Small Marketplace Premiums Pose Financial And Administrative Burdens: Evidence From Massachusetts, 2016-17.

Health insurance premiums are primarily understood to pose financial barriers to coverage. However, the need to remit monthly premium payments may also create administrative burdens that negatively affect coverage, even in cases where affordability is a negligible concern. Using 2016-17 data from the Massachusetts health insurance Marketplace and a natural experiment, we evaluated how coverage retention was affected by the introduction of nominal (less than $10 for most enrollees) monthly premiums for plans that previously had $0 premiums. Compared with plans that maintained $0 premiums, those that took on nominal premiums saw enrollment fall by 14 percent over the following year. This attrition was attributable to terminations for nonpayment; most terminations occurred at the end of January, implying that a significant number of affected enrollees never initiated premium payments. These findings suggest that even very small premiums act as enrollment barriers, which may sometimes reflect administrative burdens more than financial hardship. Several policy approaches could mitigate adverse coverage outcomes related to nominal premiums.

What Difference Does a Health Plan Make? Evidence from Random Plan Assignment in Medicaid.

Exploiting the random assignment of Medicaid beneficiaries to managed care plans, we find substantial plan-specific spending effects despite plans having identical cost sharing. Enrollment in the lowest-spending plan reduces spending by at least 25%-primarily through quantity reductions-relative to enrollment in the highest-spending plan. Rather than reducing "wasteful" spending, lower-spending plans broadly reduce medical service provision-including the provision of low-cost, high-value care-and worsen beneficiary satisfaction and health. Consumer demand follows spending: a 10 percent increase in plan-specific spending is associated with a 40 percent increase in market share. These facts have implications for the government's contracting problem and program cost growth.

The Two-Margin Problem in Insurance Markets.

Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data from Massachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.

The Liquidity Sensitivity of Healthcare Consumption: Evidence from Social Security Payments.

Insurance is typically viewed as a mechanism for transferring resources from good to bad states. Insurance, however, may also transfer resources from high-liquidity periods to low-liquidity periods. We test for this type of transfer from health insurance by studying the distribution of Social Security checks among Medicare recipients. When Social Security checks are distributed, prescription fills increase by 6-12 percent among recipients who pay small copayments. We find no such pattern among recipients who face no copayments. The results demonstrate that more-complete insurance allows recipients to consume healthcare when they need it rather than only when they have cash.

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.

The effects of plan payment rates on the market for Medicare Advantage Dual-Eligible Special Needs Plans.

OBJECTIVE: To determine the effect of higher potential benchmark payment rates on the market for Medicare Advantage (MA) Dual-Eligible Special Needs Plans (D-SNPs). DATA SOURCES/STUDY SETTING: Publicly available county-level data from 2009 to 2015 regarding the number of D-SNPs operating within the county, the enrollment in and five-star quality of score of these plans, and the benchmark amounts used to determine capitated plan payments. STUDY DESIGN: This study exploits the introduction of quality bonus payments to the MA program in 2012, and exogenous geographic variation in the potential size of these bonuses to estimate the effect of benchmark payment increases on the availability, quality, and take-up of D-SNPs. We use a difference-in-difference estimation approach to compare changes in the market for D-SNPs in counties eligible for a double bonus to those that are not. PRINCIPAL FINDINGS: The doubling of bonuses was associated with a relative 29 percent increase in the number of D-SNPs offered (P = 0.021) and 0.1-star increase in the average quality of available D-SNPs (P = 0.034). No relative increase in overall D-SNP enrollment was detected. CONCLUSIONS: These findings indicate that larger benchmark payment amounts may influence insurers' decisions of whether to participate in the D-SNP market but not dual-eligibles' decision of whether to enroll in these plans. Future research is needed to inform discussions about whether D-SNPs are a viable mechanism for integrating benefits for dual eligibles and the degree to which Medicare policies should support their continued growth.

Attention Deficit-Hyperactivity Disorder and Month of School Enrollment.

