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bioRxiv2018

Mendelian randomization does not support serum calcium in prostate cancer risk

Background: Observational studies suggest that dietary and serum calcium are risk factors for prostate cancer. However, such studies suffer from residual confounding (due to unmeasured or imprecisely measured confounders), undermining causal inference. Mendelian randomization uses randomly assigned (hence unconfounded and pre-disease onset) germline genetic variation to proxy for phenotypes and strengthen causal inference in observational studies.\n\nObjective: We tested the hypothesis that serum calcium is associated with an increased risk of overall and advanced prostate cancer.\n\nDesign: A genetic instrument was constructed using 5 single nucleotide polymorphisms robustly associated with serum calcium in a genome-wide association study (N [≤] 61,079). This instrument was then used to test the effect of a 0.5 mg/dL increase (1 standard deviation, SD) in serum calcium on risk of prostate cancer in 72,729 men in the PRACTICAL (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome) Consortium (44,825 cases, 27,904 controls) and risk of advanced prostate cancer in 33,498 men (6,263 cases, 27,235 controls).\n\nResults: We found weak evidence for a protective effect of serum calcium on prostate cancer risk (odds ratio [OR] per 0.5 mg/dL increase in calcium: 0.83, 95% CI: 0.63-1.08; P=0.12). We did not find strong evidence for an effect of serum calcium on advanced prostate cancer (OR per 0.5 mg/dL increase in calcium: 0.98, 95% CI: 0.57-1.70; P=0.93).\n\nConclusions: Our Mendelian randomization analysis does not support the hypothesis that serum calcium increases risk of overall or advanced prostate cancer.

epidemiology↗

bioRxiv2018

Careful deployment of oilseed rape crops with Rlm6 resistance gene against L. maculans is recommended to prevent the loss of efficacy of this resistance gene in French condiment mustard.

Breeding varieties for increased disease resistance is a major means to control epidemics. However, the deployment of resistance genes through space and time drives the genetic composition of the pathogen population, with predictable changes in pathotype frequencies. In France, Leptosphaeria maculans causes disease on Brassica napus oilseed rape crops but not on B. juncea condiment mustard. Prior to the deployment of winter B. napus varieties with Rlm6 resistance gene introduced from B. juncea, the aim of our study was to investigate if this deployment could impact disease control in condiment mustard. We assessed the presence of resistance genes against phoma stem canker in a set of current French B. juncea varieties and breeding lines. Rlm6 was detected in all the 12 condiment mustard varieties. Rlm5 was also detected in 8 varieties. No additional resistance genes were detected with the set of isolates used. Because frequency of isolates virulent on Rlm6 is very low, these results indicate that Rlm6 gene is a major component of disease control in the French B. juncea mustards tested. Using Rlm6 in oilseed rape varieties will very likely induce an increase in frequency of Rlm6 virulent isolates. This raises the acute concern of a wise deployment of oilseed rape around the condiment mustard growing area. Scientific knowledge on adaptation dynamics, spatial segregation of crops and cooperation between actors is currently available in order to mitigate the risk and advert negative consequences of the introduction of Rlm6 resistance gene in oilseed rape varieties.

epidemiology↗

bioRxiv2018

Quantifying the risk of local Zika virus transmission in the continental US during the 2015-2016 ZIKV epidemic

BackgroundLocal mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties of the continental United State (US), prompting the issuance of travel, prevention, and testing guidance across the continental US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US.\n\nMethodWe present a framework for the projection of ZIKV autochthonous transmission in the continental US during the 2015-2016 epidemic, using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental and detailed population data. The model generates an ensemble of travel-related case counts and simulate their potential to trigger local transmission at individual level.\n\nResultsWe estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025{degrees} x 0.025{degrees} cell level across the continental US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the one observed during the years 2015-2016. The high spatial and temporal resolutions of the model allow us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk is spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the continental US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission are among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission, and identify the key introduction routes and their contributions to ZIKV spread in the continental US.\n\nConclusionsThis framework provides quantitative risk estimates, fully captures the stochas-ticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic infections. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

epidemiology↗

bioRxiv2018

Taking sharper pictures of malaria with CAMERAs: Combined Antibodies to Measure Exposure Recency Assays

