When the measure comes back for maintenance the developer should have:
- Explored the impact of patients in remission or who are on other forms of treatment on the performance results; and
- Assessed potential unintended consequences (e.g., discouraging use of other, non-pharmacological therapies) during implementation.
The Use of Pharmacotherapy for Opioid Use Disorder measure evaluates the percentage of Medicaid or Medicare-Medicaid participants, aged 18 years and older, who have been diagnosed with an opioid use disorder (OUD) who filled a prescription for, were administered, or dispensed, a Food and Drug Administration (FDA)-approved medication to treat or manage OUD during the measurement year.
Measure Specs
General Information
Pharmacotherapy for OUD is related to improved health outcomes, therefore, a quality measure to increase access to pharmacotherapy is expected to yield better care for beneficiaries with an OUD. Improved health outcomes associated with medications for OUD include reduced opioid use, overdose risk, and transmission of HIV and hepatitis C.
While other measures evaluate pharmacotherapy administration rates, CBE #3400 includes an analysis at the state-level and requires prescription fills within the measurement year. In addition, there are typically fewer quality measures for Medicaid and high rates of OUD for this population.
References:
Leshner, A., & Mancher, M. (2019). Medications for opioid use disorder save lives. National Academies Press. https://doi.org/10.17226/25310.
CBE #3400 uses administrative claims or encounter data and pharmacy claims. For measure testing the data source is the Transformed Medicaid Statistical Information System (T-MSIS), which contains beneficiary, service utilization, administrative claims, and expenditure data for the Medicaid population, including those covered through both fee-for-service and managed care payers.
Numerator
Medicaid beneficiaries with evidence of at least one prescription filled, or who were administered or dispensed an FDA-approved medication for OUD during the measurement year.
CBE #3400 calculates the percentage of Medicaid beneficiaries ages 18 and older with an opioid use disorder (OUD) who filled a prescription for or were administered or dispensed an FDA-approved medication for OUD during the measure year. The numerator is the beneficiaries with evidence of at least one prescription filled or who were administered or dispensed an FDA-approved medication for OUD during the measurement year. The measure will be calculated both overall and stratified by four medications/mode of administration: buprenorphine; oral naltrexone; long-acting, injectable naltrexone; and methadone. The total is not a sum of the four medication cohorts. Count beneficiaries in the total denominator rate if they had at least one of the four FDA-approved medications for OUD during the measurement year. Report beneficiaries with multiple medications only once for the total rate for the denominator.
Denominator
Medicaid or Medicare-Medicaid beneficiaries aged 18 years and older with at least one encounter with a diagnosis of opioid abuse, dependence, or remission (primary or other diagnosis) at any time during the measurement year.
The CBE #3400 measure data is reported annually across 12 months. The denominator is Medicaid beneficiaries ages 18 and older with at least one encounter with a diagnosis of opioid abuse, dependence, or remission (primary or other diagnosis) at any time during the measurement year.
Exclusions
None. However, states may require exclusions, as appropriate, for their substance use disorder (SUD) programs and recipients.
None.
Measure Calculation
Please see measure score calculation diagram attachment.
Measure performance is calculated both overall and stratified by four medications/ modes of administration: buprenorphine; oral naltrexone; long-acting, injectable naltrexone; and methadone.
Not applicable—CBE #3400 measure does not involve sampling.
Supplemental Attachment
Point of Contact
Not applicable.
United States
United States
Importance
Evidence
Of the 106,699 drug overdose deaths in the United States in 2021, opioids were involved in 80,411 deaths (75.4 percent) (Centers for Disease Control and Prevention, 2023). Between 2010 and 2019, opioid use disorder affected more than 7.6 million individuals annually, compared to only about 1 million individuals who received medications for OUD (MOUD) (Krawczyk, Rivera, Jent, Keyes, Jones, & Cerda, 2022). Methadone, buprenorphine, and naltrexone are FDA-approved MOUD, which have shown improved health outcomes and decreased overdose risk by 50 percent, when compared to no treatment or treatments without medication (Santo, Clark, Hickman, Grebely, Campbell, Sordo, … & Degenhardt, 2022; Leshner & Mancher, 2019). However, there are low utilization rates of MOUD, partly due to limited treatment capacity and access (Krawczyk et al., 2022). In addition, the COVID-19 public health emergency also created challenges for individuals with OUD, including decreased treatment access and quality, increased mood symptoms, and increased substance use (Banks, Paschke, Li, Fentem, Rich, Szlyk, & Cavasoz-Rehg, 2022). Policy and research initiatives focus on the gap between OUD rates and individuals who receive MOUD (Krawczyk et al., 2022).
