Hartman, Lacey
Senior Research Fellow

p 612-625-0410
e hartm042@umn.edu

Coverage During a Crisis: Insured Rate for Californians Hits Historic High in First Year of COVID-19 Pandemic (CHCF Cross Post)

The following content is cross-posted from California Health Care Foundation. It was first published on January 12, 2022.
Author: Lacey Hartman, Senior Research Fellow, SHADAC

Despite widespread concern that economic fallout from the pandemic could slow California’s progress toward covering the uninsured, more Californians had health insurance coverage than ever before in 2020, according to results from the latest California Health Interview Survey (CHIS). A combination of pre-pandemic state and federal policies that expanded health insurance coverage, along with quick action by policymakers in 2020 to bolster those policies with additional crisis stopgaps, helped protect coverage for many Californians during the pandemic.

In this brief prepared for the California Health Care Foundation (CHCF), SHADAC researcher Lacey Hartman, MPP, provides data from the CHIS about the coverage landscape in California in 2020, highlighting both encouraging trends and persistent disparities that warrant attention, particularly as federal policies that protect coverage connected to the pandemic end or wind down. 

Key Findings

• The uninsured rate among the nonelderly California population declined significantly, from 8.4% in 2019 to 7.0% in 2020.
• Rates of uninsured dropped across several population subgroups from 2019 to 2020.
  • Californians with incomes up to 138% of the federal poverty guidelines (FPG), dropping from 12.1% to 9.6%. (These are people whose income would make them eligible for Medi-Cal, many through the Affordable Care Act [ACA] expansion of the program.)
  • Californians who identify as Latinx, from 12.9% in 2019 to 10.5%.
  • Those residing in rural areas of the state, from 9.6% to 6.4%.
  • Adults age 18 to 64, from 10.8% to 9.1%.
• Employer and individual coverage held steady statewide, and increased for some groups.
  • The overall statewide rate of employer coverage among the nonelderly was statistically unchanged from 58.8% in 2019 to 60.1% in 2020.
  • Employer coverage increased significantly from 59.2% to 60.9% among nonelderly adults, from 62.6% to 64.9% among citizens, and from 20.5% to 24.0% among those with incomes up to 138% FPG.
• Medi-Cal coverage held steady statewide, but declined significantly among Black Californians.
  • Medi-Cal coverage held steady between 2019 and 2020, covering roughly one quarter of the nonelderly population.
  • Changes by subpopulation were also limited, with the notable exception that the share of Black Californians with Medi-Cal declined from 34.5% in 2019 to 24.0% in 2020, a difference that was statistically significant, and is a continuation of recent trends.
• Despite measurable progress, critical disparities in coverage persist.
  • The uninsured rate among Latinx Californians remains almost three times as high as that of their White counterparts (10.5% compared to 3.8%).
  • Noncitizen adults are uninsured at more than three times the rate of their citizen counterparts (18.4% compared to 5.6%).
  • Californians with lower incomes are more likely to be uninsured than those with incomes above 400% FPG.

Looking Ahead

The state’s robust implementation of the Affordable Care Act and additional state policies over the years, in combination with recent state and federal policies designed to protect against coverage losses during the pandemic, has enabled the rate of coverage among Californians to rise to historic levels, even during a massive public health and economic crisis. However, there is potential for coverage expansion to slow or even reverse as policies that provided robust protection during the pandemic unwind or scale back.

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files Updated December 2021 – Features 2019 Data

This blog was updated in January 2023 to feature 2020 TMSIS data files and can be found here.

Originally published August 2020

As the coronavirus (COVID-19) crisis evolves, it has become increasingly clear that vulnerable subpopulations are being disproportionately impacted, both in terms of disease burden and economic impacts. Medicaid has always played an essential role in providing coverage and care to vulnerable populations, particularly during economic downturns. As a result, the ability to evaluate enrollment, access to services, and quality of care by race and ethnicity subpopulations is critical.

The Transformed Medicaid Statistical Information System, or T‑MSIS Analytic Files (TAF), are an enhanced set of data on beneficiaries in Medicaid and the Children’s Health Insurance Program (CHIP). The Centers for Medicare and Medicaid Services (CMS) recently released updated information about the completeness of race/ethnicity information TAF. This includes information on data completeness for data years 2014-2020, as well as notes on the data versions (e.g., 2020 data are available, but race/ethnicity data are listed as preliminary and have not been assigned an assessment score; therefore this blog will look at the most recent data available from 2019). CMS classifies data in the TAF files under various levels of “concern” in order to help guide researchers considering use of the data.  

