Social Security Disability: Action Needed to Help Agency Staff Understand and Follow Policies Related to Prescription Opioid Misuse

The United States is in the midst of an unprecedented opioid epidemic. Opioids are prescribed to treat conditions such as chronic pain. However, opioid misuse can lead to addiction, disability, overdose, and death. Prior GAO and other reports have discussed the use of prescription opioids within federal programs, particularly Medicare. Less is known about the use of opioids in relation to SSA's DI program. GAO was asked to review any correlation between prescription opioids and rates of DI claims, and any related challenges for SSA.

This report examines (1) what is known about the relationship between trends in prescription opioids and DI claims, and (2) how SSA considers potential prescription opioid misuse in its DI eligibility decisions. GAO analyzed county-level data on opioid prescriptions and DI claims from 2006 through 2017; interviewed program staff involved in DI eligibility decisions in Alabama, Kentucky, and West Virginia, selected because of their high rates of opioid prescriptions and percentage of the adult population on DI; and reviewed case files for DI beneficiaries identified by the Centers for Medicare & Medicaid Services as being at risk for prescription opioid misuse or abuse.

The numbers of opioid prescriptions and claims for the Social Security Administration's (SSA) Disability Insurance (DI) program have each declined nationally in recent years, but rates vary widely across the country. National trends show both peaking between 2010 and 2014 and then declining. GAO's analysis shows counties with the highest rates of both were concentrated in the Southeast (see figure). After accounting for economic, demographic, and other factors, GAO found that counties with higher rates of opioid prescriptions tended to have higher rates of DI claims from 2010 through 2017. These rates were also correlated with other factors. For example, counties with higher rates of each tended to have higher poverty rates. However, GAO was unable to determine whether there is a causal relationship between rates of opioid prescriptions and DI claims or other factors, given readily available data.

Program staff are required to evaluate and document substance use disorders (including opioids not taken as prescribed) when making certain DI eligibility decisions. Specifically, staff are required to evaluate potential substance use disorders for certain DI claims and deny benefits, for example, if the claimant would not be considered disabled if they stopped using drugs or alcohol. In addition, staff are generally required to document the rationale for their decision so that another reviewer can understand how they made the decision. However, staff in five of the six offices GAO visited in three states were confused about when to evaluate substance use disorders, and nine of 15 case files that GAO reviewed in which an evaluation was conducted did not have a documented rationale. SSA officials acknowledged the need to clarify policies on when to evaluate substance use disorders, and that a poorly documented rationale could lead to reversals or remands of decisions. Without ensuring that SSA's policies are understood and that staff document their rationale, the agency may expend resources re-working cases and, in turn, delay benefits to individuals eligible for assistance.

GAO recommends that SSA 1) clarify policies and procedures to help staff better evaluate substance use disorders, and 2) ensure staff document their rationale. SSA agreed with GAO's recommendations.

The United States is in the midst of an unprecedented opioid epidemic.  Physicians and other treatment providers may prescribe opioids to treat  conditions such as chronic pain. However, opioid misuse can lead to  addiction, disability, overdose, and death. Studies have shown that  prescription opioids played a role in the emergence of the opioid epidemic  in the 1990s, in part, because of the overprescribing of opioids, such as  oxycodone and hydrocodone. The number of overdose deaths from all  opioids has grown by nearly 2.5 times over the last decade, from about  19,600 deaths in 2008 to 47,600 in 2017.

Prior reports by GAO and by the Department of Health and Human  Services (HHS) Office of the Inspector General (OIG) have discussed the  use of prescription opioids within federally funded programs, particularly  Medicare. For example, GAO reported on the need for additional  oversight of Medicare to reduce beneficiaries’ risk of harm from  prescription opioid use. The HHS OIG also reported that Medicare Part  D paid for about $3.4 billion in prescription opioids in 2017, and nearly  one in three (14.1 million) Medicare Part D beneficiaries received at least  one prescription opioid that year. Of those beneficiaries, nearly 460,000  received high amounts of prescription opioids, and over 71,000 were at  serious risk of misuse or overdose. However, less is known about the  use of these opioids by individuals receiving benefits from the Social  Security Administration’s (SSA) Disability Insurance (DI) program. DI  generally provides benefits to individuals who can no longer work due to  qualifying impairments. Many individuals may apply for DI benefits  because of pain-related conditions, and may be taking prescription  opioids because of these conditions.

You asked us to identify any correlation between prescription opioids and  rates of DI claims, and any related challenges for SSA in making DI  eligibility decisions. This report examines (1) what is known about the  relationship between trends in prescription opioids and DI claims, and (2)  how SSA considers potential prescription opioid misuse in its DI eligibility  decisions.

To examine the relationship between trends in prescription opioids and DI  claims, we reviewed relevant literature and analyzed data from HHS’s  Centers for Disease Control and Prevention (CDC) and SSA. Specifically,  we reviewed existing studies on the relationship between prescription  opioids and DI claims, as well as available data from SSA. In addition, we  analyzed county-level data on the rates of opioid prescriptions from CDC  and number of DI claims from SSA from 2006 through 2017, the most  recent year of data available at the time of our review. We used  aggregate data to illustrate nationwide trends over time. We also  examined variation among counties and conducted multiple regression  analyses to examine the relationship between opioid prescriptions and DI  claims, taking into account economic, demographic, and other factors.

To examine how SSA considers prescription opioid misuse in its DI  eligibility decisions, we reviewed relevant information, interviewed  program staff, and reviewed DI case files. We reviewed relevant federal  laws, regulations, and SSA policies as well as federal standards for  internal control. We also interviewed SSA headquarters officials and staff  involved in DI eligibility decisions in six offices in Alabama, Kentucky, and  West Virginia. We selected these three states primarily because of their  high rates of opioid prescriptions and drug overdose deaths and because  a relatively high percentage of their adult population received DI benefits.  In addition, we selected and reviewed 30 case files for DI beneficiaries  who had been identified by the Centers for Medicare & Medicaid Services  (CMS) as being at risk for prescription opioid misuse or abuse. We  selected the case files to review based on whether the beneficiary had  been evaluated by SSA for an identified substance use disorder, among  other factors. The case files we reviewed may not have contained any  evidence of prescription opioid misuse or abuse because of the  timeframes we used to select them. Specifically, we selected case files  for DI beneficiaries who had been allowed in or after 2013, but who were  identified as being at risk of prescription opioid misuse or abuse in 2017.  Because these beneficiaries may have been allowed as early as 2013,  they may not have had any issues with prescription opioid misuse or  abuse at the time SSA evaluated their claim (i.e., they may have  developed potential issues after being allowed benefits). For further  details on our scope and methodology, including how we selected case  files for review, see appendix I.

