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Lack of insurance is associated with lower probability of diagnostic imaging use among US trauma patients: An instrumental variable analysis and simulation

BackgroundUninsured trauma patients have higher mortality than their insured counterparts. One possible reason is disparities in utilization of appropriate diagnostic imaging, including computed tomography (CT), X-ray, ultrasound (US), and magnetic resonance imaging (MRI). We examined the association between lack of insurance and use of diagnostic imaging.\n\nMethodsData come from the National Trauma Databank 2010-2015. Patients were determined uninsured if payment mode was self-pay or missing. The primary outcome was any diagnostic imaging procedure, and secondary outcomes included CT, X-ray, US, or MRI. Risk ratios (RRs) were adjusted for demographics, comorbidities, injury characteristics, facility characteristics. We also used the 2010 Patient Protection and Affordable Care Act as an instrumental variable (IV), with linear terms for year to account for annual trends in imaging use. Monte carlo simulations to test effect of hypothetical violations to IV assumptions of relevance, no direct effect, and no confounding.\n\nResultsOf 4,373,554 patients, 953,281 (21.8%) were uninsured. After adjusting, uninsured patients had lower chance of any imaging (RR 0.98, 95% CI 0.98 to 0.98), x-ray (RR 0.99, 95% CI 0.99 to 1.00), and MRI (RR 0.82, 95% CI 0.81 to 0.83), and higher chance of ultrasound (RR 1.01, 95% CI 1.01 to 1.02). In IV analysis, uninsured status was associated with reduction in any imaging (RR 0.60, 95% CI 0.52 to 0.70), tomography (RR 0.52, 95% CI 0.44 to 0.62) ultrasound (RR 0.46, 95% CI 0.32 to 0.65), and MRI (RR 0.19, 95% CI 0.10 to 0.37) and increased likelihood of x-ray use (RR 1.74, 95% CI 1.31 to 2.32). Simulations indicated that a direct effect RD of -0.02 would be necessary to produce observed results under the null hypothesis.\n\nDiscussionOur study suggests an association between insurance status and use of imaging that is unlikely to be driven by confounding or violations of IV assumptions. Mechanisms for this remain unclear, but could include unconscious provider bias or institutional financial constraints. Further research is warranted to elucidate mechanisms and assess whether differences in diagnostic imaging use mediate the association between insurance and mortality.

epidemiology

Within-host dynamics explain the coexistence of antibiotic-sensitive and resistant bacteria

The spread of antibiotic resistance, a major threat to human health, is poorly understood. Empirically, resistant strains gradually increase in prevalence as antibiotic consumption increases, but current mathematical models predict a sharp transition between full sensitivity and full resistance. In other words, we do not understand what drives persistent coexistence between resistant and sensitive strains of disease-causing bacteria in host populations. Without knowing what drives patterns of resistance, we cannot accurately predict the impact of potential strategies for managing resistance. Here, we show that within-host dynamics--bacterial growth, strain competition, and host immune responses--promote frequency-dependent selection for resistant strains, explaining patterns of resistance at the population level. By capturing these processes in a parsimonious mathematical framework, we resolve a long-standing conflict between theory and observation. Our models capture widespread coexistence for multiple bacteria-drug combinations across 30 European countries and explain associations between carriage prevalence and resistance prevalence among bacterial subtypes. A mechanistic understanding of resistance evolution is needed to accurately forecast the impact and effectiveness of resistance-management strategies.

epidemiology

Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization

Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20,000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.

epidemiology

The Impact of 2017 ACC/AHA Guidelines on the Prevalence of Hypertension and Eligibility for Anti-Hypertensive Therapy in the United States and China

