How Successful Is Medicare Advantage?

Context

Medicare Part C, or Medicare Advantage (MA), now almost 30 years old, has generally been viewed as a policy disappointment. Enrollment has vacillated but has never come close to the penetration of managed care plans in the commercial insurance market or in Medicaid, and because of payment policy decisions and selection, the MA program is viewed as having added to cost rather than saving funds for the Medicare program. Recent changes in Medicare policy, including improved risk adjustment, however, may have changed this picture.

Methods

This article summarizes findings from our group''s work evaluating MA''s recent performance and investigating payment options for improving its performance even more. We studied the behavior of both beneficiaries and plans, as well as the effects of Medicare policy.

Findings

Beneficiaries make “mistakes” in their choice of MA plan options that can be explained by behavioral economics. Few beneficiaries make an active choice after they enroll in Medicare. The high prevalence of “zero-premium” plans signals inefficiency in plan design and in the market''s functioning. That is, Medicare premium policies interfere with economically efficient choices. The adverse selection problem, in which healthier, lower-cost beneficiaries tend to join MA, appears much diminished. The available measures, while limited, suggest that, on average, MA plans offer care of equal or higher quality and for less cost than traditional Medicare (TM). In counties, greater MA penetration appears to improve TM''s performance.

Conclusions

Medicare policies regarding lock-in provisions and risk adjustment that were adopted in the mid-2000s have mitigated the adverse selection problem previously plaguing MA. On average, MA plans appear to offer higher value than TM, and positive spillovers from MA into TM imply that reimbursement should not necessarily be neutral. Policy changes in Medicare that reform the way that beneficiaries are charged for MA plan membership are warranted to move more beneficiaries into MA.     

Loss to Follow-Up in Cohort Studies: How Much is Too Much?

Loss to follow-up is problematic in most cohort studies and often leads to bias. Although guidelines suggest acceptable follow-up rates, the authors are unaware of studies that test the validity of these recommendations. The objective of this study was to determine whether the recommended follow-up thresholds of 60-80% are associated with biased effects in cohort studies. A simulation study was conducted using 1000 computer replications of a cohort of 500 observations. The logistic regression model included a binary exposure and three confounders. Varied correlation structures of the data represented various levels of confounding. Differing levels of loss to follow-up were generated through three mechanisms: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). The authors found no important bias with levels of loss that varied from 5 to 60% when loss to follow-up was related to MCAR or MAR mechanisms. However, when observations were lost to follow-up based on a MNAR mechanism, the authors found seriously biased estimates of the odds ratios with low levels of loss to follow-up. Loss to follow-up in cohort studies rarely occurs randomly. Therefore, when planning a cohort study, one should assume that loss to follow-up is MNAR and attempt to achieve the maximum follow-up rate possible.     

How Much Positivity Is Needed to Buffer the Impact of Parental Negativity on Children?

Previous research suggests that a couple's ability to balance adverse interactions with positivity is a better predictor of relationship outcomes than negativity per se. The purpose of this study was to extend the findings on couples to parents by examining the positive‐to‐negative ratio in parental interaction as a predictor of child adjustment. Three hundred seventy‐five parents completed questionnaires on the negative and positive interactions in their intimate relationship and their child's functioning online. Different interparental positive–negative ratios were identified by latent class analysis and then linked to children's adjustment. Children whose parents were characterized by more negativity relative to positivity scored higher in externalizing problems compared to children whose parents' negativity was outbalanced by positive interactions. Girls with parents yielding the highest positive–negative ratios were reported to show most prosocial behavior. The findings suggest that parental positivity should be expressed at least twice as much as parental negativity for the sake of children's well‐being.     

Too Much Ado about Instrumental Variable Approach: Is the Cure Worse than the Disease?

ObjectiveTo review the efficacy of instrumental variable (IV) models in addressing a variety of assumption violations to ensure standard ordinary least squares (OLS) estimates are consistent. IV models gained popularity in outcomes research because of their ability to consistently estimate the average causal effects even in the presence of unmeasured confounding. However, in order for this consistent estimation to be achieved, several conditions must hold. In this article, we provide an overview of the IV approach, examine possible tests to check the prerequisite conditions, and illustrate how weak instruments may produce inconsistent and inefficient results.MethodsWe use two IVs and apply Shea's partial R-square method, the Anderson canonical correlation, and Cragg–Donald tests to check for weak instruments. Hall–Peixe tests are applied to see if any of these instruments are redundant in the analysis.ResultsA total of 14,952 asthma patients from the MarketScan Commercial Claims and Encounters Database were examined in this study. Patient health care was provided under a variety of fee-for-service, fully capitated, and partially capitated health plans, including preferred provider organizations, point of service plans, indemnity plans, and health maintenance organizations. We used controller–reliever copay ratio and physician practice/prescribing patterns as an instrument. We demonstrated that the former was a weak and redundant instrument producing inconsistent and inefficient estimates of the effect of treatment. The results were worse than the results from standard regression analysis.ConclusionDespite the obvious benefit of IV models, the method should not be used blindly. Several strong conditions are required for these models to work, and each of them should be tested. Otherwise, bias and precision of the results will be statistically worse than the results achieved by simply using standard OLS.     

