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Spatial Models of the Growth and Spread of Methamphetamine Abuse in California

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Crime, HIV and Health: Intersections of Criminal Justice and Public Health Concerns

Abstract

This chapter examines the serious and growing public health problems related to methamphetamine abuse in the United States. It combines economic and mathematical epidemiological approaches to explaining the spread of drug abuse, treating methamphetamine use as a chronic relapsing disease that spreads through social contacts with the active facilitation of illegal drug markets. These models suggest that methamphetamine problems may exhibit typical disease characteristics such as spatial clustering and correlated growth, as would be consistent with the frequent references to methamphetamine as an epidemic. These models were tested using historical data on methamphetamine-related arrests and hospital discharges in California between 1980 and 2006. Statewide data suggest that both problem indicators grew exponentially during this period except for temporary supply reductions following the enactment of federal restrictions on the precursor chemicals used to manufacture methamphetamine. The spatial spread of methamphetamine abuse was investigated using Bayesian space-time models of arrest counts in 330 California cities. These analyses found that cities varied considerably in both their underlying levels of amphetamine-related arrests and their growth rates over time. These growth rates were strongly correlated between nearby cities, as predicted by a disease approach in which a methamphetamine ‘infection’ spreads from person to person. These analyses suggested that methamphetamine growth was highest in rural northern and southern California between 1980 and 1989, then shifted to the central valley areas during the early 1990s before moving more into urban areas after 1997.

Research for and preparation of this chapter was supported by National Institute on Drug Abuse Research Grant No. R21 DA024341 to Dr. Gruenewald and National Institute on Alcohol Abuse and Alcoholism Research Grant No. R21 AA016632 to Dr. Waller.

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Notes

  1. 1.

    California hospital discharge data diagnostic codes for amphetamine and other psychostimulant dependence (ICD9-CM 304.4) or amphetamine and related acting sympathomimetic abuse (ICD9-CM 305.7).

  2. 2.

    The code for ‘dangerous drugs’ corresponds to felony possession, transport or selling of methamphetamine, amphetamine, hallucinogens, and related drugs, and specifically does not include narcotics, opiates, marijuana, and ‘other drugs.’ Estimated from recent city level data, more than 90% of ‘dangerous drug’ arrests are exclusively related to methamphetamine. These codes do not include manufacturing or possession of precursor chemicals, which are separately detailed under ‘manufacturing’ and again strongly dominated by arrests related to methamphetamine.

  3. 3.

    Specifically, the centroid of each city was located within California and tessellations constructed around each centroid. Cities that shared a common tessellated boundary were considered as connected one to the other.

References

  • Anderson, R. M., & May, R. M. (1991). Infectious diseases of humans: Dynamics and control. London: Oxford University Press.

    Google Scholar 

  • Banerjee, A., LaScala, E. A., Gruenewald, P. J., Freisthler, B., Treno, A., & Remer, L. (2008). Social disorganization, alcohol and other drug markets, and violence: A space-time model of community structure. In Y. F. Thomas, D. Richardson, & I. Cheung (Eds.), Geography and drug addiction (pp. 117–130). New York: Springer.

    Chapter  Google Scholar 

  • Bjornstad, O. N., Finkenstadt, B. F., & Grenfell, B. T. (2002). Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series SIR model. Ecological Monographs, 72, 169–184.

    Google Scholar 

  • Cassels, S., Clark, S. J., & Morris, M. (2008). Mathematical models for HIV transmission dynamics: Tools for social and behavioral science research. Journal of Acquired Immune Deficiency Syndrome, 47, S34–S39.

    Article  Google Scholar 

  • Caulkins, J. (2005). Investing for cost-effectiveness in the face of uncertainty: Applying financial portfolio optimization to prevention programming. In T. Stockwell, P. J. Gruenewald, J. W. Toumbourou, & W. Loxley (Eds.), Preventing harmful substance use: The evidence base for policy and practice (pp. 401–414). New York: Wiley.

    Chapter  Google Scholar 

  • Caulkins, J. (2007). The need for dynamic drug policy. Addiction, 102, 4–7.

    Article  Google Scholar 

  • Cheng, W. S., Garfein, R. S., Semple, S. J., Strathdee, S. A., Zians, J. K., & Patterson, T. L. (2010). Binge use and sex and drug use behaviors among HIV(−), heterosexual methamphetamine users in San Diego. Substance Use and Misuse, 45, 115–133.

    Article  Google Scholar 

  • Cintron-Arias, A., Sanchez, F., Wang, X., Castillo-Chavez, C., Gorman, D. M., & Gruenewald, P. J. (2009). The role of nonlinear relapse on contagion amongst drinking communities. In G. Chowell, J. M. Hyman, L. M. A. Bettencourt, & C. Castillo-Chavez (Eds.), Mathematical and statistical estimation approaches in epidemiology (pp. 343–360). New York: Springer.

