Standards of Care
The Economics of Generic Drug Shortages: The Limits of Competition
Rena Conti & Marta Wosińska
Journal of Economic Perspectives, Spring 2025, Pages 79-102
Abstract:
We examine the economics of the US generic prescription drug market, which comprises the majority of medicines sold. The market is celebrated for its benefits in the form of high quality and low prices for consumers but is also increasingly challenged by shortages that may disrupt patient care. Shortages in the generic drug market present an economic puzzle -- in the face of a shortage, prices should rise, encouraging entry, yet we observe shortages increasing in number and persistence. Moreover, if shortages cause patient harm, why don't markets pay a premium for a reliable supply chain? We argue that the puzzle can be explained by the inability of generic drug prices to adjust easily due to regulatory and contracting frictions, and the coexisting presence of asymmetric information and agency problems in the US market. We conclude with a discussion of policy interventions aimed at addressing these challenges to ensure resilient US generic drug supply.
Medicaid ‘Unwinding:’ Much Of The Reduction In Medicaid-Paid Prescriptions Was Offset By Increased Commercial Coverage
Eden Volkov et al.
Health Affairs, May 2025, Pages 523-530
Abstract:
With the expiration of the Medicaid continuous coverage condition on March 31, 2023, states began returning to regular eligibility renewals in Medicaid and the Children’s Health Insurance Program (CHIP). Because of incompleteness of administrative data and lags in the availability of survey data, there is limited understanding of how this “unwinding” process has affected insurance coverage or access to care. Using data from IQVIA PayerTrak, a large, nationally representative, all-payer pharmacy transactions database, we examined the trends in prescription drug use during the unwinding period. Leveraging state variation in Medicaid coverage termination start dates, we found that although prescriptions paid for by Medicaid or CHIP fell during unwinding, this decline was mostly offset by an increase in commercial-paid prescriptions. Total prescriptions were unchanged, suggesting that the unwinding did not result in reduced access to medications.
How Do Mental Health Treatment Delays Impact Long-Term Mortality?
Sydney Costantini
American Economic Review, May 2025, Pages 1672-1707
Abstract:
With a growing mental health crisis and a shortage of behavioral health specialists, those seeking mental health treatment often face long wait times to obtain care. I study how clinic congestion affects mortality for veterans experiencing mental health emergencies. I find that longer waiting times make it more likely that patients miss their follow-up mental health visit, consequently increasing the probability that they permanently disengage from care. A 1 standard deviation increase in wait time between the emergency department visit and follow-up appointment date (11.7 days) increases two-year mortality by about 1.5 percent.
Health risk and the value of life
Daniel Bauer, Darius Lakdawalla & Julian Reif
Journal of Public Economics, May 2025
Abstract:
We extend the conventional life-cycle framework for valuing health and longevity improvements to a stochastic setting with multiple health states and apply it to data on mortality, quality of life, labor earnings, and medical spending for adults with different comorbidities. We find that sick adults are willing to pay nearly twice as much per quality-adjusted life-year (QALY) to reduce mortality risk as healthy adults, and that reducing the risk of serious illness is valued similarly to reducing the risk of mild illness. Our results provide a rational explanation for why people oppose a single threshold value for rationing care and why they invest less in prevention than in treatment.
Regulating Out-of-Network Hospital Payments: Disagreement Payoffs, Negotiated Prices, and Access
Elena Prager & Nicholas Tilipman
NBER Working Paper, April 2025
Abstract:
Recent policy proposals seek to regulate out-of-network hospital prices. We study how such regulation affects equilibrium prices, network formation, and hospital exit. We estimate a structural model of insurer-hospital bargaining that allows for out-of-network transactions between non-contracting parties. These transactions generate a notion of exit by rendering hospitals unprofitable under some regulations. Estimation relies on a novel measure of out-of-network prices. We find that reducing out-of-network prices would also lower negotiated prices, but potentially at the cost of narrower hospital networks. Aggressive regulation could induce substantial hospital exit, but only under the restrictive assumption that negotiators cannot anticipate the exits.
