Divergent geography of Salmonella Wangata and Salmonella Typhimurium epidemiology in New South Wales, Australia

Salmonella enterica in farm environments in the Ashanti Region of Ghana

BACKGROUND

Salmonella enterica are important foodborne pathogens and the third leading cause of death among diarrheal infections worldwide. This cross-sectional study investigated the frequency of antibiotic-resistant Salmonella enterica in commercial and smallholder farm environments in the Ashanti Region of Ghana. A total of 1490 environmental samples, comprising 800 (53.7%) soil (from poultry, pigs, sheep, goats and cattle farms), 409 (27.4%) pooled poultry fecal and 281 (18.9%) dust (from poultry farms) samples, were collected from 30 commercial and 64 smallholder farms. All samples were processed using standard culture methods. Isolates were identified by biochemical methods and confirmed using the VITEK 2 System. Antibiotic susceptibility testing was carried out by disk diffusion following the EUCAST guidelines. Serotyping was performed using the Kauffman White Le Minor Scheme.

RESULTS

The overall Salmonella frequency was 6.0% (n/N = 90/1490); the frequency varied according to the type of sample collected and included: 8.9% for dust (n/N = 25/281), 6.5% for soil (n/N = 52/800) and 3.2% for pooled poultry fecal samples (n/N = 13/409). Salmonella was also recovered from commercial farm environments (8.6%, n/N = 68/793) than from smallholder farms (3.2%, n/N = 22/697) (PR = 2.7, CI: 1.7 - 4.4). Thirty-four different Salmonella serovars were identified, the two most common being Rubislaw (27.8%, n/N = 25/90) and Tamale (12.2%, n/N = 11/90). Serovar diversity was highest in strains from soil samples (70.6%, n/N = 24/34) compared to those found in the dust (35.2%, n/N = 12/34) and in fecal samples (29.4%, n/N = 10/34). Salmonella frequency was much higher in the rainy season (8.4%, n/N = 85/1007) than in the dry season (1.0%, n/N = 5/483) (PR = 8.4, 95% CI: 3.3 - 20.0). Approximately 14.4% (n/N = 13/90) of the isolates were resistant to at least one of the tested antimicrobials, with 84.6% (n/N = 11/13) being resistant to multiple antibiotics. All Salmonella Kentucky (n = 5) were resistant to ciprofloxacin.

CONCLUSION

This study showed that farm environments represent an important reservoir for antibiotic-resistant Salmonella, which warrants monitoring and good husbandry practices, especially in commercial farms during the rainy season, to control the spread of this pathogen.

Gammaretroviruses, novel viruses and pathogenic bacteria in Australian bats with neurological signs, pneumonia and skin lesions

More than 70 bat species are found in mainland Australia. While most studies of bat viromes focus on sampling seemingly healthy individuals, little is known about the viruses and bacteria associated with diseased bats. We performed traditional diagnostic techniques and metatranscriptomic sequencing on tissue samples from 43 Australian bats, comprising three flying fox (Pteropodidae) and two microbat species experiencing a range of disease syndromes, including mass mortality, neurological signs, pneumonia and skin lesions. Of note, we identified the recently discovered Hervey pteropid gammaretrovirus in a bat with lymphoid leukemia, with evidence of replication consistent with an exogenous virus. The possible association of Hervey pteropid gammaretrovirus with lymphoid leukemia clearly merits additional investigation. One novel picornavirus and at least three new astroviruses and bat pegiviruses were also identified in a variety of tissue types, as well as a number of likely bacterial pathogens or opportunistic infections, most notably Pseudomonas aeruginosa.

Climate variability and change are drivers of salmonellosis in Australia: 1991 to 2019

