Initiation and Receipt of Medication for Opioid Use Disorder Among Adolescents and Young Adults in 4 State Medicaid Programs in 2018: Improving Medicaid Quality Metrics.

BACKGROUND: Medications for opioid use disorder (MOUD) in youth can reduce harms but many youths do not receive MOUD. Improving quality metrics of MOUD among youth can advance interventions for youth with opioid use disorder (OUD). METHODS: We relied on 2018 Medicaid claims data from California, Colorado, Massachusetts, and New Mexico. We calculated the percentage of youth with OUD included in the quality metric for initiation, and the percentage who initiated by state. We also calculated the percentage excluded from the quality metric for initiation because they have an existing episode of OUD care and their MOUD receipt. We compared the characteristics of those who initiated/received MOUD to those who did not and compared state estimates after adjusting for age and health conditions. RESULTS: Estimates of initiation exclude about half of the youth with OUD because they were in an existing episode of OUD care and could not be observed initiating. Among youth in a new episode of OUD care, only about 1 in 4 initiated and state estimates varied from 18.9% to 40.1%. Among youth with an existing episode of OUD care, more than half received MOUD and state estimates ranged from 35.2% to 71.3%. Youth who initiated MOUD or received MOUD with an existing OUD had more severe OUD but fewer co-occurring substance use disorders or mental or physical health diagnoses. After adjusting for age and health conditions, MOUD still varied substantially across states. CONCLUSIONS: Most youth with a new OUD diagnosis do not initiate MOUD but more than half of the youth in an existing OUD diagnosis receive MOUD. MOUD quality metrics that are disaggregated, adjusted, and inclusive of youth in an existing episode of care provide additional insight into opportunities to better support youth who might choose MOUD. State differences should be further studied for insight into policies that may affect MOUD.

Racialized economic segregation and potentially preventable hospitalizations among Medicaid/CHIP-enrolled children.

OBJECTIVE: To examine geographic variation in preventable hospitalizations among Medicaid/CHIP-enrolled children and to test the association between preventable hospitalizations and a novel measure of racialized economic segregation, which captures residential segregation within ZIP codes based on race and income simultaneously. DATA SOURCES: We supplement claims and enrollment data from the Transformed Medicaid Statistical Information System (T-MSIS) representing over 12 million Medicaid/CHIP enrollees in 24 states with data from the Public Health Disparities Geocoding Project measuring racialized economic segregation. STUDY DESIGN: We measure preventable hospitalizations by ZIP code among children. We use logistic regression to estimate the association between ZIP code-level measures of racialized economic segregation and preventable hospitalizations, controlling for sex, age, rurality, eligibility group, managed care plan type, and state. DATA EXTRACTION METHODS: We include children ages 0-17 continuously enrolled in Medicaid/CHIP throughout 2018. We use validated algorithms to identify preventable hospitalizations, which account for characteristics of the pediatric population and exclude children with certain underlying conditions. PRINCIPAL FINDINGS: Preventable hospitalizations vary substantially across ZIP codes, and a quarter of ZIP codes have rates exceeding 150 hospitalizations per 100,000 Medicaid-enrolled children per year. Preventable hospitalization rates vary significantly by level of racialized economic segregation: children living in the ZIP codes that have the highest concentration of low-income, non-Hispanic Black residents have adjusted rates of 181 per 100,000 children, compared to 110 per 100,000 for children in ZIP codes that have the highest concentration of high-income, non-Hispanic white residents (p < 0.01). This pattern is driven by asthma-related preventable hospitalizations. CONCLUSIONS: Medicaid-enrolled children's risk of preventable hospitalizations depends on where they live, and children in economically and racially segregated neighborhoods-specifically those with higher concentrations of low-income, non-Hispanic Black residents-are at particularly high risk. It will be important to identify and implement Medicaid/CHIP and other policies that increase access to high-quality preventive care and that address structural drivers of children's health inequities.

Medicaid professional fees for treatment of opioid use disorder varied widely across states and were substantially below fees paid by medicare in 2021.

