Subsequent Program Participation of Former Social Security Disability Insurance Beneficiaries and Supplemental Security Income Recipients Whose Eligibility Ceased Because of Medical Improvement

Introduction

Each year, the Social Security Administration (SSA) reviews the status of several hundred thousand Social Security Disability Insurance (DI) beneficiaries and Supplemental Security Income (SSI) recipients to determine if their medical conditions have improved enough since their last favorable determination of eligibility to allow them to engage in substantial gainful activity (SGA). To be eligible for the SSI disability program, an individual must have limited income and resources and be unable to engage in SGA because of a medically determinable physical or mental impairment that can be expected to result in death or last for at least 12 continuous months.¹ To qualify for DI, an individual must have a work history sufficient to attain insured status in addition to meeting the medical requirement.² At the time of award, or the last favorable review of eligibility, a date is set to revisit the individual's medical eligibility for continued participation. Because reviewing each case helps ensure that only eligible individuals receive payments, it is necessary for maintaining program integrity.

These periodic reviews, required by law, are called continuing disability reviews (CDRs). In order to keep the workload manageable and to limit administrative costs, SSA initiates the CDR process by using statistical models to identify individuals with characteristics indicating potential medical improvement. Based on those model results, SSA conducts a full medical review (FMR) only for cases deemed most likely to involve medical improvement. To individuals with a lower likelihood of medical improvement, SSA sends a "mailer" asking for more information to help determine if a FMR is necessary.

During a FMR, SSA and the state Disability Determination Service (DDS) collect medical information about the participant and determine whether evidence of medical improvement exists. If the individual's condition has improved since the most recent favorable decision such that he or she is able to engage in SGA, program eligibility ceases; if not, the individual continues to receive DI benefits or SSI payments and a date is set for a future review.³ CDRs are estimated to be highly cost effective, saving approximately $9.30 for every dollar spent on them (SSA 2012b).⁴ For that reason, the 2011 deficit-reduction plan exempted CDR allocations from congressional spending caps, and the Obama administration requested an increase in CDR funding in the 2012 budget.⁵

The focus on program integrity comes at a time of substantial increases in SSI and DI participation. From 1990 through 2011, the numbers of DI beneficiaries grew from about 3.0 million to 8.6 million and disabled SSI recipients increased from 3.3 million to 6.9 million (SSA 2012c, 2012d, 2012f). Although much of the increase is simply due to the aging and growth of the population, some have argued that the programs have become relatively more attractive to low-wage individuals and those with moderate disabilities, especially during economic downturns (for example, Autor and Duggan 2003, 2006; Black, Daniel, and Sanders 2002; and Rupp and Stapleton 1998). Additionally, there is some evidence that states have transferred some of the costs formerly borne under Temporary Assistance for Needy Families onto the federal SSI program (Burkhauser and Daly 2011; Schmidt and Sevak 2004; Kubik 1999, 2003; Wamhoff and Wiseman 2005/2006). The 1990 Sullivan v. Zebley Supreme Court decision greatly expanded SSI eligibility for children, although welfare reform in the mid-1990s required SSA to review cases allowed during that period. Regardless, the SSI child population grew substantially in the 1990s, and many recipients continue receiving SSI as adults. In light of the increasing program costs associated with increasing participation, it is important for SSA to ensure that only those truly eligible for DI and SSI remain in the programs.

Although a few studies have looked at DI beneficiaries who medically recover (for example, Hennessey and Dykacz 1993; Dykacz and Hennessey 1989; Treitel 1979; and Schmulowitz 1973), we have not found similar studies of SSI recipients.⁶ The DI studies have focused on earnings of former beneficiaries rather than subsequent program participation after a cessation decision. A few studies that look at subsequent return (Hennessey and Dykacz 1993; Dykacz and Hennessey 1989; Dykacz 1998) do not differentiate between medical and SGA-based recovery.⁷

Understanding what happens to individuals after their eligibility ceases because of medical improvement is important given recent calls across the government for stronger program integrity. Additionally, although actuaries from SSA and the Centers for Medicare and Medicaid Services incorporate returns in their models of the savings derived from CDRs, it is important for policymakers to better understand the impact of CDRs on program participation patterns.

In this article, we provide new information on the experiences of DI beneficiaries and SSI recipients after receiving a FMR that resulted in eligibility cessation. Specifically, we look at subsequent DI and SSI participation of former DI beneficiaries and former SSI recipients. Although this study does not address whether SSA's current CDR policy is adequate or how well the social safety net is working in general, we provide descriptive information on formerly eligible participants and highlight which subgroups are most likely to return to program participation.

