British Columbia Centre for Disease Control

Abstract Background The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting a search for effective alternatives. Antimicrobial peptides (AMPs) are short, often cationic, peptide-based molecules with antimicrobial and immunomodulatory activity, which makes them promising alternatives to conventional antibiotics in poultry production. Results From a prior machine-learning-guided screen of 875 candidate AMPs against a wide bacterial panel, 62 exhibited activity against avian pathogenic Escherichia coli (APEC) and low in vitro hemolytic and cytotoxic activity. We selected three lead AMPs from this list (named TeRu4, TeBi1, and PeNi4), and evaluated their in vitro and in vivo efficacy, safety, and immunomodulatory potential for use in poultry farming. In animal experiments, AMPs were administered via in ovo injection on d 18 of embryonic development. In APEC challenge trials, yolk sacs were inoculated with APEC post-hatch to assess early chick mortality, while in pen trials, birds were raised in a commercial production setting for 35 d. For challenged birds, TeBi1 (10 μg/egg) significantly reduced culture-positive rates for APEC in the air sac and pericardium, increased body weight by 50% and reduced cytokine transcript levels by 10%–30% on d 7 post hatch. In HD11 chicken macrophage-like cultured cells, TeRu4 (16 μg/mL) suppressed lipopolysaccharide (LPS)-induced pro-inflammatory cytokine transcript levels. In pen trials, TeRu4 (20 μg/egg) increased the survival probability of female birds by 4.9%, while TeBi1 (20 μg/egg) increased the survival probability of all birds by 4.4%, by d 35. Gene expression analysis revealed AMP- and sex-specific cytokine responses. In pen trials, no significant differences were observed in mean weights, feed conversion ratio (FCR), and flock uniformity on d 35. By integrating high-throughput in ovo automation with large-scale commercial pen trials, this study provides a systematic translational bridge from in silico AI discovery to field-relevant poultry production interventions. Conclusions These findings demonstrate that TeBi1 and TeRu4 are promising antibiotic alternatives that improve survival, modulate immune responses, and maintain normal growth performance in broiler chickens in this experimental setting.

Contemporary sample size calculations for external validation of risk prediction models require users to specify fixed values of assumed model performance metrics alongside target precision levels (e.g., 95% CI widths). However, due to the finite samples of previous studies, our knowledge of true model performance in the target population is uncertain, and so choosing fixed values represents an incomplete picture. As well, for net benefit (NB) as a measure of clinical utility, the relevance of conventional precision-based inference is doubtful. In this work, we propose a general Bayesian framework for multi-criteria sample size considerations for prediction models for binary outcomes. For statistical metrics of performance (e.g., discrimination and calibration), we propose sample size rules that target desired expected precision or desired assurance probability that the precision criteria will be satisfied. For NB, we propose rules based on Optimality Assurance (the probability that the planned study correctly identifies the optimal strategy) and Value of Information (VoI) analysis, which quantifies the expected gain in NB by learning about model performance from a validation study of a given size. We showcase these developments in a case study on the validation of a risk prediction model for deterioration among hospitalized COVID-19 patients. Compared to conventional sample size calculation methods, a Bayesian approach requires explicit quantification of uncertainty around model performance, and thereby enables flexible sample size rules based on expected precision, assurance probabilities, and VoI. In our case study, calculations based on VoI for NB suggest considerably lower sample sizes are required than when focusing on the precision of calibration metrics. This approach is implemented in the accompanying software.

BACKGROUND:British Columbia (BC), Canada, continues to experience persistently high rates of unregulated drug toxicity. While the presence of fentanyl in the drug supply is well-established, less is known about how fluctuations in its concentration may influence mortality at the population level. This study describes geographic variation and temporal trends in fentanyl concentrations across BC and assesses if they are associated with unregulated drug toxicity rates. METHODS:Using a validated machine learning model that quantifies fentanyl and fluorofentanyl concentrations in unregulated opioid samples, we estimated concentrations in samples submitted to BC drug checking services from October 2018 to June 2025. We derived monthly median concentrations by geographic health service delivery area to determine historic monthly typical fentanyl strength. We then examined the relationship between fentanyl concentrations and unregulated drug toxicity mortality rates using a generalized additive mixed model (GAMM), accounting for regional heterogeneity, temporal trends, and autocorrelation. RESULTS:Median fentanyl concentrations varied geographically and temporally across the province, peaking provincially at 11.0% in mid-2023 before declining to 5.1% in early 2025. The GAMM estimated that, on average, each 1-percentage point increase in median fentanyl concentration was associated with a 0.072 increase in the monthly drug-related mortality rate per 100,000 population (p = 0.029). CONCLUSIONS:Quantifying fentanyl concentrations from point-of-care drug checking data enables detection of geographic and temporal patterns in the unregulated drug supply and their associations with drug-related mortality. This approach offers a tool for harm reduction, drug supply monitoring, and policy response during the ongoing drug toxicity crisis.