BACKGROUND: As Medicaid is the largest payer for opioid use disorder (OUD) treatment services in the United States, information about Medicaid provider reimbursement is critical, and Medicaid payment policies influence the structure of OUD treatment services for everyone with OUD treatment needs. METHODS: We collected Medicaid professional fees for OUD treatment and related services for the District of Columbia and fifty state Medicaid programs and the Medicare program in 2021. We create three fee indexes related to OUD treatment, with an emphasis on services related to first-line medication treatments in outpatient settings. We then create Medicaid fee indexes and Medicaid-to-Medicare fee indexes. RESULTS: Weekly Medicaid fee bundles for methadone treatment at OTPs in 2021 varied widely, more than 4-fold across states. The Medicaid-to-Medicare fee index shows that the national average Medicaid fee bundle was 56 percent of Medicare fees for regular methadone treatment at OTPs in 2021. For services related to OUD treatment, Medicaid fees varied up to 5-fold and larger across the components of each of the four services, and Medicaid fees were low relative to Medicare for almost all state services examined. The Medicaid-to-Medicare fee index was 64 percent of Medicare fees in 2021, ranging from 52 percent for evaluation & management to 76 percent for toxicology testing. CONCLUSIONS: There appears to be little justification for such large variation in Medicaid fees across states. In addition, the generally low fees in Medicaid persist despite recent efforts to increase access to opioid use disorder treatment for Medicaid enrollees, and have important implications for access to life-saving treatment during the current opioid overdose crisis.

Diagnosis and treatment of substance use disorder among pregnant women in three state Medicaid programs from 2013 to 2016.

Substance use disorder (SUD) during pregnancy increases risks of adverse outcomes for mothers and children. Because Medicaid covers about half of all births and maternal SUD is a costly problem, describing the timing of enrollment and health care that Medicaid-enrolled pregnant women with SUDs receive is critical to understanding gaps in the timeliness and specificity of SUD diagnosis and treatment for pregnant women with SUDs. We used linked maternal and infant Medicaid claims and enrollment data and infant birth records from three states (n=72,086 mother-infant dyads) to estimate the share of sample women diagnosed with a specified SUD (e.g., opioid use disorder) before or during the birth month, with a specified SUD after the birth month, and with only an unspecified SUD diagnosed (e.g., drug use disorder complicating pregnancy). We also examined the timing of first observed Medicaid enrollment, SUD diagnosis and treatment, and maternal and infant costs. In the 24 months surrounding birth, 3.6% of women had a specified SUD diagnosis first observed before or during the birth month, 1.7% had a specified SUD diagnosis first observed after the birth month, and 6.0% had an SUD diagnosis that was not specified. Most women with a specified SUD diagnosis were enrolled in Medicaid before or early in pregnancy and initiated prenatal care in the first or second trimester, yet nearly one-third of these women received their specified SUD diagnosis after the birth month. Less than two-thirds of women with a specified SUD diagnosis received any SUD treatment during the study period (59.9% among those identified before or during the birth month and 63.1% among those observed after the birth month), and women with an unspecified SUD were about half as likely to get treatment (28.6%). Among treated women, more than two-thirds had the first observed treatment in the same month as their first observed SUD diagnosis. Findings point to a critical need for interventions as well as substantial opportunities to improve the identification of substance use-related needs and provision of treatment among women who birth in Medicaid. Changes in Medicaid and other public policy to reduce disincentives for pregnant and parenting women to report substance use during medical visits and to increase providers' abilities and motivation to equitably screen for as well as treat women with SUDs before, during, and after pregnancy could improve outcomes for mothers and their children. Improvements in SUD diagnosis would also improve prevalence estimates of specific types of SUD, which could contribute to better Medicaid policies aimed at prevention and treatment.

Pre-school child blood lead levels in a population-derived Australian birth cohort: the Barwon Infant Study.