CDR Process

The date for which a CDR is scheduled is called the CDR diary date. That date is set during the last favorable decision, which in many cases is the time of award. SSA categorizes diaries into one of three groups according to the individual's prospects for medical improvement, and the diary type determines the timing of the scheduled CDR. If medical improvement is expected, the diary date is within 3 years of the last favorable decision. For cases in which SSA deems medical improvement possible, a CDR is scheduled for 3 years after the last favorable decision. If medical improvement is not expected, a CDR is scheduled for 5 to 7 years after the last favorable decision. When the diary date approaches, SSA either "directly releases" the individual for a FMR or sends the individual a mailer containing a questionnaire seeking information to determine whether a FMR is necessary.⁸

To help determine who is directly released for a FMR and who receives a mailer, SSA uses a CDR profiling model based on administrative information to "score" the likelihood of medical improvement. SSA groups the results into three categories of likelihood of medical improvement—high, medium, or low—using cutoff scores that have not changed over time. Generally, high-scoring individuals undergo a FMR, and medium- or low-scoring DI beneficiaries and adult SSI recipients receive a mailer. However, as limited funding in recent years has restricted resources and experienced staff, SSA and the DDSs have further prioritized FMRs. As a result, some individuals do not receive their scheduled review until years later.⁹

If a mailer recipient's responses indicate medical improvement, SSA releases the case for a FMR;¹⁰ otherwise, the agency simply sets a new CDR diary date. For a FMR, the DDS gathers medical information from the individual's medical care sources or orders consultative examinations from the treatment provider or other physicians.¹¹ A disability examiner and medical expert then determine if the individual's condition has improved since the last favorable decision to such an extent that he or she can perform SGA. If there has been no improvement, the individual is "continued" on the program and the DDS examiner sets a date for the next CDR. If the individual has medically improved enough to perform SGA, the examiner makes a "cessation" decision, which the individual may appeal.¹² Benefits stop after a 3-month grace period (the month of the decision and the following 2 months) unless the beneficiary appeals the decision and requests continuation of benefits during the appeal.¹³ In fiscal year 2010, over 90 percent of initial CDR decisions for DI disabled-worker beneficiaries and SSI adult recipients were continuations (SSA 2012b).

The process described above has changed over time. One important example is that, as SSA moved toward statistical profiling, the agency started conducting FMRs for a sample of cases—a "profile sample"— that would not otherwise have received one. FMRs for the profile sample must be completed each year to validate the profiling model. We do not use the profile sample in our estimates because of the varying procedures under which they were drawn over the period we analyze.

Data Sources and Methodology

In this study, we use data from Social Security administrative records. The primary source is SSA's CDR Waterfall file, which contains information on all centrally initiated FMRs with a DDS determination.¹⁴ We used an extract of the CDR Waterfall file covering calendar years 2003 through 2008.¹⁵ That period includes FMRs conducted after the funding dedicated to processing CDRs was reduced. The file does not contain records for individuals who received a mailer unless their responses indicated possible medical improvement, in which case they went on to receive a FMR (subject to agency resources).

The file contains the date and result of the initial FMR decision by the DDS as well as the final appellate decision at the time the file was extracted. We use those data to identify records for which the FMR led to a final cessation and to define the year of the initial decision. We also use that file to create several variables likely to be correlated with return to program participation:

• CDR diary type (medical improvement expected, not expected, or possible);
• CDR profile score (high, medium, or low);
• whether the individual received a mailer or was directly released for a FMR;
• whether the individual had a prior CDR;
• whether a consultative examination was requested during the FMR;
• the adjudicative level of the decision under which the individual first entered the DI or SSI program (initial, reconsideration, Administrative Law Judge or higher, or unknown); and
• the disability considered to be the primary impairment prior to the FMR.

In addition, the file contains the date the individual became eligible for DI or SSI, the date of birth (used to establish age at the time of the initial decision¹⁶), sex, race, and state of residence, which may also be correlated with return to the program. For individuals receiving both SSI and DI, we use the eligibility date and adjudicative level of whichever program they entered first.

We merged the CDR Waterfall file with SSA's Numident file to obtain dates of death. If a record was missing the date of birth, we used the Supplemental Security Record and the Master Beneficiary Record (program databases covering applicants and beneficiaries for SSI and DI) to obtain it.

We also merged those files with SSA's Master Earnings File to create a measure of preeligibility earnings. We use the average earnings in the 5 years preceding the individual's date of eligibility to derive that measure. In our analyses, we include the program-specific earnings quartile of our target population. For example, we use the earnings of DI-only disabled-worker beneficiaries to define the quartiles for that group. For SSI-only recipients, we combine the two lowest quartiles because their median earnings are very close to $0.

To determine if an individual returned to DI or SSI, we merged the data described above with SSA's Disability Research File. We used that file to identify the date of the first successful postcessation application. A successful application is determined by whether benefits are awarded; postallowance technical denials are omitted. We are able to follow individuals in all of those data files through 2010.

