugged driving’ and ‘crime and nuisance’ subdomains were rescaled to .056 = (.04 + (.04/.388)) * .388 and .325 = (.234 + (.234/.388)) * .388, respectively, and finally to .147 = (.056/(.056 + .325) and 0.853 = (.325/(.056 + .325).

  4. 4.

    These index subcomponents are downloadable as a data supplement from the journal’s website.

  5. 5.

    Note that in our context, the use of a generalized mean of power 2 would have altered the order of importance of the three main dimensions in the various drug types.

  6. 6.

    A methodological break in the NSDUH sampling design precludes comparison with pre-2002 data. Aggregate 2002–2009 data facilitate more table state-level estimates, especially for heroin and methamphetamine.

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Acknowledgments

This research was supported in part by the US Office of National Drug Control Policy (Award Number: G10USCAROLINA) and the University of South Carolina, Office of Research and Graduate Education (Promising Investigator Research Award Program). We acknowledge the following people: Gianfranco Lucchese from the European Commission Joint Research Centre for creating the rose charts; Fe Caces, Terry Zobeck, and Michael Cala for support, feedback, and data; and 19 unnamed experts in drug policy and addiction fields who participated in the Analytic Hierarchy Process.

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  1. Department of Criminal Justice and Criminology, Georgia State University, 140 Decatur Street, Urban Life Building, Atlanta, GA, 30303, USA

    Eric L. Sevigny

  2. Joint Research Centre of the European Commission, Via Enrico Fermi 2749, 21027, Ispra, VA, Italy

    Michaela Saisana

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  1. Eric L. Sevigny
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  2. Michaela Saisana
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Correspondence to Eric L. Sevigny.

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Table 7 Notes on Data Sources and Indicators
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Sevigny, E.L., Saisana, M. Measuring Interstate Variations in the Consequences of Illegal Drugs: A Composite Indicator Approach. Soc Indic Res 128, 501–529 (2016). https://doi.org/10.1007/s11205-015-1042-2

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Keywords

  • Drug-related consequences
  • Composite indicator
  • Interstate variations
  • Drug data systems