Hospital Capital Expenditures Associated With Prices And Hospital Expansion Or Withering, 2010–19
Nancy Beaulieu, Andrew Hicks & Michael Chernew
Health Affairs, May 2025, Pages 546-553
Abstract:
Prices charged by hospitals in commercial markets are, on average, high and growing rapidly, and they vary within markets. The narrative around these facts has focused on hospitals gaining market power through mergers and acquisitions. Hospitals may also increase their market power by investing in capacity, services, or amenities that, although potentially desirable, increase demand and differentiate them from competitors. Independent of market-power changes, average prices may increase if volume shifts toward high-price hospitals. This study investigated the market dynamics linking hospital capital expenditures during the period 2010–19 to changes in volume, market share, and prices. We found that hospitals investing more in capital gained market share and raised prices, whereas hospitals investing relatively less in capital lost market share and increased prices less. Taken together, these forces perpetuate a cycle of expanding and withering hospitals. Study findings suggest important limits to antitrust as a mechanism to address high and rising prices, and the findings could inform policies to forestall or eliminate the financial decline of withering hospitals, thereby preserving access and promoting competition.
Assessing the Estimands and Estimates of Hospitalization Rates in Health Economics and Clinical Medicine
Aditya Jain et al.
NBER Working Paper, May 2025
Abstract:
Even though data on hospital admissions are widely used in health research, hospitalization-related quantities measured using these data are not always clearly conceptualized. Consequently, estimators of these quantities can have unclear rationales and undesirable properties. We evaluate three rate estimators for measuring hospitalization-related quantities that are of interest in health economics and clinical medicine subspecialities. Using the Grossman human capital model, we motivate the importance of measuring healthy time. We show that an upper bound on healthy time can be calculated using lengths of hospital stay without assumptions about health status outside the hospital. We find that an admission rate conventionally used in clinical research is a patient follow-up time weighted average that lacks a clear basis for the weights. We evaluate the Centers for Medicare and Medicaid Services (CMS) use of risk-standardized readmission rates to penalize hospitals under the Hospital Readmissions Reduction Program (HRRP) and find that it may inadvertently conflict with disease-specific care aimed at reducing mortality risk. We show that risk-standardized rates can be sensitive to patient case mix, potentially leading to hospital rankings that do not reflect hospital quality. We also summarize debates regarding the effectiveness of risk-standardized readmission rates in reducing readmissions.
FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: A model development and validation study
Dennis Bontempi et al.
Lancet Digital Health, forthcoming
Methods: FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb–Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan–Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes.
Findings: FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p<0·0001). We found that FaceAge can improve physicians’ survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70–0·78] to 0·8 [0·76–0·83]; p<0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not.
Screening Through Soft Spending Limits: Evidence from the Medicare Therapy Cap
Ashvin Gandhi & Maggie Shi
NBER Working Paper, April 2025
Abstract:
Governments and firms often employ soft spending limits to restrict overspending while still allowing exceptions on a case-by-case basis. This paper studies a Medicare policy which capped per-patient physical therapy spending, with exceptions for patients with documented medical need. The cap reduced spending by 8 percent without harming patient health, with the targeting improvements driven by Medicare discretion in granting exceptions rather than improved provider screening. However, the documentation requirement also introduced horizontal inequity: conditional on need, lower-income and minority patients were more likely to be screened out, as they tended to see providers with poorer documentation practices.
Understanding the Effect of Race on Medicare Advantage Enrollment
Adam Atherly et al.
Health Services Research, April 2025
Objective: To understand why Medicare Advantage (MA) has a relatively larger market share among racial minorities than traditional Medicare (TM).
Study Setting and Design: We estimate Probit models for the choice of the MA sector versus TM by Black and Hispanic beneficiaries, as compared with White beneficiaries. We use a non-linear version of the Oaxaca-Blinder decomposition to decompose differences in the probability of MA enrollment by race into differences in explanatory variable values versus differences in the coefficients on those variables, which we identify as “preferences” for MA.
