This national study analyzes county-level risk factors for methamphetamine manufacture. Neural network and probit models are used to test the effectiveness of county-level characteristics in predicting methamphetamine production levels. Data on all 3,143 counties are drawn from the U.S. DEA’s Clandestine Laboratory Surveillance System, the 2000 U.S. Census and health service resources from the 2004 Area Resource File, and the Uniform Crime Reporting Program (UCRP) for the period 2002-2005. The resulting model accurately predicted methamphetamine production levels 85% of the time. The leading variables were existing methamphetamine problems, seizures in contiguous counties, families with “female head of household,” home value, and “percentage of White population.” Several variables that factored heavily in earlier single-community studies had very little impact in this national study. This study’s results suggest a new approach to assessing community vulnerability to drug manufacturer and a need to refocus efforts in fighting the problem.
AlkanA.KoklukayaE.SubasiA. (2005). Automatic seizure detection in EEG using logistic regression and artificial neural network. Journal of Neuroscience Methods, 148, 167-176.
2.
Area Resource File: U.S. Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Professions (2006). Area Resource File [CD-ROM]. Fairfax, VA: Quality Resource Systems, Inc. [Producer and Distributor], 2006.
3.
ArthurM.HawkinsJ.PollardJ.CatalanoR.BaglioniA. (2002). Measuring risk and protective factors for substance abuse, delinquency, and other adolescent problem behaviors: The communities that care survey. Evaluation Review, 26, 575-601.
4.
BaxtW. G.ShoferF. S.SitesF. D.HollanderJ. E. (2002). A neural computational aid to the diagnosis of acute myocardial infarction. Annals of Emergency Medicine, 39, 366-373.
5.
ChiuR. K.KuoC. W. (2006). A decision support model of demand forecast for national immunisation vaccines. International Journal of Electronic Healthcare, 2, 92-104.
6.
CunninghamJ. K.LiuL.-M. (2003). Impacts of federal ephedrine and pseudoephedrine regulations on methamphetamine-related hospital admissions. Addiction, 98, 1229-1237.
7.
CunninghamJ. K.LiuL.-M. (2008). Impact of methamphetamine precursor chemical legislation, a suppression policy, on the demand for drug treatment. Social Science & Medicine, 66, 1463-1473.
8.
DalmadgeC.CainM. (2008). Utilizing neural networks to assess community-level vulnerability to the methamphetamine epidemic. Issues in Information Systems, 9, 258-266.
9.
DasA.Ben-MenachemT.CooperG. S.ChakA.SivakM. V.Jr.GonetJ. A.WongR. C. (2003). Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: Internal and external validation of a predictive model. The Lancet, 362, 1261-1266.
10.
EckJ. (1995). A general model of the geography of illicit retail marketplaces. In EckJ.WeisburdD. (Eds.), Crime and place (Vol. 4), 67-93. Monsey, NY: Criminal Justice Press.
11.
EsbensenF.HuizingaD. (1990). Community structure and drug use: From a social disorganization perspective. Justice Quarterly, 7, 691-709.
12.
FlahertyC. W.PattersonD. A. (2003). Predicting child physical abuse recurrence: Comparison of a neural network to logistic regression. Journal of Technology in Human Services, 21, 93-111.
13.
GismondiR. C.AlmeidaR. M. V. R.InfantosiA. F. C. (2002). Artificial neural networks for infant mortality modelling. Computer Methods and Programs in Biomedicine, 69, 237-247.
14.
GossE. P.VozikisG. S. (2002). Improving health care organizational management through neural network learning. Health Care Management Science, 5, 221-227.
15.
HerzD. (2000). Drugs in the heartland: Methamphetamine use in rural Nebraska. Washington, DC: National Institute of Justice.
16.
HirshbergA.AdarR. (1997). Artificial neural networks in medicine. Israel Journal of Medical Sciences, 33, 700-702.
17.
JamalA. M. M.SundarC. (1998). Modeling exchange rates with neural networks. Journal of Applied Business Research, 14, 1-6.
18.
JohnstonL. D.O’MalleyP.BachmanJ.SchulenbergJ. (2010). Monitoring the future, national survey results on drug use 1975–2009. U.S. Department of Health and Human Services, National Institutes of Health: NIH Bethesda, Maryland, Publication.
19.
JoonS.JohnsonB. (2001). Neighborhood disorder, individual religiosity, and adolescent use of illicit drugs: A test of multilevel hypothesis. Criminology, 39, 109-143.
20.
KaurH.WasanS. K. (2006). Empirical study on applications of data mining techniques in healthcare. Journal of Computer Science, 2, 194-200.
21.
LisboaP. J.TaktakA. F. (2006). The use of artificial neural networks in decision support in cancer: A systematic review. Neural Networks, 19, 408-415.
22.
LivingstoneD. J.ManallackD. T.TetkoI. V. (1997). Data modelling with neural networks: Advantages and limitations. Journal of Computer-Aided Molecular Design, 11, 135-142.
23.
LuaC.LeebT.LiancC. (2012). Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks. Decision Support Systems, 54, 584-596.
24.
McDonaldT. P.PoertnerJ.HarrisG. (2002). Predicting placement in foster care: A comparison of logistic regression and neural network analysis. Journal of Social Service Research, 28, 1-20.
25.
OettingE. R.DeffenbacherJ. L.TaylorM. J.LutherN.BeauvaisF.EdwardsR. W. (2000). Methamphetamine use by high school students: Recent trends, gender and ethnicity differences, and use of other drugs. Journal of Child & Adolescent Substance Abuse, 10, 33-50.
26.
ParryM. E.CaoQ.SongM. (2011). Forecasting new product adoption with probabilistic neural networks. Journal of Product Innovation Management, 28, 78-88.
27.
PennellS.EllettJ.RienickC.GrimesJ. (1999). Meth matters: Report on methamphetamine users in five western cities. Washington, DC: National Institute of Justice.
28.
RodriguezN.KatzC.WebbV.SchaeferD. (2005). Examining the impact of individual, community, and market factors on methamphetamine use: A tale of two cities. The Journal of Drug Issues, 35, 656-693.
29.
SeomunG. A.ChangS. O.LeeS. J.KimI. A.ParkS. A. (2005). A prediction model for patient classification according to nursing need: Using data mining techniques. Studies in Health Technology and Informatics, 122, 899.
30.
SimonS.RichardsonK.DaceyJ.GlynnS.DomierC.RawsonR.LingW. (2001). A comparison of patterns of methamphetamine and cocaine use. Journal of Addictive Diseases, 21, 35-44.
31.
Simoni-WastilaL. (2004). Gender and other factors associated with the non-medical use of abusable prescription drugs. Substance Use & Misuse, 39, 1-23.
32.
Simoni-WastilaL.StricklerG. (2004). Risk factors associated with problem use of prescription drugs. American Journal of Public Health, 94, 266-268.
TuJ. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49, 1225-1231.
36.
U.S. Drug Enforcement Administration. (2006). State factsheets. Washington, DC: Department of Justice.
37.
WeisheitR. A.WellsL. E. (2010). Methamphetamine laboratories: The geography of drug production. Western Criminology Review, 11(2), 9-26.
38.
WellsL. E.WeisheitR. A. (2014). A comparison of criminological and public health models: Geographic and social diversity associated with methamphetamine laboratory seizures. American Journal of Criminal Justice, 39, 1-21.
39.
WilsonR.ShardaR. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11, 545-557.
