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Earlier global detection of novel SARS-CoV-2 variants gives governments more time to respond. However, few countries can implement timely national surveillance, resulting in gaps in monitoring. The United Kingdom implemented large-scale community and hospital surveillance, but experience suggests it might be faster to detect new variants through testing England arrivals for surveillance. We developed simulations of emergence and importation of novel variants with a range of infection hospitalization rates to the United Kingdom. We compared time taken to detect the variant though testing arrivals at England borders, hospital admissions, and the general community. We found that sampling 10%–50% of arrivals at England borders could confer a speed advantage of 3.5–6 weeks over existing community surveillance and 1.5–5 weeks (depending on infection hospitalization rates) over hospital testing. Directing limited global capacity for surveillance to highly connected ports could speed up global detection of novel SARS-CoV-2 variants.

ABSTRACT Background Gut‐brain axis is widely implicated in the pathophysiology of Parkinson's disease (PD). We take an integrated approach to considering the gut as a target for disease‐modifying intervention, using continuous measurements of disease facets irrespective of diagnostic divide. Methods We characterised 77 participants with diagnosed‐PD, 113 without, by dietary/exogenous substance intake, faecal metabolome, intestinal inflammation, serum cytokines/chemokines, clinical phenotype including colonic transit time. Complete‐linkage hierarchical cluster analysis of metabolites discriminant for PD‐status was performed. Results Longer colonic transit was linked to deficits in faecal short‐chain‐fatty acids outside PD, to a ‘tryptophan‐containing metabolite cluster’ overall. Phenotypic cluster analysis aggregated colonic transit with brady/hypokinesia, tremor, sleep disorder and dysosmia, each individually associated with tryptophan‐cluster deficit. Overall, a faster pulse was associated with deficits in a metabolite cluster including benzoic acid and an imidazole‐ring compound (anti‐fungals) and vitamin B3 (anti‐inflammatory) and with higher serum CCL20 (chemotactic for lymphocytes/dendritic cells towards mucosal epithelium). The faster pulse in PD was irrespective of postural hypotension. The benzoic acid‐cluster deficit was linked to (well‐recognised) lower caffeine and alcohol intakes, tryptophan‐cluster deficit to higher maltose intake. Free‐sugar intake was increased in PD, maltose intake being 63% higher (p = .001). Faecal calprotectin was 44% (95% CI 5%, 98%) greater in PD [p = .001, adjusted for proton‐pump inhibitors (p = .001)], with 16% of PD‐probands exceeding a cut‐point for clinically significant inflammation compatible with inflammatory bowel disease. Higher maltose intake was associated with exceeding this calprotectin cut‐point. Conclusions Emerging picture is of (i) clinical phenotype being described by deficits in microbial metabolites essential to gut health; (ii) intestinal inflammation; (iii) a systemic inflammatory response syndrome.

Abstract Background Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Methods Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. Results The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates for R 0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R 0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. Conclusions This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

Abstract Background Peer support can enable patient engagement with healthcare services, particularly for marginalised populations. In this randomised controlled trial, the efficacy of a peer support intervention at promoting successful engagement with clinical services for chronic hepatitis C was assessed. Methods In London, UK, potential participants were approached through outreach services for problematic drug use and homelessness. Individuals positive for hepatitis C virus (HCV) after confirmatory testing were randomised using an online service to the intervention (peer support) or standard of care. The primary outcome of interest was successful engagement with clinical hepatitis services. The study was non-blinded. Absolute differences were calculated using a generalised linear model and the results compared to logistic regression. Results Three hundred sixty-four individuals consented to participate. One hundred one had chronic hepatitis C and were randomised, 63 to receive the intervention (peer support). A successful outcome was achieved by 23 individuals in this arm (36.5%) and seven (18.4%) receiving the standard of care, giving an absolute increase of 18.1% (95% confidence interval 1.0–35.2%, p value = 0.04). This was mirrored in the logistic regression (odds ratio 2.55 (0.97–6.70), p = 0.06). No serious adverse events were reported. Conclusions Peer support can improve the engagement of patients with chronic HCV with healthcare services. Trial registration ISRCTN24707359. Registered 19th October 2012.