Target Populations

All three of the target populations in this article consist of adults aged 18–59 who participated in disability programs administered by SSA until their eligibility ceased because of a FMR finding of medical improvement. The groups comprise former DI-only disabled-worker beneficiaries (individuals who did not also receive SSI payments, hereafter called "DI-only workers"), former SSI-only recipients (individuals who did not also receive DI benefits), and former disabled-worker concurrent beneficiaries (individuals who received both DI benefits and SSI payments, hereafter called "concurrent workers"). The FMRs that produced the cessation decisions were conducted during 2003–2008.

We restricted the target populations for various reasons. We removed individuals belonging to the profile sample, as well as those for whom a FMR determined reeligibility during a period of expedited reinstatement.¹⁷ We removed records with missing or inconsistent dates, such as those indicating that an individual died before becoming eligible. We also removed individuals who appealed a cessation decision and were awaiting a new decision or still had time to file an appeal between their last cessation decision and the date the file was created. Because we focus on subsequent program participation, we excluded individuals whose eligibility did not cease. We observed the members of our sample through age 62 (as discussed below in the Analytical Methods section). Therefore, we omitted individuals who reached age 60 before their initial FMR decision and those who turned 62 before their final FMR decision in order to ensure adequate followup time.¹⁸ We also excluded DI beneficiaries and SSI recipients who died before the final FMR decision or whose CDR profile score was missing. Those exclusions leave target populations of 33,376 DI-only workers, 24,514 SSI-only recipients, and 17,742 concurrent workers.¹⁹ Appendix Table A-1 presents the number of records eliminated in each step of the selection process.

Limitations

SSA's CDR process is complex and dynamic. When considering our results, the reader should remember that our primary analysis pools data for several years under varying CDR policies. For example, different types of participants may have been targeted in certain years because of perceived cost savings or changes in the profiling model. Moreover, other SSA policies can also affect a CDR decision, complicating the definitional boundaries of our target populations. For example, Section 301 of the Social Security Disability Amendments of 1980 (Public Law 96-265) allows individuals to continue receiving payments even if they have received a cessation decision as long as they participate in an approved vocational program and make progress toward their employment goals. Because our observation period for each individual begins with the date of the FMR decision, the outcomes for former participants in our target population who use the Section 301 provisions and those who do not might differ. We cannot identify Section 301 use in our data (however, usage is generally low).

Our estimates also cannot anticipate future changes in funding for CDRs, the stringency of the reviews and the eligibility requirements, and the extent to which SSA uses its profiling model. The interaction of those and other factors could lead, for example, to an increase in the number of FMRs conducted. However, depending on the underlying causes and other circumstances, an increase in CDRs could result in program returns that differ in either direction from our estimates.

Analytical Methods

In this section, we discuss the cumulative incidence functions (CIFs) and proportional hazard regressions used in our analysis. We also address collinearity issues.

Cumulative Incidence Functions (CIFs)

Our primary goal is to estimate, among DI beneficiaries and SSI recipients whose eligibility ceased because of medical improvement, the percentage who subsequently returned to either the same program or the opposite program. If returning to the program was the only possible outcome and we observed all individuals over a consistent period, we would simply divide the number of individuals who returned by the number of people whose eligibility ceased. Unfortunately, neither of those conditions holds. Our observation periods range from 2 years to 8 years, depending on the year of the individual's FMR. Additionally, certain life events will compete with that outcome in other ways; death, for example, obviously precludes program return. Also, disability is no longer a factor in the SSI eligibility determination once an individual reaches age 65, and after a person reaches full retirement age (between 65 and 67 years, depending on year of birth), disability no longer affects Social Security benefit eligibility. Accurate estimates of program return must account for such factors.

To address those issues, we compute CIFs measuring the cumulative percentage of individuals from each target population who return to DI or SSI after the final cessation decision. CIFs estimate the probability of an event (such as returning to the program) when competing risks exist (Gooley and others 1999). For our analysis, we treat attainment of age 62 (which we refer to as early retirement or, simply, retirement) and death as competing events or risks.²⁰ Once individuals attain age 62 or die, they are no longer at risk of returning and thus provide no information about the probability of program return. Without controlling for those competing events, our estimates would assume such individuals could still return later, artificially decreasing estimated returns. Dropping the individuals who experience those events from our analysis would similarly bias our results. Thus, we estimate the probability that an individual returns to the program, allowing for the risk of dying or reaching age 62 by the end of our follow-up period (December 31, 2010). Our measure of time covers the period from the date of the final FMR decision to the first of those events.

