Browsing by Subject "Econometrics"

Now showing 1 - 7 of 7
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    Designing Public Health Supply Chains: Towards Improving Health Commodity Availability in Developing Countries
    (2020-07) Karimi, Amir
    In developing countries, significant resource constraints (e.g., funding and human resource limitations) hamper the effective delivery of health commodities from upstream suppliers to the last-mile, leading to supply chain failures such as “stock-outs.” For individuals who are deprived access to basic health commodities due to stock-outs, the consequences can be dire. For example, without reliable access to contraceptives, women may suffer unintended pregnancies, imposing economic and psychological burden, and unsafe abortions that often cause death. While the prevalence of health commodity stock-outs and the consequent repercussions for clients in developing countries are well-documented, there is a paucity of rigorous empirical research into the factors that drive such stock-outs. Focusing on this context, this dissertation aims to (i) empirically evaluate and uncover the factors that contribute to health commodity stock-outs in developing countries by leveraging field data collected from health facilities and using a combination of rigorous econometric and predictive modeling techniques; (ii) generate actionable insights that public health organizations, governments, and donors can use to mitigate the risk of stock-outs in developing countries. Toward addressing these objectives, the first dissertation study focuses on the effect of factors at the upstream level of the public health supply chain and explores the impact of the distribution model (i.e., pull vs. push) on the likelihood of stock-outs. At the downstream level, the second study investigates the role of practices that health facilities can use to mitigate the likelihood of stock-outs. The third dissertation study also focuses on the downstream level of the public health supply chain by examining how the provision of training to frontline healthcare providers can help reduce the likelihood of stock-outs. Taken together, the three dissertation studies serve as the first systematic attempt in the literature to conduct a rigorous empirical evaluation of the factors driving the stock-outs of health commodities in developing countries
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    The impact of health information technology on demand for hospital inpatient services.
    (2011-05) Barrette, Eric G.
    Health Information Technology (IT) research has been focused on health IT adoption and the supply-side effects such as the quality and efficiency of health care. This demand analysis complements the existing supply-side analyses, allowing for a more complete understanding of the impact of health IT on health care markets. The impact of health IT on demand for hospital inpatient services is estimated using Medicare beneficiary inpatient hospital admissions as a measure of patient choices. Two complementary discrete choice models are used to model patients’ choices with the underlying assumption that patients are making a utility maximizing decision. Berry’s specification of a linear market share model provides mean effects of health IT on hospital market share at a national level. A patient-level conditional logit model which includes interactions of patient characteristics and health IT is also estimated for a subset of hospitals and diagnoses. Hospital inpatient admission data from 1999-2006 was obtained from the MedPAR file. The data for this study includes 100% of Medicare fee-for-service (FFS) beneficiaries over age 65. Hospital characteristics were obtained from American Hospital Association annual hospital survey. Hospital health IT system information is from the HIMSS/Dorenfest Integrated HEALTH CARE DELIVERY SYSTEM PLUS (IHDS+) DATABASE™. The impact of three technologies is evaluated: 1) Picture Archive and Communication System (PACS), 2) Computerized Physician Order Entry (CPOE) and 3) Electronic Medical Records (EMR). Combinations of these technologies are also studies. A panel data structure including hospital fixed effects is used to identify the impact of health IT on demand. The hospital fixed effects are included to control for endogeneity in hospitals’ adoption of health IT and patient choices. The health IT variable and interaction terms were jointly significant in market level and individual choice models for CPOE but did not result in significant impacts on hospital demand. Patient-level conditional logit model results are used to calculate consumer surplus welfare measures for hospitals with both EMR and CPOE systems. In 2006 approximately 10% of the analysis sample of hospitals had adopted EMR and CPOE. The change from no adoption to the 2006 adoption level produces a $228,000 increase in consumer surplus ($100/patient) for joint replacement patients and a $139,000 ($78/patient) increase for heart failure patients.
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    Mitigating Adverse Outcomes in Health and Wellness with Data Analytics: Investigation of Medical Device Recalls and Digital Exercise Program Churns
    (2024-03) Zhu, Yi
    The adverse outcomes in health and wellness, such as the failure of medical devices and the prevalence of human sedentary lifestyles, pose significant risks to patient safety, public health, and the financial stability of healthcare institutions and firms. To enhance proactive measures against these adverse outcomes and mitigate their potential harm, this dissertation employs empirical methodologies to investigate two types of adverse outcomes in health and wellness. Moreover, it also develops algorithmic approaches for predicting these outcomes. Specifically, this dissertation comprises three essays: the first essay addresses medical device recalls by proposing a design-science-based framework for predicting such recalls. This framework demonstrates superior performance compared to traditional predictive models and offers various insights for improving medical device safety regulations through different prediction problem setups. The second and third essays delve into human physical activity behaviors as recorded by digital exercise platforms. They explore the social contagion effect of churn digital exercise programs, examine the variations in the contagion effects based on individuals’ characteristics, and interpret these effects using complex contagion theory. An advanced deep learning approach is introduced to forecast exercise patterns indicative of potential digital activity program churn, leveraging the relationships between different exercise types and measures. The effectiveness of this approach is validated through experiments with both simulated and actual data, showcasing its advantages over traditional models. This dissertation provides substantial practical implications and contributes to the advancement of knowledge across the domains of information systems, healthcare, and wellness. Insights from the first essay provide critical policy recommendations for enhancing medical device safety, augmenting the understanding of predictive health information system design, and fostering data-driven methodologies for early detection of product recalls. The findings from the second and third essays emphasize the significance of leveraging social strategies to reduce churn in digital exercise programs and highlight the advantages of recommending users personalized exercise programs based on more accurate predictions of exercisers’ activities. These tailored programs aim to boost exercise adherence, thereby enhancing the overall personal wellness of exercisers. Overall, this dissertation enhances our understanding and prediction of medical device recalls and digital exercise program churn, elevating personal health and wellness outcomes.
