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The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.

Background: A previous investigation of the occurrence of childhood acute leukemia around the Belgian nuclear sites has shown positive associations around one nuclear site (Mol-Dessel). In the following years, the Belgian Cancer Registry has made data available at the smallest administrative unit for which demographic information exists in Belgium, i.e. the statistical sector. This offers the advantage to reduce the potential misclassification due to large geographical scales., Methods: The current study performed for the period 2006-2016 uses Poisson models to investigate (i) the incidence of childhood acute leukemia within 20 km around the four Belgian nuclear sites, (ii) exposure-response relationships between cancer incidence and surrogate exposures from the nuclear sites (distance, wind direction frequency and exposure by hypothetical radioactive discharges taking into account historical meteorological conditions). All analyses are carried out at statistical sector level., Results: Higher incidence rate ratios were found for children <15 years (7 cases, RR = 3.01, 95% CI: 1.43;6.35) and children <5 years (< 5 cases, RR = 3.62, 95% CI: 1.35;9.74) living less than 5 km from the site of Mol-Dessel. In addition, there was an indication for positive exposure-response relationships with the different types of surrogate exposures., Conclusion: Results confirm an increased incidence of acute childhood leukemia around Mol-Dessel, but the number of cases remains very small. Random variation cannot be excluded and the ecological design does not allow concluding on causality. These findings emphasize the need for more in-depth research into the risk factors of childhood leukemia, for a better understanding of the etiology of this disease., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months., (Copyright © 2021. Published by Elsevier B.V.)

Introduction: COVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions., Methods: We estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors., Results: The results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda., Conclusion: The analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks., Methods: We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown., Results: Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period., Conclusions: Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

Patients with sickle cell disease (SCD) have reduced functional capacity due to anemia and cardio-respiratory abnormalities. Recent studies also suggest the presence of muscle dysfunction. However, the interaction between exercise capacity and muscle function is currently unknown in SCD. The aim of this study was to explore how muscle dysfunction may explain the reduced functional capacity. Nineteen African healthy subjects (AA), and 24 sickle cell anemia (SS) and 18 sickle cell hemoglobin C (SC) patients were recruited. Maximal isometric torque (Tmax) was measured before and after a self-paced 6-min walk test (6-MWT). Electromyographic activity of the Vastus Lateralis was recorded. The 6-MWT distance was reduced in SS ( p < 0.05) and SC ( p < 0.01) patients compared to AA subjects. However, Tmax and root mean square value were not modified by the 6-MWT, showing no skeletal muscle fatigue in all groups. In a multiple linear regression model, genotype, step frequency and hematocrit were independent predictors of the 6-MWT distance in SCD patients. Our results suggest that the 6-MWT performance might be primarily explained by anemia and the self-paced step frequency in SCD patients attempting to limit metabolic cost and fatigue, which could explain the absence of muscle fatigue.

A major outbreak of the Ebola virus occurred in 2014 in Sierra Leone. We investigate the spatial heterogeneity of the outbreak among districts in Sierra Leone. The stochastic discrete-time susceptible-exposed-infectious-removed (SEIR) model is used, allowing for probabilistic movements from one compartment to another. Our model accounts for heterogeneity among districts by making use of a hierarchical approach. The transmission rates are considered time-varying. It is investigated whether or not incubation period, infectious period and transmission rates are different among districts. Estimation is done using the Bayesian formalism. The posterior estimates of the effective reproductive number were substantially different across the districts, with pronounced variability in districts with few cases of Ebola. The posterior estimates of the reproductive number at the district level varied between below 1.0 and 4.5, whereas at nationwide level it varied between below 1.0 and 2.5. The posterior estimate of the effective reproductive number reached a value below 1.0 around December. In some districts, the effective reproductive number pointed out for the persistence of the outbreak or for a likely resurgence of new cases of Ebola virus disease (EVD). The posterior estimates have shown to be highly sensitive to prior elicitation, mainly the incubation period and infectious period., Competing Interests: He authors have declared that no competing interests exist.

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof., Competing Interests: The authors have declared that no competing interests exist.

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

Background: Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects., Methods: Assuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects., Results: We observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression., Conclusions: Our findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Bayesian sequential integration is an appealing approach in drug development, as it allows to recursively update posterior distributions as soon as new data become available, thus considerably reducing the computation time. However, preclinical trials are often characterized by small sample sizes, which may affect the estimation process during the first integration steps, particularly when complex PK-PD models are used. In this case, sequential integration would not be practicable, and trials should be pooled together. This work is aimed at comparing simple Bayesian pooling with sequential integration through a simulation study. The two techniques are compared under several scenarios using linear as well as nonlinear models. The results of our simulation study encourage the use of Bayesian sequential integration with linear models. However, in the case of nonlinear models several caveats arise. This paper outlines some important recommendations and precautions in that respect.

Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications. We note that it has placed biostatistics, epidemiology, virology, infectiology, and vaccinology, and related fields in the spotlight in an unprecedented way, implying great opportunities, but also the need to communicate effectively, often amidst controversy., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The number of secondary cases is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the number of secondary cases is often modelled using a negative binomial distribution. However, this may not be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the offspring mean and its overdispersion when the data generating distribution is different from the one used for inference. We find that overdispersion estimates may be biased when there is a substantial amount of heterogeneity, and that the use of other distributions besides the negative binomial should be considered. We revisit three previously analysed COVID-19 datasets and quantify the proportion of cases responsible for 80% of transmission, p 80% , while acknowledging the variation arising from the assumed offspring distribution. We find that the number of secondary cases for these datasets is better described by a Poisson-lognormal distribution.

Mesothelioma is a rare cancer caused by exposure to asbestos. Belgium has a known long history of asbestos production, resulting in one of the highest mesothelioma mortality rates worldwide. While the production of asbestos has stopped completely, the long latency period of mesothelioma, which can fluctuate between 20 and 40 years after exposure, causes incidences still to be frequent. Mesothelioma's long incubation time affects our assessment of its geographical distribution as well. Since patients' residential locations are likely to change a number of times throughout their lives, the location where the patients develop the disease is often far from the location where they were exposed to asbestos. Using the residential history of patients, we propose the use of a convolution multiple membership model (MMM), which includes both a spatial conditional autoregressive and an unstructured random effect. Pancreatic cancer patients are used as a control population, reflecting the population at risk for mesothelioma. Results show the impact of the residential mobility on the geographical risk estimation, as well as the importance of acknowledging the latency period of a disease. A simulation study was conducted to investigate the properties of the convolution MMM. The robustness of the results for the convolution MMM is assessed via a sensitivity analysis., (© 2020 John Wiley & Sons, Ltd.)

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes . Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.

Although COVID-19 has been spreading throughout Belgium since February, 2020, its spatial dynamics in Belgium remain poorly understood, partly due to the limited testing of suspected cases during the epidemic's early phase. We analyse data of COVID-19 symptoms, as self-reported in a weekly online survey, which is open to all Belgian citizens. We predict symptoms' incidence using binomial models for spatially discrete data, and we introduce these as a covariate in the spatial analysis of COVID-19 incidence, as reported by the Belgian government during the days following a survey round. The symptoms' incidence is moderately predictive of the variation in the relative risks based on the confirmed cases; exceedance probability maps of the symptoms' incidence and confirmed cases' relative risks overlap partly. We conclude that this framework can be used to detect COVID-19 clusters of substantial sizes, but it necessitates spatial information on finer scales to locate small clusters., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

There are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. We investigate the time from symptom onset to diagnosis and hospitalization or the length of stay (LoS) in the hospital, and whether there are differences in the population. Sciensano collected information on 14,618 hospitalized patients with COVID-19 admissions from 114 Belgian hospitals between 14 March and 12 June 2020. The distributions of different event times for different patient groups are estimated accounting for interval censoring and right truncation of the time intervals. The time between symptom onset and hospitalization or diagnosis are similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient (longest delay in age group 20-60 years) and whether or not the patient lives in a nursing home (additional 2 days for patients from nursing home). The median LoS in hospital varies between 3 and 10.4 days, with the LoS increasing with age. The hospital LoS for patients that recover is shorter for patients living in a nursing home, but the time to death is longer for these patients. Over the course of the first wave, the LoS has decreased.

Oxidative stress would play a role in the pathophysiology of sickle cell anemia (SCA). We tested the impact of common SCA genetic modifiers (alpha-thalassemia, G6PD deficiency, HbF quantitative trait loci; QTL) and pro/antioxidant genes polymorphisms ( SOD2 rs4880, XO rs207454, MPO rs233322) on oxidative stress biomarkers (AOPP, MDA, MPO, XO, MnSOD, CAT, GPx) and clinical severity in 301 Senegalese SCA hydroxyurea-free children at steady-state (median age 9.1 years, sex ratio H/F = 1.3). Plasma oxidative stress biomarkers were compared with those of a control group (AA). CAT activity, AOPP, and MDA levels were higher in SCA than in AA individuals while XO, GPX, and MnSOD activities were lower. The presence of alpha-thalassemia decreased MDA level and MPO activity but no effect of the HbF QTL or G6PD deficiency was observed. SCA children who experienced their first hospitalized complication before 3 years old had higher MnSOD and CAT activities than the other children while those with no hospitalized VOC in the previous 2 years presented higher GPX activity. Age of the first hospitalized complication and AOPP levels were affected by the MPO rs2333227 SNP. Our results suggest that alpha-thalassemia modulates oxidative stress in SCA, presumably because of a reduction in the MPO activity.