Marubini and Valsecchi (1995) show that the CIFs can be estimated by

where j represents the event of interest (return to the program), $\widehat{S}(t_k)$ is the overall Kaplan-Meier survival function (that is, an estimate of the probability of neither returning, dying, nor reaching age 62 by time t_k), d_jk is the number of individuals returning at time t_k, and n_k is the count of those at risk of returning at time t_k. Thus, it is the sum of the products of the survival estimate at time t_k and the hazard at time t_k of event j, $\left(\frac{d_{jk}}{n_k}\right)$ .

As described above, we are able to track program return, death, and early retirement through December 31, 2010 (the censoring date); however, we present only the results for program return. We estimate the CIFs in monthly increments and the maximum observable time span in our data is 96 months, or 8 years.^21,22

Regressions

Because the CIF does not control for other variables that may affect return to the program, we ran Cox proportional hazard regressions on the hazard of successfully reapplying to the program to control for the characteristics of our population. Like other types of regression (such as ordinary least squares), Cox regressions provide estimates of the relative contribution of the covariates to the outcome, which in this case is the risk (or "hazard") of returning to the program over a given period of time. The exponentiated coefficients from this regression are known as hazard ratios and are interpreted similarly to odds ratios from logistic regressions: Hazard ratios greater than 1 indicate a higher risk of return relative to the reference group and those less than 1 indicate a lower risk.

The time dimension is one of the primary differences between Cox regressions and static regressions: Cox regressions estimate whether an event occurs, controlling for the timing of the event. As with the CIFs, Cox regressions control for the diverse followup times within the sample. Individuals no longer at risk of returning to the program are censored and thus drop from subsequent periods in the analysis. Unlike the CIFs, though, competing events do not hinder our ability to estimate the risk of return; that is, we can estimate the risk of return by treating competing events (death and early retirement age) as censored at the time they occur.²³

In all our empirical models, we stratify our analyses by year of initial FMR determination, state of residence, sex, and race, allowing for separate baseline hazard functions for groups identified by those characteristics but constraining the coefficients (and hazard ratios) to be equal.²⁴ We do so because the different CDR policies, funding, and resources, and the variation in state policies and economies, likely affect the baseline hazard of return in each state and year in different ways. Stratification allows the effect of the other covariates in our empirical model to be proportional to the differing baseline hazards. Although this method eliminates our ability to estimate hazard ratios for the stratification variables, it also helps satisfy the proportionality assumption discussed in the following paragraph. However, future work may further consider the distributional aspects of program return.

The Cox regressions rely on the proportionality between the hazard and each covariate being constant over time. Grambsch and Therneau (1994) suggested a test of the proportionality assumption using scaled Schoenfeld residuals.²⁵ Those residuals (essentially the covariate value for a person actually experiencing an event minus the expected value of the event) are independent of time if the proportionality assumption is satisfied. After running that test on our empirical models, we determined that our data do not satisfy the proportionality assumption for the DI-only and concurrent worker models. For the empirical model of return to DI by former DI-only workers, the problematic variables were CDR profile score, history of a prior CDR, and preeligibility earnings quartile. In addition to those variables, the age variables did not satisfy the proportional hazards assumption in the empirical model of the return to DI by concurrent workers. For the empirical model of former DI-only workers entering SSI, the problematic variables were CDR profile score, history of a prior CDR, and mailer-recipient status. For the empirical model of concurrent workers returning to SSI, the problematic variables were history of a prior CDR and diary type.

For the problematic variables, we allow the hazard ratios to take on different values at different times. To minimize the effect of imposing a functional form on the relationship with time and to keep the empirical models computationally feasible, we allow each of the variables to have different hazard ratios for each year of followup, combining the seventh and eighth years because of small cell sizes. For example, we include a separate hazard ratio to capture the effect of a high CDR profile score in the first year after the FMR, the second year after the FMR, and so on up to 7+ years after the FMR.²⁶ The resulting general empirical model is:

where h_i(t) is the hazard for stratification group i at time t, h_0i(t) is the baseline hazard,²⁷ the β_s are the coefficients, and the x_s are the main variables. The last term on the right-hand side of the equation captures the time-varying effects, where γ_m is the effect of variable z_m m years after the FMR (and is not included in the SSI-only empirical model). In the estimation, the coefficients (β_s and γ_m) are constrained to be equal across stratification groups. All empirical models use the Efron method for treating tied events.²⁸

Multicollinearity

Because of the number and the nature of the variables in our models, our estimates may suffer from multicollinearity, causing individual hazard ratios to become difficult to interpret and standard errors to be inflated. However, excluding problem variables could lead to omitted-variables bias, also causing difficult-to-interpret hazard ratios.

