Government of New Brunswick

BackgroundTreating individuals with tuberculosis (TB) infection (TBI) is an important aspect of the global strategy to eliminate TB as a public health problem, as it would help reduce the pool of individuals with TBI who are at risk of developing TB disease (TBD). Understanding factors that impact effective management of patients with TBI is helpful in informing policy.ObjectiveTo assess the proportion of immigrants with TBI accepting and completing TB preventive treatment (TPT), variables potentially related to accepting and completing TPT were examined and healthcare provider (HCP)-related factors that impact TBI management were identified.MethodsTuberculosis preventive treatment was offered to TBI-positive immigrants without a history or treatment of TBD from a pilot TBI screening study conducted in southern New Brunswick, Canada between November 2021 and November 2023. Tuberculosis preventive treatment acceptance and completion rates were calculated, and the HCP completed a questionnaire to identify factors that affected TBI management. Participant characteristics were summarized using descriptive statistics, while Fisher’s exact tests were conducted to test for independence between demographics and treatment acceptance and completion. The HCP questionnaire data were analyzed using thematic analysis.ResultsOf the 49 participants who screened positive for TBI, 11 (22.4%) were lost to follow-up prior to being assessed and offered TPT and 38 (77.6%) were offered TPT, of whom 3 (7.9%) declined, 35 (92.1%) accepted and initiated TPT, and 30 (85.7%) completed treatment. Treatment acceptance and completion were found to be independent from the participant demographics examined. Thematic analysis revealed five emerging themes regarding the management of TBI participants (i.e., supports, collaboration, communication, time, and satisfaction).ConclusionThis study demonstrates the feasibility of treating TBI in immigrants and highlights HCP-related factors that impact the management of TBI among immigrants in southern New Brunswick. Our findings may assist programs aimed at improving TBI screening and treatment.

With an estimated one million cases per year anticipated by 2030, total hip arthroplasty (THA) is a popular and successful treatment for end-stage hip osteoarthritis. The objectives of this study were to: (1) measure the incidence of disruptive bleeding; (2) determine the demographics of patients and the burden of Elixhauser comorbidity related to disruptive bleeding; and (3) assess the effect of disruptive bleeding on health care utilization (inpatient costs, hospital lengths of stay, and 90-day readmission rates). In this cohort study, patients who had primary THA in 2019 were identified using a national health care database. Multivariate logistic regressions were used to examine possible associations with disruptive bleeding after patients were grouped by Elixhauser Comorbidity Index (ECI), which ranged from 0 to ≥ 6. Both 95% confidence intervals (CIs) and odds ratios (ORs) were presented for every ECI. Generalized linear models were used to assess the effects of disruptive bleeding on hospital lengths of stay, 90-day readmission rates, and overall expenses. Disruptive hemorrhage occurred in 6,586 (6.0%) of the 109,482 patients receiving THA. Higher bleeding risk was linked to women and Medicare coverage (P < 0.01). For instance, the risks of disruptive bleeding rose from OR = 1.48 (95% CI 1.23 to 1.78, P < 0.001) for ECI = 2 to OR = 3.81 (95% CI 2.85 to 5.10, P < 0.001) as the comorbidity burden increased. Disruptive bleeding was linked to higher overall expenses ($34,700 versus $23,500 for ECI > 6; P < 0.001), longer hospital stays (6.0 days for ECI ≥ 6; P < 0.001), and higher 90-day readmissions (18.5 versus 10.2% for ECI = 5; P < 0.001). Patients who had higher comorbidity burdens are more likely to experience disruptive hemorrhage following THA, which is linked to worse short-term results and noticeably higher resource utilization.

Potato early dying disease complex (PED) leads to premature senescence and rapid decline in potato plants. Unlike potato wilt caused solely by Verticillium species, PED symptoms are more severe due to the synergistic effects of multiple pathogens, including root-lesion nematodes, fungi such as Colletotrichum and Fusarium, and soft-rot bacteria. To investigate the microbiome responsible for PED, soil and stem samples from healthy-looking and symptomatic plants were analyzed using amplicon-targeted next-generation sequencing (Illumina MiSeq and PacBio technologies). Samples were collected from four locations in New Brunswick, Canada from fields previously rotated with barley or oat. Comparative analysis of the bacterial, fungal, and eukaryotic diversity in soil samples showed minimal differences, with only bacterial alpha diversity influenced by the plant health status. Verticillium dahliae was abundant in all soil samples, and its abundance was significantly higher in the stems of diseased plants. Additional fungal species implicated in PED, including Plectosphaerella cucumerina, Colletotrichum coccodes, Botrytis sp., and Alternaria alternata, were also identified in the stems. This study highlights the complex, plant-associated microbial interactions underlying PED and provides a foundation for microbiome-informed disease management strategies.

