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bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

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↗

bioRxiv2016

A risk stratification approach for improved interpretation of diagnostic accuracy statistics

Diagnostic accuracy statistics, including predictive values, risk-differences, Youdens index and Area Under the Curve (AUC), assess the promise of novel biomarkers proposed as diagnostic tests. We reinterpret these statistics in light of risk-stratification (how well a biomarker separates those at higher risk from those at lower risk) to better understand their implications for public-health programs. We introduce an intuitively simple statistic, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS demonstrates that conventional predictive values can mislead because they do not account for disease prevalence or test-positivity rates. Little risk-stratification is possible for rare diseases, demonstrating a \"high-bar\" to justify population-based screening. Importantly, we demonstrate that the risk-difference, Youdens index, and AUC measure only multiplicative relative gains in risk-stratification: AUC=0.6 achieves only 20% of maximum risk-stratification (AUC=0.9 achieves 80%). However, large relative gains in risk-stratification might not imply large absolute gains if disease is rare or if the test is rarely positive. We illustrate MRS by our experience comparing the performance of cervical cancer screening tests in China vs. the USA. The test with the worst AUC=0.72 in China (visual inspection with ascetic acid) provides twice the risk-stratification of the test with best AUC=0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics.

Epidemiology↗

bioRxiv2016

Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective

BackgroundIn order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols \"mixing\" and \"cycling\" have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups.\n\nObjectivesWe aim to investigate a model that comes closer to clinical practice and compare the predictions to the standard model.\n\nMethodsWe set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account.\n\nResultsThe alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance.\n\nConclusionsWhile the traditional models provide valuable insight, care is needed in the interpretation of results.

epidemiology↗

bioRxiv2016

A broad sense measure of health and its properties in the HRS

Measuring health is a crucial component of much social research. Two approaches are typical. Health may be measured via either narrowly targeted questions about aspects of disability or chronic conditions. Alternatively, individuals may be asked to self-report their health in some global sense. Both approaches have potential drawbacks. We consider a broad sense measure of health constructed by items from five different batteries related to physical and mental wellbeing. We demonstrate that this measure predicts time until death better than self-reported health, especially for females. Although this measure has promise, we argue that future surveys on health would benefit from the inclusion of additional items focusing on issues salient to younger individuals or other non-disabled respondents.

epidemiology↗

bioRxiv2016

Statistical and biological uncertainties associated with vaccine efficacy estimates and their implications for dengue vaccine impact projections

Given the limited effectiveness of strategies based solely on vector control to reduce dengue virus (DENV) transmission, it is expected that an effective vaccine could play a pivotal role in reducing the global disease burden of dengue. Of several dengue vaccines under development, Dengvaxia(R) from Sanofi Pasteur recently became the first to become licensed in select countries and to achieve WHO recommendation for use in certain settings, despite the fact that a number of uncertainties about its profile complicate projections of its public health impact. We used a stochastic, agent-based model for DENV transmission to perform simulations of the public health impact of dengue vaccines in light of two key uncertainties: (1) \"statistical uncertainty\" about the numerical value of the vaccines efficacy against disease, and (2) \"biological uncertainty\" about the extent to which its efficacy against disease derives from the amelioration of symptoms, blocking of DENV infection, or some combination thereof. Simulations of a generic dengue vaccine showed that the proportion of disease episodes averted following 20 years of routine vaccination of nine-year olds at 80% coverage was sensitive to both the numerical value of vaccine efficacy and to the extent to which efficacy derives from blocking of DENV infection. Simulations of a vaccine resembling Dengvaxia(R) took into account that vaccine trial results substantially reduced statistical uncertainty but did not address biological uncertainty, resulting in the proportion of disease episodes averted being more sensitive to biological uncertainty than to statistical uncertainty. Taken together, our results indicate limitations associated with the use of symptomatic disease as the primary endpoint of dengue vaccine trials and highlight the importance of considering multiple forms of uncertainty in projections of a vaccines public health impact.

epidemiology↗

bioRxiv2016

Signatures of non-neutral processes within the population structure of Streptococcus pneumoniae

Populations of Streptococcus pneumoniae (SP) are typically structured into groups of closely related organisms or lineages, but it is not clear whether they are maintained by selection or neutral processes. Here, we attempt to address this question by applying a machine learning technique to SP whole genomes. Our results indicate that lineages evolved through immune selection on the groEL chaperone protein. The groEL protein is part of the groESL operon and enables a large range of proteins to fold correctly within the physical environment of the nasopharynx, thereby explaining why lineage structure is so stable within SP despite high levels of genetic transfer. SP is also antigenically diverse, exhibiting a variety of distinct capsular serotypes. Associations exist between lineage and capsular serotype but these can be easily perturbed, such as by vaccination. Overall, our analyses indicate that the evolution of SP can be conceptualized as the rearrangement of modular functional units occurring on several different timescales under different pressures: some patterns have locked in early (such as the epistatic interactions between groESL and a constellation of other genes) and preserve the differentiation of lineages, while others (such as the associations between capsular serotype and lineage) remain in continuous flux.

epidemiology↗

bioRxiv2016

A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts

The Nurses Health Study (NHS), Nurses Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)).\n\nWe observed the strongest BMI association for the FTO SNP rs55872725 ({beta}=0.45, p=3.48x10-22), and using a significance level of p=0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR=2.17, 95% CI: 1.79-2.63, p=2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.

epidemiology↗

bioRxiv2016

A structural variant encoding hybrid glycophorins is associated with resistance to severe malaria