BACKGROUNDThe 2017 American College of Cardiology (ACC)/American Heart Association (AHA) guideline recommendations for hypertension include major changes to the diagnosis of hypertension as well as suggested treatment targets for blood pressure management. To better guide future health policy interventions in the management of hypertension, we examined the effect of these guidelines on the prevalence as well as the eligibility for initiation and intensification of therapy in nationally-representative populations from the US and China.\n\nMETHODSIn the National Health and Nutrition Examination Survey (NHANES) for the most recent 2 cycles (2013-2014 and 2015-2016), and the China Health and Retirement Longitudinal Study (CHARLS) (2011-2012), we identified all adults 45 to 75 years of age who would have a diagnosis of hypertension, and would be candidates for initiation and intensification of anti-hypertensive therapy based on the 2017 ACC/AHA guidelines, compared with current guidelines.\n\nRESULTSThe adoption of the 2017 ACC/AHA guidelines for hypertension in the US would label 70.1 million individuals in the 45-75-year age group with hypertension, representing 63% of the population in this age-group. The adoption of these guidelines in China would lead to labeling of 267 million or 55% individuals in the same age-group with hypertension. This would represent a relative increase in the prevalence of hypertension by 26.8% in the US and 45.1% in China with the adoption of the new guidelines. Further, based on observed treatment patterns and current guidelines, 8.1 million Americans with hypertension are currently untreated. However, this number is expected to increase to 15.6 million after the implementation of the 2017 ACC/AHA guidelines. In China, based on current treatment patterns, 74.5 million patients with hypertension are untreated, and is estimated to increase to 129.8 million if the 2017 ACC/AHA guidelines are adopted by China. In addition, the new ACC/AHA guidelines will label 8.7 million adults in the US, and 51 million in China with hypertension who would not require treatment with an anti-hypertensive agent, compared with 1.5 million and 23.4 million in the current guidelines. Finally, even among those treated with anti-hypertensive therapy, the proportion of undertreated individuals, i.e. those above target blood pressures despite receiving anti-hypertensive therapy and candidates for intensification of therapy, is estimated to increase by 13.9 million (from 24.0% to 54.4% of the treated patients) in the US, and 30 million (41.4% to 76.2% of patients on treatment) in China, if the 2017 ACC/AHA treatment targets are adopted into clinical practice in the respective countries.\n\nConclusionsAdopting the new 2017 ACC/AHA hypertension guidelines would be associated with a substantial increase in the prevalence of hypertension in both US and China accompanied with a marked increase in the recommendation to initiate and intensify treatment in several million patients. There would be a 26.8% and 45.1% increase in those labeled with hypertension in the US and China, respectively. Further, 7.5 million and 55.3 million will be newly recommended for therapy, and 13.9 million and 30 million newly recommended for intensification of existing therapy in the US and China, respectively.

epidemiology

The Association between Tuberculosis and Diphtheria

This research investigates the now-forgotten relationship between diphtheria and tuberculosis. Historical medical reports from the 19th century are reviewed followed by a statistical regression analysis of the relationship between the two diseases in the early 20th century. Historical medical records show a consistent association between diphtheria and tuberculosis that can increase the likelihood and severity of either disease in a co-infection. The statistical analysis uses historical weekly public health data on reported cases in five American cities over a period of several years, finding a modest but statistically significant relationship between the two diseases. No current medical theory explains the association between diphtheria and tuberculosis. Alternative explanations are explored with a focus on how the diseases assimilate iron. In a co-infection, the effectiveness of tuberculosis at assimilating extracellular iron can lead to increased production of diphtheria toxin, worsening that disease, which may in turn exacerbate tuberculosis. Iron-dependent repressor genes connect both diseases.

epidemiology

Climatic influence on the anthrax niche in warming northern latitudes

Climate change is impacting ecosystem structure and function, with potentially drastic downstream effects on human and animal health. Emerging zoonotic diseases are expected to be particularly vulnerable to climate and biodiversity disturbance. Anthrax is an archetypal zoonosis that manifests its most significant burden on vulnerable pastoralist communities. The current study sought to investigate the influence of temperature increases on the landscape suitability of anthrax in the temperate, boreal, and arctic North, where observed climate impact has been rapid. This study also explored the influence of climate relative to more traditional factors, such as livestock distribution, ungulate biodiversity, and soil-water balance, in demarcating high risk landscapes. Machine learning was used to model landscape suitability as the ecological niche of anthrax in northern latitudes. The model identified climate, livestock density and wild ungulate species richness as the most influential landscape features in predicting suitability. These findings highlight the significance of warming temperatures for anthrax ecology in northern latitudes, and suggest potential mitigating effects of interventions targeting megafauna biodiversity conservation in grassland ecosystems, and animal health promotion among small to midsize livestock herds.\n\nSignificance StatementWe present evidence that a warming climate may be associated with the current distribution of anthrax risk in the temperate, boreal, and arctic North. Moreover, projected warming over the coming decades was associated with substantive expansion of this risk. In addition, livestock distribution, ungulate biodiversity, and soil-water balance were also influential to anthrax risk. While these results are sobering for the future health of livestock and pastoralist communities in the northern latitudes, the coincident modulating effect of ungulate biodiversity may suggest targeted ecosystem conservation as a possible buffer against a growing anthrax niche.