Helicobacter pylori has been implicated in the pathogenesis of Parkinson's disease (PD). Its eradication, in a randomized placebo-controlled trial, improved PD hypokinesia. Helicobacter species zoonosis might explain excess mortality from PD and non-Hodgkin lymphoma in livestock, but not arable, farmers. Indeed, Helicobacter is causally-associated with gastric lymphoma. We have previously shown that the relative-frequency, H. suis to H. pylori, was 10-times greater in 60 PD-patients than in 256 controls. We now go on to evaluate the pathological significance of H. suis, detected in gastric-biopsy DNA-extracts by ureA-based species-specific qPCR, validated by amplicon sequencing. The methodology had been cross-validated by a carR-based PCR. The pathological significance is put in context of H. pylori detection [urea-breath-test (UBT) with biopsy-culture, and, if negative, PCR], and the potential reservoir in pigs. Here, we explore, in these 60 PD-patients, associations of H. suis status with all-cause-mortality, and with orthostatic cardiovascular and blood profiling. H. suis had been detected in 19 of the 60 PD-patients on one or more occasion, only two (with co-existent H. pylori) being UBT positive. We found that the hazard-of-death (age-at-diagnosis- and gender-adjusted) was 12 (95% CI 1,103) times greater (likelihood-ratio test, P = 0.005) with H. suis-positivity (6/19) than with negativity (2/40: one lost to follow-up). UBT-values did not influence the hazard. H. suis-positivity was associated with lower standing mean-arterial-pressure [6 (1, 11) mmHg], H. pylori-positivity having no effect. The lower total lymphocyte count with H. pylori-positivity [−8 (−1, −14) %] was not seen with H. suis, where T-cell counts were higher [24 (2, 52) %]. Regarding the potential zoonotic reservoir in the UK, Helicobacter-like-organism frequency was determined in freshly-slaughtered pigs, nature ascertained by sequencing. Organisms immunostaining for Helicobacter, with corkscrew morphology typical of non-H. pylori Helicobacter, were seen in 47% of 111 pig-antra. We conclude that H. suis is associated with all-cause-mortality in PD and has a potential zoonotic reservoir.

Background: Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. Objectives: To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. Methods: Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with ‘gold-standard’ MCMC-derived inferences in terms of estimation quality and computational burden. Results: The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R0 is consistently estimated to be 1.76–1.80, dropping by 43–50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden ‘shock’ in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. Limitations: The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. Conclusions: Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. Future work recommendations: Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. Trial registration: Current Controlled Trials ISRCTN40334843. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

The incidence of sporadic cryptosporidiosis among 106,000 residents of 2 local government districts in northwest England before and after installation of membrane filtration of public water supplies was compared to that of 59,700 residents whose public water supplies remained unchanged. A national outbreak of foot and mouth disease in livestock during 2001 was associated with a decline in sporadic human cryptosporidiosis in all regions of the United Kingdom. In a Poisson regression model, membrane filtration was associated with an estimated 79% reduction (incidence ratio 0.207, 95% confidence intervals 0.099–0.431, p < 0.0001) after adjustment for the interval of the foot and mouth disease epidemic and the water source. Despite the confounding effect of that epidemic, membrane filtration of the public water supply was effective in reducing the risk for sporadic human Cryptosporidium infection in this population.

To inform the UK antimicrobial resistance strategy, a risk assessment was undertaken of the likelihood, over a five-year time-frame, of the emergence and widespread dissemination of pan-drug-resistant (PDR) Gram-negative bacteria that would pose a major public health threat by compromising effective healthcare delivery. Subsequent impact over five- and 20-year time-frames was assessed in terms of morbidity and mortality attributable to PDR Gram-negative bacteraemia. A Bayesian approach, combining available data with expert prior opinion, was used to determine the probability of the emergence, persistence and spread of PDR bacteria. Overall probability was modelled using Monte Carlo simulation. Estimates of impact were also obtained using Bayesian methods. The estimated probability of widespread occurrence of PDR pathogens within five years was 0.2 (95% credibility interval (CrI): 0.07–0.37). Estimated annual numbers of PDR Gram-negative bacteraemias at five and 20 years were 6800 (95% CrI: 400–58,600) and 22,800 (95% CrI: 1500–160,000), respectively; corresponding estimates of excess deaths were 1900 (95% CrI: 0–23,000) and 6400 (95% CrI: 0–64,000). Over 20 years, cumulative estimates indicate 284,000 (95% CrI: 17,000–1,990,000) cases of PDR Gram-negative bacteraemia, leading to an estimated 79,000 (95% CrI: 0–821,000) deaths. This risk assessment reinforces the need for urgent national and international action to tackle antibiotic resistance.