Every year, 435,000 people worldwide die from Malaria, mainly in Africa and Asia. However, malaria is a curable and preventable disease. Most countries are developing malaria elimination plans to meet sustainable development goal three, target 3.3, which includes ending the epidemic of malaria by 2030. Rwanda, through the malaria strategic plan 2012-2018 set a target to reduce malaria incidence by 42% from 2012 to 2018. Assessing the health policy and taking a decision using the incidence rate approach is becoming more challenging. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. We used a spatio-temporal model to estimate the excess probability for decision making at a small area on evaluating reduction of incidence. SIR and BYM models were developed using routine data from Health facilities for the period from 2012 to 2018 in Rwanda. The fitted model was used to generate relative risk (RR) estimates comparing the risk with the malaria risk in 2012, and to assess the probability of attaining the set target goal per area. The results showed an overall increase in malaria in 2013 to 2018 as compared to 2012. Ofall sectors in Rwanda, 47.36% failed to meet targeted reduction in incidence from 2012 to 2018. Our approach of using excess probability method to evaluate attainment of target or identifying threshold is a relevant statistical method, which will enable the Rwandan Government to sustain malaria control and monitor the effectiveness of targeted interventions., Competing Interests: The authors have declared that no competing interests exist.

Background: Cardiovascular diseases remain as the leading cause of morbidity and mortality in industrialized countries. Ageing and gender strongly modulate the risk to develop cardiovascular diseases but very few studies have investigated the impact of gender on cardiovascular diseases in the elderly, which represents a growing population. The purpose of this study was to test the impact of gender and physical activity level on several biochemical and clinical markers of cardiovascular risk in elderly individuals., Methods: Elderly individuals (318 women (75.8 ± 1.2 years-old) and 227 men (75.8 ± 1.1 years-old)) were recruited. Physical activity was measured by a questionnaire. Metabolic syndrome was defined using the National Cholesterol Education Program Expert Panel's definition. Polysomnography and digital tonometry were used to detect obstructive sleep apnea and assess vascular reactivity, respectively. Blood was sampled to measure several oxidative stress markers and adhesion molecules., Results: The frequency of cardiovascular diseases was significantly higher in men (16.4%) than in women (6.1%) ( p < 0.001). Body mass index (25.0 ± 4.3 vs. 25.8 ± 3.13 kg.m -2 ) and glycaemia (94.9 ± 16.5 vs. 101.5 ± 22.6 mg.dL -1 ) were lower, and High Density Lipoprotein (HDL) (74.6 ± 17.8 vs. 65.0 ± 17.2 mg.dL -1 ) was higher in women compared to men ( p < 0.05). Oxidative stress was lower in women than in men (uric acid: 52.05 ± 13.78 vs. 59.84 ± 13.58, advanced oxidation protein products: 223 ± 94 vs. 246 ± 101  μ mol.L -1 , malondialdehyde: 22.44 ± 6.81 vs. 23.88 ± 9.74 nmol.L -1 ). Physical activity was not associated with lower cardiovascular risk factors in both genders. Multivariate analyses showed an independent effect of gender on acid uric ( β = 0.182; p = 0.020), advanced oxidation protein products ( β = 0.257; p < 0.001), and HDL concentration ( β = -0.182; p = 0.026)., Conclusion: These findings suggest that biochemical cardiovascular risk factors are lower in women than men which could explain the lower cardiovascular disease proportion observed in women in the elderly., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Antoine Raberin et al.)

