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

BACKGROUND: Despite 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: Our 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.

Can risk rating increase the ability of voluntary deductibles to reduce moral hazard?

Several regulated health insurance markets include the option for consumers to choose a voluntary deductible. An important motive for this option is to reduce moral hazard. In return for a voluntary deductible, consumers receive a premium rebate, which is typically community rated. Under community rating, voluntary deductibles are particularly attractive for low-risk consumers. Since these people use relatively little medical care, the total moral hazard reduction might be relatively small compared to the total healthcare spending. This paper examines the potential moral hazard reduction under risk-rated premiums. We use Chile as a case study due to institutional features that make it a valid benchmark for other countries. Our simulations show that in the presence of self-selection and under a uniform percentage moral hazard reduction across risk types, the absolute moral hazard reduction from a voluntary deductible is indeed expected to be larger in a system with risk-rated premiums than in a system with community-rated premiums. Nevertheless, sensitivity checks show that this conclusion might no longer hold as the percentage moral hazard reduction is lower for high-risk individuals compared to low-risk individuals.

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.

Low-dose ipilimumab plus nivolumab combined with IL-2 and hyperthermia in cancer patients with advanced disease: exploratory findings of a case series of 131 stage IV cancers - a retrospective study of a single institution.

The 3-year overall survival (OS) rate of patients with previously treated or untreated stage III or IV melanoma has by now reached 63% using ipilimumab and nivolumab therapy. However, immune-related adverse events (irAEs) of grade 3 or 4 occurred in 59% of patients leading to discontinuation of therapy in 24.5% of patients and one death. Therapy with checkpoint inhibitors could be safer and more effective in combination with hyperthermia and fever inducing therapies. We conducted a retrospective analysis to test the safety and efficacy of a new combination immune therapy in 131 unselected stage IV solid cancer patients with 23 different histological types of cancer who exhausted all conventional treatments. Treatment consisted of locoregional- and whole-body hyperthermia, individually dose adapted interleukin 2 (IL-2) combined with low-dose ipilimumab (0.3 mg/kg) plus nivolumab (0.5 mg/kg). The objective response rate (ORR) was 31.3%, progression-free survival (PFS) was 10 months, survival probabilities at 6 months was 86.7% (95% CI, 81.0-92.8%), at 9 months was 73.5% (95% CI, 66.2-81.7%), at 12 months was 66.5% (95% CI, 58.6-75.4%), while at 24 months survival was 36.6% (95% CI:28.2%; 47.3%). irAEs of World Health Organization (WHO) Toxicity Scale grade 1, 2, 3, and 4 were observed in 23.66%, 16.03%, 6.11%, and 2.29% of patients, respectively. Our results suggest that the irAEs profile of the combined treatment is safer than that of the established protocols without compromising efficacy.

Incorporating self-reported health measures in risk equalization through constrained regression.

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit 'risk adjuster' in risk equalization models. This study examines an alternative way to exploit this information, namely through 'constrained regression' (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.

Examining unpriced risk heterogeneity in the Dutch health insurance market.

A major challenge in regulated health insurance markets is to mitigate risk selection potential. Risk selection can occur in the presence of unpriced risk heterogeneity, which refers to predictable variation in health care spending not reflected in either premiums by insurers or risk equalization payments. This paper examines unpriced risk heterogeneity within risk groups distinguished by the sophisticated Dutch risk equalization model of 2016. Our strategy is to combine the administrative dataset used for estimation of the risk equalization model (n = 16.9 million) with information derived from a large health survey (n = 387k). The survey information allows for explaining and predicting residual spending of the risk equalization model. Based on the predicted residual spending, two metrics are used to indicate unpriced risk heterogeneity at the individual level and at the level of certain (risk) groups: the correlation coefficient between residual spending and predicted residual spending, and the mean absolute value of predicted residual spending. The analyses yield three main findings: (1) the health survey information is able to explain some residual spending of the risk equalization model, (2) unpriced risk heterogeneity exists both in morbidity and in non-morbidity groups, and (3) unpriced risk heterogeneity increases with predicted spending by the risk equalization model. These findings imply that the sophisticated Dutch risk equalization model does not completely remove unpriced risk heterogeneity. Further improvement of the model should focus on broadening and refining the current set of morbidity-based risk adjusters.

Can premium differentiation counteract adverse selection in the Dutch supplementary health insurance? A simulation study.

