Incentives For Selection Research Articles

Implementation of socio-economic variables in risk adjustment systems: A quantitative analysis using the example of Germany

At least in Western Europe, competitive social health insurance systems have implemented systems of morbidity-based risk adjustment to establish a level playing field for insurers. However, insured persons with specific socio-economic characteristics are still underfunded, leaving incentives for risk selection. In Germany, there is an ongoing debate about (re)implementing socio-economic variables to reduce this undercompensation. This study analyses whether the following four socio-economic groups are systematically under- or over‐compensated under the present risk equalization system in Germany: insured persons with co‐payment exemption (1), recipients of basic income support for unemployment (2), of benefits of social long-term care-insurance (3) and insured persons with reduced earning capacity (4). On this basis, several attempts of incorporating these variables into the German risk adjustment system, allowing a better fit for the socio-economically disadvantaged groups, are examined. With a data set of about 9.2 million insured persons, the performance of the modifications is demonstrated for the German system. The disparate outcomes of the various models in different dimensions necessitate the consideration of trade-offs and their incorporation into the implementation of a model designed to mitigate the undercompensation of the affected insured groups.

A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients' health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest.

A critical review of the use of R2 in risk equalization research.

Nearly all empirical studies that estimate the coefficients of a risk equalization formula present the value of the statistical measure R2. The R2-value is often (implicitly) interpreted as a measure of the extent to which the risk equalization payments remove the regulation-induced predictable profits and losses on the insured, with a higher R2-value indicating a better performance. In many cases, however, we do not know whether a model with R2 = 0.30 reduces the predictable profits and losses more than a model with R2 = 0.20. In this paper we argue that in the context of risk equalization R2 is hard to interpret as a measure of selection incentives, can lead to wrong and misleading conclusions when used as a measure of selection incentives, and is therefore not useful for measuring selection incentives. The same is true for related statistical measures such as the Mean Absolute Prediction Error (MAPE), Cumming's Prediction Measure (CPM) and the Payment System Fit (PSF). There are some exceptions where the R2 can be useful. Our recommendation is to either present the R2 with a clear, valid, and relevant interpretation or not to present the R2. The same holds for the related statistical measures MAPE, CPM and PSF.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information

BackgroundDespite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness.MethodsWe study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated– though imperfect– risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a ‘high risk pool’ using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers’ incentives for risk selection and cost control, and compare this to the situation without HRP.ResultsThe results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending.ConclusionOur main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Pricing above value: Selling to a market with selection problems

This paper shows that selection incentives in downstream markets distort upstream prices. It is possible for inputs to be priced above the value that the good has for final consumers. We apply this idea to pharmaceutical companies selling drugs to a health insurance market with selection problems. We specify the conditions under which drugs are sold at prices exceeding treatment value. Another feature of the model is an excessive private incentive to reduce market size, e.g. in the form of personalized medicine.

Unravelling risk selection in Spanish general government employee mutual funds: evidence from cancer hospitalizations in the public health network

Government employees in Spain are covered by public Mutual Funds that purchase a uniform basket of benefits, equal to the ones served to the general population, from private companies. Companies apply as private bidders for a fixed per capita premium hardly adjusted by age. Our hypothesis is that this premium does not cover risks, and companies have incentives for risk selection, which are more visible in high-cost patients. We focus on a particularly costly disease, cancer, whose prevalence is similar among government employees and the general population. We compare hospitalisations in the public hospitals of the government employees that have chosen public provision and the general population. We analysed a database of hospital discharges in the Valencian Community from 2010 to 2015 (3 million episodes). Using exact matching and logistic models, we find significant risk selection; thus, in hospitalised government employees, the likelihood for a solid metastatic carcinoma and non-metastatic cancer to appear in the registry is 31% higher than in the general population. Lymphoma shows the highest odds ratio of 2.64. We found quantitatively important effects. This research provides indirect evidence of risk selection within Spanish Mutual Funds for government employees, prompting action to reduce incentives for such a practice. More research is needed to figure out if what we have observed with cancer patients occurs in other conditions.

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.

How and Why the Choice of Success Criteria Can Impact Therapy Service Delivery: A Worked Example From a Psychological Therapy Service for Anxiety and Depression.

