Clean Bills of Health
Pricing Universal Health Care: How Much Would the Use of Medical Care Rise?
Adam Gaffney et al.
Health Affairs, January 2021, Pages 105-112
Abstract:
The return of a Democratic administration to the White House, coupled with coronavirus disease 2019 (COVID-19) pandemic-induced contractions of job-based insurance, may reignite debate over public coverage expansion and its costs. Decades of research demonstrate that uninsured people and people with copays and deductibles use less care than people with first-dollar coverage. Hence, most economic analyses of Medicare for All proposals and other coverage expansions project increased utilization and associated costs. We review the utilization surges that such analyses have predicted and contrast them with the more modest utilization increments observed after past coverage expansions in the US and other affluent nations. The discrepancy between predicted and observed utilization changes suggests that analysts underestimate the role of supply-side constraints -- for example, the finite number of physicians and hospital beds. Our review of the utilization effects of past coverage expansions suggests that a first-dollar universal coverage expansion would increase ambulatory visits by 7-10 percent and hospital use by 0-3 percent. Modest administrative savings could offset the costs of such increases.
Can Health Information Technology Save Lives During a Pandemic?
Seth Benzell et al.
University of Texas Working Paper, December 2020
Abstract:
During the COVID-19 pandemic, US deaths per case decreased from 7.46% in April 2020 to 1.82% in September 2020. In addition to increased testing, a leading explanation for this decline is that hospitals learned over time how to better treat patients diagnosed with COVID-19. Hospitals use health information technologies (IT) to develop dynamic capabilities that enable greater organizational resilience based on health information sharing across providers. This suggests that health IT helps hospitals to save lives during a pandemic, especially during periods when knowledge about the disease improves over time. Using county-level data on health IT intensity, COVID-19 cases and deaths, we show that counties with greater hospital IT intensity exhibit fewer COVID-19 deaths. This result is strengthened when instrumenting for health IT intensity with measures of local internet quality. Using a battery of controls, we are able to exclude several non-causal explanations of health IT’s relationship to deaths, including county prosperity, demographics, COVID-19 cases, pre-COVID-19 hospital mortality rates, mobility and timing of pandemic exposure. Using fixed effects estimation, we observe that a standard deviation increase in a county’s health IT intensity is associated with .043 (95% CI [.068, .018]) fewer COVID-19 deaths per 1000 residents. Consistent with the learning hypothesis, we show that high-IT intensity counties are only significantly better at treating COVID-19 cases several months into the pandemic. Counties with hospitals that participate in COVID-19 related clinical trials also experienced faster learning. We provide evidence that faster learning from clinical trials is, locally, a substitute for learning from health IT, while health IT plays a role in helping hospitals learn from clinical trials in other counties, providing evidence of a spillover effect.
An algorithmic approach to reducing unexplained pain disparities in underserved populations
Emma Pierson et al.
Nature Medicine, January 2021, Pages 136-140
Abstract:
Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3-16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2-11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.
Income Responses to the Affordable Care Act: Evidence from a Premium Tax Credit Notch
Bradley Heim et al.
Journal of Health Economics, forthcoming
Abstract:
We examine responses to the ACA subsidy for Marketplace health insurance in the first year of subsidy availability. Drawing on federal tax data and focusing on a notch in the schedule where eligibility is lost, we document that taxpayers lowered their income to remain eligible for the subsidy. The observed bunching is modest relative to the size of the notch, which, consistent with larger responses we detect in additional analyses among certain subgroups, is likely explained by significant optimization frictions. Finally, we find suggestive evidence that increased deductions drive some of the response, while reduced labor supply also plays a role.
Public and Private Options in Practice: The Military Health System
Michael Frakes, Jonathan Gruber & Timothy Justicz
NBER Working Paper, December 2020
Abstract:
Recent debates over health care reform, including in the context of the Military Health System (MHS) and Veterans Health Administration, highlight the dispute between public and private provision of health care services. Using novel data on childbirth claims from the MHS and drawing on the combination of plausibly exogenous patient moves and heterogeneity across bases in the availability of base hospitals, we identify the impact of receiving obstetrical care on versus off military bases. We find evidence that off-base care is associated with slightly greater resource intensity, but also notably better outcomes, suggesting marginal efficiency gains from care privatization.