We tested for multicollinearity by first looking for high correlation coefficients between our variables, but did not find any we deemed especially problematic (that is, greater than 0.30). We also formally tested for multicollinearity by estimating the variance inflation factor for each variable, which is 1/(1-R²) where the R² comes from a regression using each independent variable as the dependent variable. Because multicollinearity applies to the independent variables, functional form is irrelevant. Variance inflation factors above 10 signify multicollinearity issues. Very few variance inflation factors exceeded 4, and only one was above 10. The problematic variables were CDR profile score and years in the program. Many of our variables are included in the model estimating the CDR profile score, so its status as potentially problematic is not surprising. We also ran separate regressions subsetting on each value of our independent variables; and although hazard ratios differ across regressions, and levels of significance vary, we did not discern any consistent patterns. Additionally, there are large differences in population when we subset by those variables, which may also affect statistical significance.

Given the lack of clear evidence for multicollinearity from the variance inflation factor, low correlation coefficients, and results from subgroup-specific regressions, we do not exclude any variables from our Cox regressions or present subgroup-specific regressions. We generally focus on the direction of the hazard ratios, not their magnitudes. Thus, our regressions should be viewed as primarily exploratory or descriptive in nature, suggesting groups to focus on more closely in future research.

Characteristics of the Formerly Eligible Population

Table 1 shows demographic and programmatic characteristics of our target populations of former DI-only workers, SSI-only recipients, and concurrent workers. It covers all cases in which eligibility cessation was the outcome of a FMR conducted during calendar years 2003–2008 and for which potential appeals have expired or been exhausted. The majority (74 percent) of formerly eligible DI-only workers are aged 30–49 (with 31 percent aged 30–39 and 43 percent aged 40–49). Former SSI-only recipients are somewhat younger, with 36 percent younger than 30 and another 28 percent aged 30–39. The age distribution of concurrent workers falls somewhere in the middle, with two-thirds between ages 30 and 49 and one-quarter who are younger than 30.

The most common impairments among former DI-only workers are certain mental disorders (combined and categorized under "other mental disorders") and musculoskeletal system diseases (30 percent and 16 percent of the target population, respectively). Among former SSI-only recipients, we see the largest proportions in the other mental disorders (35 percent) and intellectual disabilities (20 percent) categories. Nearly 40 percent of former concurrent workers have other mental disorders, far outnumbering individuals in any other diagnosis category. Those impairments are similarly the most common among DI disabled-worker beneficiaries and SSI adult recipient populations overall (SSA 2012a, 2012b).

The most common diary type in each of the target populations is possible medical improvement, with 68 percent of former DI-only workers, 80 percent of former SSI-only recipients, and 65 percent of former concurrent workers. Those expected to medically improve comprise the next largest share of each target population, with 28 percent of the DI-only group, 14 percent of the SSI-only group, and 33 percent of the concurrent group. Very few individuals are not expected to medically improve. This is not surprising because those judged least likely to medically recover would generally not receive a FMR, thus excluding them from our target population.

Pluralities of former DI-only and concurrent workers (both more than 41 percent) and a majority of former SSI-only recipients (71 percent) had been program participants for 6 years or longer; another one-quarter of DI-only and concurrent workers had participated for 4 to 5 years. Those large shares may result from a decline in CDR funding and a growing backlog of cases. We note that over three-quarters of each target population did not have a CDR prior to the current one, and over 60 percent have had medical improvement deemed possible (meaning a CDR scheduled every 3 years).

About 70 percent of DI-only and concurrent workers and 90 percent of SSI-only recipients had their FMR during the first half of our study period (2003–2005). The decline in FMRs in the latter half of the period is most likely due to a decrease in the number of cases sent for review because of lower funding. Year-to-year differences in the percentage of FMRs may also be related to changing CDR policies in SSA. Appendix Tables B-1 through B-3 report statistics for each target population in the first (2003) and last (2008) FMR years we analyze.²⁹

Return to DI and SSI

In this section, we present estimates of the return to DI and SSI within 8 years of a final cessation decision. We begin with the estimates of the CIFs for the full target populations and follow with estimates for subsetting characteristics. We then turn to the regression results, focusing separately on each target population's return to DI and SSI.

CIF Results

We estimate the CIFs of return to DI and SSI for each beneficiary type, that is, the probability that a former participant successfully applies for DI or SSI by a given month. As stated earlier, we follow individuals until they successfully reapply for SSI or DI (depending on the empirical model), they attain age 62, they die, or December 31, 2010, whichever occurred first.³⁰ We present estimates of program return to DI in Chart 1 and to SSI in Chart 2.

Recall that we are measuring the time from the final FMR cessation decision to the application that leads to a new award. Given the large volume of appeals and the SSA backlog, it likely takes several more months until the first payment is received by those who return. However, in most circumstances, back payments will cover the time from favorable eligibility determination to first payment.