Motor imagery (MI) is the capacity to mentally perform one or a set of movements without concomitant overt action. MI training has been show to enhance the subsequent motor performance. While the benefits of MI to manage stress have been extensively documented, the reverse impact of stress on MI received far less attention. The present study thus aimed to evaluate whether acute stress might influence MI abilities. Thirty participants were assigned either to a stress or a control group. The Socially Evaluated Cold Pressor Test (SECPT) was used to induce stress, with heart rate, electrodermal activity, salivary cortisol, and self-report perceived levels of stress being monitored during the experiment. Stress induction was followed by both implicit (laterality judgment) and explicit (sequential pointing) MI tasks. Main results showed a deleterious impact of stress on implicit MI, while explicit MI was not altered. These exploratory findings provide a deeper understanding of stress effects on cognition, and practically support that under stressful conditions, as during a sport competition or rehabilitation contexts, explicit MI should be prioritized., Competing Interests: Declaration of competing interest We have no competing interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In Belgium, variations in thyroid cancer incidence were observed around the major nuclear sites. The present ecological study investigates whether there is an excess incidence of thyroid cancer among people living in the vicinity of the four nuclear sites at the smallest Belgian geographical level. Rate ratios were obtained from a Bayesian hierarchical model for areas of varying sizes around the nuclear sites. Focused hypothesis tests and generalized additive models were performed to test the hypothesis of a gradient in thyroid cancer incidence with increasing levels of surrogate exposures. No evidence was found for more incident cases of thyroid cancer near the two nuclear power plants. Regarding the two industrial and research nuclear sites, no evidence for a higher incidence in the vicinity of Mol-Dessel was observed, whereas a slightly nonsignificant higher incidence was found in the close vicinity of Fleurus. In addition, significant gradients for thyroid cancer incidence were observed with the different types of surrogate exposure considered in the 20 km area around the site of Fleurus (decreasing distance, increasing wind direction frequency and increasing exposure to estimated hypothetical radioactive discharges of iodine-131). In the investigation at the smallest Belgian geographical level, variations in thyroid cancer incidence were found around the Belgian nuclear sites. Significant exposure-response relationships were also observed for the site of Fleurus. Further investigations into these findings could be useful to allow inferring causal relationships on the origin of variations in incidence and to provide information at the individual level., (© 2019 Sciensano. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Pre-term birth is a major health concern that occurs in approximately 10% of births worldwide. Despite high incidence rate, long-term consequences of pre-term birth remain unclear. Recent evidence suggests that elevated oxidative stress observed in pre-term born infants could persist into adulthood. Given that oxidative stress is known to play an important role in response to physical activity and hypoxia, we investigated whether oxidative stress responses to acute exercise in normoxia and hypoxia may be differently modulated in pre-term vs. full-term born adults. Twenty-two pre-term born and fifteen age-matched full-term born controls performed maximal incremental cycling tests in both normoxia (FiO2: 0.21) and normobaric hypoxia (FiO2: 0.13; simulated altitude of 3800 m) in blinded and randomized manner. Plasma levels of oxidative stress (advanced oxidation protein products [AOPP] and malondialdehyde), antioxidant (ferric reducing antioxidant power, glutathione peroxidase, catalase [CAT] and superoxide dismutase [SOD]) and nitrosative stress markers (nitrotyrosine, nitrite and total nitrite and nitrate [NOx]) were measured before and immediately after each test. AOPP (+24%, P<0.001), CAT (+38%, P<0.001) and SOD (+12%, P=0.018) and NOx (+17%, P=0.024) significantly increased in response to exercise independently of condition and birth status. No difference in response to acute exercise in normoxia was noted between pre-term and full-term born adults in any of measured markers. Hypoxic exposure during exercise resulted in significant increase in AOPP (+45%, P=0.008), CAT (+55%, P=0.019) and a trend for an increase in nitrite/nitrate content (+35%, P=0.107) only in full-term and not pre-term born individuals. These results suggest that prematurely born adult individuals exhibit higher resistance to oxidative stress response to exercise in hypoxia., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.

New Findings: What is the central question of this study? What are the effects of repeated subclinical vaso-occlusions on nuclear factor erythroid 2 related factor 2 (Nrf2) and oxidative stress balance regulation in the kidney of transgenic SAD mice? What is the main finding and its importance? In response to hypoxia-reoxygenation, nuclear Nrf2 protein expression decreased in the kidney of SAD mice while haem oxygenase transcripts were increased. This suggest that in SAD mice, other transcription factors than Nrf2 could be involved in renal antioxidant gene regulation in response to hypoxia-reoxygenation., Abstract: Hypoxia-reoxygenation (H/R) stress is known to increase oxidative stress in transgenic sickle mice and can cause organ failure. Here we described the effects of H/R on nuclear factor erythroid 2-related factor 2 (Nrf2) as a putative regulator of redox status in the kidneys of SAD mice investigating Nrf2-regulated antioxidant enzymes. Transgenic SAD mice and healthy C57Bl/6J mice were exposed to 4 h of hypoxia followed by various times of reoxygenation at ambient air (2 or 6 h). Regardless of the conditions (i.e. normoxia or H/R), SAD mice expressed higher renal oxidative stress levels. Nuclear Nrf2 protein expression decreased after 2 h post-hypoxia only in the medulla region of the kidney and only in SAD mice. Simultaneously, haem oxygenase transcripts were affected by H/R stimulus with a significant enhancement after 2 h post-hypoxia. Similarly, hypoxia inducible factor-1α staining increased after 2 h post-hypoxia in SAD mice in both cortex and medulla areas. Our data confirm that the kidneys are organs that are particularly sensitive to H/R stimuli in sickle cell SAD mice. Also, these results suggest an effect of the duration of recovery period (short vs. long) and specific responses according to kidney areas, medulla vs. cortex, on Nrf2 expression in response to H/R stimuli in SAD mice., (© 2019 The Authors. Experimental Physiology © 2019 The Physiological Society.)

