The Fertility Education Initiative: responding to the need for enhanced fertility and reproductive health awareness amongst young people in the United Kingdom

The need for fertility education arises from changing patterns of family formation in recent times. Young people feel unprepared for how best to plan their career and family and have little idea of the various factors that may influence their fertility later in their life. Research shows young people would like to know more and need the information to be conveyed in a way that is engaging and helps them to integrate it at their current life stage. The Fertility Education Initiative (FEI) was founded in 2016 to address the need for improved fertility and reproductive health awareness and ensure young people are equipped with the relevant information to meet their reproductive and family building needs. This paper serves as a historical record of the genesis of the FEI and its impact to date

Effect of real-time computer-aided polyp detection system (ENDO-AID) on adenoma detection in endoscopists-in-training: a randomized trial

Background The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown. Methods We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID(OIP-1), Olympus Co., Japan) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years’ experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate-level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate. Results 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in CADe than control group (57.5% vs 44.5%, adjusted relative risk 1.41, 95%CI 1.17-1.72, p<0.001). The ADRs for <5mm (40.4% vs 25.0%) and 5-10mm adenomas (36.8% vs 29.2%) were higher in CADe group. The ADRs were higher in CADe group in both right (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in CADe group among beginners (60.0% vs 41.9%) and intermediate-level endoscopists (56.5% vs 45.5%). Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate were higher in CADe group (52.1% vs 35.0%). Conclusions Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov: NCT04838951

Dynamic Causality Analysis of COVID-19 Pandemic Risk and Oil Market Changes

Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis, including a preparedness risk score and a severity risk score, Granger-cause changes in oil future prices. In our empirical analysis, we observe 49% to 60% of days in 2020 to 2021 during which the pandemic scores significantly affected oil futures. We also find an asymmetric lead–lag relationship, indicating that there is a tendency for oil futures to move significantly when the pandemic is less severe but not when it is more severe. This study adopts preparedness risk score and severity risk score as proxy variables to measure the impact of the COVID-19 pandemic risk on oil market. The asymmetric lead–lag behavior between pandemic risk and oil future prices provides insights on oil demand and consumption during the COVID-19 pandemic

Utilization and Cost of Gender-affirming Surgery in the United States from 2012-2019.

OBJECTIVE: To characterize the trends in and characteristics associated with the utilization and cost of gender-affirming surgery (GAS) in the United States from 2012-2019. SUMMARY BACKGROUND DATA: GAS is one option among gender-diverse (GD) people to transition from their biologic anatomy to the anatomy congruent with their gender. Little is known about its utilization and cost trends and whether patient and hospital characteristics are associated with differences in utilization and cost. METHODS: This serial cross-sectional study collected retrospective data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS), a representative pool of inpatient visits in the United States. Records from 2012-2019 that indicated ages 18 or older, GD diagnoses, and GAS procedures were identified using the International Classification of Diseases, Ninth and Tenth Revisions. Within this cohort, demographics, utilization, and cost were collected and analyzed using descriptive statistics and multivariable regression models. RESULTS: 6,325 records with GD diagnoses and GAS procedures were identified. From 2012-2019, utilization increased by more than 5-fold (0.9 to 5.0 per 100,000 records among all records), and the mean, inflation-adjusted cost increased by 36% ($19,451 to$26,517). This cost trend was similar by type of surgery, and genital surgery had consistently higher costs than chest surgery from 2012 to 2019 (genital: $21,487 to$26,712, chest: $13,238 to$21,309). Lower odds of utilization were found in records with Medicaid (OR = 0.27, 95% CI [0.22-0.35], P CONCLUSIONS: As demand for GAS increases with varying utilization and costs based on patient and hospital characteristics, there will likely be a need for more qualified surgeons, increased insurance coverage, and policies to ensure equitable access to GAS

Dynamic causality analysis of COVID-19 pandemic risk and oil market changes

Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead-lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis, including a preparedness risk score and a severity risk score, Granger-cause changes in oil future prices. In our empirical analysis, we observe 49% to 60% of days in 2020 to 2021 during which the pandemic scores significantly affected oil futures. We also find an asymmetric lead-lag relationship, indicating that there is a tendency for oil futures to move significantly when the pandemic is less severe but not when it is more severe. This study adopts preparedness risk score and severity risk score as proxy variables to measure the impact of the COVID-19 pandemic risk on oil market. The asymmetric lead-lag behavior between pandemic risk and oil future prices provides insights on oil demand and consumption during the COVID-19 pandemic

Standardized local assortativity in networks and systemic risk in financial markets.

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure. This coefficient provides a lens through which the granular level of details can be observed, as well as capturing possible pattern (dis)formation in dynamic networks. We demonstrate how the standardized local assortative coefficient discovers the presence of (dis)assortative hubs in static networks on a granular level, and how it tracks systemic risk in dynamic financial networks

Do Scholars Respond Faster Than Google Trends in Discussing COVID-19 Issues? An Approach to Textual Big Data

Background: The COVID-19 pandemic has posed various difficulties for policymakers, such as the identification of health issues, establishment of policy priorities, formulation of regulations, and promotion of economic competitiveness. Evidence-based practices and data-driven decision-making have been recognized as valuable tools for improving the policymaking process. Nevertheless, due to the abundance of data, there is a need to develop sophisticated analytical techniques and tools to efficiently extract and analyze the data. Methods: Using Oxford COVID-19 Government Response Tracker, we categorize the policy responses into 6 different categories: (a) containment and closure, (b) health systems, (c) vaccines, (d) economic, (e) country, and (f) others. We proposed a novel research framework to compare the response times of the scholars and the general public. To achieve this, we analyzed more than 400,000 research abstracts published over the past 2.5 years, along with text information from Google Trends as a proxy for topics of public concern. We introduced an innovative text-mining method: coherent topic clustering to analyze the huge number of abstracts. Results: Our results show that the research abstracts not only discussed almost all of the COVID-19 issues earlier than Google Trends did, but they also provided more in-depth coverage. This should help policymakers identify core COVID-19 issues and act earlier. Besides, our clustering method can better reflect the main messages of the abstracts than a recent advanced deep learning-based topic modeling tool. Conclusion: Scholars generally have a faster response in discussing COVID-19 issues than Google Trends

Fig 7 -

Density ridge plot of the conditional multiple correlation of , l = 0, ⋯, N, and the loss Lj,t+1 given (left panel), and conditional multiple correlation of , dj,t−l, l = 0, ⋯, N, and the loss Lj,t+1 given (right panel), where N = 0, ⋯, 5; the solid black line represents the median of each distribution.</p

Density ridge plot of , where <i>k</i> = 1, ⋯, 10; the solid black line represents the median of each distribution.

Density ridge plot of , where k = 1, ⋯, 10; the solid black line represents the median of each distribution.</p