Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison.

In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

Coding-Driven Changes In Measured Risk In Accountable Care Organizations.

Claims data, which form the foundation of risk adjustment in payment for health care services, may reflect efforts to capture more-or more severe-clinical conditions rather than true changes in health status. This can distort payments. We quantify this in the context of Medicare's accountable care organization (ACO) program by comparing risk scores derived from two different measurement approaches. One approach uses diagnoses coded on claims based on Centers for Medicare and Medicaid Services Hierarchical Condition Categories (HCC), and the other uses self-reported, survey-based health data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). During 2013-16 HCC-based risk scores grew faster than CAHPS-based risk scores (2.1 percent versus 0.3 percent annually), and the gap in HCC- and CAHPS-based risk score growth varied widely across ACOs. The average gap in risk score growth appears to be the result primarily of HCC coding practices rather than poor performance of the CAHPS model, suggesting that coding practices (not necessarily driven by ACO contracts) may account for most of the observed risk score growth for ACO beneficiaries.

Improving the Performance of Risk Adjustment Systems: Constrained Regressions, Reinsurance, and Variable Selection.

Modifications of risk-adjustment systems used to pay health plans in individual health insurance markets typically seek to reduce selection incentives at the individual and group levels by adding variables to the payment formula. Adding variables can be costly and lead to unintended incentives for upcoding or service utilization. While these drawbacks are recognized, they are hard to quantify and difficult to balance against the concrete, measurable improvements in fit that may be achieved by adding variables to the formula. This paper takes a different approach to improving the performance of health plan payment systems. Using the HHS-HHC V0519 model from the Marketplaces as a starting point, we constrain fit at the individual and group level to be as good or better than the current payment model while reducing the number of variables in the model. We introduce three elements in the design of plan payment: reinsurance, constrained regressions, and machine learning methods for variable selection. The fit performance of our alternative formulas with many fewer variables is as good or better than the current HHS-HHC V0519 formula.

The Evolution of Supply and Demand in Markets for Generic Drugs.

Policy Points Much concern about generic drug markets has emerged in recent policy debates. Important changes in regulations, the structure of purchasing, and the length of the drug supply chain have affected generic drug markets. Effective price competition remains the rule in generic markets for large-selling drugs. Smaller markets and those for injectable products often have less price competition and are more susceptible to supply disruptions. CONTEXT: The image of generic drugs as a commodity sold in competitive markets is an oversimplification, as evidenced by increasing accounts of price spikes, sustained high price-cost margins, and market disruptions. The mismatch between the canonical economic model of generic drug markets and reality motivated our empirical project. METHODS: To explore recent changes in those factors impacting the supply and demand for generic drugs, we studied, from a variety of sources, the data on price, competition, supply disruptions and recalls, changes to the supply chain, and buy-side concentration. We examined quarterly data through 2018 for a cohort of 77 molecules that lost patent protection during the so-called patent cliff between 2010 and 2013. FINDINGS: On the supply side, we found that for large-market oral solids, generic entry and price declines were consistent with past studies showing a significant number of market entrants and substantial reductions in the average price of a molecule. In smaller markets for oral solids and injectable products, we observed fewer entrants, higher rates of exit, smaller price reductions, and, in some cases, considerable price instability. The number of reported shortages increased across all generic market types over time, with the rate of shortage increases especially pronounced in markets for injectable products. The number of product recalls also rose over our study period. Although we did not estimate causal effects, we did find several changes in the market environment for generic drugs that may contribute to these phenomena. The demand side for generics has become more concentrated. Supply chains rely more on producers outside the United States (particularly from China and India). Contracting practices have undergone changes that may inhibit competition in product supply. FDA regulatory scruitiny has increased. CONCLUSIONS: Competition in generic drug markets varies widely by market size and product form. Recent changes in demand-side market structure imply more downward pressure on prices stemming from buy-side concentration. The FDA's greater regulatory oversight puts upward pressure on costs, and the lengthening of the supply chain increases production uncertainty for producers. Demand and supply-side changes point to further market instabilities across all generic markets due to producers' changing economic position.

Very high and low residual spenders in private health insurance markets: Germany, The Netherlands and the U.S. Marketplaces.

We study the extremely high and low residual spenders in individual health insurance markets in three countries. A high (low) residual spender is someone for whom the residual-spending less payment (from premiums and risk adjustment)-is high (low), indicating that the person is highly underpaid (overpaid). We begin with descriptive analysis of the top and bottom 1% and 0.1% of residuals building to address the question of the degree of persistence in membership at the extremes. Common findings emerge among the countries. First, the diseases found among those with the highest residual spending are also disproportionately found among those with the lowest residual spending. Second, those at the top of the residual spending distribution (where spending exceeds payments the most) account for a massively high share of the unexplained variance in the predictions from the risk adjustment model. Third, in terms of persistence, we find that membership in the extremes of the residual spending distribution is highly persistent, raising concerns about selection-related incentives targeting these individuals. As our results show, the one-in-a-thousand people (on both sides of the residual distribution) play an outsized role in creating adverse incentives associated with health plan payment systems. In response to the observed importance of the extremes of the residual spending distribution, we propose an innovative combination of risk-pooling and reinsurance targeting the predictively undercompensated group. In all three countries, this form of risk sharing substantially improves the overall fit of payments to spending. Perhaps surprisingly, by reducing the burden on diagnostic indicators to predict high payments, our proposed risk sharing policy reduces the gap between payments and spending not only for the most undercompensated individuals but also for the most overcompensated people.