Most health insurers in the Netherlands apply community-rating and open enrolment for supplementary health insurance, although it is offered at a free market. Theoretically, this should result in adverse selection. There are four indications that adverse selection indeed has started to occur on the Dutch supplementary insurance market. The goal of this paper is to analyze whether premium differentiation would be able to counteract adverse selection. We do this by simulating the uptake and premium development of supplementary insurance over 25 years using data on healthcare expenses and background characteristics from 110,261 insured. For the simulation of adverse selection, it is assumed that only insured for whom supplementary insurance is expected not to be beneficial will consider opting out of the insurance. Therefore, we calculate for each insured the financial profitability (by making assumptions about the consumer's expected claims and the premium set by the insurer), the individual's risk attitude and the probability to opt out or opt in. The simulation results show that adverse selection might result in a substantial decline in insurance uptake. Additionally, the simulations show that if insurers were to differentiate their premium to 28 age and gender groups, adverse selection could be modestly counteracted. Finally, this paper shows that if insurers would apply highly refined risk-rating, adverse selection for this type of supplementary insurance could be counteracted completely.

Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts.

A voluntary deductible in health insurance: the more years you opt for it, the lower your premium?

Adverse selection regarding a voluntary deductible (VD) in health insurance implies that insured only opt for a VD if they expect no (or few) healthcare expenses. This paper investigates two potential strategies to reduce adverse selection: (1) differentiating the premium to the duration of the contract for which the VD holds (ex-ante approach) and (2) differentiating the premium to the number of years for which insured have opted for a VD (ex-post approach). It can be hypothesized that premiums will decrease with the duration of the contract or the number of years for which insured have opted for a VD, providing an incentive to insured to opt for a deductible also in (incidental) years they expect relatively high expenses. To test this hypothesis, we examine which premium patterns would occur under these strategies using data on healthcare expenses and risk characteristics of over 750,000 insured from 6 years. Our results show that, under the assumptions made, only without risk equalization the premiums could decrease with the duration of the contract or the number of years for which insured have opted for a VD. With (sophisticated) risk equalization, decreasing premiums seem unfeasible, both under the ex-ante and ex-post approach. Given these findings, we are sceptical about the feasibility of these strategies to counteract adverse selection.

A method to simulate incentives for cost containment under various cost sharing designs: an application to a first-euro deductible and a doughnut hole.

Many health insurance schemes include deductibles to provide consumers with cost containment incentives (CCI) and to counteract moral hazard. Policymakers are faced with choices on the implementation of a specific cost sharing design. One of the guiding principles in this decision process could be which design leads to the strongest CCI. Despite the vast amount of literature on the effects of cost sharing, the relative effects of specific cost sharing designs-e.g., a traditional deductible versus a doughnut hole-will mostly be absent for a certain context. This papers aims at developing a simulation model to approximate the relative effects of different deductible modalities on the CCI. We argue that the CCI depends on the probability that healthcare expenses end up in the deductible range and the expected healthcare expenses given that they end up in the deductible range. Our empirical application shows that different deductible modalities result in different CCIs and that the CCI under a certain modality differs across risk-groups.

Neurotoxicity screening of (illicit) drugs using novel methods for analysis of microelectrode array (MEA) recordings.

Annual prevalence of the use of common illicit drugs and new psychoactive substances (NPS) is high, despite the often limited knowledge on the health risks of these substances. Recently, cortical cultures grown on multi-well microelectrode arrays (mwMEAs) have been used for neurotoxicity screening of chemicals, pharmaceuticals, and toxins with a high sensitivity and specificity. However, the use of mwMEAs to investigate the effects of illicit drugs on neuronal activity is largely unexplored. We therefore first characterised the cortical cultures using immunocytochemistry and show the presence of astrocytes, glutamatergic and GABAergic neurons. Neuronal activity is concentration-dependently affected following exposure to six neurotransmitters (glutamate, GABA, serotonin, dopamine, acetylcholine and nicotine). Most neurotransmitters inhibit neuronal activity, although glutamate and acetylcholine transiently increase activity at specific concentrations. These transient effects are not detected when activity is determined during the entire 30min exposure window, potentially resulting in false-negative results. As expected, exposure to the GABAA-receptor antagonist bicuculline increases neuronal activity. Exposure to a positive allosteric modulator of the GABAA-receptor (diazepam) or to glutamate receptor antagonists (CNQX and MK-801) reduces neuronal activity. Further, we demonstrate that exposure to common drugs (3,4-methylenedioxymethamphetamine (MDMA) and amphetamine) and NPS (1-(3-chlorophenyl)piperazine (mCPP), 4-fluoroamphetamine (4-FA) and methoxetamine (MXE)) decreases neuronal activity. MXE most potently inhibits neuronal activity with an IC50 of 0.5µM, whereas 4-FA is least potent with an IC50 of 113µM. Our data demonstrate the importance of analysing neuronal activity within different time windows during exposure to prevent false-negative results. We also show that cortical cultures grown on mwMEAs can successfully be applied to investigate the effects of different (illicit) drugs on neuronal activity. Compared to investigating multiple single endpoints for neurotoxicity or neuromodulation, such as receptor activation or calcium channel function, mwMEAs can provide information on integrated aspects of drug-induced neurotoxicity more rapidly. Therefore, this approach could contribute to a faster insight in possible health risks and shorten the regulation process.

