Second Birth Fertility in Germany: Social Class, Gender, and the Role of Economic Uncertainty.

Building on a thick strand of the literature on the determinants of higher-order births, this study uses a gender and class perspective to analyse second birth progression rates in Germany. Using data from the German Socio-Economic Panel from 1990 to 2020, individuals are classified based on their occupation into: upper service, lower service, skilled manual/higher-grade routine nonmanual, and semi-/unskilled manual/lower-grade routine nonmanual classes. Results highlight the "economic advantage" of men and women in service classes who experience strongly elevated second birth rates. Finally, we demonstrate that upward career mobility post-first birth is associated with higher second birth rates, particularly among men.

Formative Assessment of Diagnostic Testing in Family Medicine with Comprehensive MCQ Followed by Certainty-Based Mark.

INTRODUCTION: The choice of diagnostic tests in front of a given clinical case is a major part of medical reasoning. Failure to prescribe the right test can lead to serious diagnostic errors. Furthermore, unnecessary medical tests are a waste of money and could possibly generate injuries to patients, especially in family medicine. METHODS: In an effort to improve the training of our students to the choice of laboratory and imaging studies, we implemented a specific multiple-choice questions (MCQ), called comprehensive MCQ (cMCQ), with a fixed and high number of options matching various basic medical tests, followed by a certainty-based mark (CBM). This tool was used in the assessment of diagnostic test choice in various clinical cases of general practice in 456 sixth-year medical students. RESULTS: The scores were significantly correlated with the traditional exams (standard MCQ), with matched themes. The proportion of "cMCQ/CBM score" variance explained by "standard MCQ score" was 21.3%. The cMCQ placed students in a situation closer to practice reality than standard MCQ. In addition to its usefulness as an assessment tool, those tests had a formative value and allowed students to work on their ability to measure their doubt/certainty in order to develop a reflexive approach, required for their future professional practice. CONCLUSION: cMCQ followed by CBM is a feasible and reliable evaluation method for the assessment of diagnostic testing.

Efficacy of metformin on glycemic control and weight in drug-naive type 2 diabetes mellitus patients: A systematic review and meta-analysis of placebo-controlled randomized trials.

AIM OF THE STUDY: Metformin is recommended as the first-line treatment of type 2 diabetes mellitus. Despite its common use, few studies have been conducted to precisely measure the efficacy of metformin versus placebo as a first-line treatment. This study aims to assess the precise effects of metformin monotherapy on glycemic control and weight in drug-naive patients with type 2 diabetes mellitus. METHODS: Medline® and Cochrane databases were searched until March 19, 2016 to perform a systematic review and meta-analysis of placebo-controlled randomized trials evaluating metformin monotherapy in drug-naive patients with type 2 diabetes mellitus. Assessed outcomes include glycemic control (fasting plasma glucose, glycosated hemoglobin) and weight. RESULTS: Overall, 16 studies (1140 patients) were selected. Compared to placebo, metformin monotherapy was associated with decreased glycosated hemoglobin by 0.95% at 3 months (95% CI: 0.50 to 1.39, I2=87%) and 1.32% at 6 months (95% CI: 1.01 to 1.62, I2=71%), and decreased fasting plasma glucose by 1.92mmol/L at 1 month (95% CI: 0.11 to 3.74, I2=88%), 1.79mmol/L at 3 months (95% CI: 0.92 to 2.66, I2=88%) and 2.14mmol/L at 6 months (95% CI: 1.17 to 3.12, I2=82%). No significant difference was demonstrated for the comparisons of weight due to relatively small number of studies retrieved from the literature resulting in insufficient statistical power. CONCLUSION: This study provides the precise effects of metformin monotherapy regarding the decreases in fasting plasma glucose and glycosated hemoglobin that physician can expected in drug-naive patients with type 2 diabetes mellitus. No evidence was found for the effects on weight.

Peer Role-Play for Training Communication Skills in Medical Students: A Systematic Review.

