BackgroundWeather conditions have been linked to adverse mental health outcomes, and rising concern about climate change has increased interest in these associations. However, most existing research focuses on extreme weather events, such as heatwaves, or on acute clinical outcomes, such as suicide. Evidence is more limited regarding population-level variations in mental health–related healthcare utilisation across the full range of daily weather conditions.ObjectiveTo examine associations between daily weather conditions and unscheduled mental health–related healthcare contacts in England using large-scale national surveillance data.MethodsWe conducted a retrospective observational study across nine English regions from 1 January 2014 to 31 December 2022. Outcomes were daily counts of unscheduled mental health–related contacts to emergency departments (EDs), general practice out-of-hours (GP OOH) services, and the NHS 111 telephone advice line. Weather exposures included mean daily temperature (°C), hours of full sunshine, and total daily rainfall (mm). Associations were estimated using distributed lag non-linear models at regional level and combined through two-stage multivariate meta-analysis. Models were adjusted for seasonality, long-term trends, day of week, public holidays, and population size.ResultsMental health–related unscheduled healthcare contacts showed modest but consistent associations with temperature and sunshine. Across services, relative risks (demand) increased with rising temperatures up to around 18 °C and were higher on days with fewer hours of sunshine. Sunshine demonstrated the clearest pattern, with increased utilisation on low-sunshine days across all healthcare settings. Rainfall was not consistently associated with healthcare contacts. Age-stratified analyses showed a U-shaped relationship between temperature and ED attendances among adults aged over 64 years, with higher utilisation during both colder and warmer conditions. Overall variations in daily healthcare demand were modest, typically within ±10–20% of baseline levels.ConclusionIn England, short-term variations in temperature and sunshine are associated with changes in unscheduled mental health–related healthcare utilisation, whereas rainfall shows little consistent effect. Although effect sizes were modest, these findings highlight the role of everyday weather conditions in influencing mental health–related healthcare demand and may support planning and preparedness efforts for mental health services under current and future climate conditions.

Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot keep pace with expanding evidence. We assessed whether an LLM-assisted workflow could generate a rapid, repeatable, and policy-relevant living evidence base for climate-sensitive foodborne disease. We combined structured PubMed searches (2010–2023), gold-standard human labelling, and iterative refinement of a GPT?4?Turbo?based auto-labeller within the SysRev platform. Pathogens of public-health importance in England were selected a priori. Model performance was evaluated against human reviewers using recall, precision, specificity, accuracy, and balanced accuracy. The refined inclusion model achieved 89·2% recall, 59·2% precision, 84·5% specificity, and 85·4% accuracy across 1,044 screened abstracts, identifying 436 studies for inclusion. Post-hoc re-evaluation of discordant abstracts showed that records excluded by the model but included during initial human screening did not meet the refined inclusion criteria. Frequently identified climate exposures included rainfall, temperature, seasonality, and humidity; norovirus, Salmonella, Campylobacter, and Cryptosporidium were the most common pathogens. An LLM-assisted workflow can generate living evidence for climate-sensitive foodborne disease with high recall and improved screening consistency. The approach is scalable, auditable, and suitable for secure institutional environments, supporting horizon scanning and climate-health risk assessment. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Only published literature was analysed no ethics approval was required. The workflow aligns with guidance on responsible AI use in the public sector and is suitable for deployment in secure institutional environments. While a single LLM implementation was used, the workflow is model agnostic and designed to be transferable across LLMs and institutional platforms. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Inclusion criteria, prompt evolution, and code for label updates are available in the Supplementary Material. De?identified screening data can be made available upon request for research purposes.

Wastewater-based surveillance (WBS) can monitor for the presence of human health pathogens in the population. During COVID-19, WBS was widely used to determine wastewater SARS-CoV-2 RNA concentration (concentrations) providing information on community COVID-19 cases (cases). However, studies examining the relationship between concentrations and cases tend to be localised or focussed on small-scale institutional settings. Few have examined this relationship in multiple settings, over long periods, with large sample numbers, nor attempted to quantify the relationship between concentrations and cases or detail how catchment characteristics affected these.This 18-month study (07/20–12/21) explored the correlation and quantitative relationship between concentrations and cases using censored regression. Our analysis used >94,000 wastewater samples collected from 452 diverse sampling sites (259 Sewage Treatment Works (STW) and 193 Sewer Network Sites (SNS)) covering ~65 % of the English population. Wastewater concentrations were linked to ~6 million diagnostically confirmed COVID-19 cases.High correlation coefficients were found between concentrations and cases (STW: median r = 0.66, IQR: 0.57–0.74; SNS: median r = 0.65, IQR: 0.54–0.74). The quantitative relationship (regression coefficient) between concentrations and cases was variable between catchments. Catchment and sampling characteristics (e.g. size of population and grab vs automated sampling) had significant but small effects on correlation and regression coefficients.During the last six months of the study correlation coefficients reduced and regression coefficients became highly variable between catchments. This coincided with a shift towards younger cases, a highly vaccinated population and rapid emergence of the variant Omicron.The English WBS programme was rapidly introduced at scale during COVID-19. Laboratory methods evolved and study catchments were highly diverse in size and characteristics. Despite this diversity, findings indicate that WBS provides an effective proxy for establishing COVID-19 dynamics across a wide variety of communities. While there is potential for predicting COVID-19 cases from wastewater concentration, this may be more effective at smaller scales.