    Chapter  Google Scholar 

  • Cunningham, J. K., & Liu, L. M. (2003). Impacts of federal ephedrine and pseudoephedrine regulations on methamphetamine-related hospital admissions. Addiction, 98, 1229–1237.

    Article  Google Scholar 

  • Cunningham, J. K., & Liu, L. M. (2005). Impacts of federal precursor chemical regulations on methamphetamine arrests. Addiction, 100, 479–488.

    Article  Google Scholar 

  • Curtis, R., Friedman, S. R., Neaigus, A., Jose, B., Goldstein, M., & Ildefonso, G. (1995). Street-level drug markets: Network structure and HIV risk. Social Networks, 17, 229–249.

    Article  Google Scholar 

  • Ditton, J., & Frischer, M. (2001). Computerized projection of future heroin epidemics: A necessity for the 21st century? Substance Use and Misuse, 36, 151–166.

    Article  Google Scholar 

  • Eck, J. E. (1995). A general model of the geography of illicit retail market places. In J. E. Eck & D. Weisburd (Eds.), Crime and place: Crime prevention studies (Vol. 4, pp. 67–94). Monsey: Criminal Justice Press.

    Google Scholar 

  • Greene, J., & McClintock, C. (1985). Triangulation in evaluation: Design and analysis issues. Evaluation Review, 9, 523–545.

    Article  Google Scholar 

  • Grenfell, B. T., Bjornstad, O. N., & Kappey, J. (2001). Travelling waves and spatial hierarchies in measles epidemics. Nature, 414, 716–723.

    Article  Google Scholar 

  • Grenfell, B. T., Bjornstad, O. N., & Finkenstadt, B. F. (2002). Dynamics of measles epidemics: Scaling noise, determinism and predictability with the TSIR model. Ecological Monographs, 72, 185–202.

    Article  Google Scholar 

  • Groff, E., & Birks, D. (2008). Simulating crime prevention strategies: A look at possibilities. Policing, 2, 175–184.

    Article  Google Scholar 

  • Gruenewald, P. J., Johnson, F. W., Ponicki, W. R., Remer, L. G., & LaScala, E. A. (2010). Assessing correlates of the growth and extent of methamphetamine abuse and dependence in California. Substance Use and Misuse, 45(12), 1948–1970.

    Article  Google Scholar 

  • Hohman, M., Oliver, R., & Wright, W. (2004). Methamphetamine abuse and manufacture: The child welfare response. Social Work, 49, 373–381.

    Article  Google Scholar 

  • Holder, H. D., & Treno, A. J. (2005). Moving toward a common evidence base for alcohol and other drug prevention policy. In T. Stockwell, P. J. Gruenewald, J. W. Toumbourou, & W. Loxley (Eds.), Preventing harmful substance use: The evidence base for policy and practice (pp. 351–366). Chichester: Wiley.

    Chapter  Google Scholar 

  • Hollingsworth, T. D. (2009). Controlling infectious disease outbreaks: Lessons from mathematical modeling. Journal of Public Health Policy, 30, 328–341.

    Article  Google Scholar 

  • Hsieh, Y. H., & Cheng, Y. S. (2006). Real-time forecast of multiphase outbreak. Emerging Infectious Diseases, 12, 122–127.

    Article  Google Scholar 

  • Hunt, L., & Chambers, C. (1976). The heroin epidemics: A study of heroin use in the United States, 1965–1975. New York: Spectrum.

    Google Scholar 

  • Hunt, E. D., Sumner, M., Scholten, T. J., & Frabutt, J. M. (2008). A data-driven strategy to implement a focused deterrence model and understand the elements of drug markets. In Y. F. Thomas, D. Richardson, & I. Cheung (Eds.), Geography and drug addiction (pp. 117–130). New York: Springer.

    Google Scholar 

  • Illinois Attorney General. (2010). Recognizing a structure containing a meth lab. Retrieved from http://www.illinoisattorneygeneral.gov/methnet/recognizingmeth/outside.html

  • Iritani, B. J., Halfors, D. D., & Bauer, D. J. (2007). Crystal methamphetamine use among young adults in the USA. Addiction, 102, 1102–1113.

    Article  Google Scholar 

  • Kaye, S., Darke, S., Duflou, J., & McKetin, R. (2008). Methamphetamine-related fatalities in Australia: Demographics, circumstances, toxicology and major organ pathology. Addiction, 103, 1353–1360.

    Article  Google Scholar 

  • LaScala, E. A., Freisthler, B., & Gruenewald, P. J. (2005). Population ecologies of drug use, drinking and related problems. In T. Stockwell, P. J. Gruenewald, J. W. Toumbourou, & W. Loxley (Eds.), Preventing harmful substance use: The evidence base for policy and practice (pp. 67–78). West Sussex: Wiley.