We conducted this performance audit from June 2018 to January 2020 in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.

In addition to retirement benefits to older individuals and their families,  SSA administers the nation’s largest disability benefit program, the  Disability Insurance (DI) program. DI generally pays benefits to  individuals if they are unable to work due to qualifying impairments that  are expected to last at least 1 year or result in death. In fiscal year 2018,  SSA paid DI benefits to more than 10 million beneficiaries each month for  a total of about $144 billion that year. In addition to monthly financial  benefits, which averaged about $1,234 per disabled worker in 2018,  those eligible for DI also gain access to Medicare after a 2-year waiting  period, which can help pay for their medical costs, including prescription  opioids.

Disabled workers claiming DI benefits must meet work and other  requirements to be considered eligible for DI. First, they must have  worked for a specified amount of time covered by Social Security as well  as worked within a specified timeframe before becoming disabled, based  on their age. If these work requirements are met, SSA will assess a  number of medical and vocational requirements, including whether the  claimant earned more than a set monthly amount, the severity of any  impairments they have, and whether they are able to continue working in  a similar or other capacity given their age, education, and prior work  history (see fig. 1).

DI claimants may also apply concurrently for SSA’s Supplemental  Security Income (SSI) program, which provides income to individuals who  are aged, blind, or disabled with limited income and resources. Such  claimants may be deemed eligible for both programs if they meet certain  income and resource requirements in addition to those for DI. Under  SSI, they may receive additional financial benefits as well as access to  Medicaid.

Several different program staff are involved in processing DI claims. First,  staff in SSA field offices receive applications and determine whether  claimants meet nonmedical eligibility requirements, such as having a  sufficient work history. Claims for those who meet these requirements are  then forwarded to state government Disability Determination Services  (DDS) offices, where DDS staff review the claimant’s eligibility based on  the medical and vocational requirements outlined in figure 1 above.  Specifically, DDS examiners assemble any medical and vocational  information for the claim. This can involve contacting a claimant’s  treatment providers, and third parties such as family members, friends,  and employers, and referring the claimant for consultative exams, such as  with physicians or psychologists if recent treatment records are  unavailable. DDS examiners then confer with DDS medical consultants,  such as in-house or contracted physicians and psychologists, to  determine whether the claimant meets the law’s requirements for having  a disability. DDS examiners use all of this information to decide whether  claimants are eligible for DI.

Claimants who are dissatisfied with the initial DDS decision have several  opportunities to appeal. First, they generally may request a  “reconsideration” of the claim, which is conducted by a DDS examiner  who was not involved in the original decision. Next, they may request a  hearing before an SSA administrative law judge, who may collect new  evidence and ask other witnesses, such as medical and vocational  experts, to testify at the hearing. If their claim is denied at this hearings  level, claimants may request that it be reviewed by the Appeals Council,  which is comprised of SSA administrative appeals judges and appeals  officers. Beyond the Appeals Council, the claimant may appeal to a  federal district court.

Staff at each level of the process must document their decision in a  claimant’s case file, in accordance with the agency’s policies. For  example, staff are generally required to document the medical evidence  they reviewed, any assessments regarding the claimant’s severity of  impairments and vocational abilities, and the rationale for their decisions.

For allowed DI claims, federal law requires beneficiaries’ cases to be  periodically reviewed within specified timeframes to ensure the  beneficiary continues to meet DI requirements. DDS examiners conduct  such reviews, called continuing disability reviews, conferring with medical  consultants and making a decision regarding a beneficiary’s disability in  comparison to the evidence from when the claim was allowed to  determine if medical improvement has occurred. According to SSA,  benefits typically continue unless evidence exists that a beneficiary’s  impairment has medically improved and that they are able to return to  work.

Musculoskeletal conditions, which are pain-related, make up the largest  proportion of impairments allowed by SSA for DI benefits. Specifically,  these conditions, such as back and joint impairments, made up nearly 33  percent of impairments for disabled workers in 2018. Treatments for  pain-related symptoms can include prescription opioids.

Opioid Prescriptions and DI Claims Have Declined in Recent Years; Our Analysis Shows a Correlation between Them

Opioid Prescriptions and DI Claims Have Declined in Recent Years, but Few Studies or Data Sources Provide Information on the Relationship between Them

Nationwide data show that trends in the numbers of opioid prescriptions  and DI claims have followed a similar pattern, with both peaking between  2010 and 2014 and then declining. From 2006 through 2017, total opioid  prescriptions peaked at about 255 million prescriptions in 2012, and then  decreased in each of the following years (see fig. 2).

Similarly, DI claims peaked at a maximum of about 1.1 million claims in  2014 and have steadily declined since (see fig. 3). Claims in which  individuals applied concurrently for DI and the SSI program (i.e., DI/SSI  concurrent claims) peaked a little earlier—at about 1.3 million claims in  2010—before also steadily declining.

While trends in opioid prescriptions and DI claims have moved in the  same general direction over time, few studies and data sources provide  information on the relationship between these trends. For example, we  identified two studies, both funded by SSA, that examined the relationship  between prescription opioids and disability. One preliminary study in 2017  found a positive correlation between prescription opioids and DI claims,  but noted that this correlation was not statistically significant in every  model. Researchers for this study noted that additional data and  analysis are needed to refine the results. A second study in 2018 did not  identify a direct relationship between opioid misuse and disability, but  found that they may have an indirect relationship because of other factors  such as having poor health, which may lead to unemployment due to  disability. Other studies have examined the relationship between  prescription opioids and employment, but not DI claims specifically. One  such study noted that, based on available data, it is difficult to separate  the effects of prescription opioid use and disability on employment  outcomes. The study noted further that disentangling the relationship  between prescription opioid use and disability is an area in need of  additional work.

In addition to funding research, SSA collects some administrative data on  substance use among DI claimants, including use of prescription opioids.  However, these data have limitations for analyzing prescription opioid  use. Specifically, SSA collects administrative data on the medications  claimants report using when filing their claim, which may include  prescription opioids. However, these data may be incomplete because  claimants may not report all substances they use. Further, researchers  working on a study funded by SSA said analyzing these data is  challenging because many claimants manually enter the names of their  medications into an optional free-text field on their electronic applications  rather than selecting from a dropdown menu, and that these entries often  include misspellings or alternative names. SSA also collects  administrative data on whether its staff evaluated a substance use  disorder while processing a DI claim. However, SSA headquarters  officials told us that staff are not required to record this information in the  administrative data unless substance use disorders are the basis for a  denial. Further, these data only indicate whether a substance use  disorder involved alcohol or other drugs. They do not include additional  details on the types of drugs involved (e.g., opioids versus  methamphetamines). According to SSA headquarters officials, these  details are not necessary for evaluating the claim or managing the  process for DI eligibility decisions.

