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Association of environmental markers with childhood type 1 diabetes mellitus revealed by a long questionnaire on early life exposures and lifestyle in a case-control study

BackgroundThe incidence of childhood type 1 diabetes (T1D) incidence is rising in many countries, supposedly because of changing environmental factors, which are yet largely unknown.\n\nPurposeTo unravel environmental markers associated with T1D. Methods: Cases were children with T1D from the French Isis-Diab cohort. Controls were schoolmates or friends of the patients. Parents were asked to fill a 845-item questionnaire investigating the childs environment before diagnosis. The analysis took into account the matching between cases and controls. A second analysis used propensity score methods.\n\nResultsWe found a negative association of several lifestyle variables, gastroenteritis episodes, dental hygiene, hazelnut cocoa spread consumption, wasp and bee stings with T1D, consumption of vegetables from a farm and death of a pet by old age.\n\nConclusionsThe found statistical association of new environmental markers with T1D calls for replication in other cohorts and investigation of new environmental areas.

Epidemiology

Structure in the variability of the basic reproductive number (R0) for Zika epidemics in the Pacific islands

Before the outbreak that reached the Americas in 2015, Zika virus (ZIKV) circulated in Asia and the Pacific: these past epidemics can be highly informative on the key parameters driving virus transmission, such as the basic reproduction number (R0). We compare two compartmental models with different mosquito representations, using surveillance and seroprevalence data for several ZIKV outbreaks in Pacific islands (Yap, Micronesia 2007, Tahiti and Moorea, French Polynesia 2013-2014, New Caledonia 2014). Models are estimated in a stochastic framework with state-of-the-art Bayesian techniques. R0 for the Pacific ZIKV epidemics is estimated between 1.5 and 4.1, the smallest islands displaying higher and more variable values. This relatively low range of R0 suggests that intervention strategies developed for other flaviviruses should enable as, if not more effective control of ZIKV. Our study also highlights the importance of seroprevalence data for precise quantitative analysis of pathogen propagation, to design prevention and control strategies.

Epidemiology

Projected spread of Zika virus in the Americas

We use a data-driven global stochastic epidemic model to project past and future spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution, and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester, and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents an early modeling effort aimed at projecting the potential magnitude and timing of the ZIKV epidemic that might be refined as new and more accurate data from the region become available.

Epidemiology

Cross-scale dynamics and the evolutionary emergence of infectious diseases

AO_SCPLOWBSTRACTC_SCPLOWWhen emerging pathogens encounter new host species for which they are poorly adapted, they must evolve to escape extinction. Pathogens experience selection on traits at multiple scales, including replication rates within host individuals and transmissibility between hosts. We analyze a stochastic model linking pathogen growth and competition within individuals to transmission between individuals. Our analysis reveals a new factor, the cross-scale reproductive number of a mutant virion, that quantifies how quickly mutant strains increase in frequency when they initially appear in the infected host population. This cross-scale reproductive number combines with viral mutation rates, single-strain reproductive numbers, and transmission bottleneck width to determine the likelihood of evolutionary emergence, and whether evolution occurs swiftly or gradually within chains of transmission. We find that wider transmission bottlenecks facilitate emergence of pathogens with short-term infections, but hinder emergence of pathogens exhibiting cross-scale selective conflict and long-term infections. Our results provide a framework to advance the integration of laboratory, clinical and field data in the context of evolutionary theory, laying the foundation for a new generation of evidence-based risk assessment of emergence threats.

Epidemiology

Hypothesis Tests for Neyman’s Bias in Case-Control Studies

Survival bias is a long-recognized problem in case-control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neymans bias or \"prevalence-incidence bias.\" It occurs in case-control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neymans bias may be present in case-control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neymans bias in the former two cases, but not the last case.

Epidemiology

Survival of Captive Chimpanzees: Impact of Location and Transfers

To inform the retirement of NIH-owned chimpanzees, we analyzed the outcomes of 764 NIH-owned chimpanzees that were located at various points in time in at least one of 4 specific locations. All chimpanzees considered were alive and at least 10 years of age on January 1, 2005; transfers to a federal sanctuary began a few months later. During a median follow-up of just over 7 years, there were 314 deaths. In a Cox proportional hazards model that accounted for age, sex, and location (which was treated as a time-dependent covariate), age and sex were strong predictors of mortality, but location was only marginally predictive. Among 273 chimpanzees who were transferred to the federal sanctuary, we found no material increased risk in mortality in the first 30 days after arrival. During a median follow-up at the sanctuary of 3.5 years, age was strongly predictive of mortality, but other variables - sex, season of arrival, and ambient temperature on the day of arrival - were not predictive. We confirmed our regression findings using random survival forests. In summary, in a large cohort of captive chimpanzees, we find no evidence of materially important associations of location of residence or recent transfer with premature mortality.