Abstract The mpox epidemic in the UK began in May 2022, with rates of new cases unexpectedly and rapidly declining during August 2022. Interpreting trends in infection requires disentangling the underlying growth rate of cases from the delay from symptom onset to presenting to healthcare. We developed a nowcasting Bayesian method which incorporates time-varying delays (EpiLine) to quantify the changes in the delay from symptom onset to healthcare presentation and the underlying mpox growth rate over the period May-August 2022 in the UK. We show that the mean delay between symptom onset and healthcare presentation for mpox in the UK decreased from 22 days in early May 2022 to 10 days by early June and 8 days in August 2022. When we account for these dynamic delays, the time-varying growth rate declined gradually and continuously in the UK during the May-August 2022 period. Not accounting for varying time delays would have incorrectly characterised the growth rate by a sharp increase followed by a rapid decline in mpox cases. Our results highlight the importance of correctly quantifying the delay between symptom onset to healthcare presentation when characterising the epidemic growth of mpox in the UK. The gradual reduction in the rate of epidemic spread, which pre-dated the vaccine roll-out, is consistent with gradual risk reduction or acquired immunity amongst the highest risk individuals. Our study highlights the need for public health agencies to record the delays from symptom onset to healthcare presentation early in an outbreak.

Abstract Background Understanding how the COVID-19 pandemic evolved under control measures is crucial to tackle the SARS-CoV-2 virus spread. Laos, a country bordering China but with late occurrence and low burden of COVID-19 compared to its neighbouring countries, was used for a case study. Methods A transmission model with disease reporting was proposed to investigate the impact of control measures on the SARS-CoV-2 virus spread in Laos from April 2021 to May 2022. It was assumed that the transmission rate changed with people’s behaviours, control measures and emerging variants; susceptibility decreased with vaccination and infection. Bayesian inference was used for model calibration to data of confirmed cases, deaths, and recoveries, and the deviance information criterion was used to select the best model variant. Results Our model including Non-pharmaceutical interventions (NPIs), behaviour change, vaccination, and changing variants well explained the three waves in Laos. The Alpha variant was estimated to have a basic reproduction number of 1.55 (95% CrI: 1.47–1.64) and was replaced by the Delta variant from September 2021 which was 1.88 (95% CrI: 1.77–2.01) times more transmissible; the Delta variant was replaced by Omicron variant from March 2022 which was 3.33 (95% CrI: 2.84–3.74) times more transmissible. The Delta variant was the most severe with a case fatality rate of 1.05% (95% CrI: 0.96–1.15%) while the Alpha variant and Omicron variant were much milder. The ascertainment rate was low and variable: first decreasing from 13.2 to 1.8% by 23 May 2021, and then increasing to 23.4% by 15 March 2022. Counterfactual simulations indicated that vaccination played strong roles in reducing infections even under the emergence of immune escape variants while behaviour change delayed but might not flatten the peak of outbreaks. Conclusions The three waves of Laos’ epidemics were due to the invasion of more transmissible and immune escape variants that affected the herd immunity built via vaccination and infection. Even with immunity waning and the escape of new variants, vaccination was still the major contributor to control COVID-19 and combining behaviour changes and vaccination would best suppress future outbreaks of COVID-19.

Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4–6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021–December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.

Abstract Background Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death rate associated both with the longer-term impact of the pandemic (e.g. infection, containment measures and reduced service provision by the health and other systems) and the responses that followed (e.g. curtailment of containment measures, vaccination and the response of health and other systems to backlogs). Following the relaxing of social distancing regimes and reduction in the availability of testing, across many countries, it becomes critical to measure the impact of COVID-19 infection. However, prolonged periods of mortality in excess of the expected across entire populations has raised doubts over the validity of using unadjusted historic estimates of mortality to calculate the expected numbers of deaths that form the baseline for computing numbers of excess deaths because many individuals died earlier than they would otherwise have done: i.e. their mortality was displaced earlier in time to occur during the pandemic rather than when historic rates predicted. This is also often termed “harvesting” in the literature. Methods We present a novel Cox-regression-based methodology using time-dependent covariates to estimate the profile of the increased risk of death across time in individuals who contracted COVID-19 among a population of hip fracture patients in England (N = 98,365). We use these hazards to simulate a distribution of survival times, in the presence of a COVID-19 positive test, and then calculate survival times based on hazard rates without a positive test and use the difference between the medians of these distributions to estimate the number of days a death has been displaced. This methodology is applied at the individual level, rather than the population level to provide a better understanding of the impact of a positive COVID-19 test on the mortality of groups with different vulnerabilities conferred by sociodemographic and health characteristics. Finally, we apply the mortality displacement estimates to adjust estimates of excess mortality using a “ball and urn” model. Results Among the exemplar population we present an end-to-end application of our methodology to estimate the extent of mortality displacement. A greater proportion of older, male and frailer individuals were subject to significant displacement while the magnitude of displacement was higher in younger females and in individuals with lower frailty: groups who, in the absence of COVID-19, should have had a substantial life expectancy. Conclusion Our results indicate that calculating the expected number of deaths following the first wave of the pandemic in England based solely on historical trends results in an overestimate, and excess mortality will therefore be underestimated. Our findings, using this exemplar dataset are conditional on having experienced a hip fracture, which is not generalisable to the general population. Fractures that impede mobility in the weeks that follow the accident/surgery considerably shorten life expectancy and are in themselves markers of significant frailty. It is therefore important to apply these novel methods to the general population, among whom we anticipate strong patterns in mortality displacement – both in its length and prevalence – by age, sex, frailty and types of comorbidities. This counterfactual method may also be used to investigate a wider range of disruptive population health events. This has important implications for public health monitoring and the interpretation of public health data in England and globally.

Summary: Background: The protection of fourth dose mRNA vaccination against SARS-CoV-2 is relevant to current global policy decisions regarding ongoing booster roll-out. We aimed to estimate the effect of fourth dose vaccination, prior infection, and duration of PCR positivity in a highly-vaccinated and largely prior-COVID-19 infected cohort of UK healthcare workers. Methods: Participants underwent fortnightly PCR and regular antibody testing for SARS-CoV-2 and completed symptoms questionnaires. A multi-state model was used to estimate vaccine effectiveness (VE) against infection from a fourth dose compared to a waned third dose, with protection from prior infection and duration of PCR positivity jointly estimated. Findings: 1298 infections were detected among 9560 individuals under active follow-up between September 2022 and March 2023. Compared to a waned third dose, fourth dose VE was 13.1% (95% CI 0.9 to 23.8) overall; 24.0% (95% CI 8.5 to 36.8) in the first 2 months post-vaccination, reducing to 10.3% (95% CI −11.4 to 27.8) and 1.7% (95% CI −17.0 to 17.4) at 2–4 and 4–6 months, respectively. Relative to an infection >2 years ago and controlling for vaccination, 63.6% (95% CI 46.9 to 75.0) and 29.1% (95% CI 3.8 to 43.1) greater protection against infection was estimated for an infection within the past 0–6, and 6–12 months, respectively. A fourth dose was associated with greater protection against asymptomatic infection than symptomatic infection, whilst prior infection independently provided more protection against symptomatic infection, particularly if the infection had occurred within the previous 6 months. Duration of PCR positivity was significantly lower for asymptomatic compared to symptomatic infection. Interpretation: Despite rapid waning of protection, vaccine boosters remain an important tool in responding to the dynamic COVID-19 landscape; boosting population immunity in advance of periods of anticipated pressure, such as surging infection rates or emerging variants of concern. Funding: UK Health Security Agency, Medical Research Council, NIHR HPRU Oxford, Bristol, and others.