Our County-Level Analysis Shows Wide Variation in Rates of Opioid Prescriptions and DI Claims, and Differences by Geographic Region

Given the limitations with the claimant-level data described above, we  analyzed county-level data for 2006 through 2017 and found that rates of  opioid prescriptions and DI claims varied widely across counties.  Specifically, the rate of opioid prescriptions ranged from nearly 0 to 396  opioid prescriptions per 100 people per year across all counties in 2017.  Likewise, the rate of DI claims ranged from nearly 0 to 16.4 DI claims per  1,000 people. Most counties, however, were clustered around the median  of 65 opioid prescriptions per 100 people and 3.7 DI claims per 1,000  people (see fig. 4).

In examining counties with the highest rates of opioid prescriptions and DI  claims (i.e., counties in the top third of the distributions for each rate), we  found that those with the highest rates of both were generally  concentrated in the Southeast (see fig. 5). Specifically, almost 30  percent of counties in the Southeast were among the highest for rates of  both in 2017. In comparison, many counties in the West were among the  highest for rates of opioid prescriptions, but not for DI claims. Conversely,  many counties in the Northeast were among the highest for rates of DI  claims, but not for opioid prescriptions. We also observed that these  geographic differences were generally consistent over a 10-year period  we analyzed.

Rates of Opioid Prescriptions and DI Claims Are Correlated, Even After Accounting for Economic, Demographic, and Other Factors

Our analysis shows a positive correlation between rates of opioid  prescriptions and DI claims, as well as correlations between these rates  and other factors (see fig. 6). Specifically, we conducted regression  analyses to examine the relationship between rates of opioid  prescriptions and DI claims at the county level from 2010 through 2017,  taking into account economic, demographic, and other factors.  However, we were unable to determine whether there is a causal  relationship between rates of prescription opioids and DI claims or other  factors, given readily available data. Further, given the small numbers of  DI claims in most counties, we would not expect differences in the rate of  DI claims to fully explain differences in the rate of opioid prescriptions.

Correlation between opioid prescriptions and DI claims. We found  that rates of opioid prescriptions and DI claims were positively  correlated before and after accounting for other factors. Specifically,  counties with higher rates of opioid prescriptions tended to have  higher rates of DI claims and vice versa from 2010 through 2017. We  would expect this correlation, given that many DI claimants  experience pain, and prescription opioids are intended to help  manage pain.

Correlations between opioid prescriptions and other factors. Our  analysis showed that rates of opioid prescriptions were correlated with  poverty rates, population size, and access to health insurance. In  particular, counties with higher rates of opioid prescriptions tended to  have higher poverty, be less urban and with small- to mid-size  populations, and have more people with health insurance from 2010  through 2017.

Correlations between DI claims and other factors. Our analysis  showed that rates of DI claims were also correlated with poverty rates,  as well as unemployment, age, and race. In particular, counties with  higher rates of DI claims tended to have higher unemployment and  poverty from 2010 through 2017. Those with higher rates of DI claims  also tended to have higher percentages of older adult and white  populations.

SSA Has Policies to Evaluate Potential Prescription Opioid Misuse, but Staff Faced Challenges Understanding and Following These Policies

SSA’s Policies Require Staff to Evaluate Potential Substance Use Disorders in Certain DI Claims, but These Disorders Are Seldom the Key Factor in Denying Benefits

SSA’s policies require staff to deny DI benefits to claimants if substance  use disorders (including opioids not taken as prescribed) are “material” to  the impairments that preclude the claimant from work. For example,  substance use disorders would be considered material to the claimant’s  impairment if (1) they are the claimant’s only impairment, or (2) the  claimant would not be considered disabled if they stopped using drugs or  alcohol. To illustrate, program staff described an example, under SSA’s  policies, in which they would deny a claimant with a mental health  condition, such as depression, who also has a substance use disorder. In  particular, if staff determined that substance use was affecting the  claimant’s depression, and their mental health would improve to the point  of non-disability in the absence of drugs or alcohol, SSA would deny the  claim. In contrast, they may allow a claimant with permanent liver  damage, even if caused by drug or alcohol use, because the damage is  irreversible and would continue to be disabling even if the claimant were  to stop using these substances.

SSA uses a six-step process, referred to as the Drug Addiction and  Alcoholism (DAA) evaluation, to determine whether substance use  disorders are material to a claimant’s impairments. In the first two steps  of this process, SSA determines whether a claimant is disabled and  whether one of the claimant’s “medically determinable impairments” is a  substance use disorder. Medically determinable impairments include  physical or psychological abnormalities identified through medically  acceptable diagnostic techniques and documented in objective evidence  from an acceptable medical source, such as a physician or psychologist.  If the answer is “yes” to both questions in the first two steps of the DAA  evaluation, program staff use the remaining steps to help determine  whether the substance use disorder is material to the claimant’s disability  (see fig. 7).

In conducting DAA evaluations, program staff can involve medical experts  to assist them. At the initial level, DDS examiners confer with DDS  medical consultants, such as in-house or contracted physicians and  psychologists. At the hearings level, administrative law judges can also  seek opinions from medical experts during the claimant’s hearing.

Substance use disorders are seldom the key factor in DI eligibility  decisions, according to SSA data and staff. Specifically, SSA data show  that DAA evaluations of substance use disorders—aside from those that  involved alcohol only—were the reason for a denial in about 0.1 percent  of all decisions at the initial level and 0.3 percent of all decisions at the  hearings level in 2017.

Staff in our three selected states cited these potential reasons for why  substance use disorders are seldom the key factor in DI eligibility  decisions:

Claimants with substance use disorders may not have qualifying  impairments. Staff explained that those who do not have any  impairment severe enough to meet SSA’s disability standards can be  denied without a DAA evaluation.

Medical records do not include enough evidence of a substance  use disorder to warrant a DAA evaluation. Staff said some  claimants may not have any evidence of a substance use disorder in  their file because they may not report all substances they are taking or  lack past medical treatment. In addition, staff said those with  suspected substance use disorders may not have enough evidence of  a disorder in their medical records to warrant a DAA evaluation. For  example, they said pain clinics will often discharge a claimant from the  clinic (i.e., stop providing services) due to drug-seeking behaviors.  However, these pain clinics may not always document the reasons  why the claimant was discharged. Further, staff said isolated  instances of drug-seeking behaviors or discharges from pain clinics  documented in medical records may not necessarily mean that a DAA  evaluation is warranted.