Epidemiology

Planning horizon affects prophylactic decision-making and epidemic dynamics

Human behavior can change the spread of infectious disease. There is limited understanding of how the time in the future over which individuals make a behavioral decision, their planning horizon, affects epidemic dynamics. We developed an agent-based model (along with an ODE analog) to explore the decision-making of self-interested individuals on adopting prophylactic behavior. The decision-making process incorporates prophylaxis efficacy and disease prevalence with individuals' payoffs and planning horizon. Our results show that for short and long planning horizons individuals do not consider engaging in prophylactic behavior. In contrast, individuals adopt prophylactic behavior when considering intermediate planning horizons. Such adoption, however, is not always monotonically associated with the prevalence of the disease, depending on the perceived protection efficacy and the disease parameters. Adoption of prophylactic behavior reduces the peak size while prolonging the epidemic and potentially generates secondary waves of infection. These effects can be made stronger by increasing the behavioral decision frequency or distorting an individuals perceived risk of infection.

Epidemiology

Analysis of infection biomarkers within a Bayesian framework reveals their role in pneumococcal pneumonia diagnosis in HIV patients

BackgroundHIV patients are more likely to contract bacterial pneumonia and more likely to die from the infection. Unfortunately, there are few tests to quickly diagnosis the etiology of these dangerous infections. Several biomarkers may be useful for diagnosing the most common pneumonia-causing organism, S. pneumoniae, but studies utilizing the standard statistical approach provide little concrete guidance for the HIV-infected population.\n\nMethodology and FindingsUsing a Bayesian approach, I analyze data from a cohort of 280 HIV patients with x-ray confirmed community acquired pneumonia. First, I use a variety of techniques to establish predictor significance and to identify their optimal cutoffs. Next, in lieu of cutoffs, I find the continuous and combined likelihood ratios for every value of each biomarker, and I compute the associated posttest probabilities. As expected, I find the three biomarkers with good clinical yield and a statistically significant association with S. pneumoniae are C-reactive protein (CRP), procalcitonin (PCT), and lytA gene PCR (lytA). Based on Bayesian clinical yield, optimal cutoffs are largely equivocal. The optimal dichotomous cutoff for CRP is essentially any value between 10 mg/dL and 30 mg/dL ({bigtriangleup}pPosttest {approx} 0.49). The optimal cutoff for PCT is any value between 2 ng/mL and 40 ng/mL ({bigtriangleup}pposttest {approx} 0.35). The optimal cutoff for lytA is any value less than 6 log10 copies/mL ({bigtriangleup}pposttest {approx} 0.45). Further, I find that continuous likelihood ratios provide much more accurate posttest probabilities than dichotomous cutoffs. For example, starting with the empirical pretest probability, a lytA approaching 0 copies/mL lowers the probability of S. pneumoniae infection to less than 15%, while a result of 10 copies/mL raises the probability to greater than 65%. However, a lytA value just above or below the suggested cutoff of 8000 copies/mL or my new optimal cutoff of 30,000 copies/mL leaves the posttest probability of infection essentially unchanged from the pretest probability.\n\nConclusionCRP, PCT, and lytA all provide significant value in diagnosing the etiology of pneumonia in HIV patients. The optimal dichotomous cutoffs for lytA, CRP, and PCT need to be adjusted for pneumococcal diagnosis in this population. However, continuous and combined likelihood ratios avoid discarding valuable quantitative information, and a combined likelihood ratio can be easily computed without the need for prior logistic regression. Importantly, there is significant overlap between these biomarkers such that only one of the three biomarkers at a time should be used to update clinical probabilities. Thus, it is ill-advised to combine the likelihood ratios of different biomarkers to produce a posttest probability. Finally, I provide a simple web application to quantitatively calculate the posttest probability of S. pneumoniae infection in HIV patients with x-ray confirmed pneumonia: http://meyerapps.org/pneumococcal_etiology_hiv.

Epidemiology

Telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: Do they measure the same thing?