This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe2O3T vs. Gd and Al2O3 vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power.

ObjectiveTo examine the patterns in the supply and use of psychiatric services in 3 Canadian provinces: British Columbia, Manitoba, and Ontario.MethodsWe conducted a repeated cross-sectional analysis spanning fiscal years 2012/13 to 2021/22, using patient- and psychiatrist-level data aggregated into administrative health regions. Descriptive statistics and linear regression were used to assess patterns and relationships between the per capita number of psychiatrists ("supply") and measures of use of psychiatric services ("utilization"), including any psychiatrist contact, psychiatric consultation (1-2 visits with the same psychiatrist), and ongoing psychiatric care (3 or more visits with the same psychiatrist).ResultsThe number of psychiatrists per capita remained stable within the 3 provinces during the study period. In 2021/22, Vancouver had the highest number in British Columbia (45 psychiatrists per 100,000 individuals), compared to 14 per 100,000 in lower-supply regions. Toronto had the highest number in Ontario (38 per 100,000), compared to 9 in lower supply regions. Winnipeg had the highest number in Manitoba (25 per 100,000), compared to 7 in the lower supply regions. In 2021/22, the per capita number of psychiatrists was moderately correlated with any psychiatrist contact (R2 = 0.290) and ongoing psychiatric care (R2 = 0.411), but weakly correlated with psychiatric consultation (R2 = 0.005). The relationship between supply and utilization diminishes with higher levels of regional supply.ConclusionsPsychiatrists were unevenly distributed within and across provinces. While more psychiatrists are needed, the moderate and diminishing relationships between their numbers and utilization suggest that increasing this number alone is unlikely to fully address unmet needs for mental healthcare. Strategies to improve access will need to directly target uneven distributions. Further research is needed to understand the factors influencing psychiatrists' practice choices and ways to better support them in increasing their access to care.

Demand for health data and analytics to support research, policy, and practice continues to rise, accelerated by the Covid-19 pandemic. Despite the importance of the government analytics workforce in driving academic-based data sharing and linkage platforms, little is known about how public sector managers assess capacity in health analytics. This case study describes findings from consultations among middle managers of analytics services in a Canadian provincial health ministry. Data collection involved a mixed-questions survey to gauge the functional perspective of managers on organisational and human resource analytics capacity within the New Brunswick Department of Health. The repeated cross-sectional survey was implemented in two rounds, with a baseline collected before the Covid-19 global outbreak (in 2016) and a follow-up after the pandemic emergency response (in 2022). The post-pandemic period was associated with perceptions of a growing role for public service personnel in handling analytics. Recruitment and retention of skilled analytics professionals emerged as the top priority for capacity building, including needs-based planning, competitive compensation packages to address skills shortages, professional development and promotion opportunities, and tracking key performance indicators for employee satisfaction. Government health analytics professionals play a critical role in advancing administrative data use and re-use. Enhanced knowledge sharing is needed on best practices in supply-demand monitoring for analytics professionals and planning for human resources surge capacity in the public service, lest significant innovation potential for health system improvement be left untapped.

ABSTRACT The Saint John (Wolastoq) River (SJR) is a major international river flowing through the State of Maine, USA, and the Province of New Brunswick, Canada. In terms of river length and basin area, it is one of the largest rivers on the eastern seaboard of North America south of the St. Lawrence River system. Subject to seasonal ice cover, ice conditions vary along the SJR due to natural differences in climate and terrain, and due to anthropogenetic changes, such as the construction of dams. Ice formation, growth, and breakup along the SJR affects its hydraulic and ecological regimes and leads to the potential for ice jamming that has caused severe flooding and ice runs. Ice affects river erosion, flooding, ecology, water quality, and recreational uses of the SJR. An overview of typical winter climate and ice season characteristics along the SJR from Dickey, Maine, USA to Saint John, New Brunswick, and some of its environmental consequences are summarized in this review paper. Ice processes and phenomena that occur along the SJR occur along other northern rivers, and can cause similar environmental concerns and consequences that should not be ignored in river engineering and basin management.

Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change.