Search

Objective: To estimate the effects of prenatal public health insurance targeting noncitizens on the health of U.S.‐born children of noncitizen mothers beyond birth outcomes. Data Sources and Study Setting: This paper uses the restricted version of the 1998–2014 National Health Interview Survey with state‐level geographic identifiers. Study Design: The empirical strategy compares outcomes in states that adopted the Children's Health Insurance Plan (CHIP) Unborn Child Option with states that never adopted or adopted it at different times, controlling for differences in the pre‐treatment period. I use a flexible event‐study analysis to quantify the effects of the Unborn Child Option on noncitizen women's health insurance coverage, health care utilization, and their children's health. Data Collection/Extraction Methods: All data are derived from pre‐existing sources. Principal Findings: The study finds that the impact of the Unborn Child Option is a 4.7%‐point increase in public health insurance coverage (p < 0.01) and 0.48 more doctor's office visits (p < 0.1) annually among noncitizens of childbearing ages. Subsequently, the reform leads to a 7%‐point rise in the rate of parents reporting their 4–6‐year‐old children are in "excellent" or "very good" health (p < 0.01). While no improvements are evident at birth and at younger ages, observed health improvements begin to appear by preschool age. Conclusions: The study contributes to the literature by providing evidence that certain benefits of in‐utero public health insurance targeting noncitizens may appear several years after birth, specifically around preschool age. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the influence of hospitals' organizational culture in China to the hospital influence, the impact of system organization and regulation to the health expenditures and satisfaction, and racial disparities in childhood insurance.

Objective. To assess the impact of the Children's Health Insurance Program (CHIP) on the distribution of health insurance coverage for low-income children. Data Source. The primary data for the study were from the 1997, 1999, and 2002 National Survey of America's Families (NSAF), which includes a total sample of 62,497 children across all 3 years, supplemented with data from other data sources. Study Design. The study uses quasi-experimental designs and tests the sensitivity of the results to using instrumental variable and difference-in-difference approaches. A detailed Medicaid and CHIP eligibility model was developed for this study. Balanced repeated replicate weights were used to account for the complex sample of the NSAF. Descriptive and multivariate analyses were conducted. Principle Findings. The results varied depending on the approach utilized but indicated that the CHIP program led to significant increases in public coverage (14–20 percentage points); and declines in employer-sponsored coverage (6–7 percentage points) and in uninsurance (7–12 percentage points). The estimated share of CHIP enrollment attributable to crowd-out ranged from 33 to 44 percent. Smaller crowd-out effects were found for Medicaid-eligible children. Conclusions. Implementation of the CHIP program resulted in large increases in public coverage with estimates of crowd-out consistent with initial projections made by the Congressional Budget Office. This paper demonstrates that public health insurance expansions can lead to substantial reductions in uninsurance without causing a large-scale erosion of employer coverage. [ABSTRACT FROM AUTHOR]

Objective. This paper uses a unique database to examine premiums paid by beneficiaries for Medigap supplemental coverage. Average premiums charged by insurers are reported, as well as premiums by enrollee age and gender, and additional policy characteristics. Marginal prices for Medigap benefits are estimated using hedonic price regressions. In addition, the paper considers how additional policy characteristics and geographic differences in the use and cost of medical care affect premiums. Data Sources/Study Setting. A comprehensive database on premiums paid by beneficiaries for newly issued Medigap policies in the year 2000 along with state-level characteristics. Study Design. Hedonic pricing equations are used to estimate implicit prices for Medigap benefits. Data Collection/Extraction Methods. The Centers for Medicare & Medicaid Services contracted for the creation of a detailed database on Medigap premiums. Data were collected in three stages. First, letters were sent directly to insurers requesting premium data. Second, letters were directly to state insurance commissioner's offices requesting premium data. Last, each state insurance commissioner's office was visited to collect missing data. Principal Findings. With the exceptions of the part B deductible and drug benefit, Medigap supplemental insurance is priced consistent with the actuarial value of benefits offered under the standardized plans. Premiums vary substantially based on rating method, whether the policy is guaranteed issue, Medigap Select, or explicitly for smokers. Premiums increase with enrollee age, but do not vary between men and women. The relationship between premiums and enrollee age varies across rating methods. Attained-age policies show the strongest relationship between age and premiums, while community-rated premiums, by definition, do not vary with age. Medigap supplemental insurance premiums are higher in states with poorer health, greater utilization, and greater managed care penetration. Conclusions. Despite the high cost, Medigap plans are generally priced in accordance with the actuarial value of benefits. The primary exception is the drug benefit, which appears to be subject to substantial adverse selection. Benefits such as the part B deductible and at-home recovery benefit offer little value to consumers. Several states require insurers to community rate premiums. Such regulation has important implications for premiums, and research needs to consider the impact of such regulation on the Medigap market. [ABSTRACT FROM AUTHOR]

Objective. To measure the change in U.S. women and children's health insurance coverage as a result of welfare reform (i.e. the creation of Temporary Assistance for Needy Families or TANF) in 1996. Data Source. 1992–1999 longitudinal data from the Survey of Income and Program Participation (SIPP) merged with data on the timing of state implementation of welfare reform after 1996. Two key advantages of the SIPP data are that they permit matching type of insurance coverage to the welfare policy environment in each state in each month, and permit controlling for individual-level fixed effects. Study Design. We measure how much insurance coverage changed after welfare reform using a difference in differences method that eliminates the influence of time-invariant unobserved individual heterogeneity and of statewide trends in insurance coverage. Models also control for individual, state, and year fixed effects, individual-level characteristics such as education, age, and number of children, plus state-level variables such as real per capita income, real minimum wage, and Medicaid eligibility. Data Collection/Extraction Methods. We limit our analysis to the SIPP data specific to the month just completed prior to the interview; as a result, we have up to twelve observations for each individual in the SIPP. This paper uses pooled data from the 1992–1996 panels of the SIPP covering the period 1992–1999. Publicly available state identifiers permit the merger of state policies and macroeconomic variables with the SIPP. Principal Findings. TANF implementation is associated with an 8.1 percent increase in the probability that a welfare-eligible woman was uninsured. Welfare reform had less of an impact on the health insurance coverage of children. For example, TANF implementation was associated with a 3.0 percent increase in the probability that a welfare-eligible child lacked health insurance. Conclusions. An unintended consequence of welfare reform was to adversely impact the health insurance coverage of economically vulnerable women and children, and that this impact was several times larger than the previous literature implies. [ABSTRACT FROM AUTHOR]

Nearly one in three adults of ages 19-29 lack health insurance, representing the highest uninsured rate of any age group. To help address this gap, 38 states have enacted laws requiring insurers to permit young adults to enroll as dependents on their parents' plans. This paper evaluates their impact on coverage for young adults. This study uses data for individuals ages 19-29 from the Current Population Survey's Annual Demographic Supplement for calendar years 2000-2008. Linear probability models are used to obtain difference-in-differences estimates of the impact of dependent coverage expansions in 19 early-adopting states on young adults' insurance status. The models also address possible policy endogeneity due to the nonrandom enactment of expansion policies across states. State young adult dependent coverage policies yielded small increases in dependent coverage ranging from 1.52 percentage points for all young adults to 3.84 percentage points for those ages 19-25 residing with parents. These increases were largely offset by declines in employer-sponsored insurance (ESI) in the young adults' own name. No significant impact on young adult uninsured rates was observed. Adult dependent coverage expansions have had a relatively small impact on enrollment as an ESI dependent and appear to have the unintended consequence of reducing ESI policyholder coverage. This policy did not achieve a reduction in uninsured rates as policy makers had intended. Federal reform efforts to expand dependent coverage are likely to be more successful because reform will be accompanied by subsidies and enrollment mandates. [ABSTRACT FROM AUTHOR]