Some claimants have qualifying impairments, despite having  substance use disorders. Staff said substance use disorders may  not be the reason a claimant cannot work and may have little or no  effect on a claimant’s impairments. For example, in one case file we  reviewed, an administrative law judge conducted a DAA evaluation  because of the claimant’s substance use disorders, likely involving  alcohol and prescription medications, including opioids. The judge  allowed the claim after determining that the claimant’s back issues  were disabling, independent of the substance use disorders.

Use of substances as prescribed by a treatment provider,  including opioids, is not considered a substance use disorder.  Program staff explained that, per SSA’s policies, they would not  consider the use of opioids as prescribed to be a substance use  disorder warranting a DAA evaluation, even if they thought the  claimant was using unusually high amounts. SSA headquarters  officials added that the use of prescription opioids could be  considered a substance use disorder and result in a denial if medical  records from an acceptable medical source included information  about excessive or inappropriate use.

Evaluating Substance Use Disorders Can Be Complex, and Staff Faced Challenges Understanding and Following SSA’s Policies

Staff told us that making DI eligibility decisions for claims involving  substance use disorders, including prescription opioids not taken as  prescribed, can be complex. For example, staff in our three selected  states noted challenges with subjectivity in conducting DAA evaluations,  particularly when the claim involves mental health conditions. They said  that certain conditions, such as depression or psychosis, can be  exacerbated by substance use disorders. Thus, they said evaluating  whether these conditions would continue to be disabling in the absence of  drug or alcohol use can be difficult and subjective.

We found that program staff faced challenges understanding or following  SSA’s policies, based on our interviews with staff in three selected states  and our review of 30 case files for DI beneficiaries, which included 15 in  which a DAA evaluation had been conducted. Specifically, we found  challenges with two aspects of the DAA evaluation process:

Determining when to conduct a DAA evaluation. SSA  headquarters officials told us that their policies do not require an  official diagnosis of a substance use disorder from a treatment  provider to conduct a DAA evaluation. Rather, they said a DAA  evaluation is required if the potential disorder is considered a  medically determinable impairment as defined by the current edition of  the American Psychiatric Association’s Diagnostic and Statistical  Manual of Mental Disorders—which includes descriptions of many  types of substance use disorders—and documented by an acceptable  medical source. However, program staff in five of the six offices we  visited in the three selected states, including DDS managers and  examiners participating in group interviews and three administrative  law judges, told us they believed they should not conduct a DAA  evaluation unless they see an official diagnosis documented in the  medical evidence.

SSA headquarters officials discussed why staff may be confused  about when to conduct a DAA evaluation, and acknowledged the  potential effects. Specifically, they said staff may be confused about  the policies for determining what is considered a medically  determinable impairment for substance use disorders. Officials said  there must be evidence of substance use that is consistent with the  general definition of a substance use disorder as defined in the  Diagnostic and Statistical Manual of Mental Disorders. They said staff  may mistakenly interpret this requirement to mean that they need an  official diagnosis to conduct a DAA evaluation. In fact, SSA’s  operations manual for determining DI eligibility may also cause  confusion. Though officials told us that SSA’s policies do not require  an official diagnosis, the operations manual states that staff should  only conduct a DAA evaluation when “an acceptable medical source  establishes that a claimant is diagnosed with a substance use  disorder.” SSA headquarters officials acknowledged that confusion  about when to conduct a DAA evaluation could result in evaluations  not being done when they should be, as well as claims being  evaluated for substance use disorders unnecessarily when they do  not meet the standards for being a medically determinable  impairment.

Documenting the rationale for why substance use disorders did  not affect the claimant’s impairment. SSA’s policies for the DAA  evaluation process generally require staff to document sufficient  information about their evaluations so that a subsequent reviewer can  understand the rationale for the decision, which is in keeping with  federal standards for internal control. These policies also indicate  that a single statement documenting that “DAA is not material” to the  claimant’s impairments is not sufficient, and that documentation  should be included in the determination and decision, or in other  appropriate documents for DDS staff. In the 15 case files in which  SSA had conducted a DAA evaluation, nine did not include a  documented rationale. For example, in one case file we reviewed, a  DDS examiner initially denied a claim for mental health issues after  determining that these issues would not be disabling in the absence of  the claimant’s substance use disorders, which involved  benzodiazepines. An administrative law judge at the hearings level  later allowed the claim, but did not document a rationale for why the  claimant’s substance use disorders did not affect the claimant’s  impairments.

SSA headquarters officials agreed that a documented rationale was  inappropriately missing in four of the nine case files mentioned above,  although they did not indicate why the documentation was missing.  For the remaining case files, while they agreed that there was no  documented rationale, they asserted that neither a DAA evaluation  nor a documented rationale was required. For example, for four of  these five case files, officials stated that the substance use disorder  was not established as a medically determinable impairment, that the  claimant’s impairments were disabling by themselves regardless of  whether there was any history of substance use disorders, and that  the impairments were irreversible or could not improve to the point of  non-disability. Nonetheless, a DAA evaluation was conducted in these  case files, underscoring staff’s confusion about when an evaluation is  necessary. Furthermore, regardless of whether a documented  rationale was required in these case files, such documentation, if  included, would ensure the rationale for the decision is clear to a  subsequent reviewer, a recommended practice in federal internal  control standards.

SSA headquarters officials acknowledged that a poorly documented  rationale could lead to reversals or remands if staff conducting  appeals or quality reviews are unable to understand the decision. This  could result in increased processing time for those conducting  appeals and quality reviews, as well as for staff who may be required  to revisit their decision. For example, in one case file we reviewed, an  administrative law judge allowed a claim for mental health issues that  had previously been denied at the initial level as a result of the  claimant’s substance use disorders involving prescription opioids,  alcohol, and marijuana. The case file was later randomly selected for  quality review by the Appeals Council, which remanded the case back  to the administrative law judge due, in part, to the lack of documented  rationale regarding the claimant’s substance use disorders. As a  result of the remand, the administrative law judge held a new hearing  and issued a new decision that still allowed the claim, but provided a  rationale for the DAA decision.