Antibodies directed against malaria parasites are easy and inexpensive to measure but remain an underutilized surveillance tool due to a lack of consensus on what to measure and how to interpret results. High throughput screening of antibodies from well-characterized cohorts offers a means to substantially improve existing assays by rationally choosing the most informative sets of responses and analytical methods. Recent data suggest that high-resolution data on malaria exposure can be obtained from a small number of samples by measuring a handful of properly chosen antibody responses. In this review, we will discuss how standardized multi-antibody assays can be developed and efficiently integrated into existing surveillance activities, with great potential to greatly augment the breadth and quality of information available to direct and monitor malaria control and elimination efforts.

epidemiology↗

bioRxiv2018

The effect of education and general cognitive ability on smoking: A Mendelian randomisation study

Recent analyses have shown educational attainment to be associated with a number of health outcomes. This association may, in part, be due to an effect of educational attainment on smoking behaviour. In this study we apply a multivariable Mendelian randomisation design to determine whether the effect of educational attainment on smoking behaviour could be due to educational attainment or general cognitive ability. We use individual data from the UK Biobank study (N = 120,050) and summary data from large GWAS studies of educational attainment, cognitive ability and smoking behaviour. Our results show that more years of education are associated with a reduced likelihood of smoking which is not due to an effect of general cognitive ability on smoking behaviour. Given the considerable physical harms associated with smoking, the effect of educational attainment on smoking is likely to contribute to the health inequalities associated with differences in educational attainment.

epidemiology↗

bioRxiv2018

The effect of body mass index on smoking behaviour and nicotine metabolism: a Mendelian randomization study

BackgroundGiven clear evidence that smoking lowers weight, it is possible that individuals with higher body mass index (BMI) smoke in order to lose or maintain their weight.\n\nMethods and FindingsWe undertook Mendelian randomization analyses using 97 genetic variants associated with BMI. We performed two sample Mendelian randomization analyses of the effects of BMI on smoking behaviour in UK Biobank (N=335,921) and the Tobacco and Genetics consortium genomewide association study (GWAS) (N[≤]74,035) respectively, and two sample Mendelian randomization analyses of the effects of BMI on cotinine levels (N[≤]4,548) and nicotine metabolite ratio (N[≤]1,518) in published GWAS, and smoking-related DNA methylation in the Avon Longitudinal Study of Parents and Children (N[≤]846).\n\nIn inverse variance weighted Mendelian randomization analysis, there was evidence that higher BMI was causally associated with smoking initiation (OR for ever vs never smoking per one SD increase in BMI: 1.19, 95% CI: 1.11 to 1.27) and smoking heaviness (1.45 additional cigarettes smoked per day per SD increase in BMI, 95% CI: 1.03 to 1.86), but little evidence for a causal effect with smoking cessation. Results were broadly similar using pleiotropy robust methods (MR-Egger, median and weighted mode regression). These results were supported by evidence for a causal effect of BMI on DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) locus. There was no strong evidence that BMI was causally associated with cotinine, but suggestive evidence for a causal negative association with the nicotine metabolite ratio.\n\nConclusionsThere is a causal bidirectional association between BMI and smoking, but the relationship is likely to be complex due to opposing effects on behaviour and metabolism. It may be useful to consider BMI and smoking together when designing prevention strategies to minimise the effects of these risk factors on health outcomes.

epidemiology↗

bioRxiv2018

Identification of 12 genetic loci associated with human healthspan

The mounting challenge of preserving the quality of life in an aging population directs the focus of longevity science to the regulatory pathways controlling healthspan. To understand the nature of the relationship between the healthspan and lifespan and uncover the genetic architecture of the two phenotypes, we studied the incidence of major age-related diseases in the UK Biobank (UKB) cohort. We observed that the incidence rates of major chronic diseases increase exponentially. The risk of disease acquisition doubled approximately every eight years, i.e., at a rate compatible with the doubling time of the Gompertz mortality law. Assuming that aging is the single underlying factor behind the morbidity rates dynamics, we built a proportional hazards model to predict the risks of the diseases and therefore the age corresponding to the end of healthspan of an individual depending on their age, gender, and the genetic background. We suggested a computationally efficient procedure for the determination of the effect size and statistical significance of individual gene variants associations with healthspan in a form suitable for a Genome-Wide Association Studies (GWAS). Using the UKB sub-population of 300,447 genetically Caucasian, British individuals as a discovery cohort, we identified 12 loci associated with healthspan and reaching the whole-genome level of significance. We observed strong (|{rho}g| > 0.3) genetic correlations between healthspan and the incidence of specific age-related disease present in our healthspan definition (with the notable exception of dementia). Other examples included all-cause mortality (as derived from parental survival, with{rho} g = -0.76), life-history traits (metrics of obesity, age at first birth), levels of different metabolites (lipids, amino acids, glycemic traits), and psychological traits (smoking behaviour, cognitive performance, depressive symptoms, insomnia). We conclude by noting that the healthspan phenotype, suggested and characterized here, offers a promising new way to investigate human longevity by exploiting the data from genetic and clinical data on living individuals.