Objective. To illustrate the impact of moral hazard for estimating relative rates of underinsurance and to present an adjustment method to correct for this source of bias. Data Sources/Study Setting. Secondary data from the 2005 Medical Expenditure Panel Survey (MEPS) are used in this study. We restrict attention to households that report having employer-sponsored insurance (ESI) for all members during the entire 2005 calendar year. Study Design. Individuals or households are often classified as underinsured if out-of-pocket spending on medical care relative to income exceeds some threshold. In this paper, we show that, without adjustment, this common threshold measure of underinsurance will underestimate the number with low levels of insurance coverage due to moral hazard. We propose an adjustment method and apply it to the specific case of estimating the difference in rates of underinsurance among small- versus large-firm workers with full-year ESI. Data Collection/Extraction. Data were abstracted from the MEPS website. All analyses were performed in Stata 9.2. Principal Findings. Applying the adjustment, we find that the underinsurance rate of small-firm households increases by approximately 20 percent with the adjustment for moral hazard and the difference in underinsurance rates between large-firm and small-firm households widens substantially. Conclusions. Adjusting for moral hazard makes a sizeable difference in the estimated prevalence of underinsurance using a threshold measure. [ABSTRACT FROM AUTHOR]

Objective. To identify the effect of insurance coverage on prescription utilization by Medicare beneficiaries. Data Sources/Study Setting. Secondary data from the 1999 Medicare Current Beneficiary Survey (MCBS) Cost and Use files, a nationally representative survey of Medicare enrollees. Study Design. The paper uses a cross-sectional design with (1) a standard regression framework to estimate the impact of prescription coverage on utilization controlling for potential selection bias with covariate control based on the Diagnostic Cost Group/Hierarchical Condition Category (DCG/HCC) risk adjuster, and (2) a multistage residual inclusion method using instrumental variables to control for selection bias and identify the insurance coverage effect. Data Collection/Extraction Methods. Data were extracted from the 1999 MCBS. Study inclusion criteria are community-dwelling MCBS respondents with full-year Medicare enrollment and supplemental medical insurance with or without full-year drug benefits. The final sample totaled 5,270 Medicare beneficiaries. Principal Findings. Both the model using the DCG/HCC risk adjuster and the model using the residual inclusion method produced similar results. The estimated price elasticity of demand for prescription drugs for the Medicare beneficiaries in our sample was −0.54. Conclusions. Our results confirm that selection into prescription coverage is predictable based on observable health. Our results further confirm prior estimates of price sensitivity of prescription drug demand for Medicare beneficiaries, though our estimate is slightly above prior results. [ABSTRACT FROM AUTHOR]

Objective. To document the process used in assessing the public health impact of proposed health insurance benefit mandates in California as part of the California Health Benefits Review Program (CHBRP) to serve as a guide for other states interested in incorporating a public health impact analysis into their state mandated benefit review process. Background. As of September 2004, of the 26 states that require reviews of mandated benefit legislation, 25 required an assessment of the cost impact, 12 required an assessment of the medical efficacy, and only 6 had language requiring an assessment of the public health impact. Methodology. This paper presents the methodology used to calculate the overall public health impact of each mandate. This includes a discussion of data sources, required data elements, and the methods used to quantify the impact of a mandated health insurance benefit on: overall public health, on gender and racial disparities in health outcomes, on premature death, and on the economic loss associated with disease. In addition we identify the limitations of this type of analysis. Conclusions. The approach that California has adopted to review proposed health benefit mandates represents a leap forward in its consideration of the impact of such mandates on the health of the population. the approach is unique in its specific requirements to address public health impacts as well as the attempt to quantify these impacts by the CHBRP team. The requirement to make available this information to the state government has the potential, ultimately, to increase the availability of health insurance products in California that will maximize public health. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify the factors that determine the prevalence of private medical insurance (PMI) in England.Secondary data sources are the British Household Panel Survey (BHPS) 1997–2000, Laing's Healthcare Market Review 1999–2000, the United Kingdom (U.K.) Department of Health's National Health Service Waiting Times Team, and the Work Force Statistics Branch of the Department of Health.Logistic regression models for panel data were used to compare non-PMI subscribers with individual subscribers and those with employer-provided PMI.The BHPS data are collected by the Institute for Social and Economic Research at the University of Essex. Other data used were collected by Laing and Buisson and the U.K. Department of Health.Individual PMI is more prevalent among the well-educated and healthy. Income, age, sex, and political preference are key determinants of PMI prevalence for both individual and employer paid PMI. Individuals are also likely to reflect on information with regard to waiting times in deciding whether or not to purchase PMI cover. The withdrawal of the tax subsidy in 1997 to PMI subscribers over 60 years of age did not impact on their rate of withdrawal from PMI coverage relative to the rate among all PMI subscribers, but may have discouraged potential new subscribers.Current trends in the PMI market suggest that, over time, individually purchased PMI is likely to be partially displaced by PMI purchased as part of a company-based plan. However, having PMI is linked to economic factors in both groups, suggesting a similar segment of the population valuing the responsiveness that PMI provides. Geographic factors relating to waiting times and supply-side factors are associated with both individual and company-based PMI. The withdrawal of the tax subsidy to individual subscribers older than age of 60 resulted in a significant decline in the demand for PMI. In particular, the number of new subscribers in this group declined substantially. [ABSTRACT FROM AUTHOR]

Cogitates the challenges of studying the effects of managed care in the U.S. Changes to managed care plans in the medical care practice; Decline in the utilization management strategies; Impact of the reductions in selective contracting to health maintenance organizations.

Objective: To evaluate the effects on racial disparities in health insurance coverage from the changes in the Premium Tax Credit (PTC) implemented in March 2021 as part of the American Rescue Plan Act (ARPA). Data Sources and Study Setting: We use nationally representative individual‐level data from the Household Pulse Survey (HPS), which provides demographic, economic, and health insurance information for United States residents during the period April 2020–August 2022. Study Design: While the PTC changes applied to all states, the 14 states that did not expand Medicaid received substantially more benefits than the expansion states since they had more uninsured individuals eligible for the PTC than the expansion states. In our analysis, the treatment (control) group includes all Medicaid nonexpansion (expansion) states. We use a difference‐in‐difference regression analysis to estimate the increase in the probability of insurance coverage after the expansion of the PTC. Furthermore, we conduct sensitivity and heterogeneity analyses. Data Collection/Extraction Methods: We focus on survey respondents ages 18–64. Principal Findings: The expanded PTC increased the probability of an individual having coverage through the Health Insurance Exchange (HIX) in a nonexpansion state by 0.95 (95% CI: 0.6136, 1.2900), 1.75 (95% CI: 1.1795, 2.3291), and 1.75 (95% CI: 1.1815, 2.3269) percentage points among White, Black, and Hispanic respondents, respectively. It also increased overall health insurance coverage among all groups. Conclusions: The expanded PTC boosted HIX and overall health insurance coverage and reduced racial disparities. [ABSTRACT FROM AUTHOR]