The geroscience hypothesis posits that therapies to retard biological processes of aging can prevent disease. To test such \"geroprotective\" therapies in humans, surrogate endpoints are needed for extension of disease-free lifespan. Methods to quantify biological aging could provide such surrogate endpoints, but different methods have not been systematically evaluated in the same humans. We studied seven measures of biological aging in 964 middle-aged humans in the Dunedin Study: telomere-length, three epigenetic-clocks, and three biomarker-composites. Agreement between these different measures of biological aging was low. We also compared associations between biological aging measures and outcomes that geroprotective therapies will seek to modify: physical functioning, cognitive decline, and subjective signs of aging. The 71-CpG epigenetic clock and the biomarker composites were consistently related to these outcomes. Effect-sizes were modest. Quantification of biological aging is a young field. Next steps are to move toward systematic evaluation and refinement of methods.

Epidemiology

Virus genomes reveal the factors that spread and sustained the West African Ebola epidemic.

The 2013-2016 epidemic of Ebola virus disease in West Africa was of unprecedented magnitude, duration and impact. Extensive collaborative sequencing projects have produced a large collection of over 1600 Ebola virus genomes, representing over 5% of known cases, unmatched for any single human epidemic. In a comprehensive analysis of this entire dataset, we reconstruct in detail the history of migration, proliferation and decline of Ebola virus throughout the region. We test the association of geography, climate, administrative boundaries, demography and culture with viral movement among 56 administrative regions. Our results show that during the outbreak viral lineages moved according to a classic gravity model, with more intense migration between larger and more proximate population centers. Despite a strong attenuation of international dispersal after border closures, localized cross-border transmission beforehand had already set the seeds for an international epidemic, rendering these measures relatively ineffective in curbing the epidemic. We use this empirical evidence to address why the epidemic did not spread into neighboring countries, showing that although these regions were susceptible to developing significant outbreaks, they were also at lower risk of viral introductions. Finally, viral genome sequence data uniquely reveals this large epidemic to be a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help inform approaches to intervention in such epidemics in the future.

Epidemiology

Remote effect of Insecticide-treated nets and the personal protection against malaria mosquito bites

Experimental huts are part of the WHO process for testing and evaluation of Insecticide Treated Nets (ITN) in semi-field conditions. Experimental Hut Trials (EHTs) mostly focus on two main indicators (i.e. mortality and blood feeding reduction) that serve as efficacy criteria to obtain WHO interim recommendation. However, several other outputs that rely on counts of vectors collected in the huts are neglected although they can give useful information about vectors behavior and personal protection provided by ITNs. In particular, EHTs allow to measure the deterrent effect and personal protection of ITNs.\n\nTo provide a better assessment of ITNs efficacy, we performed a retrospective analysis of the deterrence and the personal protection against malaria transmission for 12 unwashed and 13 washed ITNs evaluated through EHTs conducted in West Africa.\n\nA significant deterrent effect was shown for six of the 12 unwashed ITNs tested. When washed 20 times, only three ITNs had significant deterrent effect (Rate Ratios (RR)<1; p<0.05) and three showed an apparent \"attractiveness\" (RR>1; p<0.01). When compared to the untreated net, all unwashed ITNs showed lower number of blood-fed Anopheles indicating a significant personal protection (RR<1, p<0.05). However, when washed 20 times, three ITNs that were found to be attractive did not significantly reduced human-vector contact (p>0.05).\n\nCurrent WHO efficacy criteria do not sufficiently take into account the deterrence effect of ITNs. Moreover the deterrence variability is rarely discussed in EHT's reports. Our findings highlighted the long range effect (deterrent or attractive) of ITNs that may have significant consequences for personal/community protection against malaria transmission. Indicators measuring the deterrence should be further considered for the evaluation of ITNs.

Epidemiology

Design of Vaccine Trials during Outbreaks with and without a Delayed Vaccination Comparator

Conducting vaccine efficacy trials during outbreaks of emerging pathogens poses particular challenges. The Ebola ca suffit trial in Guinea used a novel ring vaccination cluster randomized design to target populations at highest risk of infection. Another key feature of the trial was the use of a delayed vaccination arm as a comparator, in which clusters were randomized to immediate vaccination or vaccination 21 days later. This approach, chosen to improve ethical acceptability of the trial, complicates the statistical analysis as participants in the comparison arm are eventually protected by vaccine. Furthermore, for infectious diseases, we observe time of illness onset and not time of infection, and we may not know the time required for the vaccinee to develop a protective immune response. As a result, including events observed shortly after vaccination may bias the per protocol estimate of vaccine efficacy. We provide a framework for approximating the bias and power of any given per protocol analysis period as functions of the background infection hazard rate, disease incubation period, and vaccine immune response. We use this framework to provide recommendations for designing standard vaccine efficacy trials and trials with a delayed vaccination comparator. Briefly, narrower analysis periods within the correct window can minimize or eliminate bias but may suffer from reduced power. Designs should be reasonably robust to misspecification of the incubation period and time to develop a vaccine immune response.

