Timing of onset of menses after GnRH agonist treatment for central precocious puberty.

OBJECTIVES: To understand possible predictors of the onset of menses after gonadotropin-releasing hormone agonist treatment cessation in girls with central precocious puberty (CPP). METHODS: This exploratory post hoc analysis of a phase 3 and 4 trial of girls with CPP treated with once-monthly intramuscular leuprolide acetate examined onset of menses after treatment completion using a time-to-event analysis. Pretreatment and end-of-treatment chronologic age (CA), bone age (BA)/CA ratio, and Tanner breast stage; pretreatment menses status; and end-of-treatment BA and body mass index (BMI) were studied as potential factors influencing the onset of menses. RESULTS: Median time to first menses after stopping treatment was 18.3 months among 35 girls (mean age at onset of treatment, 6.8 years) examined. Of 26 girls experiencing menses, 11 (42 %) menstruated at 16-21 months after stopping treatment. Most girls with pretreatment BA/CA≥1.4 started menstruating very close to 18 months after stopping treatment; those with less advanced BA/CA experienced menses at 9-18 months. End-of-treatment BA/CA≥1.2 was associated with a quicker onset of menses (14.5 vs. 18.5 months for BA/CA<1.2, p=0.006). End-of-treatment BA≥12 years predicted longer time to menses. No relationship with time to menses was observed for pretreatment menarche status, pretreatment or end-of-treatment Tanner breast stage (<3/≥3) or CA (<6/≥6 or ≤11/>11), or end-of-treatment BMI percentiles (<85.6/≥85.6 and <92.6/≥92.6). CONCLUSIONS: Pretreatment menarche status or CA do not appear to predict onset of menses, but pre- and end-of-treatment BA/CA may be helpful in anticipating time to first menses after stopping treatment.

Characterizing attitudes related to future child-bearing in young women diagnosed with early-stage breast cancer.

PURPOSE: This study characterizes attitudes and decision-making around the desire for future children in young women newly diagnosed with early-stage breast cancer and assesses how clinical factors and perceived risk may impact these attitudes. METHODS: This is a prospective study in women < 45 years with newly diagnosed stage 1-3 breast cancer. Patients completed a REDCap survey on fertility and family-building in the setting of hypothetical risk scenarios. Patient, tumor, and treatment characteristics were collected through surveys and medical record. RESULTS: Of 140 study patients [median age = 41.4 (range 23-45)], 71 (50.7%) were interested in having children. Women interested in future childbearing were younger than those who were not interested (mean = 35.2 [SD = 5.2] vs 40.9 years [3.90], respectively, p < 0.001), and more likely to be childless (81% vs 31%, p < 0.001). 54 women (77.1% of patients interested in future children) underwent/planned to undergo oocyte/embryo cryopreservation before chemotherapy. Interest in future childbearing decreased with increasing hypothetical recurrence risk, however 17% of patients wanted to have children despite a 75-100% hypothetical recurrence risk. 24.3% of patients wanted to conceive < 2 years from diagnosis, and 35% of patients with hormone receptor positive tumors were not willing to complete 5 years of hormone therapy. CONCLUSION: Many young women diagnosed with early-stage breast cancer prioritize childbearing. Interest in having a biologic child was not associated with standard prognostic risk factors. Interest decreased with increasing hypothetical recurrence risk, though some patients remained committed to future childbearing despite near certain hypothetical risk. Individual risk assessment should be included in family-planning discussions throughout the continuum of care as it can influence decision-making.

Clinical characteristics and outcomes in patients with metastatic breast cancer and pseudocirrhosis: a single center retrospective cohort study.

