Adverse selection and network design under regulated plan prices: Evidence from Medicaid.

Health plans for the poor increasingly limit access to specialty hospitals. We investigate the role of adverse selection in generating this equilibrium among private plans in Medicaid. Studying a network change, we find that covering a top cancer hospital causes severe adverse selection, increasing demand for a plan by 50% among enrollees with cancer versus no impact for others. Medicaid's fixed insurer payments make offsetting this selection, and the contract distortions it induces, challenging, requiring either infeasibly high payment rates or near-perfect risk adjustment. By contrast, a small explicit bonus for covering the hospital is sufficient to make coverage profitable.

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.

The Behavioral Foundations of Default Effects: Theory and Evidence from Medicare Part D.

We show in two natural experiments that default rules in Medicare Part D have large, persistent effects on enrollment and drug utilization of low-income beneficiaries. The implications of this phenomenon for welfare and optimal policy depend on the sensitivity of passivity to the value of the default option. Using random assignment to default options, we show that beneficiary passivity is extremely insensitive, even when enrolling in the default option would result in substantial drug consumption losses. A third natural experiment suggests that variation in active choice is driven by random transitory shocks rather than the inherent attentiveness of some beneficiaries.

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.

Performance Metrics of Substance Use Disorder Care Among Medicaid Enrollees in New York, New York.

Importance: There is limited evaluation of the performance of Medicaid managed care (MMC) private plans in covering substance use disorder (SUD) treatment. Objective: To compare the performance of MMC plans across 19 indicators of access, quality, and outcomes of SUD treatment. Design Setting and Participants: This cross-sectional study used administrative claims and mandatory assignment to plans of up to 159 016 adult Medicaid recipients residing in 1 of the 5 counties (boroughs) of New York, New York, from January 2009 to December 2017 to identify differences in SUD treatment access, patterns, and outcomes among different types of MMC plans. Data from the latest years were received from the New York State Department of Health in October 2019, and analysis began soon thereafter. Approximately 17% did not make an active choice of plan, and a subset of these (approximately 4%) can be regarded as randomly assigned. Exposures: Plan assignment. Main Outcomes and Measures: Percentage of the enrollees achieving performance measures across 19 indicators of access, process, and outcomes of SUD treatment. Results: Medicaid claims data from 159 016 adults (mean [SD] age, 35.9 [12.7] years; 74 261 women [46.7%]; 8746 [5.5%] Asian, 73 783 [46.4%] Black, and 40 549 [25.5%] White individuals) who were auto assigned to an MMC plan were analyzed. Consistent with national patterns, all plans achieved less than 50% (range, 0%-62.1%) on most performance measures. Across all plans, there were low levels of treatment engagement for alcohol (range, 0%-0.4%) and tobacco treatment (range, 0.8%-7.2%), except for engagement for opioid disorder treatment (range, 41.5%-61.4%). For access measures, 4 of the 9 plans performed significantly higher than the mean on recognition of an SUD diagnosis, any service use for the first time, and tobacco use screening. Of the process measures, total monthly expenditures on SUD treatment was the only measure for which plans differed significantly from the mean. Outcome measures differed little across plans. Conclusions and Relevance: The results of this cross-sectional study suggest the need for progress in engaging patients in SUD treatment and improvement in the low performance of SUD care and limited variation in MMC plans in New York, New York. Improvement in the overall performance of SUD treatment in Medicaid potentially depends on general program improvements, not moving recipients among plans.

Racial/Ethnic Disparities in Substance Use Treatment in Medicaid Managed Care in New York City: The Role of Plan and Geography.