BACKGROUND: Younger children in a school grade cohort may be more likely to receive a diagnosis of attention deficit-hyperactivity disorder (ADHD) than their older peers because of age-based variation in behavior that may be attributed to ADHD rather than to the younger age of the children. Most U.S. states have arbitrary age cutoffs for entry into public school. Therefore, within the same grade, children with birthdays close to the cutoff date can differ in age by nearly 1 year. METHODS: We used data from 2007 through 2015 from a large insurance database to compare the rate of ADHD diagnosis among children born in August with that among children born in September in states with and states without the requirement that children be 5 years old by September 1 for enrollment in kindergarten. ADHD diagnosis was determined on the basis of diagnosis codes from the International Classification of Diseases, 9th Revision. We also used prescription records to compare ADHD treatment between children born in August and children born in September in states with and states without the cutoff date of September 1. RESULTS: The study population included 407,846 children in all U.S. states who were born in the period from 2007 through 2009 and were followed through December 2015. The rate of claims-based ADHD diagnosis among children in states with a September 1 cutoff was 85.1 per 10,000 children (309 cases among 36,319 children; 95% confidence interval [CI], 75.6 to 94.2) among those born in August and 63.6 per 10,000 children (225 cases among 35,353 children; 95% CI, 55.4 to 71.9) among those born in September, an absolute difference of 21.5 per 10,000 children (95% CI, 8.8 to 34.0); the corresponding difference in states without the September 1 cutoff was 8.9 per 10,000 children (95% CI, -14.9 to 20.8). The rate of ADHD treatment was 52.9 per 10,000 children (192 of 36,319 children; 95% CI, 45.4 to 60.3) among those born in August and 40.4 per 10,000 children (143 of 35,353 children; 95% CI, 33.8 to 47.1) among those born in September, an absolute difference of 12.5 per 10,000 children (95% CI, 2.43 to 22.4). These differences were not observed for other month-to-month comparisons, nor were they observed in states with non-September cutoff dates for starting kindergarten. In addition, in states with a September 1 cutoff, no significant differences between August-born and September-born children were observed in rates of asthma, diabetes, or obesity. CONCLUSIONS: Rates of diagnosis and treatment of ADHD are higher among children born in August than among children born in September in states with a September 1 cutoff for kindergarten entry. (Funded by the National Institutes of Health.).

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).

Measuring efficiency of health plan payment systems in managed competition health insurance markets.

Adverse selection in health insurance markets leads to two types of inefficiency. On the demand side, adverse selection leads to plan price distortions resulting in inefficient sorting of consumers across health plans. On the supply side, adverse selection creates incentives for plans to inefficiently distort benefits to attract profitable enrollees. Reinsurance, risk adjustment, and premium categories address these problems. Building on prior research on health plan payment system evaluation, we develop measures of the efficiency consequences of price and benefit distortions under a given payment system. Our measures are based on explicit economic models of insurer behavior under adverse selection, incorporate multiple features of plan payment systems, and can be calculated prior to observing actual insurer and consumer behavior. We illustrate the use of these measures with data from a simulated market for individual health insurance.

Imperfect risk adjustment, risk preferences, and sorting in competitive health insurance markets.

I develop a model of insurer price-setting and consumer welfare under risk-adjustment, a policy commonly used to combat inefficient sorting due to adverse selection in health insurance markets. I use the model to illustrate graphically that risk-adjustment causes health plan prices to be based on costs not predicted by the risk-adjustment model ("residual costs") rather than total costs, either weakening or exacerbating selection problems depending on the correlation between demand and costs predicted by the risk-adjustment model. I then use a structural model to estimate the welfare consequences of risk-adjustment, finding a welfare gain of over $600 per person-year.

Computational health economics for identification of unprofitable health care enrollees.

Health insurers may attempt to design their health plans to attract profitable enrollees while deterring unprofitable ones. Such insurers would not be delivering socially efficient levels of care by providing health plans that maximize societal benefit, but rather intentionally distorting plan benefits to avoid high-cost enrollees, potentially to the detriment of health and efficiency. In this work, we focus on a specific component of health plan design at risk for health insurer distortion in the Health Insurance Marketplaces: the prescription drug formulary. We introduce an ensembled machine learning function to determine whether drug utilization variables are predictive of a new measure of enrollee unprofitability we derive, and thus vulnerable to distortions by insurers. Our implementation also contains a unique application-specific variable selection tool. This study demonstrates that super learning is effective in extracting the relevant signal for this prediction problem, and that a small number of drug variables can be used to identify unprofitable enrollees. The results are both encouraging and concerning. While risk adjustment appears to have been reasonably successful at weakening the relationship between therapeutic-class-specific drug utilization and unprofitability, some classes remain predictive of insurer losses. The vulnerable enrollees whose prescription drug regimens include drugs in these classes may need special protection from regulators in health insurance market design.