Objective In September 2020, 15 861 SARS-CoV-2 case records failed to upload from the Second Generation Surveillance System (SGSS) to the Contact Tracing Advisory Service (CTAS) tool, delaying the contact tracing of these cases. This study used CTAS data to determine the impact of this delay on population health outcomes: transmission events, hospitalisations and mortality. Previously, a modelling study suggested a substantial impact.Design Observational study.Setting England.Population Individuals testing positive for SARS-CoV-2 and their reported contacts.Main outcome measures Secondary attack rates (SARs), hospitalisations and deaths among primary and secondary contacts were calculated, compared with all other concurrent, unaffected cases. Affected SGSS records were matched to CTAS records. Successive contacts and cases were identified and matched to hospital episode and mortality outcomes.Results Initiation of contact tracing was delayed by 3 days on average in the primary cases in the delay group (6 days) compared with the control group (3 days). This was associated with lower completion of contact tracing: 80% (95% CI: 79% to 81%) in delay group and 83% (95% CI: 83% to 84%) in control group. There was some evidence to suggest increased transmission to non-household contacts among those affected by the delay. The SAR for non-household contacts was higher among secondary contacts in the delay group than the control group (delay group: 7.9%, 95% CI: 6.5% to 9.2%; control group: 5.9%, 95% CI: 5.3% to 6.6%). There did not appear to be a significant difference between the delay and control groups in the odds of hospitalisation (crude OR: 1.1 (95% CI: 0.9 to 1.2)) or death (crude OR: 0.7 (95% CI: 0.1 to 4.0)) among secondary contacts.Conclusions Our analysis suggests that the delay in contact tracing had a limited impact on population health outcomes; however, contact tracing was not completed for all individuals, so some transmission events might not be captured.

This study investigates trends in mortality and length of stay for people hospitalised with COVID-19 in England until September 2021. It shows that risks were higher for unvaccinated people and those with multiple comorbidities, and that busier hospitals had higher mortality rates at the start of the pandemic but this effect lessened over time.

Objectives Problem drug use (PDU) prevalence is an essential part of evidence base to formulate policy, inform service provision, and assess interventions. Opiate and crack use, particularly injecting, is associated with infectious diseases. Multiple data sources, where PDU are observed, are linked to estimate the hidden population of PDU using capture-recapture. Approach Datasets consisted of (1) community treatment, (2) prison treatment, (3) probation (Ministry of Justice), and (4) drug-related deaths, for financial year 2018/19. The following offenders’ attributes and geography are used: first and second initials, date of birth, gender, multiple areas of residence and area of prison release. Geography information consists of local authority code and region name. Probabilistic linkage approach of Fellegi–Sunter is applied through FastLink package in R, where pairs of records are classified as match, possible match, or non-match at 85% threshold. Estimates of error rates, sensitivity and specificity are used to test the results. Results There were 138,341 records in community treatment, 41,700 records in prison, 18,849 in probation and 2,368 deaths from opiate or cocaine. The total number of individuals observed in at least one linked dataset is estimated at 170,307 records at 85% threshold. The number of exact and possible matches overlapping between community treatment and prison is estimated at 17,544 records, from which 13,731 (78.3%) are exact matches, and 3,813 (21.7%) are possible matches at 85% threshold. The number of exact and possible matches overlapping between linked community treatment/prison and probation is estimated at 12,586 records, from which 9,872 (78.4%) are exact and 2,714 (21.6%) are possible matches at 85% threshold. False discovery rate was estimated at 0.3%, sensitivity and specificity were 99.8%. Conclusions Information on PDUs attending community treatment is poor and relying on exact matches underestimate the overlap between data sources. This tend to bias hidden population estimates derived from capture-recapture. Probabilistic approaches, and use of multiple candidate geographic areas for matching, maximise linkages between individuals and thus likely to improve estimates.

Abstract COVID-19 is reported to have been brought under control in China. To understand the COVID-19 outbreak in China and provide potential lessons for other parts of the world, in this study we apply a mathematical model with multiple datasets to estimate the transmissibility of the SARS-CoV-2 virus and the severity of the illness associated with the infection, and how both were affected by unprecedented control measures. Our analyses show that before 19th January 2020, 3.5% (95% CI 1.7–8.3%) of infected people were detected; this percentage increased to 36.6% (95% CI 26.1–55.4%) thereafter. The basic reproduction number (R 0) was 2.33 (95% CI 1.96–3.69) before 8th February 2020; then the effective reproduction number dropped to 0.04(95% CI 0.01–0.10). This estimation also indicates that control measures taken since 23rd January 2020 affected the transmissibility about 2 weeks after they were introduced. The confirmed case fatality rate is estimated at 9.6% (95% CI 8.1–11.4%) before 15 February 2020, and then it reduced to 0.7% (95% CI 0.4–1.0%). This shows that SARS-CoV-2 virus is highly transmissible but may be less severe than SARS-CoV-1 and MERS-CoV. We found that at the early stage, the majority of R 0 comes from undetected infectious people. This implies that successful control in China was achieved through reducing the contact rates among people in the general population and increasing the rate of detection and quarantine of the infectious cases.

