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Background: Hip fractures are costly and common in older adults, but there is limited understanding of how treatment patterns and outcomes might differ between countries. Methods: We performed a retrospective serial cross‐sectional cohort study of adults aged ≥66 years hospitalized with hip fracture between 2011 and 2018 in the US, Canada, England, the Netherlands, Taiwan, and Israel using population‐representative administrative data. We examined mortality, hip fracture treatment approaches (total hip arthroplasty [THA], hemiarthroplasty [HA], internal fixation [IF], and nonoperative), and health system performance measures, including hospital length of stay (LOS), 30‐day readmission rates, and time‐to‐surgery. Results: The total number of hip fracture admissions between 2011 and 2018 ranged from 23,941 in Israel to 1,219,696 in the US. In 2018, 30‐day mortality varied from 3% (16% at 1 year) in Taiwan to 10% (27%) in the Netherlands. With regards to processes of care, the proportion of hip fractures treated with HA (range 23%–45%) and THA (0.2%–10%) differed widely across countries. For example, in 2018, THA was used to treat approximately 9% of patients in England and Israel but less than 1% in Taiwan. Overall, IF was the most common surgery performed in all countries (40%–60% of patients). IF was used in approximately 60% of patients in the US and Israel, but only 40% in England. In 2018, rates of nonoperative management ranged from 5% of patients in Taiwan to nearly 10% in England. Mean hospital LOS in 2018 ranged from 6.4 days (US) to 18.7 days (England). The 30‐day readmission rate in 2018 ranged from 8% (in Canada and the Netherlands) to nearly 18% in England. The mean days to surgery in 2018 ranged from 0.5 days (Israel) to 1.6 days (Canada). Conclusions: We observed substantial between‐country variation in mortality, surgical approaches, and health system performance measures. These findings underscore the need for further research to inform evidence‐based surgical approaches. [ABSTRACT FROM AUTHOR]

Key Points: Question: How do treatment patterns and outcomes for older patients presenting with acute myocardial infarction differ for low- vs high-income individuals across 6 countries. Findings: In this study of 289 376 patients aged 66 years or older hospitalized with ST-segment elevation myocardial infarction (STEMI) and 843 046 hospitalized with non-STEMI across 6 health systems, adjusted 30-day and 1-year mortality rates were higher for low-income patients, whereas rates of cardiac catheterization and percutaneous coronary interventions were lower. High-income patients also had shorter length of stay and lower rates of readmissions. Meaning: These results suggest that income-based disparities were present even in countries with universal health insurance and robust social safety net systems. Importance: Differences in the organization and financing of health systems may produce more or less equitable outcomes for advantaged vs disadvantaged populations. We compared treatments and outcomes of older high- and low-income patients across 6 countries. Objective: To determine whether treatment patterns and outcomes for patients presenting with acute myocardial infarction differ for low- vs high-income individuals across 6 countries. Design, Setting, and Participants: Serial cross-sectional cohort study of all adults aged 66 years or older hospitalized with acute myocardial infarction from 2013 through 2018 in the US, Canada, England, the Netherlands, Taiwan, and Israel using population-representative administrative data. Exposures: Being in the top and bottom quintile of income within and across countries. Main Outcomes and Measures: Thirty-day and 1-year mortality; secondary outcomes included rates of cardiac catheterization and revascularization, length of stay, and readmission rates. Results: We studied 289 376 patients hospitalized with ST-segment elevation myocardial infarction (STEMI) and 843 046 hospitalized with non-STEMI (NSTEMI). Adjusted 30-day mortality generally was 1 to 3 percentage points lower for high-income patients. For instance, 30-day mortality among patients admitted with STEMI in the Netherlands was 10.2% for those with high income vs 13.1% for those with low income (difference, −2.8 percentage points [95% CI, −4.1 to −1.5]). One-year mortality differences for STEMI were even larger than 30-day mortality, with the highest difference in Israel (16.2% vs 25.3%; difference, −9.1 percentage points [95% CI, −16.7 to –1.6]). In all countries, rates of cardiac catheterization and percutaneous coronary intervention were higher among high- vs low-income populations, with absolute differences ranging from 1 to 6 percentage points (eg, 73.6% vs 67.4%; difference, 6.1 percentage points [95% CI, 1.2 to 11.0] for percutaneous intervention in England for STEMI). Rates of coronary artery bypass graft surgery for patients with STEMI in low- vs high-income strata were similar but for NSTEMI were generally 1 to 2 percentage points higher among high-income patients (eg, 12.5% vs 11.0% in the US; difference, 1.5 percentage points [95% CI, 1.3 to 1.8 ]). Thirty-day readmission rates generally also were 1 to 3 percentage points lower and hospital length of stay generally was 0.2 to 0.5 days shorter for high-income patients. Conclusions and Relevance: High-income individuals had substantially better survival and were more likely to receive lifesaving revascularization and had shorter hospital lengths of stay and fewer readmissions across almost all countries. Our results suggest that income-based disparities were present even in countries with universal health insurance and robust social safety net systems. This serial cross-sectional cohort study compares whether treatment patterns and outcomes for patients presenting with acute myocardial infarction differ for low- vs high-income individuals across 6 countries. [ABSTRACT FROM AUTHOR]

