Biased efficacy estimates in phase-III dengue vaccine trials due to heterogeneous exposure and differential detectability of primary infections across trial arms.

Vaccine efficacy (VE) estimates are crucial for assessing the suitability of dengue vaccine candidates for public health implementation, but efficacy trials are subject to a known bias to estimate VE toward the null if heterogeneous exposure is not accounted for in the analysis of trial data. In light of many well-characterized sources of heterogeneity in dengue virus (DENV) transmission, our goal was to estimate the potential magnitude of this bias in VE estimates for a hypothetical dengue vaccine. To ensure that we realistically modeled heterogeneous exposure, we simulated city-wide DENV transmission and vaccine trial protocols using an agent-based model calibrated with entomological and epidemiological data from long-term field studies in Iquitos, Peru. By simulating a vaccine with a true VE of 0.8 in 1,000 replicate trials each designed to attain 90% power, we found that conventional methods underestimated VE by as much as 21% due to heterogeneous exposure. Accounting for the number of exposures in the vaccine and placebo arms eliminated this bias completely, and the more realistic option of including a frailty term to model exposure as a random effect reduced this bias partially. We also discovered a distinct bias in VE estimates away from the null due to lower detectability of primary DENV infections among seronegative individuals in the vaccinated group. This difference in detectability resulted from our assumption that primary infections in vaccinees who are seronegative at baseline resemble secondary infections, which experience a shorter window of detectable viremia due to a quicker immune response. This resulted in an artefactual finding that VE estimates for the seronegative group were approximately 1% greater than for the seropositive group. Simulation models of vaccine trials that account for these factors can be used to anticipate the extent of bias in field trials and to aid in their interpretation

Group versus individual approach? A meta-analysis of the effectiveness of interventions to promote physical activity

The purpose of the study was to conduct a meta-analysis to empirically compare the relative merits of different contexts typically employed in the physical activity intervention literature for five categories of outcomes: adherence, social interaction, quality of life, physiological effectiveness, and functional effectiveness. \ud Four contexts were examined: home-based programmes not involving contact from researchers or health-care professionals, home-based programmes that involved some contact, standard exercise classes, and exercise classes where group-dynamics principles were used to increase cohesiveness (‘true groups’). Standard literature searches produced 44 relevant studies containing 214 effect sizes. Results revealed a common trend across dependent variables; exercising in a true group was superior to exercising in a standard exercise class, which in turn, did not differ from exercising at home with contact. Furthermore, exercising at home with contact was superior to exercising at home without contact. These results have implications for practitioners in terms of the importance of contact and social support in physical activity interventions

Entrepreneurial motives and performance:Why might better educated entrepreneurs be less successful?

In a sample of newly created French firms, the impact of an entrepreneurís education on the firm's survival varies widely depending on his previous labor market situation. While it is strongly positive for the overall population, it is much weaker or insignificant for entrepreneurs who were previously unemployed or poorly matched. Our theoretical entrepreneurship model shows that these differences may be attributed to differences in unobserved human capital for better educated entrepreneurs across different initial states in the labor market. Empirical results are consistent with the theory if employers have limited information about potential entrepreneurs'human capital

Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques

[EN] The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 <= AUC <= 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 <= p <= 0.18) and were predictive of death for pre-frail and frail participants (0.001 <= p <= 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.This work was supported by the following grants: Grant PID2020-113839RB-I00 funded by MCIN/AEI/10.13039/501100011033 to C.B. DM acknowledges financial support from the Conselleria d ' Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2018/021 and AEST/2019/037) .Mirón-Mombiela, R.; Ruiz-España, S.; Moratal, D.; Borrás, C. (2023). Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mechanisms of Ageing and Development. 215. https://doi.org/10.1016/j.mad.2023.11186021

Using a Penalized Likelihood to Detect Mortality Deceleration

In this paper, we suggest a novel method for detecting mortality deceleration. We focus on the gamma-Gompertz frailty model and suggest the subtraction of a penalty in the log-likelihood function as an alternative to traditional likelihood inference and hypothesis testing. Over existing methods, our method offers advantages, such as avoiding the use of a p-value, hypothesis testing, and asymptotic distributions. We evaluate the performance of our approach by comparing it with traditional likelihood inference on both simulated and real mortality data. Results have shown that our approach is more accurate in detecting mortality deceleration and provides more reliable estimates of the underlying parameters. The proposed method is a significant contribution to the literature as it offers a powerful tool for analyzing mortality patterns

The gut–muscle axis in older subjects with low muscle mass and performance: a proof of concept study exploring fecal microbiota composition and function with shotgun metagenomics sequencing

The gut microbiota could influence the pathophysiology of age‐related sarcopenia through multiple mechanisms implying modulation of chronic inflammation and anabolic resistance. The aim of this study was to compare the fecal microbiota composition and functionality, assessed by shotgun metagenomics sequencing, between two groups of elderly outpatients, differing only for the presence of primary sarcopenia. Five sarcopenic elderly subjects and twelve non‐sarcopenic controls, classified according to lower limb function and bioimpedance‐derived skeletal muscle index, provided a stool sample, which was analyzed with shotgun metagenomics approaches, to determine the overall microbiota composition, the representation of bacteria at the species level, and the prediction of bacterial genes involved in functional metabolic pathways. Sarcopenic subjects displayed different fecal microbiota compositions at the species level, with significant depletion of two species known for their metabolic capacity of producing short‐chain fatty acids (SCFAs), Faecalibacterium prausnitzii and Roseburia inulinivorans, and of Alistipes shahii. Additionally, their fecal metagenome had different representation of genes belonging to 108 metabolic pathways, namely, depletion of genes involved in SCFA synthesis, carotenoid and isoflavone biotransformation, and amino acid interconversion. These results support the hypothesis of an association between microbiota and sarcopenia, indicating novel possible mediators, whose clinical relevance should be investigated in future studies