Arbitration Over Out-Of-Network Medical Bills: Evidence From New Jersey Payment Disputes
Benjamin Chartock et al.
Health Affairs, January 2021, Pages 130-137
Abstract:
In 2018 New Jersey implemented a final-offer arbitration system to resolve payment disputes between insurers and out-of-network providers over surprise medical bills. Similar proposals are being considered by Congress and other states. In this article we examine how arbitration decisions compare with other relevant provider payment amounts by linking administrative data from New Jersey arbitration cases to Medicare and commercial insurance claims data. We find that decisions track closely with one of the metrics that arbitrators are shown - the eightieth percentile of provider charges - with the median decision being 5.7 times prevailing in-network rates for the same services. It is not a foregone conclusion that arbitrators will select winning offers based on proximity to this target, although our findings suggest that it is a strong anchor. The amount that providers can expect to receive through the arbitration process also affects their bargaining leverage with insurers, which could affect in-network negotiated rates more broadly. Therefore, basing arbitration decisions or a payment standard on unilaterally set provider-billed charges appears likely to increase health care costs relative to other surprise billing solutions and perversely incentivizes providers to inflate their charges over time.
Family Spillover Effects of Marginal Diagnoses: The Case of ADHD
Petra Persson, Xinyao Qiu & Maya Rossin-Slater
NBER Working Paper, January 2021
Abstract:
The health care system commonly relies on information about family medical history in the allocation of screenings and in diagnostic processes. At the same time, an emerging literature documents that treatment for “marginally diagnosed” patients often has minimal impacts. This paper shows that reliance on information about relatives' health can perpetuate marginal diagnoses across family members, thereby raising caseloads and health care costs, but without improving patient well-being. We study Attention Deficit Hyperactivity Disorder (ADHD), the most common childhood mental health condition, and document that the younger siblings and cousins of marginally diagnosed children are also more likely to be diagnosed with and treated for ADHD. Moreover, we find that the younger relatives of marginally diagnosed children have no better adult human capital and economic outcomes than the younger relatives of those who are less likely to be diagnosed. Our analysis points to a simple adjustment to physician protocol that can mitigate these marginal diagnosis spillovers.
How do Physicians Respond to Malpractice Allegations? Evidence from Florida Emergency Departments
Caitlin Carroll, David Cutler & Anupam Jena
NBER Working Paper, January 2021
Abstract:
A substantial literature has studied the influence of malpractice pressure on physician behavior. However, these studies generally focus on malpractice pressure stemming from state laws that govern liability exposure, which may be unknown or not salient to physicians. We test how physicians respond to malpractice allegations made directly against them. Our sample is Emergency Department physicians in Florida, where we have the universe of data on patients and how they are treated along with a census of malpractice complaints. We find that physicians oversee 9% fewer discharges after malpractice allegations and treat each discharge 4% more expensively after an allegation. These effects are true for both allegations that result in money paid and allegations which are dropped. Further, the increase in treatment is generalized, i.e., not limited to patients with conditions similar to what the physician is reported for. The results suggest significant, if modest, impacts of malpractice claims on medical practice.
The Behavioral Foundations of Default Effects: Theory and Evidence from Medicare Part D
Zarek Brot-Goldberg et al.