Overpaying morbidity adjusters in risk equalization models.

Most competitive social health insurance markets include risk equalization to compensate insurers for predictable variation in healthcare expenses. Empirical literature shows that even the most sophisticated risk equalization models-with advanced morbidity adjusters-substantially undercompensate insurers for selected groups of high-risk individuals. In the presence of premium regulation, these undercompensations confront consumers and insurers with incentives for risk selection. An important reason for the undercompensations is that not all information with predictive value regarding healthcare expenses is appropriate for use as a morbidity adjuster. To reduce incentives for selection regarding specific groups we propose overpaying morbidity adjusters that are already included in the risk equalization model. This paper illustrates the idea of overpaying by merging data on morbidity adjusters and healthcare expenses with health survey information, and derives three preconditions for meaningful application. Given these preconditions, we think overpaying may be particularly useful for pharmacy-based cost groups.

Potential determinants of deductible uptake in health insurance: How to increase uptake in The Netherlands?

In health insurance, voluntary deductibles are offered to the insured in return for a premium rebate. Previous research has shown that 11 % of the Dutch insured opted for a voluntary deductible (VD) in health insurance in 2014, while the highest VD level was financially profitable for almost 50 % of the population in retrospect. To explain this discrepancy, this paper identifies and discusses six potential determinants of the decision to opt for a VD from the behavioral economic literature: loss aversion, risk attitude, ambiguity aversion, debt aversion, omission bias, and liquidity constraints. Based on these determinants, five potential strategies are proposed to increase the number of insured opting for a VD. Presenting the VD as the default option and providing transparent information regarding the VD are the two most promising strategies. If, as a result of these strategies, more insured would opt for a VD, moral hazard would be reduced.

How profitable is a voluntary deductible in health insurance for the consumer?

To counteract moral hazard in health insurance, insured can be offered a voluntary deductible (VD) in return for a premium rebate. In the Dutch mandatory basic health insurance however, only 11 per cent of the insured opted for a VD in 2014. Several determinants could affect the decision to opt for a VD. This paper examines one of these determinants: the financial profitability. A VD is profitable for the consumer if the out-of-pocket expenses do not exceed the offered premium rebate. The empirical analyses, based upon individual-level data on costs and characteristics of over 800,000 Dutch insured, show that a VD of €500 on top of the mandatory deductible of €360 would have been financially profitable for 48 per cent of the Dutch insured given the average premium rebate of € 240 in 2014. If the whole population had a VD, most insured would obtain either the maximum loss (44 per cent) or the maximum gain (41 per cent). A VD is profitable for males, young insured, healthy insured and insured with few healthcare expenses in the past. To further reduce moral hazard, the following strategies can be used to increase the number of insured opting for a VD: provide insured with information regarding the VD and introduce a shifted deductible.

Is there one measure-of-fit that fits all? A taxonomy and review of measures-of-fit for risk-equalization models.

This study provides a taxonomy of measures-of-fit that have been used for evaluating risk-equalization models since 2000 and discusses important properties of these measures, including variations in analytic method. It is important to consider the properties of measures-of-fit and variations in analytic method, because they influence the outcomes of evaluations that eventually serve as a basis for policymaking. Analysis of 81 eligible studies resulted in the identification of 71 unique measures that were divided into 3 categories based on treatment of the prediction error: measured based on squared errors, untransformed errors, and absolute errors. We conclude that no single measure-of-fit is best across situations. The choice of a measure depends on preferences about the treatment of the prediction error and the analytic method. If the objective is measuring financial incentives for risk selection, the only adequate evaluation method is to assess the predictive performance for non-random groups.

Improving the prediction model used in risk equalization: cost and diagnostic information from multiple prior years.