epidemiology

Household Members Do Not Contact Each Other at Random: Implications for Infectious Disease Modelling

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts and thus easily spread within households. Epidemic models, used to gain insight in infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now there was no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) in 2010-2011. We analyzed data from 318 households totaling 1266 individuals with household sizes ranging from 2 to 7 members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between father and child is smaller than for any other pair except for older siblings. Epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulation. However, ignoring the contact density when inferring from an epidemic model will result in biased estimates of within-household transmission rates. Further research on the implementation of within-household contact networks in epidemic models is necessary.\n\nSignificance StatementHouseholds have a pivotal role in the spread of airborne infectious diseases. Households are bridging units between schools and workplaces, and social contacts within households are frequent and intimate, allowing for rapid disease spread. Infectious disease models typically assume that members of a household contact each other randomly. Until now there was no direct empirical evidence to support this assumption. In this paper, we present the first social contact survey specifically designed to study contact networks within households with young children. We investigate which factors drive contacts between household members on one particular day by means of a statistical model. Our results suggest the importance of connectedness within households over heterogeneity in number of contacts.

epidemiology

Zika: An ongoing threat to women and infants

Recent data from Rio de Janeiro shows a sharp drop in the number of notified cases of Zika in the summer of 2016-17, compared to the previous summer. This is probably due to herd immunity built up after the previous year's epidemic. There is still a much higher incidence among women than men, almost certainly due to sexual transmission. An unexpected feature of the new data is that there are proportionally far more cases in children under 15 months than in older age classes. By comparing the incidence for 2016-17 with that of 2015-16, we can deduce the proportion of reported cases for men and women, and also verify that the disparity of incidence between them is still present. Women and children still represent risk groups with regard to Zika infection, even during a non-epidemic season.

epidemiology

The relationship between varicella (chickenpox) and scarlet fever in contemporary Hong Kong

Scarlet fever epidemics have reemerged in China, the UK, and Hong Kong. This research tests whether scarlet fever epidemics in Hong Kong are linked to varicella epidemics. Varicella infection is a known risk for invasive Group A Streptococcal infections, and historical research shows a connection between varicella and scarlet fever. This analysis examines the relationship between these two disease in Hong Kong from 2011 to 2015 and compares varicella rates before and after the reintroduction of scarlet fever. Analysis shows that scarlet fever and varicella have synchronous annual epidemic cycles, and a mathematical model of the relationship between scarlet fever and varicella is estimated. Varicella rates were unchanged by the return of scarlet fever, but annual varicella cycles may have influenced the size and timing of scarlet fever outbreaks. Vaccination policies for varicella may need to be adjusted to limit scarlet fever epidemics.

epidemiology

A multivariate analysis of CalEnviroScreen: comparing environmental and socioeconomic stressors versus chronic disease

BackgroundThe health-risk assessment paradigm is shifting from single stressor evaluation towards cumulative assessments of multiple stressors. Recent efforts to develop broad-scale public health hazard datasets provide an opportunity to develop and evaluate multiple exposure hazards in combination.\n\nMethodsWe performed a multivariate study of the spatial relationship between 12 indicators of environmental hazard, 5 indicators of socioeconomic hardship, and 3 health outcomes. Indicators were obtained from CalEnviroScreen (version 3.0), a publicly available environmental justice screening tool developed by the State of California Environmental Protection Agency. The indicators were compared to the total rate of hospitalization for 14 ICD-9 disease categories (a measure of disease burden) at the zip code tabulation area population level. We performed principal component analysis to visualize and reduce the CalEnviroScreen data and spatial autoregression to evaluate associations with disease burden.\n\nResultsCalEnviroScreen was strongly associated with the first principal component (PC) from a principal component analysis (PCA) of all 20 variables (Spearman {rho} = 0.95). In a PCA of the 12 environmental variables, two PC axes explained 43% of variance, with the first axis indicating industrial activity and air pollution, and the second associated with ground-level ozone, drinking water contamination and PM2.5. Mass of pesticides used in agriculture was poorly or negatively correlated with all other environmental indicators, and with the CalEnviroScreen calculation method, suggesting a limited ability of the method to capture agricultural exposures. In a PCA of the 5 socioeconomic variables, the first PC explained 66% of variance, representing overall socioeconomic hardship. In simultaneous autoregressive models, the first environmental and socioeconomic PCs were both significantly associated with the disease burden measure, but more model variation was explained by the socioeconomic PCs.\n\nConclusionsThis study supports the use of CalEnviroScreen for its intended purpose of screening California regions for areas with high environmental exposure and population vulnerability. Study results further suggest a hypothesis that, compared to environmental pollutant exposure, socioeconomic status has greater impact on overall burden of disease.

