Primary Care Physicians In Medicare Advantage Were Less Costly, Provided Similar Quality Versus Regional Average.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9 percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8 percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4 percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

Sex-Based Disparities in Acute Myocardial Infarction Treatment Patterns and Outcomes in Older Adults Hospitalized Across 6 High-Income Countries: An Analysis From the International Health Systems Research Collaborative.

BACKGROUND: Sex differences in acute myocardial infarction treatment and outcomes are well documented, but it is unclear whether differences are consistent across countries. The objective of this study was to investigate the epidemiology, use of interventional procedures, and outcomes for older females and males hospitalized with ST-segment-elevation myocardial infarction (STEMI) and non-ST-segment-elevation myocardial infarction (NSTEMI) in 6 diverse countries. METHODS: We conducted a serial cross-sectional cohort study of 1 508 205 adults aged ≥66 years hospitalized with STEMI and NSTEMI between 2011 and 2018 in the United States, Canada, England, the Netherlands, Taiwan, and Israel using administrative data. We compared females and males within each country with respect to age-standardized hospitalization rates, rates of cardiac catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery within 90 days of hospitalization, and 30-day age- and comorbidity-adjusted mortality. RESULTS: Hospitalization rates for STEMI and NSTEMI decreased between 2011 and 2018 in all countries, although the hospitalization rate ratio (rate in males/rate in females) increased in virtually all countries (eg, US STEMI ratio, 1.58:1 in 2011 and 1.73:1 in 2018; Israel NSTEMI ratio, 1.71:1 in 2011 and 2.11:1 in 2018). Rates of cardiac catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery were lower for females than males for STEMI in all countries and years (eg, US cardiac catheterization in 2018, 88.6% for females versus 91.5% for males; Israel percutaneous coronary intervention in 2018, 76.7% for females versus 84.8% for males) with similar findings for NSTEMI. Adjusted mortality for STEMI in 2018 was higher for females than males in 5 countries (the United States, Canada, the Netherlands, Israel, and Taiwan) but lower for females than males in 5 countries for NSTEMI. CONCLUSIONS: We observed a larger decline in acute myocardial infarction hospitalizations for females than males between 2011 and 2018. Females were less likely to receive cardiac interventions and had higher mortality after STEMI. Sex disparities seem to transcend borders, raising questions about the underlying causes and remedies.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community 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.

Variation in care for patients presenting with hip fracture in six high-income countries: A cross-sectional cohort study.

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.

Differences in Treatment Patterns and Outcomes of Acute Myocardial Infarction for Low- and High-Income Patients in 6 Countries.

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.

Adaptive metrics for an evolving pandemic A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and often lack 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 of risk metrics. 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 novel 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-week-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-week-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. Significance Statement: In the rapidly-evolving COVID-19 pandemic, public health risk metrics often become less relevant over time. Risk metrics are designed to predict future severe disease and mortality based on currently-available surveillance data, such as cases and hospitalizations. However, the relationship between cases, hospitalizations, and mortality has varied considerably over the course of the pandemic, in the context of new variants and shifts in vaccine- and infection-induced immunity. We propose an adaptive approach that regularly updates metrics based on the relationship between surveillance inputs and future outcomes of policy interest. Our method captures changing pandemic dynamics, requires only hospitalization input data, and outperforms static risk metrics in predicting high-risk states and counties.

Changes in prenatal care and birth outcomes after federally qualified health center expansion.

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.

Variation in revascularisation use and outcomes of patients in hospital with acute myocardial infarction across six high income countries: cross sectional cohort study.

OBJECTIVES: To compare treatment and outcomes for patients admitted to hospital with a primary diagnosis of ST elevation or non-ST elevation myocardial infarction (STEMI or NSTEMI) in six high income countries with very different healthcare delivery systems. DESIGN: Retrospective cross sectional cohort study. SETTING: Patient level administrative data from the United States, Canada (Ontario and Manitoba), England, the Netherlands, Israel, and Taiwan. PARTICIPANTS: Adults aged 66 years and older admitted to hospital with STEMI or NSTEMI between 1 January 2011 and 31 December 2017. OUTCOMES MEASURES: The three categories of outcomes were coronary revascularisation (percutaneous coronary intervention or coronary artery bypass graft surgery), mortality, and efficiency (hospital length of stay and 30 day readmission). Rates were standardised to the age and sex distribution of the US acute myocardial infarction population in 2017. Outcomes were assessed separately for STEMI and NSTEMI. Performance was evaluated longitudinally (over time) and cross sectionally (between countries). RESULTS: The total number of hospital admissions ranged from 19 043 in Israel to 1 064 099 in the US. Large differences were found between countries for all outcomes. For example, the proportion of patients admitted to hospital with STEMI who received percutaneous coronary intervention in hospital during 2017 ranged from 36.9% (England) to 78.6% (Canada; 71.8% in the US); use of percutaneous coronary intervention for STEMI increased in all countries between 2011 and 2017, with particularly large rises in Israel (48.4-65.9%) and Taiwan (49.4-70.2%). The proportion of patients with NSTEMI who underwent coronary artery bypass graft surgery within 90 days of admission during 2017 was lowest in the Netherlands (3.5%) and highest in the US (11.7%). Death within one year of admission for STEMI in 2017 ranged from 18.9% (Netherlands) to 27.8% (US) and 32.3% (Taiwan). Mean hospital length of stay in 2017 for STEMI was lowest in the Netherlands and the US (5.0 and 5.1 days) and highest in Taiwan (8.5 days); 30 day readmission for STEMI was lowest in Taiwan (11.7%) and the US (12.2%) and highest in England (23.1%). CONCLUSIONS: In an analysis of myocardial infarction in six high income countries, all countries had areas of high performance, but no country excelled in all three domains. Our findings suggest that countries could learn from each other by using international comparisons of patient level nationally representative data.