Objective: To examine the effects of hospital and insurer markets concentration on transaction prices for inpatient hospital services.Data Sources: Measures of hospital and insurer markets concentration derived from American Hospital Association and HealthLeaders-InterStudy data are linked to 2005-2008 inpatient administrative data from Truven Health MarketScan Databases.Study Design: Uses a reduced-form price equation, controlling for cost and demand shifters and accounting for possible endogeneity of market concentration using instrumental variables (IV) technique.Principal Findings: The findings suggest that greater hospital concentration raises prices, whereas greater insurer concentration depresses prices. A hypothetical merger between two of five equally sized hospitals is estimated to increase hospital prices by about 9 percent (p < .001). A similar merger of insurers would depress prices by about 15.3 percent (p < .001). Over the 2003-2008 periods, the estimates imply that hospital consolidation likely raised prices by about 2.6 percent, while insurer consolidation depressed prices by about 10.8 percent. Additional analysis using longer panel data and applying hospital fixed effects confirms the impact of hospital concentration on prices.Conclusion: The findings provide support for strong antitrust enforcement to curb rising hospital service prices and health care costs. [ABSTRACT FROM AUTHOR]

The article discusses the role of state regulators and insurance companies in the U.S. in the U.S. Affordable Care Act to shape the markets and determine insurance plan affordability by referring to the article, Individual Market Health Plan Affordability after Cost Sharing Reduction Subsidy Cuts, published within the issue. Topics discussed include the affordability of the individual health insurance market, the termination of payments for cost-sharing reduction, and Medicaid expansion.

Objective: To study the impact of Medicaid funding structures before and after the implementation of the Affordable Care Act (ACA) on health care access for Latinos in New York (Medicaid expansion), Florida (Medicaid non‐expansion), and Puerto Rico (Medicaid block grant). Data Sources: Pooled state‐level data for New York, Florida, and Puerto Rico from the 2011–2019 Behavioral Risk Factor Surveillance System and data from the 2011–2019 American Community Survey and Puerto Rico Community Survey. Study Design: Cross‐sectional study using probit with predicted margins to separately compare four health care access measures among Latinos in New York, Florida, and Puerto Rico (having health insurance coverage, having a personal doctor, delayed care due to cost, and having a routine checkup). We also used difference‐in‐differences to measure the probability percent change of having any health insurance and any public health insurance before (2011–2013) and after (2014–2019) the ACA implementation among citizen Latinos in low‐income households. Data Collection: The sample consisted of Latinos aged 18–64 residing in New York, Florida, and Puerto Rico from 2011 to 2019. Principal Findings: Latinos in Florida had the lowest probability of having health care access across all four measures and all time periods compared with those in New York and Puerto Rico. While Latinos in Puerto Rico had greater overall health care access compared with Latinos in both states, health care access in Puerto Rico did not change over time. Among citizen Latinos in low‐income households, New York had the greatest post‐ACA probability of having any health insurance and any public health insurance, with a growing disparity with Puerto Rico (9.7% any [1.6 SE], 5.2% public [1.8 SE]). Conclusions: Limited Medicaid eligibility (non‐expansion of Florida's Medicaid program) and capped Medicaid funds (Puerto Rico's Medicaid block grant) contributed to reduced health care access over time, particularly for citizen Latinos in low‐income households. [ABSTRACT FROM AUTHOR]

Specifically, Medicaid policy can improve health equity through several pathways, including (1) collecting member demographics and social determinants of health, (2) monitoring and evaluating health equity in Medicaid policy, and (3) incentivizing health equity. MONITORING AND EVALUATING HEALTH EQUITY IN MEDICAID Without federal Medicaid analytic data sets that enable and accelerate equity-focused research, coalitions of the willing are taking the first steps in driving policy change by innovating in this space to monitor and evaluate health equity. Currently, 92% of Americans have health insurance, the highest coverage rate in our nation's history.1 Much of the growth in coverage comes from states expanding Medicaid eligibility over the past decade and maintaining enrollment in Medicaid programs in response to the COVID-19 crisis. Platforms like MODRN provide cross-state evidence needed to facilitate evidence-based policy making in Medicaid programs, including emerging efforts to incentivize equity in Medicaid managed care contracts. [Extracted from the article]

Objective: To examine whether the financial burden of hospitalizations affects the health care utilization of household members of the admitted patient. Data Sources: We utilized health care claims data from the Massachusetts All‐Payer Claims Database, 2010–2015, to identify emergency hospitalizations of patients on family insurance plans and the health care utilization of the family members on those plans. Study Design: We used an event‐study analysis to compare health care spending and utilization of family members of a hospitalized individual and family members of an individual who was hospitalized 1 year later. We examine whether such hospitalizations were associated with changes in medical spending, the frequency of ambulatory office visits, other ambulatory care, and preventive care. Data Collection/Extraction Methods: The analyses include household members of patients with an emergency admission and a length of stay between 5 and 90 days. Principal Findings: Unexpected hospital admissions reduced household members' health care spending and utilization by more than 6.4% (95% confidence interval [CI]: −8.2%, −4.5%) on average in the year following the hospitalization. Household members had fewer ambulatory visits with primary care physicians (PCPs), fewer referrals to specialists, and reduced utilization of other ambulatory care, including high‐value preventive services. These changes were observed for both children and adults and were exacerbated if members of the household had previously been on Medicaid. The reduction in utilization was less pronounced when the admitted patient and household member shared the same PCP and when their health insurance plan had a family deductible. Conclusions: Compared with families without a hospitalized family member, family members of hospitalized individuals reduced their medical spending and utilization, including a substantial reduction in the use of preventive care. This study highlights the challenges of providing continuity in care when families face financial hardship. [ABSTRACT FROM AUTHOR]

Objective: To assess three possible determinants of individuals' response in their private insurance purchases to the availability of the Partnership for Long-Term Care (PLTC) insurance program: bequest motives, financial literacy, and program awareness.Data Sources: The health and retirement study (HRS) merged with data on states' implementation of the PLTC program.Study Design: Individual-level decision on private long-term care insurance is regressed on whether the PLTC program is being implemented for a given state-year, asset dummies, policy determinant variable, two-way and three-way interactions of these variables, and other controls, using fixed effects panel regression.Data Extraction Methods: Analysis used a sample between 50 and 69 years of age from 2002 to 2010, resulting in 12,695 unique individuals with a total of 39,151 observations.Principal Findings: We find mild evidence that intent to bequest influences individual purchase of insurance. We also find that program awareness is necessary for response, while financial literacy notably increases responsiveness.Conclusions: Increasing response to the PLTC program among the middle class (the stated target group) requires increased efforts to create awareness of the program's existence and increased education about the program's benefits, and more generally, about long-term care risks and needs. [ABSTRACT FROM AUTHOR]