Salmonellosis is a climate-sensitive gastroenteritis with over 92 million cases and over 50,000 deaths a year globally. Australia has high rates of salmonellosis compared with other industrialised nations. This study used a negative binomial time-series regression model to investigate the association between Australian salmonellosis notifications and monthly climate variables including El Niño Southern Oscillation (ENSO) and mean temperature anomaly from 1991 to 2019. Between 1991 and 2019 in Australia there were 275,753 salmonellosis notifications and the median annual rate for salmonellosis was 40.1 per 100,000 population. Salmonellosis notifications exhibited strong seasonality, reaching a peak in summer and a minimum in winter. There was an estimated increase of 3.4 % in salmonellosis cases nationally per 1 °C increase in monthly mean temperature anomaly (incidence rate ratio [IRR] of 1.034, 95 % confidence interval [CI]: 1.009, 1.059). Similar associations between salmonellosis and mean temperature anomaly were found for some states. Mean temperature anomaly exhibited an upward trend of 0.9 °C over the period 1991 to 2019. Additionally, a positive association was found between salmonellosis in Australia and ENSO whereby El Niño periods were associated with 7.9 % more salmonellosis cases compared to neutral periods (IRR 1.079, 95 % CI: 1.019, 1.143). A similar ENSO association was detected in the two eastern states of New South Wales and Queensland. This study suggests public health preventative measures to reduce salmonellosis could be enhanced in some regions during El Niño as well as during times of increased temperatures.

Source attribution of salmonellosis by time and geography in New South Wales, Australia

BACKGROUND

Salmonella is a major cause of zoonotic illness around the world, arising from direct or indirect contact with a range of animal reservoirs. In the Australian state of New South Wales (NSW), salmonellosis is believed to be primarily foodborne, but the relative contribution of animal reservoirs is unknown.

METHODS

The analysis included 4543 serotyped isolates from animal reservoirs and 30,073 serotyped isolates from domestically acquired human cases in NSW between January 2008 and August 2019. We used a Bayesian source attribution methodology to estimate the proportion of foodborne Salmonella infections attributable to broiler chickens, layer chickens, ruminants, pigs, and an unknown or unsampled source. Additional analyses included covariates for four time periods and five levels of rurality.

RESULTS

A single serotype, S. Typhimurium, accounted for 65-75% of included cases during 2008-2014 but < 50% during 2017-2019. Attribution to layer chickens was highest during 2008-2010 (48.7%, 95% CrI 24.2-70.3%) but halved by 2017-2019 (23.1%, 95% CrI 5.7-38.9%) and was lower in the rural and remote populations than in the majority urban population. The proportion of cases attributed to the unsampled source was 11.3% (95% CrI 1.2%-22.1%) overall, but higher in rural and remote populations. The proportion of cases attributed to pork increased from approximately 20% in 2009-2016 to approximately 40% in 2017-2019, coinciding with a rise in cases due to Salmonella ser. 4,5,12:i:-.

CONCLUSION

Layer chickens were likely the primary reservoir of domestically acquired Salmonella infections in NSW circa 2010, but attribution to the source declined contemporaneously with increased vaccination of layer flocks and tighter food safety regulations for the handling of eggs.

Occurrence of Salmonella enterica in grey-headed flying foxes from New South Wales

Salmonella enterica and Campylobacter jejuni are significant foodborne zoonotic pathogens causing gastroenteritis in humans. Domestic animals are commonly implicated as reservoirs of S. enterica and C. jejuni, but both are also detected in wild animals. Salmonella enterica serovar Typhimurium is the most common cause of human salmonellosis in Australia; however, Salmonella enterica serovar Wangata is associated with sporadic human outbreaks in New South Wales and wild animals may be a potential reservoir. To determine if wild grey-headed flying foxes (GHFF; Pteropus poliocephalus) are reservoirs of Salmonella and Campylobacter, faecal samples were collected from three GHFF colonies in New South Wales and cultured for the presence of Salmonella and Campylobacter. One Salmonella isolate was cultured from 254 GHFF faecal samples (0.39%). Whole genome sequencing was used to genetically characterise the Salmonella isolate and perform phylogenetic analysis. The GHFF isolate was determined to be Salmonella Typhimurium ST19. The GHFF isolate carried a virulence plasmid and other virulence factors, but did not exhibit antimicrobial resistance. Phylogenetic analysis determined that the GHFF isolate was most closely related to a cluster of six isolates: four from human salmonellosis cases in Queensland and two from Australian livestock. Neither Campylobacter nor Salmonella Wangata were cultured from the 254 GHFF faecal samples. This study concluded that wild GHFF in New South Wales are not major reservoirs for Salmonella, and the zoonotic risks associated with S. enterica carriage by urban GHFF are low for the general public.