Medicaid Expansion's Spillover to the Criminal Justice System: Evidence from Six Urban Counties.

Spillovers from the Affordable Care Act Medicaid expansion to other social-sector outcomes have received little attention. One that may be especially salient for public policy is the impact of expanded eligibility on jail-related outcomes. This study compares recidivism outcomes in three non-expansion counties to nearby expansion counties before and after Medicaid expansion. Using forty-eight months of arrest data from six urban county jails, we conduct comparative interrupted time series analyses to describe changes in the probability of rearrest and the number of arrests before and after Medicaid expansion. Consistent with previous literature, we find mixed results. In two case studies, Medicaid expansion is associated with decreased rates of recidivism. In the other, we find differential increases in jail-based recidivism after Medicaid expansion. We use contextual information from site visits and stakeholder interviews to understand the factors that may mediate and moderate the relationship between Medicaid expansion and return to jail.

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.

The vexing relationship between socioeconomic status and health.

In a recent issue of this Journal, Politzer, Shmueli, and Avni estimate the economic costs of health disparities due to socioeconomic status (SES) in Israel (Politzer et al., Isr J Health Policy Res 8: 46, 2019). Using three measures of SES, the socioeconomic ranking of localities, individual income, and individual education, Politzer and colleagues estimate welfare loss due to higher mortality, productivity loss due to poorer health, excess health care treatment costs, and excess disability payments for individuals with below median SES relative to those with above median SES. They find the economic costs of health disparities are substantial, adding up to between 1.1 and 3.1 billion USD annually-between 0.7 and 1.6% of Israel's GDP.This paper is useful and informative. It is, to our knowledge, the first comprehensive quantification of the economic costs stemming from health disparities in Israel. In spite of many social policies designed to level economic opportunity and social welfare generally, by most measures, Israel is among the most unequal in the distribution of income among all OECD countries (Cornfeld and Danieli, Isr Econ Rev 12:51-95, 2015). Politzer and colleagues expose the magnitude and sources of health-related loss that Israel faces because of such inequality and shows how the costs of inequality are borne to some degree by all members of society. This short commentary discusses the complicated relationship between SES and health and puts the findings from Politzer and colleagues in the context of the international literature on the subject.

Improving risk-equalization in Switzerland: Effects of alternative reform proposals on reallocating public subsidies for hospitals.

The Swiss healthcare financing system is on the verge of one of its largest reforms. The Swiss parliament is currently debating how to reallocate about 20 % of total health expenditures. Swiss cantons make substantial tax-funded contributions to health expenditures by paying 55 % of hospital inpatient costs. As health insurers are fully responsible for all outpatient costs, the present system may provide unintended incentives to treat patients in inpatient settings. This paper presents and evaluates three alternative reform proposals for the reallocation of the cantonal contribution. Two proposals are currently under consideration in the Swiss parliament, suggesting either partial cost-sharing (20 %) of all healthcare costs or inclusion of cantonal contributions into the risk-equalization fund. A third option is developed in this paper, which proposes using the cantonal funds to pay a share of insurer's expenses above a high-cost threshold. The high-cost risk-sharing alternative is clearly superior: it mitigates the incentive to discriminate against sicker individuals, improves incentives for cost control, and reduces risk of loss for insurers. The paper adds results from Switzerland to an international literature on the properties of adding high-cost risk sharing to a risk equalization model.

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.

Extreme under and overcompensation in morbidity-based health plan payments: The case of Switzerland.

In 2020, the Swiss insurer payment model will include a set of sophisticated morbidity indicators in the form of Pharmaceutical Cost Groups (PCGs), added to a payment model currently largely based on age, gender, and a crude morbidity indicator. Adding powerful risk adjustors reduces underpayment for previously highly underpaid groups but creates a new group of the highly overpaid. We characterize the diseases and patterns of health care spending in most extremely under and overpaid in the new Swiss payment model. We define extremely under and overpaid to be those in the top and bottom 1 and .1 percentiles of the distribution of spending less payment, respectively. The under and overpaid share some of the same health conditions, among them kidney disease. The highly underpaid account for a massively disproportionate share of the unexplained variance in the new payment model. Membership in the tails of the distribution of spending residuals after risk adjustment is persistent, implying that the highly over and underpaid merit special attention in design of insurer payment models.

Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment.

Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R2, when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this "risk adjustment" system is to convey incentives to health plans such that they provide health care services fairly, a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R2 for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric.

Data transformations to improve the performance of health plan payment methods.