References:
Banks, D. E., Paschke, M. E., Li, X., Fentem, A., Rich, A., Szlyk, H. S., & Cavazos-Rehg, P. (2022). Opioid use disorder and COVID-19: Treatment and recovery factors among vulnerable populations at the intersection of two U.S. epidemics. Journal of Psychoactive Drugs, 54(4), 300–308. https://doi.org/10.1080/02791072.2022.2079443
Centers for Disease Control and Prevention. (2023). Drug overdose deaths remained high in 2021. Drug Overdose Deaths. Retrieved from https://www.cdc.gov/drugoverdose/deaths
Krawczyk, N., Rivera, B., Jent, V., Keyes, K., Jones, C., & Cerda, M. (2022). Has the treatment gap for opioid use disorder narrowed in the U.S.?: A yearly assessment from 2010 to 2019. International Journal of Drug Policy, 110. https://doi.org/10.1016/j.drugpo.2022.103786
Leshner, A., & Mancher, M. (2019). Medications for opioid use disorder save lives. National Academies Press. https://doi.org/10.17226/25310
Santo, T., Clark, B., Hickman, M., Grebely, J., Campbell, G., Sordo, L. … & Degenhardt, L. (2022). Association of opioid agonist treatment with all-cause mortality and specific causes of death among people with opioid dependence: a systematic review and meta-analysis. JAMA Psychiatry, 78(9):979–993. doi:10.1001/jamapsychiatry.2021.0976
Measure Impact
Nearly four out of five Americans with OUD do not receive treatment (Madras, Ahmad, Wen, & Sharfstein, 2020). In addition to physical health effects, individuals with OUD also experience mental health challenges, including suicide (Madras, Ahmad, Wen, & Sharfstein, 2020). Although many individuals with OUD may desire and would benefit from treatment, barriers remain. These barriers include providers’ stigma, providers’ lack of training, systems that do not focus on patient needs, laws that restrict treatment access, and financial restraints (Madras, Ahmad, Wen, & Sharfstein, 2020).
References:
Madras, B. K., Ahmad, N. J., Wen, J., & Sharfstein, J. S. (2020). Improving access to evidence-based medical treatment for opioid use disorder: strategies to address key barriers within the treatment system. NAM Perspectives, 2020, 10.31478/202004b. https://doi.org/10.31478/202004b
Performance Gap
The distribution of performance scores for CBE #3400 are presented in Exhibit 1 (within the perforamnce gap attachment). Scores demonstrate room for improvement with a median score for all treatments of 52.6 percent. Buprenorphine was the most prevalent treatment with a median rate of 34.5 percent, followed by methadone (16.1 percent), oral naltrexone (2.8 percent) and injectable naltrexone (1.3 percent). Deciles for each treatment modality are presented therein.
Exhibit 2, within the performance gap attachment, examines performance scores by several beneficiary characteristics, including age band, biological sex, race or ethnicity, and dual-eligibility status. Chi-square and probability were calculated to determine whether differences in performance scores based on these characteristics were statistically significant. The performance rates reflect only the rate for all treatments as the best indicator of overall care.
Significant differences in performance were found for all characteristics. Notably, the over 65 age group and dually eligible beneficiaries had much lower performance than their respective cohorts within the age and dual-eligibility status categories. Dually eligible beneficiaries had a treatment rate of 8.3 percent versus a rate of 59.0 percent for non-dually eligible beneficiaries while those over age 64 had a treatment rate of only 3.8 percent versus rates ranging from 36.6 percent to 65.9 percent for younger age groups.
Equity
Equity
As shown above, in Exhibit 2 (within the performance gap attachment) and Exhibit 6 (within the supplemental attachment), some potential social risk factors were examined to identify performance gaps. These factors include age band, biological sex, race or ethnicity, and dual eligibility status. Statistically significant differences in performance have been identified, which demonstrate an opportunity for improving health equity based on these risk factors.