OBJECTIVE: The aim was to assess the magnitude of health care disparities in treatment for substance use disorder (SUD) and the role of health plan membership and place of residence in observed disparities in Medicaid Managed Care (MMC) plans in New York City (NYC). DATA SOURCE: Medicaid claims and managed care plan enrollment files for 2015-2017 in NYC. RESEARCH DESIGN: We studied Medicaid enrollees with a SUD diagnosis during their first 6 months of enrollment in a managed care plan in 2015-2017. A series of linear regression models quantified service disparities across race/ethnicity for 5 outcome indicators: treatment engagement, receipt of psychosocial treatment, follow-up after withdrawal, rapid readmission, and treatment continuation. We assessed the degree to which plan membership and place of residence contributed to observed disparities. RESULTS: We found disparities in access to treatment but the magnitude of the disparities in most cases was small. Plan membership and geography of residence explained little of the observed disparities. One exception is geography of residence among Asian Americans, which appears to mediate disparities for 2 of our 5 outcome measures. CONCLUSIONS: Reallocating enrollees among MMC plans in NYC or evolving trends in group place of residence are unlikely to reduce disparities in treatment for SUD. System-wide reforms are needed to mitigate disparities.

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.

Upcoding: Evidence from Medicare on Squishy Risk Adjustment.

In most US health insurance markets, plans face strong incentives to "upcode" the patient diagnoses they report to the regulator, as these affect the risk-adjusted payments plans receive. We show that enrollees in private Medicare plans generate 6% to 16% higher diagnosis-based risk scores than they would under fee-for-service Medicare, where diagnoses do not affect most provider payments. Our estimates imply that upcoding generates billions in excess public spending and significant distortions to firm and consumer behavior. We show that coding intensity increases with vertical integration, suggesting a principal-agent problem faced by insurers, who desire more intense coding from the providers with whom they contract.

Does Scheduling a Postdischarge Visit with a Primary Care Physician Increase Rates of Follow-up and Decrease Readmissions?

BACKGROUND: Driven in part by Medicare's Hospital Readmissions Reduction Program, hospitals are focusing on improving the transition from inpatient to outpatient care with particular emphasis on early follow-up with a primary care physician (PCP). OBJECTIVE: To assess whether the implementation of a scheduling assistance program changes rates of PCP follow-up or readmissions. DESIGN: Retrospective cohort study. SETTING: An urban tertiary care center PATIENTS: A total of 20,918 adult patients hospitalized and discharged home between September 2008 and October 2015. INTERVENTION: A postdischarge appointment service to facilitate early PCP follow-up. MAIN MEASURES: Primary outcomes were rates of follow-up visits with a PCP within seven days of discharge and hospital readmission within 30 days of discharge. Our first analysis assessed differences in outcomes among patients with and without the use of the service. In a second analysis, we exploited the fact that the service was not available on weekends and conducted an instrumental variable analysis that used the interaction between the intervention and day of the week of admission. RESULTS: In our multivariable analysis, use of the appointment service was associated with much higher rates of PCP follow-up (+31.9 percentage points, 95% CI: 30.2, 33.6; P < .01) and a decrease in readmission (-3.8 percentage points, 95% CI: -5.2, -2.4; P < .01). In the instrumental variable analysis, use of the service also increased the likelihood of a PCP follow-up visit (33.4 percentage points, 95% CI: 7.9, 58.9; P = .01) but had no significant impact on readmissions (-2.5 percentage points, 95% CI: -22.0, 17.0; P = .80). CONCLUSIONS: The postdischarge appointment service resulted in a substantial increase in timely PCP followup, but its impact on the readmission rate was less clear.

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.

Screening in Contract Design: Evidence from the ACA Health Insurance Exchanges.

We study insurers' use of prescription drug formularies to screen consumers in the ACA Health Insurance exchanges. We begin by showing that exchange risk adjustment and reinsurance succeed in neutralizing selection incentives for most, but not all, consumer types. A minority of consumers, identifiable by demand for particular classes of prescription drugs, are predictably unprofitable. We then show that contract features relating to these drugs are distorted in a manner consistent with multidimensional screening. The empirical findings support a long theoretical literature examining how insurance contracts offered in equilibrium can fail to optimally trade off risk protection and moral hazard.

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.