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Essays on health economics
Full text linkThis dissertation is composed of three essays studying agents' behavior in the health care sector - insurers, patients, and providers - facing different regulatory settings.
The first essay studies how vertical integration between pharmacies and
insurers affects Medicare Part D premiums. I propose a model of insurer-pharmacy bargaining, which suggests that exposure to a vertically integrated firm should increase other insurers' premiums while lowering the integrated insurer's. I test this using plan-level data from 2006 to 2017, exploiting CVS's acquisition of Target pharmacies. Exposure to this integration increases non-CVS premiums as expected; however, CVS premiums are unchanged. I estimate a demand model of plan choice to show that consumers value a large network and having CVS pharmacies in their plan's network.
The second essay (joint with Randall P. Ellis and Wenjia Zhu), estimates within-year price elasticities of demand for detailed health care services. We use an instrumental variable strategy, in which individual monthly cost shares are instrumented by employer-year-plan-month average cost shares. We show that using backward myopic prices gives more plausible results than using forward myopia. Using 171 million person-months from 73 employers between 2008-2014, we estimate an overall demand elasticity by backward myopic consumers of -0.44, with high demand elasticities for pharmaceuticals, specialists visits, MRIs and mental health/substance abuse, and lower for prevention and emergency departments.
The third essay (joint with Luis Filipe), evaluates how doctors in an emergency department react to the number of patients waiting for treatment. Our outcomes reflect the time spent with the patient, the intensity of treatment and discharge destination. Using visit-level data in one Lisbon-area hospital, we use a fixed effects model to exploit variation in the queue size, while addressing endogeneity using the number of arrivals to the hospital as an instrumental variable. Results show that doctors discharge patients more rapidly as queues increase, and this effect is stronger for patients that do not have life-threatening conditions. We also find that the intensity of diagnosis/treatment procedures decrease when patients face longer queues, driven by the extensive margin. Finally, doctors are less likely to admit patients to inpatient care
Essays on health care demand and spending
Full text linkThis dissertation examines various aspects of U.S. health care markets using the claim and enrollment files from a large set of employment-based insurance plans containing detailed records of service utilizations by individual consumers and their corresponding costs.
The first chapter, joint with Xiaoxi Zhao, studies the impact of two different types of cost sharing: coinsurance, in which the consumer out-of-pocket cost is calculated as a fraction of total fees, and copayments, in which the consumer cost is a fixed dollar amount regardless of the fee level charged. The paper’s focus is on how these two types of cost sharing affects consumer demand and health care spending given estimated price elasticities for categories of health care services. It is well documented in the literature that health care consumption decreases with consumer out-of-pocket costs and yet remarkably little is known about whether coinsurance and copayments affect consumer demand differently. Using a dataset in which we have no information about the plan policies, we first infer the type of the observed consumer out-of-pocket costs, i.e., a coinsurance or a copayment, for a given insurance policy and a given type of service from the claims and enrollment files. We then estimate the price elasticity for this given type of service paired with the inferred type of out-of-pocket costs using a set of novel instruments and fixed effect regressions. The results show that consumption decreases with both coinsurance and copayments. Specifically, consumer demand is found to be more elastic by 0.2 to 0.5 percentage points when coinsurance is used for cost sharing instead of copayments. Our model is among the first to quantify in monetary terms the savings generated by different types of cost sharing that are widely adopted in insurance policies.
The second chapter, joint with Randall P. Ellis, Heather E. Hsu, Tzu-Chun Kuo, Bruno Martins, Jeffery J. Siracuse, Ying Liu and Arlene S. Ash, uses piecewise linear regression models on monthly time series data to assess changes in diagnostic category prevalence associated with the transition from International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the Tenth Revision (ICD-10-CM) in October 2015. Private insurance claims from 2010 to 2017 are mapped into three widely used diagnostic categories: the Department of Health and Human Services Hierarchical Condition Categories (HHS-HCC); the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS); and the World Health Organization’s disease chapters (WHO). The analytic sample contains information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnosis. In all three classification systems, the ICD-10-CM implementation is associated with statistically significant changes in monthly prevalence among 58–59% of diagnostic categories. This interrupted time series analysis and cross-sectional study finds increases or decreases of 20% or more associated with the ICD-10-CM transition for nearly 1 in 6 (16%) diagnostic categories in 2 of 3 influential diagnostic classification systems, suggesting that diagnostic classification systems developed with ICD-9-CM data may need to be refined for use with ICD-10-CM data for disease surveillance, performance assessment, or risk-adjusted payment.
The third chapter, joint with Corinne Andriola, examines the performance of three risk adjustment frameworks at predicting the health care spending by people with rare diseases, i.e., diseases that affect fewer than 0.05% of the population. Three risk adjustment models are considered: the Health and Human Services Hierarchical Condition Categories (HHS-HCC), the Agency for Healthcare Research and Quality Clinical Classification System Refined (CCSR), and the Diagnostic Items (DXIs) introduced in Ellis et al, (2021). Due to their low prevalence rate, rare conditions are largely excluded from HHS-HCC and CCSR risk adjustment formulas, resulting in health insurance plans and providers having incentives to undertreat rare disease patients. The more informative and flexible DXIs model, however, is likely to give more attention to rare diseases. To evaluate their predictive power, the three risk-adjustment models are estimated on the same development sample (N=59.2 million) using both OLS and stepwise regressions, and then validated on a validation sample (N=6.6 million) to test for overfitting. The regression results show that, compared to other disease classification systems, the DXIs lower the average residual spending for people with rare diseases by at least 25% across all the regression models considered
Essays on incentive design and healthcare delivery reform in health economics
Full text linkThis dissertation considers mechanisms to improve healthcare delivery andreduce healthcare costs, both through the incentives created by risk adjustment methodology and the study of an intervention to improve care for children with medical complexity.
