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There is ongoing debate regarding whether critically important antimicrobials (CIA) should be used to treat infections in food-producing animals. In this systematic review, we determined whether CIA and non-CIA have comparable efficacy to treat nonsevere bovine clinical mastitis caused by the most commonly reported bacteria that cause mastitis worldwide. We screened CAB Abstracts, Web of Science, MEDLINE, Scopus, and PubMed for original epidemiological studies that assessed pathogen-specific bacteriological cure rates of antimicrobials used to treat nonsevere clinical mastitis in lactating dairy cows. Network models were fit using risk ratios of bacteriological cure as outcome. A total of 30 studies met inclusion criteria. Comparisons of cure rates demonstrated that CIA and non-CIA had comparable efficacy for treatment of nonsevere clinical mastitis in dairy cattle. Additionally, for cows with nonsevere clinical mastitis caused by Escherichia coli and Klebsiella spp., bacteriological cure rates were comparable for treated versus untreated cows; therefore, there was no evidence to justify treatment of these cases with CIA. Our findings supported that CIA in general are not necessary for treating nonsevere clinical mastitis in dairy cattle, the disease that accounts for the majority of antimicrobial usage in dairy herds worldwide. Furthermore, our findings support initiatives to reduce or eliminate use of CIA in dairy herds.

Glucocorticoid (GC) levels are increasingly and widely used as biomarkers of hypothalamic–pituitary–adrenal (HPA) axis activity to study the effects of environmental changes and other perturbations on wildlife individuals and populations. However, identifying the intrinsic and extrinsic factors that influence GC levels is a key step in endocrinology studies to ensure accurate interpretation of GC responses. In muskoxen, qiviut (fine woolly undercoat hair) cortisol concentration is an integrative biomarker of HPA axis activity over the course of the hair’s growth. We gathered data from 219 wild muskoxen harvested in the Canadian Arctic between October 2015 and May 2019. We examined the relationship between qiviut cortisol and various intrinsic (sex, age, body condition and incisor breakage) and extrinsic biotic factors (lungworm and gastrointestinal parasite infections and exposure to bacteria), as well as broader non-specific landscape and temporal features (geographical location, season and year). A Bayesian approach, which allows for the joint estimation of missing values in the data and model parameters estimates, was applied for the statistical analyses. The main findings include the following: (i) higher qiviut cortisol levels in males than in females; (ii) inter-annual variations; (iii) higher qiviut cortisol levels in a declining population compared to a stable population; (iv) a negative association between qiviut cortisol and marrow fat percentage; (v) a relationship between qiviut cortisol and the infection intensity of the lungworm Umingmakstrongylus pallikuukensis, which varied depending on the geographical location; and (vi) no association between qiviut cortisol and other pathogen exposure/infection intensity metrics. This study confirmed and further identified important sources of variability in qiviut cortisol levels, while providing important insights on the relationship between GC levels and pathogen exposure/infection intensity. Results support the use of qiviut cortisol as a tool to monitor temporal changes in HPA axis activity at a population level and to inform management and conservation actions.

In an emerging epidemic, public health officials must move quickly to contain the spread. Information obtained from statistical disease transmission models often informs the development of containment strategies. Inference procedures such as Bayesian Markov chain Monte Carlo allow researchers to estimate parameters of such models, but are computationally expensive. In this work, we explore supervised statistical and machine learning methods for fast inference via supervised classification, with a focus on deep learning. We apply our methods to simulated epidemics through two populations of swine farms in Iowa, and find that the random forest performs well on the denser population, but is outperformed by a deep learning model on the sparser population.

This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SINR) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus (TSWV) disease.

Glucocorticoid (GC) levels are increasingly and widely used as biomarkers of hypothalamic-pituitary-adrenal (HPA) axis activity to study the effects of environmental changes and other perturbations on wildlife individuals and populations. However, identifying the intrinsic and extrinsic factors that influence GC levels is a key step in endocrinology studies to ensure accurate interpretation of GC responses. In muskoxen, qiviut (fine woolly undercoat hair) cortisol concentration is an integrative biomarker of HPA axis activity over the course of the hair's growth. We gathered data from 219 wild muskoxen harvested in the Canadian Arctic between October 2015 and May 2019. We examined the relationship between qiviut cortisol and various intrinsic (sex, age, body condition and incisor breakage) and extrinsic biotic factors (lungworm and gastrointestinal parasite infections and exposure to bacteria), as well as broader non-specific landscape and temporal features (geographical location, season and year). A Bayesian approach, which allows for the joint estimation of missing values in the data and model parameters estimates, was applied for the statistical analyses. The main findings include the following: (i) higher qiviut cortisol levels in males than in females; (ii) inter-annual variations; (iii) higher qiviut cortisol levels in a declining population compared to a stable population; (iv) a negative association between qiviut cortisol and marrow fat percentage; (v) a relationship between qiviut cortisol and the infection intensity of the lungworm