OBJECTIVES: To investigate blood lead levels in an Australian birth cohort of children; to identify factors associated with higher lead levels. DESIGN, SETTING: Cross-sectional study within the Barwon Infant Study, a population birth cohort study in the Barwon region of Victoria (1074 infants, recruited June 2010 - June 2013). Data were adjusted for non-participation and attrition by propensity weighting. PARTICIPANTS: Blood lead was measured in 523 of 708 children appraised in the Barwon Infant Study pre-school review (mean age, 4.2 years; SD, 0.3 years). MAIN OUTCOME MEASURE: Blood lead concentration in whole blood (µg/dL). RESULTS: The median blood lead level was 0.8 µg/dL (range, 0.2-3.7 µg/dL); the geometric mean blood lead level after propensity weighting was 0.97 µg/dL (95% CI, 0.92-1.02 µg/dL). Children in houses 50 or more years old had higher blood lead levels (adjusted mean difference [AMD], 0.13 natural log units; 95% CI, 0.02-0.24 natural log units; P = 0.020), as did children of families with lower household income (per $10 000, AMD, -0.035 natural log units; 95% CI, -0.056 to -0.013 natural log units; P = 0.002) and those living closer to Point Henry (inverse square distance relationship; P = 0.002). Associations between hygiene factors and lead levels were evident only for children living in older homes. CONCLUSION: Blood lead levels in our pre-school children were lower than in previous Australian surveys and recent surveys in areas at risk of higher exposure, and no children had levels above 5 µg/dL. Our findings support advice to manage risks related to exposure to historical lead, especially in older houses.

Pregnant women with opioid use disorder and their infants in three state Medicaid programs in 2013-2016.

BACKGROUND: Maternal opioid use disorder (OUD) has serious consequences for maternal and infant health. Analysis of Medicaid enrollee data is critical, since Medicaid bears a disproportionate share of costs. METHODS: This study analyzes linked maternal and infant Medicaid claims data and infant birth records in three states in the year before and after a delivery in 2014-2015 (2013-2016) examining health, health care use, treatment, and neonatal outcomes. Diagnosis and procedure codes identify OUD and other substance use disorders (SUDs). RESULTS: In the year before and after delivery, 2.2 percent of the sample had an OUD diagnosis, and 5.9 percent had a SUD diagnosis other than OUD. Of the women with OUD, 72.8% had treatment for a SUD in the year before and after delivery, but most had none in an average enrolled month, and only 8.8% received any methadone treatment in a given month. Pregnant women with OUD had delayed and lower rates of prenatal care compared to women with other substance use disorders (SUDs). Infants of mothers with OUD did not differ from infants of mothers with other SUDs in rate of preterm or low birth weight but had higher NICU admission rates and longer birth hospitalizations. Health care costs for women with an OUD were higher than those with other SUDs. CONCLUSIONS: There is an urgent need for comprehensive, evidence-based OUD treatment integrated with maternity care. To fill critical gaps in care, workforce and infrastructure innovations can facilitate delivery of preventive and treatment services coordinated across settings.

Medicaid/CHIP Participation Reached 93.7 Percent Among Eligible Children In 2016.

Children's participation in Medicaid and the Children's Health Insurance Program (CHIP) rose by 5 percentage points between 2013 and 2016. As a result, 1.7 million fewer Medicaid/CHIP-eligible children were uninsured in 2016. Participation was lower among adults than among children, and nearly 6 million Medicaid-eligible adults were uninsured in 2016.

Children Eligible for Medicaid or CHIP: Who Remains Uninsured, and Why?

