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Training and Technical Assistance Webinar Series

Use of Administrative Records to Analyze Drug Abuse and Enforcement

May 21, 2015

Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Training and Technical Assistance Webinar Series

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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Training and Technical Assistance Webinar Series

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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Training and Technical Assistance Webinar Series

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Justice Research and Statistics Association 720 7th Street, NW, Third Floor Washington, DC 20001 Administrative Data: Challenges and Techniques For Use to Assess Drug Crime

PREPARED BY SAMUEL GONZALES OPERATIONS ANALYST WITH THE STATISTICAL ANALYSIS CENTER AT THE CRIMINAL JUSTICE COORDINATING COUNCIL Administrative Data: Challenges and Techniques

Goals •To introduce participants to 3 administrative data sets used for the Georgia Statewide Drug Enforcement Strategy •Highlight challenges in our analysis and the technical solutions we use to overcome those challenges •Provide insight for the need for data surveillance •Spark a conversation on how to tackle administrative data

Administrative Data: Challenges and Techniques

Data Sets The Georgia Department of Corrections Administrative Data • We focused on Intake data collected by the Georgia Department of Corrections from 2009 to 2013. Drug Overdose Deaths Data • The drug overdose data was collected from the Medical Examiner’s Offices at the Georgia Bureau of Investigation and Cobb, DeKalb, Fulton and Gwinnett counties from 2010 to 2013. • The data does not include all individuals from County Coroner’s Offices. Only those deaths referred to the Medical Examiner’s Offices. • The data did not include toxicity levels, so if multiple drugs were identified, we cannot attribute death to one drug. Treatment Episode Data Set • The data is collected by the Georgia Department of Behavioral Health and Developmental Disabilities (DBHDD) for the Substance Abuse and Mental Health Services Administration (SAMHSA). • The data is for only criminal justice initiated treatment episodes

Georgia Department of Corrections Data

•Had a variable that indicated Drug Possession or Drug Sales •Had another variable that better described the crime and included the drug •Problem was that we wanted to do a cross-tabulation of drug by crime type but the information was in the same variable. Georgia Department of Corrections Data

•Solved by Cleaning the data in Excel •Filtered to see common phrases •Searched and removed unwanted words like “of” •Searched the common phrases and replaced them with an added comma •Then text to columns and we have separated the drug from primary offense

Georgia Department of Corrections Data

Drug by Primary Offense from 2009-2013 POSSESSION WITH INTENT SALE AND Drug Type MANUFACTURE POSSESSION TRAFFICING OTHER Totals TO DISTRIBUTION DISTRIBUTE 0 3964 1615 2753 1004 0 9336

MARIJUANA 0 449 2909 1349 258 0 4965 METH - 666 1922 935 473 675 0 4671 0 305 0 99 45 0 449 MDMA/ 0 68 0 23 70 0 161 EXTACY PARAPHENALIA 0 56 0 0 0 0 56

EPHEDRINE 0 36 0 0 0 0 36

AMPHETAMINE 0 0 0 0 14 0 14

LSD 0 4 0 1 0 0 5 COUTERFIT 0 0 0 1 0 0 1 DRUGS OTHER 0 0 362 0 0 0 362

UNKNOWN 81 380 0 562 75 1567 2665

Totals 747 7184 5821 5261 2141 1567 22721 Overdose Deaths Data

• We wanted to get an idea of the drugs used most in combination Overdose Deaths Data

Top 20 Drugs Found in Toxicology Reports from 2010-2013

1400

1168 1200

1000 962

800 752 673 623 600 513

388 400 312 307 274 259 200 193 190 186 168 200 155 155 148 145

0 Overdose Deaths Data

•Needed to convert all drug names, which were “string” variables, to numeric variables •The catch was that you have to recode all drug variables the same and not use auto recode so you can do a multiple response set analysis in SPSS •Once we had each individual drug found in toxicology reports as separate variables with associated analysis values, we could run a frequency using the multiple response commands in SPSS •Separating each drug into its own variable also allowed us to determine which drugs were most frequently found in combination

