The prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions – A scoping review and pooled analysis

Animal slurries and wastewater treatment sludges (WWTS) represent valuable biofertilisers in high-income, temperate regions and support transformative agri-food systems as sustainable, agricultural waste management practice. However, the presence of enteric pathogens in land-spread biowastes pose a public health risk, with food and water being critical transmission pathways. A dearth of spatiotemporally representative pathogen prevalence and concentration data from high-income, temperate regions exists to estimate the risk, achievable through quantitative microbial risk assessment (QMRA). A spatiotemporally explicit scoping review was undertaken of four waste-pathogen combinations (W-PCs) (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-Cryptosporidium parvum, broiler litter-Campylobacter jejuni, and WWTS-norovirus genogroups GI/GII) from land-spreading in high-income, temperate regions. W-PC prevalence and concentration data from 46 farm-level studies were extracted, harmonised, and pooled, to obtain representative data for meta-analyses, distribution fitting, and QMRA from land-spreading across these regions in addition to providing individual study prevalence and concentrations. Pooled mean prevalence and the total number of biowaste samples across extracted studies for each W-PC ranged from 17 % for STEC O157/O26 (N = 14,204) to 48 % for norovirus GI/GII (N = 1027). These general estimates included specific and non-specific data (i.e., serogroups, species and subspecies, or genogroups), and thus, should be interpreted with a level of caution. Pooled mean and SD concentrations ranged from norovirus GI/GII 1.3, 0.5 log₁₀ gc ml^-1 to C. jejuni 5.1, 0.7 log₁₀ CFU g^-1. Spatiotemporal heterogeneity, unstandardised reporting, and study design biases were found across studies. Therefore, increased standardised data and reporting in primary studies are required for more accurate QMRA estimates. Furthermore, pooling heterogeneous secondary data as though they were homogeneous introduces general error, and hence, highlights the requirement for future meta-analyses and distribution fitting of these data to characterise the inter- and intra- study variability in addition to uncertainty and variability from environmental sources.