Data Sources and Analytic Sample: We combined 2020 Medicare Current Beneficiary Survey (MCBS) data with CMS data on MA plan payment levels aggregated to the county level, star ratings, and measures of market competition.
Principal Findings: In the Black/White beneficiary comparison, 83% of the 17% point difference in the probability of MA enrollment was explained by differences in preferences (p < 0.001) while only 17% was explained by differences in attributes (p < 0.05). In contrast, in the Hispanic/White beneficiary comparison, 72% of the difference was explained by differences in attributes (p < 0.001) and 28% was explained by differences in preferences (p < 0.01). Attributes associated with differing rates of MA enrollment by race included both market-level characteristics (e.g., payment levels) and personal characteristics (age, level of pain, and chronic disease count). Preferences associated with differing rates of MA enrollment included coefficients of sector characteristics such as payment rates and the number of four-star+ plans available and age.
Conclusions: In this study, we find that the higher MA enrollment rate for Black versus White beneficiaries is largely associated with differences in preferences, while the higher enrollment rate for Hispanic beneficiaries is more associated with differences in attributes. Differences in preferences for MA sector characteristics were significant in explaining higher MA enrollment rates for both groups compared with White beneficiaries, suggesting that changes in payment rates will disproportionately impact racial minorities, particularly for Black beneficiaries. However, the reasons for different preferences for MA among racial groups remain somewhat of a puzzle, particularly given that we control for demographics, health, and market characteristics.
Updated Medicaid-To-Medicare Fee Index: Medicaid Physician Fees Still Lag Behind Medicare Physician Fees
Laura Skopec, Avani Pugazhendhi & Stephen Zuckerman
Health Affairs, May 2025, Pages 531-538
Abstract:
Medicaid plays a critical role in the US health insurance system, but a history of low physician fees has limited physicians’ participation in the program. Recent Centers for Medicare and Medicaid Services rules have sought to encourage states to increase their Medicaid physician fees to at least 80 percent of Medicare fees, based on the methods used in the Urban Institute’s periodic surveys of Medicaid physician fees. In this article, we show that between 2019 and 2024, Medicaid fees increased slightly, from 72 percent of Medicare fees to 75 percent of Medicare fees for a basket of twenty-seven common physician services. In addition, because the current Medicaid population includes far more nonelderly, nondisabled adults than when the original basket of services was chosen, we present an updated index that better reflects current patterns of spending and service use. We found that, based on this updated index, Medicaid physician fees were approximately 71 percent of Medicare physician fees in 2024, including 69 percent for office visits, 68 percent for hospital and emergency department visits, 87 percent for obstetric care, and 79 percent for other services under our updated approach.
Enrollment in Medicare is associated with fewer outpatient mental healthcare visits among those with mental health symptoms
Grace McCormack et al.
Health Services Research, April 2025
Study Setting and Design: We employ a fuzzy regression discontinuity design, comparing the likelihood of having an outpatient mental health visit or a psychotropic drug fill among individuals younger than or older than the age 65 Medicare eligibility threshold.
Data Sources and Analytic Sample: We analyze 2014–2021 Medical Expenditure Panel Survey data. Our primary sample is restricted to individuals with probable mental health symptoms as indicated by their score on the Kessler K6 psychological distress scale (K6) and Patient Health Questionnaire-2 instrument (PHQ-2) and who have incomes less than 400% of the federal poverty level.
Principal Findings: Among individuals with probable mental health symptoms and low or moderate incomes, enrolling in Medicare (combining the effect of MA [Medicare Advantage] and TM [traditional Medicare]) is associated with a 24.9 percentage point reduction (95% CI −49.1 to −0.8; p = 0.043) in the likelihood of having any type of outpatient mental health visit and a 31.3 percentage point reduction (95% CI −54.2 to −8.4; p = 0.008) in the likelihood of having a prescription drug fill for a psychotropic drug. Effects of MA and TM on mental healthcare utilization are not statistically different from each other. We observe no impact of enrolling in Medicare on the likelihood of having a visit to a primary care provider, having a visit to a non-mental healthcare specialist, or having a fill for a prescribed non-psychotropic drug.