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. 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. 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. 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’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. 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. 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. 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. 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. Our three sets of analyses examined: 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. 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. 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. 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. 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. 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. 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.