The Drug Abuse Warning Network (DAWN) public-use data file includes information on one or more substances contributing to an Emergency Departments (ED) visit. In other words, some cases report only a single substance and other cases report multiple substances (i.e. cocaine, simvastatin, and Benadryl all present in the same ED visit). Beginning with the 2009 DAWN public-use file, information is included for up to 22 drugs reported in the ED visit; an increase from the 16 drug mentions available in previous years of DAWN. Within the DAWN data, there is currently no automated way to search for a specific drug name across all drug mentions (up to 22) for each case in order to produce the total number of ED cases involving a particular substance. For instance, a particular case reports only a single substance (i.e. codeine) that is provided in variable, DRUGID_1, with 11 for codeine’s code. Then the remaining variables, DRUGID_2 to DRUGID_22, will have missing codes. Suppose another case reports four substances (i.e. ibuprofen, simvastatin, codeine, and warfarin). These substances are provided in the first four variables, DRUGID_1 to DRUGID_4, with 14 in DRUGID_1 for ibuprofen, with 468 in DRUGID_2 for simvastatin, with 11 in DRUDID_3 for codeine, with 21 in DRUGID_4 for warfarin, and the remaining DRUGID variables having missing codes.
Determining the number of DAWN ED cases that involve a particular drug is important for many types of analyses and reports.
Some information may be obtained via the Excel files available on the SAMHSA website. These tables provide weighted national estimates for a particular drug or category. Tables that provide weighted estimates for some metropolitan areas are also available.
For users looking for a specific drug that is not included in the SAMHSA tables or for those interested in more detailed statistical analysis, we provide the following programming code to create a new variable that specifically answers the question, "Is Drug _______ present at the time of ED visit, Yes/No?". This FAQ provides sample code for the following five formats: online analysis system (SDA), SAS, SPSS, Stata, and R. Some knowledge of either SDA, SAS, SPSS, Stata, or R is required.
NOTE: The sample SDA, SAS, SPSS, STATA, and R code below use the DRUGID variables and the DRUGID value/code that corresponds with codeine in the 2009 DAWN for illustration purposes. When creating your own "Is substance _____ present in the persons system" variable, you will want to find and replace information via the following steps to obtain the drug information you are specifically seeking.
- Determine the year of DAWN data for which you wish to create the customized variable.
- Decide which substance name/categorization variable is best suited for the information you are seeking. Select one category from DRUGID, SDLED_1, SDLED_2, SDLED_3, SDLED_4, SDLED_5, or SDLED_6.
- In the PDF or HTML codebook, find the numeric value that corresponds to the drug name/category you are investigating.
- Decide on a name for your newly computed variable (it should not be a variable name that already exists in the original DAWN data file).
- Replace the variable name, the drug ID/category code, and the final new variable name information in the sample code with the information you identified in the previous steps.
Example 1: SDA
The SDA system can be used to obtain the number of cases in which a particular drug was reported. The first step is to use the PDF codebook appendix to look up the drug id number for the substance you are interested in. For this example we have selected codeine (drug id number = 12) as the drug of interest.
In SDA, select the "Compute a new variable" option from the "Create Variables" dropdown. See Exhibit 1, below.
Exhibit 1
Once you are in the "Compute a new variable" function, the field "Expression to Define the New Variable" is shown. See Exhibit 2, below.
Exhibit 2
The code to enter into the "Expression to Define the New Variable" field for this example is (it assumes codeine):
IF (DRUGID_1 eq 11 OR DRUGID_2 eq 11 OR DRUGID_3 eq 11 OR DRUGID_4 eq 11 OR DRUGID_5 eq 11 OR DRUGID_6 eq 11 OR DRUGID_7 eq 11 OR DRUGID_8 eq 11 OR DRUGID_9 eq 11 OR DRUGID_10 eq 11 OR DRUGID_11 eq 11 OR DRUGID_12 eq 11 OR DRUGID_13 eq 11 OR DRUGID_14 eq 11 OR DRUGID_15 eq 11 OR DRUGID_16 eq 11 OR DRUGID_17 eq 11 OR DRUGID_18 eq 11 OR DRUGID_19 eq 11 OR DRUGID_20 eq 11 OR DRUGID_21 eq 11 OR DRUGID_22 eq 11)
CODEINE = 1
ELSE
CODEINE = 0
It is very important to specify "Yes" for "Include missing-data values in the computation?". Otherwise, nearly all cases would be missing for the new computed variable due to the high number of missing cases that exist by the time you get to DRUGID_22.
In the SDA Compute Program, specifying a variable label, value labels, and descriptive text (question text) is optional. These options are useful to add if you intend to paste SDA output results into a document (note: all tables and graphs produced by SDA can be copied and pasted into Excel or Word).
As with all other functions in SDA, nearly every field and option has help documentation that can be selected by clicking on the field of interest.