Feasibility
Feasibility
CBE #3400 was assessed via qualitative survey of a multi-stakeholder panel. Results of the feasibility assessment, as captured in two survey questions, indicate that CBE #3400 is likely minimally challenging to report, and places minimal burden on users.
In general, there are no concerns with capturing data for this measure using the data elements defined in the specification. All respondents indicated either No or Not Sure/Do Not Know when asked if there are any challenges with capturing data for the CBE #3400 measure using the described elements. The other survey question related to feasibility showed that 80 percent of respondents either strongly agreed or agreed that reporting this measure does not place undue burden on entities to collect the data (i.e., 40 percent strongly agreed, and 40 percent agreed). One respondent indicated, Do Not Know or Not Applicable. These findings suggest that the measure is likely minimally burdensome to report, and feasibility is not a concern.
Results from the qualitative survey related to measure feasibility for CBE #3400 appear in Exhibit 3 and Exhibit 4 (within the supplemental attachment).
No changes were made to the final measure specifications in response to the feasibility assessment. There was high agreement that the measure is likely minimally burdensome to report.
Proprietary Information
Scientific Acceptability
Testing Data
As described above, CBE #3400 uses administrative claims or encounter data and pharmacy claims. For measure testing, the data source is the T-MSIS, which contains beneficiary, service utilization, administrative claims, and expenditure data for the Medicaid population, including those covered through both fee-for-service and managed care payers. The measurement period for scientific acceptability testing was calendar year 2021.
The same data were used for all aspects of testing.
As shown in Exhibit 5, within the supplemental attachment, for the 50 states and the District of Columbia, which were included in our analysis, there were 2,591,296 Medicaid beneficiaries in the eligible population who had at least one OUD diagnosis in the measurement year of 2021. Denominator counts for measured entities (eligible state-level Medicaid beneficiaries) ranged from 472 to 64,311 with a median of 13,467 beneficiaries, per entity.
As shown in Exhibit 6, within the supplemental attachment, 50.4 percent of beneficiaries were in the 25 to 44 age category followed by 33.1 percent in the 45 to 64 grouping. Approximately 51 percent of the beneficiaries eligible for the denominator were female. Non-Hispanic white beneficiaries accounted for about 62 percent of the denominator, followed by other/multiracial (14.2 percent), Black, non-Hispanic (13.5 percent) and Hispanic beneficiaries of all races (approximately 10.3 percent). Approximately 21.8 percent of denominator were dually eligible beneficiaries.
Reliability
Reliability was calculated in accordance with the methods described in The Reliability of Provider Profiling: A Tutorial (2009). This approach calculates the ability of the measure to distinguish between the performances of different reporting entities (in this case, state or district). Specifically, the testing calculated the signal-to-noise ratio for each entity, with higher scores indicating greater reliability. The reliability score is estimated using a beta-binomial model and is a function of the facility’s sample size and score on the measure, as well as the variance across measured entities.
References:
Adams, J. (2009). The reliability of provider profiling: A tutorial. https://doi.org/10.7249/ tr653
The distribution of the state estimates of signal-to-noise reliability are shown in Exhibit 7 (within the reliability attachment). The estimates for the “all treatments” score ranged from 0.99218 to 0.99996, with a median of 0.99979. Median reliability scores for the four individual treatment rates ranged from 0.99668 (oral naltrexone) to 0.99976 (methadone).
The signal-to-noise analyses showed that the reliability of CBE #3400 is excellent. Although high signal-to-noise reliability is not indicative of high-quality health care, it does indicate that the measure may be used to distinguish between states with respect to health care quality.
High reliability for CBE #3400 is likely supported by large enough sample sizes at the state level. The average number of beneficiaries in the denominator for the overall rate was about 30,000 (ranging from 472 to 64,311).
Validity
Convergent Validity. To assess convergent validity, the measure developer examined the correlations between CBE #3400 and the HEDIS® Initiation and Engagement of Alcohol and Other Drug Abuse or Dependence Treatment (IET) measure (measurement year 2021, 18 and older age stratification). The HEDIS IET measure assesses the percentage of individuals with new episode of alcohol or other drug abuse or dependence who initiated treatment within 14 days and received ongoing treatment within 34 days after initiation.
