disability-chronic-pain

Code repository for analyzing associations between disability / chronic pain and incident opioid use disorder in 2016-2019 CMS data as described in Hoffman, Milazzo, Williams, et al. (2023).

Study duration

• 01_clean_tafdedts.R defines dates of enrollment via the TAFDEDTS (Demographics - Dates) CMS file. For the analysis, enrollment time was counted in calendar years (relevant variables washout_start_dt = washout start date, washout_cal_end_dt = washout end date (6 months after washout start date), study_cal_end_dt = study end date (24 months after washout start date)). Note: There are other variables for study duration that have _cont suffixes. This is an alternate way of defining the study duration and can be ignored.

Eligibility criteria

• 02_clean_tafdebse.R cleans the TAFDEBSE (Demographics Base) CMS file. Creates several basic study eligibility indicators (e.g. age, state, sex) and uses eligibility codes to create more complex study eligibility indicators (e.g. dual eligible status, pregnancy).

• 03_initial_cohort_exclusions/clean_tafoth.R and 03_initial_cohort_exclusions/clean_tafihp.R determines whether the beneficiary had remaining exclusion criterias (e.g. severe intellectual disabilities, dementia, schizophrenia) in the washout period using the Other Services and Inpatient Hospital TAF files' ICD-10 codes.

  • The specific ICD-10 codes used for exclusion criteria can be found in ICD_codes/disabilities.R, with the exception of palliative care, which is directly listed in the cleaning files (ICD10 code "Z515").

• 03_initial_cohort_exclusions/add_LTC_variables.R creates a long term care exclusion indicator by categorizing the PTNT_DSCHRG_STUS_CD in the Inpatient Hospital file.

Variable definitions

Demographics

Demographics are extracted from the Demographics - Base TAF file in the script 02_clean_tafdebse.R.

Psychiatric Comorbidities

The ICD-10 codes used to define ADHD, Anxiety, Bipolar, Depression, and Other Mental Illness psychiatric comorbidities can be found in input/ICD_codes in individual csv files. The R script to search these ICD-10 codes in Other Services and Inpatient Hospital files is in 04_define_comorbidity_vars/define_*.R.

Concurrent Prescriptions

The NDC codes used to concurrent prescriptions can be found in NDC_codes in individual csv files. The R script to search these NDCs in Inpatient Hospital and Pharmacy files is in 05_define_prescription_vars/define_*.R.

OUD

Diagnosis codes

The diagnosis codes used for the outcomes of OUD opioid abuse diagnosis and non-fatal unintentional opioid overdose are located in scripts/ICD_codes/OUD.R. These are called in scripts/06_define_OUD_components/define_oud_*.R where * is in {mcneely, hillary, abuse, poison}.

MOUD

NDC codes for buprenorphine and naltrexone are in input/MOUD_codes/best_moud_ndc_list_20230720.csv. Buprenorphine required additional logic for the type of administration (e.g. tablet, injection) and this data are contained in input/MOUD_codes/best_bup_moud_ndc_list.rds. The HCPCS codes for buprenorphine, naltrexone, and methadone are in input/MOUD_codes/best_moud_hcpcs_list_20230720.csv. These codes are extracted and MOUD start/stop dates are defined in scripts/06_define_OUD_components/define_moud_*.R where * is in {bup, nal, met}.

Probable misuse

Probable misuse was calculated over 6 month rolling windows in the script 06_define_OUD_components/define_oud_probable_misuse.R according to the methodology defined in the paper.

Composite OUD outcome

All OUD codes are combined and a composite outcome created in the script 06_define_OUD_components/merge_oud_vars.R.

Flat file creation

A final flat file for analysis was created using the scripts in the folder 07_combine_cohort. All censoring variables were merged in add_censoring_variables.R, all exclusion criteria were gathered in merge_exclusion_criteria.R, and the final cohort with all eligibility criteria applied was created in merge_final_cohort.R.

Analysis

The code for the adjusted associations is found in R/tmle_mlr3.R. This code relies on the mlr3 and mlr3superlearner R packages.

This code was applied for final analyses in 09_adjusted_analysis/tmle_superefficient.R after data cleaning specific to the software (e.g. dummy variables) was done in 09_adjusted_analysis/clean_analytic_file.R.

Additional notes

At times the CMS data files were too large to analyze all 17 million + beneficiaries at once. The solution to this was to create partitions, e.g. filter Other Services to only the records of 1 million beneficiaries, and then run data cleaning processes on this partition before recombining.