    Chapter  Google Scholar 

  • Lipton, R., Gorman, D., Wieczorek, W., Gruenewald, P. J., & Bannerjee, A. (2009). Geography and public health: Putting people in places. In M. Khosrow-Pour (Ed.), Encyclopedia of information science and technology (2nd ed., pp. 1634–1645). Hershey: Information Science Reference, IGI Global Inc.

    Google Scholar 

  • Liu, L., & Eck, J. (2008). Artificial crime analysis: Using computer simulations and geographic information systems. Hershey: Information Science Reference, IGI Global Inc.

    Book  Google Scholar 

  • Lu, M., & Burnum, J. (2008). Spatial patterns of clandestine methamphetamine labs in Colorado Springs, Colorado. In Y. F. Thomas, D. Richardson, & I. Cheung (Eds.), Geography and drug addiction (pp. 193–207). New York: Springer.

    Chapter  Google Scholar 

  • Mounteney, J., Fry, C., McKeganey, N., & Haugland, S. (2010). Challenges of reliability and validity in the identification and emerging of drug trends. Substance Use and Misuse, 45, 266–287.

    Article  Google Scholar 

  • Mubayi, A., Castillo-Chavez, C., Chowell, G., Kribs-Zaleta, C., Siddiqui, N. A., Kumar, N., & Das, P. (2010). Transmission dynamics and underreporting of Kala-azar in the Indian state of Bihar. Journal of Theoretical Biology, 262, 177–185.

    Article  Google Scholar 

  • National Drug Intelligence Center. (2008). National methamphetamine threat assessment (Publication No. 2008-Q0317-006). Washington, DC: US Department of Justice.

    Google Scholar 

  • National Drug Intelligence Center. (2009). Situation report: Pseudoephedrine smurfing fuels surge in large-scale methamphetamine production in California (Publication No. 2009-S0787-007). Washington, DC: US Department of Justice.

    Google Scholar 

  • National Institute on Drug Abuse. (2006). Methamphetamine abuse and addiction (NIH Publication No. 06–4210). Bethesda: National Institutes of Health, DHHS.

    Google Scholar 

  • Rava, L., Calvani, M. G., Heisterkamp, S., Wiessing, L., & Rossi, C. (2001). Incidence indicators for policy-making: Models, estimation and implications. In United Nations Office on Drugs and Crime (Ed.), Bulletin on narcotics, volume LIII – Dynamic drug policy: Understanding and controlling drug epidemics (pp. 135–155). New York: United Nations.

    Google Scholar 

  • Rawson, R. A., Marinelli-Casey, P., Anglin, M. D., Dickow, A., Frazier, Y., Gallagher, C., Galloway, G. P., Herrell, J., Huber, A., McCann, M. J., Obert, J., Pennell, S., Reiber, C., Vandersloot, D., & Zweben, J. (2004). A multi-site comparison of psychosocial approaches for the treatment of methamphetamine dependence. Addiction, 99, 708–717.

    Article  Google Scholar 

  • Rengert, G., Ratcliffe, J. H., & Chakravorty, S. (2005). Policing illegal drug markets: Geographic approaches to crime reduction. Monsey: Criminal Justice Press.

    Google Scholar 

  • Rigg, N. R., Chou, C. P., & Pentz, M. A. (2009). Preventing growth in amphetamine use: Long-term effects of the Midwestern Prevention Project (MPP) from early adolescence to early adulthood. Addiction, 104, 1691–1699.

    Article  Google Scholar 

  • Rossi, C. (2001). A mover-stayer type model for epidemics of problematic drug use. In United Nations Office on Drugs and Crime (Ed.), Bulletin on narcotics, volume LIII: Dynamic drug policy – Understanding and controlling drug epidemics (pp. 39–64). New York: United Nations.

    Google Scholar 

  • Sanchez, F., Castillo-Chavez, C., Gorman, D., & Gruenewald, P. (2007). Drinking as an epidemic – A simple mathematical model with recovery and relapse. In K. Witkiewitz & G. A. Marlatt (Eds.), Therapist’s guide to evidence-based relapse prevention (pp. 354–368). New York: Elsevier.

    Google Scholar 

  • Scott, J. C., Woods, S. P., Matt, G. E., Meyer, R. A., Heaton, R. K., Atkinson, J. H., & Grant, I. (2007). Neurocognitive effects of methamphetamine: A critical review and meta-analysis. Neuropsychological Review, 17, 275–297.

    Article  Google Scholar 

  • Smith, D. L., Lucey, B., Waller, L. A., Childs, J. E., & Real, L. A. (2002). Predicting the spatial dynamics of rabies epidemics on heterogeneous landscapes. Proceedings of the National Academy of Sciences, 99, 3668–3672.