Objective: To quantify impacts of early Affordable Care Act (ACA) Medicaid expansions on Medicaid participation for primary care physicians. Data Sources: The study uses secondary Medicaid Analytic eXtract (MAX) data from the United States for 2009–2012, as well as secondary National Plan and Provider Enumeration System (NPPES) data from the United States for 2015. Study Design: The study uses a quasi‐experimental difference‐in‐differences study design where the policy change is Medicaid expansion in six states that adopted early ACA Medicaid expansions during 2010 and 2011: California, Connecticut, the District of Columbia, Minnesota, New Jersey, and Washington. The key outcome variables are five monthly measures of physician participation: the number of Medicaid visits, the number of Medicaid patients, seeing at least 1 Medicaid patient, seeing at least 25 Medicaid patients, and seeing at least 50 Medicaid patients. Data Collection/Extraction Methods: The sample consists of all physicians who were active between 2005 and 2015, according to the NPPES. Principal Findings: For primary care physicians, Medicaid expansion led to a 29% increase in Medicaid visits (5.88 per month; 95% CI: 2.49–9.27), a 29% increase in Medicaid patients (4.59 per month; 95% CI: 2.16–7.02), and did not affect the probability of any Medicaid participation. Medicaid expansion also led to a 22% increase in the probability of seeing at least 25 Medicaid patients per month (4.58 percentage points; 95% CI: 1.27–7.89) and a 31% increase in the probability of seeing at least 50 Medicaid patients per month (2.99 percentage points; 95% CI: 0.99–4.99). Conclusions: Early ACA Medicaid expansions led to increased Medicaid visits for primary care physicians but did not affect the probability of any Medicaid participation. Primary care physicians who had previously served Medicaid patients responded to early ACA Medicaid expansions by serving substantially more Medicaid patients. [ABSTRACT FROM AUTHOR]

Objective: To examine factors associated with accurate reporting of private and public health insurance coverage. Data sources: Minnesota health plan enrollment records provided the sample for the Comparing Health Insurance Measurement Error (CHIME) study, a survey conducted in 2015 that randomly assigned enrollees to treatments that included health insurance questions from the American Community Survey (ACS) or the redesigned Current Population Survey Annual Social and Economic Supplement (CPS). Study design: Reverse record check study that compared CHIME study survey responses to enrollment records of coverage type (direct purchase on and off the Marketplace, Medicaid, or MinnesotaCare), service use, subsidy receipt, and duration of coverage from a major insurer. Data collection methods: Using matched enrollment and CHIME survey data and logistic regression, we examined correlates of accurate insurance type reporting, including characteristics of the insurance coverage, the covered individual, respondent, and family. Principal findings: Reporting accuracy across treatment and coverage type is high (77%–84%). As with past research, accurate reporting of public insurance is higher for people with characteristics consistent with eligibility for public insurance for both survey treatments. For the ACS treatment, reports of direct purchase insurance are more accurate for enrollees who receive a premium subsidy. Conclusions: Given the complexity of health insurance measurement and frequently changing policy environment, differences in reporting accuracy across treatments or coverage types are not surprising. Several results have important implications for data editing and modeling routines. First, adding premium and subsidy questions in federal surveys should prove useful given the finding that subsidy receipt is associated with reporting accuracy. Second, across both survey treatments, people whose opportunity structures (race, ethnicity, and income) match public program eligibility are accurate reporters of this coverage. This evidence supports using these commonly collected demographic variables in simulation, imputation, and editing routines. [ABSTRACT FROM AUTHOR]

Objective To examine the robustness of findings regarding state-level adult dependent coverage expansions using detailed outcomes that specify coverage source. Data Sources This study uses the 2001-2009 files of the Current Population Survey's Annual Social and Economic Supplement, covering calendar years 2000-2008, and considers young adults ages 19 through 29. Study Design Difference-in-differences methods were used to estimate the effect of state-level dependent coverage expansions on finely detailed categories of coverage, and falsification tests were used to evaluate the models themselves. Principal Findings Certain published results on state-level parental coverage expansions are flawed, with reported increases driven by changes in spousal coverage. Other published results appear to be in fact driven by parental coverage, but they are not robust to alternative model adjustments. Conclusions This study shows evidence that one study's results on 'dependent' coverage are in fact driven by changes in rates of spousal coverage. Results from a second study, though not robust to use of a more conventional DD model, would seem to apply most strongly to individuals at ages at which one would typically have lost parental coverage before reform, consistent with a 'passive' effect rather than an 'active' effect that enrolls previously uninsured youths. [ABSTRACT FROM AUTHOR]

Objective: To examine nonprofit hospitals' financial and spending allocations when the private sector payment rate is higher than the Medicare's payment rate. Data Sources: Hospital financial data for 2014–2018 from Center for Medicare and Medicaid Services Hospital Cost Reports, hospital characteristics from the American Hospital Association (AHA) Annual Survey. Study Design Hospital and year level fixed effects regressions modeling each hospital's (1) operating net income per discharge equivalent (DE); (2) administrative cost per DE; (3) patient care cost per DE; (4) registered nurse per bed; charity care cost per DE; and (5) provision of unprofitable services as a function of the private sector to Medicare payment ratio (PMR). Data Collection/Extraction Methods: Hospital/year‐level data from hospital cost reports merged with AHA data. Samples included general short‐term hospitals with nonprofit ownership, excluding critical access hospitals. Principal Findings: The final sample included a total of 8862 hospital‐year observations, with a mean PMR of 1.62. Nonprofit hospitals having a 0.1 higher PMR were associated with $257 (95% CI: $181–$334) increase in operating net income per DE; $66 (95% CI: $32–$99) increase in administrative cost per DE; $170 (95% CI: $120–$220) increase in patient care cost per DE; and $18 (95% CI: $10–$25) increase in charity care cost per DE. We found hospitals hired 0.86 (95% CI: −0.08 to 1.81) more registered nurses per 100 beds, but no evidence on providing more beds for unprofitable services, such as obstetric care, burn care, alcohol/drug abuse treatment, or psychiatric care. Conclusions: Higher private sector prices led primarily to greater surplus and administrative cost for nonprofit hospitals and smaller increases in spending on services that will directly benefit patients. [ABSTRACT FROM AUTHOR]

The article presents the response of authors to the comments made on their study "Assessing the Health Effects of Medicare Coverage for Previously Uninsured Adults: A Matter of Life and Death?" by J.M. McWilliams. In response to the comment, the authors clarified how their specification is sufficiently flexible to appropriately model mortality. They added that they modeled health trajectories on the rate at which health states change between waves of the Health and Retirement Study (HRS).

The author reflects on the impact of medicare coverage policies on health care utilization in the U.S. The author states that there are significant limitations to enforcement of coverage policies in the U.S. He notes that coverage decisions vary when there is no consensus about whether a particular procedure is effective. Furthermore, a suggestion to medicare carriers to focus their efforts on the elimination of forms of care that are inappropriate is presented.