Well-defined measures of therapeutic benefit are essential for evaluating therapies and services. However, there is no single gold standard for defining 'successful' outcomes. We therefore examined the potential impact of adopting different success criteria. We analysed data for 7,064 patients undergoing psychological therapy in a single UK IAPT (Increasing Access to Psychological Therapy) Service, each patient being assessed for depression (PHQ-9) and anxiety (GAD-7) both at the start and end of treatment. Predictors of successful outcomes based on these measures were analysed separately for three different success criteria: based either on assessing clinically significant change, or reliable change, in depression and anxiety. The choice of criteria had little bearing on which variables predicted successful outcomes. However, the direction of the relationship between initial PHQ-9 or GAD-7 score and outcome success reverses when the criteria used to judge success are changed: successful outcomes are less probable under clinically significant change criteria for patients entering the service with more severe depression and/or anxiety but are more probable for such patients under reliable change criteria. Relevant for clinicians, researchers, and policymakers, the choice of success criteria adopted can substantially change the incentives for patient selection into a therapy service. Our analysis highlights how the methods used to evaluate treatment outcomes could impact the priorities and organisation of therapeutic services, which could then impact on who is offered treatment. We recommend further investigations of success criteria in other conditions or treatments to determine the reproducibility of the effects we found.

Addressing unanticipated interactions in risk equalization: A machine learning approach to modeling medical expenditure risk

Adverse selection harms market efficiency and access to essential services, particularly for disadvantaged groups. Risk equalization policies attempt to mitigate this by compensating agents for risk disparities, but often fall short of addressing interactions between risk factors. Using health insurance data from the Netherlands, we present a machine learning approach to capture unanticipated interactions that impact medical expenditure risk. We compare our novel approach to a state-of-the-art statistical model. We find that our approach explains an additional 1.5% of medical expenditure, equivalent to 571 million euros over all individuals in the Dutch market. In particular, this translates into better compensation for low- and high-cost groups that are especially vulnerable to adverse selection. These findings confirm the significance of risk factor interactions in explaining medical expenditure risk, and support the adoption of machine learning alongside statistical models to further mitigate selection incentives in risk equalization policies.

Linear pooling of potentially related density forecasts in crop insurance

AbstractAccurate pricing of crop insurance policies relies on forecasts of probability densities of crop yields. Yield densities are dynamic, time series data on yields are often limited, and yield data are spatially correlated. We examine linear pooling of potentially related, but almost surely misspecified, crop yield density forecasts. The pooled forecasts combine densities from other spatial units based on out‐of‐sample forecast performance. The pooled densities result in more accurate premium rates which can reduce incentives for adverse selection. The approach is applicable to any insurance setting where the statistical model for the loss variable is likely to be misspecified and the underlying data‐generating processes are potentially related.

Heteroscedasticity of residual spending after risk equalization: a potential source of selection incentives in health insurance markets with premium regulation

Many community-rated health insurance markets include risk equalization (also known as risk adjustment) to mitigate risk selection incentives for competing insurers. Empirical evaluations of risk equalization typically quantify selection incentives through predictable profits and losses net of risk equalization for various groups of consumers (e.g. the healthy versus the chronically ill). The underlying assumption is that absence of predictable profits and losses implies absence of selection incentives. This paper questions this assumption. We show that even when risk equalization perfectly compensates insurers for predictable differences in mean spending between groups, selection incentives are likely to remain. The reason is that the uncertainty about residual spending (i.e., spending net of risk equalization) differs across groups, e.g., the risk of substantial losses is larger for the chronically ill than for the healthy. In a risk-rated market, insurers are likely to charge a higher profit mark-up (to cover uncertainty in residual spending) and a higher safety mark-up (to cover the risk of large losses) to chronically ill than to healthy individuals. When such differentiation is not allowed, insurers face incentives to select in favor of the healthy. Although the exact size of these selection incentives depends on contextual factors, our empirical simulations indicate they can be non-trivial. Our findings suggest that – in addition to the equalization of differences in mean spending between the healthy and the chronically ill – policy measures might be needed to diminish (or compensate insurers for) heteroscedasticity of residual spending across groups.

