Trends in Opioid Use among Social Security Disability Insurance Applicants

Abstract

Several factors suggest that opioid use may be common among applicants to Social Security Disability Insurance (SSDI): the prevalence of opioid use, the suspected link between opioid use and declining rates of work, and the large share of new SSDI awardees who have conditions associated with opioid use.  However, research-ready data on opioid use by these applicants, who are generally not eligible for Medicare, have not been available.  SSDI applicants are required to report their medications, but they often do so in an open-ended text field, which requires additional coding before analysis.

Our study is the first to provide statistics on opioid use among SSDI applicants.  We used an innovative machine-learning method to identify opioids in medication text fields in SSDI administrative data.  Specifically, we examined the prevalence of reported opioid use in a 30 percent random sample of initial-level SSDI applications stored in the Social Security Administration’s Structured Data Repository (SDR) from 2007 through 2017, considering differences by demographic and other factors.  We supplemented the SDR with two SSA administrative data sources: the Disability Analysis File, which provided award information, and the Numerical Identification System, which provided information on deaths.  Using these sources, we produced statistics on the association between (1) opioid use among SSDI applicants and (2) SSDI award and death.  Understanding the prevalence of reported opioid use among these individuals and the association between opioid use and later SSDI application outcomes may help in forecasting the future composition of the SSDI caseload.

The paper found that:

• Over the 11-year analysis period, more than 30 percent of SSDI applicants reported using one or more opioids.  This is higher than the rate of opioid use in the general population (29 versus 19 percent in 2016).
• Reported rates of opioid use among SSDI applicants varied over the analysis period. Rates increased from 2007 to a peak of 32 percent in 2012, followed by a decline to the period low of 26 percent in 2017.
• Reported opioid use varied by age and demographic characteristics.  SSDI applicants ages 40–49 were the most likely age group to report opioid use; women were 3-4 percentage points more likely to report opioid use than men; and people with some college were the most likely education group to report opioid use.
• Reported opioid use is also correlated with application type.  SSDI-only applicants who reported opioid use were 4-6 percentage points more likely to report opioid use than concurrent SSDI and SSI applicants.
• Reported opioid use varied greatly between geographic areas.  Applicants from Rhode Island, Massachusetts, and Washington, DC, reported lower-than-average rates of opioid use in 2007 and consistently throughout the analysis period.  Conversely, applicants from Delaware, Nevada, and Michigan consistently reported the highest rates of opioid use.
• There was a positive and statistically significant association between (1) reported opioid use and SSDI awards and (2) reported opioid use SSDI award and death. These associations do not demonstrate a causal relationship.

The policy implications of the findings are:

• Application for SSDI provides an opportunity to identify opioid users, understand more about the nature of their use, and, if warranted, connect them with helpful services and supports.
• Given the prevalence of reported opioid use among SSDI applicants, our study may open the door to future research on how opioid use affects post-adjudication well-being—for example, applicants’ employment outcomes after they are awarded or denied SSDI. Future studies might also consider tracking the trajectory of opioid use from application through award using Medicare data.
• This study also serves as a template for using previously untapped information in SSA administrative files. The machine learning approach used to identify opioid use in free-form text could be applied to other key indicators of interest to SSA.