DOI: 10.5061/dryad.np5hqc05n
title: Data from: Government assistance and Medicaid: The relationship with drug treatment and medication for opioid use disorder among women in the United States

description: Introduction: Social safety net programs (e.g., Medicaid and government
 assistance) may facilitate drug use disorder (DUD) treatment receipt. We
 explored the association of social safety net programs with drug treatment
 and medication for opioid use disorder (MOUD)receipt among women with DUD
 and opioid use disorder (OUD), respectively. Methods: We
 used data from women ages 18-64 who met criteria for
 past-year DSM-5 DUD (n=2,784) and OUD (n=458) in the 2022 public-use
 National Survey for Drug Use and Health. We estimated the odds of
 past-year DUD treatment among women with DUD and past-year MOUD treatment
 among women with OUD by government assistance and/or Medicaid receipt in
 primary analyses, followed by secondary categorizations of exposure (any
 government assistance; number of programs received), using separate
 logistic regressions, controlling for sociodemographics.
 Results: In primary analyses, women with DUD receiving
 both Medicaid and government assistance were more likely to report
 past-year DUD treatment (aOR: 3.60, 95% CI=1.36, 9.51) compared to women
 receiving neither. Women with past-year OUD receiving both Medicaid and
 government assistance were more likely to report MOUD (aOR: 3.41, 95%
 CI=1.01, 11.61) compared to those receiving neither. Secondary analysis
 results were in the same direction. Conclusion: Drug treatment and MOUD
 receipt among women with DUD and OUD, respectively, increased when
 Medicaid was combined with other forms of government assistance. Treatment
 costs and other barriers, such as lack of insurance, childcare, and
 employment support, are critical determinants of drug treatment; our
 findings suggest that government support programs may help to buffer these
 known barriers.
description: # Government assistance and Medicaid: the relationship with drug treatment
 and medication for opioid use disorder among women in the United States
 Dataset DOI: [10.5061/dryad.np5hqc05n](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_22_table1_-__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_22_table1_-__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](https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health/datafiles/2022)