Research ArticleOriginal Articles

Health Care Utilization and Comorbidity History of North Carolina Medicaid Beneficiaries in a Controlled Substance “Lock-in” Program

Rebecca B. Naumann, Stephen W. Marshall, Jennifer L. Lund, Asheley C. Skinner, Christopher Ringwalt and Nisha C. Gottfredson
North Carolina Medical Journal May 2019, 80 (3) 135-142; DOI: https://doi.org/10.18043/ncm.80.3.135

Abstract

BACKGROUND Medicaid “lock-in” programs (MLIPs) are a widely used strategy for addressing potential misuse of prescription drugs among beneficiary populations. However, little is known about the health care needs and attributes of beneficiaries selected into these programs. Our goal was to understand the characteristics of those eligible, enrolled, and retained in a state MLIP.

METHODS Demographics, comorbidities, and health care utilization were extracted from Medicaid claims from June 2009 through June 2013. Beneficiaries enrolled in North Carolina's MLIP were compared to those who were MLIP-eligible, but not enrolled. Among enrolled beneficiaries, those completing the 12-month MLIP were compared to those who exited prior to 12 months.

RESULTS Compared to beneficiaries who were eligible for, but not enrolled in the MLIP (N = 11,983), enrolled beneficiaries (N = 5,424) were more likely to have: 1) substance use (23% versus 14%) and mental health disorders, 2) obtained controlled substances from multiple pharmacies, and 3) visited more emergency departments (mean: 8.3 versus 4.2 in the year prior to enrollment). One-third (N = 1,776) of those enrolled in the MLIP exited the program prior to completion.

LIMITATIONS Accurate information on unique prescribers visited by beneficiaries was unavailable. Time enrolled in Medicaid differed for beneficiaries, which may have led to underestimation of covariate prevalence.

CONCLUSIONS North Carolina's MLIP appears to be successful in identifying subpopulations that may benefit from provision and coordination of services, such as substance abuse and mental health services. However, there are challenges in retaining this population for the entire MLIP duration.

Between 2000 and 2013, the annual prescription drug overdose death rate in the United States more than doubled from 2.8 to 7.1 deaths per 100,000 population [1, 2]. Of the 22,767 lives lost to prescription drug overdoses in 2013, 7 out of 10 deaths involved an opioid analgesic and 3 out of 10 involved a benzodiazepine [1, 2]. Because both types of drugs act as central nervous system depressants, combined use considerably increases risk of overdose [3]. North Carolina has followed national trends, and during the same time period, more than 8,000 people in the state died from a prescription opioid overdose [1].

Medicaid beneficiaries are a high-risk population for prescription drug overdose. They are prescribed opioids at twice the rate of persons without Medicaid benefits and have prescription opioid overdose death rates 3 to 8 times that of those without Medicaid benefits [4-8]. With the goal of curbing potential misuse of prescription drugs in Medicaid populations, several states have implemented Medicaid “lock-in” programs (MLIPs) [9, 10]. MLIPs are designed to identify Medicaid beneficiaries demonstrating potential overutilization of high-risk prescription drugs (eg, opioids, benzodiazepines) and to limit access, generally by requiring beneficiaries to use a single prescriber and/or pharmacy to obtain these drugs [10].

Despite limited evaluation of these programs and knowledge of the populations impacted [11, 12], “lock-in” programs are increasingly being implemented in new beneficiary populations [13-15]. To understand and improve the utility of these programs, more information is needed about both the specific attributes of beneficiaries selected into these programs, including their health care needs, and the effects of these programs. Examining the attributes of the population impacted by the MLIP can provide key insights into the generalizability of observed program impacts on other target populations and opportunities for improved care models among “lock-in” program populations. Therefore, the purpose of this study was to obtain a thorough understanding of the demographics, health care utilization, and comorbidities of beneficiaries enrolled in a state MLIP. Comparisons were made between the general North Carolina Medicaid population, those enrolled in North Carolina's MLIP, and individuals found eligible for MLIP enrollment but not enrolled in the program. Additionally, to gain a more complete understanding of those impacted by the program, we examined the attributes of those retained in the MLIP for the entire 1-year program period as compared to those who exited the MLIP prior to program completion.