Mozambique has a high burden of HIV and is currently ranked sixth worldwide for adult prevalence. In Mozambique, HIV prevalence is not uniformly distributed geographically and throughout the population. We investigated the spatial distribution of HIV infection among adolescents and young people in Mozambique using the 2009 AIDS Indicator Survey (AIS). Generalized geoadditive modeling, combining kriging and additive modeling, was used to study the geographical variability of HIV risk among young people. The nonlinear spatial effect was assessed through radial basis splines. The estimation process was done using two-stage iterative penalized quasi-likelihood within the framework of a mixed-effects model. Our estimation procedure is an extension of the approach by Vandendijck et al., estimating the range (spatial decay) parameter in a binary context. The results revealed the presence of spatial patterns of HIV infection. After controlling for important covariates, the results showed a greater burden of HIV/AIDS in the central and northern regions of the country. Several socio-demographic, biological, and behavioral factors were found to be significantly associated with HIV infection among young people. The findings are important, as they can help health officials and policy makers to design targeted interventions for responding to the HIV epidemic.

Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance.

Recently, the generalized-growth model was introduced as a flexible approach to characterize growth dynamics of disease outbreaks during the early ascending phase. In this work, by using classical maximum likelihood estimation to obtain parameter estimates, we evaluate the impact of varying levels of overdispersion on the inference of the growth scaling parameter through comparing Poisson and Negative binomial models. In particular, under exponential and sub-exponential growth scenarios, we evaluate, via simulations, the error rate of making an incorrect characterization of early outbreak growth patterns. Simulation results show that the ability to correctly identify early outbreak growth patterns can be affected by overdispersion even when accounted for using the Negative binomial model. We exemplify our findings using data on five different outbreaks. Overall, our results show that estimates should be interpreted with caution when data are overdispersed., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer's effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

Disease mapping is a scientific field that aims to understand and predict disease risk based on counts of observed cases within small regions of a study area of interest. Hierarchical model-based approaches that borrow information from neighbouring areas via conditional autoregressive (CAR) random effects on the local disease rates have gained a lot of popularity, thanks to the readily implemented Markov chain Monte Carlo methods. Nowadays, many software implementations to model risk distributions exist. Many of these applications differ, to varying degrees, in the underlying methodology. This paper provides an in-depth comparison between analysis results, coming from R-packages CARBayes, R2OpenBUGS, NIMBLE, R2BayesX, R-INLA, and RStan. We investigate CAR models typically used in disease mapping for spatially discrete count data. Data about diabetics in children and young adults in Belgium are used in a case study, while simulation studies are undertaken to assess software performance in different settings., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Oxidative stress is a key feature in the pathophysiology of sickle cell disease. Endurance training has been shown to reduce oxidative stress in the heart and the liver of sickle mice. However, the effects of endurance training on skeletal muscles, which are major producers of reactive oxygen species during exercise, are currently unknown. The aim of this study was to evaluate the effect of sickle genotype on prooxidant/antioxidant response to individualized endurance training in skeletal muscles of sickle mice. Healthy and homozygous Townes sickle mice were divided into trained or sedentary groups. Maximal aerobic speed and V̇O 2 peak were determined using an incremental test on a treadmill. Trained mice ran at 40% to 60% of maximal aerobic speed, 1 h/day, 5 days/week for 8 weeks. Oxidative stress markers, prooxidant/antioxidant response, and citrate synthase enzyme activities were assessed in the gastrocnemius , in the plantaris , and in the soleus muscles. Maximal aerobic speed and V̇O 2 peak were significantly reduced in sickle compared to healthy mice (-57% and -17%; p < 0.001). NADPH oxidase, superoxide dismutase, and catalase activities significantly increased after training in the gastrocnemius of sickle mice only. A similar trend was observed for citrate synthase activity in sickle mice ( p = 0.06). In this study, we showed an adaptive response to individualized endurance training on the prooxidant/antioxidant balance in the gastrocnemius, but neither in the plantaris nor in the soleus of trained sickle mice, suggesting an effect of sickle genotype on skeletal muscle response to endurance treadmill training., Competing Interests: The authors have no competing interests, including specific financial interests, relationships, and/or affiliations relevant to the subject matter or materials included in this manuscript.