epidemiology

Inference about causation between body mass index and DNA methylation in blood from a twin family study

BackgroundSeveral studies have reported DNA methylation in blood to be associated with body mass index (BMI), but only a few have investigated causal aspects of the association. We used a twin family design to assess this association at two life points and applied a novel analytical approach to investigate the evidence for causality.\n\nMethodsThe methylation profile of DNA from peripheral blood was measured for 479 Australian women (mean age 56 years) from 130 twin families. Linear regression was used to estimate the associations of methylation at ~410 000 cytosine-guanine dinucleotides (CpG), and of the average methylation at ~20 000 genes, with current BMI, BMI at age 18-21 years, and the change between the two (BMI change). A novel regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess causation.\n\nResultsAt 5% false discovery rate, nine, six and 12 CpGs at 24 loci were associated with current BMI, BMI at age 18-21 years and BMI change, respectively. The average methylation of BHLHE40 and SOCS3 loci was associated with current BMI, and of PHGDH locus was associated with BMI change. From the ICE FALCON analyses with BMI as the predictor and methylation as the outcome, a womans methylation level was associated with her co-twins BMI, and the association disappeared conditioning on her own BMI, consistent with BMI causing methylation. To the contrary, using methylation as the predictor and BMI as the outcome, a womans BMI was not associated with her co-twins methylation level, consistent with methylation not causing BMI.\n\nConclusionFor middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with current BMI, BMI at age 18-21 years and BMI change. Our study suggests that BMI has a causal effect on peripheral blood DNA methylation.

epidemiology

Trends in Escherichia coli bloodstream infection, urinary tract infections and antibiotic susceptibilities in Oxfordshire, 1998-2016: an observational study