epidemiology↗

bioRxiv2018

Challenges in estimating the impact of vaccination with sparse data

BackgroundThe synthetic control (SC) model is a powerful tool to quantify the population-level impact of vaccines, because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller subnational datasets, we evaluated the performance of SC models with sparse time series data. To obtain more robust estimates of vaccine effects from noisy time series, we proposed a possible alternative approach, \"STL+PCA\" method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome.\n\nMethodsUsing both the SC and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine (PCV10) on pneumonia hospitalizations among cases <12 months and 80+ years of age during 2004-2014 at the subnational level in Brazil. The performance of these models was also compared using simulation analyses.\n\nResultsThe SC model was able to adjust for trends unrelated to PCV10 in larger states but not in smaller states. The simulation analysis confirmed that the SC model failed to select an appropriate set of control diseases when the time series were sparse and noisy, thereby generating biased estimates of the impact of vaccination when secular trends were present. The STL+PCA approach decreased bias in the estimates for smaller populations.\n\nConclusionsEstimates from the SC model might be biased when data are sparse. The STL+PCA model provides more accurate evaluations of vaccine impact in smaller populations.

epidemiology↗

bioRxiv2018

Prenatal alcohol exposure and facial morphology in a UK cohort

High levels of prenatal alcohol exposure are known to cause an array of adverse outcomes including foetal alcohol syndrome (FAS); however, the effects of low to moderate exposure are less-well characterised. Previous findings suggest that differences in normal-range facial morphology may be a marker for alcohol exposure and related adverse effects. Therefore, in the Avon Longitudinal Study of Parents and Children, we tested for an association between maternal alcohol consumption and six FAS-related facial phenotypes in their offspring, using both self-report questionnaires and the maternal genotype at rs1229984 in ADH1B as measures of maternal alcohol consumption. In both self-reported alcohol consumption (N=4,233) and rs1229984 genotype (N=3,139) analyses, we found no strong statistical evidence for an association between maternal alcohol consumption and facial phenotypes tested. The directions of effect estimates were compatible with the known effects of heavy alcohol exposure, but confidence intervals were largely centred around zero. We conclude that, in a sample representative of the general population, there is no strong evidence for an effect of prenatal alcohol exposure on normal-range variation in facial morphology.

epidemiology↗

bioRxiv2018

Investigating the combined association of BMI and alcohol consumption on liver disease and biomarkers: a Mendelian randomization study of over 90 000 adults from the Copenhagen General Population Study

BackgroundBody mass index (BMI) and alcohol consumption are suggested to independently and interactively increase the risk of liver disease. We assessed this combined effect using factorial Mendelian randomization (MR).\n\nMethodsWe used multivariable adjusted regression and MR to estimate individual and joint associations of BMI and alcohol consumption and liver disease biomarkers (alanine aminotransferase (ALT) y-glutamyltransferase (GGT)) and incident liver disease. We undertook a factorial MR study splitting participants by median of measured BMI or BMI allele score then by median of reported alcohol consumption or ADH1B genotype (AA/AG and GG), giving four groups; low BMI/low alcohol (-BMI/-alc), low BMI/high alcohol (-BMI/+alc), high BMI/low alcohol (+BMI/-alc) and high BMI/high alcohol (+BMI/+alc).\n\nResultsIndividual positive associations of BMI and alcohol with ALT, GGT and incident liver disease were found. In the factorial MR analyses, considering the +BMI/+alc group as the reference, mean circulating ALT and GGT levels were lowest in the -BMI/-alc group (2.32% (95% CI: -4.29, -0.35) and -3.56% (95% CI: -5.88; -1.24) for ALT and GGT respectively). Individuals with -BMI/+alc and +BMI/-alc had lower mean circulating ALT and GGT compared to the reference group (+BMI/+alc). For incident liver disease multivariable factorial analyses followed a similar pattern to those seen for the biomarkers, but little evidence of differences between MR factorial categories for odds of liver disease.\n\nConclusionsConsistent results from multivariable regression and MR analysis, provides compelling evidence for the individual adverse effects of BMI and alcohol consumption on liver disease. Intervening on both BMI and alcohol may improve the profiles of circulating liver biomarkers. However, this may not reduce clinical liver disease risk.