Predictable profits and losses in a health insurance market with risk equalization: A multiple-contract period perspective

Many social health insurance systems rely on ‘regulated competition’ among insurers to improve efficiency. In the presence of community-rated premiums, risk equalization is an important regulatory feature to mitigate risk-selection incentives in such systems. Empirical studies evaluating selection incentives have typically quantified group-level (un)profitability for one contract period. However, due to switching barriers, a multiple contract period perspective might be more relevant. In this paper, using data from a large health survey (N≈380k) we identify subgroups of chronically ill and healthy individuals in year t and follow these groups over three consecutive years. Using administrative data covering the entire Dutch population (N≈17m), we then simulate the mean per person predictable profits and losses (i.e. spending predicted by a sophisticated risk-equalization model minus actual spending) of these groups over the three follow-up years. We find that most of the groups of chronically ill are persistently unprofitable on average, while the healthy group is persistently profitable. This implies that selection incentives might be stronger than initially thought, underscoring the necessity of eliminating predictable profits and losses for the adequate functioning of competitive social health insurance markets.

Improving diagnosis-based cost groups in the Dutch risk equalization model: the effects of a new clustering method and allowing for multimorbidity

Health insurance markets with community-rated premiums typically use risk equalization (RE) to compensate insurers for predictable profits on people in good health and predictable losses on those with a chronic disease. Over the past decades RE models have evolved from simple demographic models to sophisticated health-based models. Despite the improvements, however, non-trivial predictable profits and losses remain. This study examines to what extent the Dutch RE model can be further improved by redesigning one key morbidity adjuster: the Diagnosis-based Cost Groups (DCGs). This redesign includes (1) revision of the underlying hospital diagnoses and treatments (‘dxgroups’), (2) application of a new clustering procedure, and (3) allowing multi-qualification. We combine data on spending, risk characteristics and hospital claims for all individuals with basic health insurance in the Netherlands in 2017 (N = 17 m) with morbidity data from general practitioners (GPs) for a subsample (N = 1.3 m). We first simulate a baseline RE model (i.e., the RE model of 2020) and then modify three important features of the DCGs. In a second step, we evaluate the effect of the modifications in terms of predictable profits and losses for subgroups of consumers that are potentially vulnerable to risk selection. While less prominent results are found for subgroups derived from the GP data, our results demonstrate substantial reductions in predictable profits and losses at the level of dxgroups and for individuals with multiple dxgroups. An important takeaway from our paper is that smart design of morbidity adjusters in RE can help mitigate selection incentives.

The interplay between risk adjustment and risk rating in voluntary health insurance

AbstractMany regulated health insurance markets include risk adjustment (aka risk equalization) to mitigate selection incentives for insurers. Empirical studies on the design and evaluation of risk‐adjustment algorithms typically focus on mandatory health insurance schemes. This paper considers risk adjustment in the context of voluntary health insurance, as found in Chile, Ireland, and Australia. In addition to the challenge of mitigating selection by insurers, regulators of these voluntary schemes have to deal with selection by consumers in and out of the market. A strategy for mitigating selection by consumers is to apply some form of risk rating. Our paper shows how risk adjustment and risk rating interact: (1) risk rating reduces the need for risk adjustment and (2) risk adjustment reduces premium variation across rating factors, thereby increasing incentives for consumers to select in and out of the market.

Selection Incentives in the Dutch Basic Health Insurance: To What Extent Does End-of-Life Spending Contribute to Predictable Profits and Losses for Selective Groups?

Existing risk-equalization models in individual health insurance markets with premium-rate restrictions do not completely compensate insurers for predictable profits/losses, confronting insurers with risk selection incentives. To guide further improvement of risk-equalization models, it is important to obtain insight into the drivers of remaining predictable profits/losses. This article studies a specific potential driver: end-of-life spending (defined here as spending in the last 1–5 years of life). Using administrative (N = 16.9 m) and health survey (N = 384 k) data from the Netherlands, we examine the extent to which end-of-life spending contributes to predictable profits/losses for selective groups. We do so by simulating the predictable profits/losses for these groups with and without end-of-life spending while correcting for the overall spending difference between these two situations. Our main finding is that—even under a sophisticated risk-equalization model—end-of-life spending can contribute to predictable losses for specific chronic conditions.