Objective. To examine the implications for statistical power of using predicted probabilities for a dichotomous independent variable, rather than the actual variable. Data Sources/Study Setting. An application uses 271,479 observations from the 2000 to 2002 CAHPS Medicare Fee-for-Service surveys. Study Design and Data. A methodological study with simulation results and a substantive application to previously collected data. Principle Findings. Researchers often must employ key dichotomous predictors that are unobserved but for which predictions exist. We consider three approaches to such data: the classification estimator (1); the direct substitution estimator (2); the partial information maximum likelihood estimator (3, PIMLE). The efficiency of (1) (its power relative to testing with the true variable) roughly scales with the square of one less the classification error. The efficiency of (2) roughly scales with the R2 for predicting the unobserved dichotomous variable, and is usually more powerful than (1). Approach (3) is most powerful, but for testing differences in means of 0.2–0.5 standard deviations, (2) is typically more than 95 percent as efficient as (3). Conclusions. The information loss from not observing actual values of dichotomous predictors can be quite large. Direct substitution is easy to implement and interpret and nearly as efficient as the PIMLE. [ABSTRACT FROM AUTHOR]

Objective. To examine whether known Medicaid enrollees misreport their health insurance coverage in surveys and the extent to which misreports of lack of coverage bias estimates of uninsurance. Data Source. Primary survey data from the Medicaid Undercount Experiment. Study Design. Analyze new data from surveys of Medicaid enrollees in California, Florida, and Pennsylvania and summarize existing research examining bias in coverage estimates due to misreports among Medicaid enrollees. Data Collection Method. Subjects were randomly drawn from Medicaid administrative records and were surveyed by telephone. Principal Findings and Conclusions. Cumulative evidence shows that a small percentage of Medicaid enrollees mistakenly report being uninsured, resulting in modest upward bias in estimates of uninsurance. A somewhat larger percentage of enrollees report having some other type of coverage than no coverage, biasing Medicaid enrollment estimates downward but not biasing estimates of uninsurance significantly upward. Implications for policy makers' confidence in survey estimates of coverage are discussed. [ABSTRACT FROM AUTHOR]

Objective. To provide guidelines for identifying composite hypotheses and addressing the probability of false rejection for multiple hypotheses. Data Sources and Study Setting. Examples from the literature in health services research are used to motivate the discussion of composite hypothesis tests and multiple hypotheses. Methods. This article is a didactic presentation. Principal Findings. It is not rare to find mistaken inferences in health services research because of inattention to appropriate hypothesis generation and multiple hypotheses testing. Guidelines are presented to help researchers identify composite hypotheses and set significance levels to account for multiple tests. Conclusions. It is important for the quality of scholarship that inferences are valid: properly identifying composite hypotheses and accounting for multiple tests provides some assurance in this regard. [ABSTRACT FROM AUTHOR]

Determines the effects of competition for both Medicare and patients enrolled in health maintenance organizations on the quality decisions of hospitals in Southern California. Analysis of secondary discharge data from the Office of Statewide Health Planning and Development for the State of California; Formulation of competition measures; Increase in the degree of competition associated with hospital mortality rates.

Objective: To estimate the impact of a new, two‐sided risk model accountable care network (ACN) on Washington State employees and their families. Data Sources/Study Setting: Administrative data (January 2013‐December 2016) on Washington State employees. Study Design: We compared monthly health care utilization, health care intensity as measured through proxy pricing, and annual HEDIS quality metrics between the five intervention counties to 13 comparison counties, analyzed separately by age categories (ages 0‐5, 6‐18, 19‐26, 18‐64). Data Collection/Extraction Methods: We used difference‐in‐difference methods and generalized estimating equations to estimate the effects after 1 year of implementation for adults and children. Principal Findings: We estimate a 1‐2 percentage point decrease in outpatient hospital visits due to the introduction of ACNs (adults: −1.8, P <.01; age 0‐5: −1.2, P =.07; age 6‐18: −1.2, P =.06; age 19‐26; −1.2, P <.01). We find changes in primary and specialty care office visits; the direction of impact varies by age. Dependents age 19‐26 were also responsive with inpatient admissions declines (−0.08 percentage points, P =.02). Despite changes in utilization, there was no evidence of changes in intensity of care and mixed results in the quality measures. Conclusions: Washington's state employee ACN introduction changed health care utilization patterns in the first year but was not as successful in improving quality. [ABSTRACT FROM AUTHOR]

Objective: The Affordable Care Act allows insurers to charge up to 50% higher premiums to tobacco users, making tobacco use the only behavioral factor that can be used to rate premiums in the nongroup insurance market. Some states have set more restrictive limits on rating for tobacco use, and several states have outlawed tobacco premium surcharges altogether. We examined the impact of state level tobacco surcharge policy on health insurance enrollment decisions among smokers.Study Design: We compared insurance enrollment in states that did and did not allow tobacco surcharges, using a difference-in-difference approach to compare the policy effects among smokers and nonsmokers. We also used geographic variation in tobacco surcharges to examine how the size of the surcharge affects insurance coverage, again comparing smokers to nonsmokers.Data Collection: We linked data from two components of the Current Population Survey-the 2015 and 2019 Annual Social and Economic Supplement and the Tobacco Use Supplement, which we combined with data on marketplace plan premiums. We also collected qualitative data from a survey of smokers who did not have insurance through an employer or public program.Principal Findings: Allowing a tobacco surcharge reduced insurance enrollment among smokers by 4.0 percentage points (P = .01). Further, smokers without insurance through an employer or public program were 9.0 percentage points less likely (P < .01) to enroll in a nongroup plan if they were subject to a tobacco surcharge. In states with surcharges, enrollment among smokers was 3.4 percentage points lower (P < .01) for every 10 percentage point increase in the tobacco surcharge.Conclusions: Tobacco use is the largest cause of preventable illness in the United States. State tobacco surcharge policy may have a substantial impact on whether tobacco users choose to remain insured and consequently their ability to receive care critical for preventing and treating tobacco-related disease. [ABSTRACT FROM AUTHOR]

Objective: To estimate the impact of the major components of the ACA (Medicaid expansion, subsidized Marketplace plans, and insurance market reforms) on health care access and self‐assessed health during the first 2 years of the Trump administration (2017 and 2018). Data Source: The 2011‐2018 waves of the Behavioral Risk Factor Surveillance System (BRFSS), with the sample restricted to nonelderly adults. The BRFSS is a commonly used data source in the ACA literature due to its large number of questions related to access and self‐assessed health. In addition, it is large enough to precisely estimate the effects of state policy interventions, with over 300 000 observations per year. Design: We estimate difference‐in‐difference‐in‐differences (DDD) models to separately identify the effects of the private and Medicaid expansion portions of the ACA using an identification strategy initially developed in Courtemanche et al (2017). The differences come from: (a) time, (b) state Medicaid expansion status, and (c) local area pre‐2014 uninsured rates. We examine ten outcome variables, including four measures of access and six measures of self‐assessed health. We also examine differences by income and race/ethnicity. Principal Findings: Despite changes in ACA administration and the political debate surrounding the ACA during 2017 and 2018, including these fourth and fifth years of postreform data suggests continued gains in coverage. In addition, the improvements in reported excellent health that emerged with a lag after ACA implementation continued during 2017 and 2018. Conclusions: While gains in access and self‐assessed health continued in the first 2 years of the Trump administration, the ongoing debate at both the federal and state level surrounding the future of the ACA suggests the need to continue monitoring how the law impacts these and many other important outcomes over time. [ABSTRACT FROM AUTHOR]