The measure developer also examined correlations between CBE #3400 and CBE #3453, Continuity of Care After Inpatient or Residential Treatment for Substance Use Disorder, which measures the percentage of discharges from inpatient or residential treatment for substance use disorder (SUD) for Medicaid beneficiaries, ages 18–64, which were followed by a treatment service for SUD. Two rates are reported, continuity within 7 and 14 days after discharge.
Face Validity. Results of the face-validity assessment, as captured in the TEP survey (questions 4 through 7), indicated agreement from TEP respondents in support of the measure’s validity.
See validity attachment.
As presented in Exhibit 7, CBE #3400 is very strongly correlated with performance score data for the HEDIS® IET measure. The measure yields Pearson correlation coefficients of 0.78 with the IET 14-day treatment initiation score and 0.76 with the IET 34-day treatment initiation score, demonstrating strong correlation for both scores. Correlations with CBE #3453 were also strong. CBE #3400 yields Pearson correlation coefficients of 0.70 with the CBE #3453 7-day continuity of care score and 0.72 with the CBE #3453 14-day continuity of care score. In addition to strong Pearson correlation coefficients, all correlations were statistically significant with p<0.0001, further indicating a strong validity rating for CBE #3400.
Results in Exhibit 8 suggest that the measure, as specified, truly evaluates what it intends to assess. The majority of respondents either strongly agreed or agreed that NQF 3400 assesses access to pharmacotherapy for the adult Medicaid population. One respondent indicated Do Not Know or Not Applicable, noting a lack of experience in this area.
Exhibit 9 shows that the measure is useful in understanding and comparing the quality of care between different entities measured. The majority of respondents either strongly agreed or agreed that comparing scores on this measure differentiates good from poor performance, were undecided or indicated Do Not Know or Not Applicable, noting a lack of experience in this area. One undecided respondent stated that access to pharmacotherapy may not be sufficiently assessed by a fill of one prescription but did not provide additional detail.
In Exhibit 10, the findings suggest that the data elements defined in this measure for identifying beneficiaries for the denominator are reasonable and useful in identifying the population of interest. The majority of respondents agreed that identifying individuals who had at least one encounter with an ICD-10 diagnosis code for opioid abuse, dependence, or remission (primary or other) at any time during the measurement year is an appropriate way of identifying Medicaid beneficiaries with an OUD. Other respondents indicated Do Not Know or Not Applicable.
The results in Exhibit 11 show that the majority of TEP respondents agree that the data elements used to define the measure’s numerator criteria are reasonable and useful in capturing access to pharmacotherapy for OUD. The majority of respondents agreed that access to pharmacotherapy for OUD can be identified by at least one prescription filled, or administration or dispensed an FDA-approved medication for OUD through use of pharmacy claims (National Drug Codes [NDC]) or HCPCS coding of medical service. One of the respondents who agreed added that methadone would be an exception, as further research is needed to understand how methadone dispensing from methadone clinics is tracked. One respondent was undecided on this topic, given the long-term nature of treatment and recovery for individuals with OUD which often requires continued pharmacotherapy, and another respondent indicated Do Not Know or Not Applicable for this question. The team notes that the one respondent who was undecided on this question provided feedback on the face validity of the numerator specification, rather than numerator data element validity.
Risk Adjustment
Use & Usability
Use
Purpose: The Adult Core Set is required by statute (Section 1139B of the Social Security Act); it directs the Secretary of Health and Human Services to identify and publish a set of core healthcare quality measures for those aged 18 years and older who are enrolled in Medicaid. Beginning in 2024, use of the behavioral health measures within the Adult Core Set (including CBE #3400) is mandatory for states. The Core Set is refreshed annually so that states may address changes to required measures prior to mandatory reporting. State data that derived from the Adult Core Set measures are documented within published chart packs and datasets that highlight publicly reportable measures.
Geographic area and percentage of accountable entities and patients included: CBE #3400 is mandatory for use by states who provide Medicaid to adults aged 18 years and older.
Level of analysis and care setting: CBE #3400 is measured at the state level. CBE #3400 does not focus on a setting, but rather documentation of disbursement of medications for OUD.
Purpose: The goal of IAP was to improve the health and health care of Medicaid beneficiaries and to reduce costs by supporting states’ ongoing payment and delivery system reforms. Medicaid IAP supported state Medicaid agencies to build capacity in key program and functional areas by offering targeted technical support, tool development, and cross-state learning opportunities. The goal of the Medicaid IAP Reducing SUD area was to support states to introduce policy, program, and payment reforms to better identify individuals with SUD, expand coverage for effective treatment, enhance care and practices delivered to beneficiaries, and develop payment mechanisms for SUD services that will provide better outcomes.