BackgroundThe incidence of Escherichia coli bloodstream infections (EC-BSIs), particularly those caused by antibiotic-resistant strains, is increasing in the UK and internationally. This is a major public health concern but the evidence base to guide interventions is limited.\n\nMethodsIncidence of EC-BSIs and E. coli urinary tract infections (EC-UTIs) in one UK region (Oxfordshire) were estimated from anonymised linked microbiological and hospital electronic health records, and modelled using negative binomial regression based on microbiological, clinical and healthcare exposure risk factors. Infection severity, 30-day allcause mortality, and community and hospital co-amoxiclav use were also investigated.\n\nFindingsFrom 1998-2016, 5706 EC-BSIs occurred in 5215 patients, and 228376 EC-UTIs in 137075 patients. 1365(24%) EC-BSIs were nosocomial (onset >48h post-admission), 1863(33%) were community (>365 days post-discharge), 1346(24%) were quasi-community (31-365 days post-discharge), and 1132(20%) were quasi-nosocomial ([&le;]30 days postdischarge). 1413(20%) EC-BSIs and 36270(13%) EC-UTIs were co-amoxiclav-resistant (41% and 30%, respectively, in 2016). Increases in EC-BSIs were driven by increases in community (10%/year (95% CI:7%-13%)) and quasi-community (8%/year (95% CI:7%-10%)) cases. Changes in EC-BSI-associated 30-day mortality were at most modest (p>0{middle dot}03), and mortality was substantial (14-25% across groups). By contrast, co-amoxiclav-resistant EC-BSIs increased in all groups (by 11%-19%/year, significantly faster than susceptible EC-BSIs, pheterogeneity<0{middle dot}001), as did co-amoxiclav-resistant EC-UTIs (by 13%-29%/year, pheterogeneity*0{middle dot}001). Co-amoxiclav use in primary-care facilities was associated with subsequent co-amoxiclav-resistant EC-UTIs (p=0{middle dot}03) and all EC-UTIs (p=0{middle dot}002).\n\nInterpretationCurrent increases in EC-BSIs in Oxfordshire are primarily community-associated, with high rates of co-amoxiclav resistance, nevertheless not impacting mortality. Interventions should target primary-care facilities with high co-amoxiclav usage.\n\nFundingNational Institute for Health Research.\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for publications from inception up until October 26, 2017, with the terms \"Escherichia coli\", \"E. coli\", \"bacteraemia\", \"bloodstream infection\", restricting the search to English language articles, and also reviewed references from retrieved articles. Escherichia coli (E. coli) is the most common cause of bloodstream infection, and the incidence of E. coli bloodstream infection, and particularly antibiotic-resistant infections, is increasing in the UK and internationally. Although the UK government aims to reduce healthcare-associated E. coli bloodstream infection, there is only limited evidence to inform appropriate interventions.\n\nAdded value of this studyWe investigated potential drivers for these increases in incidence by exploiting available linked electronic health records over 19 years for ~5200 patients with E. coli bloodstream infection and ~140000 with E. coli urinary tract infection, together with community antimicrobial prescribing data for the most recent six years. Our study identified several findings with significant implications for health policy and patient care: O_LIIncreases in the incidence of E. coli bloodstream infections were driven mainly by non-hospital-associated cases; however, neither patients with previous urinary tract infections nor having previously had urine specimens sent from catheters appeared to be driving the increases\nC_LIO_LICo-amoxiclav-resistant bloodstream infections rose significantly faster than co-amoxiclav-susceptible bloodstream infections, with the greatest number of co-amoxiclav-resistant bloodstream infections in 2016 being in patients discharged more than a month previously (i.e. community-associated)\nC_LIO_LIHigher co-amoxiclav use in primary care was associated with higher rates of both co-amoxiclav-resistant E. coli urinary tract infections and E. coli urinary tract infections overall, supporting drives to reduce broad-spectrum and inappropriate antibiotic use in primary care\nC_LIO_LIDespite substantial increases in co-amoxiclav-resistant bloodstream infections there was no evidence that mortality was increasing in these cases; this does not support moving to broader empiric antibiotic prescribing in hospitals (i.e. carbapenems, piperacillin-tazobactam)\nC_LI\n\nImplications of all available adviceThis suggests that government strategies to effectively reduce E. coli bloodstream infections should target community settings, as well as healthcare-associated settings. The absence of an increased mortality signal suggests that co-amoxiclav resistant E. coli infections are either being successfully treated by dual empiric therapy in severe cases (e.g. with concomitant gentamicin), can be \"rescued\" once isolate susceptibilities become available, or currently deployed phenotypic susceptibility testing breakpoints do not adequately correlate with clinical outcome.

epidemiology

Circulating selenium and prostate cancer risk: a Mendelian randomization analysis

In the Selenium and Vitamin E Cancer Prevention Trial (SELECT), selenium supplementation (causing a median 114 g/L increase in circulating selenium) did not lower overall prostate cancer risk, but increased risk of high-grade prostate cancer and type 2 diabetes. Mendelian randomization analysis uses genetic variants to proxy modifiable risk factors and can strengthen causal inference in observational studies. We constructed a genetic risk score comprising eleven single-nucleotide polymorphisms robustly (P<5x10-8) associated with circulating selenium in genome-wide association studies. In a Mendelian randomization analysis of 72,729 men in the PRACTICAL Consortium (44,825 cases, 27,904 controls), 114 g/L higher genetically-elevated circulating selenium was not associated with prostate cancer (OR: 1.01; 95% CI: 0.89-1.13). Concordant with findings from SELECT, selenium was weakly associated with advanced (including high-grade) prostate cancer (OR: 1.21; 95% CI: 0.98-1.49) and type 2 diabetes (OR: 1.18; 95% CI: 0.97-1.43; in a type 2 diabetes GWAS meta-analysis with up to 49,266 cases, 249,906 controls). Mendelian randomization mirrored the outcome of selenium supplementation in SELECT and may offer an approach for the prioritization of interventions for follow-up in large-scale randomized controlled trials.

