Objectives: The medical home model is a widely accepted model of team-based primary care. We examined five components of the medical home model in order to better understand their unique contributions to child health outcomes. Methods: We analyzed data from the 2016–2017 National Survey of Children’s Health (NSCH) to assess five key medical home components – usual source of care, personal doctor/nurse, family-centered care, referral access, and coordinated care – and their associations with child outcomes. Health outcomes included emergency department (ED) visits, unmet health care needs, preventive medical visits, preventive dental visits, health status, and oral health status. We used multivariate regression controlling for child characteristics including age, sex, primary household language, race/ethnicity, income, parental education, health insurance coverage, and special healthcare needs. Results: Children who were not white, living in non-English households, with less family income or education, or who were uninsured had lower rates of access to a medical home and its components. A medical home was associated with beneficial child outcomes for all six of the outcomes and the family-centered care component was associated with better results in five outcomes. ED visits were less likely for children who received care coordination (aOR 0.81, CI 0.70–0.94). Conclusions for Practice: Our study highlights the role of key components of the medical home and the importance of access to family-centered health care that provides needed coordination for children. Health care reforms should consider disparities in access to a medical home and specific components and the contributions of each component to provide quality primary care for all children.

Objectives: The medical home model is a widely accepted model of team-based primary care. We examined five components of the medical home model in order to better understand their unique contributions to child health outcomes. Methods: We analyzed data from the 2016–2017 National Survey of Children’s Health (NSCH) to assess five key medical home components – usual source of care, personal doctor/nurse, family-centered care, referral access, and coordinated care – and their associations with child outcomes. Health outcomes included emergency department (ED) visits, unmet health care needs, preventive medical visits, preventive dental visits, health status, and oral health status. We used multivariate regression controlling for child characteristics including age, sex, primary household language, race/ethnicity, income, parental education, health insurance coverage, and special healthcare needs. Results: Children who were not white, living in non-English households, with less family income or education, or who were uninsured had lower rates of access to a medical home and its components. A medical home was associated with beneficial child outcomes for all six of the outcomes and the family-centered care component was associated with better results in five outcomes. ED visits were less likely for children who received care coordination (aOR 0.81, CI 0.70–0.94). Conclusions for Practice: Our study highlights the role of key components of the medical home and the importance of access to family-centered health care that provides needed coordination for children. Health care reforms should consider disparities in access to a medical home and specific components and the contributions of each component to provide quality primary care for all children.

Objectives: The medical home model is a widely accepted model of team-based primary care. We examined five components of the medical home model in order to better understand their unique contributions to child health outcomes. Methods: We analyzed data from the 2016–2017 National Survey of Children’s Health (NSCH) to assess five key medical home components – usual source of care, personal doctor/nurse, family-centered care, referral access, and coordinated care – and their associations with child outcomes. Health outcomes included emergency department (ED) visits, unmet health care needs, preventive medical visits, preventive dental visits, health status, and oral health status. We used multivariate regression controlling for child characteristics including age, sex, primary household language, race/ethnicity, income, parental education, health insurance coverage, and special healthcare needs. Results: Children who were not white, living in non-English households, with less family income or education, or who were uninsured had lower rates of access to a medical home and its components. A medical home was associated with beneficial child outcomes for all six of the outcomes and the family-centered care component was associated with better results in five outcomes. ED visits were less likely for children who received care coordination (aOR 0.81, CI 0.70–0.94). Conclusions for Practice: Our study highlights the role of key components of the medical home and the importance of access to family-centered health care that provides needed coordination for children. Health care reforms should consider disparities in access to a medical home and specific components and the contributions of each component to provide quality primary care for all children.

Objectives: The medical home model is a widely accepted model of team-based primary care. We examined five components of the medical home model in order to better understand their unique contributions to child health outcomes. Methods: We analyzed data from the 2016–2017 National Survey of Children’s Health (NSCH) to assess five key medical home components – usual source of care, personal doctor/nurse, family-centered care, referral access, and coordinated care – and their associations with child outcomes. Health outcomes included emergency department (ED) visits, unmet health care needs, preventive medical visits, preventive dental visits, health status, and oral health status. We used multivariate regression controlling for child characteristics including age, sex, primary household language, race/ethnicity, income, parental education, health insurance coverage, and special healthcare needs. Results: Children who were not white, living in non-English households, with less family income or education, or who were uninsured had lower rates of access to a medical home and its components. A medical home was associated with beneficial child outcomes for all six of the outcomes and the family-centered care component was associated with better results in five outcomes. ED visits were less likely for children who received care coordination (aOR 0.81, CI 0.70–0.94). Conclusions for Practice: Our study highlights the role of key components of the medical home and the importance of access to family-centered health care that provides needed coordination for children. Health care reforms should consider disparities in access to a medical home and specific components and the contributions of each component to provide quality primary care for all children.