In July 2022, a genetically linked and geographically dispersed cluster of 12 cases of Shiga toxin-producing Escherichia coli (STEC) O103:H2 was detected by the UK Health Security Agency using whole genome sequencing. Review of food history questionnaires identified cheese (particularly an unpasteurized brie-style cheese) and mixed salad leaves as potential vehicles. A case-control study was conducted to investigate exposure to these products. Case food history information was collected by telephone. Controls were recruited using a market research panel and self-completed an online questionnaire. Univariable and multivariable analyses were undertaken using Firth Logistic Regression. Eleven cases and 24 controls were included in the analysis. Consumption of the brie-style cheese of interest was associated with illness (OR 57.5, 95% confidence interval: 3.10-1,060). Concurrently, the production of the brie-style cheese was investigated. Microbiological sample results for the cheese products and implicated dairy herd did not identify the outbreak strain, but did identify the presence of stx genes and STEC, respectively. Together, epidemiological, microbiological, and environmental investigations provided evidence that the brie-style cheese was the vehicle for this outbreak. Production of unpasteurized dairy products was suspended by the business operator, and a review of practices was performed.

BackgroundLaboratory surveillance detected an unprecedented increase in Cryptosporidium spp. (predominantly Cryptosporidium hominis) in England and Wales in August 2023. Cases are not routinely followed up in all of England and Wales, and initial investigations identified no common exposures.AimTo perform a retrospective case-case study investigation of the increase in Cryptosporidium spp. in England and Wales.MethodsWe conducted an unmatched case-case study with 203 cases of laboratory-confirmed C. hominis and 614 comparator cases of laboratory-confirmed Campylobacter spp. reported between 14 August and 30 September 2023. We fitted a multilevel logistic regression model, with random intercepts for geographical region, to estimate adjusted odds ratios (aOR) for exposures. We present the final model as aOR and 95% confidence intervals (CI).ResultsMultivariable analysis identified associations with swimming pool use (aOR: 5.3, 95% CI: 2.3-9.3), travel to Spain (aOR: 6.5, 95% CI: 3.5-12.3) and young age, with children 0-4 years having the strongest association of being a case (aOR: 3.6, 95% CI: 1.5-8.6). We also identified associations with swimming in a river, and travel to France or Türkiye, but there was low frequency of exposure among cases and comparator cases.ConclusionsFollowing the largest recorded increase of Cryptosporidium spp. and in particular C. hominis cases in England and Wales, we identified several exposures, suggesting that causation was likely to be multifactorial. We recommend development of a standardised questionnaire to enable rapid investigation of future case increases, which will improve existing surveillance and inform public health actions.