Objective: To add to the evidence base on causal linkages between health insurance coverage and health status, controlling for sociodemographic factors, by analyzing longitudinal data. Data Source: Secondary data from the Panel Study of Income Dynamics (PSID), 2009‐17, which is a longitudinal, multigenerational study covering a wide array of socioeconomic topics that began in 1968 but has only recently begun collecting useful information on individual health insurance. Study Design: 2017 data on self‐reported health status, work limitations, and death were analyzed as outcomes based upon the degree of exposure to health insurance in 2011‐17. All variables were collected biannually for four years beginning in 2011. Having health insurance at each point in time was, in turn, modeled as a function of several sociodemographic factors. Data Extraction Methods: Data were downloaded using the crosswalk tool available at the PSID website. Because individual health insurance questions were only asked of heads and spouses in households beginning in 2011, we analyzed only these records. Principal Findings: Among respondents who were not in fair or poor health in 2009, each additional 2 years of subsequent reported insurance coverage reduced the chance of reporting fair or poor health in 2017 by 10 percent; however, this effect was not present for black respondents. Conclusions: Our results suggest that the effect of health insurance on health status may compound over time, although unevenly by race. Since people who report fair or poor health status represent the bulk of utilization and spending, our findings provide evidence in support of viewing coverage expansions as investments that will pay dividends in the form of lower utilization over time. More work is needed to produce detailed estimates of cost savings, which may in turn influence policy, as well as to understand and address the source of racial disparity. [ABSTRACT FROM AUTHOR]

Objective: To provide new evidence on the effects of large-scale public health insurance expansions, associated with the Affordable Care Act (ACA), on the availability of specialty mental health care treatment in the United States. We measure availability with the probability that a provider accepts Medicaid.Data Source/study Setting: The National Mental Health Services Survey (N-MHSS) 2010-2018.Study Design: A quasi-experimental differences-in-differences design using observational data.Data Collection: The N-MHSS provides administrative data on the universe of specialty mental health care providers in the United States. Response rates are above 90 percent in all years. Data cover 85 019 provider/year observations.Principal Findings: ACA-Medicaid expansion increases the probability that a provider accepts Medicaid by 1.69 percentage points, 95 percent confidence interval: [0.0017,0.0321], which corresponds to an increase from 87.27 percent pre-expansion to 90.27 percent postexpansion in expansion states or a 1.94 percent increase. We observe spillovers to Medicare, although this finding is sensitive to specification.Conclusions: This study provides evidence on the impact of ACA-Medicaid expansion on accepted forms of payment for specialty mental health care treatment. Findings suggest that expansion increases availability of providers who deliver valuable care for enrollees with severe mental illness. These findings may help policy makers reflecting on the future directions of the US health care delivery system. [ABSTRACT FROM AUTHOR]

Objective: To examine the association of a national insurer's reference-based pricing (RBP), program for outpatient advanced imaging-a benefit design to encourage patients to choose lower-price facilities.Data Source/study Setting: Administrative and medical claims data for three self-insured employers that introduced RBP and a comparison group without RBP.Study Design: Difference-in-difference comparison of pre-RBP (2014) and post-RBP (2015-6) care between intervention and comparison groups.Data Collection/extraction Method: We identified 137 680 imaging procedures (4602 intervention group; 133 078 comparison group) in 2014-2016.Principal Findings: In the first post-RBP year (2015), there was no change in choice of facility; by the second year, RBP-exposed enrollees were 21.9 pp (95% CI: 18.5, 25.3) more likely to choose a lower-priced facility and net prices were $101.05 (95% CI: -$130.65, -$71.46), a difference of 8.1 percent lower. RBP was associated with higher patient out-of-pocket spending in the first post-RBP year ($31.82; 95% CI: $10.91, $52.73). There was no change in utilization, and higher-priced providers did not lower prices in the postperiod. Net savings represented 0.3 percent of outpatient spending.Conclusions: Reference-based pricing for advanced imaging was associated with a shift to lower-priced facilities, but net impact on outpatient spending was modest. Patients paid increased out-of-pocket costs, though the amount declined after the first year of the program. [ABSTRACT FROM AUTHOR]

Objective: To measure the accuracy of survey-reported data on features and type of health insurance coverage.Data Source: Enrollment records from a private insurer were used as sample for primary survey data collection in spring of 2015 using the Current Population Survey health insurance module.Study Design: A reverse record check study where households with individuals enrolled in a range of public and private health insurance plans (including the marketplace) were administered a telephone survey that included questions about general source of coverage (eg, employer), program name (eg, Medicaid), portal, premium, and subsidies.Data Collection/extraction Methods: Survey data were matched back to enrollment records, which indicated coverage status at the time of the survey. Concordance between the records and survey data was assessed.Principal Findings: Correct reporting of general source of coverage ranged from 77.8 percent to 98.3 percent across coverage type, premium ranged from 91.6 percent to 96.4 percent, and subsidy ranged from 83.0 percent to 91.0 percent. Using a conceptual algorithm to categorize coverage type resulted in sensitivity of 98.3 percent for employer-sponsored enrollees, and 70.6 percent-77.6 percent for the other coverage types, while specificity ranged from 93.9 percent to 99.4 percent across coverage types.Conclusions: Survey reports of features of coverage suggest they are viable items to include in an algorithm to categorize coverage type. Findings have implications beyond the CPS, particularly for marketplace enrollees. [ABSTRACT FROM AUTHOR]

Objective: To examine the effects of insurance and hospital market concentration on hospital patients' experience of care, as hospitals may compete on quality for favorable insurance contracts.Data Sources/study Setting: Secondary data for 2008-2015 on patient experience from Hospital Compare's patient survey data, hospital characteristics from the American Hospital Association (AHA) Annual Survey, and insurance market characteristics from HealthLeaders-InterStudy.Study Design: Hospital/year-level regressions predict each hospital's patient experience measure as a function of insurance and hospital market concentration and hospital fixed effects. The model is identified by longitudinal variation in insurance and hospital concentration.Data Collection/extraction Methods: Hospital/year-level data from Hospital Compare and the AHA merged by market/year to insurance and hospital concentration measures.Principal Findings: Changes in patient satisfaction are positively associated with increases in insurance concentration and negatively associated with increases in hospital concentration. Moving from a market with 20th percentile insurance concentration and 80th percentile hospital concentration to a market with 80th percentile insurance concentration and 20th percentile hospital concentration increases the share of patients that rated the hospital highly from 66.9 percent (95% CI: 66.5-67.2 percent) to 67.9 percent (95% CI: 67.5-68.3 percent) and the share of patients that definitely recommend the hospital from 69.7 percent (95% CI: 69.4-70.0 percent) to 70.8 percent (95% CI: 70.5-71.2 percent). The relationship for insurance concentration is stronger in more concentrated hospital markets, while the relationship for hospital concentration is stronger in less concentrated hospital markets.Conclusions: These findings add to the evidence on the harms of hospital consolidation but suggest that insurer consolidation may improve patient experience. [ABSTRACT FROM AUTHOR]

Objective: To study the extent to which risk equalization (RE) in competitive health insurance markets can be improved by including an indicator for being healthy.Study Setting/data Sources: This study is conducted in the context of the Dutch individual health insurance market. Administrative data on spending and risk characteristics (2011-2014) for the entire population (N = 16.6 m) as well as health survey data from a large sample (N = 387 k) are used.Study Design: The indicator for being healthy is low spending in three consecutive prior years. "Low spending" is defined in three ways: belonging to the bottom 60%, 70%, or 80% of the annual spending distribution. Versions of the Dutch RE model 2017 with and without the indicator are compared on individual-level payment fit and, using the survey data, group-level payment fit.Principal Findings: All three alternative models outperform the Dutch RE model 2017. However, significant unpriced risk heterogeneity remains. Compared with the 60% threshold, the 80% threshold comes with a larger improvement in fit but identifies a less selective group.Conclusions: The performance of the RE model can be improved by adding an indicator for being healthy based on multiple-year low spending. However, risk-selection potential remains, warranting high priority to further improvement of RE. [ABSTRACT FROM AUTHOR]