SSA headquarters officials told us about efforts that could help ensure  staff understand and follow policies for the DAA evaluation process. For  example, they discussed training on DAA evaluation and documentation  requirements. For DDS examiners, they said this training includes  presentation slides and videos on these topics. Similarly, for new  administrative law judges and other hearings-level staff, they said  mandatory trainings include a module on the DAA policies. While SSA  headquarters officials said they generally do not offer additional training  beyond this, they noted that DDS examiners and administrative law  judges are able to revisit the training materials and receive more local,  ongoing training and resource materials as needed.

We found examples of local, ongoing training and resource materials on  the DAA evaluation process during our interviews in our three selected  states. For example, one DDS office we visited had developed a DAA  flowchart for its internal website, as well as a question and answer section  derived from existing SSA information. Another DDS office had developed  its own guidance specifically on documentation requirements for DAA  evaluations. DDS managers and examiners in this office said they had  sought clarification from the SSA office overseeing their region in  developing the guidance, which was used during a local training for  disability examiners in January 2019.

In addition to training and guidance, SSA headquarters officials told us  that compliance with policies for the DAA evaluation process is examined  as part of the agency’s larger quality review processes. These processes  are designed to ensure that cases are decided accurately. They include  national and local reviews of randomly selected decisions at the initial  level, as well as national reviews at the hearings level. Identified errors  are reported back to the respective offices for correction. However, these  reviews do not target claims involving substance use disorders. SSA  headquarters officials said the agency does not track how often they  review such claims at the initial level. DDS managers in the three  selected states who are involved in local quality reviews also told us that  such claims are not targeted for review.

Despite SSA’s efforts to train staff on the DAA requirements, provide  guidance, and conduct quality reviews that may cover DAA evaluations,  we found that confusion about implementing the policies remains and  staff are not always documenting the rationale for their evaluations as  required. If SSA does not clarify its policies regarding when to conduct a  DAA evaluation, as well as ensure that staff document the rationale for  these evaluations, staff may not be in compliance with the policies.  Further, if SSA does not take action, staff conducting subsequent appeals  and quality reviews may not have the information needed to effectively  examine prior evaluations of substance use disorders. Thus, the agency  may expend resources re-working cases and, in turn, delay benefits to  individuals eligible for assistance.

The DI program helps people with eligible impairments even if they are  also struggling with substance use disorders, including opioids not taken  as prescribed, if the impairments would continue to be disabling in the  absence of drugs or alcohol. Many people with disabilities have chronic  pain for which prescription opioids are used as a legitimate treatment  option. Thus, it is not surprising that many people who apply for DI  benefits have opioid prescriptions, or that we would observe a positive  correlation between these rates.

Though SSA data show that substance use disorders are seldom the key  factor in denying benefits, the agency nonetheless has a responsibility to  show accountability for the decisions made by staff. Evaluating substance  use disorders can be complex. However, without clarification to help staff  better understand the policies for evaluating such disorders and ensuring  staff document the rationale for their decisions, SSA likely cannot know  whether claims are thoroughly assessed and efficiently examined as they  move through subsequent reviews. Such inefficiencies can result in  delayed benefits to those eligible for assistance. Further, while our review  focused on prescription opioids, any improvements SSA makes to this  process could help the agency stay ahead of shifting trends in the  broader opioid epidemic.

We are making the following two recommendations to SSA:

The Commissioner of the Social Security Administration should clarify  policies and procedures to remind staff that a diagnosis of a  substance use disorder is not necessary to conduct a Drug Addiction  and Alcoholism evaluation. (Recommendation 1)

The Commissioner of the Social Security Administration should  ensure that staff document their rationale for decisions involving the  Drug Addiction and Alcoholism evaluation process. (Recommendation  2)

We provided a draft of this report to SSA and HHS for review and  comment. SSA provided technical comments, which we incorporated as  appropriate, and formal comments.  As part of its technical comments,  SSA suggested that we revise the language of Recommendation 1 to  focus more directly on the cause of staff’s confusion about when to  conduct a DAA evaluation (i.e., staff’s misconception that a diagnosis of a  substance use disorder is required). We agreed with this suggestion, and  revised the recommendation accordingly. A letter conveying SSA’s formal  comments is reproduced in appendix IV. SSA agreed with our  recommendations. Regarding both recommendations, SSA stated that it  will continue to train staff on the agency’s policies and procedures related  to substance use disorders and the DAA evaluation process, as well as  the importance of fully documenting these evaluations. HHS did not  provide any comments.

We are sending copies to the appropriate congressional committees, the  Commissioner of the Social Security Administration, the Secretary of  Health and Human Services, and other interested parties. The report will  also be available at no charge on the GAO website at www.gao.gov.

If you or your staff have any questions about this report, please contact  me at (202) 512-7215 or curdae@gao.gov. Contact points for our Offices  of Congressional Relations and Public Affairs may be found on the last  page of this report. GAO staff who made key contributions to this report  are listed in appendix V.

Appendix I: Objectives, Scope, and Methodology

We examined (1) what is known about the relationship between trends in  prescription opioids and Disability Insurance (DI) claims, and (2) how the  Social Security Administration (SSA) considers potential prescription  opioid misuse in its DI eligibility decisions. This appendix provides a  detailed account of the information and methods we used to answer these  objectives. Section 1 provides an overview of our methods and key data  sources. Sections 2 through 4 provide additional details on the three main  methods we used to answer our objectives.

Section 1: Overview of Methods and Key Data Sources

To answer our first objective on the relationship between trends in  prescription opioids and DI claims, we reviewed relevant literature and  analyzed data from the Department of Health and Human Services’  (HHS) Centers for Disease Control and Prevention (CDC) and SSA.  Specifically, we reviewed existing studies and interviewed several  researchers currently examining the relationship between prescription  opioids and DI claims. We also reviewed available data from SSA on  prescription opioid use among DI claimants. In addition, we analyzed  county-level data on the rates of opioid prescriptions from CDC and  number of DI claims from SSA from 2006 through 2017, the most recent  year of data available at the time of our review. We used aggregate data  to illustrate nationwide trends over time. We also examined variation  among counties, including differences among those with the highest rates  of opioid prescriptions and DI claims. Lastly, we used these data to  conduct multiple regression analyses to examine the relationship between  opioid prescriptions and DI claims, taking into account economic,  demographic, and other factors. We discuss these analyses in greater  detail in Section 2.