STATEMENT: Peer role-play (PRP) is a simulation-based training method (SBTM) in which medical students alternately play the patient's and clinician's role. This review aimed to assess the effectiveness of PRP for improving the communication skills of medical students. A systematic search was conducted in the MedLine, PsycInfo, and ERIC databases. Studies were qualitatively analyzed according to the Kirkpatrick evaluation level (Kirkpatrick level) and the Medical Education Research Study Quality Instrument.Twenty-two studies were included. Studies assessing the "reaction" of students (Kirkpatrick level 1, n = 15) found that PRP was appreciated, whereas those assessing the effect of PRP on "learning" (Kirkpatrick level 2, n = 12) found that PRP improves communication skills but no more than other SBTMs. No study assessed real-life "attitudes" or "clinical outcomes" (Kirkpatrick levels 3 and 4), whereas 2 studies found that using PRP had a better cost-efficacy ratio than the use of simulated patients. Compared with other SBTMs, PRP improved communication skills similarly in medical students and seemed less expensive.

Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival.

The promotion time cure model is a survival model acknowledging that an unidentified proportion of subjects will never experience the event of interest whatever the duration of the follow-up. We focus our interest on the challenges raised by the strong posterior correlation between some of the regression parameters when the same covariates influence long- and short-term survival. Then, the regression parameters of shared covariates are strongly correlated with, in addition, identification issues when the maximum follow-up duration is insufficiently long to identify the cured fraction. We investigate how, despite this, plausible values for these parameters can be obtained in a computationally efficient way. The theoretical properties of our strategy will be investigated by simulation and illustrated on clinical data. Practical recommendations will also be made for the analysis of survival data known to include an unidentified cured fraction.

Small-Sided Games in Elite Soccer: Does One Size Fit All?

PURPOSE: To compare the peak intensity of typical small-sided games (SSGs) with those of official matches in terms of running demands and mechanical work (MechW) over different rolling average durations and playing positions. METHODS: Data were collected in 21 players (25 [5] y, 181 [7] cm, and 77 [7] kg) belonging to an elite French football team. SSG data were collected over 2 seasons during typical training sessions (249 files, 12 [4] per player) and official matches (n = 12). Players' locomotor activity was recorded using 5-Hz Global Positioning System. Total distance (m), high-speed distance (HS, distance above 14.4 km·h-1, m), and MechW (a.u.) were analyzed during different rolling average periods (1-15 min). The SSGs examined were 4v4+goalkeepers (GKs), 6v6+GKs, 8v8+GKs, and 10v10+GKs. RESULTS: Peak total distance and HS during 4v4, 6v6, and 8v8 were likely-to-most likely lower than during matches (effect size: -0.59 [±0.38] to -7.36 [±1.20]). MechW during 4v4 was likely-to-most likely higher than during matches (1-4 min; 0.61 [±0.77] to 2.30 [±0.64]). Relative to their match demands, central defenders performed more HS than other positions (0.63 [±0.81] to 1.61 [±0.52]) during 6v6. Similarly, central midfielders performed less MechW than the other positions during 6v6 (0.68 [±0.72] to 1.34 [±0.99]) and 8v8 (0.73 [±0.50] to 1.39 [±0.32]). CONCLUSION: Peak locomotor intensity can be modulated during SSGs of various formats and durations to either overload or underload match demands, with 4v4 placing the greatest and the least emphasis on MechW and HS, respectively. Additionally, in relation to match demands central defenders and central midfielders tend to be the most and least overloaded during SSGs, respectively.

Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone.

The 2014 Ebola outbreak in Sierra Leone is analyzed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model. The discrete time-stochastic model for the epidemic evolution is coupled to a set of ordinary differential equations describing the dynamics of the expected proportions of subjects in each epidemic state. The unknown parameters are estimated in a Bayesian framework by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO during the follow-up of specific Ebola cases. The time-varying disease transmission rate is modeled in a flexible way using penalized B-splines. Our framework represents a valuable stochastic tool for the study of an epidemic dynamic even when only irregularly observed and possibly aggregated data are available. Simulations and the analysis of the 2014 Sierra Leone Ebola data highlight the merits of the proposed methodology. In particular, the flexible modeling of the disease transmission rate makes the estimation of the effective reproduction number robust to the misspecification of the initial epidemic states and to underreporting of the infectious cases.

Inference in dynamic systems using B-splines and quasilinearized ODE penalties.

Nonlinear (systems of) ordinary differential equations (ODEs) are common tools in the analysis of complex one-dimensional dynamic systems. We propose a smoothing approach regularized by a quasilinearized ODE-based penalty. Within the quasilinearized spline-based framework, the estimation reduces to a conditionally linear problem for the optimization of the spline coefficients. Furthermore, standard ODE compliance parameter(s) selection criteria are applicable. We evaluate the performances of the proposed strategy through simulated and real data examples. Simulation studies suggest that the proposed procedure ensures more accurate estimates than standard nonlinear least squares approaches when the state (initial and/or boundary) conditions are not known.

Psychometric and Physiological Responses to a Preseason Competitive Camp in the Heat With a 6-Hour Time Difference in Elite Soccer Players.

PURPOSE: To examine in elite soccer players after traveling across 6 time zones some psychometric and physiological responses to a competitive camp in the heat. METHODS: Data from 12 elite professional players (24.6 ± 5.3 y) were analyzed. They participated in an 8-d preseason summer training camp in Asia (heat index 34.9°C ± 2.4°C). Players' activity was collected during all training sessions and the friendly game using 15-Hz GPS. Perceived training/playing load was estimated using session rating of perceived exertion (RPE) and training/match duration. Psychometric measures of wellness were collected on awakening before, during, and after the camp using simple questionnaires. Heart-rate (HR) response to a submaximal 4-min run (12 km/h) and the ratio between velocity and force-load (accelerometer-derived measure, a marker of neuromuscular efficiency) response to four ~60-m runs (22-24 km/h) were collected before, at the end of, and after the camp. RESULTS: After a large increase, the RPE:m/min ratio decreased substantially throughout the camp. There were possible small increases in perceived fatigue and small decreases in subjective sleep quality on the 6th day. There were also likely moderate (~3%) decreases in HR response to the submaximal run, both at the end of and after the camp, which were contemporary to possible small (~8%) and most likely moderate (~19%) improvements in neuromuscular efficiency, respectively. CONCLUSIONS: Despite transient increases in fatigue and reduced subjective sleep quality by the end of the camp, these elite players showed clear signs of heat acclimatization that were associated with improved cardiovascular fitness and neuromuscular running efficiency.

Modelling the potential of focal screening and treatment as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region.

BACKGROUND: Focal screening and treatment (FSAT) of malaria infections has recently been introduced in Peru to overcome the inherent limitations of passive case detection (PCD) and further decrease the malaria burden. Here, we used a relatively straightforward mathematical model to assess the potential of FSAT as elimination strategy for Plasmodium falciparum malaria in the Peruvian Amazon Region. METHODS: A baseline model was developed to simulate a scenario with seasonal malaria transmission and the effect of PCD and treatment of symptomatic infections on the P. falciparum malaria transmission in a low endemic area of the Peruvian Amazon. The model was then adjusted to simulate intervention scenarios for predicting the long term additional impact of FSAT on P. falciparum malaria prevalence and incidence. Model parameterization was done using data from a cohort study in a rural Amazonian community as well as published transmission parameters from previous studies in similar areas. The effect of FSAT timing and frequency, using either microscopy or a supposed field PCR, was assessed on both predicted incidence and prevalence rates. RESULTS: The intervention model indicated that the addition of FSAT to PCD significantly reduced the predicted P. falciparum incidence and prevalence. The strongest reduction was observed when three consecutive FSAT were implemented at the beginning of the low transmission season, and if malaria diagnosis was done with PCR. Repeated interventions for consecutive years (10 years with microscopy or 5 years with PCR), would allow reaching near to zero incidence and prevalence rates. CONCLUSIONS: The addition of FSAT interventions to PCD may enable to reach P. falciparum elimination levels in low endemic areas of the Amazon Region, yet the progression rates to those levels may vary substantially according to the operational criteria used for the intervention.

A Bayesian design space for analytical methods based on multivariate models and predictions.

The International Conference for Harmonization (ICH) has released regulatory guidelines for pharmaceutical development. In the document ICH Q8, the design space of a process is presented as the set of factor settings providing satisfactory results. However, ICH Q8 does not propose any practical methodology to define, derive, and compute design space. In parallel, in the last decades, it has been observed that the diversity and the quality of analytical methods have evolved exponentially, allowing substantial gains in selectivity and sensitivity. However, there is still a lack of a rationale toward the development of robust separation methods in a systematic way. Applying ICH Q8 to analytical methods provides a methodology for predicting a region of the space of factors in which results will be reliable. Combining design of experiments and Bayesian standard multivariate regression, an identified form of the predictive distribution of a new response vector has been identified and used, under noninformative as well as informative prior distributions of the parameters. From the responses and their predictive distribution, various critical quality attributes can be easily derived. This Bayesian framework was then extended to the multicriteria setting to estimate the predictive probability that several critical quality attributes will be jointly achieved in the future use of an analytical method. An example based on a high-performance liquid chromatography (HPLC) method is given. For this example, a constrained sampling scheme was applied to ensure the modeled responses have desirable properties.

How representative are clinical study patients with allergic rhinitis in primary care?

BACKGROUND: Guidelines are the cornerstone of health care decision making and are based on the best available evidence, ideally large randomized controlled trials (RCTs). Although guidelines target typical patients, RCTs are often based on narrow inclusion and exclusion criteria. OBJECTIVES: We explored to what extent typical patients, such as those consulting general practitioners for allergic rhinitis, differ from patients enrolled in RCTs. METHODS: We conducted a prospective cohort study including all the consecutive patients with allergic rhinitis cared for by general practitioners in the Languedoc-Roussillon region of France within 2 weeks during the grass pollen season. We evaluated how the characteristics of these patients differed from those of patients included in the 4 largest placebo-controlled RCTs of persistent and intermittent allergic rhinitis. RESULTS: Three hundred eleven patients seen by 48 general practitioners were enrolled in this study. Only 7.4% (95% CI, 4.5% to 10.3%) of the patients would have been enrolled in the RCTs. The primary reasons for this difference were as follows: diagnosis of allergy based on skin test results, serum specific IgE levels, or both (20.4%); severity of allergic rhinitis (11.5%); other chronic diseases (11.4%); history of sinusitis (10.4%); and asthma comorbidity (10.1%). A sensitivity analysis excluding contraception and the diagnosis of allergy showed that the percentage of representative patients increased to 20.2% (95% CI, 15.8% to 24.7%). CONCLUSION: Only a small proportion of patients with allergic rhinitis seen in the primary care setting for allergic rhinitis would be eligible for RCTs. Thus guideline developers and health decision makers need to make careful judgments about the directness of the evidence from RCTs conducted in highly controlled settings.

Smooth estimation of survival functions and hazard ratios from interval-censored data using Bayesian penalized B-splines.

We discuss the use of Bayesian P-spline and of the composite link model to estimate survival functions and hazard ratios from interval-censored data. If one further assumes proportionality of the hazards, the proposed strategy provides a smoothed estimate of the baseline hazard along with estimates of global covariate effects. The frequentist properties of our Bayesian estimators are assessed by an extensive simulation study. We further illustrate the methodology by two examples showing that the proportionality of the hazards might also be found inappropriate from interval-censored data.

Pharmacokinetic parameters estimation using adaptive Bayesian P-splines models.

In preclinical and clinical experiments, pharmacokinetic (PK) studies are designed to analyse the evolution of drug concentration in plasma over time i.e. the PK profile. Some PK parameters are estimated in order to summarize the complete drug's kinetic profile: area under the curve (AUC), maximal concentration (C(max)), time at which the maximal concentration occurs (t(max)) and half-life time (t(1/2)).Several methods have been proposed to estimate these PK parameters. A first method relies on interpolating between observed concentrations. The interpolation method is often chosen linear. This method is simple and fast. Another method relies on compartmental modelling. In this case, nonlinear methods are used to estimate parameters of a chosen compartmental model. This method provides generally good results. However, if the data are sparse and noisy, two difficulties can arise with this method. The first one is related to the choice of the suitable compartmental model given the small number of data available in preclinical experiment for instance. Second, nonlinear methods can fail to converge. Much work has been done recently to circumvent these problems (J. Pharmacokinet. Pharmacodyn. 2007; 34:229-249, Stat. Comput., to appear, Biometrical J., to appear, ESAIM P&S 2004; 8:115-131).In this paper, we propose a Bayesian nonparametric model based on P-splines. This method provides good PK parameters estimation, whatever be the number of available observations and the level of noise in the data. Simulations show that the proposed method provides better PK parameters estimations than the interpolation method, both in terms of bias and precision. The Bayesian nonparametric method provides also better AUC and t(1/2) estimations than a correctly specified compartmental model, whereas this last method performs better in t(max) and C(max) estimations.We extend the basic model to a hierarchical one that treats the case where we have concentrations from different subjects. We are then able to get individual PK parameter estimations. Finally, with Bayesian methods, we can get easily some uncertainty measures by obtaining credibility sets for each PK parameter.

Effect of a collaborative approach on the quality of prescribing for geriatric inpatients: a randomized, controlled trial.

OBJECTIVES: To evaluate the effect of pharmaceutical care provided in addition to acute Geriatric Evaluation and Management (GEM) care on the appropriateness of prescribing. DESIGN: Randomized, controlled trial, with the patient as unit of randomization. SETTING: Acute GEM unit. PARTICIPANTS: Two hundred three patients aged 70 and older. INTERVENTION: Pharmaceutical care provided from admission to discharge by a specialist clinical pharmacist who had direct contacts with the GEM team and patients. MEASUREMENTS: Appropriateness of prescribing on admission, at discharge, and 3 months after discharge, using the Medication Appropriateness Index (MAI), Beers criteria, and Assessing Care of Vulnerable Elders (ACOVE) underuse criteria and mortality, readmission, and emergency visits up to 12 months after discharge. RESULTS: Intervention patients were significantly more likely than control patients to have an improvement in the MAI and in the ACOVE underuse criteria from admission to discharge (odds ratio (OR)=9.1, 95% confidence interval (CI)=4.2-21.6 and OR=6.1, 95% CI=2.2-17.0, respectively). The control and intervention groups had comparable improvements in the Beers criteria. CONCLUSION: Pharmaceutical care provided in the context of acute GEM care improved the appropriate use of medicines during the hospital stay and after discharge. This is an important finding, because only limited data exist on the effect of various strategies to improve medication use in elderly inpatients. The present approach has the potential to minimize risk and improve patient outcomes.

Discrimination of shifts in a soil microbial community associated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays.

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro transcribed 16S rRNA derived from uncontaminated and 2,4,6-trinitrotoluene contaminated soils to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and mismatch (MM) variants, thermal dissociation was used to analyze the nucleic acid bound to each PM-MM probe set. Functional ANOVA of the dissociation curves generally discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greaterthan the Td. Comparison of signal intensities measured prior to dissociation analysis from hybridizations of the two soil samples revealed significant differences in domain-, group-, and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridizations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary.

Functional ANOVA with random functional effects: an application to event-related potentials modelling for electroencephalograms analysis.

The differential effects of basic visual or auditory stimuli on electroencephalograms (EEG), named event related potentials (ERPs), are often used to evaluate the impact of treatments on brain performances. In the present paper, we propose a P-splines based model that can be used to evaluate treatment effect on the timing and the amplitude of some peaks of the ERPs curves. Functional ANOVA is an adaptation of linear model or analysis of variance to analyse functional observations. The changes in the functional of interest effects are generally described using smoothing splines. Eilers and Marx proposed to work with P-splines, a combination of B-splines and difference penalties on coefficients. We define a P-splines model for ERPs curves combined with random effects. In particular, we show that it is a useful alternative to classical strategies requiring the visual and usually imprecise localization of specific ERP peaks from curves with a low signal-to-noise ratio.

Bayesian proportional hazards model with time-varying regression coefficients: a penalized Poisson regression approach.

One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi-parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B-splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log-linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coefficients to vary in a smooth way with time. Penalized B-splines models will be proposed for each of these coefficients. We show how the regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis-adjusted Langevin algorithm.

Parametric accelerated failure time models with random effects and an application to kidney transplant survival.

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data.

On the joint analysis of longitudinal responses and early discontinuation in randomized trials.

Our focus is on the joint analysis of longitudinal nonnormal responses and early discontinuation in (pre)-clinical trials. Separate models are fitted to the two series (response and discontinuation) to account for covariate and time effects. The serial dependence and the dependence between response and drop-out are also modeled. This is done using particular dependence functions, called copulas. Copulas are used to create a joint distribution with given marginal distributions. Applications are given for the analysis of heart rate/morbidity in toxicology and pain severity/intake of rescue medications in a trial on migraine. Using copulas, the level of dependence between two variables remains invariant to changes in the marginal distribution of either variable. This proves interesting in modeling the association in a longitudinal setting when responses change over time.