Objective: To evaluate whether the expansion of Federally Qualified Health Centers (FQHCs) improved late prenatal care initiation, low birth weight, and preterm birth among Medicaid‐covered or uninsured individuals. Data Sources and Study Setting: We identified all FQHCs in California using the Health Resources and Services Administration's Uniform Data System from 2000 to 2019. We used data from the U.S. Census American Community Survey to describe area characteristics. We measured outcomes in California birth certificate data from 2007 to 2019. Study Design: We compared areas that received their first FQHC between 2011 and 2016 to areas that received it later or that had never had an FQHC. Specifically, we used a synthetic control with a staggered adoption approach to calculate non‐parametric estimates of the average treatment effects on the treated areas. The key outcome variables were the rate of Medicaid or uninsured births with late prenatal care initiation (>3 months' gestation), with low birth weight (<2500 grams), or with preterm birth (<37 weeks' gestation). Data Collection/Extraction Methods: The analysis was limited to births covered by Medicaid or that were uninsured, as indicated on the birth certificate. Principal Findings: The 55 areas in California that received their first FQHC in 2011–2016 were more populous; their residents were more likely to be covered by Medicaid, to be low‐income, or to be Hispanic than residents of the 48 areas that did not have an FQHC by the end of the study period. We found no statistically significant impact of the first FQHC on rates of late prenatal care initiation (ATT: −10.4 [95% CI −38.1, 15.0]), low birth weight (ATT: 0.2 [95% CI −7.1, 5.4]), or preterm birth (ATT: −7.0 [95% CI −15.5, 2.3]). Conclusions: Our results from California suggest that access to primary and prenatal care may not be enough to improve these outcomes. Future work should evaluate the impact of ongoing initiatives to increase access to maternal health care at FQHCs through targeted workforce investments. [ABSTRACT FROM AUTHOR]

An editorial is presented discussing the effects of Medicaid expansion under the Affordable Care Act on health care workers' financial outcomes, particularly examining whether the increased revenue for provider organizations is passed on to workers. Topics include the methodological considerations of the study, the impact on low and high-income workers, and the broader implications for the allocation of public resources in the healthcare sector.

Objective: To formalize comparative interrupted time series (CITS) using the potential outcomes framework; compare two version of CITS--a standard linear version and one that adds postperiod group-by-time parameters--to two versions of difference-in-differences (DID)--a standard version with time fixed effects and one that adds group-specific pretrends; and reanalyze three previously published papers using these models. Data Sources: Outcome data for reanalyses come from two counties' jail booking and release data, Medicaid prescription drug rebate data from the Centers for Medicare and Medicaid Services (CMS), and acute hepatitis C incidence from the Centers for Disease Control and Prevention. Study Design: DID and CITS were compared using potential outcomes, and reanalyses were conducted using the four described pre--post study designs. Data Collection/Extraction Methods: Data from county jails were provided by sheriffs. Data from CMS are publicly available. Data for the third reanalysis were provided by the authors of the original study. Principal Findings: Though written differently and preferred by different research communities, the general version of CITS and DID with group-specific pretrends are the same: they yield the same counterfactuals and identify the same treatment effects. In a reanalysis with evidence of divergent preperiod trends, failing to account for this in standard DID led to an 84% smaller effect estimate than the more flexible models. In a second reanalysis with evidence of nonlinear outcome trends, failing to account for this in linear CITS led to a 28% smaller effect estimate than the more flexible models. Conclusion: We recommend detailing a causal model for treatment selection and outcome generation and the required counterfactuals before choosing an analytical approach. The more flexible versions of DID and CITS can accommodate features often found in real data, namely, nonlinearities and divergent preperiod outcome trends. [ABSTRACT FROM AUTHOR]

Objective: To define confounding bias in difference-in-difference studies and compare regression- and matching-based estimators designed to correct bias due to observed confounders. Data sources: We simulated data from linear models that incorporated different confounding relationships: time-invariant covariates with a time-varying effect on the outcome, time-varying covariates with a constant effect on the outcome, and time-varying covariates with a time-varying effect on the outcome. We considered a simple setting that is common in the applied literature: treatment is introduced at a single time point and there is no unobserved treatment effect heterogeneity. Study design: We compared the bias and root mean squared error of treatment effect estimates from six model specifications, including simple linear regression models and matching techniques. Data collection: Simulation code is provided for replication. Principal findings: Confounders in difference-in-differences are covariates that change differently over time in the treated and comparison group or have a timevarying effect on the outcome. When such a confounding variable is measured, appropriately adjusting for this confounder (ie, including the confounder in a regression model that is consistent with the causal model) can provide unbiased estimates with optimal SE. However, when a time-varying confounder is affected by treatment, recovering an unbiased causal effect using difference-in-differences is difficult. Conclusions: Confounding in difference-in-differences is more complicated than in cross-sectional settings, from which techniques and intuition to address observed confounding cannot be imported wholesale. Instead, analysts should begin by postulating a causal model that relates covariates, both time-varying and those with time-varying effects on the outcome, to treatment. This causal model will then guide the specification of an appropriate analytical model (eg, using regression or matching) that can produce unbiased treatment effect estimates. We emphasize the importance of thoughtful incorporation of covariates to address confounding bias in difference-in-difference studies. [ABSTRACT FROM AUTHOR]

Background/Objectives: Paid home care providers (caregivers) are in close, regular contact with people for whom they care (clients). This study evaluated whether caregivers, by noting changes in physical and mental health, could prevent hospitalizations. Design Stepped wedge cluster randomized trial with 4 sequences, 244 clusters, an open cohort, and continuous outcome assessment. Setting: Franchises of a national home care company. Participants: Eligible clusters were all franchises operating at the study start, excluding those in a previous pilot. The sample included all clients who received private‐pay home care services from an eligible franchise, could be linked to Medicare enrollment records, and were enrolled in fee‐for‐service Medicare. Intervention: A telephone‐ and app‐based questionnaire at the end of each caregiving shift asked caregivers whether they noted changes in mental or physical health of their client. This generated a change‐in‐condition report that staff at the franchise office addressed at their discretion (e.g., by contacting clients or primary care providers). The control condition was no questionnaire. The study was unblinded. Clusters were randomized to four treatment sequences in eight strata of franchise characteristics. Measurements The primary outcome was hospitalization during months of home care enrollment. Secondary outcomes were emergency department (ED) visits and mortality. Results: Two hundred and forty‐four franchises were randomized to the four sequences (n = 40, 66, 68, 70 franchises, respectively) and 40,137 people were observed during 276,938 person‐months of home care enrollment. We found no evidence of impact on hospitalization (odds ratio [OR] 1.03, 95% confidence interval [CI]: 0.97, 1.10), ED visits (OR 1.03, 95% CI: 0.98, 1.08), or mortality (OR 1.07, 95% CI: 0.96, 1.19). Conclusion: A technology‐enabled intervention to identify health changes among home care recipients did not show evidence of impact on hospitalizations, ED visits, or mortality. Providing change‐in‐condition reports to home care staff, without a structured response to manage these changes, was not effective at improving care. [ABSTRACT FROM AUTHOR]