To answer our second objective on how SSA considers potential  prescription opioid misuse in its DI eligibility decisions, we reviewed  relevant information, interviewed program staff, and reviewed DI case  files. We reviewed relevant federal laws, regulations, and SSA policies,  as well as federal standards for internal control. We also interviewed  SSA headquarters officials and staff involved in DI eligibility decisions in  six offices in Alabama, Kentucky, and West Virginia. We discuss the  criteria we used to select these states in Section 3. Lastly, we selected  and reviewed 30 case files for DI beneficiaries who had been identified by  the Centers for Medicare & Medicaid Services (CMS) as being at risk for  prescription opioid misuse or abuse in 2017. We discuss the data and  criteria we used to select these case files in Section 4.

To answer our objectives, we used a variety of electronic data from data  sources administered by CDC, SSA, and other federal agencies. Tables 1  and 2 summarize the key data sources and how they were used for each  objective. For each data source, we conducted a reliability assessment by  completing two or more of these steps: conduct electronic tests for  completeness and accuracy, review relevant documentation, and  interview knowledgeable officials about how the data are collected and  maintained. We found that the data we used were sufficiently reliable for  the purposes of our analyses. However, our analytical approach was  limited by the availability of data, as discussed below and in appendix II.

Section 2: Analyses of County-Level Data on Opioid Prescriptions and DI Claims

To answer our first objective on what is known about the relationship  between trends in prescription opioids and DI claims, we conducted three  sets of analyses using county-level data on the rates of opioid  prescriptions and number of DI claims from 2006 through 2017. The data  on opioid prescriptions are from CDC and represent the number of opioid  prescriptions filled by retail (i.e., non-hospital) pharmacies per 100 people  per year in each county. Though other datasets on prescription opioids  exist, we chose to use CDC data because they show the actual number  of prescriptions filled in each county, were publicly available at the time of  our study, and included data through 2017.

SSA provided data on the number of DI claims, which we used to  calculate rates. We chose to include claims from individuals who are  generally subject to a disability determination, such as disabled workers,  widow(er)s, and adult children. We excluded individuals who are  generally not subject to these determinations, such as dependent  spouses and children under age 18. We examined DI only claims  separately from DI/Supplemental Security Income (SSI) concurrent  claims, and also examined similar data for DI allowances. We calculated  rates of DI claims per 1,000 people per year in each county using  population data from the U.S. Census Bureau. We used county-level data  because claimant-level data, such as prescription opioid use by DI  claimants, were not readily available.

Nationwide trends. We used aggregate data from CDC on opioid  prescriptions and data on DI claims from SSA to examine trends  nationwide from 2006 through 2017.

County variation. We used the data to examine variation among  counties in their rates of opioid prescriptions and DI claims.  Specifically, we examined the distribution of these rates among all  counties. We had data available on both rates of opioid prescriptions  and DI claims for 2,953 out of 3,142 counties nationwide. We then  examined counties with the highest rates of opioid prescriptions and  DI claims. We defined counties with the highest rates as those in the  top third of the statistical distributions for each rate (i.e., at least 984  counties for each rate in 2017). Of these counties, 527 were in the  top third of the statistical distribution for both rates. We plotted these  counties with the highest rates on a U.S. map to observe any  geographic differences across the Midwest, Southeast, Northeast,  and West.

In addition, we identified two counties to feature as illustrative examples.  To select these counties, we first calculated the number of years from  2010 through 2017 a given county ranked in the top 10 for rates of opioid  prescriptions and DI claims in each geographic region. We then selected  two of these high-rate counties to serve as examples from different  geographic regions and with different major industries.

Regressions on the relationship between opioid prescriptions  and DI claims. We used the county-level data to conduct regression  analyses to examine the relationship between rates of opioid  prescriptions and DI claims. In our regression models, we analyzed  rates of opioid prescriptions and DI claims. In addition, we used data  from a variety of sources to control for other county-level factors.  Specifically, economic factors we accounted for included  unemployment and poverty rates; demographic factors included sex,  age, and race; and other factors included state, year, population  size/degree of urbanization, and access to health insurance (i.e.,  uninsured rates). See table 1 above for additional information on the  sources of these data, as well as appendix II for a detailed discussion  of our regression analyses, including our models and limitations.

Section 3: Interviews with Program Staff in Selected States

To answer our second objective on how SSA considers potential  prescription opioid misuse in its DI eligibility decisions, we conducted site  visits to Alabama, Kentucky, and West Virginia. We selected these three  states primarily because of their high rates of opioid prescriptions in 2016  and drug overdose deaths in 2017, and because a high percentage of  their adult population received DI benefits in 2015.

In each state, we visited one Disability Determination Services (DDS)  office and one Hearing Office. These six offices included the Birmingham  DDS and Birmingham Hearing Office in Alabama, the Frankfort DDS and  Louisville Hearing Office in Kentucky, and the Charleston DDS and  Charleston Hearing Office in West Virginia. We selected offices that were  relatively larger, were nearest to or in counties with the highest rates of  opioid prescriptions in the state in 2016, and where the DDS and Hearing  Office were in close proximity, among other reasons.

At each office, we interviewed a range of staff involved in making DI  eligibility decisions. Specifically, for each DDS, we conducted group  interviews with managers, disability examiners, and medical consultants.  We initially conducted an exploratory site visit to the Frankfort DDS in  Kentucky, where we met with all available managers, disability  examiners, and medical consultants. In the remaining visits, we met with  all available managers, but randomly selected five disability examiners  and five medical consultants for the group interviews. Each group  included between 5 and 15 participants. For each Hearing Office, we  conducted individual interviews with three randomly selected  administrative law judges, as well as the chief administrative law judge.  For the purposes of our report, we include state government DDS staff in  our general references to “program staff.”

We used semi-structured interview protocols for all interviews that  included open-ended questions about SSA’s processes for making  decisions on claims involving potential prescription opioid misuse and any  challenges doing so, among other topics. Because those we interviewed  provided answers in response to open-ended questions, not all  respondents commented on every process or challenge. In addition,  because we visited a non-probability sample of DDS and Hearing Offices  in three selected states, the results of our review cannot be generalized to  all offices and states.