Validation Using American Community Survey (ACS) Data
While past versions of the Data Quality (DQ) assessment were based only on the percentage of beneficiaries with missing race and/or ethnicity values, the more recent releases also validate the data against an external benchmark, the U.S. Census Bureau's American Community Survey (ACS). This was done using analysis of the ACS 5-year public use microdata Sample (PUMS) File. The analysis calculated the distribution of self-reported race for all individuals who identified having the following health insurance: “Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability” (2018 ACS Questionnaire). Categories of race in the ACS can be combined to mirror those reported in the TAF (see Table 1). For more information on the construction of the race/ethnicity codes in the TAF we encourage you to read the excellent Background and Methods Resource, published by CMS.

Table 1. Crosswalk of Race and Ethnicity Variables between the TAF and ACS    

Table 2. Race and Ethnicity Data Quality Assessment, 2019 T-MSIS Analytic File (TAF)²

Notes: Though the T-MSIS includes all 50 states, the District of Columbia (D.C.), and the U.S. territories of Puerto Rico and the Virgin Islands, the latter two territories are excluded from the 2019 TAF (Data Version: Release 1) because they do not have 2019 Data Quality (DQ) Assessments or other associated information in the DQ Atlas and are therefore considered “unclassified.”
Source: Medicaid.gov. (n.d.). DQ Atlas: Race and Ethnicity [2019 data set]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16. Accessed December 17, 2021.

Using T-MSIS Data
CMS notes that some states may not have complete data on race and ethnicity because they follow the guidance from the Office of Management and Budget (OMB) that establishes self-identification as the preferred means of obtaining this information, and not all beneficiaries disclose this information. In addition, their evaluation of these data looks only at “missingness” and not other quality issues; for example, states that do not report certain race/ethnicity groups that comprise a substantial proportion of the state’s population. CMS encourages TAF users interested in using the race/ethnicity data for research to further assess its validity by comparing TAF distributions to external benchmarks such as state-level data on race/ethnicity and Medicaid/CHIP enrollment from the American Community Survey (ACS).

Data Quality Atlas
CMS’s Data Quality Atlas provides an interactive tool where users can generate maps (see Figure 1 below), tables, and a variety of other data quality measures related to the TAF. The tool provides a quick way to assess the completeness of multiple variables that may be relevant to specific research questions. It also provides helpful context for the completeness of certain variables compared to other variables. For instance, while all states fall into the “low concern” category for the completeness of age data, over half of the states are classified as having “unusable” family income data.

Figure 1. Data Quality Assessments of Beneficiary Information by U.S. State/Territory: 2019 Race and Ethnicity Data 

Notes: Green = low concern; yellow = medium concern; orange = high concern; red = unusable; grey = unclassified.
Source: Medicaid.gov. (n.d.). DQ Atlas: Race and Ethnicity [2019 Data set: Version: Release 1]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16&tafVersionId=23. Accessed December 20, 2021.

Looking Ahead
Over time, expectations are that the completeness and quality of data in the TAF will continue to improve. In the meantime, CMS’s investments in making the strengths and limitations of these data transparent are critically important for guiding researchers.

States are also intently focused on understanding and addressing the completeness and quality of race and ethnicity data across their data systems in order to better understand and address persistent health disparities. SHADAC, with support from the Robert Wood Johnson Foundation (RWJF) for its State Health and Value Strategies (SHVS) initiative, has been closely tracking the extent to which states are reporting critical health equity measures related to the coronavirus pandemic and vaccination efforts.

SHADAC researchers have also been exploring strategies for filling gaps in Medicaid race, ethnicity, and language (REL) data via Medicaid, and have produced an issue brief that provides a 50-state summary of how Medicaid agencies collect this data through their application process. 

Sources
1. Medicaid.gov. (n.d.). DQ Atlas: Background and methods resource [PDF file]. Available from https://www.medicaid.gov/dq-atlas/downloads/data_by_topic/TAF_DQ_Race_Ethnicity/TAF_DQ_Race_Ethnicity.pdf. Accessed December 20, 2021.
2. Medicaid.gov. (n.d.). Exploring data quality (DQ) assessments by topic. https://www.medicaid.gov/dq-atlas/landing/topics/info. Accessed December 20, 2021.

Borrowing Proven Policy Strategies to Vaccinate Kids Against COVID-19

With the authorization of the first COVID-19 vaccine for children age five and older, most kids in the United States are now eligible to be immunized. Recent experience with other vaccines—measles and chickenpox, for example—shows the country is capable of vaccinating kids widely and equitably, but the challenges that have emerged in the effort to vaccinate adults against COVID-19 also demonstrate that success will take planning and hard work.

In order to meet their COVID-19 vaccination goals to vaccinate kids against COVID-19, states can borrow strategies that have historically proved effective in immunizing kids against other contagious diseases and resulted in dramatic reductions in certain vaccination rate disparities.

