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.     

人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

基于文本挖掘和生物医学数据库的新型冠状病毒肺炎药物发现

目的 基于文本挖掘技术和生物医学数据库对新型冠状病毒肺炎（COVID-19）相关文献进行数据挖掘分析，探究COVID-19及其主要症状发热、咳嗽、呼吸障碍相关基因靶点，筛选潜在有效的化学药和中药。方法 使用GenCLiP 3网站获取COVID-19和其主要症状咳嗽、发热、呼吸障碍共4个关键词的共有靶点，在METASCAPE数据库中对其进行基因本体（GO）和通路富集分析，再利用String数据库和Cytoscape软件构建共有靶点的蛋白质相互作用网络，筛选获得核心基因，运用DGIdb数据库、SymMap数据库针对核心基因进行中西医治疗药物预测。结果 获得COVID-19及其主要症状共有基因靶点28个，其中有IL2、IL1B、CCL2等核心基因16个，使用DGIdb数据库筛选获得与16个关键靶点相互作用的化学药包括沙利度胺、来氟米特、环孢素等28种，中药包括虎杖、黄芪、芦荟等70味。结论 COVID-19及其主要症状的病理机制可能和CD4、KNG1、VEGFA等28个共有基因相关，可能通过介导TNF、IL-17等信号通路参与COVID-19病理过程。潜在有效药物可能通过作用相关靶点通路起到治疗COVID-19的作用。     

基于真实世界数据探讨中医药治疗糖尿病肾病用药规律的研究＊

目的 基于临床信息系统分析总结真实世界的数据，采用数据挖掘的方法探讨中医药治疗糖尿病肾病的用药规律。方法 收集2018年1月-2020年12月上海中医药大学附属曙光医院宝山分院健康信息系统确诊的糖尿病肾病的门诊或住院患者的诊疗信息，建立Excel数据库，采用Excel 2010软件统计高频药物的四气、五味、归经及功效；使用SPSS Modeler 18.0软件中的Apriori算法分析关联规则，采用web节点建构药对关联网状图；运用SPSS 25统计软件进行因子分析。结果 本研究最终纳入477例接受中药饮片治疗的DKD患者，在1203条方剂信息中，涉及中药462种；使用频数排名前5位的中药分别是黄芪、黄精、石斛、山茱萸、麦冬；使用频数前5类的中药类别分别是补气药、补阴药、清热燥湿药、活血调经药、息风止痉药；在30味高频药物中，苦、甘、辛药味最为常见；药性寒、温数量接近；归脾经、肝经、肺经、肾经居多；关联规则提示，药物组合中置信度最高的组合为地龙-当归-僵蚕，因子分析共得到5个有效因子，累积贡献率为47.33%。结论 中医药治疗DKD在补益气血阴阳的同时，兼顾对瘀血、湿邪和痰饮的治疗，结合证型，可考虑使用黄芪、黄精、石斛、山茱萸、麦冬、金蝉花等药物的使用，清热燥湿药物如黄连、黄芩、黄柏可适当加入，为使邪有去处，大黄、车前子或可增添疗效。     

Research hotspots and effectiveness of repetitive transcranial magnetic stimulation in stroke rehabilitation

Repetitive transcranial magnetic stimulation, as a relatively new type of rehabilitation treatment, is a painless and non-invasive method for altering brain excitability. Repetitive transcranial magnetic stimulation has been widely used in the neurorehabilitation of stroke patients. Here, we used CiteSpace software to visually analyze 315 studies concerning repetitive transcranial magnetic stimulation for stroke rehabilitation from 1999 to 2019, indexed by Web of Science, to clarify the research hotspots in different periods and characterize the gradual process of discovery in this field. We found that four main points were generally accepted:(1) repetitive transcranial magnetic stimulation has a positive effect on motor function recovery in patients with subcortical stroke;(2) it may be more advantageous for stroke patients to receive low-frequency repetitive transcranial magnetic stimulation in the unaffected hemispheres than to receive high-frequency repetitive transcranial magnetic stimulation in affected hemisphere;(3) low-frequency repetitive transcranial magnetic stimulation has become a potential therapeutic tool for patients with non-fluent aphasia after chronic stroke for neurological rehabilitation and language recovery; and(4) there are some limitations to these classic clinical studies, such as small sample size and low test efficiency. Our assessment indicates that prospective, multi-center, large-sample, randomized controlled clinical trials are still needed to further verify the effectiveness of various repetitive transcranial magnetic stimulation programs for the rehabilitation of stroke patients.     

人工智能在耳鼻咽喉头颈外科的运用及展望

通过对大数据深度学习,近年人工智能技术已逐渐渗透到医学各个领域,实现一定程度应用。虽然耳鼻咽喉头颈外科专业近几年发表相关文献数量急剧增长,但大部分临床医生对于人工智能的研究还比较陌生。介绍人工智能的基本原理,列举、分析其在耳鼻喉科的主要研究情况,探讨目前人工智能技术实际应用的局限,展望未来人工智能技术在耳鼻喉科可能的应用。     

基于数据挖掘的国医大师刘祖贻治疗脑梗死恢复期方药规律研究

目的挖掘分析国医大师刘祖贻治疗脑梗死恢复期的处方用药规律,总结其学术思想。方法收集整理刘祖贻治疗脑梗死恢复期的病案处方,录入中医传承辅助平台软件中构建数据库,再使用该软件相关功能进行数据挖掘,分析处方中的组方用药规律。结果①共得处方147首,涉及药物179味,累计使用频次2 164次。②使用频次在20次及以上的药物,共有22味,其中前10味高频药物为黄芪、丹参、山楂、葛根、枸杞子、地龙、制何首乌、川芎、石菖蒲、淫羊藿。③黄芪用量从15 g至120 g均可见,最常用剂量为30 g,常用剂量范围主要集中于30～60 g。④药物功效分类排名靠前的为补虚药、活血化瘀药、平肝息风药等。⑤高频药组前5位为丹参-黄芪、黄芪-山楂、葛根-黄芪、葛根-丹参、丹参-山楂;关联度较高的药组有地龙-黄芪、葛根-地龙-黄芪、丹参-地龙-黄芪等。⑥通过聚类分析算法提取出核心组合12个,进而演化出潜在新方6首,如"白芍、威灵仙、桂枝、鸡血藤、白芥子""全蝎、菊花、蜈蚣、刺蒺藜"等。结论国医大师刘祖贻治疗脑梗死恢复期的用药以益气温阳、填精益髓、活血通络、息风化痰为主,重用黄芪,方以自拟芪仙通络方加减,体现出"气阳主用""脑髓阳生阴长"等学术思想。