State policy makers are under increasing pressure to address the prohibitive cost of health care given the lack of action at the federal level. In 2020, the U.S. spent more on health care than any other country in the world—$4.1 trillion, representing 19.7% of the Gross Domestic Product (GDP). States are trying to better understand their role in health care spending and to think creatively about strategies to address health care cost growth. One way they are doing this is through the development and use of state-based all-payer claims databases (APCDs). APCDs are health data organizations that hold transactional information from public (Medicare and Medicaid) and private health insurers (commercial plans and some self-insured employers) and transform this data to useful information on health care costs and trends. This paper describes the use of APCDs by states and recent efforts that have provided opportunities and challenges for states interested in this unique opportunity to inform health policy. While challenges exist, there is new funding for state APCD improvements in the No Surprises Act and potential new federal interest will help states enhance their APCD capacity to better understand their markets, educate consumers and create actionable market information.

State policy makers are under increasing pressure to address the prohibitive cost of health care given the lack of action at the federal level. In 2020, the U.S. spent more on health care than any other country in the world—$4.1 trillion, representing 19.7% of the Gross Domestic Product (GDP). States are trying to better understand their role in health care spending and to think creatively about strategies to address health care cost growth. One way they are doing this is through the development and use of state-based all-payer claims databases (APCDs). APCDs are health data organizations that hold transactional information from public (Medicare and Medicaid) and private health insurers (commercial plans and some self-insured employers) and transform this data to useful information on health care costs and trends. This paper describes the use of APCDs by states and recent efforts that have provided opportunities and challenges for states interested in this unique opportunity to inform health policy. While challenges exist, there is new funding for state APCD improvements in the No Surprises Act and potential new federal interest will help states enhance their APCD capacity to better understand their markets, educate consumers and create actionable market information.

A number of federal and state policy changes have been put in place during the COVID-19 pandemic to improve access to medications for opioid use disorder (MOUD), including methadone, buprenorphine, and naltrexone. In this brief, we explore these policy changes and recommend that policymakers keep particular changes in place beyond the emergency period. Specifically, policymakers should make permanent (1) federal policies allowing buprenorphine initiation via telemedicine and state policies lifting buprenorphine prescribing restrictions that go beyond federal requirements; (2) federal policies allowing opioid treatment programs to dispense more doses of metha-done and state policies lifting methadone dispensing restrictions; and (3) the prohibition of Medicaid prior authorization requirements for MOUD.

This study aimed to assess the relationship between food security and health outcomes among older adults (age 65+) in the U.S. We used a pooled sample (2011–2015, N = 37,292) from the National Heal...

The global COVID-19 pandemic is causing unprecedented job loss and financial strain. It is unclear how those most directly experiencing economic impacts may seek assistance from disparate safety net programs. To identify self-reported economic hardship and enrollment in major safety net programs before and early in the COVID-19 pandemic, we compared individuals with COVID-19 related employment or earnings reduction with other individuals. We created a set of questions related to COVID-19 economic impact that was added to a cross-sectional, nationally representative online survey of American adults (age ≥18, English-speaking) in the AmeriSpeak panel fielded from April 23-27, 2020. All analyses were weighted to account for survey non-response and known oversampling probabilities. We calculated unadjusted bivariate differences, comparing people with and without COVID- 19 employment and earnings reductions with other individuals. Our study looked primarily at awareness and enrollment in seven major safety net programs before and since the pandemic (Medicaid, health insurance marketplaces/exchanges, unemployment insurance, food pantries/free meals, housing/renters assistance, SNAP, and TANF). Overall, 28.1% of all individuals experienced an employment reduction (job loss or reduced earnings). Prior to the pandemic, 39.0% of the sample was enrolled in ≥1 safety net program, and 50.0% of individuals who subsequently experienced COVID-19 employment reduction were enrolled in at least one safety net program. Those who experienced COVID-19 employment reduction versus those who did not were significantly more likely to have applied or enrolled in ≥1 program (45.9% versus 11.7%, p<0.001) and also significantly more likely to specifically have enrolled in unemployment insurance (29.4% versus 5.4%, p < .001) and SNAP (16.8% versus 2.8%, p = 0.028). The economic devastation from COVID-19 increases the importance of a robust safety net.

Medicaid provides essential coverage for health care and long-term services and supports (LTSS) to low-income older adults and disabled individuals but eligibility is complicated and restrictive. In light of the current public health emergency, states have been given new authority to streamline and increase the flexibility of Medicaid LTSS eligibility, helping them enroll eligible individuals and ensure that current beneficiaries are not inadvertently disenrolled. Though state budgets are under increased pressure during the economic crisis created by the coronavirus, we caution states against cutting Medicaid LTSS eligibility or services to balance their budgets. These services are critical to an especially vulnerable population during a global pandemic.