This issue brief, produced for State Health & Value Strategies with funding from the Robert Wood Johnson Foundation, highlights strategies and tools that have led to prior successful U.S. efforts to achieve high childhood vaccination rates—and dramatic strides toward health equity—and it identifies how those strategies could be applied in the context of the current COVID-19 crisis.

Key strategies: 

• States can leverage children’s higher rates of health insurance to facilitate COVID-19 vaccination in traditional health care settings, where clinicians can educate parents and kids on vaccine benefits and administer them immediately.
• Vaccines for children-enrolled providers offer a ready infrastructure for immunizing uninsured and under-insured children.
• State Medicaid programs can promote vaccination by setting COVID-19 immunization rate targets for MCOs and health care providers.
• States can disseminate and encourage evidence-based practices (e.g., the 4 Pillars Immunization Toolkit) to help health care providers improve immunization rates.
• Implementing immunization requirements for children to attend schools or childcare has shown to result in higher vaccination rates.

Unlike adults, the U.S. has a proven playbook of initiatives for vaccinating children widely and equitably. States can and should borrow those evidence-based strategies in order to protect children and communities against COVID-19 through successful vaccination.

Click on the image above to read the full brief.

SHADAC Staff and External Authors

p 612-624-4802
e shadac@umn.edu

Brief: Strategies to Increase Access to Medications for Opioid Use Disorder during the COVID-19 Pandemic and Beyond

Impacts of the COVID-19 pandemic have been demonstrably far-ranging, including changes to substance use-related behaviors.¹ Evidence indicates that opioid use disorders (OUDs), overdoses, and overdose deaths grew as a result of COVID-19-related isolation and economic anxiety, and mortality data indicate that opioid overdose deaths, in particular, reached record levels in 2020.

SHADAC researchers Carrie Au-Yeung and Lynn Blewett, along with Hennepin Healthcare researcher Tyler Winkelman, authored a new Milbank Foundation policy brief that examines the federal and state policies changes put in place to improve access to medications for opioid use disorder (MOUD) during the COVID-19 pandemic, during which time MOUD became especially challenging to access because MOUD patients are typically required to have as many as six in-person clinic visits a week. The authors conclude with federal and state policy recommendations based on their analysis.

What is MOUD?
MOUD – sometimes referred to as medication-assisted treatment, or MAT – is the use of medications (methadone, buprenorphine, and naltrexone) to treat OUD, reduce opioid use, and lower the risk of overdose mortality. MOUD is frequently coupled with counseling and behavioral therapies and is the standard of care for OUD.

What policy solutions were put into place to make MOUD more accessible during COVID-19?
A number of policy solutions were put into place at the federal and state level in order to reduce the number of in-person clinic visits required for MOUD and to make MOUD more accessible in general. These solutions involved service delivery rules, prescribing rules, dispensing and refill rules, drug-testing requirements, counseling requirements, Medicaid prior authorization rules, and Medicaid coverage of telehealth.

For example, at the state level:

• Telehealth visits were allowed to replace in-person MOUD visits around prescriptions, assessments, and counseling.
• Some states loosened restrictions on dispensing and refill rules for MOUD and also allowed opioid treatment programs (OTPs) to deliver medications to quarantined or otherwise homebound patients.
• Some states gave more latitude to practitioners to use their own clinical judgment regarding toxicology screening and drug testing, and relaxed state-level counseling requirements or suspending them altogether.
• Individual states temporarily suspended certain Medicaid prior authorization requirements for MOUD, extended prior authorizations through the end of the COVID-19 emergency period, and/or expedited prior authorizations through documentation flexibility.
•  All 50 states and the District of Columbia (D.C.) modified Medicaid policies regarding telehealth to allow for more virtual visits: some upped payment rates for telehealth visits to match in-person visits, others extended policies to cover a wider array of providers, and others expanded “telehealth” to cover audio-only (telephonic) visits.

At the federal level, policy solutions included:

• In-person examination requirements for buprenorphine initiation were removed while for methadone, initiation must be in person, but subsequent visits can occur via telemedicine.
• In April 2021, the US Department of Health and Human Services altered prescribing rules to exempt eligible physicians and practitioners from federal training requirements to obtain a waiver to prescribe buprenorphine small numbers of patients (i.e., fewer than 30).
• Dispensing and refill rules, such as the standard take-home schedule for methadone, was relaxed by the Substance Abuse and Mental Health Services Administration (SAMHSA).
• The American Society for Addiction Medicine suggested pausing, limiting, or using alternative protocols for drug-testing requirements in certain areas to combat community spread of COVID-19.

Recommendation
The need to expand access to MOUD will not end with the COVID-19 emergency, as the number of individuals who need treatment far exceeds the capacity of MOUD providers and facilities. Some of the policy changes that have made MOUD more accessible during the pandemic have already been made permanent at the state level (e.g., expanding the definition of “telehealth” in Medicaid). The continuation of other changes, however, will require ongoing support from state and national policymakers.