Background Whole-genome sequencing (WGS) is the gold standard diagnostic tool to identify and genetically characterise emerging pathogen mutations (variants), but cost, capacity, and timeliness limit its use when large populations need rapidly assessing. We assessed the potential of genotyping assays to provide accurate and timely variant information at scale by retrospectively examining surveillance for SARS-CoV-2 variants in England between March and September, 2021, when genotyping assays were used widely for variant detection. Methods We chose a panel of four RT-PCR genotyping assays to detect circulating variants of SARS-COV-2 in England and developed a decision algorithm to assign a probable SARS-CoV-2 variant to samples using the assay results. We extracted surveillance data from the UK Health Security Agency databases for 115 934 SARS-CoV-2-positive samples (March 1-Sept 6, 2021) when variant information was available from both genotyping and WGS. By comparing the genotyping and WGS variant result, we calculated accuracy metrics (ie, sensitivity, specificity, and positive predictive value [PPV]) and the time difference between the sample collection date and the availability of variant information. We assessed the number of samples with a variant assigned from genotyping or WGS, or both, over time. Findings Genotyping and an initial decision algorithm (April 10-May 11, 2021 data) were accurate for key variant assignment: sensitivities and PPVs were 0.99 (95% CI 0.99-0.99) for the alpha, 1.00 (1.00-1.00) for the beta, and 0.91 (0.80-1.00) for the gamma variants; specificities were 0.97 (0.96-0.98), 1.00 (1.00-1.00), and 1.00 (1.00-1.00), respectively. A subsequent decision algorithm over a longer time period (May 27-Sept 6, 2021 data) remained accurate for key variant assignment: sensitivities were 0.91 (95% CI 0.74-1.00) for the beta, 0.98 (0.98-0.99) for the delta, and 0.93 (0.81-1.00) for the gamma variants; specificities were 1.00 (1.00-1.00), 0.96 (0.96-0.97), and 1.00 (1.00-1.00), respectively; and PPVs were 0.83 (0.62-1.00), 1.00 (1.00-1.00), and 0.78 (0.59-0.97), respectively. Genotyping produced variant information a median of 3 days (IQR 2-4) after the sample collection date, which was faster than with WGS (9 days [8-11]). The flexibility of genotyping enabled a nine-times increase in the quantity of samples tested for variants by this method (from 5000 to 45 000). Interpretation RT-PCR genotyping assays are suitable for high-throughput variant surveillance and could complement WGS, enabling larger scale testing for known variants and timelier results, with important implications for effective public health responses and disease control globally, especially in settings with low WGS capacity. However, the choice of panels of RT-PCR assays is highly dependent on database information on circulating variants generated by WGS, which could limit the use of genotyping assays when new variants are emerging and spreading rapidly.

Following an outbreak of Salmonella Typhimurium in Wales in July 2021 associated with sheep meat and offal, further genetically related cases were detected across the UK. Cases were UK residents with laboratory-confirmed Salmonella Typhimurium in the same 5-single-nucleotide polymorphism (SNP) single-linkage cluster with specimen date between 01/08/2021-2031/12/2022. We described cases using routine (UK) and enhanced (Wales only) surveillance data. Exposures in cases in Wales were compared with non-Typhimurium Salmonella case-controls. Environmental Health Practitioners and the Food Standards Agency investigated supply chains of food premises reported by >= 2 cases. Animal, carcass, and environmental samples taken for diagnostic or monitoring purposes for gastrointestinal pathogens were included in microbiological investigations. We identified 142 cases: 75% in England, 23% in Wales and 3% in Scotland. Median age was 32 years, and 59% were male. Direct contact with sheep was associated with becoming a case (aOR: 14, 95%CI: 1.4-145) but reported by few (6/32 cases). No single food item, premises, or supplier linked all cases. Multi-agency collaboration enabled the identification of isolates in the same 5-SNP single-linkage cluster from a sheep carcass at an English abattoir and in ruminant, wildlife, poultry, and environmental samples, suggesting multiple vehicles and pathways of infection.

Routine laboratory surveillance has identified an unprecedented and ongoing exceedance of Cryptosporidium spp. across the United Kingdom, notably driven by C. hominis transmission, since 14 August 2023. Information from 477 reported cases in England and Wales, followed up with a standardised exposure questionnaire as of 25 September 2023, identified foreign travel in 250 (54%) of 463 respondents and swimming in 234 (66%) of 353 cases. A significant, common exposure has not yet been identified in first analyses.

Most Shiga toxin-producing E. coli (STEC) infections are sporadic. Routine enhanced surveillance questionnaires of confirmed STEC cases in England contained promising data to conduct a case-control study to identify non-food exposures linked to the risk of becoming infected with different STEC serotypes, including O157, O26 and all others; this study pulled eligible cases from the recorded enhanced surveillance data. Controls were recruited from the general population and answered a comparable postal questionnaire. Logistic regression was performed to identify risk factors associated with STEC infection for O157, O26 and other serotype cases. In adjusted models, travel outside of the U.K. and childcare occupations raised the risk of infection for all serotypes. Day trips within the UK, exposure to dogs and contact with soil were linked to lower infection risk. Resident region within England was often linked to decreased risk. Summer season was linked to O157 and O26, but not other STEC. Swimming in the sea was linked to increased risk of infection by O157, but not other types of STEC. Correlations between exposures and infection were similar when the analysis was repeated excluding participants with a history of foreign travel. As the first case-control study in England to include sporadic non-O157 STEC, the varying risk factors between O157 and non-O157 cases suggest there are potentially unique reservoirs for different serotypes.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

When SARS-CoV-2 Omicron emerged in 2021, S gene target failure enabled differentiation between Omicron and the dominant Delta variant. In England, where S gene target surveillance (SGTS) was already established, this led to rapid identification (within ca 3 days of sample collection) of possible Omicron cases, alongside real-time surveillance and modelling of Omicron growth. SGTS was key to public health action (including case identification and incident management), and we share applied insights on how and when to use SGTS.