Sherri Rose, Ph.D. is an associate professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research as well as Co-Director of the joint Harvard–Stanford Health Policy Data Science Lab. A renowned expert in machine learning methodology for causal inference and prediction, her applied work has focused on risk adjustment, algorithmic fairness, health program evaluation, and comparative effectiveness research. Dr. Rose's leadership positions include current roles as Co-Editor of Biostatistics and Chair of the American Statistical Association's Biometrics Section. She is also a Fellow of the American Statistical Association. Dr. Rose earned a BS in Statistics from The George Washington University and a PhD in Biostatistics from the University of California, Berkeley before completing an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins University. Prior to joining the faculty at Stanford University, she was on the faculty at Harvard Medical School in the Department of Health Care Policy. Below, an interview of Dr. Rose, conducted by her colleague, Dr. Laura Hatfield, on the occasion of her 2020 Mid-Career Award from the Health Policy Statistics Section (HPSS) of the American Statistical Association. This award recognizes leaders in health care policy and health services research who have made outstanding contributions through methodological or applied work and who show a promise of continued excellence at the frontier of statistical practice that advances the aims of HPSS. [ABSTRACT FROM AUTHOR]

Policy PointsConcerns have been raised about risk selection in the Medicare Shared Savings Program (MSSP). Specifically, turnover in accountable care organization (ACO) physicians and patient panels has led to concerns that ACOs may be earning shared‐savings bonuses by selecting lower‐risk patients or providers with lower‐risk panels.We find no evidence that changes in ACO patient populations explain savings estimates from previous evaluations through 2015. We also find no evidence that ACOs systematically manipulated provider composition or billing to earn bonuses.The modest savings and lack of risk selection in the original MSSP design suggest opportunities to build on early progress.Recent program changes provide ACOs with more opportunity to select providers with lower‐risk patients. Understanding the effect of these changes will be important for guiding future payment policy. Context: The Medicare Shared Savings Program (MSSP) establishes incentives for participating accountable care organizations (ACOs) to lower spending for their attributed fee‐for‐service Medicare patients. Turnover in ACO physicians and patient panels has raised concerns that ACOs may be earning shared‐savings bonuses by selecting lower‐risk patients or providers with lower‐risk panels. Methods: We conducted three sets of analyses of Medicare claims data. First, we estimated overall MSSP savings through 2015 using a difference‐in‐differences approach and methods that eliminated selection bias from ACO program exit or changes in the practices or physicians included in ACO contracts. We then checked for residual risk selection at the patient level. Second, we reestimated savings with methods that address undetected risk selection but could introduce bias from other sources. These included patient fixed effects, baseline or prospective assignment, and area‐level MSSP exposure to hold patient populations constant. Third, we tested for changes in provider composition or provider billing that may have contributed to bonuses, even if they were eliminated as sources of bias in the evaluation analyses. Findings: MSSP participation was associated with modest and increasing annual gross savings in the 2012‐2013 entry cohorts of ACOs that reached $139 to $302 per patient by 2015. Savings in the 2014 entry cohort were small and not statistically significant. Robustness checks revealed no evidence of residual risk selection. Alternative methods to address risk selection produced results that were substantively consistent with our primary analysis but varied somewhat and were more sensitive to adjustment for patient characteristics, suggesting the introduction of bias from within‐patient changes in time‐varying characteristics. We found no evidence of ACO manipulation of provider composition or billing to inflate savings. Finally, larger savings for physician group ACOs were robust to consideration of differential changes in organizational structure among non‐ACO providers (eg, from consolidation). Conclusions: Participation in the original MSSP program was associated with modest savings and not with favorable risk selection. These findings suggest an opportunity to build on early progress. Understanding the effect of new opportunities and incentives for risk selection in the revamped MSSP will be important for guiding future program reforms. [ABSTRACT FROM AUTHOR]

Of the fourteen states that have not expanded eligibility for Medicaid, nine are in the southern census region, and two others border that region. Ongoing debate over the merits of Medicaid expansion in these states has focused, in part, on whether the safety net provides sufficient access for uninsured low-income Americans. We analyzed longitudinal survey and vital status data from the twelve-state Southern Community Cohort Study (SCCS) for 15,356 nonelderly adult participants with low incomes, 86 percent of whom were enrolled at community health centers. In difference-in-differences analyses, we compared changes in self-reported health between participants in four expansion and eight nonexpansion states before (2008-13) and after (2015-17) Medicaid expansion. We found that a higher proportion of SCCS participants in expansion states reported increases in Medicaid coverage (a differential change of 7.6 percentage points), a lower proportion experienced a health status decline (-1.8 percentage points), and a higher proportion maintained their baseline health status (1.4 percentage points). The magnitude of estimated reductions in health declines would meaningfully affect a nonexpansion state's health ranking in our sample if that state elected to expand Medicaid. Our results suggest that for low-income adults in the South, Medicaid expansion yielded health benefits--even for those with established access to safety-net care. [ABSTRACT FROM AUTHOR]

Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures such as length of stay and spending include zero inflation, overdispersion, and skewness, all of which complicate statistical modeling. Moreover, latent subgroups of patients may have distinct patterns of utilization and relationships between that utilization and observed covariates. In this work, we apply and compare likelihood-based and parametric Bayesian mixtures of negative binomial and zero-inflated negative binomial regression models. In a simulation, we find that the Bayesian approach finds the true number of mixture components more accurately than using information criteria to select among likelihood-based finite mixture models. When we apply the models to data on hospital lengths of stay for patients with lung cancer, we find distinct subgroups of patients with different means and variances of hospital days, health and treatment covariates, and relationships between covariates and length of stay. [ABSTRACT FROM AUTHOR]

The article presents author's comments on the methods to measure the effects of nonrandomized policy interventions. It mentions hat the difference-in-differences analysis is one of the method with popularity in health policy evaluations. It mentions that regression to the mean (RTM) bias affects the simulation.