We estimate that about 20 percent of our DI-only target population and 21 percent of concurrent workers will return to DI within 8 years of an eligibility cessation due to medical improvement (Chart 1). More than one-half of those returns occur within the first few years of the FMR—at 3 years, roughly 11 percent of each group had returned.

A much smaller percentage of the SSI-only group successfully applies for DI after their SSI eligibility ceases (6 percent). Former SSI-only recipients must establish a sufficient work history to become eligible for DI. We cannot determine how many quarters of coverage those individuals had prior to entering SSI; some may only have needed a few quarters while others may have needed many. However, former SSI-only recipients with higher preparticipation earnings are more likely to subsequently enter DI than those with lower preparticipation earnings.

We estimate that almost 30 percent of the SSI-only group will return to SSI within 8 years of a final eligibility cessation (Chart 2). Unsurprisingly, concurrent workers return to SSI at about the same rate as they return to DI (22 percent). We estimate that 11 percent of former DI-only workers will successfully apply for SSI payments within 8 years.³¹

Note that the estimated CIFs at year 8 reflect the experiences of the earliest FMRs in our target population. However, the greatest risk of return, measured by the slope of the CIF, is in the first few years after the FMR. Although CIFs increase over time, they do so at diminishing rates.³²

CIFs by Subsetting Characteristics

Table 2 presents the estimated cumulative incidence of successfully applying for DI or SSI within 8 years of cessation for each target population by characteristic. The first line replicates the final values of the overall CIFs in Charts 1 and 2 (that is, the average return after 8 years).

The estimated percentages of successful DI or SSI application vary substantially across characteristics. In general, those for whom SSA does not expect medical improvement are more likely to return within 8 years than the groups for whom medical improvement is expected or deemed possible. A higher percentage of older individuals tend to return to their original program (or to either program for former concurrent workers), compared with the overall return averages. The return percentage for those with a prior CDR is lower than average across all categories; correspondingly, the percentage is higher than average among those without a prior CDR.

Among those diagnosed with schizophrenia and other psychotic disorders, circulatory system diseases, and genitourinary system diseases, we estimate higher-than-average percentages returning to each program from all three former program types. We estimate lower-than-average percentages returning among those with neoplasms, digestive system diseases, and injuries from all three target populations. The other characteristic groups show less variation across program types.

Regression Results

We estimated Cox proportional hazard regressions of the time to first successful postcessation DI or SSI application, controlling for the characteristics described earlier. Table 3 presents the hazard model results for program returns. The aggregate hazard ratios for the entire study period appear in the upper panel of Table 3 and the hazard ratios of the time-varying effects in each model are shown in the lower panel. Recall that the variables we include as time-varying are those that did not satisfy the proportionality assumptions of each Cox regression. Note that the methodology we use to estimate the time-varying effects creates separate observations for each distinct time period during which we observe an individual. Thus, an individual who, for example, has a medium CDR profile score and is observed for 4 years in the DI-only regressions will have four different observations in the data, one for each calendar year after cessation. As a result, the number of observations for DI-only and concurrent regressions shown in Table 3 is substantially higher than the total sample values given in Table 1; but the observations for the SSI-only regressions, which do not include time-varying effects, match the Table 1 values. Appendix Table C-1 presents standard errors for the regressions.

Former DI-only Workers

All else being equal, former DI-only workers have a higher risk (hazard) of returning to DI if they were older or judged less likely to improve according to the diary type. To illustrate, the hazard ratio of 1.40 for the medical improvement not expected group implies that the group, in any given year after cessation, had 1.40 times the risk of returning to DI as did the reference group (for which medical improvement was expected). Alternatively, those with higher CDR profile scores (that is, more likely to have their eligibility ceased according to SSA's profiling model) have a lower risk of return—although this effect diminishes after 3 years. For example, in the first year after cessation, the high CDR profile-score group's hazard ratio of 0.73 indicates that the risk of return to DI for members of this group was only 73 percent of that for members of the low profile-score group. In the fourth year after cessation, however, there is no difference in risk of return between the two groups (the hazard ratio is 1.06 and is not statistically significant). Former DI-only workers with a lower risk of return include those who had a prior CDR, those who required a consultative examination, and those who were on the DI program for 6 or more years (compared with those who were on DI for fewer than 2 years).

Former DI-only workers in the highest preeligibility earnings quartiles are less likely to return to DI within 4 years than are those in the lowest quartile, all else being equal. Relative to those with musculoskeletal system and connective tissue impairments, individuals with intellectual disabilities are much more likely to return to DI, as are those with schizophrenia and other psychotic disorders; other mental disorders; endocrine, nutritional, and metabolic diseases; circulatory system diseases; and genitourinary system diseases, all else being equal. Individuals with injuries have a lower risk of return than do those with musculoskeletal impairments. Also, those initially allowed at the Administrative Law Judge level or higher have a lower risk of return to DI than do those allowed at the initial adjudication level.