Implications of family risk pooling for individual health insurance markets.

While family purchase of health insurance may benefit insurance markets by pooling individual risk into family groups, the correlation across illness types in families could exacerbate adverse selection. We analyze the impact of family pooling on risk for health insurers to inform policy about family-level insurance plans. Using data on 8,927,918 enrollees in fee-for-service commercial health plans in the 2013 Truven MarketScan database, we compare the distribution of annual individual health spending across four pooling scenarios: (1) "Individual" where there is no pooling into families; (2) "real families" where costs are pooled within families; (3) "random groups" where costs are pooled within randomly generated small groups that mimic families in group size; and (4) "the Sims" where costs are pooled within random small groups which match families in demographics and size. These four simulations allow us to identify the separate contributions of group size, group composition, and family affinity in family risk pooling. Variation in individual spending under family pooling is very similar to that within "simulated families" and to that within random groups, and substantially lower than when there is no family pooling and individuals choose independently (standard deviation $12,526 vs $11,919, $12,521 and $17,890 respectively). Within-family correlations in health status and utilization do not "undo" the gains from family pooling of risks. Family pooling can mitigate selection and improve the functioning of health insurance markets.

RISK CORRIDORS AND REINSURANCE IN HEALTH INSURANCE MARKETPLACES: Insurance for Insurers.

Health Insurance Marketplaces established by the Affordable Care Act implement reinsurance and risk corridors. Reinsurance limits insurer costs associated with specific individuals, while risk corridors protect against aggregate losses. Both tighten the insurer's distribution of expected costs. This paper compares the economic costs and consequences of reinsurance and risk corridors. We simulate the insurer's cost distribution under reinsurance and risk corridors using data for a group of individuals likely to enroll in Marketplace plans from the Medical Expenditure Panel Survey. We compare reinsurance and risk corridors in terms of risk reduction and incentives for cost containment. We find that reinsurance and one-sided risk corridors achieve comparable levels of risk reduction for a given level of incentives. We also find that the policies being implemented in the Marketplaces (a mix of reinsurance and two-sided risk corridor policies) substantially limit insurer risk but perform similarly to a simpler stand-alone reinsurance policy.

Higher Incentive Payments in Medicare Advantage's Pay-for-Performance Program Did Not Improve Quality But Did Increase Plan Offerings.

OBJECTIVE: To evaluate the effects of the size of financial bonuses on quality of care and the number of plan offerings in the Medicare Advantage Quality Bonus Payment Demonstration. DATA SOURCES: Publicly available data from CMS from 2009 to 2014 on Medicare Advantage plan quality ratings, the counties in the service area of each plan, and the benchmarks used to construct plan payments. STUDY DESIGN: The Medicare Advantage Quality Bonus Payment Demonstration began in 2012. Under the Demonstration, all Medicare Advantage plans were eligible to receive bonus payments based on plan-level quality scores (star ratings). In some counties, plans were eligible to receive bonus payments that were twice as large as in other counties. We used this variation in incentives to evaluate the effects of bonus size on star ratings and the number of plan offerings in the Demonstration using a differences-in-differences identification strategy. We used matching to create a comparison group of counties that did not receive double bonuses but had similar levels of the preintervention outcomes. PRINCIPAL FINDINGS: Results from the difference-in-differences analysis suggest that the receipt of double bonuses was not associated with an increase in star ratings. In the matched sample, the receipt of double bonuses was associated with a statistically insignificant increase of +0.034 (approximately 1 percent) in the average star rating (p > .10, 95 percent CI: -0.015, 0.083). In contrast, the receipt of double bonuses was associated with an increase in the number of plans offered. In the matched sample, the receipt of double bonuses was associated with an overall increase of +0.814 plans (approximately 5.8 percent) (p < .05, 95 percent CI: 0.078, 1.549). We estimate that the double bonuses increased payments by $3.43 billion over the first 3 years of the Demonstration. CONCLUSIONS: At great expense to Medicare, double bonuses in the Medicare Advantage Quality Bonus Payment Demonstration were not associated with improved quality but were associated with more plan offerings.