epidemiology↗

bioRxiv2018

Pattern of severe injuries in Spanish children: boys and falls are alarmingly overrepresented

Background: Taking into account that injury is one of the main causes of child fatalities in developed countries, and that boys are more likely to suffer it than girls, we have explored a database of pediatric patients with severe injuries to determine whether sex and age influence the pattern of these fatalities, and the magnitude of this.\n\nMethod: Observational study of the demographic and clinical characteristics of 227 patients from a Spanish pediatric reference hospital, all of them admitted with a diagnosis of trauma.\n\nResult: Falls are the most frequent type of trauma (60.7%), followed by pedestrian traffic collisions (15%). Boys are over-represented in falls (72% vs 28% in girls) and pedestrian traffic injuries (61% vs 39 %). In boys, falls are mainly observed in public roads and during leisure activities (53.8%) whereas in girls at home (55.2%). In a logistic regression, sex and age are statistically significant predictors of severe trauma, boys (OR = 1.59) and the adolescent age group (OR = 3.7) showed the highest odds.\n\nConclusion: We have observed a clear gender-biased pattern of injury-related events: falls are the leading cause of injuries, with 2.5 boys for every girl. Falls mostly happened during outdoor leisure activities in boys and at home in girls. Pedestrian traffic injuries also show significant differences between sexes, emphasizing the role of cognitive and cultural factors in childrens behavior.

epidemiology↗

bioRxiv2018

Testing the causal effects between subjective wellbeing and physical health using Mendelian randomisation

ObjectivesTo investigate whether the association between subjective wellbeing (subjective happiness and life satisfaction) and physical health is causal.\n\nDesignWe conducted two-sample bidirectional Mendelian randomisation between subjective wellbeing and six measures of physical health: coronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol and body mass index (BMI).\n\nParticipantsWe used summary data from four large genome-wide association study consortia: CARDIoGRAMplusC4D for coronary artery disease and myocardial infarction; the Global Lipids Genetics Consortium for cholesterol measures; the Genetic Investigation of Anthropometric Traits consortium for BMI; and the Social Science Genetics Association Consortium for subjective wellbeing. A replication analysis was conducted using 337,112 individuals from the UK Biobank (54% female, mean age =56.87, SD=8.00 years at recruitment).\n\nMain outcome measuresCoronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol, BMI and subjective wellbeing.\n\nResultsThere was evidence of a causal effect of BMI on subjective wellbeing such that each 1 kg/m2 increase in BMI caused a 0.045 (95%CI 0.006 to 0.084, p=0.023) SD reduction in subjective wellbeing. Replication analyses provided strong evidence of an effect of BMI on satisfaction with health ({beta}=0.034 (95% CI: -0.042 to -0.026) unit decrease in health satisfaction per SD increase in BMI, p<2-16). There was no clear evidence of a causal effect between subjective wellbeing and the other physical health measures in either direction.\n\nConclusionsOur results suggest that a higher BMI lowers subjective wellbeing. Our replication analysis confirmed this finding, suggesting the effect in middle-age is driven by satisfaction with health. BMI is a modifiable determinant and therefore, our study provides further motivation to tackle the obesity epidemic because of the knock-on effects of higher BMI on subjective wellbeing.

epidemiology↗

bioRxiv2018

Measuring the Impact of an Open Online Prescribing Data Analysis Service on Clinical Practice: a Cohort Study in NHS England Data