How Much Risk Pooling Is There in the Individual Insurance Market?

OBJECTIVE: To examine how much pooling of risks occurs among potential purchasers in the individual market, how much pooling occurs among those who purchase coverage, and whether there is greater pooling among longer-term enrollees. DATA SOURCES: The data are administrative records for enrollees in individual insurance plans in California in 2001, and from a survey of Californians enrolled in the individual insurance market and the uninsured. STUDY DESIGN: Logit models were estimated for 5 health outcome measures to compare the insured and uninsured after adjusting for other factors that affect insurance status and health. Multivariate models were also estimated to explore the relationship between health and three measures of pooling in the market: plan type, pricing tier, and the actuarially adjusted premium paid by the enrollee. PRINCIPAL FINDINGS: Those who purchase individual health insurance are in better health than those who remain uninsured. On the other hand, a large share of people with health problems does obtain individual insurance. The distribution of subscribers across plan type and pricing tier varies with their health status. Those in poor health are less likely to purchase low benefit plans. There is less separation of risks for those who become sick after enrollment based on the measure of pricing tier. The distribution of subscribers across plan type for those who have health problems at enrollment and those who become sick differs, but so does the distribution of those who become sick and those who remain healthy. CONCLUSIONS: Despite small differences among the healthy and sick, our results support the conclusion that there is considerable risk pooling in the individual market. To some extent, this pooling occurs because underwriting happens at the time people enroll and there is greater pooling among those who become sick than those who enroll sick. Our results however suggest that health savings accounts may further fragment the market.     

Too Much of a Good Thing? Exploring the Impact of Wealth on Weight

Obesity, like many health conditions, is more prevalent among the socioeconomically disadvantaged. In our data, very poor women are three times more likely to be obese and five times more likely to be severely obese than rich women. Despite this strong correlation, it remains unclear whether higher wealth causes lower obesity. In this paper, we use nationally representative panel data and exogenous wealth shocks (primarily inheritances and lottery wins) to shed light on this issue. Our estimates show that wealth improvements increase weight for women, but not men. This effect differs by initial wealth and weight—an average‐sized wealth shock received by initially poor and obese women is estimated to increase weight by almost 10 lb. Importantly, for some females, the effects appear permanent. We also find that a change in diet is the most likely explanation for the weight gain. Overall, the results suggest that additional wealth may exacerbate rather than alleviate weight problems. Copyright © 2014 John Wiley & Sons, Ltd.     

How Much Do Outside Options Matter? The Effect of Subsidized Health Insurance on Social Security Disability Insurance Benefit Receipt

New government health insurance programs may affect participation in existing safety-net benefits that provide health insurance as a secondary aim. We examine whether the outside options for health insurance made available by the Affordable Care Act affected Social Security Disability Insurance (DI) application decisions. Using the universe of U.S. individual income tax records spanning 2007-2016, we first estimate the effect of Medicaid expansions using a state difference-in-differences identification strategy, but find small and statistically insignificant estimates. However, when we estimate the effect of being eligible for high vs. low Marketplace subsidies based on geography, we find some evidence consistent with subsidies increasing DI claiming among those with prior access to Employer Sponsored Insurance, and decreasing DI claiming otherwise. Overall, we find suggestive evidence that outside options for health insurance do matter, though magnitudes are small and results are statistically precise only for Marketplace coverage.     

How Do the Experiences of Medicare Beneficiary Subgroups Differ between Managed Care and Original Medicare?

Objective. To examine whether disparities in health care experiences of Medicare beneficiaries differ between managed care (Medicare Advantage [MA]) and traditional fee‐for‐service (FFS) Medicare. Data Sources. 132,937 MA and 201,444 FFS respondents to the 2007 Medicare Consumer Assessment of Health Care Providers and Systems (CAHPS) survey. Study Design. We defined seven subgroup characteristics: low‐income subsidy eligible, no high school degree, poor or fair self‐rated health, age 85 and older, female, Hispanic, and black. We estimated disparities in CAHPS experience of care scores between each of these groups and beneficiaries without those characteristics within MA and FFS for 11 CAHPS measures and assessed differences between MA and FFS disparities in linear models. Principal Findings. The seven subgroup characteristics had significant (p<.05) negative interactions with MA (larger disparities in MA) in 27 of 77 instances, with only four significant positive interactions. Conclusion. Managed care may provide less uniform care than FFS for patients; specifically there may be larger disparities in MA than FFS between beneficiaries who have low incomes, are less healthy, older, female, and who did not complete high school, compared with their counterparts. There may be potential for MA quality improvement targeted at the care provided to particular subgroups.     