Although the magnitudes differ, the signs and significance of the hazard ratios of subsequent SSI participation for former DI-only workers are generally similar to those for subsequent DI participation. The hazard ratios of individuals previously on DI for 6 or more years, those allowed at the Administrative Law Judge level or higher, and those with injuries are not significant in the SSI empirical model. Consistent with the means-tested nature of SSI, former DI-only workers in higher preeligibility earnings quartiles have a lower risk of successfully applying for SSI than do those with earnings in the lowest quartile.

Former SSI-only Recipients

All else held equal, former SSI-only recipients have a higher risk of successfully applying for DI if they are considered less likely to medically improve (as judged by diary type) and if they had higher preeligibility earnings. Former SSI-only recipients who were on the program for 4 years or more, received a mailer, or required a consultative examination have a lower risk of successfully applying for DI. Additionally, those with endocrine, nutritional, and metabolic diseases, genitourinary system diseases, and "other" impairments are more likely than those with musculoskeletal and connective tissue impairments to apply successfully for DI.

The characteristics influencing return to SSI by former SSI-only recipients differ from those influencing successful application for DI. For example, the diary type and mailer status hazard ratios are not statistically significant in the SSI regression. Additionally, those with a prior CDR are less likely to return to SSI, and those who were aged 18 or older at the time they first entered SSI are more likely to return to SSI. Neither of those variables is significant in the DI-return model. Older individuals are also more likely to return to SSI. As would be expected, those with higher preeligibility earnings are less likely to return to SSI, although we found them more likely to successfully apply for DI after SSI cessation.

Former Concurrent Workers

In the empirical models for former concurrent workers, those not expected to medically improve are more likely to return to each program, but those with medical improvement deemed possible are more likely to return only to SSI. In the SSI empirical model, those effects are sporadic; in cases where medical improvement is not expected, the hazard ratios are statistically significant in only the second and fifth years (1.57 and 1.71, respectively), and where improvement is deemed possible, only the first-year estimate (1.23) is significant. Individuals with higher CDR profile scores are less likely to return to SSI, but those effects fluctuate in the DI empirical model, with some hazard ratios above 1 and others below 1 in no consistent pattern. In both empirical models, those with a prior CDR and those who required a consultative examination are less likely to return to DI and SSI. The hazard ratios for the highest two earnings quartiles in the SSI-return empirical model are statistically significant, with individuals in those quartiles less likely to return to SSI. In the DI empirical model, the estimates suggest higher earners are somewhat more likely to enter DI, but the hazard ratios vary over the followup period. Older individuals are also more likely to return to each program.

Individuals with schizophrenia and other psychotic disorders are more likely to return to either program than are those with musculoskeletal and connective tissue impairments; those with neoplasms and injuries are less likely to return. Former concurrent workers with intellectual disabilities are more likely to return to the SSI program. As for the DI program, individuals with digestive systems diseases are less likely to return, while those with genitourinary system diseases are more likely.

Year-Specific Estimates

As discussed earlier, our aggregate results pool several cohort years together, resulting in heterogeneous target populations. Therefore, the estimated CIFs may mask differences in the rates of program return between yearly cohorts. To explore that possibility, we present the estimates of the CIFs for each FMR cohort year for former DI-only workers returning to DI (Chart 3) and former SSI-only recipients returning to SSI (Chart 4) through the maximum followup time.³³

For both programs, there is substantial overlap of the cohort-year estimates over time—program return is fairly similar in every followup month for each yearly cohort. However, for former DI-only workers, there is some evidence of a downward shift—the curves are somewhat flatter in successive cohorts. We compared the 95-percent confidence intervals of the 2003 and 2008 cohorts, the earliest and latest in our sample, to determine the extent of that trend. The confidence intervals for those two cohorts overlap for all but the last 3 months of their common followup time (not shown in the charts). The difference between those two cohorts at the end of the common followup period is about 2 percentage points, but over the first year and a half they are virtually identical.³⁴

That finding may result from a tightening of CDR funding over the period—inflation-adjusted CDR funding decreased from about $659 million in fiscal year 2003 to just over $300 million in fiscal year 2008.³⁵ With the drop in funding, SSA reduced the number of FMRs (for both SSI and DI) by about 400,000. Combined with the improved profiling models used during the period, the fewer FMRs were increasingly targeted to individuals less likely to qualify for benefits and arguably less likely to return to the program. Following the later cohorts for longer periods will help determine whether this is a long-standing result or an inconsequential blip in the data.³⁶

Based on a comparison of the confidence intervals, a similar trend does not appear among former SSI-only recipients, which may be due to the smaller populations with ceased SSI eligibility in each year (down to just over 400 in 2008; the confidence intervals overlap for all years). Plots of cross-program participation and former concurrent beneficiary returns show trends similar to those for same-program returns (not shown).