Objective: To demonstrate regression to the mean bias introduced by matching on preperiod variables in difference-in-differences studies.Data Sources: Simulated data.Study Design: We performed a Monte Carlo simulation to estimate the effect of a placebo intervention on simulated longitudinal data for units in treatment and control groups using unmatched and matched difference-in-differences analyses. We varied the preperiod level and trend differences between the treatment and control groups, and the serial correlation of the matching variables. We assessed estimator bias as the mean absolute deviation of estimated program effects from the true value of zero.Principal Findings: When preperiod outcome level is correlated with treatment assignment, an unmatched analysis is unbiased, but matching units on preperiod outcome levels produces biased estimates. The bias increases with greater preperiod level differences and weaker serial correlation in the outcome. This problem extends to matching on preperiod level of a time-varying covariate. When treatment assignment is correlated with preperiod trend only, the unmatched analysis is biased, and matching units on preperiod level or trend does not introduce additional bias.Conclusions: Researchers should be aware of the threat of regression to the mean when constructing matched samples for difference-in-differences. We provide guidance on when to incorporate matching in this study design. [ABSTRACT FROM AUTHOR]

Background: Health care providers who participate as an accountable care organization (ACO) in the voluntary Medicare Shared Savings Program (MSSP) have incentives to lower spending for Medicare patients while achieving high performance on a set of quality measures. Little is known about the extent to which early savings achieved by ACOs in the program have grown and been replicated by ACOs that entered the program in later years. ACOs that are physician groups have stronger incentives to lower spending than hospital-integrated ACOs.Methods: Using fee-for-service Medicare claims from 2009 through 2015, we performed difference-in-differences analyses to compare changes in Medicare spending for patients in ACOs before and after entry into the MSSP with concurrent changes in spending for local patients served by providers not participating in the MSSP (control group). We estimated differential changes (i.e., the between-group difference in the change from the pre-entry period) separately for hospital-integrated ACOs and physician-group ACOs that entered the MSSP in 2012, 2013, or 2014.Results: MSSP participation was associated with differential spending reductions in physician-group ACOs. These reductions grew with longer participation in the program and were significantly greater than the reductions in hospital-integrated ACOs. By 2015, the mean differential change in per-patient Medicare spending was -$474 (-4.9% of the pre-entry mean, P<0.001) for physician-group ACOs that entered in 2012, -$342 (-3.5% of the pre-entry mean, P<0.001) for those that entered in 2013, and -$156 (-1.6% of the pre-entry mean, P=0.009) for those that entered in 2014. The corresponding differential changes for hospital-integrated ACOs were -$169 (P=0.005), -$18 (P=0.78), and $88 (P=0.14), which were significantly lower than for physician-group ACOs (P<0.001). Spending reductions in physician-group ACOs constituted a net savings to Medicare of $256.4 million in 2015, whereas spending reductions in hospital-integrated ACOs were offset by bonus payments.Conclusions: After 3 years of the MSSP, participation in shared-savings contracts by physician groups was associated with savings for Medicare that grew over the study period, whereas hospital-integrated ACOs did not produce savings (on average) during the same period. (Funded by the National Institute on Aging.). [ABSTRACT FROM AUTHOR]

Public quality reports for Medicare Advantage health plans include 11 measures of patient experiences reported in the annual Consumer Assessment of Healthcare Providers and Systems surveys. Computing summaries at the health plan level (of multiple measures in multiple years) yields an array-structured random variable. To summarize associations among measures and years, we model the variance-covariance matrix governing the plan-level vectors of yearly quality measures as a Kronecker product of an across-measure matrix and an across-year matrix, or a sum of such Kronecker products. This approach extends separable covariance structure to Fay-Herriot models. In addition, we develop linear combinations of Kronecker products similar to principal components for array random variables. To each Kronecker-product term, we apply post hoc analyses suited to the corresponding dimension of the cross-classification: 1-way factor analysis for the across-measure factor and time-series analysis to the across-year factor. These methods draw out key patterns of variation in the quality measures over time and suggest new strategies for reporting quality information to consumers. [ABSTRACT FROM AUTHOR]

In a substantial shift in payment policy, the State of Maryland implemented a global budget program for acute care hospitals in 2010. Goals of the program include controlling hospital use and spending. Eight rural hospitals entered the program in 2010, while urban and suburban hospitals joined in 2014. Prior analyses, which focused on urban and suburban hospitals, did not find consistent evidence that Maryland's program had contributed to changes in hospital use after two years. However, these studies were limited by short follow-up periods, may have failed to isolate impacts of Maryland's payment change from other state trends, and had limited generalizability to rural settings. To understand the effects of Maryland's global budget program on rural hospitals, we compared changes in hospital use among Medicare beneficiaries served by affected rural hospitals versus an in-state control population from before to after 2010. By 2013--three years after the rural program began--there were no differential changes in acute hospital use or price-standardized hospital spending among beneficiaries served by the affected hospitals, versus the within-state control group. Our results suggest that among Medicare beneficiaries, global budgets in rural Maryland hospitals did not reduce hospital use or price-standardized spending as policy makers had anticipated. [ABSTRACT FROM AUTHOR]

Objective: To forecast out-of-pocket health care spending among older adults. Long-term forecasts allow policy makers to explore potential impacts of policy scenarios, but existing microsimulations do not incorporate details of supplemental insurance coverage and income effects on health care spending.Data Sources: Dynamic microsimulation calibrated to survey and administrative data.Study Design: We augment Urban Institute's Dynamic Simulation of Income Model (DYNASIM) with modules that incorporate demand responses and economic equilibria, with dynamics driven by exogenous technological change. A lengthy technical appendix provides details of the microsimulation model and economic assumptions for readers interested in applying these techniques.Principal Findings: The model projects total out-of-pocket spending (point of care plus premiums) as a share of income for adults aged 65 and older. People with lower incomes and poor health fare worse, despite protections of Medicaid. Spending rises 40 percent from 2012 to 2035 (from 10 to 14 percent of income) for the median beneficiary, but it increases from 5 to 25 percent of income for low-income beneficiaries and from 23 to 29 percent for the near poor who are in fair/poor health.Conclusions: Despite Medicare coverage, near-poor seniors will face out-of-pocket spending that would render them, in practical terms, underinsured. [ABSTRACT FROM AUTHOR]