The first chapter, joint with Randall Ellis, Jeffrey Siracuse, Allan Walkey, Karen Lasser, Brian Jacobson, Alex Hoagland, Ying Liu, Chenlu Song, Tzu-Chun Kuo, and Arlene Ash, characterizes a novel diagnosis classification system, the Diagnostic Items Classification (DXI) System. The system leverages the detail embedded in the International Classification of Diseases, Tenth Revision, Clinical Modification. The system performs better than benchmark risk adjustment models across a range of measures of model fit, especially for individuals with rare diseases. Addressing systemic underpayment for individuals with rare diseases has potential implications for the quality of care they receive.
The second chapter, joint with Randall Ellis, Jeffrey Siracuse, Alexander Hoagland, Heather Hsu, Allan Walkey, Karen Lasser, Tzu-Chun Kuo, and Arlene Ash proposes a regression algorithm for variable selection in risk adjustment models. Risk adjustment systems are vulnerable to gameability, particularly in the form of upv coding. This work develops a computationally feasible, transparent, and clinically informed approach to undermine gaming incentives while maintaining predictive power.
The third chapter evaluates a project to improve care for children with medical complexity (CMC): The Collaborative Improvement and Innovation Network to Advance Care for Children with Medical Complexity (CoIIN). While CMC represent a small share of the population of children, they are associated with a disproportionately large share of healthcare spending, and their families face significant burdens. I conduct a claims-based evaluation of the effect of the programs at two sites on a range of utilization outcomes, including inpatient (IP) admissions and length of stay (LOS), emergency department (ED) visits, outpatient (OP) provider encounters, and days with OP provider encounters. I do not find robust statistically significant changes in utilization from the interventions. This study lays the foundation for future work, and provides informative descriptive statistics to inform future study design
Essays on insurance design and the demand for medical care
Full text linkThis dissertation is composed of three essays that study the interplay of consumers, insurers, and providers in the health care market. These chapters address the
role of insurance plan design in shaping the incentives of market participants, and
how this translates into economic outcomes. The results presented here shed light
on how consumers respond to health care prices, and how this factors into equilibrium
pricing and welfare.
The first essay studies the impact of tiered cost sharing in health plans. Consumers in tiered plans face variation in out-of-pocket prices across provider tiers, creating an incentive to use low-cost facilities. I use detailed administrative claims data from New Hampshire, a state where these plans
have become increasingly common, to study both the demand-side and supply-side
effects. I find strong evidence that the tiered programs lead to a reduction in per-episode spending on an array of lab, endoscopic, and arthroscopic medical procedures. Expenditure reductions are driven in part by an increase in the use of low-cost providers, and in part by a decrease in prices overall.
The second essay develops a structural model of the health care market to explore the equilibrium implications of tiered cost sharing. I first employ a discrete choice model to estimate the demand for providers, exploiting variation in out-of-pocket costs across providers, plans, and time. I next estimate a model of bilateral bargaining between insurers and providers, which incorporates variation in benefit design across plans. Counterfactual simulations imply that tiered plans are more effective than other popular plans in steering consumers toward low-cost facilities.
The third essay provides new estimates of the price elasticity of demand on the
intensive margin for a suite of common medical services. I develop an instrumental variable strategy that exploits consumer inertia and average plan characteristics to account for endogenous out-of-pocket prices. I employ this method in both linear and nonlinear settings to ascertain the extent to which consumers respond to variation in out-of-pocket prices when choosing a health care provider. I find that elasticities on this margin are relatively modest, and exhibit heterogeneity across services
Essays on information and innovation in health economics
Full text linkThis dissertation consists of three essays that study the role of information acquisition and processing in health decision-making. Each chapter underscores the ways in which new information shapes the choices of health providers and consumers. Understanding these responses sheds light on critical health policy problems, including the potential overuse of low-value health services, gaps between medical evidence and practice, and inequitable access to high-value health services.
The first essay studies the role of a consumer’s family network in the formation of their risk perceptions. I assess whether people correctly interpret new risk information communicated through household health events and analyze how these
responses impact household welfare. Individuals respond to new diagnoses in ways most consistent with individual reevaluations of health risk rather than other possible explanations. To assess welfare implications, I estimate a structural model of health choices in which individuals learn about risk after health events reveal information. I find that consumers over-respond to recent, salient health events by over-weighting their risks ex-post. This leads to individual and social welfare losses, and suggests that aiding consumers in interpreting health risk information should be an important
aim of health literacy policies.
The second essay explores how health providers respond to information about innovations in mental health treatments, paying particular attention to the heterogeneous adoption costs of different practices. I compare the impact of continuing
education on takeup across innovations that incur learning costs (psychotherapy) and those that do not (psychopharmacology). I use a novel extension of an estimator proposed by Calvi et al. (2021) to estimate a dynamic treatment effect in the presence of classification error. Therapists respond more to education when learning costs are
negligent, being about three percentage points more likely to write new prescriptions following a conference.
The third essay assesses the tradeoff between adopting novel medical technologies and achieving health equity. I study the adoption of transcatheter valve replacement surgeries in Medicare patients; these surgeries disrupted the supply of medical interventions from cardiothoracic surgeons to interventional cardiologists. This transition led providers to adjust practice styles along two margins: medium-risk patients became more likely to receive surgery, and low-risk patients received fewer medical interventions overall. I incorporate these findings into a model of physician decision-making, showing that both the expansion of high-intensity intervention and the crowd-out of low-intensity treatment can be rationalized by the presence of technological spillovers. The model further highlights that crowd-out may be inequitably
distributed across the patient population when treatment appropriateness is not directly observed. I validate these predictions in my setting, showing that technology adoption resulted in disproportionately high barriers to care for low-income patients
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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