NBER Working Paper, January 2021
Abstract:
We leverage two unique natural experiments to show that, in public drug insurance for the low-income elderly in the U.S., defaults have large and persistent effects on plan enrollment and beneficiary drug utilization. We estimate that when a beneficiary's default is exogenously changed from one year to the next, 96% of beneficiaries follow that default. We then develop a general framework for choice under costly cognition that allows for the possibility that either paternalistic defaults that steer consumers to plans that suit them (Thaler and Sunstein 2008) or `shocking' defaults that trigger consumers to make active choices (Carroll et al. 2009) could be optimal. We show that optimal default design depends on a previously-overlooked parameter: The elasticity of active choice propensity with respect to the value of the default. Leveraging variation in the match value of randomly-assigned default plans, we estimate an elasticity close to zero: There is little difference in the probability of active choice between beneficiaries assigned a well-matched default versus beneficiaries assigned a poorly-matched default. We also show that this passivity has real consequences, with beneficiaries assigned poorly-matched defaults experiencing large declines in drug consumption relative to those assigned well-matched defaults. This suggests that any potential welfare gains from an active choice response induced by a poorly-matched default are likely to be small and outweighed by the welfare losses due to reductions in drug consumption among beneficiaries who follow the poorly-matched default. Using a third natural experiment and a structural model of attention, we find that the little active choice that is present in this market appears to be largely random, with two-thirds of the variation in active choice coming from within-beneficiary transitory shocks to attention. Our results show that default rules are an integral part of insurance market design and that beneficiaries are likely to benefit from paternalistic defaults rather than be hurt by them.
What Is The Value Of A Star When Choosing A Provider For Total Joint Replacement? A Discrete Choice Experiment
Adam Schwartz et al.
Health Affairs, January 2021, Pages 138-145
Abstract:
The past decade witnessed a rapid rise in the public reporting of surgeon- and hospital-specific quality-of-care measures. However, patients’ interpretations of star ratings and their importance relative to other considerations (for example, cost, distance traveled) are poorly understood. We conducted a discrete choice experiment in an outpatient setting (an academic joint arthroplasty practice) to study trade-offs that patients are willing to make in choosing a provider for a hypothetical total joint arthroplasty. Two hundred consecutive new patients presenting for hip or knee pain in 2018 were included. The average patient was willing to pay $2,607 and $3,152 extra for an additional hospital or physician star, respectively, and an extra $11.45 to not travel an extra mile for arthroplasty care. History of prior surgery and prior experience with rating systems reduced the relative value of an incremental star by $539.25 and $934.50, respectively. Patients appear willing to accept significantly higher copayments for higher quality of care, and surgeon quality seems relatively more important than hospital quality. Further study is needed to understand the value and trust patients place in publicly reported hospital and surgeon quality ratings.
Insurance Coverage, Provider Contact, and Take-Up of the HPV Vaccine
Brandyn Churchill
American Journal of Health Economics, forthcoming
Abstract:
Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States and the single biggest cause of cervical cancer, as well as certain cancers of the head and throat, anus, vulva, vagina, and penis. Between 2008 and 2012 nearly 40,000 people annually were diagnosed with an HPV-related cancer. Despite these staggering numbers and the existence of a highly effective vaccine, HPV vaccination rates remain low. In this paper, I show that state Medicaid expansions as part of the Affordable Care Act were associated with a 3-4 percentage point increase in the probability that a teenager initiated the HPV vaccine. This relationship appears to have been driven in part by increases in Medicaid coverage, the probability of having a recent check-up, and knowledge about the HPV vaccine. Supporting this pathway, I show that Medicaid expansion states saw increased searches for “pediatrician,” “Gardasil” (a trade name of the HPV vaccine), and “HPV Cancer.”
Impact of Medicaid expansion in Oregon on access to prenatal care
Marie Harvey et al.
