Essays on health care demand and spending

This 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

Short- and Long-Term Observations of Fracture Permeability in Granite by Flow-Through Tests and Comparative Observation by X-Ray CT

Having a grasp of the variation in the fracture contact area is a kernel in the understanding of the permeability evolution of fractured rocks. However, the number of studies that focus on measuring the long-term variation in the fracture contact area under different conditions is insufficient. In this study, a series of short- and long-term permeability tests under coupled conditions is performed to check the performance of permeability. The results reveal that the permeability measured in the short-term tests shows reversible behavior and a dependence on the applied confining pressures and temperature. In contrast, the permeability in the long-term tests displays irreversible behavior and an irregular change under the constant confining pressure. In order to verify the evolution of permeability, microfocus X-ray computed tomography (CT) is developed to observe the changes in the internal fracture structure under the same conditions as those in long-term permeability tests by assembling a triaxial cell with heating capability. The fracture aperture and the fracture contact-area ratio are calculated by a CT image analysis technique. The image analysis results show that the estimated aperture is seen to decrease with an increase in the confining pressure and to also decrease with time under a constant confining pressure. Moreover, the increase in the fracture contact area under the constant confining pressure observed by X-ray CT is confirmed. This also corresponds to a decrease in permeability in long-term tests. The hydraulic aperture calculated from the permeability tests and that evaluated from the CT observation have a similar decreasing trend. Therefore, the CT observation can better capture the evolution of the internal fracture contact area. These experiments underscore the importance of mechanical compaction and/or mineral dissolution at contacts in determining the rates and the magnitude of permeability evolution within rock fractures

Room-temperature nonequilibrium growth of controllable ZnO nanorod arrays

In this study, controllable ZnO nanorod arrays were successfully synthesized on Si substrate at room temperature (approx. 25°C). The formation of controllable ZnO nanorod arrays has been investigated using growth media with different concentrations and molar ratios of Zn(NO3)2 to NaOH. Under such a nonequilibrium growth condition, the density and dimension of ZnO nanorod arrays were successfully adjusted through controlling the supersaturation degree, i.e., volume of growth medium. It was found that the wettability and electrowetting behaviors of ZnO nanorod arrays could be tuned through variations of nanorods density and length. Moreover, its field emission property was also optimized by changing the nanorods density and dimension

LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202

Diagnostic category prevalence in 3 classification systems across the transition to the International Classification of Diseases, Tenth Revision, Clinical Modification

IMPORTANCE: On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. OBJECTIVE: To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants: This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. EXPOSURES: US implementation of ICD-10-CM. Main Outcomes and Measures: Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. Results: The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. CONCLUSIONS AND RELEVANCE: These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.R01 HS026485 - AHRQ HHS; UL1 TR000161 - NCATS NIH HHShttp://doi.org/10.1001/jamanetworkopen.2020.2280Published versio

Development and assessment of a new framework for disease surveillance, prediction, and risk adjustment: the diagnostic items classification system

IMPORTANCE: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: To develop an ICD-10-CM-based classification framework for predicting diverse health care payment, quality, and performance outcomes. DESIGN SETTING AND PARTICIPANTS: Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using R 2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. MAIN OUTCOMES AND MEASURES: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. RESULTS: A total of 65 901 460 person-years were split into 90$5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R 2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. CONCLUSIONS: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.Published versio

Enhanced Luminescence of Eu-Doped TiO2Nanodots

Monodisperse and spherical Eu-doped TiO2nanodots were prepared on substrate by phase-separation-induced self-assembly. The average diameters of the nanodots can be 50 and 70 nm by changing the preparation condition. The calcined nanodots consist of an amorphous TiO2matrix with Eu3+ions highly dispersed in it. The Eu-doped TiO2nanodots exhibit intense luminescence due to effective energy transfer from amorphous TiO2matrix to Eu3+ions. The luminescence intensity is about 12.5 times of that of Eu-doped TiO2film and the luminescence lifetime can be as long as 960 μs

CRL4 antagonizes SCFFbxo7-mediated turnover of cereblon and BK channel to regulate learning and memory

Intellectual disability (ID), one of the most common human developmental disorders, can be caused by genetic mutations in Cullin 4B (Cul4B) and cereblon (CRBN). CRBN is a substrate receptor for the Cul4A/B-DDB1 ubiquitin ligase (CRL4) and can target voltage- and calcium-activated BK channel for ER retention. Here we report that ID-associated CRL4CRBNmutations abolish the interaction of the BK channel with CRL4, and redirect the BK channel to the SCFFbxo7ubiquitin ligase for proteasomal degradation. Glioma cell lines harbouring CRBN mutations record density-dependent decrease of BK currents, which can be restored by blocking Cullin ubiquitin ligase activity. Importantly, mice with neuron-specific deletion of DDB1 or CRBN express reduced BK protein levels in the brain, and exhibit similar impairment in learning and memory, a deficit that can be partially rescued by activating the BK channel. Our results reveal a competitive targeting of the BK channel by two ubiquitin ligases to achieve exquisite control of its stability, and support changes in neuronal excitability as a common pathogenic mechanism underlying CRL4CRBN–associated ID

様々な拘束圧および温度条件下での岩盤不連続面構造と透水性の長期観測

京都大学0048新制・課程博士博士(工学)甲第22420号工博第4681号新制||工||1731(附属図書館)京都大学大学院工学研究科都市社会工学専攻(主査)教授 岸田 潔, 教授 三村 衛, 准教授 肥後 陽介学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA