Peri-abortion contraceptive counseling: A systematic review of randomized controlled trials

Objective To assess the effects of peri-abortion contraceptive counseling interventions. Methods We performed a systematic review of randomized controlled trials (RCTs) that compared the effect of different types of peri-abortion contraceptive counseling interventions and were published as original papers in scientific journals. The literature search was performed in June 2021 in PubMed, Central Cochrane Library (CENTRAL), Scopus, and Google Scholar; without restrictions in language or publication date. Two independent authors identified studies that met the inclusion and exclusion criteria and extracted the data. The risk of bias was assessed using the Cochrane tool, and evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology. Whenever possible, meta-analyses were performed. The protocol was registered at PROSPERO (CRD42020187354). Results Eleven RCTs were eligible for inclusion (published from 2004 to 2017), from which nine compared enhanced versus standard counseling. Pooled estimates showed that, compared to standard counseling, enhanced counseling was associated with a higher incidence of effective contraceptive use (>3 months) (relative risk [RR], 1.12; 95% confidence interval [CI], 1.09–1.16), although no significant difference was found in the incidence of long-acting reversible contraceptive use (RR, 1.25; 95% CI, 0.68–2.29), contraceptive uptake (RR, 1.06; 95% CI, 0.98–1.15), and obstetric event occurrence (RR, 0.91; 95% CI, 0.57–1.47). Certainty of evidence was very low for all outcomes. In addition, two studies compared contraceptive counseling provided by physicians versus that provided by non-physicians, which did not show significant differences. Conclusions Enhanced contraceptive counseling may favor effective contraceptive use but may not affect the rate of obstetric event occurrence. Also, the studies did not find a difference in the effects of counseling interventions given by different providers. Since evidence certainty was very low, future well-designed RCTs are needed to make informed decisions. Registration The study protocol was registered at PROSPERO (CRD42020187354).

Rare roll event modeling of unmanned catamaran

Статья посвящена вопросу моделирования редких событий, которые возникают при качке катамарана. Система управления автономного катамарана должна уметь распознавать нежелательные ситуации, которые могут привести к осуществлению редких событий. В данной статье приводится несколько методов, позволяющих проводить моделирование редких событий и делать оценку риска возникновения редкого события. Методы основываются на теории больших уклонений. Первый метод позволяет оценить возможные «ожидаемые потери» при достижении редкого события путём оценки скорости убывания вероятности компонентов вектора состояния в редком состоянии. Оценка осуществляется путём расчёта квазипотенциалов из аттрактора до порогового значения состояния. Второй метод позволяет оценить вероятность движения вдоль наиболее вероятной траектории к редкому событию. Оценка осуществляется путём сравнения вектора состояния с состояниями на наиболее вероятной траектории к редкому событию. Точность оценок зависит от вектора состояния. Приводится сравнение с результатами, полученными с помощью метода Монте-Карло. Указанные методы могут быть использованы для создания систем супервизорного управления и систем поддержки принятия решений при оценке рискованности совершения морских переходов. The article is devoted to the issue of modeling rare events that occur when a catamaran is pitching. The control system of an autonomous catamaran should be able to recognize undesirable situations that can lead to the rare events. This article provides several methods for modeling rare events and making estimation of risk of a rare event occurrence. The methods are based on the large deviations theory for dynamical systems. The first method allows to estimate possible losses via calculation of the probability decreasing rate of the state vector components in a rare state. The estimation is carried out by calculating the quasipotential from the state close to the attractor to the threshold state. The second method allows to estimate the probability of moving along the most likely trajectory to a rare event. The evaluation is carried out by comparing the studied state vector with the states on the most likely trajectory. The accuracy of the estimates depends on the studied state vector. A comparison with the results obtained using the Monte Carlo method. These methods can be used to create supervisory control systems and decision support systems when assessing the riskiness of sea navigation.

Solar Energetic Particle Event occurrence prediction using Solar Flare Soft X-ray measurements and Machine Learning

The prediction of the occurrence of Solar Energetic Particle (SEP) events has been investigated over many years and multiple works have presented significant advances in this problem. The accurate and timely prediction of SEPs is of interest to the scientific community as well as mission designers, operators, and industrial partners due to the threat SEPs pose to satellites, spacecrafts and crewed missions. In this work we present a methodology for the prediction of SEPs from the soft X-rays of solar flares associated with SEPs that were measured in 1 AU. We use an expansive dataset covering 25 years of solar activity, 1988-2013, which includes thousands of flares and more than two hundred identified and catalogued SEPs. Neural networks are employed as the predictors in the model providing probabilities for the occurrence or not of an SEP which are converted to yes/no predictions. The neural networks are designed using current and state-of the-art tools integrating recent advances in the machine learning field. The results of the methodology are extensively evaluated and validated using all the available data and it is shown that we achieve very good levels of accuracy with correct SEP occurrence prediction higher than 85% and correct no-SEP predictions higher than 92%. Finally we discuss further work towards potential improvements and the applicability of our model in real life conditions.

Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Background In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest. The Joint Latent Class Model (JLCM) allows to stratify the population into groups (classes) of patients that are homogeneous both with respect to the evolution of a longitudinal marker and to the occurrence of an event; this model is widely employed in real-life applications. However, the finite sample-size properties of this model remain poorly explored. Methods In the present paper, a simulation study is carried out to assess the impact of the number of individuals, of the censoring rate and of the degree of class separation on the finite sample size properties of the JLCM. A real-life application from the neurology domain is also presented. This study assesses the precision of class membership prediction and the impact of covariates omission on the model parameter estimates. Results Simulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates; Conclusion The JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research.

The Local Labor Market Impacts of US Megafires

As we learn to sustainably coexist with wildfire, there is an urgent need to improve our understanding of its multidimensional impacts on society. To this end, we undertake a nationwide study to estimate how megafires (wildfires > 100,000 acres in size) affect US labor market outcomes in communities located within the flame zone. Both year-of-fire and over-time dynamic impacts are studied between 2010−2017. We find that counties located within a megafire flame zone experience significantly lower per capita wage earnings across multiple sources of earnings data for up to two years after megafire event occurrence. We find preliminary evidence that impacts are nonlinear over megafire size. These results highlight a new dimension of megafire impacts and expand the scope of the potential costs of megafires that should be considered in benefit-cost analyses of wildfire control and suppression decisions, especially along sustainability dimensions.

The Unexpected Relationship Between Extreme Shear Events and AMO

The vertical shear–the change in wind speed with height-of horizontal winds is a serious threat to the safety of aircraft. Yet their global distribution is not fully understood. We creatively used a precise method to calculate different types of vertical shear at four isobaric surfaces during the period of 1979~2018. The occurrence of severe shear events has increased by 19%, and they mostly occur over the equatorial ocean and within the mid-high latitude zone of the Northern hemisphere, while light shear event occurrence has been reduced by 21%. Variations of severe shear are modulated by the Atlantic Multidecadal Oscillation (AMO), which affects the frequency of shear events by influencing the intertropical convergence zone (ITCZ). Our study implies that severe shear events are regulated by internal climate variability.