We examined case studies of 3 rural Midwestern communities to assess local health care systems' response to rapidly growing Latino populations. Currently, clinics provide free or low-cost care, and schools, public health, social services, and religious organizations connect Latinos to the health care system. However, many unmet health care needs result from lack of health insurance, limited income, and linguistic and cultural barriers. Targeted safety net funding would help meet Latino health care needs in rural communities with limited resources.

Hospital provision of uncompensated care is partly a function of insurance coverage of state populations. As states expand insurance coverage options and reduce the number of uninsured, hospital provision of uncompensated care should also decrease. Controlling for hospital characteristics and market factors, the authors estimate that increases in MinnesotaCare (a state-subsidized health insurance program for the working poor) enrollment resulted in a 5-year cumulative savings of $58.6 million in hospital uncompensated care costs. Efforts to evaluate access expansions should take into account the costs of the program and the savings associated with reductions in hospital uncompensated care.

Allocations for the State Children's Health Insurance Program (SCHIP) varied 22% per state between 1999 and 2002. The funding fluctuations present significant problems for states as they develop budget priorities under difficult fiscal conditions. We examine sources of the variation in state allocations during the first four years of SCHIP, focusing on the Current Population Survey's “child component” of the allocation formula. We consider the trade-offs in using alternative estimates from the American Community Survey and model-based estimation. Obtaining reliable estimates of need for SCHIP allocations is critical for states dependent on federal support for insurance programs.

BACKGROUND As payers, purchasers, and providers, both the public and private sectors have a stake in developing sound methods of measuring health care quality and patient safety. However, the role of the private sector in a national quality monitoring system remains largely underdeveloped. PRIVATE SECTOR ROLE IN HEALTH CARE QUALITY MONITORING There have been some attempts to pool private-sector data through health care industry efforts to measure and monitor the quality of health care services. Yet despite a number of public/private partnerships, no standard method exists for measuring and monitoring health care quality and safety across public and private payers. THE AHRQ WORKSHOP ON PRIVATE-SECTOR QUALITY MONITORING The Agency for Healthcare Research and Quality (AHRQ) sponsored a workshop in fall 2000 to address the private sector’s role in monitoring quality in the health care system. National experts developed a conceptual framework and recommendations on the design and scope of a private-sector data monitoring system. Ten key attributes of the monitoring system, such as timeliness of reports, flexibility, efficiency, and linkability, were identified. Barriers and gaps to the development of such a system include the cost of data collection, the diversity of the units of data collection, data privacy, and limitations of administrative data elements. SUMMARY A comprehensive, public/private data collection system would address the multidimensional nature of quality and use data to effectively represent this complexity to the extent possible.

OBJECTIVE To estimate the differences in functional outcomes attributable to discharge to one of four different venues for post-hospital care for each of five different types of illness associated with post-hospital care: stroke, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), hip procedures, and hip fracture, and to estimate the costs and benefits associated with discharge to the type of care that was estimated to produce the greatest improvement. STUDY SETTING/DATA SOURCES Consecutive patients with any of the target diagnoses were enrolled from 52 hospitals in three cities. Data sources included interviews with patients or their proxies, medical record reviews, and the Medicare Automated Data Retrieval System. ANALYSIS A two-stage regression model looked first at the factors associated with discharge to each type of post-hospital care and then at the outcomes associated with each location. An instrumental variables technique was used to adjust for selection bias. A predictive model was created for each patient to estimate how that person would have fared had she or he been discharged to each type of care. The optimal discharge location was determined as that which produced the greatest improvement in function after adjusting for patients' baseline characteristics. The costs of discharge to the optimal type of care was based on the differences in mean costs for each location. DATA COLLECTION/EXTRACTION METHODS Data were collected from patients or their proxies at discharge from hospital and at three post-discharge follow-up times: six weeks, six months, and one year. In addition, the medical records for each participant were abstracted by trained abstractors, using a modification of the Medisgroups method, and Medicare data were summarized for the years before and after the hospitalization. PRINCIPAL FINDINGS In general, patients discharged to nursing homes fared worst and those sent home with home health care or to rehabilitation did best. Because the cost of rehabilitation is high, greater use of home care could result in improved outcomes at modest or no additional cost. CONCLUSIONS Better decisions about where to discharge patients could improve the course of many patients. It is possible to save money by making wiser discharge planning decisions. Nursing homes are generally associated with poorer outcomes and higher costs than the other post-hospital care modalities.

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. What do we already know about this topic? Reinsurance is being considered by state and national policy makers as a strategy to stabilize the individual health insurance market. How does your research contribute to the field? Our research leverages national data resources to estimate the size and spending in the individual market across 4 large states and estimates the costs of a fully funded national reinsurance program. What are your research's implications toward theory, practice, or policy? Our estimates are in line with estimates developed by Congressional Budget Office based on proposed legislation, and contribute to evidence that can be used for ongoing policy discussions on ways to stabilize the individual health insurance market.

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. Keywords APCDs, Medicare, Medicaid, health care costs 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) (CMS 2021). The persistent rise in health care spending and lack of federal action has significant impact for states and state policymakers. First, increases in health care spending levies pressure on states which must balance their budgets each year. State spending on the Medicaid program alone accounted for almost one-fifth (17.8%) of state budgets in the 2021 fiscal year (NASBO 2021). States also finance health care for state employees, state prison health care services, and a myriad of safety net programs. Second, patient concerns about excessive costs, including both increases in health