AbstractBackgroundA rapid increase in cases due to the SARS-CoV-2 Omicron (B.1.1.529) variant in highly vaccinated populations has raised concerns about the effectiveness of current vaccines.MethodsWe used a test-negative case-control design to estimate vaccine effectiveness (VE) against symptomatic disease caused by the Omicron and Delta variants in England. VE was calculated after primary immunisation with two BNT162b2 or ChAdOx1 doses, and at 2+ weeks following a BNT162b2 booster.ResultsBetween 27 November and 06 December 2021, 581 and 56,439 eligible Omicron and Delta cases respectively were identified. There were 130,867 eligible test-negative controls. There was no effect against Omicron from 15 weeks after two ChAdOx1 doses, while VE after two BNT162b2 doses was 88.0% (95%CI: 65.9 to 95.8%) 2-9 weeks after dose 2, dropping to between 34 and 37% from 15 weeks post dose 2.From two weeks after a BNT162b2 booster, VE increased to 71.4% (95%CI: 41.8 to 86.0%) for ChAdOx1 primary course recipients and 75.5% (95%CI: 56.1 to 86.3%) for BNT162b2 primary course recipients.For cases with Delta, VE was 41.8% (95%CI: 39.4-44.1%) at 25+ weeks after two ChAdOx1 doses, increasing to 93.8% (95%CI: 93.2-94.3%) after a BNT162b2 booster. With a BNT162b2 primary course, VE was 63.5% (95%CI: 61.4 to 65.5%) 25+ weeks after dose 2, increasing to 92.6% (95%CI: 92.0-93.1%) two weeks after the booster.ConclusionsPrimary immunisation with two BNT162b2 or ChAdOx1 doses provided no or limited protection against symptomatic disease with the Omicron variant. Boosting with BNT162b2 following either primary course significantly increased protection.

In April 2018, Public Health England was notified of cases of Shigella sonnei who had eaten food from three different catering outlets in England. The outbreaks were initially investigated as separate events, but whole-genome sequencing (WGS) showed they were caused by the same strain. The investigation included analyses of epidemiological data, the food chain and microbiological examination of food samples. WGS was used to determine the phylogenetic relatedness and antimicrobial resistance profile of the outbreak strain. Ultimately, 33 cases were linked to this outbreak; the majority had eaten food from seven outlets specialising in Indian or Middle Eastern cuisine. Five outlets were linked to two or more cases, all of which used fresh coriander although a shared supplier was not identified. An investigation at one of the venues recorded that 86% of cases reported eating dishes with coriander as an ingredient or garnish. Four cases were admitted to hospital and one had evidence of treatment failure with ciprofloxacin. Phylogenetic analysis showed that the outbreak strain was part of a wider multidrug-resistant clade associated with travel to Pakistan. Poor hygiene practices during cultivation, distribution or preparation of fresh produce are likely contributing factors.

In August 2019, public health surveillance systems in Scotland and England identified seven, geographically dispersed cases infected with the same strain (defined as isolates that fell within the same five single nucleotide polymorphism single linage cluster) of Shiga toxin-producing Escherichia coli O157:H7. Epidemiological analysis of enhanced surveillance questionnaire data identified handling raw beef and shopping from the same national retailer (retailer A) as the common exposure. Concurrently, a microbiological survey of minced beef at retail identified the same strain in a sample of minced beef sold by retailer A, providing microbiological evidence of the link. Between September and November 2019, a further four primary and two secondary cases infected with the same strain were identified; two cases developed haemolytic uraemic syndrome. None of the four primary cases reported consumption of beef from retailer A and the transmission route of these subsequent cases was not identified, although all four primary cases visited the same petting farm. Generally, outbreaks of STEC O157:H7 in the UK appear to be distinct, short-lived events; however, on-going transmission linked to contaminated food, animals or environmental exposures and person-to-person contact do occur. Although outbreaks of STEC caused by contaminated fresh produce are increasingly common, undercooked meat products remain a risk of infection.