A large proportion of adult patients with sickle cell disease (SCD) develops kidney disease and is at a high risk of mortality. The contribution of advanced glycation end products and their receptor (AGE/RAGE) axis has been established in the pathogenesis of multiple kidney diseases. The aim of the present study was to determine the implication of RAGE in the development of SCD-related kidney complications in a mouse model of SCD, as this has never been investigated. 8-week-old AA (normal) and SS (homozygous SCD) Townes mice were treated with a specific RAGE antagonist (RAP) or vehicle (NaCl). After 3 weeks of treatment, red blood cell count, hematocrit, and hemoglobin levels were significantly higher in RAP-treated SS mice. Reticulocyte count and sickle cell count were reduced in RAP-SS compared to their NaCl-treated littermates. The lower NADPH oxidase activity in the kidney of RAP-treated mice compared to NaCl-treated mice suggests limited ROS production. RAP-treated SS mice had decreased NF-κB protein expression and activation as well as reduced TNF-α mRNA expression in the kidney. Glomerular area, interstitial fibrosis, tubular iron deposits, and KIM-1 protein expression were significantly reduced after RAP treatment. In conclusion, this study provides evidence supporting the pathogenic role of RAGE in kidney injuries in sickle cell mice.

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed., (© 2019 John Wiley & Sons, Ltd.)

Sickle cell disease (SCD) is associated with chronic activation of coagulation and an increased risk of venous thromboembolism. Erythrocyte sickling, the primary pathologic event in SCD, results in dramatic morphological changes in red blood cells (RBCs) because of polymerization of the abnormal hemoglobin. We used a mouse model of SCD and blood samples from sickle patients to determine if these changes affect the structure, properties, and dynamics of sickle clot formation. Sickling of RBCs and a significant increase in fibrin deposition were observed in venous thrombi formed in sickle mice. During ex vivo clot contraction, the number of RBCs extruded from sickle whole blood clots was significantly reduced compared with the number released from sickle cell trait and nonsickle clots in both mice and humans. Entrapment of sickled RBCs was largely factor XIIIa-independent and entirely mediated by the platelet-free cellular fraction of sickle blood. Inhibition of phosphatidylserine, but not administration of antisickling compounds, increased the number of RBCs released from sickle clots. Interestingly, whole blood, but not plasma clots from SCD patients, was more resistant to fibrinolysis, indicating that the cellular fraction of blood mediates resistance to tissue plasminogen activator. Sickle trait whole blood clots demonstrated an intermediate phenotype in response to tissue plasminogen activator. RBC exchange in SCD patients had a long-lasting effect on normalizing whole blood clot contraction. Furthermore, RBC exchange transiently reversed resistance of whole blood sickle clots to fibrinolysis, in part by decreasing platelet-derived PAI-1. These properties of sickle clots may explain the increased risk of venous thromboembolism observed in SCD., (© 2019 by The American Society of Hematology.)

Public health and governmental organizations have acknowledged the importance of obtaining information of various characteristics for small areas, such as counties. Spatial smoothing models have been developed to gain reliable information on the geographical distribution of the outcome of interest. When the geographical analysis is based on survey data, two issues pose challenges: (1) the complex design of the survey and (2) the presence of missing data due to non-response. We investigate the influence of missing data and the adjustment thereof in the context of the 2013 Florida Behavioral Risk Factor Surveillance System (BRFSS) health survey. We focus on the application and comparison of the Hajek ratio estimator and two model-based approaches for estimation of the spatial trend of the prevalence of having no health insurance coverage. The model-based methods are compared using the Deviance Information Criterion which show the benefits of modeling the weights as flexibly as possible. Methods are extended towards subgroup analyses and the estimation of area-specific standardized rates, where household incomes was identified as an important factor to include in the analysis., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Analysis of clustered data is often performed using random effects regression models. In such conditional models, a cluster-specific random effect is often introduced into the linear predictor function. Parameter interpretation of the covariate effects is then conditioned on the random effects, leading to a subject-specific interpretation of the regression parameters. Recently, Marginalized Multilevel Models (MMM) and the Bridge distribution models have been proposed as a unified approach, which allows one to capture the within-cluster correlations by specifying random effects while still allowing for marginal parameter interpretation. In this paper, we investigate these two approaches, and the conditional Generalized Linear Mixed Model (GLMM), in the context of right-truncated, interval-censored time-to-event data, further characterized by clustering and additional overdispersion. While these models have been applied in literature to model the mean, here we extend their application to modeling the hazard function for the survival endpoints. The models are applied to analyze data from the HET-CAM VT experiment which was designed to assess the potential of a compound to cause injection site reaction. Results show that the MMM and Bridge distribution approaches are useful when interest is in the marginal interpretation of the covariate effects.

