Feasibility of hepatitis C elimination by screening and treatment alone in high-income countries.

BACKGROUND AND AIMS: Despite the availability of highly effective direct-acting antiviral therapy, chronic hepatitis C (CHC) continues to cause a major public health burden. In many high-income countries, treatment rates have been declining, which was exacerbated by the impact of the COVID-19 pandemic, threatening the ability to meet the World Health Organization (WHO)'s targets for eliminating HCV as a public health threat by 2030. We sought to model the impact of CHC in Canada, a resource-rich country with ongoing immigration from HCV-endemic regions; which relies exclusively on risk-based screening for case identification. APPROACH AND RESULTS: We developed an agent-based model to characterize the HCV epidemic in a high-income country with ongoing immigration. Combinations of prevention such as harm reduction, screening, and treatment strategies were considered. Model parameters were estimated from the literature and calibrated against historical HCV data. Sensitivity analyses were performed to assess uncertainty. Under the current status quo of risk-based screening, we predict the incidence of CHC-induced decompensated cirrhosis, HCC, and liver-related deaths would decrease by 79.4%, 76.1%, and 62.1%, respectively, between 2015 and 2030, but CHC incidence would only decrease by 11.1%. The results were sensitive to HCV transmission rate and an annual number of people initiating treatment. CONCLUSIONS: Current risk-based screening, and subsequent treatment, will be inadequate to achieve WHO goals. With extensive scale-up in screening, and treatment, the mortality target may be achievable, but the target for preventing new CHC cases is unlikely reachable, highlighting the importance of developing enhanced harm-reduction strategies for HCV elimination.

Impact of new direct-acting antiviral therapy on the prevalence and undiagnosed proportion of chronic hepatitis C infection.

BACKGROUND: Patients with chronic hepatitis C (CHC) can be cured with the new highly effective interferon-free combination treatments (DAA) that were approved in 2014. However, CHC is a largely silent disease, and many individuals are unaware of their infections until the late stages of the disease. The impact of wider access to effective treatments and improved awareness of the disease on the number of infections and the number of patients who remain undiagnosed is not known in Canada. Such evidence can guide the development of strategies and interventions to reduce the burden of CHC and meet World Health Organization's (WHO) 2030 elimination targets. The purpose of this study is to use a back-calculation framework informed by provincial population-level health administrative data to estimate the prevalence of CHC and the proportion of cases that remain undiagnosed in the three most populated provinces in Canada: British Columbia (BC), Ontario and Quebec. METHODS: We have conducted a population-based retrospective analysis of health administrative data for the three provinces to generate the annual incidence of newly diagnosed CHC cases, decompensated cirrhosis (DC), hepatocellular carcinoma (HCC) and HCV treatment initiations. For each province, the data were stratified in three birth cohorts: individuals born prior to 1945, individuals born between 1945 and 1965 and individuals born after 1965. We used a back-calculation modelling approach to estimate prevalence and the undiagnosed proportion of CHC. The historical prevalence of CHC was inferred through a calibration process based on a Bayesian Markov chain Monte Carlo (MCMC) algorithm. The algorithm constructs the historical prevalence of CHC for each cohort by comparing the model-generated outcomes of the annual incidence of the CHC-related health events against the data set of observed diagnosed cases generated in the retrospective analysis. RESULTS: The results show a decreasing trend in both CHC prevalence and undiagnosed proportion in BC, Ontario and Quebec. In 2018, CHC prevalence was estimated to be 1.23% (95% CI: .96%-1.62%), .91% (95% CI: .82%-1.04%) and .57% (95% CI: .51%-.64%) in BC, Ontario and Quebec respectively. The CHC undiagnosed proportion was assessed to be 35.44% (95% CI: 27.07%-45.83%), 34.28% (95% CI: 26.74%-41.62%) and 46.32% (95% CI: 37.85%-52.80%) in BC, Ontario and Quebec, respectively, in 2018. Also, since the introduction of new DAA treatment in 2014, CHC prevalence decreased from 1.39% to 1.23%, .97% to .91% and .65% to .57% in BC, Ontario and Quebec respectively. Similarly, the CHC undiagnosed proportion decreased from 38.78% to 35.44%, 38.70% to 34.28% and 47.54% to 46.32% in BC, Ontario and Quebec, respectively, from 2014 to 2018. CONCLUSIONS: We estimated that the CHC prevalence and undiagnosed proportion have declined for all three provinces since the new DAA treatment has been approved in 2014. Yet, our findings show that a significant proportion of HCV cases remain undiagnosed across all provinces highlighting the need to increase investment in screening. Our findings provide essential evidence to guide decisions about current and future HCV strategies and help achieve the WHO goal of eliminating hepatitis C in Canada by 2030.