Individual-level models incorporate individual-specific covariate information, such as spatial location, to model infectious disease transmission. However, fitting these models with traditional Bayesian methods becomes cumbersome as model complexity or population size increases. We consider a spatial individual-level model with a binary susceptibility covariate. A method for fitting this model to aggregate-level data using traditional Metropolis-Hastings MCMC and then disaggregating the results to obtain individual-level estimates for epidemic metrics is proposed. This so-called "Cluster-Aggregate-Disaggregate" (CAD) method is compared to two approximate Bayesian computation (ABC) algorithms in a simulation study. The methods are also applied to a data set from the 2001 U.K. foot and mouth disease epidemic. While the CAD and ABC methods both performed reasonably well at capturing epidemic metrics, the CAD method was found to be much easier to implement and reduced computation time (relative to the traditional model-fitting method) more consistently than the ABC methods.

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network.

Objectives To quantify rheumatologists’ beliefs about the effectiveness of triple therapy (MTX + HCQ + SSZ) and other commonly used initial treatments for RA. Methods In a Bayesian belief elicitation exercise, 40 rheumatologists distributed 20 chips, each representing 5% of their total weight of belief on the probability that a typical patient with moderate–severe early RA would have an ACR50 response within 6 months with MTX (oral and s.c.), MTX + HCQ (dual therapy) and triple therapy. Parametric distributions were fit, and used to calculate pairwise median relative risks (RR), with 95% credible intervals, and estimate sample sizes for new trials to shift these beliefs. Results In the pooled analysis, triple therapy was perceived to be superior to MTX (RR 1.97; 1.35, 2.89) and dual therapy (RR 1.32; 1.03, 1.73). A pessimistic subgroup (n = 10) perceived all treatments to be similar, whereas an optimistic subgroup (n = 10) believed triple therapy to be most effective of all (RR 4.03; 2.22, 10.12). Similar variability was seen for the comparison between oral and s.c. MTX. Assuming triple therapy is truly more effective than MTX, a trial of 100 patients would be required to convince the pessimists; if triple therapy truly has no–modest effect (RR Conclusion Rheumatologists’ beliefs regarding the effectiveness of triple therapy vary, which may partially explain the variability in its use. Owing to the strength of beliefs, some may be reluctant to shift, even with new evidence.

Geographically dependent individual level models (GD-ILMs) are a class of statistical models that can be used to study the spread of infectious disease through a population in discrete-time in which covariates can be measured both at individual and area levels. The typical ILMs to illustrate spatial data are based on the distance between susceptible and infectious individuals. A key feature of GD-ILMs is that they take into account the spatial location of the individuals in addition to the distance between susceptible and infectious individuals. As a motivation of this article, we consider tuberculosis (TB) data which is an infectious disease which can be transmitted through individuals. It is also known that certain areas/demographics/communities have higher prevalent of TB (see Section 4 for more details). It is also of interest of policy makers to identify those areas with higher infectivity rate of TB for possible preventions. Therefore, we need to analyze this data properly to address those concerns. In this article, the expectation conditional maximization algorithm is proposed for estimating the parameters of GD-ILMs to be able to predict the areas with the highest average infectivity rates of TB. We also evaluate the performance of our proposed approach through some simulations. Our simulation results indicate that the proposed method provides reliable estimates of parameters which confirms accuracy of the infectivity rates.

Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at emergency departments can improve staffing and resource allocation decisions within hospitals. In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. We assessed the accuracy and reliability of our model with 1 to 4 weeks ahead forecast targets using real-time hospital-level data on weekly ILI visit volumes during the 2012-2018 flu seasons in the Alberta Children’s Hospital, located in Calgary, Alberta, Canada. Our results suggest the forecasting performance of the stacked ensemble meets or exceeds the performance of the individual models over all forecast targets.