Using urban landscape pattern to understand and evaluate infectious disease risk

COVID-19 case numbers in 161 sub-districts of Wuhan were investigated based on landscape epidemiology, and their landscape metrics were calculated based on land use/land cover (LULC). Initially, a mediation model verified a partially mediated population role in the relationship between landscape pattern and infection number. Adjusted incidence rate (AIR) and community safety index (CSI), two indicators for infection risk in sub-districts, were 25.82∼63.56 ‱ and 3.00∼15.87 respectively, and central urban sub-districts were at higher infection risk. Geographically weighted regression (GWR) performed better than OLS regression with AICc differences of 7.951∼181.261. The adjusted R² in GWR models of class-level index and infection risk were 0.697 to 0.817, while for the landscape-level index they were 0.668 to 0.835. Secondly, 16 key landscape metrics were identified based on GWR, and then a prediction model for infection risk in sub-districts and communities was developed. Using principal component analysis (PCA), development intensity, landscape level, and urban blue-green space were considered to be principal components affecting disease infection risk, explaining 73.1 % of the total variance. Cropland (PLAND and LSI), urban land (NP, LPI, and LSI) and unused land (NP) represent development intensity, greatly affecting infection risk in urban areas. Landscape level CONTAG, DIVISION, SHDI, and SHEI represent mobility and connectivity, having a profound impact on infection risk in both urban and suburban areas. Water (PLAND, NP, LPI, and LSI) and woodland (NP, and LSI) represent urban blue-green spaces, and were particularly important for infection risk in suburban areas. Based on urban landscape pattern, we proposed a framework to understand and evaluate infection risk. These findings provide a basis for risk evaluation and policy-making of urban infectious disease, which is significant for community management and urban planning for infectious disease worldwide.

Detection of spatial and spatio-temporal Salmonella Heidelberg and Salmonella Typhimurium human case clusters focused around licensed abattoirs in Ontario in 2015, and their potential relation to known outbreaks

Salmonellosis is one of several zoonotic diseases for which individuals with occupational animal contact, including abattoir workers, are at an increased risk. If meat is contaminated during slaughter, this can increase the risk of enteric illness for consumers. In this study, we investigated whether reported cases of Salmonella Heidelberg and Typhimurium were clustered around abattoirs in Ontario in 2015 and whether there was any evidence (laboratory/exposure) to suggest an abattoir at the centre of a cluster might be the source of exposure. Data for each reported case of S. Heidelberg and S. Typhimurium in Ontario in 2015 were collected. Multi-focused and non-focused spatial and space-time cluster detection tests were performed for each serotype, with and without cases linked to known outbreaks, using Poisson and space-time permutation models. Focused tests included the location of abattoirs operational in all or part of 2015. Laboratory data and exposure information were used to explore the relatedness of cases within identified clusters. Focused spatial tests identified clusters of S. Heidelberg and S. Typhimurium around abattoirs. Focused space-time permutation tests identified 2 significant space-time clusters of S. Heidelberg; one cluster (n = 11 cases) included 8 of 9 cases associated with a known outbreak and the other cluster (n = 18 cases) was not part of a previously identified outbreak. Review of laboratory and risk factor information suggested that cases within each cluster shared a common exposure. Cases were not asked about goat or sheep meat consumption. The focused cluster test, particularly with the space-time permutation model, could assist in identifying outbreaks associated with a particular physical location, such as an abattoir. Improvements to the current case investigation process, such as consistent collection and reporting of high-risk occupation information and more detailed food consumption history, could assist in outbreak identification when coupled with this statistic.

Multidrug-Resistant Salmonella enterica Isolated in Paca (Cuniculus paca) Carcasses from the Belen Market, Iquitos, Perú