Insurers, employers, and states increasingly encourage price transparency so that patients can compare health care prices across providers. However, the evidence on whether price transparency tools encourage patients to receive lower-cost care and reduce overall spending remains limited and mixed. We examined the experience of a large insured population that was offered a price transparency tool, focusing on a set of "shoppable" services (lab tests, office visits, and advanced imaging services). Overall, offering the tool was not associated with lower shoppable services spending. Only 12 percent of employees who were offered the tool used it in the first fifteen months after it was introduced, and use of the tool was not associated with lower prices for lab tests or office visits. The average price paid for imaging services preceded by a price search was 14 percent lower than that paid for imaging services not preceded by a price search. However, only 1 percent of those who received advanced imaging conducted a price search. Simply offering a price transparency tool is not sufficient to meaningfully decrease health care prices or spending. [ABSTRACT FROM AUTHOR]

Objective: To study how differences in quality score adjustments across Medicare Advantage contracts change comparisons for individuals and contracts.Data Sources: Responses to the Medicare Advantage implementation of the Consumer Assessments of Healthcare Providers and Systems (CAHPS) survey from 2010 to 2014.Study Design: We fit national-and state-level hierarchical models to predict CAHPS scores for individuals and contracts, adjusted for self-reported education, general health, and mental health. We allow the effects of these variables on quality measures to vary across contracts with a hierarchical model.Data Collection/extraction Methods: We perform secondary data analysis.Principal Findings: For average consumers, standard adjustment is sufficient to represent variation in contract quality standardized to a common population. For people with characteristics far from average, personalized reporting using their characteristics and contract-specific coefficients can substantially change the expected quality measures across contracts. This effect is stronger when comparing among contracts within a state than across all contracts.Conclusions: Customized reporting may help consumers select the best Medicare Advantage plan, but policies should protect against unintended consequences. [ABSTRACT FROM AUTHOR]

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as theCDCCommunity Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context. [ABSTRACT FROM AUTHOR]

Patient-centered care involves patients in decision-making about their medical care. Effective shared decision-making requires relevant evidence on the expected health outcomes of treatment, assessment of patient preferences for possible health outcomes, and a method to synthesize this information. Few tools exist to help clinicians and patients synthesize clinical evidence and personal preferences. We develop a statistical framework that combines multiple data sources on expected treatment outcomes with individual preferences to produce a personalized preference-weighted outcome score (PWOS). The PWOS can be calculated for multiple treatments and compared to determine which will provide the best balance of risks and benefits given a patient's preferences. We demonstrate our method by evaluating adjuvant chemotherapy regimens for colorectal cancer. We begin by identifying heterogeneity in patient preferences for health outcomes associated with colorectal cancer disease and treatment, finding classes of patients who value efficacy and side effects differently. Then we fit a hierarchical Bayesian model to randomized clinical trials and produce posterior distributions of expected outcomes under three chemotherapy regimens. We use the PWOS to combine distinct sets of patient preferences with these expected outcomes. Our method preserves estimation uncertainty and accounts for correlation among outcomes. In a simulation study of applying PWOS to make decisions at the population level, we find that our methods are most useful when there are large differences in individual preferences and small distinctions in treatment efficacy (i.e., preference-sensitive settings). [ABSTRACT FROM AUTHOR]

Insurance transitions--sometimes referred to as "churn"--before and after childbirth can adversely affect the continuity and quality of care. Yet little is known about coverage patterns and changes for women giving birth in the United States. Using nationally representative survey data for the period 2005-13, we found high rates of insurance transitions before and after delivery. Half of women who were uninsured nine months before delivery had acquired Medicaid or CHIP coverage by the month of delivery, but 55 percent of women with that coverage at delivery experienced a coverage gap in the ensuing six months. Risk factors associated with insurance loss after delivery include not speaking English at home, being unmarried, having Medicaid or CHIP coverage at delivery, living in the South, and having a family income of 100-185 percent of the poverty level. To minimize the adverse effects of coverage disruptions, states should consider policies that promote the continuity of coverage for childbearing women, particularly those with pregnancy-related Medicaid eligibility. [ABSTRACT FROM AUTHOR]

In 2011 CareFirst BlueCross BlueShield, a large mid-Atlantic health insurance plan, implemented a payment and delivery system reform program. The model, called the Total Care and Cost Improvement Program, includes enhanced payments for primary care, significant financial incentives for primary care physicians to control spending, and care coordination tools to support progress toward the goal of higher-quality and lower-cost patient care. We conducted a mixed-methods evaluation of the initiative's first three years. Our quantitative analyses used spending and utilization data for 2010-13 to compare enrollees who received care from participating physician groups to similar enrollees cared for by nonparticipating groups. Savings were small and fully shared with providers, which suggests no significant effect on total spending (including bonuses). Our qualitative analysis suggested that early in the program, many physicians were not fully engaged with the initiative and did not make full use of its tools. These findings imply that this and similar payment reforms may require greater time to realize significant savings than many stakeholders had expected. Patience may be necessary if payer-led reform is going to lead to system transformation. [ABSTRACT FROM AUTHOR]