PURPOSE: Pseudocirrhosis is a term used to describe changes in hepatic contour that mimic cirrhosis radiographically, but lack the classic pathologic features of cirrhosis. This radiographic finding is frequently found in patients with metastatic breast cancer (MBC), but the risk factors and clinical consequences are poorly understood. METHODS: In this retrospective study, we identified patients with MBC and pseudocirrhosis who were treated at a single center from 2002 to 2021. We used chart extraction and radiology review to determine demographic characteristics, treatment history, imaging features, and complications of pseudocirrhosis. RESULTS: 120 patients with MBC and pseudocirrhosis were identified with the following BC subtypes: hormone receptor (HR) positive, HER2 negative (n = 99, 82.5%), HR+/HER2+ (n = 14, 11.7%), HR- /HER2+ (n = 3, 2.5%), and triple negative (TNBC; n = 4, 3.3%). All patients had liver metastases and 82.5% (n = 99) had > 15 liver lesions. Thirty-six patients (30%) presented with de novo metastatic disease. Median time from MBC diagnosis to pseudocirrhosis was 29.2 months. 50% of patients had stable or responding disease at the time of pseudocirrhosis diagnosis. Sequelae of pseudocirrhosis included radiographic ascites (n = 97, 80.8%), gastric/esophageal varices (n = 68, 56.7%), splenomegaly (n = 26, 21.7%), GI bleeding (n = 12, 10.0%), and hepatic encephalopathy (n = 11, 9.2%). Median survival was 7.9 months after pseudocirrhosis diagnosis. Radiographic ascites was associated with shorter survival compared to no radiographic ascites (42.8 vs. 76.2 months, p = < 0.001). CONCLUSIONS: This is the largest case series of patients with MBC and pseudocirrhosis. Nearly all patients had HR+ MBC and extensive liver metastases. Survival was short after pseudocirrhosis and prognosis worse with radiographic ascites.

Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective.

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.

New Metrics for Assessing the State Performance in Combating the COVID-19 Pandemic.

Previous research has noted that many factors greatly influence the spread of COVID-19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID-19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision-making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county-level covariates and unobservable state-level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model-based metrics-the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID-19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.

Phased Implementation of COVID-19 Vaccination: Rapid Assessment of Policy Adoption, Reach and Effectiveness to Protect the Most Vulnerable in the US.

The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet became economically and administratively difficult to enforce as the pandemic continued. In late December 2020, COVID-19 vaccines were first approved in the US and states began a phased implementation of COVID-19 vaccination. However, there is limited quantitative evidence regarding the effectiveness of the phased COVID-19 vaccination. This study aims to provide a rapid assessment of the adoption, reach, and effectiveness of the phased implementation of COVID-19 vaccination. We utilize an event-study analysis to evaluate the effect of vaccination on the state-level daily COVID-19 case growth rate. Through this analysis, we assert that vaccination was effective in reducing the spread of COVID-19 shortly after the first shots were given. Specifically, the case growth rate declined by 0.124, 0.347, 0.345, 0.464, 0.490, and 0.756 percentage points corresponding to the 1-5, 6-10, 11-15, 16-20, 21-25, and 26 or more day periods after the initial shots. The findings could be insightful for policymakers as they work to optimize vaccine distribution in later phases, and also for the public as the COVID-19 related health risk is a contentious issue.

The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space-time disaggregation.

On symmetric semiparametric two-sample problem.

We consider a two-sample problem where data come from symmetric distributions. Usual two-sample data with only magnitudes recorded, arising from case-control studies or logistic discriminant analyses, may constitute a symmetric two-sample problem. We propose a semiparametric model such that, in addition to symmetry, the log ratio of two unknown density functions is modeled in a known parametric form. The new semiparametric model, tailor-made for symmetric two-sample data, can also be viewed as a biased sampling model subject to symmetric constraint. A maximum empirical likelihood estimation approach is adopted to estimate the unknown model parameters, and the corresponding profile empirical likelihood ratio test is utilized to perform hypothesis testing regarding the two population distributions. Symmetry, however, comes with irregularity. It is shown that, under the null hypothesis of equal symmetric distributions, the maximum empirical likelihood estimator has degenerate Fisher information, and the test statistic has a mixture of χ2 -type asymptotic distribution. Extensive simulation studies have been conducted to demonstrate promising statistical powers under correct and misspecified models. We apply the proposed methods to two real examples.