Overdose Deaths Data Overdose Deaths Data

Syntax for Recoding a Drug RECODE DrugA ('1,1-Difluoroethane'=1) ('1,3-Dimethylamylamine'=2) ('25I-NBOMe'=3) ('3,4-Methylenedioxyamphetamine'=4) ('10-Monoacetyl '=5) ('7-Amino'=10) ('Acetaminophen'=7) ('Adderall'=8) ('Alpha-Hydroxyalprazolam'=9) ('Alprazolam'=10) (''=11) ('Amlodipine'=12) ('Amoxatine'=13) ('Amphetamine'=14) ('Anaphylaxis'=15) (''=16) ('Aspirin'=17) (''=18) ('Baclofen'=19) (''=20) ('Benzodiazepine'=21) ('Benzonatate'=22) ('Benzoylecgonine'=23) ('Benztropine'=24) (''=25) ('Bupivacaine'=26) (''=27) (''=28) (''=29) ('Butalbital'=30) ('Butalnotal'=31) ('Caffeine'=32) ('Carbamazepine'=33) ('Carboxyhemoglobin'=34) (''=35) ('Chloral Hydrate'=36) ('Chlorazepine'=37) ('Chlordiazepoxide'=38) ('Chlorethan'=39) ('Chloroquine'=40) ('Chlorpheniramine'=41) ('Chlorpromazine'=42) (''=43) (''=44) ('Clonazepam'=45) ('Clonidine'=46) ('Clozapine'=47) ('Cocaethylene'=48) ('Cocaine'=49) (''=50) (''=51) ('Cyclobenzaprine'=52) (''=53) ('Desmethldoxepin'=54) ('Destromethorphan'=55) (''=56) ('Detromethophan'=57) (''=58) ('Diazepam'=59) ('Difluoroethane'=60) ('Diltiazem'=61) (''=62) ('Donepezil'=63) (''=64) (''=65) (''=66) ('Ecstasy'=67) ('EDDP'=68) ('Ephedrine'=69) ('Estazolam'=70) (' Glycol'=71) (''=72) ('Flecainide'=73) (''=74) ('Fluphenazine'=75) (''=76) ('Gabapentin'=77) ('GHB'=78) ('Guetiapine'=79) (''=80) ('Helium'=81) (''=82) (''=83) (''=84) ('Hydroxychloroquine'=85) ('Hydroxyzine'=86) (''=87) ('Insulin'=88) ('Isobutyl Nitrite'=89) ('Isopropanol'=90) (''=91) ('Kratom'=92) ('Lamotrigine'=93) ('Levetiracetam'=94) ('Lidocaine'=95) ('Lidoderm'=96) (''=97) ('Lorazepam'=98) ('Loxapine'=99) (''=100) ('Meperidine'=101) (''=102) ('Mesoridazine'=103) ('Metabolite'=104) ('Metaclopramide'=105) ('Metaxalone'=106) ('Metclopramide'=107) (''=108) (''=109) ('Methocarbamol'=110) ('Methodone'=111) (''=112) ('Methotrimeprazine'=113) ('Methylone'=114) ('Metoclopramide'=115) ('Metoprolol'=116) ('Midazolam'=117) (''=118) ('Morphine'=119) ('Multiple Drug'=120) (''=121) (''=122) ('Nifedipine'=123) ('Nonvenlafaxine'=124) (''=125) ('Nordiazepam'=126) ('Nordiazpam'=127)('Norfentanyl'=128) ('Norketamine'=129) ('Normeperidine'=130) (''=131) (''=132) ('Norvenlafaxine'=133) (''=134) (''=135) (''=135) (''=137) ('Oxazepam'=138) (''=139) (''=140) (''=141) ('Perphenazine'=142) ('Phenazepam'=143) (''=144) ('Phentermine'=145) ('Phentobarbital'=146) ('Phenytoin'=147) ('Piroxicam'=148) (''=149) (''=150) ('Propoxyphene'=151) ('Propranolol'=152) ('Pseudoephedrine'=153) (''=154) ('Ranitidine'=155) (''=156) (''=157) ('Salicylate'=158) (''=159) (''=160) ('Synthetic '=161) ('Synthetic '=162) ('Tapentado'=163) (''=164) ('Temazepam'=165) ('THC Metabolite'=166) ('Theobromine'=167) ('Thiordazine'=168) (''=169) (''=170) (''=171) ('Triazolam'=172) (''=173) ('Unknown'=174) ('Valproic acid'=175) (''=176) ('Verapamil'=177) ('Vicodin'=178) ('Warfarin'=179) (''=180) ('Ziprasidone'=181) (''=182) (''=183) INTO DrugA_Recode. VARIABLE LABELS DrugA_Recode 'DrugA_Recode'. EXECUTE. Overdose Deaths Data