Methods

North Carolina MLIP enrollment

North Carolina's MLIP originated in October 2010 [16]. Medicaid beneficiaries were eligible for the MLIP if, within 2 consecutive calendar months, they filled: 1) more than 6 opioid prescriptions, 2) more than 6 benzodiazepine prescriptions, or 3) opioid or benzodiazepine prescriptions that were written by more than 3 different prescribers [16]. Each month a vendor, contracting with the North Carolina Division of Medical Assistance (DMA), reviewed prescription dispensing data for all Medicaid beneficiaries in the previous 2 calendar months to determine who met MLIP eligibility criteria. The vendor then ranked the MLIP-eligible pool of beneficiaries using a proprietary algorithm. This was combined with a clinical review process by pharmacists employed by the vendor. Approximately 200 of the highest-ranking beneficiaries (due to resource constraints) were then recommended to the DMA for MLIP enrollment each month. Therefore, not everyone who was eligible was selected for MLIP enrollment. The specific algorithm and review process details were proprietary and thus unavailable; however, as outlined below, our analysis was structured to gain insight into the attributes considered in these processes, as well as characteristics that may not have been included in these processes but could indicate important health needs of the beneficiaries examined.

Upon approval from the DMA, the approximately 200 selected beneficiaries each month were each sent a letter notifying them of their upcoming enrollment in the program and that the MLIP restricted them to using one prescriber and one pharmacy location to obtain prescriptions categorized as opioids or benzodiazepines for a 1-year period. Beneficiaries were given 30 days to choose a preferred prescriber and pharmacy before these mandatory restrictions began. Those who did not respond to the DMA were assigned to a prescriber and pharmacy. Once restrictions began, claims submitted for opioids or benzodiazepines that were not associated with the beneficiary's assigned MLIP prescriber and pharmacy were denied.

Data and Study Cohorts

North Carolina Medicaid claims data from June 2009 through June 2013 were obtained from North Carolina DMA's Data Retrieval Information and Validation Engine (DRIVE). Data available through DRIVE included beneficiaries' demographic information, periods of enrollment in Medicaid and the MLIP (if applicable), and adjudicated pharmacy and medical claims.

The overall study population consisted of adults aged 18–64 years enrolled in Medicaid at any point between June 2010 and December 2012. First, the MLIP-eligible population was identified by examining Medicaid-reimbursed opioid and benzodiazepine prescription fills from June 2010 through December 2012. Consistent with MLIP eligibility criteria, beneficiaries with more than 6 opioid or benzodiazepine prescriptions in a consecutive 2-month period were defined as MLIP-eligible (see Figure 1).

FIGURE 1.
FIGURE 1.

Classification of Persons who Qualified for the North Carolina Medicaid Lock-in Program (MLIP) from June 2010 through December 2012, Stratified by Enrollment in the MLIP and Time Spent in the MLIP

Within the MLIP-eligible population, a second study cohort was then identified: a cohort that was enrolled in the MLIP. As specified in Figure 1, this cohort was then further stratified based on time spent in the MLIP, categorized as (Group 1) those spending no time in the MLIP because they no longer possessed Medicaid coverage during the time they would have been enrolled; (Group 2) those who were enrolled in the MLIP for part of their assigned period but discontinued Medicaid coverage at some point during their entire observed and assigned MLIP period; (Group 3) those who possessed Medicaid coverage during the proportion of their MLIP period observed in our data (ie, through June 2013), but their entire 1-year MLIP period exceeded the time observed in our dataset (ie, they were administratively censored); and (Group 4) those who were observed for their full 12-month MLIP enrollment period and possessed Medicaid coverage during the entire time. Due to similarities, the first 2 groups and last 2 groups were collapsed in several analyses in which the combined first 2 groups were termed the “early exiters” and the combined last 2 groups were termed the “completers.”