Example 2: SAS
data work.tmp;
set 'drivename:\filepath\filename';
Codeine=0;
if (drugid_1= 11 or drugid_2= 11 or drugid_3= 11 or drugid_4= 11 or drugid_5= 11
or drugid_6= 11 or drugid_7= 11 or drugid_8= 11 or drugid_9= 11 or drugid_10= 11
or drugid_11= 11 or drugid_12= 11 or drugid_13= 11 or drugid_14= 11 or drugid_15= 11
or drugid_16= 11 or drugid_17= 11 or drugid_18= 11 or drugid_19= 11 or drugid_20= 11
or drugid_21= 11 or drugid_22= 11) then Codeine=1;
run;
Example 3: SPSS
compute Codeine=0.
if (drugid_1= 11 or drugid_2= 11 or drugid_3= 11 or drugid_4= 11 or drugid_5= 11 or drugid_6= 11
or drugid_7= 11 or drugid_8= 11 or drugid_9= 11 or drugid_10= 11 or drugid_11= 11 or drugid_12= 11
or drugid_13= 11 or drugid_14= 11 or drugid_15= 11 or drugid_16= 11 or drugid_17= 11 or drugid_18= 11
or drugid_19= 11 or drugid_20= 11 or drugid_21= 11 or drugid_22= 11)
Codeine=1.
Example 4: Stata
*drop Codeine
gen Codeine=0
replace Codeine=1 if drugid_1== 11 | drugid_2== 11 | drugid_3== 11 | drugid_4== 11 | drugid_5== 11 | drugid_6== 11 | drugid_7== 11 | drugid_8== 11 | drugid_9== 11 | drugid_10== 11 | drugid_11== 11 | drugid_12== 11 | drugid_13== 11 | drugid_14== 11 | drugid_15== 11 | drugid_16== 11 | drugid_17== 11 | drugid_18== 11 | drugid_19== 11 | drugid_20 == 11 | drugid_21== 11 | drugid_22== 11
Example 5: R
# get R format downloaded DAWN 2009 PUF file into R console
load("d:/~/Desktop/ICPSR_31921/DS0001/31921-0001-Data.rda")
# da31921.001.rda is created (by default) from above load command
dawn9 = da31921.0001 # make a copy of data file with a shorter name
rm(da31921.0001) # removing the duplicate data
codeine01 = as.numeric(as.numeric(dawn9$DRUGID_1)==11)
codeine01[is.na(codeine01)] <- 0 # replacing NA by 0
codeine02 = as.numeric(as.numeric(dawn9$DRUGID_2)==11)
codeine02[is.na(codeine02)] <- 0
codeine03 = as.numeric(as.numeric(dawn9$DRUGID_3)==11)
codeine03[is.na(codeine03)] <- 0
codeine04 = as.numeric(as.numeric(dawn9$DRUGID_4)==11)
codeine04[is.na(codeine04)] <- 0
codeine05 = as.numeric(as.numeric(dawn9$DRUGID_5)==11)
codeine05[is.na(codeine05)] <- 0
codeine06 = as.numeric(as.numeric(dawn9$DRUGID_6)==11)
codeine06[is.na(codeine06)] <- 0
codeine07 = as.numeric(as.numeric(dawn9$DRUGID_7)==11)
codeine07[is.na(codeine07)] <- 0
codeine08 = as.numeric(as.numeric(dawn9$DRUGID_8)==11)
codeine08[is.na(codeine08)] <- 0
codeine09 = as.numeric(as.numeric(dawn9$DRUGID_9)==11)
codeine09[is.na(codeine09)] <- 0
codeine10 = as.numeric(as.numeric(dawn9$DRUGID_10)==11)
codeine10[is.na(codeine10)] <- 0
codeine11 = as.numeric(as.numeric(dawn9$DRUGID_11)==11)
codeine11[is.na(codeine11)] <- 0
codeine12 = as.numeric(as.numeric(dawn9$DRUGID_12)==11)
codeine12[is.na(codeine12)] <- 0
codeine13 = as.numeric(as.numeric(dawn9$DRUGID_13)==11)
codeine13[is.na(codeine13)] <- 0
codeine14 = as.numeric(as.numeric(dawn9$DRUGID_14)==11)
codeine14[is.na(codeine14)] <- 0
codeine15 = as.numeric(as.numeric(dawn9$DRUGID_15)==11)
codeine15[is.na(codeine15)] <- 0
codeine16 = as.numeric(as.numeric(dawn9$DRUGID_16)==11)
codeine16[is.na(codeine16)] <- 0
codeine17 = as.numeric(as.numeric(dawn9$DRUGID_17)==11)
codeine17[is.na(codeine17)] <- 0
codeine18 = as.numeric(as.numeric(dawn9$DRUGID_18)==11)
codeine18[is.na(codeine18)] <- 0
codeine19 = as.numeric(as.numeric(dawn9$DRUGID_19)==11)
codeine19[is.na(codeine19)] <- 0
codeine20 = as.numeric(as.numeric(dawn9$DRUGID_20)==11)
codeine20[is.na(codeine20)] <- 0
codeine21 = as.numeric(as.numeric(dawn9$DRUGID_21)==11)
codeine21[is.na(codeine21)] <- 0
codeine22 = as.numeric(as.numeric(dawn9$DRUGID_22)==11)
codeine22[is.na(codeine22)] <- 0
codeine = codeine01 + codeine02 + codeine03 + codeine04 + codeine05 + codeine06 + codeine07 + codeine08 + codeine09 + codeine10 + codeine11 + codeine12 + codeine13 + codeine14 + codeine15 + codeine16 + codeine17 + codeine18 + codeine19 + codeine20 + codeine21 + codeine22
rm(codeine01, codeine02, codeine03, codeine04, codeine05, codeine06, codeine07, codeine08, codeine09, codeine10, codeine11, codeine12, codeine13, codeine14, codeine15, codeine16, codeine17, codeine18, codeine19, codeine20, codeine21, codeine22)
table(codeine) # to display the counts