Meta-analysis of individual participant data (IPD) is increasingly utilised to improve the estimation of treatment effects, particularly among different participant subgroups. An important concern in IPD meta-analysis relates to partially or completely missing outcomes for some studies, a problem exacerbated when interest is on multiple discrete and continuous outcomes. When leveraging information from incomplete correlated outcomes across studies, the fully observed outcomes may provide important information about the incompleteness of the other outcomes. In this paper, we compare two models for handling incomplete continuous and binary outcomes in IPD meta-analysis: a joint hierarchical model and a sequence of full conditional mixed models. We illustrate how these approaches incorporate the correlation across the multiple outcomes and the between-study heterogeneity when addressing the missing data. Simulations characterise the performance of the methods across a range of scenarios which differ according to the proportion and type of missingness, strength of correlation between outcomes and the number of studies. The joint model provided confidence interval coverage consistently closer to nominal levels and lower mean squared error compared with the fully conditional approach across the scenarios considered. Methods are illustrated in a meta-analysis of randomised controlled trials comparing the effectiveness of implantable cardioverter-defibrillator devices alone to implantable cardioverter-defibrillator combined with cardiac resynchronisation therapy for treating patients with chronic heart failure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Home care recipients are often hospitalized for potentially avoidable reasons. A pilot program (Intervention in Home Care to Improve Health Outcomes (In-Home)) was designed to help home care providers identify acute clinical changes in condition and then manage the condition in the home and thereby avoid a costly hospitalization. Caregivers answer simple questions about the care recipient's condition during a telephone-based 'clock-out' at the end of each shift. Responses are electronically captured in the agency management software that caregivers use to 'clock-in,' manage care, and 'clock-out' on every shift. These are transmitted to the agency's care manager, who follows up on the change in condition and escalates appropriately. A description of the In-Home model is presented, and pilot data from 22 home care offices are reported. In the pilot, caregivers reported a change in condition after 2% of all shifts, representing an average of 1.9 changes per care recipient in a 6-month period. Changes in behavior and skin condition were the most frequently recorded domains. Interviews with participating caregivers and care managers suggested positive attitudes regarding the intervention; challenges included resistance to change on the part of home care staff and difficulties in applying a uniform intervention to individuals with varying needs in home care offices with varying capacities. In an ongoing randomized trial, the success of the overall program will be measured primarily according to the potential reduction in avoidable hospitalizations of home care recipients and the effect this potential reduction has on spending and healthcare outcomes. [ABSTRACT FROM AUTHOR]

Purpose Elderly patients with cancer are under-represented in clinical trials and risk greater toxicity from chemotherapy. These patients and their physicians need better evidence to decide among guideline-recommended regimens. We test whether patients with extensive-stage small-cell lung cancer (ES SCLC) have noninferior survival and less hospital-based health care after carboplatin/etoposide compared with cisplatin/etoposide. Methods We analyzed SEER-Medicare data for beneficiaries with ES SCLC diagnosed at age 67 years and older between 1995 and 2009. Among patients treated with first-line chemotherapy in the ambulatory setting, 831 received cisplatin/etoposide and 2,846 received carboplatin/etoposide. Propensity score matching (2:1 ratio) yielded 778 cisplatin/etoposide and 1,502 carboplatin/etoposide patients. Results Survival was nearly identical in the two groups: 35.7 weeks for cisplatin/etoposide and 35.9 weeks for carboplatin/etoposide. The hazard ratio of 1 (95% Cl, 0.91 to 1.09) excluded our prespecified threshold, indicating noninferiority. Mortality at 6 months was indistinguishable: 35% for cisplatin/etoposide and 34% for carboplatin/etoposide. After carboplatin/etoposide, patients were less likely to be admitted to a hospital (80% v86%, P < .001) and had fewer hospitalizations (median 1 v 2, odds ratio 0.76,95% Cl, 0.65 to 0.9), ED visits (median 1 v 2, odds ratio 0.82, 95% Cl, 0.7 to 0.96), and ICU stays (median 0 v 0, odds ratio 0.82, 95% Cl, 0.69 to 0.99). Conclusion First-line carboplatin/etoposide is associated with similar survival and less subsequent hospital-based health care use than cisplatin/etoposide among elderly patients with ES SCLC treated in ambulatory settings. [ABSTRACT FROM AUTHOR]

Background: In the Medicare Shared Savings Program (MSSP), accountable care organizations (ACOs) have financial incentives to lower spending and improve quality. We used quasi-experimental methods to assess the early performance of MSSP ACOs.Methods: Using Medicare claims from 2009 through 2013 and a difference-in-differences design, we compared changes in spending and in performance on quality measures from before the start of ACO contracts to after the start of the contracts between beneficiaries served by the 220 ACOs entering the MSSP in mid-2012 (2012 ACO cohort) or January 2013 (2013 ACO cohort) and those served by non-ACO providers (control group), with adjustment for geographic area and beneficiary characteristics. We analyzed the 2012 and 2013 ACO cohorts separately because entry time could reflect the capacity of an ACO to achieve savings. We compared ACO savings according to organizational structure, baseline spending, and concurrent ACO contracting with commercial insurers.Results: Adjusted Medicare spending and spending trends were similar in the ACO cohorts and the control group during the precontract period. In 2013, the differential change (i.e., the between-group difference in the change from the precontract period) in total adjusted annual spending was -$144 per beneficiary in the 2012 ACO cohort as compared with the control group (P=0.02), consistent with a 1.4% savings, but only -$3 per beneficiary in the 2013 ACO cohort as compared with the control group (P=0.96). Estimated savings were consistently greater in independent primary care groups than in hospital-integrated groups among 2012 and 2013 MSSP entrants (P=0.005 for interaction). MSSP contracts were associated with improved performance on some quality measures and unchanged performance on others.Conclusions: The first full year of MSSP contracts was associated with early reductions in Medicare spending among 2012 entrants but not among 2013 entrants. Savings were greater in independent primary care groups than in hospital-integrated groups. [ABSTRACT FROM AUTHOR]