Syntax for Labeling a Variable

VALUE LABELS DrugA_Recode 1'1,1-Difluoroethane' 2'1,3-Dimethylamylamine' 3'25I-NBOMe' 4'3,4-Methylenedioxyamphetamine' 5'10-Monoacetyl Morphine' 6'7-Amino' 7'Acetaminophen' 8'Adderall' 9'Alpha-Hydroxyalprazolam' 10'Alprazolam' 11'Amitriptyline' 12'Amlodipine' 13'Amoxatine' 14'Amphetamine' 15'Anaphylaxis' 16'Aripiprazole' 17'Aspirin' 18'Atomoxetine' 19'Baclofen' 20'Barbiturate' 21'Benzodiazepine' 22'Benzonatate' 23'Benzoylecgonine' 24'Benztropine' 25'Brompheniramine' 26'Bupivacaine' 27'Buprenorphine' 28'Bupropion' 29'Buspirone' 30'Butalbital' 31'Butalnotal' 32'Caffeine' 33'Carbamazepine' 34'Carboxyhemoglobin' 35'Carisoprodol' 36'Chloral Hydrate' 37'Chlorazepine' 38'Chlordiazepoxide' 39'Chlorethan' 40'Chloroquine' 41'Chlorpheniramine' 42'Chlorpromazine' 43'Citalopram' 44'Clomipramine' 45'Clonazepam' 46'Clonidine' 47'Clozapine' 48'Cocaethylene' 49'Cocaine' 50'Codeine' 51'Cotinine' 52'Cyclobenzaprine' 53'Desipramine' 54'Desmethldoxepin' 55'Destromethorphan' 56'Desvenlafaxine' 57'Detromethophan' 58'Dextromethorphan' 59'Diazepam' 60'Difluoroethane' 61'Diltiazem' 62'Diphenhydramine' 63'Donepezil' 64'Doxepin' 65'Doxylamine' 66'Duloxetine' 67'Ecstasy' 68'EDDP' 69'Ephedrine' 70'Estazolam' 71'Ethylene Glycol' 72'Fentanyl' 73'Flecainide' 74'Fluoxetine' 75'Fluphenazine' 76'Fluvoxamine' 77'Gabapentin' 78'GHB' 79'Guetiapine' 80'Haloperidol' 81'Helium' 82'Heroin' 83'Hydrocodone' 84'Hydromorphone' 85'Hydroxychloroquine' 86'Hydroxyzine' 87'Imipramine' 88'Insulin' 89'Isobutyl Nitrite' 90'Isopropanol' 91'Ketamine' 92'Kratom' 93'Lamotrigine' 94'Levetiracetam' 95'Lidocaine' 96'Lidoderm' 97'Lithium' 98'Lorazepam' 99'Loxapine' 100'Meclizine' 101'Meperidine' 102'Meprobamate' 103'Mesoridazine' 104'Metabolite' 105'Metaclopramide' 106'Metaxalone' 107'Metclopramide' 108'Methadone' 109'Methamphetamine' 110'Methocarbamol' 111'Methodone' 112'Methorphan' 113'Methotrimeprazine' 114'Methylone' 115'Metoclopramide' 116'Metoprolol' 117'Midazolam' 118'Mirtazapine' 119'Morphine' 120'Multiple Drug' 121'Naloxone' 122'Nicotine' 123'Nifedipine' 124'Nonvenlafaxine' 125'Norbuprenorphine' 1210'Nordiazepam' 127'Nordiazpam' 128'Norfentanyl' 129'Norketamine' 130'Normeperidine' 131'Norpropoxyphene' 132'Nortriptyline' 133'Norvenlafaxine' 134'Olanzapine' 135'Opiate' 1310'Opiates' 137'Orphenadrine' 138'Oxazepam' 139'Oxycodone' 140'Oxymorphone' 141'Paroxetine' 142'Perphenazine' 143'Phenazepam' 144'Phenobarbital' 145'Phentermine' 146'Phentobarbital' 147'Phenytoin' 148'Piroxicam' 149'Promethazine' 150'Propofol' 151'Propoxyphene' 152'Propranolol' 153'Pseudoephedrine' 154'Quetiapine' 155'Ranitidine' 156'Risperidone' 157'Rocuronium Bromide' 158'Salicylate' 159'Scopolamine' 160'Sertraline' 161'Synthetic Cannabinoid' 162'' 163'Tapentado' 164'Tapentadol' 165'Temazepam' 166'THC Metabolite' 167'Theobromine' 168'Thiordazine' 169'Topiramate' 170'Tramadol' 171'Trazodone' 172'Triazolam' 173'Trihexyphenidyl' 174'Unknown' 175'Valproic acid' 177Venlafaxine' 177'Verapamil' 178'Vicodin' 179'Warfarin' 180'Zaleplon' 181'Ziprasidone' 182'Zolpidem' 183'Zopiclone'. EXECUTE.