The goal of risk equalization in regulated competitive health insurance markets

Different opinions exist about the goal of risk equalization in regulated competitive health insurance markets. There seems to be consensus that an element of the goal of risk equalization is ‘to remove the predictable over- and undercompensations of subgroups of insured’ or, equivalently, ‘to achieve a level playing field for each risk composition of an insurer’s portfolio’ or, equivalently, ‘to remove the incentives for risk selection’. However, the role of efficiency appears to be a major issue: should efficiency also be an element of the goal of risk equalization, or should it be a restriction to the goal, or should efficiency not be an element of the goal or a restriction to the goal? If efficiency plays a role, a comprehensive analysis of the total effect of risk equalization on efficiency needs to be done. An improvement of the performance of a risk equalization scheme has both negative and positive effects on efficiency. Negative effects include the reduction in efficiency via cost- or utilization-based risk adjusters. Positive effects result from leveling the playing field and reducing the incentives for risk selection, which increase efficiency as the outcome of a competitive market. In practice many regulators and policy makers take efficiency into consideration by looking at the negative effects, but hardly at the positive effects. The definition of the goal of risk equalization has consequences for the design and evaluation of risk equalization schemes and for the equalization payments. We describe relevant potential goals, tradeoffs and possible solutions.

Do health insurers use target marketing as a tool for risk selection? Evidence from the Netherlands

In health care systems based on managed competition, enrolees can choose between insurers who are positioned as prudent buyers of care on their behalf. To avoid risk selection, insurers are compensated through a system of risk equalisation. The Dutch system of risk equalisation is generally considered to be one of the most sophisticated in the world. Empirical evidence, however, shows there are still consumer segments that are profitable for insurers. To examine whether insurers use target marketing for attracting these segments, we assessed promotional material used by Dutch insurers during the switching season of 2019. Our findings provide preliminary evidence that large insurers with different brands primarily use their sub brands as strategic vehicles to improve their competitive positions by targeting these brands at financially favourable groups and price sensitive buyers. By contrast, the more visible main brands are targeted at a much broader spectrum of consumer groups to display the insurer's social character. Only a minority of insurers’ marketing expressions are targeted at actual users of care. Despite continuous improvements in the risk equalisation system, on average this group is still unprofitable for insurers. From a health policy perspective, further improvements are key to motivate health insurers to target their efforts at improving care for the chronically ill and to eliminate incentives for risk selection.

Research on Product Quality, Platform Governance and Government Supervision

As an increasingly important form of industrial organization in the new economic era, the platform economy has become an important engine for promoting economic transformation and development. With the development of the platform economy, the monopoly power of platform companies has become stronger and stronger, and more and more problems in the operation of internet platforms have been exposed. What follows is the corporate governance of platforms and the government governance of platform enterprises. The governance intensity of low-quality business by platforms is often lower than the optimal level in society, resulting in damage to consumer rights and social welfare. To clarify the selection incentives and impacts of platform governance strategies, based on the social reality of platform sales of counterfeit products, in the situation where there are both high-quality and low-quality merchants, this paper compares the governance strength of platforms in the oligopoly market and the monopoly market respectively by constructing a game model, and further analyzes the regulatory strategies in different market environments from the perspective of government supervision.By comparing the equilibrium results under different market structures and governance models, this paper finds that the quality difference between the two types of products is the key to determining the governance intensity of platforms. The greater the quality difference, the stronger the platform governance intensity. The difference in the governance intensity of platforms under different market structures depends on network externality and product quality differences: The stronger the network externality and the greater the quality difference, the stronger the governance intensity under the monopoly market; the weaker the network externality and the smaller the quality difference, the stronger the governance intensity under the oligopolistic market. This paper finally analyzes how to regulate platform governance from the perspective of government supervision. Compared with the social optimal, the strength of platform governance is weak, and the increase in network externality and quality differences will further enlarge the gap between the two. Therefore, the intensity of government supervision should be determined according to the market environment, increasing with the increase of network externality and product quality differences.This paper enriches the theoretical exploration of platform economy from the perspective of business governance, and meticulously describes the relationship between governance intensity and network externality as well as business quality. The research conclusions have a certain enlightening effect on the choice of platform self-governance strategies, the choice of government governance intensity, the protection of consumer rights and the promotion of social welfare.

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.

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

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. 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. 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). 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.