The authors recommend continued efforts around three regulatory strategies in particular:

1. National and state policymakers should encourage the continuation of federal policies allowing buprenorphine initiation via telemedicine and the removal of state-level restrictions that go beyond federal requirements.
2. National and state policymakers should also promote the continuation of federal policies allowing OTPs to dispense more doses of methadone and the removal of any additional state-level methadone dispensing restrictions that exist.
3. Policymakers should work to prohibit Medicaid prior authorization requirements for MOUD and to make permanent any temporary suspensions of prior authorization requirements for these medications that were implemented during the COVID-19 emergency.

The authors note that, though there are other policy avenues for expanding access to MOUD, these three areas are particularly relevant for reducing logistical barriers to treatment. Moreover, evidence and testimony from providers and patients indicates that these mechanisms have been safe and effective during the COVID-19 pandemic and that continuing them beyond the pandemic period is warranted.

For more detail on policy changes to MOUD during the COVID crisis and recommendations for extending these policies beyond the pandemic, click on the image above to read the full brief. 

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Explore Physician Acceptance of New Medicaid Patients through Two New Measures on SHADAC’s State Health Compare and in a New MACPAC Factsheet

Authors: Robert Hest and Julia Ngep

In order for the more than 80 million Medicaid beneficiaries to access needed care in a timely manner, there must be a sufficient number of health care providers to serve these patients. There has long been concern that providers are less likely to accept Medicaid patients than patients with other types of health insurance coverage. Because Medicaid policies—and providers’ responses to those policies—differ substantially across states, state-level data is critical for monitoring the providers’ decisions to accept Medicaid patients and for understanding the factors that influence those decisions.

Using data from the 2011-2017 National Electronic Health Records Survey (NEHRS),¹ SHADAC performed an analysis to examine and compare physician acceptance of new Medicaid patients at the state level and by physician and practice characteristics. This analysis was performed under contract with the Medicaid and CHIP Payment and Access Commission (MACPAC) and is presented in two new measures on SHADAC’s State Health Compare web tool as well as in a new MACPAC factsheet.

State Health Compare Measures

Physicians who accept new patients
Physicians who accept new patients measures the percent of physicians who accept new patients by type of coverage: private, Medicare, and Medicaid. Data years were pooled where single-year estimates were not possible and are available for 2011-2013 and 2014-2017.

In 2014-2017, state-level physician acceptance of Medicaid patients ranged from 42.2 percent in New Jersey to 99.4 percent in North Dakota, acceptance of Medicare patients ranged from 77.1 percent in Georgia to 98.3 percent in North Dakota, and acceptance of private patients ranged from 80.3 percent in the District of Columbia (D.C.) to 100.0 percent in Nebraska.

Physicians who accept new Medicaid patients
Physicians who accept new Medicaid patients measures the percent of physicians who accept new Medicaid patients by the following physician/practice characteristics:

• Setting (private solo/group versus total) available for pooled data years 2011-2012 and 2014-2017;
• The share of Medicaid existing patients (above versus below national average) available for pooled data years 2011 & 2013 and 2014-2017;
• And the ratio of mid-level providers (above versus below national average) available for pooled data years 2014-2017.

At the national level in the most recent time period: physicians in private solo/group practices were less likely to accept new Medicaid patients compared to all physicians (70.4% versus 74.0%); physicians with an above-average share of existing Medicaid patients were more likely to accept new Medicaid patients compared to all physicians (87.4% versus 63.8%); and physicians with an above-average ratio of mid-level providers were more likely to accept new Medicaid patients compared with all physicians (80.5% versus 73.7%).

New MACPAC Factsheet

Physician Acceptance of New Medicaid Patients: Findings from the National Electronic Health Records Survey
This new MACPAC factsheet analyzes physician acceptance of new Medicaid patients at the national and state levels. As Medicaid programs vary by each state and there is little information on physician participation in Medicaid at the state level, this analysis provides an important update of previous MACPAC work analyzing physician acceptance at the national level and prior literature analyzing state-level physician acceptance, last updated for data year 2013.

The analysis found significant differences in rates of physician acceptance of new patients by coverage type, with physicians being more likely to accept private and Medicare patients compared with Medicaid patients. Acceptance of Medicaid patients varied significantly by state and by various patient, physician, and practice characteristics. These include source of patient coverage (Medicaid, Medicare, and private), physician specialty, practice setting, existing Medicaid caseload, and presence of mid-level providers. The analysis found that nationally, rates of physician acceptance were stable over time and increased significantly in a handful of states, with no states experiencing significant decreases in rates of acceptance.