Geographic area and percentage of accountable entities and patients included: The IAP covered all 50 states.
Level of analysis and care setting: The measure is included in the IAP for optional reporting at the state level.
Usability
Usability of the measure was assessed via qualitative survey of a multi-stakeholder group. The results indicate that 80 percent of the respondents strongly agree or agree that the measure assesses the quality of care (i.e., access to OUD pharmacotherapy) provided to Medicaid beneficiaries who are at high-risk due to their OUD diagnosis. Overall, these results suggest that the majority of TEP respondents agreed that CBE #3400 is useful for assessing care quality among the Medicaid OUD population. Furthermore, 60 percent of respondents either strongly agree or agree that the results from the measure can be used by entities to guide decision-making and improve healthcare quality and health outcomes. These findings suggest that the majority of TEP respondents support the use of CBE #3400 for decision making and health care quality improvement efforts. There was inconclusive feedback on whether the results of CBE #3400 supplies meaningful information to the individuals or entities who use the measure’s data. 50 percent of respondents either strongly agreed or agreed, 25 percent were undecided, and another 25 percent indicated Do Not Know or Not Applicable. These findings suggest that the measure may be meaningful to the measure’s users, but more information may be needed to make this determination.
Feedback on the continued use of CBE #3400 is collected via public comment when the Adult Core Set is evaluated annually, via the annual review and selection process. To date, no substantive comments on CBE #3400 have been received.
To date, no substantive comments on CBE #3400 have been received.
Mandatory use and reporting of CBE #3400 as part of the Medicaid Adult Core Set began in 2024. Data on trends in performance are not available at this time but will be included in annual updates.
To date, no unexpected findings have been identified.
Comments
Staff Preliminary Assessment
CBE #3400 Staff Assessment
Importance
Strengths:
- Evidence suggests that opioid deaths involved over 80,000 Americans in 2021. Further, opioid use disorder (OUD) affected over 7.6 million Americans between 2010-2019. Medications have reduced overdose risk significantly (~50%) compared to other treatment options (including lack of treatment). The developer discussed barriers to treatment (e.g., laws, provider's absent training). This process measure evaluates the percentage of adults with an OUD who took prescribed FDA-approved medication.
Performance gap scores are clearly reported, with significant differences being found.
Limitations:
- Patients must be Medicaid or Medicare-Medicaid participants. The logic model shows the process from OUD diagnosis to remission. However, no thorough description of the logic model is provided beyond the four simple components (i.e., diagnosis --> medicine initiation --> adverse event reduction --> remission).
Rationale:
- There are no current measures that include a state-level analysis and require medication fulfillment within the measurement year.
Feasibility Acceptance
Strengths:
- Data is comprised of administrative claims or encounter data as well as pharmacy claims. Data collection does not involve sampling. The developer employs the Transformed Medicaid Statistical Information System (T-MSIS) as the data source.
Limitations:
- None identified.
Rationale:
- Data is comprised of administrative claims or encounter data. Data collection does not involve sampling. The qualitative survey conducted indicated that there are minimal challenges for data collection and minimal burden to report. No changes were suggested nor made in response to panel feedback.
Scientific Acceptability
Strengths:
- The measure is clear and well defined.
State-level reliability is conducted on 2021 data four five treatment types using the beta-binomial method (the measure is not risk-adjusted). Average state-level reliability is >0.6 for all reliability deciles for all five treatments across 51 states. The estimated reliability for the first decile for each treatment type is greater than 0.98.
Limitations:
- Reliability is estimated on a state level, and does not address entity level reliability. The developer does state that the measure can be used to distinguish between states.
Rationale:
- The measure is well defined. Reliability is assessed at the state level. Reliability statistics are above the established thresholds.
Strengths:
- Measure performance investigates four medications/ modes of administration (i.e., buprenorphine, oral naltrexone, long-acting injectable naltrexone methadone.) The measure developer also examined correlations between CBE #3400 and CBE#3453. Strong correlation (0.7) was reported.
Limitations:
- None identified.