OBJECTIVE: To assess how many uninsured children are eligible for coverage through Medicaid or the Children's Health Insurance Program (CHIP) but not participating and examine the reasons low-income uninsured children are unenrolled. METHODS: Medicaid/CHIP eligibility and participation are estimated for a sample of over 1.4 million children in the 2008 and 2012 American Community Survey. Medicaid/CHIP experience and enrollment barriers are examined for 2300 uninsured children in families with incomes below 200% of the federal poverty level in the 2011-2012 National Survey of Children's Health. RESULTS: Despite increases in the number eligible for Medicaid or CHIP between 2008 and 2012, participation rose nationwide by 6 percentage points; by 2012, 21 states and the District of Columbia had participation rates for children of 90% or higher. The number of eligible but uninsured declined from 4.9 to 3.7 million, but 68% of uninsured children in 2012 qualified for Medicaid or CHIP. Interest in enrolling uninsured children in Medicaid or CHIP is high (more than 90% of parents say they would enroll their child), but despite the high rates of prior enrollment, many families had knowledge gaps and perceived difficulties with enrollment. CONCLUSIONS: Addressing enrollment/retention barriers and raising Medicaid/CHIP participation in low-performing states hold promise for reducing the number of eligible but uninsured children given the diverse set of states with high participation and the high expressed interest in enrolling children.

Comparing errors in Medicaid reporting across surveys: evidence to date.

OBJECTIVE: To synthesize evidence on the accuracy of Medicaid reporting across state and federal surveys. DATA SOURCES: All available validation studies. STUDY DESIGN: Compare results from existing research to understand variation in reporting across surveys. DATA COLLECTION METHODS: Synthesize all available studies validating survey reports of Medicaid coverage. PRINCIPAL FINDINGS: Across all surveys, reporting some type of insurance coverage is better than reporting Medicaid specifically. Therefore, estimates of uninsurance are less biased than estimates of specific sources of coverage. The CPS stands out as being particularly inaccurate. CONCLUSIONS: Measuring health insurance coverage is prone to some level of error, yet survey overstatements of uninsurance are modest in most surveys. Accounting for all forms of bias is complex. Researchers should consider adjusting estimates of Medicaid and uninsurance in surveys prone to high levels of misreporting.

Variation in Medicaid eligibility and participation among adults: implications for the Affordable Care Act.

Steep declines in the uninsured population under the Affordable Care Act (ACA) will depend on high enrollment among newly Medicaid-eligible adults. We use the 2009 American Community Survey to model pre-ACA eligibility for comprehensive Medicaid coverage among nonelderly adults. We identify 4.5 million eligible but uninsured adults. We find a Medicaid participation rate of 67% for adults; the rate is 17 percentage points lower than the national Medicaid participation rate for children, and it varies substantially across socioeconomic and demographic subgroups and across states. Achieving substantial increases in coverage under the ACA will require sharp increases in Medicaid participation among adults in some states.

Who and where are the children yet to enroll in Medicaid and the children's health insurance program?

Kathleen Sebelius, secretary of health and human services, has issued a challenge to enroll the millions of uninsured children eligible for public insurance in Medicaid or the Children's Health Insurance Program (CHIP). This paper provides estimates of the rates at which children in the various states participated in these programs in 2008 as well as the number who were eligible for them but uninsured. According to our coverage estimates, an estimated 7.3 million children were uninsured on an average day in 2008, of whom 4.7 million (65 percent) were eligible for Medicaid or CHIP but not enrolled. Participation rates varied across states from 55 percent to 95 percent, and ten states had participation rates close to or above 90 percent. Thirty-nine percent of eligible uninsured children (1.8 million) live in just three states--California, Texas, and Florida--and 61 percent (2.9 million) live in ten states. Meeting Secretary Sebelius's challenge means achieving success in these populous states, in part through tools and resources available under the 2009 CHIP reauthorization law.

Understanding the current population survey's insurance estimates and the Medicaid 'undercount'.

The widely cited Census Bureau estimates of the number of uninsured people, based on the Current Population Survey, probably overstate the number of uninsured people. This is because of a Medicaid "undercount": Fewer people report to survey takers that they're covered by Medicaid than program administrative data show are enrolled. Our study finds that the undercount can be explained by the inability of people to recall their insurance status accurately from the previous year. We suggest that other data sources, such as Census's American Community Survey, should be studied to determine whether they would provide better estimates of the uninsured.