A real data-driven simulation strategy to select an imputation method for mixed-type trait data.

Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly complete information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.

Impact of direct-acting antiviral treatment on health utility in patients with chronic hepatitis C in hospital and community settings.

BACKGROUND: Direct-acting antiviral agents (DAAs) have transformed chronic hepatitis C (CHC) treatment. Continued affordable access to DAAs requires updated cost-effectiveness analyses (CEA). Utility is a preference-based measure of health-related quality of life (HRQoL) used in CEA. This study evaluated the impact of DAAs on utilities for patients with CHC in two clinical settings. METHODS: This prospective longitudinal study included patients aged ≥18 years, diagnosed with CHC and scheduled to begin DAA treatment, from two tertiary care hospital clinics and four community clinics in Toronto, Calgary, and Montreal. Patients completed two utility instruments (EQ-5D-5L and Health Utilities Index 2/3 (HUI2/3)) before treatment, 6 weeks after treatment initiation, and 12 weeks and 1 year after treatment completion. We measured utilities for all patients, and for hospital-based and community-based groups. RESULTS: Between 2017 and 2020, 209 patients (126 hospital-based, 83 community-based; average age 53 years; 65% male) were recruited, and 143 completed the 1-year post-treatment assessment. Pre-treatment, utilities were (mean ± standard deviation) 0.77 ± 0.21 (EQ-5D-5L), 0.69 ± 0.24 (HUI2) and 0.58 ± 0.34 (HUI3). The mean changes at 1-year post-treatment were 0.035, 0.038 and 0.071, respectively. While utilities for hospital-based patients steadily improved, utilities for the community-based cohort improved between baseline and 12-weeks post-treatment, but decreased thereafter. DISCUSSION: This study suggests that utilities improve after DAA treatment in patients with CHC in a variety of settings. However, community-based patients may face challenges related to comorbid health and social conditions that are not meaningfully addressed by treatment. Our study is essential for valuing health outcomes in CHC-related CEA.

ATQ: alert time quality, an evaluation metric for assessing timely epidemic detection models within a school absenteeism-based surveillance system.

BACKGROUND: Wellington-Dufferin-Guelph Public Health (WDGPH) has conducted an absenteeism-based influenza surveillance program in the WDG region of Ontario, Canada since 2008, using a 10% absenteeism threshold to raise an alert for the implementation of mitigating measures. A recent study indicated that model-based alternatives, such as distributed lag seasonal logistic regression models, provided improved alerts for detecting an upcoming epidemic. However model evaluation and selection was primarily based on alert accuracy, measured by the false alert rate (FAR), and failed to optimize timeliness. Here, a new metric that simultaneously evaluates epidemic alert accuracy and timeliness is proposed. The alert time quality (ATQ) metric is investigated as a model selection criterion on both a simulated and real data set. METHODS: The ATQ assessed alerts on a gradient, where alerts raised incrementally before or after an optimal day were considered informative, but were penalized for lack of timeliness. Summary statistics of ATQ, average alert time quality (AATQ) and first alert time quality (FATQ), were used for model evaluation and selection. Alerts raised by ATQ and FAR selected models were compared. Daily elementary school absenteeism and laboratory-confirmed influenza case data collected by WDGPH were used for demonstration and evaluation of the proposed metric. A simulation study that mimicked the WDG population and influenza demographics was conducted for further evaluation of the proposed metric. RESULTS: The FATQ-selected model raised acceptable first alerts most frequently, while the AATQ-selected model raised first alerts within the ideal range most frequently. CONCLUSIONS: Models selected by either FATQ or AATQ would more effectively predict community influenza activity with the local community than those selected by FAR.

Feasibility of hepatitis B elimination in high-income countries with ongoing immigration.