epidemiology

Assessing the genetic effect mediated through gene expression from summary eQTL and GWAS data

Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data into transcriptome-wide association studies (TWAS) based on predicted expression can boost power to detect novel disease loci or pinpoint the susceptibility gene at a known disease locus. However, it is often the case that multiple eQTL genes colocalize at disease loci, making the identification of the true susceptibility gene challenging, due to confounding through linkage disequilibrium (LD). To distinguish between true susceptibility genes (where the genetic effect on phenotype is mediated through expression) and colocalization due to LD, we examine an extension of the Mendelian Randomization Egger regression method that allows for LD while only requiring summary association data for both GWAS and eQTL. We derive the standard TWAS approach in the context of Mendelian Randomization and show in simulations that the standard TWAS does not control Type I error for causal gene identification when eQTLs have pleiotropic or LD-confounded effects on disease. In contrast, LD Aware MR-Egger regression can control Type I error in this case while attaining similar power as other methods in situations where these provide valid tests. However, when the direct effects of genetic variants on traits are correlated with the eQTL associations, all of the methods we examined including LD Aware MR-Egger regression can have inflated Type I error. We illustrate these methods by integrating gene expression within a recent large-scale breast cancer GWAS to provide guidance on susceptibility gene identification.

epidemiology

Zoonotic Babesia microti in the northeastern U.S.: evidence for the expansion of a specific parasite lineage

The recent range expansion of human babesiosis in the northeastern United States, once found only in restricted coastal sites, is not well understood. This study sought to utilize a large number of samples to examine the population structure of the parasites on a fine scale to provide insights into the mode of emergence across the region. 228 B. microti samples collected in endemic northeastern U.S. sites were genotyped using published VNTR markers. The genetic diversity and population structure were analysed on a geographic scale using Phyloviz and TESS. Three distinct populations were detected in northeastern US, each dominated by a single ancestral type. In contrast to the limited range of the Nantucket and Cape Cod populations, the mainland population dominated from New Jersey eastward to Boston. Ancestral populations of B. microti were sufficiently isolated to differentiate into distinct populations. Despite this, a single population was detected across a large geographic area of the northeast that historically had at least 3 distinct foci of transmission, central New Jersey, Long Island and southeastern Connecticut. We conclude that a single B. microti genotype has expanded across the northeastern U.S. The biological attributes associated with this parasite genotype that have contributed to such a selective sweep remain to be identified.\n\nAuthor summaryBabesiosis is a disease caused by a protozoan parasite, Babesia microti, related to malaria. The disease is acquired by the bite of the deer tick, the same tick that transmits Lyme disease. Although Lyme disease rapidly emerged over a wide range within the last 40 years, babesiosis remained rare with an extremely focal distribution. Within the last decade, the number of reports of babesiosis cases has increased from an expanded area of risk, particularly across the mainland of southern New England. We determined whether the expanded risk may be due to local intensification of transmission as opposed to introduction of the parasite. Historical fragmentation of the landscape suggests that sites of B. microti transmission should have been isolated and thus evidence of multiple genetically distinct populations should be found. By a genetic fingerprinting method, we found that samples from the new mainland sites were all genetically similar. We conclude that one parasite genetic lineage has recently expanded its distribution and now dominates, suggesting that it has some phenotypic attribute that may confer a selective advantage over others.

epidemiology

Automated detection of sleep-boundary times using wrist-worn accelerometry

ObjectiveCurrent polysomnography-validated measures of sleep status from wrist-worn accelerometers cannot be used in fully automated analysis as they rely on self-reported sleep-onset and -end (sleep-boundary) information. We set out to develop an automated, data-driven approach to sleep-boundary detection from wrist-worn accelerometer data.\n\nMethodsOn three separate occasions, participants were asked to wear a GENEActiv(R) wrist-worn accelerometer for nine days and concurrently complete sleep diaries with lights-off, asleep and wake-up information. We developed and evaluated three data-driven methods for sleep-boundary detection: a change-point detection based method, a thresholding method and a random forest classifier based method. Mean absolute errors between automatically-derived and self-reported sleep-onset and wake-up times were recorded in addition to kappa statistics for the minute-by-minute performance of each of the methods.\n\nResults46 participants provided 972 days of accelerometer recordings with corresponding self-reported sleep information. The three sleep-boundary detection methods resulted in mean absolute errors in sleep-onset and wake-up times per individual of 36 min, 34 min and 33 min and kappa statistics of 0.87, 0.89 and 0.89, respectively.\n\nConclusionOur methods provide a data-driven approach to detect sleep-onset and -end times without the need for self-reported sleep-boundary information. The methods are likely to be of particular use for large-scale studies where the collection of self-reported sleep diaries is impractical.\n\nSignificanceObjective measures of sleep are needed to reliably detect associations with health outcomes. This work lays the foundation for studies of objectively measured sleep duration and its health consequences in large studies.