Finally, to place our findings within the context of the larger Medicaid population, these distinct cohorts were compared to a sample of the general North Carolina Medicaid population within the same age range and time period (ie, any North Carolina Medicaid beneficiary aged 18–64 years with at least one pharmacy claim between October 2009 and September 2010).

Measures

For MLIP-eligible beneficiaries, demographic characteristics were assessed at the time they became MLIP-eligible. For the general Medicaid sample, demographic characteristics were assessed at the time of the first pharmacy claim in our data. Demographic characteristics included age, sex, race, urbanicity of county of residence [18], drug overdose death rate in county of residence [19], Medicaid aid category [20], and Medicaid class code [20]. For the MLIP-eligible population, beneficiary-level clinical characteristics were also examined, including controlled substance-related characteristics, overall health care utilization, and other comorbid conditions in the 12 months prior to MLIP eligibility. Controlled substance-related characteristics included MLIP eligibility criteria met, number of unique pharmacies visited in the 2-month period prior to MLIP eligibility, and history of medication-assisted treatment or overdose in the previous year [21, 22]. Health care utilization measures included numbers of emergency department (ED) visits and inpatient admissions and the number of days with Medicaid coverage in the prior year. Finally, the prevalence of various pain-related, mental health, substance use-related, and other comorbid diagnoses was estimated. Detailed reference information regarding the definitions used to define each specific condition has been previously published [23].

Statistical Methods

The prevalence of demographic and clinical characteristics of beneficiaries enrolled in the MLIP was estimated and compared to those who were eligible, but not enrolled. These groups were also compared to the general Medicaid population with respect to key demographic characteristics. Lastly, prevalences of demographic and clinical characteristics of beneficiaries enrolled in the MLIP, stratified by time spent in the MLIP, were compared. For categorical variables, counts and percentages were obtained. For continuous variables, means and standard deviations were calculated. For heavily skewed continuous variables (ie, health care utilization measures), means and 25th, 50th (median), and 75th percentiles were reported.

For all variables, standardized differences between those enrolled in the MLIP and those eligible but not enrolled were calculated, as well as between MLIP “early exiters” and “completers” [24]. Standardized differences provide a measure of the similarity or dissimilarity of 2 groups with respect to specific covariates. This study was approved by the University of North Carolina at Chapel Hill's Institutional Review Board.

Results

Demographics of MLIP-eligible, MLIP-enrolled, and MLIP Completers

Between June 2010 and December 2012, a total of 17,407 North Carolina Medicaid beneficiaries aged 18–64 years received more than 6 opioid prescriptions and/or more than 6 benzodiazepine prescriptions through Medicaid in a 2-consecutive-calendar-month period, qualifying them for the MLIP (see Table 1). Compared to the general North Carolina Medicaid population, those who met MLIP eligibility criteria tended to be older (mean age: 39.8 versus 35.1), male (34.9% versus 25.7%), white (75.5% versus 52.6%), from counties with high overdose death rates, and less likely to receive Medicaid benefits due to a pregnancy (2.4% versus 10.0%).

View this table:
TABLE 1.

Demographic Characteristicsa of Adults < 65 Years with Medicaid Coverage Overall and who met Medicaid Lock-in Program (MLIP) Eligibility Criteria from June 2010 through December 2012, Stratified by Enrollment in the MLIP and Time Spent in the MLIP

Among those eligible for the MLIP, 31% were enrolled in the MLIP (see Table 1). Compared to those not enrolled, MLIP-enrolled beneficiaries were more often younger (mean age: 37.1 versus 41.0), female (69.1% versus 63.2%), and less often qualified for Medicaid benefits due to disability (36.1% versus 48.3%) (see Table 1, Figure 2A).

FIGURE 2.
FIGURE 2.