Background Clostridioides difficile infection (CDI) is an opportunistic disease that lacks a gold-standard test. Nucleic acid amplification tests such as real-time polymerase chain reaction (PCR) demonstrate an excellent limit of detection (LOD), whereas antigenic methods are able to detect protein toxin. Latent class analysis (LCA) provides an unbiased statistical approach to resolving true disease. Methods A cross-sectional study was conducted in patients with suspected CDI (N = 96). Four commercial real-time PCR tests, toxin antigen detection by enzyme immunoassay (EIA), toxigenic culture, and fecal calprotectin were performed. CDI clinical diagnosis was determined by consensus majority of 3 experts. LCA was performed using laboratory and clinical variables independent of any gold standard. Results Six LCA models were generated to determine CDI probability using 4 variables including toxin EIA, toxigenic culture, clinical diagnosis, and fecal calprotectin levels. Three defined zones as a function of real-time PCR cycle threshold (Ct) were identified using LCA: CDI likely (>90% probability), CDI equivocal (10%), CDI unlikely ( Conclusions The clinical implication of applying LCA to CDI is to report Ct values assigned to probability zones based on the commercial real-time PCR platform. A broad range of equivocation suggests clinical judgment is essential to the confirmation of CDI.

SummaryBackgroundThe government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood.MethodsRates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented.FindingsDaily case rates ranged from 0·03 to 30·05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2·18/10 million/day). AverageRtwas 1·99 (95% CI 1·93-2·06) for India; it ranged from 1·38 to 2·78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value.InterpretationThe Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates andRtwill aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India.FundingNon-funded.

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: The primary objective is to compare the benefits and harms of different disease‐modifying anti‐rheumatic drugs (DMARDs) as initial therapy and after failure of conventional synthetic DMARDs or biologic or targeted synthetic DMARDs in adults with rheumatoid arthritis through a network meta‐analysis (NMA). A secondary objective is to rank the interventions for both benefits and harms. This protocol describes the approach for separate NMAs for the three populations of interest (described below), which we intend to publish as three separate Cochrane Reviews. 1) Disease‐modifying anti‐rheumatic drugs for rheumatoid arthritis as initial therapy: a systematic review and network meta‐analysis 2) Disease‐modifying anti‐rheumatic drugs for rheumatoid arthritis after failure of conventional synthetic disease‐modifying anti‐rheumatic drugs: a systematic review and network meta‐analysis 3) Disease‐modifying anti‐rheumatic drugs for rheumatoid arthritis after failure of biologic or targeted synthetic therapy: a systematic review and network meta‐analysis

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.

The government of India implemented social distancing interventions to contain the COVID‐19 epidemic. However, effects of these interventions on epidemic dynamics are yet to be understood. Rates of laboratory‐confirmed COVID‐19 infections per day and effective reproduction number (Rt) were estimated for 7 periods (Pre‐lockdown, Lockdown Phases 1 to 4 and Unlock 1–2) according to nationally implemented interventions with phased relaxation. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID‐19 are presented. Daily case rates ranged from 0.03 to 285.60/10 million people across 7 discrete periods in India. From 18 May to 31 July 2020, the NCT of Delhi had the highest case rate (999/10 million people/day), whereas Madhya Pradesh had the lowest (49/10 million/day). Average Rt was 1.99 (95% CI 1.93–2.06) and 1.39 (95% CI 1.38–1.40) for the entirety of India during the period from 22 March 2020 to 17 May 2020 and from 18 May 2020 to 31 July 2020, respectively. Median mobility in India decreased in all contact domains during the period from 22 March 2020 to 17 May 2020, with the lowest being 21% in retail/recreation, except home which increased to 129% compared to the 100% baseline value. Median mobility in the ‘Grocery and Pharmacy’ returned to levels observed before 22 March 2020 in Unlock 1 and 2, and the enhanced mobility in the Pharmacy sector needs to be investigated. The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID‐19 epidemic progression in India. The identified daily COVID‐19 case rates and Rt will aid national and state governments in formulating ongoing COVID‐19 containment plans. Furthermore, these findings may inform COVID‐19 public health policy in developing countries with similar settings to India.