Objective: To compare access at community health centers (CHCs) vs private offices (non-CHCs) under the Affordable Care Act.Data Source: Ten state primary care audit conducted in 2012/2013 and 2016.Study Design: CHCs and non-CHCs were called. We calculated difference in differences comparing CHCs vs non-CHCs by caller insurance type.Principal Findings: In both rounds, Medicaid and uninsured callers had higher appointment rates at CHC than non-CHCs. CHC appointment rates significantly increased between 2012/2013 and 2016 for both employer-sponsored and Medicaid callers, with no significant wait time changes. Appointment rates increased (13.5% points, P < 0.001) and wait times decreased (-5.7 days, P = 0.017) at CHCs relative to non-CHCs for employer-sponsored insurance.Conclusion: Appointment availability at CHCs improved after ACA implementation, without increased wait times. [ABSTRACT FROM AUTHOR]

Objective: Spanish-preferring Medicare beneficiaries are underrepresented in national patient experience surveys. We test a method for improving their representation via higher response rates.Data Sources/study Setting: 2009-2010 Medicare CAHPS surveys; Medicare population.Study Design: We used surname and address to predict Spanish-language preference for a national sample of 177 139 beneficiaries. We randomized half of the 10 000 non-Puerto Rico beneficiaries with the highest predicted probabilities of Spanish preference (>10 percent) to bilingual mailings (intervention) and half to standard English-only mailings (control).Data Collection: Medicare CAHPS Survey data were collected through mail surveys with telephone follow-up of nonrespondents.Principal Findings: Mail response rate was higher for intervention (28.7 percent) than control (23.9 percent) (P < 0.0001); phone response rates among mail nonrespondents were similar in intervention and control arms (15.8 percent vs 15.7 percent, P = 0.90). Targeted bilingual mailings induced 6.5 percent of those who would not have responded to respond by mail and 54.0 percent of those who would have responded in English to respond in Spanish. Beneficiaries with greater Spanish probabilities showed greater increases in response rates, a higher proportion of responses in Spanish, and lower control response rates among.Conclusions: Targeted bilingual mailing of mixed-mode surveys using commonly available surname and address information can efficiently increase representation of this underrepresented group. [ABSTRACT FROM AUTHOR]

Objective: To estimate and describe factors driving variation in spending for breast cancer patients within geographic region.Data Source: Surveillance, Epidemiology, and End Results (SEER)-Medicare database from 2009-2013.Study Design: The proportion of variation in monthly medical spending within geographic region attributed to patient and physician factors was estimated using multilevel regression models with individual patient and physician random effects. Using sequential models, we estimated the contribution of differences in patient and disease characteristics or use of cancer treatment modalities to patient-level and physician-level variance in spending. Services associated with high spending physicians were estimated using linear regression.Data Extraction Method: A total of 20 818 women with a breast cancer diagnosis in 2010-2011.Principal Findings: We observed substantial between-patient and between-provider variation in spending following diagnosis and at the end-of-life. Immediately following diagnosis, 48% of between-patient and 31% of between-physician variation were driven by differences in delivery of cancer treatment modalities to similar patients. At the end-of-life, patients of high spending physicians had twice as many inpatient days, double the chemotherapy spending, and slightly more hospice days.Conclusions: Similar patients receive very different treatments, which yield significant differences in spending. Efforts to reduce unwanted variation may need to target treatment choices within patient-doctor discussions. [ABSTRACT FROM AUTHOR]

Objective: To measure the Medicaid undercount and analyze response error in the 2007‐2011 Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Data Sources/Study Setting: Medicaid Statistical Information System (MSIS) 2006‐2010 enrollment data linked to the 2007‐2011 CPS ASEC person records. Study Design: By linking individuals across datasets, we analyze false‐negative error and false‐positive error in reports of Medicaid enrollment. We use regression analysis to identify factors associated with response error in the 2011 CPS ASEC. Principal Findings: We find that the Medicaid undercount in the CPS ASEC ranged between 22 and 31% from 2007 to 2011. In 2011, the false‐negative rate was 40%, and 27% of Medicaid reports in CPS ASEC were false positives. False‐negative error is associated with the duration of enrollment in Medicaid, enrollment in Medicare and private insurance, and Medicaid enrollment in the survey year. False‐positive error is associated with enrollment in Medicare and shared Medicaid coverage in the household. Conclusions: Survey estimates of Medicaid enrollment and estimates of the uninsured population are affected by both false‐positive response error and false‐negative response error, and these response errors are non‐random. [ABSTRACT FROM AUTHOR]

Objective: To compare medication adherence, cost, and utilization in Medicare beneficiaries attributed to nurse practitioners (NP) and primary care physicians (PCP).Data: Medicare Part A, B, and D claims and beneficiary summary file data, years 2009-2013.Study Design: We used propensity score-weighted analyses combined with logistic regression and generalized estimating equations to test differences in good medication adherence (proportion of days covered (PDC >0.8); office-based and specialty care costs; and ER visits.Data Extraction: Beneficiaries with prescription claims for anti-diabetics, renin-angiotensin system antagonists (RASA), or statins.Principal Findings: There were no differences in good medication adherence (PDC >0.8) between NP and PCP attributed beneficiaries taking anti-diabetics or RASA. Beneficiaries taking statins had a slightly higher probability of good adherence when attributed to PCPs (74.6% vs 75.5%; P < 0.05). NP attributed beneficiaries had lower office-based and specialty care costs and were less likely to experience an ER visit across all three medication cohorts (P < 0.01).Conclusions: Examining the impact of NP and PCP provided care on outcomes beyond the primary care setting is important to the Medicare program in general but will also help practices seeking to meet benchmarks under alternative payment models that incentivize higher quality and lower costs. [ABSTRACT FROM AUTHOR]

Objective: To estimate health care utilization and costs incurred by homeless Veterans relative to nonhomeless Veterans and to examine the impact of a VA homelessness program on these outcomes.Data Sources/study Setting: Combined Department of Veterans Affairs (VA) administrative and Medicare claims data.Study Design: Observational study using longitudinal data from Veterans engaged with the VA system and enrolled in Medicare. Veterans with administrative evidence of homelessness at any point during 2006-2010 were matched on period of military service to Veterans with no evidence of homelessness.Principal Findings: Experience of homelessness was associated with 1.37 (95 percent CI = 1.34-1.40) and 0.16 (95 percent CI = 0.14-0.17) more outpatient encounters per quarter in VA and non-VA settings, respectively, and 1.31 (95 percent CI = 1.30-1.32) and 0.49 (95 percent CI = 0.48-0.49) more inpatient days per quarter in VA and non-VA hospitals, respectively. These were associated with higher costs. Relative to stably housed Veterans less than 65 years of age, those enrolled in a VA homelessness program had 94.4 percent (95 percent CI = 90.7 percent-98.1 percent) more VA outpatient visits but 5.5 percent (95 percent CI = 3.0 percent-7.9 percent) fewer Medicare outpatient visits.Conclusions: Homelessness was associated with an increase in VA and Medicare utilization and cost. A VA homelessness program decreased use of Medicare outpatient services. [ABSTRACT FROM AUTHOR]