Standardized Differencesa in Characteristicsb of Beneficiariesc who were Enrolled versus Not Enrolled (reference group) in the Medicaid Lock-in Program (MLIP) (Panel A) and Among Those Enrolled, Differences in Characteristics Between MLIP “Early Exiters” versus “Completers” (reference group) (Panel B)

Among those enrolled, 41% remained in the program for a full 12 months, and another 25% remained in the MLIP until the point of administrative censoring (ie, end of study observation period). Together, these beneficiaries are referred to as “completers.” Another 25% spent less than 12 months in the MLIP despite our ability to follow them and observe them for a longer period of time, and 8% spent no time in the MLIP. Together, these beneficiaries are referred to as “early exiters.” The 2 groups constituting MLIP “completers” were generally similar in terms of characteristics, as were the 2 groups constituting “early exiters.”

Compared to MLIP “completers,” the “early exiters” tended to be younger, white, more often from counties with high overdose death rates, more often received aid as a family with dependent children or due to a pregnancy, and more often qualified as medically needy (see Table 1, Figure 2B).

Substance-related and Health Care Utilization of MLIP-eligible, MLIP-enrolled, and MLIP Completers

Nearly all of those who became eligible for the MLIP met the opioid eligibility criterion; however, those enrolled in the MLIP also visited more unique pharmacies to fill their opioid and/or benzodiazepine prescriptions than did those not enrolled (see Table 2; Figure 2). Twenty-nine percent of those enrolled obtained these drugs from more than 3 different pharmacies in a 2-month period, as opposed to 7.8% of those not enrolled. Moreover, “early exiters” had an even higher prevalence than “completers” of using many different pharmacies.

View this table:
TABLE 2.

Controlled Substance-related Characteristics of Adults < 65 years who met Medicaid Lock-in Program (MLIP) Eligibility Criteria from June 2010 through December 2012, Stratified by Enrollment in the MLIP and Time Spent in the MLIP

With the exception of ED use, other health care utilization measures were generally similar between those who were and were not enrolled in the MLIP. Those enrolled had, on average, twice as many ED visits (mean visits: 8.3 versus 4.2) in the year prior to becoming eligible (see Table 3; Figure 2). MLIP-enrolled and non-enrolled cohorts tended to have similar Medicaid coverage in the prior year (mean days with coverage in past year: 310.1 versus 308.7). However, stratification by time spent in the MLIP revealed that “early exiters” tended to have less stable Medicaid coverage in the prior year (ie, fewer days enrolled in Medicaid in the prior year).

View this table:
TABLE 3.

Overall Health Care Utilization and Comorbid Conditionsa of Adults < 65 years who met Medicaid Lock-in Program (MLIP) Eligibility Criteria from June 2010 through December 2012, Stratified by Enrollment in the MLIP and Time Spent in the MLIP

Comorbid Conditions of MLIP-eligible, MLIP-enrolled, and MLIP Completers

Beneficiaries enrolled in the MLIP tended to a have a higher prevalence of pain, mental health, and substance use-related conditions (see Table 3; Figure 2). Of note, nearly a quarter of those enrolled had a substance use disorder diagnosis in the year prior (23.3%), almost double that of those not enrolled (13.5%). The prevalence of other comorbid conditions was generally similar between MLIP-enrolled and non-enrolled cohorts (absolute standardized differences all less than 10%) except that the latter had a higher proportion of recent cancer diagnoses (13.3% versus 0.8%). Stratification by time spent in the MLIP revealed an even higher prevalence of pain-, mental health-, and substance use-related conditions among those who completed the MLIP (eg, range of standardized differences for pain conditions comparing “early exiters” to “completers”: −3% to −26%; for mental health- and substance use-related conditions: −6% to −17%).