BACKGROUND & AIMS: Addressing HBV is vital to meeting the World Health Organization (WHO)'s viral hepatitis elimination goals, as 47% of viral hepatitis complications can be attributed to HBV. The objective of this study is to develop an agent-based model determining which integrated strategies involving vaccination, screening, and treatment would achieve the WHO's goals. METHODS: We developed an agent-based model to characterize the HBV epidemic in a high-income country with ongoing immigration. The spread of HBV was simulated through sexual networks and perinatal transmission. Model parameters were estimated from the literature and calibrated against historical HBV data. Sensitivity analyses were performed to assess the uncertainty. RESULTS: We predict that under the current strategies, the incidence of acute hepatitis B, and HBV-attributable decompensated cirrhosis and hepatocellular carcinoma would decrease by 64.5%, 9.4%, and 10.5% between 2015-2030, respectively. However, the incidence of chronic hepatitis B and liver-related deaths would increase by 26.6% and 1.0% between 2015-2030, respectively. Results were sensitive to the number of immigrants and HBV prevalence in immigrants. CONCLUSIONS: The results suggest that the current vaccination, screening, and treatment strategies will be inadequate to achieve WHO elimination goals. Even with extensive integrated scale-up in vaccination, screening, and treatment, the morbidity and mortality targets may not be reachable, highlighting the need for a re-evaluation of the global strategy for HBV, the importance of developing curative therapy for HBV, and of establishing tailored strategies to prevent long-term sequelae and improve health in immigrants. LAY SUMMARY: We have developed a model that reflects the dynamics of hepatitis B virus (HBV) transmission in a high-income country with ongoing immigration, which enabled us to forecast the epidemiology of HBV for policy-level decision making. Our analysis suggests that current vaccination, screening, and treatment strategies are inadequate to achieve the WHO goals of eliminating chronic hepatitis B. Even with extensive integrated scale-up in vaccination, screening, and treatment, the morbidity and mortality targets may not be reachable.

Time Costs and Out-of-Pocket Costs in Patients With Chronic Hepatitis C in a Publicly Funded Health System.

OBJECTIVES: Chronic hepatitis C (CHC) infection affects more than 70 million people worldwide and imposes considerable health and economic burdens on patients and society. This study estimated 2 understudied components of the economic burden, patient out-of-pocket (OOP) costs and time costs, in patients with CHC in a tertiary hospital clinic setting and a community clinic setting. METHODS: This was a multicenter, cross-sectional study with hospital-based (n = 174) and community-based (n = 101) cohorts. We used a standardized instrument to collect healthcare resource use, time, and OOP costs. OOP costs included patient-borne costs for medical services, nonprescription drugs, and nonmedical expenses related to healthcare visits. Patient and caregiver time costs were estimated using an hourly wage value derived from patient-reported employment income and, where missing, derived from the Canadian census. Sensitivity analysis explored alternative methods of valuing time. Costs were reported in 2020 Canadian dollars. RESULTS: The mean 3-month OOP cost was $55 (95% confidence interval [CI] $21-$89) and $299 (95% CI $170-$427) for the community and hospital cohorts, respectively. The mean 3-month patient time cost was $743 (95% CI $485-$1002) (community) and $465 (95% CI $248-$682) (hospital). The mean 3-month caregiver time cost was $31 (95% CI $0-$63) (community) and $277 (95% CI $174-$380) (hospital). Patients with decompensated cirrhosis bore the highest costs. CONCLUSIONS: OOP costs and patient and caregiver time costs represent a considerable economic burden to patient with CHC, equivalent to 14% and 21% of the reported total 3-month income for the hospital-based and community-based cohorts, respectively.

The Effects of Ecological Traits on the Rate of Molecular Evolution in Ray-Finned Fishes: A Multivariable Approach.

Myriad environmental and biological traits have been investigated for their roles in influencing the rate of molecular evolution across various taxonomic groups. However, most studies have focused on a single trait, while controlling for additional factors in an informal way, generally by excluding taxa. This study utilized a dataset of cytochrome c oxidase subunit I (COI) barcode sequences from over 7000 ray-finned fish species to test the effects of 27 traits on molecular evolutionary rates. Environmental traits such as temperature were considered, as were traits associated with effective population size including body size and age at maturity. It was hypothesized that these traits would demonstrate significant correlations with substitution rate in a multivariable analysis due to their associations with mutation and fixation rates, respectively. A bioinformatics pipeline was developed to assemble and analyze sequence data retrieved from the Barcode of Life Data System (BOLD) and trait data obtained from FishBase. For use in phylogenetic regression analyses, a maximum likelihood tree was constructed from the COI sequence data using a multi-gene backbone constraint tree covering 71% of the species. A variable selection method that included both single- and multivariable analyses was used to identify traits that contribute to rate heterogeneity estimated from different codon positions. Our analyses revealed that molecular rates associated most significantly with latitude, body size, and habitat type. Overall, this study presents a novel and systematic approach for integrative data assembly and variable selection methodology in a phylogenetic framework.