The standard mass action, which assumes that infectious disease transmission occurs in well-mixed populations, is popular for formulating compartmental epidemic models. Compartmental epidemic models often follow standard mass action for simplicity and to gain insight into transmission dynamics as it often performs well at reproducing disease dynamics in large populations. In this work, we formulate discrete time stochastic susceptible-infected-removed models with linear (standard) and nonlinear mass action structures to mimic varying mixing levels. Using simulations and real epidemic data, we demonstrate the sensitivity of the basic reproduction number to these mathematical structures of the force of infection. Our results suggest the need to consider nonlinear mass action in order to generate more accurate estimates of the basic reproduction number although its uncertainty increases due to the addition of one growth scaling parameter., (© 2018 John Wiley & Sons, Ltd.)

Maternal mortality remains very high in Mozambique, with estimates from 2015 showing a maternal mortality ratio of 489 deaths per 100,000 live births, even though the rates tend to decrease since 1990. Pregnancy related hemorrhage, gestational hypertension and diseases such as malaria and HIV/AIDS are amongst the leading causes of maternal death in Mozambique, and a significant number of these deaths occur within health facilities. Often, the analysis of data on maternal mortality involves the use of counts of maternal deaths as outcome variable. Previously we showed that a class of hierarchical zero-inflated models were very successful in dealing with overdispersion and clustered counts when analyzing data on maternal deaths and related risk factors within health facilities in Mozambique. This paper aims at providing additional insights over previous analyses and presents an extension of such models to account for spatial variation in a disease mapping framework of facility-based maternal mortality in Mozambique., Competing Interests: The authors have declared that no competing interests exist.

Coadministration of 2 or more compounds can alter both the pharmacokinetics and pharmacodynamics of individual compounds. While experiments on pharmacodynamic drug-drug interactions are usually performed in an in vitro setting, this experiment focuses on an in vivo setting. The change over time of a safety biomarker is modeled using an indirect response model, in which the virtual pharmacokinetic profile of one compound drives the effect of the other. Several experiments at different dose level combinations were performed sequentially. While a traditional frequentist analysis consists of estimating the model parameters based on all the data simultaneously, in this work, we consider a Bayesian inference framework allowing to incorporate the results from a historical dose-response experiment., (© 2018 John Wiley & Sons, Ltd.)

We assess the relationship between epidemic size and the scaling of epidemic growth of Ebola epidemics at the level of administrative areas during the 2014-16 Ebola epidemic in West Africa. For this purpose, we quantify growth scaling parameters from the ascending phase of Ebola outbreaks comprising at least 7 weeks of epidemic growth. We then study how these parameters are associated with observed epidemic sizes. For validation purposes, we also analyse two historic Ebola outbreaks. We find a high monotonic association between the scaling of epidemic growth parameter and the observed epidemic size. For example, scaling of growth parameters around 0.3-0.4, 0.4-0.6 and 0.6 are associated with epidemic sizes on the order of 350-460, 460-840 and 840-2500 cases, respectively. These results are not explained by differences in epidemic onset across affected areas. We also find the relationship between the scaling of epidemic growth parameter and the observed epidemic size to be consistent for two past Ebola outbreaks in Congo (1976) and Uganda (2000). Signature features of epidemic growth could become useful to assess the risk of observing a major epidemic outbreak, generate improved diseases forecasts and enhance the predictive power of epidemic models. Our results indicate that the epidemic growth scaling parameter is a useful indicator of epidemic size, which may have significant implications to guide control of Ebola outbreaks and possibly other infectious diseases.