In this article, we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis-Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. The use of the package is demonstrated through examples involving both simulated and real data., Comment: 15 pages, 10 figures. This paper will be submitted to the R journal

Objective To quantify patient preferences for maintenance therapy of Crohn’s disease and understand the impact on treatment selection. Methods We conducted a discrete-choice experiment in patients with Crohn’s disease (n = 155) to measure the importance of attributes relevant to choosing between different medical therapies for maintenance of Crohn’s disease. The attributes included efficacy and withdrawals due to adverse events, as well as dosing and other rare risks of treatment. From the discrete-choice experiment we estimated the part-worth (importance) of each attribute level, and explored preference heterogeneity through latent class analysis. We then used the part-worths to apply weights across each outcome from a prior network meta-analysis to estimate patients’ preferred treatment in pairwise comparisons and for the overall group of treatments. Results The discrete-choice experiment revealed that maintaining remission was the most important attribute. Patients would accept a rare risk of infection or cancer for a 14% absolute increased chance of remission. Latent class analysis demonstrated that 45% of the cohort was risk averse, either to adverse events or requiring a course of prednisone. When these preferences were used in modelling studies to compare pairs of treatments, there was a ≥ 78% probability that all biologic treatments were preferred to azathioprine and methotrexate, based on the balance of benefits and harms. When comparing all treatments, adalimumab was preferred by 53% of patients, who were motivated by efficacy, and vedolizumab was preferred by 30% who were driven by the preference to avoid risks. However, amongst biologic treatment options, there was considerable uncertainty regarding the preferred treatment at the individual patient level. Conclusion Patients with Crohn’s disease from our population were, on average, focused on the benefits of treatment, supporting intensive treatment approaches aimed at maintaining remission. Important preference heterogeneity was identified, however, highlighting the importance of shared decision making when selecting treatments.

BACKGROUND: Incidence of Inflammatory Bowel Disease (IBD)—Crohn’s Disease (CD) and ulcerative colitis (UC)—is decreasing in some provinces, but increasing in pediatrics. Even with this decrease in incidence, prevalence will continue to rise until incidence equals to mortality. Decision makers require accurate data on the current and future burden of IBD for resource planning to ensure IBD patients receive proper care. AIMS: 1. Assess current and forecast future IBD incidence trends; 2. determine IBD mortality rates; and, 3. use mortality to calculate the threshold that incidence must approximate to stabilize prevalence. METHODS: Using population-based data from Alberta, annual incidence (per 100,000 persons) is calculated from 2010 to 2015, stratified by pediatric (

Here, we address the issue of experimental design for animal and crop disease transmission experiments, where the goal is to identify some characteristic of the underlying infectious disease system via a mechanistic disease transmission model. Design for such non-linear models is complicated by the fact that the optimal design depends upon the parameters of the model, so the problem is set in simulation-based, Bayesian framework using informative priors. This involves simulating the experiment over a given design repeatedly using parameter values drawn from the prior, calculating a Monte Carlo estimate of the utility function from those simulations for the given design, and then repeating this over the design space in order to find an optimal design or set of designs. Here we consider two agricultural scenarios. The first involves an experiment to characterize the effectiveness of a vaccine-based treatment on an animal disease in an in-barn setting. The design question of interest is on which days to make observations if we are limited to being able to observe the disease status of all animals on only two days. The second envisages a trial being carried out to estimate the spatio-temporal transmission dynamics of a crop disease. The design question considered here is how far apart to space the plants from each other to best capture those dynamics. In the in-barn animal experiment, we see that for the prior scenarios considered, observations taken very close to the beginning of the experiment tend to lead to designs with the highest values of our chosen utility functions. In the crop trial, we see that over the prior scenarios considered, spacing between plants is important for experimental performance, with plants being placed too close together being particularly deleterious to that performance.

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.

Summary Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods.

Properties of statistical alarms have been well studied for simple disease surveillance models, such as normally distributed incidence rates with a sudden or gradual shift in mean at the start of an outbreak. It is known, however, that outbreak dynamics in human populations depend significantly on the heterogeneity of the underlying contact network. The rate of change in incidence for a disease such as influenza peaks early on during the outbreak, when the most highly connected individuals get infected, and declines as the average number of connections in the remaining susceptible population drops. Alarm systems currently in use for detecting the start of influenza seasons generally ignore this mechanism of disease spread, and, as a result, will miss out on some early warning signals. We investigate the performance of various alarms on epidemics simulated from an undirected network model with a power law degree distribution for a pathogen with a relatively short infectious period. We propose simple custom alarms for the disease system considered, and show that they can detect a change in the process sooner than some traditional alarms. Finally, we test our methods on observed rates of influenza-like illness from two sentinel providers (one French, one Spanish) to illustrate their use in the early detection of the flu season.