BackgroundOpenPrescribing is a freely accessible service that enables any user to view and analyse NHS primary care prescribing data at the level of individual practices. This tool is intended to improve the quality, safety, and cost-effectiveness of prescribing.\n\nObjectivesWe set out to measure the impact of OpenPrescribing being viewed on subsequent prescribing.\n\nMethodsHaving pre-registered our protocol and code, we measured three different metrics of prescribing quality (mean percentile across 34 existing OpenPrescribing quality measures, available \"price-per-unit\" savings, and total \"low-priority prescribing\" spend) to see if they changed after CCG and practice pages were viewed. We also measured whether practices whose data were viewed on OpenPrescribing differed in prescribing, prior to viewing, to those who were not. We used fixed effects and between effects linear panel regression, to isolate change over time and differences between practices respectively. We adjusted for month of prescribing in the fixed effects model, to remove underlying trends in outcome measures.\n\nResultsWe found a reduction in available price-per-unit savings for both practices and CCGs after their pages were viewed. The saving was greater at the practice level (-{pound}40.42 per thousand patients per month, 95% confidence interval -54.04 to -26.01) than at CCG level (-{pound}14.70 per thousand patients per month, 95% confidence interval -25.56 to -3.84). We estimate a total saving since launch of {pound}243k at practice level and {pound}1.47m at CCG level between the feature launch and end of follow-up (August to November 2017) among practices viewed. If the observed savings from practices viewed were extrapolated to all practices, this would generate {pound}26.8m in annual savings for the NHS, approximately 20% of the total possible savings from this method. The other two measures were not different after CCGs/practices were viewed. Practices which were viewed had worse prescribing quality scores overall, prior to viewing.\n\nConclusionsWe found a clinically significant positive impact from use of OpenPrescribing, specifically for the class of savings opportunities that can only be identified by using this tool. We also show that it is possible to conduct a robust analysis of the impact of such an online service on clinical practice.

epidemiology↗

bioRxiv2018

Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far.\n\nHere, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.\n\nAuthor summaryThe obvious goals of antimicrobial drug therapy are rapid patient recovery and low disease prevalence in the population. However, achieving these goals is complicated by the rapid evolution and spread of antimicrobial resistance. A sustainable treatment strategy needs to account for the risk of resistance and keep it in check. One parameter of treatment is the drug dosage, which can vary within certain limits. It has been proposed that lower doses may, in some cases, be more suitable than higher doses to reduce the risk of resistance evolution in any one patient. However, if lower doses prolong the period of infectiousness, such a strategy has consequences for the pathogen dynamics of both strains at the population level. Here, we set up a nested model of within-host and between-host dynamics for an acute self-limiting infection. We explore the consequences of drug dosing on several measures of treatment success: the risk of resistance at the individual and population levels and the outbreak probability and the disease burden of an epidemic. Our analysis shows that trade-offs may exist between optimal treatments under these various criteria. The criterion given most weight in the decision process ultimately depends on the disease and population under consideration.

epidemiology↗

bioRxiv2018

Association of intrauterine alcohol exposure and offspring depression:A negative control analysis of maternal and partner consumption.

BackgroundPrevious research has suggested that intrauterine alcohol exposure is associated with a variety of adverse outcomes in offspring. However, few studies have investigated its association with offspring internalising disorders in late adolescence.\n\nMethodsUsing data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we investigated the associations of maternal drinking in pregnancy with offspring depression at age 18. We also examined partner drinking as a negative control for intrauterine exposure for comparison.\n\nResultsOffspring of mothers that consumed any alcohol at 18 weeks gestation were at increased risk of having a diagnosis of depression (OR 1.15, 95% CI 1.00 to 1.32), but there was no clear evidence of association between partners alcohol consumption during pregnancy and increased risk of offspring depression (OR 0.90, 95% CI 0.78 to 1.04).\n\nConclusionsMaternal drinking in pregnancy was associated with increased risk of offspring depression at age 18. Residual confounding may explain this association, but the negative control comparison of paternal drinking provides some evidence that it may be causal, and this warrants further investigation.

epidemiology↗

bioRxiv2018

Colistin resistance prevalence in Escherichia coli from domestic animals in intensive breeding farms of Jiangsu Province, China