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    Organizational, Operational, and Behavioral Causes of Product Recalls
    (2015-06) Ball, George
    Research germane to product recalls and their causes is limited. With recall rates rising in many industries, it is timely and pertinent to comprehensively investigate recalls. The focus of my dissertation is on product recalls and their causes, with the objective of recall understanding and prevention. I study three important phases in the product recall process at multiple organizational levels in the high-risk medical device industry: plant-level causes, recall decision-making, and causes and effects of firm and regulator responsiveness within the recall event. First, I study the relationship between Food and Drug Administration (FDA) plant inspections and future recalls. Using a 7-year panel dataset and recurrent event Cox proportional hazard and propensity score matching models, I find that adverse plant inspection outcomes serve as warning signs for future recalls. I incorporate FDA investigator experience to identify reasons for, and effects of, investigator complacency in repeated plant inspections. Repeated visits to the same site by an inspector increases the recall risk and also reduces the predictability of inspection outcomes as a leading indicator of future recalls. FDA investigator rotation is shown to be an effective solution to compensate for investigator complacency. Second, I explore behavioral factors thainfluence managers' decision to recall. Recall guidance provided by the FDA allows for broad managerial interpretation so it is crucial to study which factors influence managers to choose to recall. Using actual industry managers with recall experience in a controlled experiment, I find that product defects which are undetectable to physician customers pre-use are more likely to lead to a recall than detectable ones. When managers have a deeper understanding about the root cause of a defect, they are also more likely to recall. I also study individual dispositional factors unique to each manager, and surprisingly find that the level of cognitive reflection, as measured by the Cognitive Reflection Test (CRT), is the most important predictor of a recall decision in the experiment. Finally, I study firm and regulator recall responsiveness. Responsiveness is critical in this domain: the longer a faulty medical device remains on the marketplace, the more consumers are at risk. Using an 11-year panel dataset with time-stamps for over 4,000 recalls, and multiple hazard and fixed effects panel models, I find that higher recall severity leads to slower firm and faster FDA responsiveness. However, taking longer to close a recall reduces a firm's future recalls, and this may be attributed to learning mechanisms. FDA response times also reduce future recalls.
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    Three Essays in Applied Microeconomics
    (2018-03) Jatusripitak, Napat
    This dissertation consists of three independent chapters that study how to improve public policies and reduce the level of social injustice through the lens of microeconomics and the innovative use of new data sets. In the first chapter, I test the impact of neighborhood heterogeneity on the private contribution of local public goods. Using a panel data set containing over two million non-emergency service requests and detailed census-tract level data on socioeconomic characteristics from the American Community Survey, I find that, contrary to the prevailing view in the literature, racial and linguistic heterogeneity have little to no negative effect on private voluntary contributions to local public goods. Income inequality, on the other hand, reduces private contributions by a significant margin. In the second chapter, my coauthors and I examine how job transfer rules and preferences affect labor market efficiency and access to quality teachers. To do so, we recover teacher and school preferences using data from Minneapolis Public Schools’ web-based internal teacher labor market. Overall, we find that the average teacher prefers schools serving already-advantaged students and the average school prefers applicants who are more effective, hold an advanced degree, and not in their early-career. These preferences help explain why we observe the troubling sorting patterns among teachers and suggest that further liberalizing the teacher labor market may exacerbate the inequitable distribution of quality teachers. Finally, the third chapter evaluates a hidden social cost of air pollution beyond hospital admissions and premature deaths: student achievement. Given the strength of evidence linking academic performance to long-term life outcomes and the fact that disadvantaged and marginalized communities tend to get more exposure to air pollution, this additional cost should be identified and quantified correctly. Using an exogenous source of variation in the levels of air pollution from the closure of an airport terminal, I find that the closure led to a roughly 2 percent of a standard deviation increase in high-stakes test scores.
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    Three Essays In Development Economics
    (2020-05) Bloem, Jeffrey
    Although the world has witnessed a remarkable reduction in global poverty, vexing challenges persist. Whereas globally poor individuals of the previous generation overwhelmingly lived in poor countries, today the global poor are largely split between two groups: (i) poor, fragile, and conflict-riddled countries or (ii) fast-growing but increasingly economically unequal countries. Answers to questions about what can be done to promote inclusive economic development and reduce poverty will differ critically across these contexts. My research aims to make valuable contributions toward answering important contextualized questions by evaluating policies, testing new theories, and credibly using quantitative data.
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    What raw statistics have the greatest effect on wRC+ in Major League Baseball in 2017?
    (2018) Sanford, Gavin D
    Major League Baseball has different statistics for hitters, fielders, and pitchers. The game has followed the same rules for over a century and this has allowed for statistical comparison. As technology grows, so does the game of baseball as there is more areas of the game that people can monitor and track including pitch speed, spin rates, launch angle, exit velocity and directional break. The website QOPBaseball.com is a newer website that attempts to correctly track every pitches horizontal and vertical break and grade it based on these factors (Wilson, 2016). Fangraphs has statistics on the direction players hit the ball and what percentage of the time. The game of baseball is all about quantifying players and being able give a value to their contributions. Sabermetrics have given us the ability to do this in far more depth. Weighted Runs Created Plus (wRC+) is an offensive stat which is attempted to quantify a player’s total offensive value (wRC and wRC+, Fangraphs). It is Era and park adjusted, meaning that the park and year can be compared without altering the statistic further. In this paper, we look at what 2018 statistics have the greatest effect on an individual player’s wRC+.