Medicaid plays a significant role in financing long-term services and supports (LTSS) for low-income elderly (65+) in the United States. We modeled the impact of changing income, home equity, and asset limitations on Medicaid eligibility across states. We found that one in five elderly adults (10 million individuals) meet all three tests and would be financially eligible for Medicaid LTSS. Imposing additional restrictions on income allowances and eligibility thresholds had greatest impact on financial eligibility for Medicaid LTSS. Few states have opted to restrict financial eligibility and are instead looking for ways to keep people living independently in the community.

Reinsurance, an insurance product designed to protect health insurers against the financial risk of covering high-cost enrollees, has attracted bipartisan policy interest as a mechanism to stabilize individual health insurance markets. Three states-Alaska, Minnesota, and Oregon-have implemented state-based reinsurance programs under the Affordable Care Act's 1332 State Innovation Waivers, and reinsurance waivers have been approved though not yet enacted in Maine, Maryland, New Jersey, and Wisconsin. In this article, we estimate the costs of implementing national and state-based reinsurance programs using health spending data from the 2007-2016 Medical Expenditure Panel Survey and state demographic and health insurance coverage data from the 2015-2017 Current Population Survey Annual Social and Economic Supplement. We project that a reinsurance program with an 80% payment rate for expenditures between $40,000 and $250,000 would cost $30.1 billion from 2020-2022. We observed considerable variation in reinsurance programs and estimated costs between the 4 states we examined: California, Florida, Illinois, and Texas. Our projections provide updated estimates of the costs of implementing federal reinsurance programs for the individual health insurance market.

Introduction: Prior to the Affordable Care Act, as many as 1.3 million veterans lacked health insurance. With the passage of the Affordable Care Act, veterans now have new pathways to coverage through Medicaid expansion in those states that chose to expand Medicaid and through private coverage options offered through the Health Insurance Marketplace. We examined the impact of the ACA on health insurance coverage for veterans in expansion and non-expansion states and for urban and rural veterans. Methods: We examined changes in veterans' health insurance coverage following the first year of the ACA, focusing on whether they lived in an urban or rural area and whether they live in a Medicaid expansion state. We used data on approximately 200,000 non-elderly communitydwelling veterans, obtained from the 2013-2014 American Community Survey and estimated differences in the adjusted probability of being uninsured between 2013 and 2014 for both urban and rural areas. Adjusted probabilities were computed by fitting logistic regressions controlling for age, gender, race, marital status, poverty status, education, and employment. Results: There were an estimated 10.1 million U.S. non-elderly veterans in 2013; 82% lived in predominantly urban areas (8.3 million), and the remaining 18% (1.8 million) lived in predominately rural areas. Most veterans lived in the South (43.6%), and rural veterans were more likely to be Southerners than their urban counterparts. On every marker of economic well-being, rural veterans fared worse than urban veterans. They had a statistically significant higher chance of having incomes below 138% of FPG (20.0% versus 17.0%), of being out of the labor force (29.1% versus 23.0%), and of having no more than a high school education (39.6% versus 28.8%). Rural veterans were also more likely to experience at least one functional limitation. Overall, veterans in Medicaid expansion states experienced a significantly larger increase in insurance compared to veterans living in non-expansion states. For rural veterans in Medicaid expansion states, the increase in insurance was 3.5 percentage points, compared with 1.2 percentage points in non-expansion states. Conclusion: Our analysis found a substantial 24% relative decline in the rate of uninsurance for U.S. Veterans, from 9.3 to 7.1% between 2013 and 2014. We found that coverage gains in rural areas were due to gains in Medicaid and individual market coverage. Residence in a Medicaid expansion state was particularly influential for rural veterans - the increase in the insured rate was three times larger in Medicaid expansion states versus non-expansion states. The ACA has had a positive and significant impact on the ability of U.S. Veterans to obtain health insurance coverage specifically for low-income veterans living in rural areas. The poverty rate among Veterans is rising and is particularly an issue for the more recent Gulf War veterans. Providing affordable and accessible health insurance options is part of our commitment to those who have served our country. Our analysis also presents yet another reason for the 17 non-expansion states to consider a Medicaid expansion.

Rich federal data resources provide essential data inputs for monitoring the health and health care of the US population and are essential for conducting health services policy research. The six household surveys we document in this article cover a broad array of health topics, including health insurance coverage (American Community Survey, Current Population Survey), health conditions and behaviors (National Health Interview Survey, Behavioral Risk Factor Surveillance System), health care utilization and spending (Medical Expenditure Panel Survey), and longitudinal data on public program participation (SIPP). New federal activities are linking federal surveys with administrative data to reduce duplication and response burden. In the private sector, vendors are aggregating data from medical records and claims to enhance our understanding of treatment, quality, and outcomes of medical care. Federal agencies must continue to innovate to meet the continuous challenges of scarce resources, pressures for more granular data, and new multimode data collection methodologies.