The global dissemination of colistin resistance has received a great deal of attention. Recently, the plasmid-mediated colistin resistance encoded by mcr-1 and mcr-2 genes in Escherichia coli (E.coli) strains from animals, food, and patients in China have been reported continuously. To make clear the colisin resistance and mcr gene spread in domestic animals in Jiangsu Province, we collected fecael swabs from pigs, chicken and cattle at different age distributed in intensive feeding farms. The selected chromogenic agar and mcr-PCR were used to screen the colisin resistance and mcr gene carriage. Colistin resistant E.coli colonies were identified from 54.25 % (440/811) pig faecal swabs, from 35.96 % (443/1232) chicken faecal swabs, and 26.92 % (42/156) from cattle faecal swabs. Of all the colisin resistant E.coli colonies, the positive amplifications of mcr-1 were significantly higher than mcr-2. The mcr-1 prevalence was 68.86 % (303/440) in pigs, 87.58 % (388/443) in chicken, and 71.43 % (30/42), compared with 46.82 % (206/440) in pigs, 14.90 % (66/443) in chicken, and 19.05 % (8/42) in cattle of prevalence of mcr-2. Co-occurrence of mcr-1 and mcr-2 was identified in 20 % (88/440) in pigs, 7.22 % (32/443) in chickens, and in 9.52 % (4/42) cattle. These data indicate that mcr was the most important colistin resistance mechanism. Interventions and alternative options are necessary to minimise further dissemination of mcr between food-producing animals and human.\n\nIMPORTANCEColistin is recognized one of the last defence lines for the treatment of highly resistant bacteria, but the emergence of resistance that conferred by a transferable plasmid-mediated mcr genes to this vital antibiotic is extremely disturbing. Here, we used E. coli as an index to monitor drug resistance in domestic animals (pigs, chicken and cattle). It was found that the colistin resistance widely occurred at all ages of domestic animals and the mcr-dependent mechanism dominated in E.coli. We also found that the elder and adult animals were a reservoir of resistant strains, suggesting a potential food safety issue and greater public health problems.

epidemiology↗

bioRxiv2018

Geographic Latitude, Cholesterol, and Blood Pressure

Background Sunlight has been hypothesized to play a role in variation in cardiovascular disease according to geographic latitude. Objectives To evaluate the plausibility of sunlight as a factor in populations average cholesterol and blood pressure Methods We analyzed World Health Organization data including 180 or more countries age-standardized average cholesterol, age-standardized mean systolic blood pressure (BP), and age-standardized prevalence of raised BP, by geographic latitude, over decades. We also performed analysis by ultraviolet B light (UVB) exposure. Results Mean cholesterol increases with the distance of a country from the Equator. This relationship has changed very little since 1980. Similarly, in 1975, mean systolic BP and prevalence of raised BP were higher in countries farther from the Equator. However, the relationship between latitude and BP has changed dramatically; by 2015, the opposite pattern was observed in women. Countries average UVB exposure has a stable relationship with cholesterol over recent decades, but a changing relationship with BP. Conclusions Since sunlight exposure in a country is relatively fixed and its relationship with BP has changed dramatically in recent decades, countries average sunlight exposure is an unlikely explanation for contemporary country-level variation in BP. However, our findings are consistent with a putative effect of sunlight on countries average cholesterol, as well as a no longer detectable effect on BP decades ago. A parsimonious potential explanation for the relationship between light and cholesterol is that 7-dehydrocholesterol can be converted to cholesterol, or in the presence of ultraviolet light, can instead be converted to vitamin D.

epidemiology↗

bioRxiv2018

National and Regional Influenza-Like-Illness Forecasts for the USA

Health planners use forecasts of key metrics associated with influenza-like-illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.\n\nAuthor summaryIt is estimated that there are between 3 and 5 million worldwide annual seasonal cases of severe influenza illness, and between 290 000 and 650 000 respiratory deaths [1]. Influenza-like-illness (ILI) describes a set of symptoms and is a practical way for health-care workers to easily estimate likely influenza cases. The Centers for Disease Control (CDC) collects and disseminates ILI information, and has, for the last several years, run a forecasting challenge (the CDC Flu Challenge) for modelers to predict near-term weekly incidence, week of season onset, week of peak, and intensity of peak. We have developed a modeling framework that accounts for a range of mechanisms thought to be important for influenza transmission, such as climatic conditions, school vacations, and coupling between different regions. In this study we describe our forecast procedure for the 2016-17 season and highlight which features of our models resulted in better or worse forecasts. Most notably, we found that when the dynamics of different regions are coupled together, the forecast accuracy improves. We also found that the most accurate forecasts required some level of forecaster interaction, that is, the procedure could not be completely automated without a reduction in accuracy.

epidemiology↗