Section 4: Case File Reviews for DI Beneficiaries

To gain a deeper understanding of how SSA considers potential  prescription opioid misuse in its DI eligibility decisions, we selected and  reviewed 30 case files from SSA involving DI beneficiaries who had been  identified by CMS as being at risk of opioid misuse or abuse. To select  case files, we used a dataset from CMS on Medicare Part D beneficiaries  that we matched with SSA data on DI beneficiaries. The CMS dataset  contained information on Medicare Part D beneficiaries who CMS  identified as being at risk of prescription opioid misuse or abuse in 2017.  CMS identifies beneficiaries as being at risk of prescription opioid misuse  or abuse if they received high amounts of opioids (had an average daily  morphine dose equivalent of 90 mg or more) and appeared to have  coordination of care issues (either had three or more opioid prescribers  and three or more opioid dispensing pharmacies, or five or more  prescribers regardless of the number of pharmacies) during a 6-month  period. We identified DI beneficiaries within this larger dataset of  Medicare Part D beneficiaries using an identifier in CMS’s data. This  identifier signified that DI eligibility was a beneficiary’s reason for  Medicare enrollment, since those eligible for DI may gain access to  Medicare after a 2-year waiting period. We then worked with SSA to  match these data on DI beneficiaries within CMS’s dataset with SSA data.  Specifically, we obtained information for analysis from SSA’s database on  various demographic characteristics of this population of DI beneficiaries,  including their sex, age, race, and impairments. We also obtained  administrative data on beneficiaries’ claims.

Using the CMS dataset on Medicare Part D beneficiaries that we matched  with SSA’s data on DI beneficiaries, we identified 30,273 DI beneficiaries  who had been identified by CMS as being at risk of prescription opioid  misuse or abuse in 2017. See appendix III for additional demographic and  other information on this population.

From the DI beneficiaries we identified, we selected 30 case files to  review based on a number of claims characteristics related to potential  prescription opioid misuse and SSA’s processing of the claim. First, we  only selected case files for individuals who had been allowed benefits  during or after 2013, when SSA formalized its policies for evaluating  substance use disorders, including prescription opioids. In addition, we  randomly selected 15 case files where the beneficiary had been  evaluated by SSA for an identified substance use disorder and 15 where  they had not. As part of the selection of 30 case files, we also randomly  selected 16 case files where the beneficiary had self-reported the use of a  prescription opioid and 14 where they had not, and 14 case files where  the beneficiary had their case reviewed for potential medical improvement  (called a continuing disability review) and 16 where they had not (these  characteristics were not mutually exclusive).

To systematically collect information on how or whether SSA considered  potential prescription opioid misuse in each case file, we developed a  data collection instrument to conduct our review of them. We designed  the instrument to examine SSA’s implementation of its process for making  DI eligibility decisions for claims involving substance use disorders,  including opioids not taken as prescribed. For example, the instrument  included questions about how SSA identifies and evaluates such  disorders when making decisions, any documentation of this process, and  how SSA reviews case files for potential medical improvement after  allowing benefits. The instrument was not intended to examine the  accuracy of decisions. In addition, we shared the instrument with SSA  officials in advance, who provided notes on where the needed information  could be found in the case files. Two GAO analysts independently  reviewed each case file using the instrument, then met to review coding  decisions and reconcile any differences between their reviews. We also  discussed the results of our review with SSA headquarters officials.  These officials provided comments on our observations for each case file,  which we took into consideration.

Though we examined information on all of the case file characteristics  described above, we ultimately focused on SSA’s implementation of its  process for evaluating the beneficiary for an identified substance use  disorder. We did not focus on SSA’s implementation of its process for  examining whether the beneficiary had self-reported the use of a  prescription opioid. This is because the use of opioids as prescribed is not  considered a substance use disorder under SSA’s policies. In addition,  we learned during our case file review that beneficiaries had multiple  opportunities to self-report such use that would not be captured in SSA’s  administrative data, and that program staff also had multiple opportunities  to examine such use when collecting and reviewing medical evidence. In  addition, we did not focus on SSA’s implementation of its process for  reviewing the beneficiary for potential medical improvement because we  learned during our case file review that substance use disorders seldom  factor into SSA decisions about whether to continue or cease DI benefits.  According to SSA, benefits typically continue unless evidence exists that  a beneficiaries’ impairment has medically improved and that they are able  to return to work. SSA headquarters officials told us that staff would not  evaluate a substance use disorder during the continuing disability review  unless the beneficiary has medically improved and a new impairment that  may be affected by a substance use disorder is to be assessed.

Several limitations exist with our review of case files. Because we  selected from a population of DI beneficiaries, the sample did not include  case files for claimants who were ultimately denied. However, we did not  see this as a significant limitation because SSA’s policies regarding the  DAA evaluation are the same regardless of whether a claim is ultimately  allowed or denied. In addition, 16 of the 30 case files we reviewed had  been denied at the initial level before being allowed on appeal at later  adjudicative levels. In addition, the case files may not have contained any  evidence of prescription opioid misuse or abuse because of the  timeframes we used to select them. Specifically, we selected case files  for DI beneficiaries who had been allowed during or after 2013, but who  were identified as being at risk of prescription opioid misuse or abuse in  2017. Because these beneficiaries may have been allowed benefits as  early as 2013, they may not have had any issues with prescription opioids  at the time SSA evaluated their claim (i.e., they may have developed  potential issues after being allowed benefits). Lastly, because we  reviewed a non-probability sample of 30 case files, the results of our  review cannot be generalized to the larger population of DI beneficiaries.

Appendix II: Regression Analyses of County- Level Data

We used regression models and other data analyses to address our first  objective on the relationship between opioid prescriptions and Disability  Insurance (DI) claims. This technical appendix outlines the data,  methodology, limitations, and results for the regression analyses in our  report.

We used county-level data from data sources administered by the  Department of Health and Human Services’ Centers for Disease Control  and Prevention (CDC) and other federal agencies from 2010 through  2017. These data included the rates of opioid prescriptions from CDC.  We also used ZIP code-level data from the Social Security Administration  (SSA) on the number of DI claims, which we transformed into county-level  data using ZIP code-to-county crosswalk data from the Department of  Housing and Urban Development. Similarly, we examined the numbers  of DI allowances and DI/Supplemental Security Income (SSI) concurrent  claims and allowances from SSA as well. In addition, we used data on a  number of economic, demographic, and other factors. Economic factors  included unemployment and poverty rates; demographic factors included  sex, age, and race; and other factors included state, year, population  size/degree of urbanization, and access to health insurance (i.e.,  uninsured rates). We used data from 2010 through 2017 because those  were the years in which we had data for all of our factors, with the  exception of degree of urbanization. We had data on degree of  urbanization for 2013, and assumed that this factor was consistent from  2010 through 2017. For a list of the county-level data that we used in our  analyses and their sources, see table 1 in appendix I.

Table 3 provides summary statistics for factors included in our regression  models. These include the mean, median, standard deviation, and range  for the factors among counties from 2010 through 2017.