For the Cox regressions, recall that stratification imposes identical hazard ratio estimates on each yearly stratum. To obtain yearly estimates, we also ran proportional hazard regressions for each yearly cohort to reveal any systematic changes in the estimated hazard ratios over time. Table 4 presents year-specific Cox regressions of same-program return (Appendix Table D-1 presents standard errors). For the DI-only population, we show regressions for the 2003 and 2008 cohorts. For the SSI-only population we show regressions for the 2003 cohort and, because the 2008 cohort is small, a pooled 2007/2008 cohort. We limit the regressions to the maximum followup period for the 2008 cohort (36 months, counting the month of eligibility cessation as month 1). As in the prior regressions, we continue to stratify by state, sex, and race, and allow for time-varying effects of variables that do not pass proportional hazards tests. Additionally, some variable categories needed to be combined because of small sample sizes; thus, the yearly models differ from those for pooled regressions.

Some hazard ratios change in magnitude and for others the direction of the risk of return changes. The only effect that is statistically significant and consistent across target populations for both years is the decreased risk of returning for those who have had a prior CDR. We also see an increased risk of return for individuals who are older (with the exception of the 2007/2008 SSI regression). In general, few hazard ratios are statistically significant at commonly accepted levels and even fewer are significant in the 2008 and 2007/2008 regressions. However, this is likely due to small sample sizes, leaving us unable to determine the extent to which the hazard ratios have changed over time.

Conclusion

In this article, we provide data to address the question: Do individuals who lose disability benefits because of medical improvement return to DI or SSI? We estimate that for adults whose program eligibility ceased because of medical improvement, 30 percent of former SSI-only recipients and 22 percent of former concurrent workers will return to SSI within 8 years. We estimate that about 20 percent of former DI-only workers and about 21 percent of former concurrent workers will return to DI within 8 years of the cessation decision.

Our empirical models use several variables that are also used by SSA in the profiling model that predicts the likelihood of medical recovery and therefore determines who receives a FMR. Thus, the CDR profile score is highly significant in our empirical models for former DI-only workers and, to some extent, for former concurrent workers who return to SSI. In our view, that result demonstrates the usefulness of the profiling model not just for determining who is likely to improve medically at the time of the FMR, but also who is likely to stay off the program in the future.

If funding restricts the number of CDRs to less-than-optimal levels, then some individuals whose eligibility could have ceased will instead continue receiving benefits. Against that scenario, increasing the number of CDRs would likely increase overall savings. However, the program return rate for individuals receiving those additional CDRs could exceed that for individuals undergoing current (restricted-level) CDRs within a particular type of CDR (for example, DI worker, SSI adult, SSI child); in that case, the cost/savings ratio would decline. To understand why, consider that beneficiaries whose eligibility ceases are among the least likely to have a severe disability. Thus, if the number of CDRs within a particular category were to increase above current restricted levels, then beneficiaries losing eligibility in CDRs they otherwise would not receive are likely to have somewhat more severe disabilities, and be somewhat more likely to return to the program, than those losing eligibility in current-level CDRs. Consequently, an increase in certain CDRs could lead to a higher program return rate within that category, thereby decreasing the savings per dollar spent even though overall program savings would still increase. It is important to reiterate that savings per dollar spent is highly dependent on the composition of CDR types as well as assumptions regarding interest rates and cost-of-living adjustments.

By limiting our analysis to post-FMR outcomes before age 62, our results likely describe a lower bound on program return. Individuals may be eligible for SSI based on their disability (and income and resources) until they reach age 65; thereafter, the disability requirement no longer applies. Similarly, individuals can receive DI benefits until they reach their full retirement age. Eligibility at those older ages may be amplified by worsening health. Thus, some individuals in our target population may still return to SSI or DI after what we termed early retirement; however, relatively few people reach age 62 during our observation period, so the effect of those sample restrictions on our estimates may be of little import.

One broader concern not considered in this article is the general health of individuals whose disability program participation ceases because they have medically improved to the point where they no longer meet SSA's eligibility requirements. Such individuals may still have substantial disabilities and limitations. We also cannot tell if those who return to the programs do so because their original disability worsens, or if they reapply because of a new disabling condition.³⁷

This article focused on a program-integrity aspect of FMRs. Although most formerly eligible individuals remain off the program, we did not consider their economic situation. Future research should examine the extent to which formerly eligible beneficiaries and recipients reenter the labor force. The availability of employment opportunities likely affects program return. Additionally, further exploration of income (especially at the family level) and use of other programs (for example, vocational rehabilitation) for formerly eligible beneficiaries may also shed more light on why some individuals return to the program and others do not.

Appendices