Estimating chronic hepatitis C prevalence in British Columbia and Ontario, Canada, using population-based cohort studies.

Patients identified as having chronic hepatitis C (CHC) infection can be effectively and rapidly treated using direct-acting antiviral agents. However, there remains a substantial burden of subclinical undetected infection. This study estimates the prevalence and undiagnosed proportion of CHC in British Columbia (BC) and Ontario, Canada, using a model-based approach, informed by provincial population-level health administrative data. A two-step approach was used: Step 1) Two population-based retrospective analyses of administrative health data for a cohort of British Columbians and a cohort of Ontarians with CHC were conducted to generate population-level statistics of CHC-related health events; Step 2) using a validated natural history model of hepatitis C virus (HCV) infection, the historical prevalence of CHC was back-calculated from the data collected in Step 1. Our retrospective study found that, in BC and Ontario, the number of newly diagnosed CHC cases is declining yearly while the complications of the disease are increasing yearly. BC had a 2014 CHC prevalence of 1.04% (95% CI: 0.84%-1.44%), with 33.3% (95% CI: 25.5%-42.0%) of CHC cases undiagnosed. Ontario had a 2014 CHC prevalence of 0.91% (95% CI: 0.83%-1.02%) with 36.0% (95% CI: 31.2%-38.9%) of CHC cases undiagnosed. Our study offers robust estimates based on the integration of a validated natural history model with population-level health administrative data on HCV-related events, which can provide vital evidence for policymakers to develop appropriate policies to achieve elimination targets. Our approach can also be applied to produce robust region-specific estimates in other countries.

Precision medicine and gut dysbiosis.

Clostridioides difficile Infection (CDI) is a leading cause of healthcare-associated infections in Canada, affecting the gastrointestinal tract which can lead to fever, abdominal pain, and diarrhea. Effective treatment for patients with Recurrent CDI (rCDI) can be achieved by Fecal Microbiota Transplantation (FMT) by introducing the gut micro-organisms of a healthy person (donor) into the bowel of the affected individual. Research has shown that an increase in the specific bacterial phyla post-FMT may be partly responsible for this gut restoration and elimination of disease. Furthermore, in understanding the key bacteria associated with successful FMT, full treatment plans can be developed for the individual needs of the patient by matching an infected individual with a donor possessing ideal microbiota for the specific patient. This development of precision medicine and more systematic adoption of FMT can be the next step toward more rapid resolution of rCDI.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers.

BACKGROUND: The detection of signatures of selection has the potential to elucidate the identities of genes and mutations associated with phenotypic traits important for livestock species. It is also very relevant to investigate the levels of genetic diversity of a population, as genetic diversity represents the raw material essential for breeding and has practical implications for implementation of genomic selection. A total of 1151 animals from nine goat populations selected for different breeding goals and genotyped with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip were included in this investigation. RESULTS: The proportion of polymorphic SNPs ranged from 0.902 (Nubian) to 0.995 (Rangeland). The overall mean HO and HE was 0.374 ± 0.021 and 0.369 ± 0.023, respectively. The average pairwise genetic distance (D) ranged from 0.263 (Toggenburg) to 0.323 (Rangeland). The overall average for the inbreeding measures FEH, FVR, FLEUT, FROH and FPED was 0.129, -0.012, -0.010, 0.038 and 0.030, respectively. Several regions located on 19 chromosomes were potentially under selection in at least one of the goat breeds. The genomic population tree constructed using all SNPs differentiated breeds based on selection purpose, while genomic population tree built using only SNPs in the most significant region showed a great differentiation between LaMancha and the other breeds. We hypothesized that this region is related to ear morphogenesis. Furthermore, we identified genes potentially related to reproduction traits, adult body mass, efficiency of food conversion, abdominal fat deposition, conformation traits, liver fat metabolism, milk fatty acids, somatic cells score, milk protein, thermo-tolerance and ear morphogenesis. CONCLUSIONS: In general, moderate to high levels of genetic variability were observed for all the breeds and a characterization of runs of homozygosity gave insights into the breeds' development history. The information reported here will be useful for the implementation of genomic selection and other genomic studies in goats. We also identified various genome regions under positive selection using smoothed FST and hapFLK statistics and suggested genes, which are potentially under selection. These results can now provide a foundation to formulate biological hypotheses related to selection processes in goats.

Imputing genotypes using regularized generalized linear regression models.