The conventional method for developing health care plan payment systems uses observed data to study alternative algorithms and set incentives for the health care system. In this paper, we take a different approach and transform the input data rather than the algorithm, so that the data used reflect the desired spending levels rather than the observed spending levels. We present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our systematic approach for data transformations in two Medicare applications: underprovision of care for individuals with chronic illnesses and health care disparities by geographic income levels. Empirically comparing our method to two other common approaches shows that the "side effects" of these approaches vary by context, and that data transformation is an effective tool for addressing misallocations in individual health insurance markets.

Association between High Proportions of Seriously Mentally Ill Nursing Home Residents and the Quality of Resident Care.

OBJECTIVES: To examine the association between the quality of care delivered to nursing home residents with and without a serious mental illness (SMI) and the proportion of nursing home residents with SMI. DESIGN: Instrumental variable study. Relative distance to the nearest nursing home with a high proportion of SMI residents was used to account for potential selection of patients between high- and low-SMI facilities. Data were obtained from the 2006-2010 Minimum Data Set assessments linked with Medicare claims and nursing home information from the Online Survey, Certification, and Reporting database. SETTING: Nursing homes with high (defined as at least 10% of a facility's population having an SMI diagnosis) and low proportions of SMI residents. PARTICIPANTS: A total of 58 571 Medicare nursing residents with an SMI diagnosis (ie, schizophrenia or bipolar disorder) and 558 699 individuals without an SMI diagnosis who were admitted to the same nursing homes. MEASUREMENTS: Outcomes were nursing home quality measures: (1) use of physical restraints, (2) any hospitalization in the last 3 months, (3) use of an indwelling catheter, (4) use of a feeding tube, and (5) presence of pressure ulcer(s). RESULTS: For individuals with SMI, admission to a high-SMI facility was associated with a 3.7 percentage point (95% confidence interval [CI] = 1.4-6.0) increase in the probability of feeding tube use relative to individuals admitted to a low-SMI facility. Among individuals without SMI, admission to a high-SMI facility was associated with a 1.7 percentage point increase in the probability of catheter use (95 CI = .03-3.47), a 3.8 percentage point increase in the probability of being hospitalized (95% CI = 2.16-5.44), and a 2.1 percentage point increase in the probability of having a feeding tube (95% CI = .43-3.74). CONCLUSION: Admission to nursing homes with high concentrations of residents with SMI is associated with worse outcomes for both residents with and without SMI. J Am Geriatr Soc 67:2346-2352, 2019.

The Comparative Advantage of Medicare Advantage.

We ascertain the degree of service-level selection in Medicare Advantage (MA) using individual level data on the 100 most frequent HCC's or combination of HCC's from two national insurers in 2012-2013. We find differences in the distribution of beneficiaries across HCC's between TM and MA, principally in the smaller share of MA enrollees with no coded HCC, consistent with greater coding intensity in MA. Among those with an HCC code, absolute differences between MA and TM shares of beneficiaries are small, consistent with little service-level selection. Variation in HCC margins does not predict differences between an HCC's share of MA and TM enrollees, although one cannot a priori sign a relationship between margin and service-level selection. Margins are negatively associated with the importance of post-acute care in the HCC. Margins among common chronic disease classes amenable to medical management and typically managed by primary care physicians are larger than among diseases typically managed by specialists. These margin differences by disease are robust against a test for coding effects and suggest that the average technical efficiency of MA relative to TM may vary by diagnosis. If so, service-level selection on the basis of relative technical efficiency could be welfare enhancing.

Market Concentration and Potential Competition in Medicare Advantage.

Issue: Medicare Advantage (MA), the private option to traditional Medicare, now serves roughly 37 percent of beneficiaries. Congress intended MA plans to achieve efficiencies in the provision of health care that lead to savings for Medicare through managed competition among private health plans. Goal: Two elements are needed for savings to accrue: a sound payment policy and effective competition among the private plans. This brief examines the latter. Methods: We use data from 2009­17 to describe market structure in MA, including the insurers offering plans and enrollment in each U.S. county. We measure both actual and potential competitors for each county for each year. Key Findings and Conclusions: MA markets are highly concentrated and have become more concentrated since 2009. From 2009­17, 70 percent or more of enrollees were in highly concentrated markets, dominated by two or three insurers. Since the payment system used to reimburse insurers selling in the MA market relies on competition to spur efficiency and premiums that more closely reflect insurers' actual costs, these developments suggest that taxpayers and beneficiaries will overpay. We also find an average of six potential entrants into MA markets, which points to a source of competition that may be activated in MA. To tap into potential competition, further research is needed to understand the factors affecting entry into MA markets.

College Football Players Less Likely to Report Concussions and Other Injuries with Increased Injury Accumulation.

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.

Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data.

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

Deriving risk adjustment payment weights to maximize efficiency of health insurance markets.

Risk-adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a risk-adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of risk-adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating risk-adjustment weights. We show that when the number of risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate risk-adjustment payment weights in the Netherlands (N = 16.5 million).