Dataset DOI: 10.5061/dryad.np5hqc05n

Description of the data and file structure

Uploaded files are a supplement to Government assistance and Medicaid: the relationship with drug treatment and medication for opioid use disorder among women in the United States

‘Rmd’ files refer to R Markdown documents which knit to HTML output.

‘R’ files can be used in the R Linux interface or in R Studio or a similar code editor. 

‘Rds’ files are data files that we use for our intermediate or ‘working’ dataset.

‘Rdata’ file is data provided by SAMHSA for the full dataset.  

Individual File Descriptions

• NSDUH21_22_working.rds - This is the working dataset for the paper. We pared down the variables in the public use NSDUH dataset from over 2000 variables down to 44. There are 56069 observations. If users want to review the entire, unedited, NSDUH data in its various formats, they can access the data at the link included at the end of this README.  Each variable is described below. 

  ·       ANALWT2_C: Survey weight for person-level estimates.

  ·       verep: Survey variance estimation replicate.

  ·       VESTR_C: Survey variance estimation stratum.

  ·       year: Survey year.

  ·       irsex: Respondent’s sex/gender. categorical.

  ·       NEWRACE2:  race/ethnicity. categorical.

  ·       AGE3: Age group. categorical.

  ·       IRHHSIZ2: Household size. categorical.

  ·       eduhighcat: Highest level of education completed. categorical.

  ·       IRWRKSTAT18: Employment status. categorical

  ·       IRPINC3: Personal income category.

  ·       IRFAMIN3: Family income category.

  ·       irmarit: Marital status. categorical.

  ·       COUTYP4: County type (e.g., urban/rural classification). categorical.

  ·       IRKI17_2: Number of children under 17 years old. categorical.

  ·       irmedicr: Covered by Medicare. Binary.

  ·       irmcdchp: Covered by Medicaid or CHIP. categorical.

  ·       irchmpus: Covered by CHAMPUS, TRICARE, or VA. categorical.

  ·       irprvhlt: Covered by private health insurance. categorical.

  ·       irfstamp: Received food stamps. Binary

  ·       irothhlt: Covered by other health insurance. Binary

  ·       IRINSUR4: Health insurance coverage status. categorical.

  ·       irfampmt: Family received public assistance payments. Binary.

  ·       irfamssi: Family received Supplemental Security Income (SSI). Binary.

  ·       irfamsvc: Family received other welfare services. Binary

  ·       govtprog: Participation in government assistance programs. Binary.

  ·       POVERTY3: Poverty level category.

  ·       txyremril: Received treatment for illicit drugs in emergency room in past year. Binary.

  ·       txyrresil: Received treatment inpatient for illicit drugs in the past year. Binary.

  ·       txyroutil: Received treatment outpatient for illicit drugs in the past year. Binary.

  ·       irherrc: Received treatment for heroin use in the past year. Binary.

  ·       irpnranyrec: Received treatment for prescription pain reliever misuse in the past year. Binary.

  ·       iroxcnanyyr: Used oxycodone products in the past year. Imputation revised. Binary.

  ·       oxycnanyyr: Used any oxycodone products in the past year. Binary.

  ·       irpnrnmrec: Received treatment for prescription pain reliever misuse in the past 30 days. Binary.

  ·       heryr: Used heroin in the past year. Binary.

  ·       irherfm: Frequency of heroin use in the past year. Categorical.

  ·       IRPNRNM30FQ: Frequency of prescription pain reliever misuse in the past 30 days. Categorical.

  ·       rxbuprany: Used buprenorphine in the past year. Categorical.

  ·       svyropiany: Opioid use disorder severity. Categorical.

  ·       OPMATYR2: Ever received treatment for substance use (old variable).  Binary

  ·       UD5OPIANY: Opioid use disorder. Binary.

  ·       TXEVRRCVD2: Received treatment for substance use – past year.  Binary.

  ·       unique_id: Unique identifier for each respondent.

• e_value_v2.R - Code here can be used to recreate the tables contained in the supplement to the publication. Individual estimate values and their corresponding e-values can be found by executing this code, allowing anyone reviewing this repository to evaluate the conclusions that were drawn in the paper. 
• model_gov_assistance_ADJUSTED_age_stratified.Rmd - This markdown file lays out the steps, and provides the code needed to identify the effect of government assistance (yes/no) on substance use outcomes including medication for opioid use treatment (MOUD) and drug treatment. The models are for both unadjusted and adjusted versions of the analysis. All analysis incorporate NSDUH’s survey weights which include weights, strata, and cluster (PSUs). 
• models_edited_adjustedments_GOVCOUNT.Rmd -  This markdown file lays out the steps, and provides the code needed to identify the effect of government assistance (categorical - 0,1,2+) on substance use outcomes including medication for opioid use treatment (MOUD) and drug treatment. The models are for both unadjusted and adjusted versions of the analysis. All analysis incorporate NSDUH’s survey weights which include weights, strata, and cluster (PSUs). 
• models_edited_adjustedments_MEDGOV_ref_medicaid_only.Rmd - This markdown file lays out the steps, and provides the code needed to identify the effect of government assistance (categorical - none, medicaid only, government assistance only, both) on substance use outcomes including medication for opioid use treatment (MOUD) and drug treatment. The models are for both unadjusted and adjusted versions of the analysis. All analysis incorporate NSDUH’s survey weights which include weights, strata, and cluster (PSUs). Note that the reference on these models is the ‘medicaid only’ group. 
• models_edited_adjustedments_MEDGOV_ref_none.Rmd - This markdown file lays out the steps, and provides the code needed to identify the effect of government assistance (categorical - none, medicaid only, government assistance only, both) on substance use outcomes including medication for opioid use treatment (MOUD) and drug treatment. The models are for both unadjusted and adjusted versions of the analysis. All analysis incorporate NSDUH’s survey weights which include weights, strata, and cluster (PSUs). Note that the reference on these models is the ‘none’ group. 
• nsduh_s_22table1-__DUD_V5.Rmd - This markdown file lays out the steps and the R code to create the descriptive table for the low income people with drug use disorder as described in the paper and in the accompanying tables. The characteristics explored include age, race/ethnicity, education, employment, marital status, number of children, percent of federal poverty line, the exposure variables, and the outcome variables. 
• nsduh_s_22table1-__OUD_V5.Rmd - This markdown file lays out the steps and the R code to create the descriptive table for the low income people with opioid use disorder as described in the paper and in the accompanying tables. The characteristics explored include age, race/ethnicity, education, employment, marital status, number of children, percent of federal poverty line, the exposure variables, and the outcome variables.

Access information

The full, raw data and documentation including for data in other formats (SAS, STATA, etc.) and codebooks from SAMHSA and NSDUH can be found at the following link:

• https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health/datafiles/2022