As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits. However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted by GLMNET, is proposed to impute missing genotypes. The method aims to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly the specification of the number of neighboring SNPs to be included for imputing the missing genotype. The performance of GLMNET-based method is compared to the conventional multinomial regression method and two phase-based methods: fastPHASE and BEAGLE. Two simulation scenarios are evaluated: a sparse-missing model, and a small-panel expansion model. The sparse-missing model simulates a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of individuals is only genotyped at a subset of the SNPs of the large panel. Each imputation method is tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Results show that the proposed GLMNET method outperforms the other methods in the small panel expansion scenario and fastPHASE performs slightly better than the GLMNET method in the sparse-missing scenario.

CAR T-cell Therapy for Diffuse Large B-cell Lymphoma in Canada: A Cost-Utility Analysis.

BACKGROUND: Chimeric antigen receptor (CAR) T-cell therapy is a novel cell therapy for treating non-Hodgkin lymphoma. The development of CAR T-cell therapy has transformed oncology treatment by offering a potential cure. However, due to the high cost of these therapies, and the large number of eligible patients, decision makers are faced with difficult funding decisions. Our objective was to assess the cost-effectiveness of tisagenlecleucel for adults with relapsed/refractory diffuse large B-cell lymphoma in Canada using updated survival data from the recent JULIET trial. METHODS: We developed an individual-simulated discrete event simulation model to assess the costs and quality-adjusted life-years (QALY) of tisagenlecleucel compared with salvage chemotherapy. Survival estimates were obtained from a published clinical trial and retrospective analysis. If patients remained progression free for 5 y, they were assumed to be in long-term remission. Costing and utility data were obtained from reports and published sources. A Canadian health care payer perspective was used, and outcomes were modeled over a lifetime horizon. Costs and outcomes were discounted at 1.5% annually, with costs reported in 2021 Canadian dollars. A probabilistic analysis was used, and model parameters were varied in 1-way sensitivity analyses and scenario analyses. RESULTS: After we incorporated the latest clinical evidence, tisagenlecleucel led to an additional cost of $503,417 and additional effectiveness of 2.48 QALYs, with an incremental cost-effectiveness ratio of $202,991 compared with salvage chemotherapy. At a willingness-to-pay threshold of $100,000/QALY, tisagenlecleucel had a 0% likelihood of being cost-effective. CONCLUSIONS: At the current drug price, tisagenlecleucel was not found to be a cost-effective option. These results heavily depend on assumptions regarding long-term survival and the price of CAR T. Real-world evidence is needed to reduce uncertainty. HIGHLIGHTS: For patients with diffuse large B-cell lymphoma who failed 2 or more lines of systemic therapy, CAR T was not found to be a cost-effective treatment option at a willingness-to-pay threshold of $100,000.These results heavily depend on the expected long-term survival. The uncertainty in the model may be improved using real-world evidence reported in the future.

Urinary Metabolite Profiling to Non-Invasively Monitor the Omega-3 Index: An Exploratory Secondary Analysis of a Randomized Clinical Trial in Young Adults.

The Omega-3 Index (O3I) reflects eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in erythrocytes. While the O3I is associated with numerous health outcomes, its widespread use is limited. We investigated whether urinary metabolites could be used to non-invasively monitor the O3I in an exploratory analysis of a previous placebo-controlled, parallel arm randomized clinical trial in males and females (n = 88) who consumed either ~3 g/d olive oil (OO; control), EPA, or DHA for 12 weeks. Fasted blood and first-void urine samples were collected at baseline and following supplementation, and they were analyzed via gas chromatography and multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), respectively. We tentatively identified S-carboxypropylcysteamine (CPCA) as a novel urinary biomarker reflecting O3I status, which increased following both EPA and DHA (p < 0.001), but not OO supplementation, and was positively correlated to the O3I (R = 0.30, p < 0.001). Additionally, an unknown dianion increased following DHA supplementation, but not EPA or OO. In ROC curve analyses, CPCA outperformed all other urinary metabolites in distinguishing both between OO and EPA or DHA supplementation groups (AUC > 80.0%), whereas the unknown dianion performed best in discriminating OO from DHA alone (AUC = 93.6%). Candidate urinary biomarkers of the O3I were identified that lay the foundation for a non-invasive assessment of omega-3 status.

Multiple-platform data integration method with application to combined analysis of microarray and proteomic data.

