In a study appearing in JAMA, Kathryn Rough, Sc.M., of Brigham and Women’s Hospital, Boston, and colleagues examined the association between implementation of the Centers for Medicare & Medicaid Services (CMS) suppression policy of substance abuse-related claims and rates of diagnoses for nonsubstance abuse conditions in Medicaid data.
In a change from longstanding practice, the CMS recently began suppressing substance abuse-related claims in the Medicare and Medicaid Research Identifiable Files to comply with a 1987 federal regulation barring third party payers from releasing information from federally funded substance abuse treatment programs without patient consent. CMS removes any claim containing a diagnostic or procedure code related to substance abuse, meaning that the entire encounter captured by the claim is deleted (including all diagnosis and procedure codes). Therefore, important diagnoses linked to substance abuse might also be suppressed.
This study included Medicaid data for 2000-2006 prior to implementation of the suppression policy (i.e., containing substance abuse codes) and data for 2007-2010 after the policy was enacted, allowing comparison of data without vs with claim suppression. The researchers calculated annual inpatient and outpatient rates of diagnoses for 6 conditions that commonly co-occur with substance abuse (hepatitis C, human immunodeficiency virus, cirrhosis, tobacco use, depression, and anxiety) and 4 conditions unrelated to substance abuse (type II diabetes, stroke, hypertension, and kidney disease).
The authors found that conditions unrelated to substance abuse appeared generally unassociated with the CMS suppression practices. However, implementation of the policy coincided with sudden and substantial decreases in the rates of inpatient diagnoses for conditions commonly co-occurring with substance abuse, and anxiety showed significant reductions in outpatient diagnosis rates.
“Underestimation of diagnoses has the potential to bias health services research studies and epidemiological analyses for which affected conditions are outcomes or confounders. In studies of health care utilization, the number of missing claims may vary in a nonrandom fashion between groups defined by demographics, disease, or locality. Comparisons between groups may lead to spurious conclusions – a hospital that regularly admits substance abusers will have artificially low rates of readmission, giving a false appearance of better performance.”