The consumption of wildlife meat is traditionally accepted in the Peruvian Amazon; however, little is known about the pathogens present in this type of food. One of the most frequently consumed species is a rodent, the paca (Cuniculus paca) or "majaz" in the Peruvian language. The objective of this study was to determine the presence of Salmonella enterica and its antimicrobial resistance profile in paca carcasses sold in the Belen Market of Iquitos-Peru. An observational, descriptive, cross-sectional study was carried out. Fresh and smoked paca carcasses (72 samples) were evaluated during the low-rain period (July 2019) in the traditional market of Iquitos, in the Amazonian Region. Meat samples were swabbed, and International Standards Organization (ISO) 6579-1: 2017 protocol was followed to Salmonella isolation. Antimicrobial susceptibility analysis was performed by the disk diffusion method. In addition, serotyping was performed by using the Kauffmann-White scheme. A total of 25 strains of S. enterica were isolated in the paca carcasses, mainly in fresh carcasses (48.6%). The serovars isolated were Agona (45.8%), Infantis (41.7%), Wangata (8.3%), and Javiana (4.2%). A considerable number of the isolated strains were multidrug resistant (40%). The highest prevalence of resistance corresponded to trimethoprim-sulfamethoxazole (64%) followed by nitrofurantoin (44%), chloramphenicol (40%), cefotaxime (40%), and nalidixic acid (40%). Ten strains isolated (40%) were identified as producers of extended spectrum beta lactamases, all in S. enterica serovar Infantis. This study describes the presence of Salmonella Infantis with multidrug resistance profiles in wildlife meat carcasses, making the consumption of this type of products a risk factor for the development of foodborne diseases in the Amazon region. Institutional Review Board: Approval Resolution of Thesis Project: N° 024-DACMVZ-DAFCVB-U.CIENTÍFICA-2019.

Spatial and space-time clustering and demographic characteristics of human nontyphoidal Salmonella infections with major serotypes in Toronto, Canada

Nontyphoidal Salmonella enterica (NTS) causes a substantial health burden to human populations in Canada and worldwide. Exposure sources and demographic factors vary by location and can therefore have a major impact on salmonellosis clustering. We evaluated major NTS serotypes: S. Enteritidis (n = 620), S. Typhimurium (n = 150), S. Thompson (n = 138), and S. Heidelberg (n = 136) reported in the city of Toronto, Canada, between January 1, 2015, and December 31, 2017. Cases were analyzed at the forward sortation area (FSA)—level (an area indicated by the first three characters of the postal code). Serotype-specific global and local clustering of infections were evaluated using the Moran's I method. Spatial and space-time clusters were investigated using Poisson and multinomial scan statistic models. Case-case analyses using a multinomial logistic regression model was conducted to compare seasonal and demographic factors among the different serotypes. High infection rate FSAs clustered in the central region of Toronto for S. Enteritidis, in the south-central region for S. Typhimurium, in north-west region for S. Thompson, and in the south-east region for S. Heidelberg. The relative risk ratio of S. Enteritidis infections was significantly higher in cases who reported travel outside of Ontario. The relative risk ratio of infections was significantly higher in summer for S. Typhimurium, and in fall for S. Thompson. The relative risk ratio of infection was highest for the 0–9 age group for S. Typhimurium, and the 20–39 age group for S. Heidelberg. Our study will aid public health stakeholders in designing serotype-specific geographically targeted disease prevention programs.

Spatial Trends in Salmonella Infection in Pigs in Spain

Salmonella is one of the most important foodborne pathogens worldwide. Its main reservoirs are poultry and pigs, in which infection is endemic in many countries. Spain has one of the largest pig populations in the world. Even though Salmonella infection is commonly detected in pig farms, its spatial distribution at the national level is poorly understood. Here we aimed to report the spatial distribution of Salmonella-positive pig farms in Spain and investigate the presence of potential spatial trends over a 17-year period. For this, data on samples from pigs tested for Salmonella in 2002-2013, 2015, 2017, and 2019 as part of the Spanish Veterinary Antimicrobial Resistance Surveillance program, representing 3,730 farms were analyzed. The spatial distribution and clustering of Salmonella-positive pig farms at the province level were explored using spatial empirical Bayesian smoothing and global Moran's I, local Moran's I, and the Poisson model of the spatial scan statistics. Bayesian spatial regression using a reparameterized Besag-York-Mollié Poisson model (BYM2 model) was then performed to quantify the presence of spatially structured and unstructured effects while accounting for the effect of potential risk factors for Salmonella infection at the province level. The overall proportion of Salmonella-positive farms was 37.8% (95% confidence interval: 36.2-39.4). Clusters of positive farms were detected in the East and Northeast of Spain. The Bayesian spatial regression revealed a West-to-East increase in the risk of Salmonella infection at the province level, with 65.2% (50% highest density interval: 70-100.0%) of this spatial pattern being explained by the spatially structured component. Our results demonstrate the existence of a spatial variation in the risk of Salmonella infection in pig farms at the province level in Spain. This information can help to optimize risk-based Salmonella surveillance programs in Spain, although further research to identify farm-level factors explaining this pattern are needed.