Importance:There is a global call to end cervical cancer, and various jurisdictions are still determining optimal strategies to accelerate elimination. Human papillomavirus (HPV)-negative testing confers lower risk of future precancer vs normal cytology; high-quality longitudinal data are needed comparing risk after a negative HPV test vs negative cotest (HPV and cytology). Objective:To compare long-term risk of cervical precancer based on HPV, cytology, and cotest screening results. Design, Setting, and Participants:This cohort study linked data from a randomized clinical trial to a comprehensive screening program in British Columbia. Participants were recruited between 2006 and 2012 and followed from trial exit to 10 years postexit. Eligible participants were women who completed trial exit cotesting. Data were analyzed between January and April 2025. Exposure:HPV and cytology status from exit cotesting were considered, stratified by status of each test. Main Outcome and Measures:Cumulative risk of precancer was calculated over follow-up using Kaplan-Meier techniques. Risk was compared among groups who tested HPV-negative with normal cytology, HPV-negative with abnormal cytology, HPV-positive with normal cytology, and HPV-positive with abnormal cytology. Additionally, risk among those who were HPV-negative (regardless of cytology result), with normal cytology (regardless of HPV results), or were cotest negative were compared in order to simulate outcomes in primary HPV screening, cytology, and cotest programs, respectively. Results:In this cohort of 8078 women (median [IQR] age at exit screen, 49 [41-57] years; 1636 Asian [22.4%], 223 Indigenous [3.0%], 5568 White [76.1%]) who participated in a British Columbia-based cervical cancer screening trial, the HPV-positive with abnormal cytology group had the highest cumulative incidence risk (CIR) of cervical intraepithelial neoplasia grade 2 or higher at the end of follow-up (CIR, 43.47%; 95% CI, 23.45%-58.26%), followed by the HPV-positive and cytology-negative group (CIR, 22.21%; 95% CI, 11.49%-31.62%). The HPV-negative with abnormal cytology (CIR, 4.83%; 95% CI, 0%-10.03%) and the HPV-negative with normal cytology (CIR, 0.37%; 95% CI, 0.13%-0.60%) groups had significantly lower CIR at the end of follow-up. Less than 1% of the population was HPV-negative with abnormal cytology (69 of 8078 [0.85%]). Women who were HPV-negative regardless of cytology results (CIR, 0.41%; 95% CI, 0.17%-0.65%) had a similar risk as those who cotested negative (CIR, 0.37%; 95% CI, 0.13%-0.60%); both groups had lower risk than those with normal cytology results (regardless of HPV result) (CIR, 1.28%; 95% CI, 0.78%-1.78%) throughout follow-up. Conclusions and Relevance:In this cohort study of cervical cancer screen testing approaches and risk of cervical precancer, after a negative HPV test (regardless of cytology results) risk of precancer remained acceptably low throughout long-term follow-up. This suggests that cotesting yielded limited benefits, while increasing costs, relative to primary HPV testing.

BACKGROUND:Although digital health literacy (DHL) is recognised as a determinant of access to digital sexually transmitted and blood-borne infection (STBBI) testing, empirical evidence about its contribution to access disparities remains limited. We applied multidimensional DHL measures to examine inequities in awareness and use of GetCheckedOnline, British Columbia's (BC) publicly funded digital STBBI testing service. METHODS:We analysed data from GetCheckedOnline's 2022 community survey of English-speaking BC residents aged ≥16 years who were sexually active in the past year. Outcomes were awareness and use of GetCheckedOnline (yes/no). DHL was measured using latent factors from the eHealth Literacy Scale: Information Navigation, Resource Appraisal and Confidence in Use. Structural equation modelling (SEM) was used to estimate associations and mediation pathways between DHL, sociodemographic characteristics and service outcomes. Model fit was assessed using standard SEM indices. RESULTS:Among 1657 respondents (mean age 33 years, SD 11.77), Information Navigation was positively associated with awareness (β=0.162, p<0.001) and use (β=0.063, p=0.020) of GetCheckedOnline. Confidence in Use was positively associated with awareness (β=0.206, p=0.014) and use (β=0.115, p=0.020). In contrast, Resource Appraisal was negatively associated with awareness (β=-0.263, p=0.006) and use (β=-0.150, p=0.010). DHL factors mediated the effects of age, income, education and digital access on both outcomes. CONCLUSIONS:DHL operates as a multidimensional and socially patterned determinant of access to digital STBBI testing services. While information navigation and confidence in use facilitate access, higher resource appraisal may reduce use, potentially reflecting concerns about service fit, privacy or trust. Findings highlight the need for digital interventions that are not only accessible but also contextually relevant, trusted and responsive to the needs of diverse users.