Preterm birth is a global health issue that can induce lifelong medical sequela. Presently, at least one in ten newborns are born prematurely. At birth, preterm newborns exhibit higher levels of oxidative stress (OS) due to the inability to face the oxygen rich environment in which they are born into. Moreover, their immature respiratory, digestive, immune and antioxidant defense systems, as well as the potential numerous medical interventions following a preterm birth, such as oxygen resuscitation, nutrition, phototherapy and blood transfusion further contribute to high levels of OS. Although the acute effects seem well established, little is known regarding the long-term effects of preterm birth on OS. This matter is especially important given that chronically elevated OS levels may persist into adulthood and consequently contribute to the development of numerous non-communicable diseases observed in people born preterm such as diabetes, hypertension or lung disorders. The purpose of this review is to summarize the current knowledge regarding the consequences of preterm birth on OS levels from newborn to adulthood. In addition, the effects of physical activity and hypoxia, both known to disrupt redox balance, on OS modulation in preterm individuals are also explored., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To investigate the associations between several sickle cell disease genetic modifiers (beta-globin haplotypes, alpha-thalassemia, and glucose-6-phosphate dehydrogenase deficiency) and the level of oxidative stress and to evaluate the association between oxidative stress and the rates of vaso-occlusive events., Study Design: Steady-state oxidative and nitrosative stress markers, biological variables, genetic modulators, and vaso-occlusive crisis events requiring emergency admissions were measured during a 2-year period in 62 children with sickle cell anemia (58 SS and 4 Sβ 0 ). Twelve ethnic-matched children without sickle cell anemia also participated as healthy controls (AA) for oxidative and nitrosative stress level measurement., Results: Oxidative and nitrosative stress were greater in patients with sickle cell anemia compared with control patients, but the rate of vaso-occlusive crisis events in sickle cell anemia was not associated with the level of oxidative stress. The presence of alpha-thalassemia, but not glucose-6-phosphate dehydrogenase deficiency or beta-globin haplotype, modulated the level of oxidative stress in children with sickle cell anemia., Conclusion: Mild hemolysis in children with alpha-thalassemia may limit oxidative stress and could explain the protective role of alpha-thalassemia in hemolysis-related sickle cell complications., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Chronic systemic inflammation is a pathophysiological feature of sickle cell disease (SCD). Considering that regular exercise exerts multiple beneficial health effects including anti-inflammatory actions, we investigated whether a treadmill training program could minimize the inflammatory state in transgenic sickle cell (SS) mice. To test this hypothesis, SS mice were subjected to a treadmill training protocol of 1h/day, 5days a week for 8weeks. Exercise training increased the percent of venous oxyhemoglobin and sharply decreased the percent of carboxyhemoglobin suggesting that exercise training may limit the proportion of erythrocytes that were deoxygenated in the venous circulation. Exercise training attenuated systemic inflammation as attested by a significant drop in white blood cell (WBC) count and plasma Th1/Th2 cytokine ratio. There was reduction in interleukin-1β and endothelin-1 mRNA expression in trained sickle mice. The spleen/body mass ratio was significantly decreased in trained sickle mice and there was a strong correlation between the magnitude of congestion and the relative spleen mass in all animals (trained and untrained). We conclude that moderate intensity exercise training, without any noticeable complications, may be associated with limited baseline blood deoxygenation and inflammation in sickle cell mice, and reduce sequestration of sickle erythrocytes/congestion in the spleen., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Sickle cell disease (SCD) is the most common inherited disease in the world. Red blood cell sickling, blood cell-endothelium adhesion, blood rheology abnormalities, intravascular haemolysis, and increased oxidative stress and inflammation contribute to the pathophysiology of SCD. Because acute intense exercise may alter these pathophysiological mechanisms, physical activity is usually contra-indicated in patients with SCD. However, recent studies in sickle-cell trait carriers and in a SCD mice model show that regular physical activity could decrease oxidative stress and inflammation, limit blood rheology alterations and increase nitric oxide metabolism. Therefore, supervised habitual physical activity may benefit patients with SCD. This article reviews the literature on the effects of acute and chronic exercise on the biological responses and clinical outcomes of patients with SCD., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

It is our primary focus to study the spatial distribution of disease incidence at different geographical levels. Often, spatial data are available in the form of aggregation at multiple scale levels such as census tract, county, state, and so on. When data are aggregated from a fine (e.g. county) to a coarse (e.g. state) geographical level, there will be loss of information. The problem is more challenging when excessive zeros are available at the fine level. After data aggregation, the excessive zeros at the fine level will be reduced at the coarse level. If we ignore the zero inflation and the aggregation effect, we could get inconsistent risk estimates at the fine and coarse levels. Hence, in this paper, we address those problems using zero inflated multiscale models that jointly describe the risk variations at different geographical levels. For the excessive zeros at the fine level, we use a zero inflated convolution model, whereas we consider a regular convolution model for the smoothed data at the coarse level. These methods provide a consistent risk estimate at the fine and coarse levels when high percentages of structural zeros are present in the data.

In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.

Chronic activation of coagulation is one of the features of sickle cell disease (SCD). Increased tissue factor expression, phosphatidylserine exposure, thrombin generation and fibrinolysis, as well as decreased levels of natural anticoagulants have been reported in SCD patients and in the mouse models of SCD. Consistent with this, patients with SCD are prone to develop thrombotic complications. In addition, the altered morphology of sickle red blood cells (RBC) may also alter the properties and dynamics of clot formation in SCD patients. Clinical data and results from animal models have revealed complex interactions between coagulation, chronic hemolysis, and inflammation suggesting that activation of coagulation may contribute to the pathophysiology of SCD.