Plasmodium falciparum invades human red blood cells by a series of interactions between host and parasite surface proteins. Here we analyse whole genome sequence data from worldwide human populations, including 765 new genomes from across sub-Saharan Africa, and identify a diverse array of large copy number variants affecting the host invasion receptor genes GYPA and GYPB. We find that a nearby reported association with severe malaria is explained by a complex structural variant that involves the loss of GYPB and gain of two hybrid genes, each with a GYPB extracellular domain and GYPA intracellular domain. This variant reduces the risk of severe malaria by 40% and has recently risen in frequency in parts of Kenya. We show that the structural variant encodes the Dantu blood group antigen, and therefore a serologically distinct red cell phenotype. These findings demonstrate that structural variation of red blood cell invasion receptors is associated with natural resistance to P. falciparum malaria.

genomics↗

bioRxiv2016

Pre-Hospital Midazolam for Treatment of Status Epilepticus Before and After the Rapid Anticonvulsant Medication Prior to Arrival Trial (RAMPART): A National Observational Cohort Study

BackgroundImplementation of evidence-based treatment for pre-hospital status epilepticus can improve outcomes. We hypothesized that publication of a pivotal pre-hospital clinical trial (RAMPART), demonstrating superiority of intramuscular midazolam over intravenous lorazepam, altered the national utilization rates of midazolam for pre-hospital treatment of status epilepticus, while upholding its safety and efficacy outside the trial setting.\n\nMethods and FindingsThis is a retrospective, observational cohort study of pre-hospital patient encounters throughout the United States in the National Emergency Medicine Services Information System database, from January 2010 through December 2014. We compared the rates and odds of midazolam use as the first-line treatment for status epilepticus among all adult and pediatric benzodiazepine-treated seizures before and after RAMPART publication (February 2012). Secondary analyses were conducted for rates of airway interventions and rescue therapy, as proxies for safety and efficacy of seizure termination. 156,539 benzodiazepine-treated seizures were identified. Midazolam use increased from 26.1% in January 2010 to 61.7% in December 2014 (difference +35.6%, 95% CI, 32.7%-38.4%). The annual rate of midazolam adoption increased significantly from 5.9% per year to 8.9% per year after the publication of RAMPART (difference +3.0% per year; 95%CI, 1.6%-4.5% per year; adjusted OR 1.24; 95%CI, 1.17-1.32). Overall frequency of rescue therapy and airway interventions changed little after the publication of RAMPART.\n\nConclusionsThese data are consistent with effective, ongoing, but incomplete clinical translation of the RAMPART results. The effects of the trial, however, cannot be isolated. The safety and effectiveness of midazolam for treatment of seizures in prehospital clinical practice appear consistent with trial data, which should encourage continuing increases in utilization.

epidemiology↗

bioRxiv2016

Non-polio enteroviruses in faeces of children diagnosed with acute flaccid paralysis in Nigeria.

BackgroundThe need to investigate the contribution of non-polio enteroviruses to acute flaccid paralysis (AFP) cannot be over emphasized as we move towards a poliovirus free world. Hence, we aim to identify non-polio enteroviruses recovered from the faeces of children diagnosed with AFP in Nigeria.\n\nMethodsNinety-six isolates, (95 unidentified and one previously confirmed Sabin poliovirus 3) recovered on RD cell culture from the stool of children <15 years old diagnosed with AFP in 2014 were analyzed. All isolates were subjected to RNA extraction, cDNA synthesis and three different PCR reactions (one panenterovirus 5'-UTR and two VP1 amplification assays). VP1 amplicons were then sequenced isolates identified.\n\nResults93.75% (90/96) of the isolates were detected by at least one of the three assays as an enterovirus. Precisely, 79.17% (76/96), 6.25% (6/96), 7.295% (7/96) and 6.25% (6/96) of the isolates were positive for both, positive and negative, negative and positive, as well as negative for both the 5'-UTR and VP1 assays, respectively. In this study, sixty-nine (69) of the 83 VP1 amplicons sequenced were identified as 27 different enterovirus types. The most commonly detected were CV-B3 (10 isolates) and EV-B75 (5 isolates). Specifically, one, twenty-four and two of the enterovirus types identified in this study belong to EV-A, EV-B and EV-C respectively.\n\nDiscussionThis study reports the circulating strains of 27 non-polio enterovirus types in Nigerian children with AFP in 2014 and Nigerian strains of CV-B2, CV-B4, E17, EV-B80, EV-B73, EV-B97, EV-B93, EV-C99 and EV-A120.

epidemiology↗

bioRxiv2016

Enterovirus A119 in a child with Acute Flaccid Paralysis, Nigeria

The oldest EV-A119 record was in 2008 in a chimpanzee in Cameroon and subsequently in more non-human primates and healthy children. Here we report for the first time the detection of EV-A119 in a child with Acute Flaccid Paralysis, thus suggesting possible association with a clinical condition in humans.

epidemiology↗

bioRxiv2016

Can you catch Ebola from a stork bite? Inductive reasoning influences on zoonosis risk perception

Emerging zoonoses are a prominent global health threat. Human beliefs are central to drivers of emerging zoonoses, yet little is known about the factors that influence perceived risks of animal contact. We present an inductive account of zoonosis risk perception, suggesting that beliefs about the range of animals that are able to transmit diseases to each other influence zoonosis risk perception. Consistent with our account, in Study 1, we find that participants who endorse higher likelihoods of cross-species disease transmission have stronger intention to report animal bites. In Study 2, using real world descriptions of Ebola virus from the WHO and CDC, we find that communications conveying a broader range of animals as susceptible increase intentions to report animal bites and decrease perceived safety of wild game meat. These results suggest that cognitive factors may be harnessed to modulate zoonosis risk perception and combat emerging infectious diseases.

epidemiology↗