Rationale:
- Measure performance investigates four medications/ modes of administration (i.e., buprenorphine, oral naltrexone, long-acting injectable naltrexone methadone.) The measure developer also examined correlations between CBE #3400 and CBE#3453. Strong correlation (0.7) was reported.
Equity
Strengths:
- The developer describes risk factors examined to identify performance gaps (e.g., race). Statistically significant differences in the measure rates were found across age band, biological sex, race or ethnicity, and dual-eligibility status.
Limitations:
- The measure simply highlights the gaps but does not provide description of how gaps will be addressed or improved.
Rationale:
- In sum, the developer described meaningful differences in measure rates for patients of different ages, races, sex, and dual eligibility status.
Use and Usability
Strengths:
- The measure is currently in use. The developer notes that, as of 2024, the measure is required as part of the Medicaid Adult Core Set.
The developer reported 80% of respondents indicated strong agreement or agreement that the measure assesses access to OUD pharmacotherapy "provided to Medicaid beneficiaries who are at high-risk due to their OUD diagnosis." Evidence suggests that the majority of TEP respondents agreed that CBE #3400 is useful for "assessing care quality among the Medicaid OUD population." Furthermore, 60% of respondents indicated strong agreement or agreement that the results "can be used by entities to guide decision-making and improve healthcare quality and health outcomes."
Evidence suggests that the majority of TEP respondents "support the use of CBE #3400 for decision making and health care quality improvement efforts."
Feedback was inconclusive regarding whether measure results provide meaningful insights to individuals or entities employing the measure’s data.
Lastly, 50% of respondents indicated strong agreement or agreement, 25% were undecided, and 25% selected 'Do Not Know or Not Applicable'.
On the whole, these findings suggest the measure may prove meaningful to the measure’s users; however additional questioning by the developer (and associated data) may be required.
Limitations:
- Public comments regarding usability were not received nor reported. The developer does not provide rationale as to why no comments were made. Based on the submission, it is not known if the developer sought input on barriers.
Trend data is not available; however the developer notes that performance information will be provided at a later date.
Rationale:
- The current use of the measure is documented; however, usability feedback was inconclusive and additional data is needed to understand barriers to use.
Committee Independent Review
Important measure but needs additional information
Importance
Agree with Staff Preliminary Assessment.
Feasibility Acceptance
Agree with Staff Preliminary Assessment.
Scientific Acceptability
Agree with Staff Preliminary Assessment.
Agree with Staff Preliminary Assessment.
Equity
Disagree with Staff Preliminary Assessment.
Lack of discussion on how this measure supports health equity advancement. Only shows the gaps in care.
Use and Usability
Agree with Staff Preliminary Staff Assessment.
The developer did not discuss how implementers can improve scores. Did not discuss either the burden of implementing and improving scores. No discussion on possible barriers to implementation and how to address low scores. There should be available information on this as this measure has been implemented and is being used.
Summary
This measure shows importance and relevance for improved quality outcomes. However, it needs to discuss better how implementation can impact service offered, how implementers can address gaps in care and barriers.
Appropriate measure for state-level assessments
Importance
Agree with staff assessment. This measure addresses an important performance gap in screening processes, with potential to save lives. Few issues were identified with the submission, and should not prevent endorsement.
Feasibility Acceptance
Agree with staff assessment. No issues with these administrative data.
Scientific Acceptability
Agree with staff assessment. Testing supports the reliability of the numerator and denominator.
Agree with staff assessment. Testing supports the validity of the measure result
Equity
Agree with staff assessment. Enormous racial disparities identified.
Use and Usability
Disagree with Staff Assessment. In my view, given the disparities and published literature that identifies distinct root causes for disparities that are within a state government's control (the level of analysis for this measure), the measure result should provide sufficient actionable information that can be used to improve performance.
Summary
Although the results are not specified to be directly useful to individual facilities or clinicians, state-based assessments of health performance, especially relative to nearby or comparable states, are useful signposts for regulatory and policymaking efforts.
support
Importance
agree with staff assessment
Feasibility Acceptance
agree with staff assessment
Scientific Acceptability
Agree with staff assessment
Need more than survey data, ideally need to check assessments against medical records to validate that you are measuring what you think you are measuring
Equity
agree with staff assessment
Use and Usability
Agree with staff assessment
Summary
An important topic with good face validity for the stakeholder who would employe the model. Needs post roll out assessment to be sure that it is measuring with high reliability across different care settings and groups.
