Effects of state reinsurance programs on health insurance exchange premiums and insurer participation

Objective

The aim of the study was to estimate the effect of the state-based reinsurance programs through the section 1332 State Innovation Waivers on health insurance marketplace premiums and insurer participation.

Data Source

2015 to 2022 Robert Wood Johnson Foundation Health Insurance Exchange Compare Datasets.

Study Design

An event study difference-in-differences (DD) model separately for each year of implementation and a synthetic control method (SCM) are used to estimate year-by-year effects following program implementation.

Data Collection/Extraction Methods

Not applicable.

Principal Findings

Reinsurance programs were associated with a decline in premiums in the first year of implementation by 10%–13%, 5%–19%, and 11%–17% for bronze, silver, and gold plans (p < 0.05). There is a trend of sustained declines especially for states that implemented their programs in 2019 and 2020. The SCM analyses suggest some effect heterogeneity across states but also premium declines across most states. There is no evidence that reinsurance programs affected insurer participation.

Conclusion

State-based reinsurance programs have the potential to improve the affordability of health insurance coverage. However, reinsurance programs do not appear to have had an effect on insurer participation, highlighting the need for policy makers to consider complementary strategies to encourage insurer participation.     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

数据挖掘法探讨周珉教授治疗原发性肝癌用药规律及经验总结

[目的]数据挖掘法分析周珉教授治疗原发性肝癌的中医用药规律,探讨相关病机并进行经验总结。[方法]收集2016年2月—2018年5月周珉教授门诊期间治疗原发性肝癌的方剂,运用"中医传承辅助系统(V2.50)"进行数据挖掘,并结合周珉教授临床经验,进行原发性肝癌病机探讨及用药规律分析。[结果]共收集治疗原发性肝癌方剂176首,涉及中药235种,列出方剂中的高频药物及组合规律。[结论]总结原发性肝癌以"湿热痰毒、气阴两伤"为基础病机,"清热化湿、健脾养阴"治则贯穿原发性肝癌治疗始末,同时,根据疾病不同发展阶段及治疗措施,权衡"扶正"与"祛邪"的主次分配,斟酌应用"攻毒散结""行气化瘀"等治法,以达到改善患者生活质量、延缓疾病进展的目的。     

Reference values of postural balance in preschoolers: Age and gender differences for 4–5 years old Tunisian children

BackgroundPreschool age is a critical stage of postural balance development. Posturography normative data during this age are crucial for surveillance of postural balance and for the timely diagnosis of any dysfunction. The aim of this study was to provide normative data, specific to Tunisian preschoolers, according to age and gender, and to examine gender- and age-related differences in postural balance.Research questionAre there postural balance gender- and age-related differences in Tunisian preschoolers?MethodsThe study included 410 (205 boys and 205 girls) preschool children aged between 4- and 5-year-old selected within a large geographic area of Tunisia. Postural balance was assessed using a stabilometric platform under 4 sensory conditions. The mean center of pressure velocity (CoP-V) parameter was selected for analysis. Differences between gender and age groups were analysed using the Mann–Whitney U. Normative data were expressed in percentiles.ResultsSignificant gender effect was found in conditions where sensory inputs are most challenged with a significantly smaller mean CoP-V in girls compared to boys (p < 0.05). Moreover, 5-year-old children had significantly better postural balance than 4-year-old ones in almost all conditions (p < 0.05).SignificanceThe normative data established for Tunisian preschoolers provide useful comparative data for pediatricians, teachers and coaches for the early evaluation and monitoring of postural balance during this age. Gender and age-related differences should be considered in this population.     

2010年-2018年甲状腺结节相关SCI论文知识图谱及数据可视化分析

目的:分析2010年至2018年甲状腺结节相关SCI论文，并建模进行定性与定量分析。方法:利用文献计量学中的引文分析、共被引分析、数据可视化、聚类分析等方法，使用CiteSpace分析工具，分析来源于Web of ScienceTM核心合集数据库中有关论文的出版情况、国家、机构、作者、知识基础与研究热点。结果:到2018年10月31日截止，研究了4 618篇论文。发表论文数:美国在国家/地区中排名第一，延世大学在研究机构中排名第一，Kwak JY在所有作者中排名第一。从2010年开始，每年发表的论文数量都在稳步增长，有相当多的论文发表在如《新英格兰杂志》、《THYROID》、《JAMA》、《CELL》等高影响因子的期刊上。甲状腺结节的SCI论文参考文献聚类为6类，分别是papillary-like nuclear feature、current statu、radiofrequency ablation、ultrasound elastography、goiter area、data system。代表研究前沿的关键词是meta-analysis、recommendation、thyroid carcinoma、shear wave elastography与bethesda system。结论:本文通过对国际上甲状腺结节的研究基础、研究热点的分析，阐述了甲状腺结节的研究趋势，为我国医学工作者的研究提供参考。     

医院信息化系统在武汉新冠肺炎防疫救治过程中暴露出的问题及其引发的思考

目的针对武汉地区医院信息化系统在新型冠状病毒肺炎(简称新冠肺炎)防疫救治过程中暴露出的问题,提出应对突发公共卫生事件时医院信息化的完善方法。方法充分分析医院信息化系统在新冠肺炎防疫救治中所暴露出的各个医院信息化系统结构不同、各个医院之间无法实施信息共享、门诊病房之间系统相互独立、数据信息更新不及时等九大问题,并提出医院信息化基础布局的建立、区域链及大数据临床中的应用等多项措施。结果医院信息化能力的提高有助于降低医护人员工作强度,提高患者的安全。结论新冠肺炎疫情的发生给我国医院信息化系统带来了严峻的挑战,但同时也给我们带来了更大的机遇。我国在未来针对突发公共卫生事件时,医院信息化将为医护人员、患者提供更有利的支持。     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.