Summary: Background: The SARS-CoV-2 Delta variant (B.1.617.2), first detected in India, has rapidly become the dominant variant in England. Early reports suggest this variant has an increased growth rate suggesting increased transmissibility. This study indirectly assessed differences in transmissibility between the emergent Delta variant compared to the previously dominant Alpha variant (B.1.1.7). Methods: A matched case-control study was conducted to estimate the odds of household transmission (≥ 2 cases within 14 days) for Delta variant index cases compared with Alpha cases. Cases were derived from national surveillance data (March to June 2021). One-to-two matching was undertaken on geographical location of residence, time period of testing and property type, and a multivariable conditional logistic regression model was used for analysis. Findings: In total 5,976 genomically sequenced index cases in household clusters were matched to 11,952 sporadic index cases (single case within a household). 43.3% (n=2,586) of cases in household clusters were confirmed Delta variant compared to 40.4% (n= 4,824) of sporadic cases. The odds ratio of household transmission was 1.70 among Delta variant cases (95% CI 1.48-1.95, p

Background: SARS-CoV-2 antibody tests are used for population surveillance and might have a future role in individual risk assessment. Lateral flow immunoassays (LFIAs) can deliver results rapidly and at scale, but have widely varying accuracy. Methods: In a laboratory setting, we performed head-to-head comparisons of four LFIAs: the Rapid Test Consortium's AbC-19TM Rapid Test, OrientGene COVID IgG/IgM Rapid Test Cassette, SureScreen COVID-19 Rapid Test Cassette, and Biomerica COVID-19 IgG/IgM Rapid Test. We analysed blood samples from 2,847 key workers and 1,995 pre-pandemic blood donors with all four devices. Findings: We observed a clear trade-off between sensitivity and specificity: the IgG band of the SureScreen device and the AbC-19TM device had higher specificities but OrientGene and Biomerica higher sensitivities. Based on analysis of pre-pandemic samples, SureScreen IgG band had the highest specificity (98.9%, 95% confidence interval 98.3 to 99.3%), which translated to the highest positive predictive value across any pre-test probability: for example, 95.1% (95% uncertainty interval 92.6, 96.8%) at 20% pre-test probability. All four devices showed higher sensitivity at higher antibody concentrations (“spectrum effects”), but the extent of this varied by device. Interpretation: The estimates of sensitivity and specificity can be used to adjust for test error rates when using these devices to estimate the prevalence of antibody. If tests were used to determine whether an individual has SARS-CoV-2 antibodies, in an example scenario in which 20% of individuals have antibodies we estimate around 5% of positive results on the most specific device would be false positives. Funding: Public Health England.

Objectives To identify key predictors of general practitioner (GP) consultations for allergic rhinitis (AR) using meteorological and environmental data.Design A retrospective, time series analysis of GP consultations for AR.Setting A large GP surveillance network of GP practices in the London area.Participants The study population was all persons who presented to general practices in London that report to the Public Health England GP in-hours syndromic surveillance system during the study period (3 April 2012 to 11 August 2014).Primary measure Consultations for AR (numbers of consultations).Results During the study period there were 186 401 GP consultations for AR. High grass and nettle pollen counts (combined) were associated with the highest increases in consultations (for the category 216-270 grains/m3, relative risk (RR) 3.33, 95% CI 2.69 to 4.12) followed by high tree (oak, birch and plane combined) pollen counts (for the category 260–325 grains/m3, RR 1.69, 95% CI 1.32 to 2.15) and average daily temperatures between 15°C and 20°C (RR 1.47, 95% CI 1.20 to 1.81). Higher levels of nitrogen dioxide (NO2) appeared to be associated with increased consultations (for the category 70–85 µg/m3, RR 1.33, 95% CI 1.03 to 1.71), but a significant effect was not found with ozone. Higher daily rainfall was associated with fewer consultations (15–20 mm/day; RR 0.812, 95% CI 0.674 to 0.980).Conclusions Changes in grass, nettle or tree pollen counts, temperatures between 15°C and 20°C, and (to a lesser extent) NO2 concentrations were found to be associated with increased consultations for AR. Rainfall has a negative effect. In the context of climate change and continued exposures to environmental air pollution, intelligent use of these data will aid targeting public health messages and plan healthcare demand.