We used linear regression models to analyze the relationship between  rates of opioid prescriptions and DI claims, and controlled for the  economic, demographic, and other factors described above at the county  level. Our unit of analysis was the county-year, meaning that the  observations are for each county each year. We had 22,789  observations, since there are over 2,977 counties and we used data from  2010 through 2017. Some of the factors we controlled for, such as  unemployment rates, sex, age, race, and access to health insurance (i.e.,  uninsured rates), were similar to what other researchers used in  examining the relationship between prescription opioids and employment  variables.

We included state and year fixed effects in our models to help account for  additional factors that could vary across states or over time and national  time trends. For example, differences in prescribing practices and  increased law enforcement strategies across states could affect rates of  opioid prescriptions. Further, factors that have previously been identified  as possibly affecting the DI population include changes in the  characteristics of the working-age population, federal policies (e.g., DI  eligibility criteria), and employment opportunities. The results should be  interpreted as changes in the dependent variable (i.e., rate of opioid  prescriptions or rate of DI claims) associated with a change in the  independent variables, within states.

Compared to the previous model, the main dependent and  independent variables are switched, but all other elements of the  model are as described above.

Though our primary focus was the relationship between rates of opioid  prescriptions and DI claims, we also examined the relationship between  rates of opioid prescriptions and DI allowances, as well as concurrent  DI/SSI claims and allowances.

We found that the data we reported on were sufficiently reliable for the  purposes of our analyses. However, our analytical approach was limited  by the availability of data. Consequently, our results should be interpreted  with caution. Specifically, we were unable to establish whether there is a  causal relationship between rates of opioid prescriptions and DI claims  (e.g., whether higher rates of opioid prescriptions could have contributed  to higher rates of DI claims or vice versa), in part because of potential  reverse causality between these variables. While we could have  potentially used an instrumental variable approach to establish a causal  relationship, we did not identify an appropriate instrument to conduct that  analysis. Moreover, individual-level data on opioid use among DI  claimants were not readily available. Though we used county-level data,  we were unable to account for variations within counties, also due to data  not being readily available. Other researchers have noted similar  limitations in their studies on prescription opioids.

In addition, the opioid prescriptions data we analyzed only count the  number of prescriptions filled, which could vary by number of pills,  dosage, and potency (i.e., the morphine dose equivalent). The data also  do not account for any potential diversion, or illicit transfer, of prescription  opioids from one county to another.

Further, we did not include county-fixed effects in our models. Though  there may be constant or long-term characteristics of counties that are  related to rates of opioid prescriptions and DI claims, we did not find  enough variation in these rates within counties in the timeframe we  analyzed to include county fixed effects in our models. In sensitivity  analyses, we did include county fixed effects in our models and found that  there was not a statistically significant relationship between rates of opioid  prescriptions and DI claims with these effects included. However, this  may be due to the large number of fixed effects introduced in the model  (our analyses included about 3,000 counties) and the relatively short  timeframe of 2010 through 2017.

Lastly, we analyzed DI claims separately from DI/SSI concurrent claims in  our models due to limitations with the units of analyses for these claims.  Specifically, the number of DI claims represents the total number of  claims an individual may have, rather than the number of individuals. For  example, one individual may have five different DI claims and all five  would be counted in the number of DI claims. On the other hand, the  number of DI/SSI concurrent claims represents the number of individuals  who had filed at least one DI and one SSI claim within a given year. The  individual may have filed two DI claims and three SSI claims that year,  but are counted as one DI/SSI concurrent claim.

Though we were unable to determine whether there is a causal  relationship between rates of opioid prescriptions and DI claims (e.g.,  whether higher rates of opioid prescriptions could have contributed to  higher rates of DI claims or vice versa), as discussed above, we did find a  significantly positive correlation between these rates across our models,  on average, from 2010 through 2017. These results were consistent  before and after accounting for the economic, demographic, and other  factors described above.

We also found correlations between rates of opioid prescriptions and  some of the other factors. These correlations are detailed in figure 6 of  our report. Table 4 also provides additional results from our regression  analyses for rates of opioid prescriptions.

In addition, we found correlations between rates of DI claims and other  factors. Similarly, these correlations are detailed in figure 6 of our report.  Table 5 provides additional results.

We also examined the relationship between rates of opioid prescriptions  and DI allowances, as well as DI/SSI concurrent claims and allowances,  and found similar results.

In various sensitivity analyses to check our results, we found that the  positive correlation between rates of opioid prescriptions and DI claims  remained consistent. For example, these results were consistent in  models that:  Included labor force participation rates instead of unemployment or  poverty rates.

Examined each year of data. Given that we did not find much variation  in rates of opioid prescriptions and DI claims within counties from  2010 through 2017, we also ran our models for each year separately  to explain variations across counties.

Accounted for counties with small populations. There were eight  counties that were omitted from our regression models because they  had no DI claims. To ensure we accounted for all counties in our  sensitivity analyses, we took an approach similar to other researchers  and aggregated counties with less than 100,000 people in each state  for each year. We ran our models when treating these counties with  small populations as one county and found similar qualitative results.

Appendix III: Characteristics of Disability Insurance Beneficiaries Identified as Being At Risk for Prescription Opioid Misuse or Abuse

Using data from the Centers for Medicare & Medicaid Services (CMS)  and the Social Security Administration (SSA), we identified 30,273  Disability Insurance (DI) beneficiaries who had been identified by CMS as  being at risk of prescription opioid misuse or abuse in 2017. Figures 8  and 9 describe the demographics of this population, including  beneficiaries’ sex, age, and race, as well as the primary impairments for  which they were allowed DI benefits.

Appendix IV: Comments from the Social Security Administration

In addition to the contact named above, the following staff members  made key contributions to this report: Erin Godtland (Assistant Director),  Nhi Nguyen (Analyst-in-Charge), Justin Gordinas, Kathleen McQueeney,  and Paul Wright. Also contributing to this report were James Bennett, Joy  Booth, Mari Calderón, Breanne Cave, Jessica Farb, Justin Fisher, Alex  Galuten, Melissa Jaynes, Lorin Obler, Jessica Orr, Oliver Richard, William  Simerl, Almeta Spencer, Shana Wallace, and Eric Wedum.

People whose disabilities keep them from working can apply for Social Security Disability Insurance. People who claim these benefits may also be on prescription opioids for chronic pain. Opioids are addictive and can be misused.

Social Security staff must deny claims if claimants who stopped using drugs or alcohol would be employable. Staff in 5 of 6 offices we visited were confused about when to evaluate a claimant’s substance use.

In more than half the cases we reviewed, the evaluations weren’t fully documented.

Our recommendations are to help staff improve the evaluation process.