Currently-used risk-equalization models do not adequately compensate insurers for predictable differences in individuals' health care expenses. Consequently, insurers face incentives for risk rating and risk selection, both of which jeopardize affordability of coverage, accessibility to health care, and quality of care. This study explores to what extent the predictive performance of the prediction model used in risk equalization can be improved by using additional administrative information on costs and diagnoses from three prior years. We analyze data from 13.8 million individuals in the Netherlands in the period 2006-2009. First, we show that there is potential for improving models' predictive performance at both the population and subgroup level by extending them with risk adjusters based on cost and/or diagnostic information from multiple prior years. Second, we show that even these extended models do not adequately compensate insurers. By using these extended models incentives for risk rating and risk selection can be reduced substantially but not removed completely. The extent to which risk-equalization models can be improved in practice may differ across countries, depending on the availability of data, the method chosen to calculate risk-adjusted payments, the value judgment by the regulator about risk factors for which the model should and should not compensate insurers, and the trade-off between risk selection and efficiency.

Diagnoses-based cost groups in the Dutch risk-equalization model: the effects of including outpatient diagnoses.

BACKGROUND: The Dutch basic health-insurance scheme for curative care includes a risk equalization model (RE-model) to compensate competing health insurers for the predictable high costs of people in poor health. Since 2004, this RE-model includes the so-called Diagnoses-based Cost Groups (DCGs) as a risk adjuster. Until 2013, these DCGs have been mainly based on diagnoses from inpatient hospital treatment. OBJECTIVES: This paper examines (1) to what extent the Dutch RE-model can be improved by extending the inpatient DCGs with diagnoses from outpatient hospital treatment and (2) how to treat outpatient diagnoses relative to their corresponding inpatient diagnoses. METHOD: Based on individual-level administrative costs we estimate the Dutch RE-model with three different DCG modalities. Using individual-level survey information from a prior year we examine the outcomes of these modalities for different groups of people in poor health. CONCLUSIONS: We find that extending DCGs with outpatient diagnoses has hardly any effect on the R-squared of the RE-model, but reduces the undercompensation for people with a chronic condition by about 8%. With respect to incentives, it may be preferable to make no distinction between corresponding inpatient and outpatient diagnoses in the DCG-classification, although this will be at the expense of the predictive accuracy of the RE-model.

Effectivity of long antigen exposition dendritic cell therapy (LANEXDC®) in the palliative treatment of pancreatic cancer.

PURPOSE: In pancreatic cancer median survival times range around 6, 6 to 6,9 months. Here we retrospectively analyzed the outcome of immunotherapy in the additional palliative treatment of pancreatic cancer with long antigen exposition dendritic cell therapy (LANEX-DC(®)) in 138 patients who were treated at our institution. PATIENTS: Data were available of 134 patients (97.1%). The median interval between first diagnosis and start of treatment was 1.4 months. RESULTS: Therapy was well tolerated and no serious side effects were observed. The survival rate after 6 months was 72.2 % and afters 9 month 50.4%. The median survival time according to Kaplan- Meier regression analysis was 8.9 months. Median survival was significantly higher in the group of patients who started immunotherapy within 2 months following diagnosis (p=0.029) or repeated immunotherapy (p=0.027). Interestingly, younger patients <= 60 years of age lived significantly longer as patients > 60 years of age (p = 0.022). CONCLUSION: We were able to demonstrate in a large retrospective analysis that additional treatment with dendritic cells (LANEX-DC(®)) is highly effective and extends the median survival times up to 8.9 months. Furthermore we were able to demonstrate that median survival can be increased by early beginning and repetition of LANEX-DC(®) treatment.

Successful treatment of advanced ovarian cancer with thermochemotherapy and adjuvant immune therapy.

We report on a 4-year progression-free survival of a 54-year-old female first diagnosed in December 2007 with advanced bilateral ovarian cancer FIGO IIIc, disseminated peritoneal carcinosis and malignant diaphragm invasion. Treatment started in January 2008 with 6 cycles of Taxol 175 mg/m(2)/carboplatin AUC 5 in 3-week intervals. Twenty-four hours following each chemotherapy session, fever-range long-duration whole-body hyperthermia (WBH) was performed at the temperature plateau of 40°C body core temperature for 6 h. Three months after completion of chemotherapy, 4 more long-duration WBH procedures were performed in monthly intervals. Importantly, long-duration WBH was paralleled with intradermal vaccination of autologous dendritic cells. No other treatment was given to the patient. Four years following the first diagnosis, the patient is still in complete remission with no evidence of disease.