Objective: The Medicare Modernization Act of 2004 allowed Medicare Advantage (MA) contracts to form provider networks in order to concentrate their patients among preferred providers. We focus on the skilled nursing facility (SNF) industry to assess patients' health when treating SNFs concentrate more patients from the same MA contract.Data Sources/study Setting: We use Medicare Beneficiary Summary File and Health, HEDIS, and the Minimum Data Set for patient attributes and OSCAR, LTCfocus.org, and Nursing Home Compare for SNF attributes. We include 1,069,436 MA enrollees newly admitted to SNF between 2012 and 2014.Study Design: Using a MA contract fixed-effect model, we examine the effect of prevalence of a patient's MA contract in the treating SNF on patient's health outcomes including 180-day survival, 30-day hospital readmission, 30-day home discharge, and nursing home length of stay. We use an Instrumental Variable (IV), the expected share of admissions in a SNF from patient's MA contract calculated using a McFadden choice model.Principal Findings: We find no relationship between SNF contract concentration and patients' outcomes after applying the IV.Conclusions: While MA plans appear to steer patients to specific SNFs, we do not observe significant returns to patient outcomes related to concentration. [ABSTRACT FROM AUTHOR]

Objective: To examine the effects of provider choice policies on workers' compensation medical and indemnity costs.Data Sources/study Setting: Pooled cross-sectional analysis of administrative claims records for workers with work-related injuries primarily in 2007-2010 across 25 states (n = 4,489,729).Study Design: We used linear and quantile regression analyses to evaluate differences in claim costs (medical and indemnity) based on whether policies give employers or injured workers control over the choice of provider.Principal Findings: We find no difference in average medical costs by provider choice policies, although a distributional analysis indicates higher developed medical costs for the costliest back injury cases in states where workers control provider choice. The evidence for indemnity costs is similar, although the point estimates also indicate (statistically insignificantly) higher average costs when policies give workers more control of the choice of provider.Conclusions: Our nuanced evidence suggests that policymakers seeking to reduce workers' compensation costs may need to focus on the highest cost cases in states where policy gives workers more control over the choice of provider, rather than the simpler and broader issue of whether policy gives workers or employers more control. [ABSTRACT FROM AUTHOR]

Objective: To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment.Data Sources: Medicare enrollment and claims data from 2008 to 2011.Data Extraction: Risk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files.Study Design: A propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries.Principal Findings: Matching improved balance on observables, but performance metrics were similar when comparing risk adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample.Conclusions: Fitting MA risk adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for risk adjustment. [ABSTRACT FROM AUTHOR]

Objective: To identify hospital/county characteristics and sources of regional heterogeneity associated with readmission penalties.Data Sources/study Setting: Acute care hospitals under the Hospital Readmissions Reduction Program from fiscal years 2013 to 2018 were linked to data from the Annual Hospital Association, Centers for Medicare and Medicaid Services, Medicare claims, Hospital Compare, Nursing Home Compare, Area Resource File, Health Inequity Project, and Long-term Care Focus. The final sample contained 3,156 hospitals in 1,504 counties.Data Collection/extraction Methods: Data sources were combined using Medicare hospital identifiers or Federal Information Processing Standard codes.Study Design: A two-level hierarchical model with correlated random effects, also known as the Mundlak correction, was employed with hospitals nested within counties.Principal Findings: Over a third of the variation in readmission penalties was attributed to the county level. Patient sociodemographics and the surrounding access to and quality of care were significantly associated with penalties. Hospital measures of Medicare volume, percentage dual-eligible and Black patients, and patient experience were correlated with unobserved area-level factors that also impact penalties.Conclusions: As the readmission risk adjustment does not include any community-level characteristics or geographic controls, the resulting endogeneity bias has the potential to disparately penalize certain hospitals. [ABSTRACT FROM AUTHOR]

Objective: The concept of shifting from volume (i.e., billing for as many patients and services as possible) to value (i.e., reducing costs while improving quality) has been a key underpinning of the development of accountable care organizations (ACOs), yet the cultural change necessary to make this shift has been previously unexplored.Data Sources/study Setting: Primary data collected through site visits to four private sector ACOs.Study Design: Cross-sectional, semi-structured interview study with analysis done at the ACO level to learn about ACO development.Data Collection: One hundred and forty-eight interviews recorded and transcribed verbatim followed by rigorous qualitative analysis using a grounded theory approach.Principal Findings: The importance of shifting organizational culture from volume to value was emphasized across sites and interviewees, particularly when defining an ACO; describing the shift in organizational focus to value; and discussing how to create value by emphasizing quality over volume. Value was viewed as more than cost-benefit, but rather encapsulated a paradigmatic cultural change in the way care is provided.Conclusions: We found that moving from volume to value is central to the culture change required of an ACO. Our findings can inform future efforts that aim to create a more effective value-based health care system. [ABSTRACT FROM AUTHOR]

Objective: To assess the effects of longitudinal patterns of health insurance and poverty on out-of-pocket expenditures among low-income late middle-aged adults.Data Sources/study Setting: Six waves (2002-2012) of the Health and Retirement Study, in combination with RAND Center for the Study of Aging data, were used.Study Design: A random coefficient regression analysis was conducted in a multilevel growth curve framework to estimate the impact of health insurance and poverty on out-of-pocket expenditures.Principal Findings: At baseline, individuals with private insurance or unstable coverage were more likely to have out-of-pocket expenditures and financial burdens than public insurance holders. Over time, the poor who had no insurance, unstable coverage, or insurance type change had higher out-of-pocket expenditures; private coverage holders had higher odds of financial burden.Conclusions: Unstable insurance coverage had a discernible effect on the long-term, out-of-pocket expenditures among low-income adults. Findings have an important policy implication to protect poor late middle-aged population; as this population enters old age, the high financial burden it faces may exacerbate persistent socioeconomic health disparity among older people with unstable insurance coverage. [ABSTRACT FROM AUTHOR]

Objective: To assess frequency, type, and extent of behavioral health (BH) nonquantitative treatment limits (NQTLs) before and after implementation of the Mental Health Parity and Addiction Equity Act of 2008 (MHPAEA).Data Sources: Secondary administrative data for Optum carve-out and carve-in plans.Study Design: Cross-tabulations and "two-part" regression models were estimated to assess associations of parity period with NQTLs.Data Collection/extraction Methods: Optum provided four proprietary BH databases, including 2008-2013 data for 40 carve-out and 385 carve-in employers from Optum's claims processing databases and 2010 data from interviews conducted by Optum's parity compliance team with 49 carve-out employers.Principal Findings: Preparity, carve-out plans required preauthorization for in-network inpatient/intermediate care; otherwise coverage was denied. Postparity, 73 percent would review later by request and half charged no penalty for late authorization. Outpatient visit authorization requirements virtually disappeared. For carve-out out-of-network inpatient/intermediate care, and for carve-ins, plans changed penalties to match medical service policies, but this did not necessarily lead to fewer requirements or lower penalties.Conclusion: After 2011, MHPAEA was associated with the transformation of BH care management, including much less restrictive preauthorization requirements, especially for in-network care provided by carve-out plans. [ABSTRACT FROM AUTHOR]