Preventive Medicine, forthcoming
Abstract:
Medicaid expansion under the Patient Protection and Affordable Care Act (ACA) has the potential to improve reproductive health by allowing low-income women access to healthcare before and early in pregnancy. The aim of this study was to examine the effects of Oregon's Medicaid expansion on timely and adequate prenatal care. We included live births in Oregon from 2012 to 2015 and used individually-linked birth certificate and Medicaid eligibility data. Outcomes were receipt of first trimester prenatal care and receipt of adequate prenatal care. We also assessed Medicaid enrollment one month prior to pregnancy. We estimated the overall effect of Medicaid expansion on prenatal care utilization using probit regression models. Additionally, we assessed the impact of Medicaid expansion on prenatal care utilization via pre-pregnancy Medicaid enrollment using bivariate probit models. Overall, receipt of first trimester prenatal care increased post-expansion by 1.5 percentage points (p < 0.01) after expansion. Receipt of adequate prenatal care also increased significantly post-expansion with an incremental increase of 2.8 percentage points (p < 0.001). Pre-pregnancy Medicaid enrollment increased following Medicaid expansion (β = 0.55, p < 0.001) and was associated with both timely (β = 0.48, p < 0.001) and adequate receipt of prenatal care (β = 0.14, p < 0.001). Using two years of post-ACA data we found that Medicaid expansion had significant positive associations with Medicaid enrollment prior to pregnancy, which subsequently increased receipt of timely and adequate prenatal care. Our study provides evidence that expanding Medicaid has positive effects on women's use of healthcare.
Vertical Integration of Healthcare Providers Increases Self-Referrals and Can Reduce Downstream Competition: The Case of Hospital-Owned Skilled Nursing Facilities
David Cutler et al.
NBER Working Paper, December 2020
Abstract:
The landscape of the U.S. healthcare industry is changing dramatically as healthcare providers expand both within and across markets. While federal antitrust agencies have mounted several challenges to same-market combinations, they have not challenged any non-horizontal affiliations - including vertical integration of providers along the value chain of production. The Clayton Act prohibits combinations that “substantially lessen” competition; few empirical studies have focused on whether this is the source of harm from vertical combinations. We examine whether hospitals that are vertically integrated with skilled nursing facilities (SNFs) lessen competition among SNFs by foreclosing rival SNFs from access to the most lucrative referrals. Exploiting a plausibly exogenous shock to Medicare reimbursement for SNFs, we find that a 1 percent increase in a patient’s expected profitability to a SNF increases the probability that a hospital self-refers that patient (i.e., to a co-owned SNF) by 2.5 percent. We find no evidence that increased self-referrals improve patient outcomes or change post-discharge Medicare spending. Additional analyses show that when integrated SNFs are divested by their parent hospitals, independent rivals are less likely to exit. Together, the results suggest vertical integration in this setting may reduce downstream competition without offsetting benefits to patients or payers.
Occupational Credentials and Job Qualities of Direct Care Workers: Implications for Labor Shortages
Jeounghee Kim
Journal of Labor Research, December 2020, Pages 403-420
Abstract:
Occupational training and credentialing requirements for direct care workers were in place for consumers’ health and safety, but their effects on job qualities and labor shortages in the direct care industry have been controversial. Using a nationally representative sample of psychiatric, nursing, and home health aides, a series of Average Treatment Effect models were analyzed to examine the effects of occupational credentials on various measures of job qualities. The findings revealed that credential-holding was related to higher annual earnings and increased probability of working full-time, year-round, and having access to employer-provided health insurance and retirement savings plans. The positive effects, however, were modest in size and suggested that, given the current wage and benefit levels for direct care workers, training and credential requirements cannot be the key to resolving job quality and labor shortage issues in the direct care industry. Implications of these findings and alternative ways to address the issues were discussed.
Robots and Labor in the Service Sector: Evidence from Nursing Homes
Karen Eggleston, Yong Suk Lee & Toshiaki Iizuka
NBER Working Paper, January 2021
Abstract:
In one of the first studies of service sector robotics using establishment-level data, we study the impact of robots on staffing in Japanese nursing homes, using geographic variation in robot subsidies as an instrumental variable. We find that robot adoption increases employment by augmenting the number of care workers and nurses on flexible employment contracts, and decreases difficulty in staff retention. Robot adoption also reduces the monthly wages of regular nurses, consistent with reduced burden of care. Our findings suggest that the impact of robots may not be detrimental to labor and may remedy challenges posed by rapidly aging populations.