Importance: There is increasing interest in using price transparency tools to decrease health care spending.Objective: To measure the association between offering a health care price transparency tool and outpatient spending.Design, Setting, and Participants: Two large employers represented in multiple market areas across the United States offered an online health care price transparency tool to their employees. One introduced it on April 1, 2011, and the other on January 1, 2012. The tool provided users information about what they would pay out of pocket for services from different physicians, hospitals, or other clinical sites. Using a matched difference-in-differences design, outpatient spending among employees offered the tool (n=148,655) was compared with that among employees from other companies not offered the tool (n=295,983) in the year before and after it was introduced.Exposure: Availability of a price transparency tool.Main Outcomes and Measures: Annual outpatient spending, outpatient out-of-pocket spending, use rates of the tool.Results: Mean outpatient spending among employees offered the tool was $2021 in the year before the tool was introduced and $2233 in the year after. In comparison, among controls, mean outpatient spending changed from $1985 to $2138. After adjusting for demographic and health characteristics, being offered the tool was associated with a mean $59 (95% CI, $25-$93) increase in outpatient spending. Mean outpatient out-of-pocket spending among those offered the tool was $507 in the year before introduction of the tool and $555 in the year after. Among the comparison group, mean outpatient out-of-pocket spending changed from $490 to $520. Being offered the price transparency tool was associated with a mean $18 (95% CI, $12-$25) increase in out-of-pocket spending after adjusting for relevant factors. In the first 12 months, 10% of employees who were offered the tool used it at least once.Conclusions and Relevance: Among employees at 2 large companies, offering a price transparency tool was not associated with lower health care spending. The tool was used by only a small percentage of eligible employees. [ABSTRACT FROM AUTHOR]

The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in recent years. More recently, a new and attractive application of this type of model has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, providing real time risk assessment using all recorded information. The aim of this article is two-fold. First, to highlight the importance of modeling the association structure between the longitudinal and event time responses that can greatly influence the derived predictions, and second, to illustrate how we can improve the accuracy of the derived predictions by suitably combining joint models with different association structures. The second goal is achieved using Bayesian model averaging, which, in this setting, has the very intriguing feature that the model weights are not fixed but they are rather subject- and time-dependent, implying that at different follow-up times predictions for the same subject may be based on different models. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The author discusses the effectiveness of cardiac implantable electrical devices. He discusses the comparison of implantable cardioverter defibrillator (ICD) or cardiac resynchronisation therapy (CRT) with pacemaker (CRT-P) or defibrillator (CRT-D) function. He also states the network meta-analytical approach which uses direct treatment comparisons such as medical therapy with CRT.

Background: Prior studies have demonstrated how price transparency lowers the test-ordering rates of trainees in hospitals, and physician-targeted price transparency efforts have been viewed as a promising cost-controlling strategy.Objective: To examine the effect of displaying paid-price information on test-ordering rates for common imaging studies and procedures within an accountable care organization (ACO).Design: Block randomized controlled trial for 1 year.Subjects: A total of 1205 fully licensed clinicians (728 primary care, 477 specialists).Intervention: Starting January 2014, clinicians in the Control arm received no price display; those in the intervention arms received Single or Paired Internal/External Median Prices in the test-ordering screen of their electronic health record. Internal prices were the amounts paid by insurers for the ACO's services; external paid prices were the amounts paid by insurers for the same services when delivered by unaffiliated providers.Main Measures: Ordering rates (orders per 100 face-to-face encounters with adult patients): overall, designated to be completed internally within the ACO, considered "inappropriate" (e.g., MRI for simple headache), and thought to be "appropriate" (e.g., screening colonoscopy).Key Results: We found no significant difference in overall ordering rates across the Control, Single Median Price, or Paired Internal/External Median Prices study arms. For every 100 encounters, clinicians in the Control arm ordered 15.0 (SD 31.1) tests, those in the Single Median Price arm ordered 15.0 (SD 16.2) tests, and those in the Paired Prices arms ordered 15.7 (SD 20.5) tests (one-way ANOVA p-value 0.88). There was no difference in ordering rates for tests designated to be completed internally or considered to be inappropriate or appropriate.Conclusions: Displaying paid-price information did not alter how frequently primary care and specialist clinicians ordered imaging studies and procedures within an ACO. Those with a particular interest in removing waste from the health care system may want to consider a variety of contextual factors that can affect physician-targeted price transparency. [ABSTRACT FROM AUTHOR]

Objective: To discuss and develop difference‐in‐difference estimators for categorical outcomes and apply them to estimate the effect of the Affordable Care Act's Medicaid expansion on insurance coverage. Data Sources: Secondary analysis of Survey on Income and Program Participation (SIPP) data on health insurance coverage types before (January 2013) and after (December 2015) Medicaid expansion in 39 US states (19 expansion and 20 non‐expansion). Study Design: We develop difference‐in‐difference methods for repeated measures (panel data) of categorical outcomes. We discuss scale‐dependence of DID assumptions for marginal and transition effect estimates and specify a new target estimand: the difference between outcome category transitions under treatment versus no treatment. We establish causal assumptions about transitions that are sufficient to identify this and a marginal target estimand. We contrast the marginal estimands identified by the transition approach versus an additive assumption only about marginal evolution. We apply both the marginal and transition approaches to estimate the effects of Medicaid expansion on health insurance coverage types (employer‐sponsored; other private, non‐group; public; and uninsured). Data Extraction: We analyzed 16,027 individual survey responses from people aged 18–62 years in the 2014 SIPP panel. Principal Findings: We show that the two identifying assumptions are equivalent (on the scale of the marginals) if either the baseline marginal distributions are identical or the marginals are constant in both groups. Applying our transitions approach to the SIPP data, we estimate a differential increase in transitions from uninsured to public coverage and differential decreases in transitions from uninsured to private, non‐group coverage and in remaining uninsured. Conclusions: By comparing the assumption that marginals are evolving in parallel to an assumption about transitions across outcome values, we illustrate the scale‐dependence of difference‐in‐differences. Our application shows that studying transitions can illuminate nuances obscured by changes in the marginals. [ABSTRACT FROM AUTHOR]

