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BACKGROUND: Human milk (HM) composition data are widely used in clinical, regulatory, and public health initiatives. The existing HM profiles in U.S. and Canadian nutrient databanks are outdated and now considered inappropriate to estimate current nutrient intakes. Recent reviews have underscored the limited North American data available to generate a new profile. OBJECTIVE: To describe concentrations and sources of variability of nutrients in HM from a large cohort collected in Canada. METHODS: The Maternal-Infant Research on Environmental Chemicals (MIREC) study recruited participants in the first trimester of pregnancy from 10 Canadian cities between 2008-2011. HM samples (n=559-835, depending on nutrient) were collected 3-10 weeks post-partum and analyzed for minerals (calcium, magnesium, phosphorus, potassium, sodium, manganese, molybdenum, zinc, copper, iodine, selenium), vitamin D (vitamin D3, 25-(OH)D3), folate vitamers (folic acid, 5-methyltetrahydrofolate, total folates), and fatty acids (panel). We examined associations between participant characteristics and log-transformed nutrient concentrations using linear regression. RESULTS: Concentrations of HM components in MIREC samples were within the range observed in literature except for manganese, which was >100 fold lower than the value in the existing Canadian nutrient databank profile (2.43 [SD 2.84] compared to 260 ng/g). In multivariable models, concentrations of folate vitamers, vitamin D and fatty acids demonstrated greater variability with maternal and sample characteristics than minerals. Factors such as relevant supplement use, body mass index (BMI), and for vitamin D, skin color and season, had a larger impact on nutrient concentrations than characteristics typically standardized in HM research, such as maternal or infant health, and method of collection. CONCLUSION: HM mineral concentrations from this study meet the methodological inclusion criteria for updating nutrient databank values and dietary reference intakes. Consideration of factors such as diet, skin colour, and BMI will be important for selecting studies for developing representative reference values based on human milk.
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Wastewater-based surveillance (WBS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) offers a complementary tool for clinical surveillance to detect and monitor coronavirus disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviours and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies; however, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outcomes. The implementation of WBS, therefore, requires a comprehensive framework to outline a pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities-Vancouver, Edmonton, Toronto, Montréal and Halifax-whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. The goal of this work is to share our insights on approaches to implement WBS. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.