Overdose Deaths Data

Syntax for Data Selection by Drug

10=Alprazolam

*Most Frequent Drugs used with Alprazolam #1

USE ALL.

COMPUTE filter_$=((DrugA_Recode=10 or DrugB_Recode=10 or DrugC_Recode=10 or DrugD_Recode=10 or DrugE_Recode=10 or DrugF_Recode=10 or DrugG_Recode=10 or DrugH_Recode=10 or DrugI_Recode=10 or DrugJ_Recode=10 or DrugK_Recode=10 or DrugL_Recode=10) and (Total_Drugs >= 2)).

VARIABLE LABELS filter_$ 'DrugA_Recode=10 or DrugB_Recode=10 or DrugC_Recode=10 or DrugD_Recode=10 ‘+ 'or DrugE_Recode=10 or DrugF_Recode=10 or DrugG_Recode=10 or DrugH_Recode=10 or DrugI_Recode=10 or 'DrugJ_Recode=10 or DrugK_Recode=10 or DrugL_Recode=10 and Total_Drugs >= 2 (FILTER)'.

VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.

FORMATS filter_$ (f1.0).

FILTER BY filter_$.

EXECUTE.

MULT RESPONSE GROUPS=$Total_Drug_Frequency 'MR Variable for Total Drug Frequency' (druga_recode drugb_recode drugcrecode drugd_recode druge_recode drugf_recode drugg_recode drugh_recode drugi_recode drugj_recode drugk_recode drugl_recode (1,183))

/FREQUENCIES=$Total_Drug_Frequency. Overdose Deaths Data

Drug with 3 Most Used Combination

Total % Drug Total 1st Combo 2nd Combo 3rd Combo Combo Combo Alprazolam Oxycodone Methadone Hydrocodone 1168 1143 98% (Benzodiazepine) (466) (308) (304) Oxycodone (Semi- Alprazolam Hydrocodone Methadone 962 823 86% synthetic ) (466) (158) (111)

Hydrocodone (Semi- Alprazolam Oxycodone Methadone 673 599 89% synthetic Opioid) (304) (158) (90)

Methadone Alprazolam Oxycodone Hydrocodone 752 532 71% (Synthetic Opioid) (308) (111) (90) Morphine Alprazolam Oxycodone Hydrocodone 513 401 78% (Opioid) (162) (85) (76) Alprazolam Oxycodone Morphine Cocaine 623 307 49% (91) (75) (55) Diphenhydramine Alprazolam Oxycodone Hydrocodone 307 280 91% (Antihistamine) (102) (84) (72)

Citalopram Alprazolam Oxycodone Hydrocodone 274 259 95% () (108) (83) (69) Diazepam Oxycodone Alprazolam Hydrocodone 259 259 100% (Benzodiazepine) (95) (92) (71) Fentanyl Alprazolam Oxycodone Hydrocodone 312 235 75% (Synthetic Opioid) (87) (65) (50)

Cocaine Alprazolam Methamphetam Heroin (Opioid) 142 67 47% (34) (18) ine (10)