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed. A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk. Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout. In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

For outbreaks of gastrointestinal disease, rapid identification of the source is crucial to enable public health intervention and prevent further cases. Outbreak investigation comprises analyses of exposure information from cases and, if required, undertaking analytical epidemiological studies. Hypothesis generation has been reliant on empirical knowledge of exposures historically associated with a given pathogen. Epidemiology studies are resource-intensive and prone to bias, one of the reasons being the difficulties in recruiting appropriate controls. For this paper, the information from cases was compared against pre-defined background exposure information. As exemplars, three past outbreaks were used, one of common and two of rare exposures. Information from historical case trawling questionnaires was used to define background exposure having removed any exposures implicated with the outbreak. The case-background approach showed good sensitivity and specificity, identifying correctly all outbreak-related exposures. One additional exposure related to a retailer was identified and four food items where all cases had been exposed. In conclusion, the case-background method, a development of the case-case design, can be used to assist with hypothesis generation or when a case-control study may not be possible to carry out.

BackgroundCampylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood.MethodsTo investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses.ResultsThe increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas.ConclusionsThe association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored.

BackgroundCryptosporidium spp. are important causes of gastroenteritis that can be transmitted from humans and animals. We elucidated the distribution of species and gp60 subtypes in human outbreaks classified by transmission vehicle.MethodsWe used a combined database of national outbreak surveillance and reference unit data to analyse outbreaks by setting, vehicle, season, and linkage with suspected sources.ResultsA total of 178 outbreaks involving 4031 laboratory confirmed cases were identified; 82 (46%) outbreaks involved recreational waters, 74 (42%) animal contact, 4 (2%) environmental contact, 4 (2%) person-to-person spread, 3 (2%) food, 2 (1%) drinking water supplies, and 9 (5%) were of unknown source. The infecting Cryptosporidium sp. was identified in 131 (74%) outbreaks; 69 were C. parvum, 60 C. hominis, and in two outbreaks cases were infected with either species. Animal contact, environmental contact, and food-borne outbreaks were exclusively C. parvum and were mainly in first half of the year. Recreational water outbreaks were predominantly C. hominis and were mainly in the second half of the year. Outbreaks attributed to person-to-person spread were exclusively C. hominis and all occurred in October. Both C. parvum and C. hominis caused drinking waterborne outbreaks. Gp60 subtypes were identified from patients in 48 C. parvum and 38 C. hominis outbreaks, revealing more subtypes among C. parvum (n=14) than C. hominis (n=7) outbreaks. Cryptosporidium hominis IbA10G2 predominated (30 outbreaks). Of C. parvum subtypes, IIaA15G2R1 predominated (17 outbreaks), followed by IIaA17G1R1 (12 outbreaks), IIaA19G1R1 (four outbreaks), and other subtypes caused three or fewer outbreaks each. Linkage between cases and suspected sources by gp60 subtype was established in nine animal contact, three swimming pool, and one drinking water outbreak.ConclusionsThe public health benefit of identifying infecting species and subtypes was twofold: (i) identifying and strengthening epidemiologic links between cases; and (ii) indicating possible exposures and sources to inform outbreak management. Gp60 subtype refined the epidemiological investigations, but a multilocus genotyping scheme would provide further benefit. Characterisation of Cryptosporidium spp. and subtypes needs to shift from predominantly supporting outbreak investigations to becoming nationally systematic.

Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed.

Indigenous, foodborne transmission of hepatitis E virus genotype 3 (HEV G3) has become recognized as an emerging problem in industrialized countries. Although mostly asymptomatic, HEV G3 infection has a range of outcomes, including mild illness, severe acute hepatitis, and, of particular concern, chronic progressive hepatitis in immunocompromised patients. Public Health England has monitored cases of acute HEV infection in England and Wales since 2003. Between 2010 and 2017, enhanced surveillance using 2 linked laboratory databases and questionnaires on clinical features and risk factors was conducted. There was a year-on-year increase in the number of infections from 2008 (183) through 2016 (1243). Then, in 2017, the number of infections declined (to 912). As reported previously, HEV G3 group 2 (also known as "G3 abcdhij") is the predominant cause of acute infections, and older men are most at risk. Consumption of pork and pork products was significantly higher among patients than in the general population, but other previously reported associations, such as consumption of shellfish, were not observed. Ongoing surveillance is required to monitor future trends and changes in the epidemiology of the virus. The changing methods of animal husbandry and processing and distribution of animal products needs to be further investigated.