Discussion

This study identified a number of differences between the North Carolina MLIP target population (as defined by program selection criteria) and the actual population enrolled in and impacted by the program. Selection for the MLIP included a prioritization process of all eligible beneficiaries since resource constraints limited the number of those eligible who could be enrolled in any given month. Those enrolled in the MLIP tended to be younger, female, and less often qualified for Medicaid benefits due to a disability. Additionally, those enrolled tended to visit more pharmacies to fill their opioid and/or benzodiazepine prescriptions, have more ED visits, have a higher prevalence of pain-, mental health-, and substance use-related conditions, and have a lower prevalence of recent cancer diagnoses relative to those eligible but not enrolled in the MLIP. Beneficiaries with cancer diagnoses were generally excluded from MLIP enrollment. These findings are consistent with previous research on characteristics of those most at risk of opioid misuse and overdose [1, 5, 30-35].

To further understand the extent to which beneficiaries were exposed to the program, we stratified the population of those enrolled by time spent in the MLIP. Those who exited the program early were more often younger, white, and from counties with high overdose death rates, compared to those who remained in the program. Additionally, we found that “early exiters” more often received aid as a family with dependent children or due to a pregnancy, visited more unique pharmacies to fill their opioid and/or benzodiazepine prescriptions, had less stable Medicaid coverage in the prior year, and a lower prevalence of diagnoses for pain-, mental health-, and substance use-related conditions. Unstable Medicaid coverage, which led to unstable MLIP exposure for some enrolled in the program, has been shown to be more prevalent among certain populations, such as younger individuals [36]. Moreover, many women only qualify for Medicaid benefits while pregnant and in the 60 days following delivery, after which they often lose coverage [37]. Other attributes, such as county overdose death rates and their potential associations with Medicaid coverage instability, warrant additional research. Many of the observed differences and overall cohort profiles illuminate both important generalizability considerations as well as care coordination barriers and opportunities for future MLIP design.

The generalizability of MLIP evaluation findings is an important consideration as the medical community continues to grapple with the surging opioid epidemic, and with this, broad implementation of “lock-in” programs. “Lock-in” programs have been increasingly utilized in new and different beneficiary populations, including private insurance plans and other Medicaid populations, and will soon be incorporated into Medicare [13-15]. While the evidence base for these programs is sparse, recent evaluation findings from North Carolina's MLIP have begun to provide some understanding of both intended and unintended consequences of the MLIP (eg, reductions in the number of Medicaid-reimbursed opioid prescriptions but increases in opioid prescriptions obtained through cash payment) [38, 39]. As the evidence base develops and as these programs are designed and refined, evaluations from other “lock-in” programs are needed that not only present a range of program impacts, but that are also coupled with a clear depiction of the affected population. Overall, North Carolina's Medicaid population was similar demographically (ie, age, sex, and race) to the national Medicaid population profile at the time of this study [40]. Therefore, from a broad demographic perspective, evaluation findings related to North Carolina's program may be generalizable to other similar Medicaid programs. However, the larger policy and prescribing landscape within which these programs are embedded should also be considered when evaluating potential generalizability of findings. Moreover, the extent to which observed program impacts (eg, reductions in Medicaid-reimbursed, but increases in cash payment for, opioid prescriptions) in this beneficiary population transfer to “lock-in” programs in private insurance, older adult, and other populations is not known and will be an important consideration for future research.