Is premium support the right medicine for Medicare?

This paper assesses the desirability of transforming Medicare into a premium-support system. I focus on three areas crucial to the future of Medicare: cost savings, beneficiary choice, and the stability of traditional Medicare. Based on my analysis of the Bipartisan Commission on the Future of Medicare plan, I find substantial problems with adopting premium support for Medicare. In particular, projections of premium-support savings are based on questionable assumptions that the slowdown in health spending during 1993-1997 can be sustained and extrapolated to future Medicare performance. Consequently, premium support may inadvertently destabilize public Medicare and erode beneficiary choice without achieving substantial savings.     

Time Is Money,But How Much? The Monetary Value of Response Time for Thai Ambulance Emergency Services

ObjectiveTo calculate the monetary value of the time factor per minute and per year for emergency services.MethodsThe monetary values for ambulance emergency services were calculated for two different time factors, response time, which is the time from when a call is received by the emergency medical service call-taking center until the response team arrives at the emergency scene, and operational time, which includes the time to the hospital. The study was performed in two steps. First, marginal effects of reduced fatalities and injuries for a 1-minute change in the time factors were calculated. Second, the marginal effects and the monetary values were put together to find a value per minute.ResultsThe values were found to be 5.5 million Thai bath/min for fatality and 326,000 baht/min for severe injury. The total monetary value for a 1-minute improvement for each dispatch, summarized over 1 year, was 1.6 billion Thai baht using response time.ConclusionsThe calculated values could be used in a cost-benefit analysis of an investment reducing the response time. The results from similar studies could for example be compared to the cost of moving an ambulance station or investing in a new alarm system.     

How Much Is a Human Life Worth? A Systematic Review

ObjectivesTo systematically review studies eliciting monetary value of a statistical life (VSL) estimates within, and across, different sectors and other contexts; compare the reported estimates; and critically review the elicitation methods used.MethodsIn June 2019, we searched the following databases to identify methodological and empirical studies: Cochrane Library, Compendex, Embase, Environment Complete, Informit, ProQuest, PubMed, Scopus, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines for reporting and a modified Consolidated Health Economic Evaluation Reporting Standards checklist to assess the quality of included studies.ResultsWe identified 1455 studies, of which we included 120 in the systematic review. A stated-preference approach was used in 76 articles, with 51%, 41%, and 8% being contingent valuation studies, discrete-choice experiments, or both, respectively. A revealed-preference approach was used in 43 articles, of which 74% were based on compensating-wage differentials. The human capital approach was used in only 1 article. We assessed most publications (87%) as being of high quality. Estimates for VSL varied substantially by context (sector, developed/developing country, socio-economic status, etc), with the median of midpoint purchasing power parity–adjusted estimates of 2019 US$5.7 million ($6.8 million, $8.7 million, and $5.3 million for health, labor market, and transportation safety sectors, respectively).ConclusionsThe large variation observed in published VSLs depends mainly on the context rather than the method used. We found higher median values for labor markets and developed countries. It is important that health economists and policymakers use context-specific VSL estimates. Methodological innovation and standardization are needed to maximize comparability of VSL estimates within, and across, sectors and methods.     

How Much Is Postacute Care Use Affected by Its Availability?

OBJECTIVE: To assess the relative impact of clinical factors versus nonclinical factors-such as postacute care (PAC) supply-in determining whether patients receive care from skilled nursing facilities (SNFs) or inpatient rehabilitation facilities (IRFs) after discharge from acute care. DATA SOURCES AND STUDY SETTING: Medicare acute hospital, IRF, and SNF claims provided data on PAC choices; predictors of site of PAC chosen were generated from Medicare claims, provider of services, enrollment file, and Area Resource File data. STUDY DESIGN: We used multinomial logit models to predict PAC use by elderly patients after hospitalizations for stroke, hip fractures, or lower extremity joint replacements. DATA COLLECTION/EXTRACTION METHODS: A file was constructed linking acute and postacute utilization data for all medicare patients hospitalized in 1999. PRINCIPAL FINDINGS: PAC availability is a more powerful predictor of PAC use than the clinical characteristics in many of our models. The effects of distance to providers and supply of providers are particularly clear in the choice between IRF and SNF care. The farther away the nearest IRF is, and the closer the nearest SNF is, the less likely a patient is to go to an IRF. Similarly, the fewer IRFs, and the more SNFs, there are in the patient's area the less likely the patient is to go to an IRF. In addition, if the hospital from which the patient is discharged has a related IRF or a related SNF the patient is more likely to go there. CONCLUSIONS: We find that the availability of PAC is a major determinant of whether patients use such care and which type of PAC facility they use. Further research is needed in order to evaluate whether these findings indicate that a greater supply of PAC leads to both higher use of institutional care and better outcomes-or whether it leads to unwarranted expenditures of resources and delays in returning patients to their homes.