Epidemiology

Industry-wide surveillance of Marek’s disease virus on commercial poultry farms: underlying potential for virulence evolution and vaccine escape

Mareks disease virus is a herpesvirus of chickens that costs the worldwide poultry industry over 1 billion USD annually. Two generations of Mareks disease vaccines have shown reduced efficacy over the last half century due to evolution of the virus. Understanding where the virus is present may give insight into whether continued reductions in efficacy are likely. We conducted a three-year surveillance study to assess the prevalence of Mareks disease virus on commercial poultry farms, determine the effect of various factors on virus prevalence, and document virus dynamics in broiler chicken houses over short (weeks) and long (years) timescales. We extracted DNA from dust samples collected from commercial chicken and egg production facilities in Pennsylvania, USA. Quantitative polymerase chain reaction (qPCR) was used to assess wild-type virus detectability and concentration. Using data from 1018 dust samples with Bayesian generalized linear mixed effects models, we determined the factors that correlated with virus prevalence across farms. Maximum likelihood and autocorrelation function estimation on 3727 additional dust samples were used to document and characterize virus concentrations within houses over time. Overall, wild-type virus was detectable at least once on 36 of 104 farms at rates that varied substantially between farms. Virus was detected in 1 of 3 broiler-breeder operations (companies), 4 of 5 broiler operations, and 3 of 5 egg layer operations. Mareks disease virus detectability differed by production type, bird age, day of the year, operation (company), farm, house, flock, and sample. Operation (company) was the most important factor, accounting for between 12% and 63.4% of the variation in virus detectability. Within individual houses, virus concentration often dropped below detectable levels and reemerged later. These data characterize Mareks disease virus dynamics, which are potentially important to the evolution of the virus.

Epidemiology

Dynamic forecasting of Zika epidemics using Google Trends

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance for Health Departments with early warning, and predictions of numbers of infection cases, which would allow them sufficient time to implement interventions. We used correlation data from ZIKV epidemics and Zika-related online search in GTs between 12 February and 25 August 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The online search data acted as an external regressor in the forecasting model, and was used with the historical ZVD epidemic data to improve the quality of the predictions of disease outbreaks. Our results showed a strong correlation between Zika-related GTs and the cumulative numbers of reported cases, both confirmed and suspected (both p<0.001; Pearson Product-Moment Correlation analysis). The predictive cumulative numbers of confirmed and suspected cases increased steadily to reach 148,510 (95% CI: 126,826-170,195) and 602,721 (95% CI: 582,753-622,689), respectively, in 21 October 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.

Epidemiology

Prevalence of Zika virus infection in wild African primates

The recent spread of Zika virus (ZIKV) is alarming due to its association with birth defects. Though the natural reservoir of ZIKV remains poorly defined, the virus was first described in a captive \"sentinel\" macaque in Africa. Here, we examined blood from 239 wild African monkeys and found variable seropositivity.

Epidemiology

Modelling the impact of curtailing antibiotic usage in food animals on antibiotic resistance in humans

1.Consumption of antibiotics in food animals is increasing worldwide and is approaching, if not already surpassing, the volume consumed by humans. It is often suggested that reducing the volume of antibiotics consumed by food animals could have a public health benefits. Although this notion is widely regarded as intuitively obvious there is a lack of robust, quantitative evidence to either support or contradict the suggestion.\n\nAs a first step towards addressing this knowledge gap, we develop a simple mathematical model for exploring the generic relationship between antibiotic consumption by food animals and levels of resistant bacterial infections in humans. We investigate the impact of restricting antibiotic consumption by animals and identify which model parameters most strongly determine that impact.\n\nOur results suggest that, for a wide range of scenarios, curtailing the volume of antibiotics consumed by food animals has, as a stand-alone measure, little impact on the level of resistance in humans. We also find that reducing the rate of transmission of resistance from animals to humans may be more effective than an equivalent reduction in the consumption of antibiotics in food animals. Moreover, the response to any intervention is strongly determined by the rate of transmission from humans to animals, an aspect which is rarely considered.

Epidemiology

MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

epidemiology

Collider Scope: How selection bias can induce spurious associations

Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited - either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e., a form of collider bias). While it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.\n\nKey MessagesSelection bias (including selective attrition) may limit the representativeness of large-scale cross-sectional and cohort studies.\n\nThis selection bias may induce collider bias (which occurs when two variables independently influence a third variable, and that variable is conditioned upon).\n\nThis may lead to substantially biased estimates of associations, including of genetic associations, even when selection / attrition is relatively modest.

Epidemiology