Assessing logistic regression applied to respondent-driven sampling studies : a simulation study with an application to empirical data

The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option

Evaluation of impact of one dose varicella vaccine on the incidence of chickenpox in Argentina.

INTRODUCTION: Varicella, also known as chickenpox is one of the most common immunizable diseases. In 1998, the World Health Organization (WHO) recommended to incorporate this vaccine in the national immunization programs, which Argentina did in 2015. OBJECTIVES: To describe the behavior of the varicella time series for the 2005-2017 period, and to evaluate the impact of the vaccine in Argentina. METHODOLOGY: An ecological observational study was performed, using the varicella cases reported in the National Health Monitoring System, and the data of the National census as secondary data sources. A model based time series analysis of the notified varicella cases in Argentina was performed, using a Negative Binomial Mixed Model. For the verification of the vaccine impact, the 2005-2014 period was selected, and a prognosis for the following years was performed. Impact was evaluated by comparing the rates and confidence intervals between the predicted and observed values. RESULTS: Argentina reported 1,775,587 varicella cases for the 2005-2017 period. The series exhibited seasonality, and, a decreasing trend in the number of cases was observed in 2016 and 2017. A reduction of the incidence rate after the implementation of the vaccine was observed. The transmission risk decreased in the country after vaccine implementation. CONCLUSIONS: This study is the first concrete evidence of the varicella incidence decline after the implementation of a single dose application program in Argentina

Fast approaches for Bayesian estimation of size of hard-to-reach populations using Network Scale-up

The Network scale-up method is commonly used to overcome difficulties in estimating the size of hard-to-reach populations. The method uses indirect information based on social network of each participant taken from the general population, but in some applications a fast computational approach would be highly recommended. We propose a Gibbs sampling method and a Monte Carlo approach to sample from the random degree model. We applied the abovementioned analytical strategies to previous data on heavy drug users from Curitiba, Brazil

Dynamic Quantile Linear Models: A Bayesian Approach

The paper introduces a new class of models, named dynamic quantile linear models, which combines dynamic linear models with distribution-free quantile regression producing a robust statistical method. Bayesian estimation for the dynamic quantile linear model is performed using an efficient Markov chain Monte Carlo algorithm. The paper also proposes a fast sequential procedure suited for high-dimensional predictive modeling with massive data, where the generating process is changing over time. The proposed model is evaluated using synthetic and well-known time series data. The model is also applied to predict annual incidence of tuberculosis in the state of Rio de Janeiro and compared with global targets set by the World Health Organization

Nicotinic acid induces antinociceptive and anti-inflammatory effects in different experimental models

AbstractAlthough in vitro studies have shown that nicotinic acid inhibits some aspects of the inflammatory response, a reduced number of in vivo studies have investigated this activity. To the best of our knowledge, the effects induced by nicotinic acid in models of nociceptive and inflammatory pain are not known. Per os (p.o.) administration of nicotinic acid (250, 500 or 1000mg/kg, −1h) inhibited the first and the second phases of the nociceptive response induced by formalin in mice. Nicotinic acid (250 or 500mg/kg, −1 and 3h) also inhibited the mechanical allodynia induced by carrageenan in rats, a model of inflammatory pain. However, in a model of nociceptive pain, exposure of mice to a hot-plate, nicotinic acid was devoid of activity. In addition to inhibiting the nociceptive response in models of inflammatory pain, nicotinic acid (250 or 500mg/kg, p.o., −1 and 3h) inhibited paw edema induced by carrageenan in mice and rats. Picolinic acid (62.5 or 125mg/kg, p.o., −1h), a nicotinic acid isomer, inhibited both phases of the nociceptive response induced by formalin, but not paw edema induced by carrageenan in mice. The other nicotinic acid isomer, isonicotinic acid, was devoid of activity in these two models. In conclusion, our results represent the first demonstration of the activity of nicotinic acid in experimental models of nociceptive and inflammatory pain and also provide further support to its anti-inflammatory activity. It is unlikely that conversion to nicotinamide represents an important mechanism to explain the antinociceptive and anti-inflammatory activities of nicotinic acid. The demonstration of new activities of nicotinic acid, a drug that has already been approved for clinical use and presents a positive safety record, may contribute to raise the interest in conducting clinical trials to investigate its usefulness in the treatment of painful and inflammatory diseases

The cost-effectiveness of controlling dengue in Indonesia using wMel Wolbachia released at scale: a modelling study.

BACKGROUND: Release of virus-blocking Wolbachia-infected mosquitoes is an emerging disease control strategy that aims to control dengue and other arboviral infections. Early entomological data and modelling analyses have suggested promising outcomes, and wMel Wolbachia releases are now ongoing or planned in 12 countries. To help inform government, donor, or philanthropist decisions on scale-up beyond single city releases, we assessed this technology's cost-effectiveness under alternative programmatic options. METHODS: Using costing data from existing Wolbachia releases, previous dynamic model-based estimates of Wolbachia effectiveness, and a spatially explicit model of release and surveillance requirements, we predicted the costs and effectiveness of the ongoing programme in Yogyakarta City and three new hypothetical programmes in Yogyakarta Special Autonomous Region, Jakarta, and Bali. RESULTS: We predicted Wolbachia to be a highly cost-effective intervention when deployed in high-density urban areas with gross cost-effectiveness below $1500 per DALY averted. When offsets from the health system and societal perspective were included, such programmes even became cost saving over 10-year time horizons with favourable benefit-cost ratios of 1.35 to 3.40. Sequencing Wolbachia releases over 10 years could reduce programme costs by approximately 38% compared to simultaneous releases everywhere, but also delays the benefits. Even if unexpected challenges occurred during deployment, such as emergence of resistance in the medium-term or low effective coverage, Wolbachia would remain a cost-saving intervention. CONCLUSIONS: Wolbachia releases in high-density urban areas are expected to be highly cost-effective and could potentially be the first cost-saving intervention for dengue. Sites with strong public health infrastructure, fiscal capacity, and community support should be prioritised