epidemiology

Statistical power of clinical trials has increased whilst effect size remained stable: an empirical analysis of 137 032 clinical trials between 1975-2017

BackgroundBiomedical studies with low statistical power are a major concern in the scientific community and are one of the underlying reasons for the reproducibility crisis in science. If randomized clinical trials, which are considered the backbone of evidence-based medicine, also suffer from low power, this could affect medical practice.\n\nMethodsWe analysed the statistical power in 137 032 clinical trials between 1975 and 2017 extracted from meta-analyses from the Cochrane database of systematic reviews. We determined study power to detect standardized effect sizes according to Cohen, and in meta-analysis with p-value below 0.05 we based power on the meta-analysed effect size. Average power, effect size and temporal patterns were examined.\n\nResultsThe number of trials with power [&ge;]80% was low but increased over time: from 9% in 1975-1979 to 15% in 2010-2014. This increase was mainly due to increasing sample sizes, whilst effect sizes remained stable with a median Cohens h of 0.21 (IQR 0.12-0.36) and a median Cohens d of 0.31 (0.19-0.51). The proportion of trials with power of at least 80% to detect a standardized effect size of 0.2 (small), 0.5 (moderate) and 0.8 (large) was 7%, 48% and 81%, respectively.\n\nConclusionsThis study demonstrates that sufficient power in clinical trials is still problematic, although the situation is slowly improving. Our data encourages further efforts to increase statistical power in clinical trials to guarantee rigorous and reproducible evidence-based medicine.

epidemiology

Cross-reactive immunity drives global oscillation and opposed alternation patterns of seasonal influenza A viruses

Several human pathogens exhibit distinct patterns of seasonality and circulate as pairs of discrete strains. For instance, the activity of the two co-circulating influenza A virus subtypes oscillates and peaks during winter seasons of the worlds temperate climate zones. These periods of increased activity are usually caused by a single dominant subtype. Alternation of dominant strains in successive influenza seasons makes epidemic forecasting a major challenge. From the start of the 2009 influenza pandemic we enrolled influenza A virus infected patients (n = 2,980) in a global prospective clinical study. Complete hemagglutinin (HA) sequences were obtained from 1,078 A/H1N1 and 1,033 A/H3N2 viruses and were linked to patient data. We then used phylodynamics to construct high resolution spatio-temporal phylogenetic HA trees and estimated global influenza A effective reproductive numbers (R) over time (2009-2013). We demonstrate that R, a parameter to define host immunity, oscillates around R = 1 with a clear opposed alternation pattern between phases of the A/H1N1 and A/H3N2 subtypes. Moreover, we find a similar alternation pattern for the number of global virus migration events between the sampled geographical locations. Both observations suggest a between-strain competition for susceptible hosts on a global level. Extrinsic factors that affect person-to-person transmission are a major driver of influenza seasonality, which forces influenza epidemics to coincide with winter seasons. The data presented here indicate that also cross-reactive host immunity is a key intrinsic driver of global influenza seasonality, which determines the outcome of competition between influenza A virus strains at the onset of each epidemic season.\n\nSignificance statementAnnual influenza epidemics coincide with winter seasons in many parts of the world. Environmental factors, such as air humidity variation or temperature change, are commonly believed to drive these seasonality patterns. Interestingly, three out of the four latest pandemics (1918, 1968 and 2009) did not spread in winter initially, but during summer. This questions to what extent other factors could also impact virus spread among humans. We demonstrate that cross-reactive host immunity is a key factor. It drives the well-known seasonal patterns of virus activity oscillation and alternation of the dominant influenza virus subtype in successive seasons. Furthermore, this factor may also explain the efficient spread of pandemic viruses during summer when cross-reactive host immunity is relatively low.

epidemiology