Difference-in-differences for categorical outcomes.

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.

Birds of a feather flock together: Comparing controlled pre-post designs.

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.

Change in condition alerts for home care recipients: A stepped wedge cluster randomized trial.

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.

Confounding and regression adjustment in difference-in-differences studies.

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 time-varying 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.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policymakers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Accuracy of US College Football Players' Estimates of Their Risk of Concussion or Injury.

Importance: Despite increased concern about the health consequences of contact sports, little is known about athletes' understanding of their own risk of sports-related injury. Objective: To assess whether college football players accurately estimate their risk of concussion and nonconcussion injury and to identify characteristics of athletes who misestimate their injury risk. Design, Setting, and Participants: In this survey study, questionnaires were given to 296 current college football players on 4 teams from the 3 of the 5 most competitive conferences of the US National Collegiate Athletic Association. Surveys were conducted between February and May 2017. Data were analyzed from June 2017 through July 2020. Main Outcomes and Measures: Multiple approaches were taken to compare athlete perceptions of their risks of concussion and nonconcussion injury with individual probabilities of these risks, which were modeled using logistic regression. Results: Of 296 male college-aged athletes from 4 football teams who participated in the survey, 265 (89%) answered all questions relevant for this study. Participating teams were similar to nonparticipating teams across nearly all measured characteristics. One hundred athletes (34%) had sustained 1 or more concussions, and 197 (68% of the 289 who responded to the question) had sustained 1 or more injuries in the previous football season. Logistic regression models of single-season injury and concussion had reasonably good fit (area under the curve, 0.75 and 0.73, respectively). Of the 265 participants for whom all relevant data were available, 111 (42%) underestimated their risk of concussion (χ2 = 98.6; P = .003). A similar proportion of athletes (113 [43%]) underestimated their risk of injury, although this was not statistically significant (χ2 = 34.0; P = .09). An alternative analytic strategy suggested that 241 athletes (91%) underestimated their risk of injury (Wilcoxon statistic, 7865; P < .001) and 167 (63%) underestimated their risk of concussion (Wilcoxon statistic, 26 768; P < .001). Conclusions and Relevance: The findings of this survey study suggest that college football players may underestimate their risk of injury and concussion. The implications for informed participation in sport are unclear given that people generally underestimate health risks. It is necessary to consider whether athletes are sufficiently informed and how much risk is acceptable for an athlete to participate in a sport.

Use of Telemedicine for Emergency Triage in an Independent Senior Living Community: Mixed Methods Study.

BACKGROUND: Older, chronically ill individuals in independent living communities are frequently transferred to the emergency department (ED) for acute issues that could be managed in lower-acuity settings. Triage via telemedicine could deter unnecessary ED transfers. OBJECTIVE: We examined the effectiveness of a telemedicine intervention for emergency triage in an independent living community. METHODS: In the intervention community, a 950-resident independent senior living community, when a resident called for help, emergency medical technician-trained staff could engage an emergency medicine physician via telemedicine to assist with management and triage. We compared trends in the proportion of calls resulting in transport to the ED (ie, primary outcome) in the intervention community to two control communities. Secondary outcomes were telemedicine use and posttransport disposition. Semistructured focus groups of residents and staff were conducted to examine attitudes toward the intervention. Qualitative data analysis used thematic analysis. RESULTS: Although the service was offered at no cost to residents, use was low and we found no evidence of fewer ED transfers. The key barrier to program use was resistance from frontline staff members, who did not view telemedicine triage as a valuable tool for emergency response, instead perceiving it as time-consuming and as undermining their independent judgment. CONCLUSIONS: Engagement of, and acceptance by, frontline providers is a key consideration in using telemedicine triage to reduce unnecessary ED transfers.

Savings or Selection? Initial Spending Reductions in the Medicare Shared Savings Program and Considerations for Reform.

Policy Points Concerns 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.

Current state of opioid stewardship.

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.

Staffing, Financial, and Administrative Oversight Models and Rates of Injury in Collegiate Athletes.

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.