Differences in ICSI utilization rates among states with insurance mandates for ART coverage.

BACKGROUND: Assisted reproductive technology (ART) insurance mandates promote more selective utilization of ART clinic resources including intracytoplasmic sperm injection (ICSI). Our objective was to examine whether ICSI utilization differs by state insurance mandates for ART coverage and assess if such a difference is associated with male factor, preimplantation genetic testing (PGT), and/or live birth rates. METHODS: In this retrospective analysis of the Centers for Disease Control (CDC) data from 2018, ART clinics in ART-mandated states (n = 8, AR, CT, HI, IL, MD, MA, NJ, RI) were compared individually to one another and with non-mandated states in aggregate (n = 42) for use of ICSI, male factor, PGT, and live birth rates. ANOVA was used to evaluate differences between ART-mandated states and non-mandated states. Individual ART-mandated states were compared using Welch t-tests. Statistical significance was determined by Bonferroni Correction. RESULTS: There were significant differences in ICSI rates (%, mean ± SD) between MA (53.3 ± 21.3) and HI (90.7 ± 19.6), p = 0.028; IL (86.5 ± 18.7) and MA, p = 0.002; IL and MD (57.2 ± 30.8), p = 0.039; IL and NJ (62.0 ± 26.8), p = 0.007; between non-mandated states in aggregate (79.9 ± 19.9) and MA, p = 0.006, and NJ (62.0 ± 26.8), p = 0.02. Male factor rates of HI (65.8 ± 16.0) were significantly greater compared to CT (18.8 ± 8.7), IL (26.0 ± 11.9), MA (26.9 ± 6.6), MD (29.3 ± 9.9), NJ (30.6 ± 17.9), and non-mandated states in aggregate (29.7 ± 13.7), all p < 0.0001. No significant differences were reported for use of PGT and/or live birth rates across all age groups regardless of mandate status. CONCLUSIONS: ICSI use varied significantly among ART-mandated states while demonstrating no differences in live birth rates. These data suggest that the prevalence of male factor and the presence of a state insurance mandate are not the only factors influencing ICSI use. It is suggested that other non-clinical factors may impact the rate of ICSI utilization in a given state.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.