BACKGROUND: It is desirable in genomic studies to select biomarkers that differentiate between normal and diseased populations based on related data sets from different platforms, including microarray expression and proteomic data. Most recently developed integration methods focus on correlation analyses between gene and protein expression profiles. The correlation methods select biomarkers with concordant behavior across two platforms but do not directly select differentially expressed biomarkers. Other integration methods have been proposed to combine statistical evidence in terms of ranks and p-values, but they do not account for the dependency relationships among the data across platforms. RESULTS: In this paper, we propose an integration method to perform hypothesis testing and biomarkers selection based on multi-platform data sets observed from normal and diseased populations. The types of test statistics can vary across the platforms and their marginal distributions can be different. The observed test statistics are aggregated across different data platforms in a weighted scheme, where the weights take into account different variabilities possessed by test statistics. The overall decision is based on the empirical distribution of the aggregated statistic obtained through random permutations. CONCLUSION: In both simulation studies and real biological data analyses, our proposed method of multi-platform integration has better control over false discovery rates and higher positive selection rates than the uncombined method. The proposed method is also shown to be more powerful than rank aggregation method.

Improving monitoring of fish health in the oil sands region using regularization techniques and water quality variables.

Trout-perch are sampled from the Athabasca River in Alberta, Canada, as a sentinel species for environmental health. The performance of trout-perch populations is known to be influenced by the quality of the water in which they reside. Using climate, environmental, and water quality variables measured in the Athabasca River near trout-perch sampling locations is found to improve model fitting and the predictability of models for the adjusted body weight, adjusted gonad weight, and adjusted liver weight of trout-perch. Given a large number of covariables, three variable selection techniques: stepwise regression, the lasso, and the elastic net (EN) are considered for selecting a subset of relevant variables. The models selected by the lasso and EN are found to outperform the models selected by stepwise regression in general, and little difference is observed between the models selected by the lasso and EN. Uranium, tungsten, tellurium, pH, molybdenum, and antimony are selected for at least one fish response.

Inference of latent event times and transmission networks in individual level infectious disease models.

Transmission networks indicate who-infected-whom in epidemics. Reconstruction of transmission networks is invaluable in applying and developing effective control strategies for infectious diseases. We introduce transmission network individual level models (TN-ILMs), a competing-risk, continuous time extension to individual level model framework for infectious diseases of Deardon et al. (2010). Through simulation study using a Julia language software package, Pathogen.jl, we explore the models with respect to their ability to jointly infer latent event times, latent disease transmission networks, and the TN-ILM parameters. We find good parameter, event time, and transmission network inference, with enhanced performance for inference of transmission networks in epidemic simulations that have higher spatial signals in their infectivity kernel. Finally, an application of a TN-ILM to data from a greenhouse experiment on the spread of tomato spotted wilt virus is presented.

Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces: Model-Based Analysis.

BACKGROUND: The development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed. OBJECTIVE: This study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia. METHODS: A model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied. RESULTS: The estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%). CONCLUSIONS: Our estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.

Single nucleotide polymorphisms in sweet, fat, umami, salt, bitter and sour taste receptor genes are associated with gustatory function and taste preferences in young adults.

Taste is a fundamental mechanism whereby compounds are detected orally, yet it is highly variable among individuals. The variability in taste that is attributable to genetics is not well-characterized despite its potential role in food selection, and therefore, eating habits that contribute to risk of overweight and obesity. In order to implicate measures of taste function and preference as potentially deterministic factors in adverse eating behaviors that lead to obesity, it must be shown that a relationship exists between genetic variation in taste receptor genes and psychophysical measures of taste in the absence high body mass index. The primary objective of this pilot study was to investigate the relationship between single nucleotide polymorphisms (SNPs) in taste receptor genes and 3 different psychophysical measures of taste in healthy young adults. Sweet, salt, umami, fat, sour, and bitter taste receptor gene SNPs were genotyped in 49 participants (ages 24.6 ± 0.6 years) who completed testing to determine oral detection threshold (DT), suprathreshold sensitivity (ST) and taste preference (PR). A simultaneous association test was conducted between each SNP and the 3 taste outcomes (DT, ST, and PR). Twelve SNPs were associated with at least one of the 3 taste outcomes. Associations were observed between SNPs in taste receptor genes and psychophysical measures of sweet, fat, umami, and salt taste. These results suggest that differences in interindividual psychophysical measures of tastes, namely DT, ST, and PR, may be partially attributed to genetic variation in taste receptor genes. Future studies are warranted to investigate if these findings have consequences for habitual dietary intake of foods that elicit these tastes.