Zoonotic Helminths of Urban Brown Rats (Rattus norvegicus) in the UK: Neglected Public Health Considerations?

Abstract

Urban brown rats (Rattus norvegicus) carry microbial human pathogens but their role as reservoir hosts for helminths of public health importance is less well known. In this study, 42 brown rats trapped on Merseyside were subject to thorough combined helminthological and pathohistological post-mortem examination. Eggs of the rodent-borne zoonotic nematode Calodium hepaticum were initially detected in histological sections of the livers of 9.5% of rats, but overall diagnostic sensitivity increased to 16.6% when entire liver tissue was disrupted and the resulting filtrates were examined for released eggs. In their rat host, mainly trapped inside the dockland, infections with C. hepaticum were associated with a chronic multifocal pyogranulomatous hepatitis with intralesional eggs and peripheral fibrosis. Mean intensity of hepatic C. hepaticum egg infections was 1041 eggs. This is the first report of C. hepaticum in an urban brown rat population in the UK and provides original data for liver egg burdens in this abundant commensal rodent. The zoonotic cestode Rodentolepis nana had a prevalence of infection of 14.3%. Rodent-specific, non-zoonotic helminths found were the spiruroid Mastophorus muris (16.0%) in the stomach, the trichuroid Trichosomoides crassicauda in the urinary bladder (31.0%); the ascarid Heterakis spumosa was the commonest helminth of the large intestine (76.2%). Many millions of brown rats inhabit cities and rural areas of the UK, and the infective stages of the zoonotic worm species, particularly C. hepaticum, are likely to be widely distributed in the environment presenting a threat to public health.

Rat-borne diseases at the horizon. A systematic review on infectious agents carried by rats in Europe 1995–2016

“…Both tapeworm species can cause enteritis, although infections often are asymptomatic [44]. H. nana has recently been recognised to have zoonotic potential in the UK [45].…”

The ecology of wildlife disease surveillance: demographic and prevalence fluctuations undermine surveillance

“…; McGarry et al . ), but given the sample sizes obtained and the temporal and geographic scales involved, it seems reasonable to assume that they are considerably lower. Simulation study 1 encompasses a wide range of real‐world wildlife disease surveillance.…”

“…For example, McGarry and co‐workers report overall prevalence of zoonotic helminths in 42 brown rats Rattus norvegicus captured in a programme of active surveillance carried out in an urban area in England between 2008 and 2011 (McGarry et al . ). These authors also present comparable results from several studies in Europe and North America, while another of the same host species conducted over a 2‐year period across a broad area of north‐western England captured just 133 individuals (Pounder et al .…”

Predicting missing links in global host-parasite networks

“…We suggest that an iterative approach to data amalgamation, link 55 prediction, and validation of potential host species offers a cost-effective solution to address-56 ing knowledge gaps in global host-parasite networks, ultimately strengthening capacities for 57 disease monitoring and control of both emerging and neglected multi-host pathogens.58Recent efforts have attempted to predict missing links in global host-parasite networks us-59 ing machine and statistical learning, such as the identification of potential rodent reservoirs of 60 zoonotic diseases from traits and zoonotic pathogen diversity (31). However, we frequently 61 lack the detailed knowledge of ecological and functional traits of interacting species required to 62 3 make accurate predictions (8,32,33). Thus, while these approaches work well for smaller net-63 works (8), they scale poorly to global-scale ecological datasets in which comparable traits are 64 unavailable for all species (20).…”

“…Recent efforts have attempted to predict missing links in global host-parasite networks us-59 ing machine and statistical learning, such as the identification of potential rodent reservoirs of 60 zoonotic diseases from traits and zoonotic pathogen diversity (31). However, we frequently 61 lack the detailed knowledge of ecological and functional traits of interacting species required to 62 3 make accurate predictions (8,32,33). Thus, while these approaches work well for smaller net-63 works (8), they scale poorly to global-scale ecological datasets in which comparable traits are 64 unavailable for all species (20).…”