    Google Scholar 

  • Sommers, I., Baskin, D., & Baskin-Sommers, A. (2006). Methamphetamine use among young adults: Health and social consequences. Addictive Behaviors, 311, 1469–1476.

    Article  Google Scholar 

  • Song, B., Castillo-Garsow, M., Rios-Soto, K. R., Mejran, M., Henso, L., & Castillo-Chavez, C. (2006). Raves, clubs and ecstasy: The impact of peer pressure. Mathematical Biosciences and Engineering, 3(1), 1–18.

    Google Scholar 

  • Substance Abuse and Mental Health Services Administration, Office of Applied Studies. (2008). Treatment episode data set (TEDS): 1996–2006 (DHHS Publication No. (SMA) 08–4347). Rockville: National Admissions to Substance Abuse Treatment Service.

    Google Scholar 

  • Sulzer, D., Sonders, M. S., Poulsen, N. W., & Galli, A. (2005). Mechanisms of neurotransmitter release by amphetamines: A review. Progress Neurobiology, 75, 406–433.

    Article  Google Scholar 

  • Urbina, A., & Jones, K. (2004). Crystal methamphetamine, its analogues, and HIV infection: Medical and psychiatric aspects of a new epidemic. Clinical Infectious Diseases, 38, 890–894.

    Article  Google Scholar 

  • Viboud, C., Bjornstad, O. N., Smith, D. L., Simonsen, L., Miller, M. A., & Grenfell, B. T. (2006). Synchrony, waves, and spatial hierarchies in the spread of influenza. Science, 312, 447–451.

    Article  Google Scholar 

  • Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160, 509–516.

    Article  Google Scholar 

  • Weisburd, D., & Mazerolle, L. G. (2000). Crime and disorder in drug hot spots: Implications for theory and practice in policing. Police Quarterly, 3(3), 331–349.

    Article  Google Scholar 

  • Wheeler, D. C., & Waller, L. A. (2008). Mountains, valleys, and rivers: The transmission of raccoon rabies over a heterogeneous landscape. Journal of Agricultural, Biological and Environmental Statistics, 13, 1–19.

    Article  Google Scholar 

  • Wyatt, S. A., & Ziedonis, D. (1998). Psychological and psychiatric consequences of amphetamines. In R. E. Tarter, R. T. Ammerman, & P. J. Ott (Eds.), Handbook of substance abuse: Neurobehavioral pharmacology (pp. 529–544). New York: Plenum Press.

    Google Scholar 

  • Yacoubian, G. S. (2007). Assessing environmental prevention strategies for reducing the prevalence of associated harms of methamphetamine use. Journal of Drug Education, 37, 31–53.

    Article  Google Scholar 

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Authors and Affiliations

  1. Prevention Research Center, Pacific Institute for Research and Evaluation, 1995 University Ave., Suite 450, Berkeley, CA, USA

    Paul J. Gruenewald Ph.D., William R. Ponicki M.A., Lillian G. Remer M.A., G.I.S.P. & Fred W. Johnson Ph.D.

  2. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Lance A. Waller Ph.D.

  3. Department of Epidemiology & Biostatistics, Texas A&M Health Science Center School of Rural Public Health, College Station, TX, USA

    Dennis M. Gorman Ph.D.

  4. Statistics Methodology and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Li Zhu Ph.D.

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  1. Paul J. Gruenewald Ph.D.
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  2. William R. Ponicki M.A.
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  3. Lillian G. Remer M.A., G.I.S.P.
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  4. Fred W. Johnson Ph.D.
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  5. Lance A. Waller Ph.D.
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  6. Dennis M. Gorman Ph.D.
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  7. Li Zhu Ph.D.
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Correspondence to Paul J. Gruenewald Ph.D. .

Editor information

Editors and Affiliations

  1. School of Criminal Justice &, Criminalistics, California State University, Los Angeles, State University Drive 5151, Los Angeles, 90032, California, USA

    Bill Sanders

  2. , Howard University, Office of Regulatory Research Compliance, 7th Street 1840, Washington, 20001, District of Columbia, USA

    Yonette F. Thomas

  3. National Institute on Drug Abuse, Div. Epidemiology, Services &, National Institutes of Health, Executive Blvd. 6001, Bethesda, 20892-9589, Maryland, USA

    Bethany Griffin Deeds

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Gruenewald, P.J. et al. (2013). Spatial Models of the Growth and Spread of Methamphetamine Abuse in California. In: Sanders, B., Thomas, Y., Griffin Deeds, B. (eds) Crime, HIV and Health: Intersections of Criminal Justice and Public Health Concerns. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8921-2_9

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