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.     

Intermediate-term outcomes of combined phacoemulsification and Aurolab aqueous drainage implant in eyes with refractory glaucoma and coexistent cataract

International Ophthalmology - To investigate the efficacy and safety of non-valved Aurolab aqueous drainage implant (AADI) surgery combined with phacoemulsification in eyes with refractory glaucoma...     

Reading the unique DNA methylation landscape of the brain: Non-CpG methylation,hydroxymethylation, and MeCP2

DNA methylation at CpG dinucleotides is an important epigenetic regulator common to virtually all mammalian cell types, but recent evidence indicates that during early postnatal development neuronal genomes also accumulate uniquely high levels of two alternative forms of methylation, non-CpG methylation and hydroxymethylation. Here we discuss the distinct landscape of DNA methylation in neurons, how it is established, and how it might affect the binding and function of protein readers of DNA methylation. We review studies of one critical reader of DNA methylation in the brain, the Rett syndrome protein methyl CpG-binding protein 2 (MeCP2), and discuss how differential binding affinity of MeCP2 for non-CpG and hydroxymethylation may affect the function of this methyl-binding protein in the nervous system.     

The impact of low-dose versus high-dose antibiotic prophylaxis regimens on surgical site infection rates after cesarean delivery

Archives of Gynecology and Obstetrics - The objective of this study was to determine if high-dose antibiotic prophylaxis with cefazolin decreases the risk of surgical site infection (SSI) after a...     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.