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.     

中医移动医疗App信息服务现状研究

以“七麦数据”网站收录的中医移动医疗App作为研究对象，采用网络调查法和文献分析法，根据“七麦数据”对移动医疗App的分类，结合中医移动医疗App的信息服务内容和特点，将筛选出的中医移动医疗App划分为医疗健康类、中医养生类、知识传播类、全面综合类，并根据“七麦数据”网站中对各类中医移动医疗App的打分及累计下载量筛选出最具代表性的12款中医移动医疗App，从全面性、人性化、安全性、实用性4个一级指标和40个二级指标对其信息服务现状进行评价，指出当前中医移动医疗App信息服务存在的问题并提出建议。     

2010—2020年北京市房山区梅毒防治效果分析

目的了解北京市房山区梅毒规划(2010—2020年)实施期间梅毒防治效果和各考核指标达标情况,为下一步制定有效的梅毒防治策略提供科学依据。方法根据《中国预防与控制梅毒规划(2010—2020年)》(以下简称《梅控规划》)终期评估的要求,通过专网、现场调查、APP答题等方式对涉及的16个指标进行收集,并将16个指标划分为梅毒防治保障措施的落实、防治工作开展和防治效果3个层面进行评估,同时将评估结果与终期评估标准进行比较。采用SPSS 18.0软件进行描述性统计分析。结果2010—2020年北京市房山区共报告梅毒病例3260例,年均报告发病率27.99/10万,年均增长0.08%,男女比例基本持平,以25岁年龄组病例数最多,历年病例均以隐性梅毒为主。保障措施与能力建设方面均达标;防治工作方面:感染梅毒的孕产妇所生婴幼儿接受规范诊疗服务的比例为50.00%,梅毒患者接受规范化治疗的比例86.11%,两指标未达标,其余均达标;防治效果中一期和二期梅毒年报告发病率增长幅度为3.80%,未达标,先天梅毒年报告发病率为9.25/10万活产数达到了《梅控规划》的工作要求。结论2010—2020年北京市房山区梅毒防治工作取得了一定成效,但部分指标仍未达标,应继续加强梅毒防治工作,尤其是需提高规范化诊疗的比例同时控制新发梅毒发病率。     

A systematic review of the methodological quality of economic evaluations in genetic screening and testing for monogenic disorders

PurposeUnderstanding the value of genetic screening and testing for monogenic disorders requires high-quality, methodologically robust economic evaluations. This systematic review sought to assess the methodological quality among such studies and examined opportunities for improvement.MethodsWe searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.ResultsAcross the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.ConclusionWe describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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