Context: Structural features of health care environments are associated with patient health outcomes, but these relationships are not well understood in sports medicine. Objective: To evaluate the association between athlete injury outcomes and structural measures of health care at universities: (1) clinicians per athlete, (2) financial model of the sports medicine department, and (3) administrative reporting structure of the sports medicine department. Design: Descriptive epidemiology study. Setting: Collegiate sports medicine programs. Patients or Other Participants: Colleges that contribute data to the National Collegiate Athletic Association (NCAA) Injury Surveillance Program. Main Outcome Measure(s): We combined injury data from the NCAA Injury Surveillance Program, sports medicine staffing data from NCAA Research, athletic department characteristics from the United States Department of Education, and financial and administrative oversight model data from a previous survey. Rates of injury, reinjury, concussion, and time loss (days) in NCAA athletes. Results: Compared with schools that had an average number of clinicians per athlete, schools 1 standard deviation above average had a 9.5% lower injury incidence (103.6 versus 93.7 per 10000 athlete-exposures [AEs]; incidence rate ratio [IRR] = 0.905, P, .001), 2.7% lower incidence of reinjury (10.6 versus 10.3 per 10000 AEs; IRR = 0.973, P = .004), and 6.7% lower incidence of concussion (6.1 versus 5.7 per 10000 AEs; IRR = 0.933, P, .001). Compared with the average, schools that had 1 standard deviation more clinicians per athlete had 16% greater injury time loss (5.0 days versus 4.2 days; IRR = 1.16, P, .001). At schools with sports medicine departments financed by or reporting to the athletics department (or both), athletes had higher injury incidences (31% and 9%, respectively). Conclusions: The financial and reporting structures of collegiate sports medicine departments as well as the number of clinicians per athlete were associated with injury risk. Increasing the number of sports medicine clinicians on staff and structuring sports medicine departments such that they are financed by and report to a medical institution may reduce athlete injury incidence. [ABSTRACT FROM AUTHOR]

Purpose The opioid epidemic continues to result in significant morbidity and mortality even within hospitals where opioids are the second most common cause of adverse events. Opioid stewardship represents one model for hospitals to promote safe and rational prescribing of opioids to mitigate preventable adverse events in alliance with new Joint Commission standards. The purpose of this study was to identify the prevalence of current hospital practices to improve opioid use. Methods A cross-sectional survey of hospital best practices for opioid use was electronically distributed via electronic listservs in March 2018 to examine the presence of an opioid stewardship program and related practices, including formulary restrictions, specialist involvement for high-risk patients, types of risk factors screened, and educational activities. Results Among 133 included hospitals, 23% reported a stewardship program and 14% reported a prospective screening process to identify patients at high risk of opioid-related adverse events (ORAEs). Among those with a prospective screening process, there was variability in ORAE risk factor screening. Formulary restrictions were dependent on specific opioids and formulations. Patient-controlled analgesia was restricted at 45% of hospitals. Most hospitals reported having a pain management service (90%) and a palliative care service providing pain management (67%). Conclusion The absence of opioid stewardship and prospectively screening ORAEs represents a gap in current practice at surveyed hospitals. Hospitals have an opportunity to implement and refine best practices such as access to pain management specialists, use of formulary restrictions, and retrospective and prospective monitoring of adverse events to improve opioid use. [ABSTRACT FROM AUTHOR]

Athletes sometimes choose not to report suspected concussions, risking delays in treatment and health consequences. How and why do athletes make these reporting decisions? Using original survey data from a cohort of college football players, we evaluate two assumptions of the current literature on injury reporting: first, that the probability of reporting a concussion or injury is constant over time; second, that athletes make reasoned deliberative decisions about whether to report their concussion or other injury. We find that athletes are much less likely to report a concussion to a medical professional than they are to report another injury (47% vs. 80%), but no association between reporting and a measure of athletes' ability to switch from fast, reactive thinking to reasoned, deliberative thinking. The likelihood of reporting decreases as the number of injuries and concussions increases, and no athlete reported more than four concussions. Sports medicine clinicians sometimes use four concussions as a time to discuss possibly curtailing sports participation, which may influence athletes' subsequent reporting behavior. Sports medicine clinicians may want to consider athlete injury history as a risk factor for concussion and injury under-reporting. [ABSTRACT FROM AUTHOR]

Characterizations of average end-of-life care for people with cancer can obscure important differences in patients' experiences. Using Medicare claims data for 14,257 patients diagnosed with extensive-stage small-cell lung cancer in the period 1995-2009, we used latent class analysis to identify classes of people with different care patterns. We characterized care trajectories from diagnosis to death using time spent in five care settings--home, hospital inpatient unit (acute), hospital intensive care unit (ICU), postacute skilled nursing facility, and hospice--and transitions across these settings. We identified four classes of patients: 66 percent spent the time primarily at home, 11 percent were primarily in hospice, 17 percent were largely in an acute setting, and 6 percent were largely in an ICU. Patients in these classes differed significantly in terms of baseline clinical characteristics, survival length, time spent in hospice, site of death, and spending. The findings show substantial heterogeneity in patterns of care for patients with advanced cancer, which should be accounted for in efforts to improve end-of-life care. [ABSTRACT FROM AUTHOR]

BACKGROUND ANO OBJECTIVES: Ordering rates for imaging studies and procedures may change if clinicians are shown the prices of those tests while they are ordering. We studied the effect of 2 forms of paid price information, single median price and paired internal/ external median prices, on how often pediatric-focused and adult-oriented clinicians (most frequently general pediatricians and adult specialists caring for pediatric-aged patients, respectively) order imaging studies and procedures for 0- to 21-year-olds. METHODS: In January 2014, we randomized 227 pediatric-focused and 279 adult-oriented clinicians to 1 of 3 study arms: Control (no price display), Single Median Price, or Paired Internal/External Median Prices (both with price display in the ordering screen of electronic health record). We used 1-way analysis of variance and paired t tests to examine how frequently clinicians (1) placed orders and (2) designated tests to be completed internally within an accountable care organization. RESULTS: For pediatric-focused clinicians, there was no significant difference in the rates at which orders were placed or designated to be completed internally across the study arms. For adult-oriented clinicians caring for children and adolescents, however, those in the Single Price and Paired Price arms placed orders at significantly higher rates than those in the Control group (Control 3.2 [SD 4.8], Single Price 6.2 [SD 6.8], P < .001 and Paired Prices 5.2 [SD 7.9], P = .04). The rate at which adult-oriented clinicians designated tests to be completed internally was not significantly different across arms. CONCLUSIONS: The effect of price information on ordering rates appears to depend on whether the clinician is pediatric-focused or adult-oriented. [ABSTRACT FROM AUTHOR]