Support
Importance
Agree with staff assessment. Would be useful if developer could detail the added value and differences between this measure and the NCQA IET measure.
Feasibility Acceptance
Agree with staff assessment.
Scientific Acceptability
Agree with staff assessment. Appears to be highly reliable at the state level.
Agree with staff assessment.
Equity
Agree with staff assessment. Appears that high missingness reported for race/ethnicity data which may limit interpretation.
Use and Usability
Disagree with staff assessment. Data provided appear to suggest that 60 percent of respondents either strongly agree or agree that the results from the measure can be used by entities to guide decision-making and improve healthcare quality and health outcomes. In addition, no public comments have been received indicating any issues with usability.
Summary
Overall important measure for state-level reporting.
Support this measure
Importance
Important for tracking to identify gaps in OUD and approved medications to treat
Feasibility Acceptance
Feasible as using claims tracking and filled pharmacy prescriptions.
Scientific Acceptability
Yes, the measure was met
Yes
Equity
Equity is addressed along age, gender, race, and type of payer. However, there is not a prior history so it is not known if these are initial or ongoing meds being prescribed. Not sure if historical info is important as it is about saving lives.
Use and Usability
Measure can be helpful in addressing disparities among health plans, states, etc. as it pertains to the linkage of persons with OUD and medication treatment linked to lives saved.
Summary
It is important to track OUD and Medication treatment as it pertains to lives saved.
Do not support
Importance
Do not agree with staff assessment. Only 2 studies were presented to support the measure. However, there is no information regarding the strength of those studies or whether other studies exist that might contradict those results.
Feasibility Acceptance
Agree with staff assessment.
Scientific Acceptability
Agree with staff assessment. (Note that the accountable entity is the state Medicaid program, so it isn’t clear what the staff assessment means by stating that it “does not address entity level reliability”).
Agree with staff assessment.
Equity
Agree with staff assessment.
Use and Usability
Agree with staff assessment.
Summary
I could support the measure if additional information regarding the underlying evidence base is provided. I’d want to know that the evidence is based on high quality studies and to know that the supporting studies are not cherry-picked (i.e., if other studies exist, the findings are consistent).
CBE #3400: Use of Pharmacotherapy for Opioid Use Disorder
Importance
Agree with Staff Preliminary Assessment
Feasibility Acceptance
Agree with Staff Preliminary Assessment
Scientific Acceptability
Agree with Staff Preliminary Assessment
Agree with Staff Preliminary Assessment
Equity
Do not agree with Staff Preliminary Assessment. There is no clear discussion of how this measure will be used to improve equity. It is widespread and will capture many races and ethnicities, but there is no discussion of how this data will improve health equity.
Use and Usability
Agree with Staff Preliminary Assessment
Summary
This is a valuable measure that is already used. Claims data submission minimizes reporting burden which supports feasibility. Usability was assessed and does not appear to be a halting concern. However, I do not believe equity has been fully examined. The researchers identified health disparities, but did not include clarity as to how this measure will improve health equity.
Important and well…
Importance
Agree with staff assessment
Feasibility Acceptance
Agree with staff assessment
Scientific Acceptability
Agree with staff assessment
Agree with staff assessment
Equity
Agree with staff assessment
Use and Usability
Agree with staff assessment
Summary
Important and well-constructed measure
support
Importance
agree with staff assessment
Feasibility Acceptance
agree with staff assessment
Scientific Acceptability
agree with staff assessment
agree with staff assessment
Equity
minimal consideration of equity impact described suggests a possible ability to address some inequities
Use and Usability
presently it seems usable but this conclusion warrants reconsideration once use and performance data become available
Summary
Currently, the supplied information is adequate to support endorsement, a decision that can be reexamined once real world use and performance are available.
Public Comments
Public Comment Shared During May 29 Listening Session
Hi, thank you for holding this again today. And once again, I'm sorry I didn't read all the materials, but I'm just curious on this, did they look at Medicare Advantage? Or was that data available for this? I think it's a very important measure. So, I'm just curious about that. But because people aren't aware among the older population, there is a quite higher than would be anticipated incidence of overdose. And it could be because they are on other medications. So, I just wanted once again affirm the importance of this opportunity. Thank you.