IntroductionOne of the leading challenges in the 2013-2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning.MethodologyWe performed a systematic literature review to identify all published data on EVD clinical symptoms in adult patients. Data was dual extracted, and random effects meta-analysis performed for each symptom to identify symptoms with the greatest risk for EVD infection.ResultsSymptoms usually presenting late in illness that were more than twice as likely to predict a diagnosis of Ebola, were confusion (pOR 3.04, 95% CI 2.18-4.23), conjunctivitis (2.90, 1.92-4.38), dysphagia (1.95, 1.13-3.35) and jaundice (1.86, 1.20-2.88). Early non-specific symptoms of diarrhoea (2.99, 2.00-4.48), fatigue (2.77, 1.59-4.81), vomiting (2.69, 1.76-4.10), fever (1.97, 1.10-4.52), muscle pain (1.65, 1.04-2.61), and cough (1.63, 1.24-2.14), were also strongly associated with EVD diagnosis.ConclusionsThe existing literature fails to provide a unified position on the symptoms most predictive of EVD, but highlights some early and late stage symptoms that in combination will be useful for future risk stratification. Confirmation of these findings across datasets (or ideally an aggregation of all individual patient data) will aid effective future clinical assessment, risk stratification tools and emergency epidemic response planning.

BACKGROUND:Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions. METHODS:We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included. RESULTS:We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29th or 30th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models. CONCLUSION:Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity.

BACKGROUND:Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups. METHODS AND FINDINGS:We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t-1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%-63.2%) of incidence in adults aged 15-44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%-28.2%, and 6.07%, CI 2.83%-9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%-3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%-3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates. CONCLUSIONS:Our estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups.

Abstract Background Providing sexual health services in primary care is an essential step towards universal provision. However they are not offered consistently. We conducted a national pilot of an educational intervention to improve staff’s skills and confidence to increase chlamydia testing rates and provide condoms with contraceptive information plus HIV testing according to national guidelines, known as 3Cs&HIV. The effectiveness of the pilot on chlamydia testing and diagnosis rates in general practice was evaluated. Methods The pilot was implemented using a step-wedge design over three phases during 2013 and 2014 in England. The intervention combined educational workshops with posters, testing performance feedback and continuous support. Chlamydia testing and diagnosis rates in participating general practices during the control and intervention periods were compared adjusting for seasonal trends in chlamydia testing and differences in practice size. Intervention effect modification was assessed for the following general practice characteristics: chlamydia testing rate compared to national median, number of general practice staff employed, payment for chlamydia screening, practice urban/rurality classification, and proximity to sexual health clinics. Results The 460 participating practices conducted 26,021 tests in the control period and 18,797 tests during the intervention period. Intention-to-treat analysis showed no change in the unadjusted median tests and diagnoses per month per practice after receiving training: 2.7 vs 2.7; 0.1 vs 0.1. Multivariable negative binomial regression analysis found no significant change in overall testing or diagnoses post-intervention (incidence rate ratio (IRR) 1.01, 95 % confidence interval (CI) 0.96–1.07, P = 0.72; 0.98 CI 0.84–1.15, P = 0.84, respectively). Stratified analysis showed testing increased significantly in practices where payments were in place prior to the intervention (IRR 2.12 CI 1.41–3.18, P