Alprazolam Oxycodone Amphetamine Methamphetamine 388 199 51% (69) (46) (41) Treatment Episode Data Set

•The data was at the individual level and each person was given a unique identifier •This allowed us to analyze multiple treatment episodes for an individual •The problem was that each individual was added separately (multiple rows or separate cases) instead of each treatment episode recorded in separate a variable •We solved this problem by restructuring the data set from “Cases to Variables” Treatment Episode Data Set

Syntax for Cases to Variables SORT CASES BY list variables and include a space between them. These will be the ID variable/s or variable/s used for the match. CASESTOVARS /ID = list variables and include a space between them. These are your matching variable/s. /AUTOFIX=YES/NO /COUNT= Name variable to include a count of the cases. EXECUTE.

Subcommand Definitions • ID subcommand specifies variables that identify the rows from the original data that should be grouped together in the new data file • INDEX subcommand names the variables in the original data that should be used to create the new columns. INDEX variables are also used to name the new columns • FIXED subcommand names the variables that should be copied from the original data that do not vary within row groups in the original data. • AUTOFIX subcommand evaluates candidate variables and classifies them as either fixed or as the source of a variable group. Will override the FIXED command. • Label AUTOFIX as YES and it will evaluate all candidate variables and classifies them as variable or fixed and NO will evaluate all candidate variables and compare to the FIXED command and if there are differences it will issue a warning • DROP subcommand specifies the subset of variables to exclude from the new data file. • COUNT subcommand creates a new variable that contains the number of rows in the original data that were used to generate the row in the new data file. *There are more subcommands in the SPSS Help menu if you have more needs than what are listed above

Treatment Episode Data Set

Treatment Episodes by Drug When the Initial Treatment was for Heroin

Treatment 1 Treatment 2 Treatment 3 Treatment 4 Treatment 5 Treatment 6 Heroin 100.00% 63.33% 80.00% 0.00% 0.00% 0.00% Marijuana/Hashish - 3.33% 0.00% 0.00% 0.00% 0.00% - 10.00% 20.00% 0.00% 0.00% 0.00% Cocaine/Crack - 3.33% 0.00% 0.00% 0.00% 0.00% Methamphetamine - 6.66% 0.00% 0.00% 0.00% 0.00% Other Opiates and - 10.00% 0.00% 0.00% 0.00% 0.00% Synthetics Benzodiazepines - 3.33% 0.00% 0.00% 0.00% 0.00% Total of Individuals 186 30 5 0 0 0 Treated (n)

% Receiving Additional - 16.13% 16.67% 0.00% 0.00% 0.00% Treatment

% Of Total Receiving - 16.13% 2.69% 0.00% 0.00% 0.00% Additional Treatment Treatment Episode Data Set

Treatment Episodes by Drug When the Initial Treatment was for Opiates or Synthetics

Treatment 1 Treatment 2 Treatment 3 Treatment 4 Treatment 5 Treatment 6

Other Opiates and Synthetics 100.00% 59.52% 50.00% 50.00% 0.00% 0.00% Marijuana/Hashish - 5.55% 5.55% 0.00% 0.00% 0.00% Alcohol - 11.11% 22.22% 0.00% 0.00% 0.00% Cocaine/Crack - 0.79% 0.00% 0.00% 0.00% 0.00% Methamphetamine - 2.38% 0.00% 0.00% 0.00% 0.00% Benzodiazepines - 7.14% 0.00% 0.00% 0.00% 0.00% Heroin - 3.97% 5.55% 0.00% 0.00% 0.00%

Total of Individuals Treated (n) 951 126 18 2 0 0

% Receiving Additional - 13.25% 14.29% 11.11% 0.00% 0.00% Treatment

% Of Total Receiving - 13.25% 1.89% 0.21% 0.00% 0.00% Additional Treatment Administrative Data: Challenges and Techniques

Conclusion •Sometimes administrative data sets need a lot of work to pull out important information, but they can be great resources •Data mining and trend analysis can help identify problems, anticipate future needs or help quantify what people say in the field •Using many different data sets can help paint a broad picture and show how one issue can effect different areas Administrative Data: Challenges and Techniques

Questions

Samuel Gonzales, Operations Analyst [email protected] Let us know if you want any of our syntax files!