BackgroundIt is unclear if malaria causes deranged liver enzymes. This has implications both in clinical practice and in research, particularly for antimalarial drug development.MethodWe performed a retrospective cohort study of returning travelers (n = 4548) who underwent a malaria test and had enzymes measured within 31 days in Calgary, Canada, from 2010 to 2017. Odds ratios of having an abnormal alkaline phosphatase (ALP), alanine aminotransferases (ALT), aspartate aminotransferases (AST), and total bilirubin (TB) were calculated using multivariable longitudinal analysis with binomial response.ResultsAfter adjusting for gender, age, and use of hepatotoxic medications, returning travelers testing positive for malaria had higher odds of having an abnormal TB (odds ratio [OR], 12.64; 95% confidence interval [CI], 6.32–25.29; P < .001) but not ALP (OR, 0.32; 95% CI, 0.09–1.10; P = .072), ALT (OR, 1.01; 95% CI, 0.54–1.89; P = .978) or AST (OR, 1.26; 95% CI, 0.22–7.37; P = .794), compared with those who tested negative. TB was most likely to be abnormal in the “early” period (day 0–day 3) but then normalized in subsequent intervals. Returning travelers with severe malaria (OR, 2.56; 95% CI, 0.99–6.62; P = .052) had borderline increased odds of having an abnormal TB, but malaria species (OR, 0.70; 95% CI, 0.24–2.05; P = .511) did not.ConclusionsIn malaria-exposed returning travelers, the TB is abnormal, especially in the early period, but no abnormalities are seen for ALT, AST, or ALP.

peer-reviewed BACKGROUND & AIMS: Inflammatory bowel diseases (IBDs) exist worldwide, with high prevalence in North America. IBD is complex and costly, and its increasing prevalence places a greater stress on health care systems. We aimed to determine the past current, and future prevalences of IBD in Canada. METHODS: We performed a retrospective cohort study using population-based health administrative data from Alberta (2002–2015), British Columbia (1997– 2014), Manitoba (1990–2013), Nova Scotia (1996–2009), Ontario (1999–2014), Quebec (2001–2008), and Saskatchewan (1998–2016). Autoregressive integrated moving average regression was applied, and prevalence, with 95% prediction intervals (PIs), was forecasted to 2030. Average annual percentage change, with 95% confidence intervals, was assessed with log binomial regression. RESULTS: In 2018, the prevalence of IBD in Canada was estimated at 725 per 100,000 (95% PI 716–735) and annual average percent change was estimated at 2.86% (95% confidence interval 2.80%–2.92%). The prevalence in 2030 was forecasted to be 981 per 100,000 (95% PI 963–999): 159 per 100,000 (95% PI 133–185) in children, 1118 per 100,000 (95% PI 1069–1168) in adults, and 1370 per 100,000 (95% PI 1312–1429) in the elderly. In 2018, 267,983 Canadians (95% PI 264,579–271,387) were estimated to be living with IBD, which was forecasted to increase to 402,853 (95% PI 395,466–410,240) by 2030. CONCLUSION: Forecasting prevalence will allow health policy makers to develop policy that is necessary to address the challenges faced by health systems in providing high-quality and cost-effective care.

Statistical inference for spatial models of infectious disease spread is often very computationally expensive. These models are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework, which requires multiple iterations of the computationally cumbersome likelihood function. We here propose a method of inference based on so-called emulation techniques. Once again the method is set in a Bayesian MCMC context, but avoids calculation of the computationally expensive likelihood function by replacing it with a Gaussian process approximation of the likelihood function built from simulated data. We show that such a method can be used to infer the model parameters and underlying characteristics of the spatial disease system, and this can be done in a computationally efficient manner. The Canadian Journal of Statistics 44: 480–501; 2016 © 2016 Statistical Society of Canada

A class of complex statistical models, known as individual-level models, have been effectively used to model the spread of infectious diseases. These models are often fitted within a Bayesian Markov chain Monte Carlo framework, which can have a sig nificant computational expense due to the complex nature of the likelihood function associated with this class of models. Increases in population size or duration of the modeled epidemic can contribute to this computational burden. Here, we explore the effect of reducing this computational expense by aggregating the data into spatial clusters, and therefore reducing the overall population size. Individual-level models, reparameterized to account for this aggregation effect, may then be fitted to the spatially aggregated data. The ability of two reparameterized individual-level models, when fitted to this reduced data set, to identify a covariate effect is investigated through a simulation study.