Even with our limited view of complete “lock-in” program effects, these programs theoretically provide a unique opportunity to efficiently deliver services capable of improving patient health and saving health care dollars. This study showed that beneficiaries enrolled in the MLIP tended to have a high prevalence of comorbidities, including pain, mental health, and substance use-related conditions, and tended to show signs of uncoordinated care (eg, high use of EDs and multiple pharmacies). The ability of “lock-in” programs to more effectively target the complex health needs of this beneficiary population is unknown, but has strong potential. In 2014, the Association for Community Affiliated Plans supported implementation of innovative MLIP pilot projects in Medicaid populations in 4 different states [41]. These pilot projects offered a more holistic MLIP model, as compared to the more traditional MLIP model (like the one administered in North Carolina). Program elements included connections to pain specialists, risk screenings, evaluation of barriers to critical needs (eg, transportation, housing), connections to resources, and screening and referral to substance use disorder treatment resources. While evaluation research was limited to short-term outcomes, preliminary results revealed cost savings and improved care coordination. Pending further evaluation, such models, particularly when targeted to the needs of specific “lock-in” program beneficiary populations, may serve as a more effective framework. Based on our findings, inclusion and coordination of substance use disorder and mental health screenings and connection to substance use disorder, mental health, and alternative pain therapy services could serve as a useful starting point for improving and piloting a more comprehensive MLIP model in North Carolina. Discussions around improved models of care within a MLIP framework also require some consideration of Medicaid “churn” (ie, moving between an insured and uninsured status and/or between different coverage sources). While a complete discussion of “churn” and coverage issues is beyond the scope of this paper, refining MLIPs to improve care coordination within a larger system prone to coverage lapses and care disruptions for populations typically enrolled in MLIPs is an important barrier to address and warrants further research [42].

Our findings should be viewed in light of 3 limitations. First, the Medicaid data available did not include accurate information on numbers of unique prescribers visited. Therefore, we were unable to use the third MLIP criterion in constructing our MLIP-eligible population. However, given that almost all of the MLIP-enrolled cohort met the first criterion (ie, more than 6 opioid prescriptions) and that there were likely relatively few people who visited several unique prescribers but did not also meet the prescription thresholds, this missing information was not expected to have excluded many beneficiaries from our analysis. Second, our measurement of overdoses in the prior year only captured overdoses involving some interaction with the health care system while a person had Medicaid coverage. Third, the presence of diagnoses (eg, pain diagnoses) and measures of health care utilization (eg, methadone treatment) in the year prior to meeting MLIP eligibility may be underestimated, particularly for “early exiters,” as they also tended to have less Medicaid coverage in the prior year. However, research suggests that inclusion of any available data in a lookback period to assess presence of covariates results in less misclassification than restricting the data to a common lookback period [43].

Conclusion

Understanding demographic and clinical profiles of the population impacted by the MLIP provides key insights into the generalizability of MLIP impacts to other beneficiary populations and opportunities for tailored “lock-in” program design improvements. Future work is needed to examine which enrollment criteria are most useful for selecting beneficiaries who could benefit from such programs. Additionally, evaluations are needed to examine a broad range of potential positive and negative impacts of these programs, combined with a clear description of studied populations, so that future program designs can be informed by the most comprehensive and relevant research. While “lock-in” program administrators should aim to gain a thorough understanding of the specific beneficiary populations impacted by their programs, our findings can help prepare administrators of new, similar programs for the magnitude of substance use, mental health disorders, and other comorbidities that may be likely in their populations.

Acknowledgments

The authors thank the NC Division of Medical Assistance and the Division of Mental Health, Developmental Disabilities, and Substance Abuse for their support in obtaining the data.

Potential conflicts of interest. All authors have no relevant conflicts of interest.

This research was supported by Cooperative Agreement U01 CE002160-01 from the National Center for Injury Prevention and Control at the Centers for Disease Control and Prevention (NCIPC/CDC) and award R49-CE001495 to the University of North Carolina for an Injury Control Research Center from NCIPC/CDC. Dr. Naumann received fellowship support from the University of North Carolina's Royster Society of Fellows. Dr. Gottfredson received support through an award from NIH (K01 DA035153).

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Health Care Utilization and Comorbidity History of North Carolina Medicaid Beneficiaries in a Controlled Substance “Lock-in” Program
Rebecca B. Naumann, Stephen W. Marshall, Jennifer L. Lund, Asheley C. Skinner, Christopher Ringwalt, Nisha C. Gottfredson
North Carolina Medical Journal May 2019, 80 (3) 135-142; DOI: 10.18043/ncm.80.3.135
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