Impact of dydrogesterone use in cycles with low progesterone levels on the day of frozen embryo transfer.

PURPOSE: This study aims to evaluate whether the clinical outcomes of cycles with frozen embryo transfer (FET) in hormonal replacement treatment supplemented with dydrogesterone (DYD) following detection of low circulating levels of progesterone (P4) were comparable to the results of cycles with otherwise normal serum P4 values. METHODS: Extended analyses of a retrospective cohort that included FET cycles performed between July 2019 and March 2022 after a cycle of artificial endometrial preparation using valerate-estradiol and micronized vaginal P4 (400 mg twice daily). Whenever the serum P4 value was considered low on the morning of the planned transfer, 10 mg of DYD three times a day was added as a supplement. Only single-embryo transfers of a blastocyst were considered. The primary endpoint was live birth rate. RESULTS: Five-hundred thirty-five FET cycles were analyzed, of which 136 (25.4%) underwent treatment with DYD. There were 337 pregnancies (63%), 207 live births (38.6%), and 130 miscarriages (38.5%). The P4 values could be modeled by a gamma distribution, with a mean of 14.5 ng/ml and a standard deviation of 1.95 ng/ml. The variables female age on the day of FET, ethnicity, and weight were associated with a variation in the serum P4 values. There were no differences in the results between cycles with or without the indication for DYD supplementation. CONCLUSIONS: Live birth rate did not vary significantly in females with low and normal serum P4 levels on the day of FET when DYD was used as rescue therapy.

Nowcasting influenza epidemics using non-homogeneous hidden Markov models.

Timeliness of a public health surveillance system is one of its most important characteristics. The process of predicting the present situation using available incomplete information from surveillance systems has received the term nowcasting and has high public health interest. Generally in Europe, general practitioners' sentinel networks support the epidemiological surveillance of influenza activity, and each week's epidemiological bulletins are usually issued between Wednesday and Friday of the following week. In this work, we have developed a non-homogeneous hidden Markov model (HMM) that, on a weekly basis, uses as covariates an early observation of influenza-like illness (ILI) incidence rate and the number of ILI cases tested positive to nowcast the current week ILI rate and the probability that the influenza activity is in an epidemic state. We use Bayesian inference to find estimates of the model parameters and nowcasted quantities. The results obtained with data provided by the Portuguese influenza surveillance system show the additional value of using a non-homogeneous HMM instead of a homogeneous one. The use of a non-homogeneous HMM improves the surveillance system timeliness in 2 weeks.

Time series methods for obtaining excess mortality attributable to influenza epidemics.

The occurrence of influenza epidemics during winters, in the northern hemisphere countries, is known to be associated with observed excess mortality for all causes. A large variety of methods have been developed in order to estimate, from weekly or monthly mortality time series, the number of influenza-associated deaths in each season. The present work focus on the group of methods characterised by fitting statistical models to interrupted mortality time series. The study objective is to find a common ground between these methods in order to describe and compare them. They are unified in a single class, being categorised according to three main parameters: the model used to fit the interrupted time series and obtain a baseline, the a priori chosen type of periods used to estimate the influenza epidemic periods and the procedure used to fit the model to the time series (iterative or non-iterative). This generalisation led quite naturally to the construction of a set of user friendly R-routines, package flubase, implementing all these models. These routines were applied to data on about 20 years of weekly Portuguese number of deaths by pneumonia and influenza showing that, in this case, the parameter that had the highest impact on influenza-associated deaths estimates was the a priori chosen type of period used.

Towards joint disease mapping.

This article discusses and extends statistical models to jointly analyse the spatial variation of rates of several diseases with common risk factors. We start with a review of methods for separate analyses of diseases, then move to ecological regression approaches, where the rates from one of the diseases enter as surrogate covariates for exposure. Finally, we propose a general framework for jointly modelling the variation of two or more diseases, some of which share latent spatial fields, but with possibly different risk gradients. In our application, we consider mortality data on oral, oesophagus, larynx and lung cancers for males in Germany, which all share smoking as a common risk factor. Furthermore, the first three cancers are also known to be related to excessive alcohol consumption. An empirical comparison of the different models based on a formal model criterion as well as on the posterior precision of the relative risk estimates strongly suggests that the joint modelling approach is a useful and valuable extension over individual analyses.