Background: A deep infection of the surgical site is reported in 0.7% of all cases of total hip arthroplasty (THA). This often leads to revision surgery that is invasive, painful and costly. A range of strategies is employed in NHS hospitals to reduce risk, yet no economic analysis has been undertaken to compare the value for money of competing prevention strategies. Objectives: To compare the costs and health benefits of strategies that reduce the risk of deep infection following THA in NHS hospitals. To make recommendations to decision-makers about the cost-effectiveness of the alternatives. Design: The study comprised a systematic review and cost-effectiveness decision analysis. Setting: 77,321 patients who had a primary hip arthroplasty in NHS hospitals in 2012. Interventions: Nine different treatment strategies including antibiotic prophylaxis, antibiotic-impregnated cement and ventilation systems used in the operating theatre. Main outcome measures: Change in the number of deep infections, change in the total costs and change in the total health benefits in quality-adjusted life-years (QALYs). Data sources: Literature searches using MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature and the Cochrane Central Register of Controlled Trials were undertaken to cover the period 1966–2012 to identify infection prevention strategies. Relevant journals, conference proceedings and bibliographies of retrieved papers were hand-searched. Orthopaedic surgeons and infection prevention experts were also consulted. Review methods: English-language papers only. The selection of evidence was by two independent reviewers. Studies were included if they were interventions that reported THA-related deep surgical site infection (SSI) as an outcome. Mixed-treatment comparisons were made to produce estimates of the relative effects of competing infection control strategies. Results: Twelve studies, six randomised controlled trials and six observational studies, involving 123,788 total hip replacements (THRs) and nine infection control strategies, were identified. The quality of the evidence was judged against four categories developed by the National Institute for Health and Care Excellence Methods for Development of NICE Public Health Guidance (http://publications.nice.org.uk/methods-for-the-development-of-nice-public-health-guidance-third-edition-pmg4), accessed March 2012. All evidence was found to fit the two highest categories of 1 and 2. Nine competing infection control interventions [treatments (Ts) 1–9] were used in a cohort simulation model of 77,321 patients who had a primary THR in 2012. Predictions were made for cases of deep infection and total costs, and QALY outcomes. Compared with a baseline of T1 (no systemic antibiotics, plain cement and conventional ventilation) all other treatment strategies reduced risk. T6 was the most effective (systemic antibiotics, antibiotic-impregnated cement and conventional ventilation) and prevented a further 1481 cases of deep infection, and led to the largest annual cost savings and the greatest gains to QALYs. The additional uses of laminar airflow and body exhaust suits indicate higher costs and worse health outcomes. Conclusions: T6 is an optimal strategy for reducing the risk of SSI following THA. The other strategies that are commonly used among NHS hospitals lead to higher cost and worse QALY outcomes. Policy-makers, therefore, have an opportunity to save resources and improve health outcomes. The effects of laminar air flow and body exhaust suits might be further studied if policy-makers are to consider disinvesting in these technologies. Limitations: A wide range of evidence sources was synthesised and there is large uncertainty in the conclusions. Funding: The National Institute for Health Research Health Technology Assessment programme and the Queensland Health Quality Improvement and Enhancement Programme (grant number 2008001769).

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

BACKGROUND:We report a widespread foodborne outbreak of Cryptosporidium parvum in England and Scotland in May 2012. Cases were more common in female adults, and had no history of foreign travel. Over 300 excess cases were identified during the period of the outbreak. Speciation and microbiological typing revealed the outbreak strain to be C. parvum gp60 subtype IIaA15G2R1. METHODS:Hypothesis generation questionnaires were administered and an unmatched case control study was undertaken to test the hypotheses raised. Cases and controls were interviewed by telephone. Controls were selected using sequential digit dialling. Information was gathered on demographics, foods consumed and retailers where foods were purchased. RESULTS:Seventy-four laboratory confirmed cases and 74 controls were included in analyses. Infection was found to be strongly associated with the consumption of pre-cut mixed salad leaves sold by a single retailer. This is the largest documented outbreak of cryptosporidiosis attributed to a food vehicle.

BACKGROUND:One pathway through which pandemic influenza strains might emerge is reassortment from coinfection of different influenza A viruses. Seasonal influenza vaccines are designed to target the circulating strains, which intuitively decreases the prevalence of coinfection and the chance of pandemic emergence due to reassortment. However, individual-based analyses on 2009 pandemic influenza show that the previous seasonal vaccination may increase the risk of pandemic A(H1N1) pdm09 infection. In view of pandemic influenza preparedness, it is essential to understand the overall effect of seasonal vaccination on pandemic emergence via reassortment. METHODS AND FINDINGS:In a previous study we applied a population dynamics approach to investigate the effect of infection-induced cross-immunity on reducing such a pandemic risk. Here the model was extended by incorporating vaccination for seasonal influenza to assess its potential role on the pandemic emergence via reassortment and its effect in protecting humans if a pandemic does emerge. The vaccination is assumed to protect against the target strains but only partially against other strains. We find that a universal seasonal vaccine that provides full-spectrum cross-immunity substantially reduces the opportunity of pandemic emergence. However, our results show that such effectiveness depends on the strength of infection-induced cross-immunity against any novel reassortant strain. If it is weak, the vaccine that induces cross-immunity strongly against non-target resident strains but weakly against novel reassortant strains, can further depress the pandemic emergence; if it is very strong, the same kind of vaccine increases the probability of pandemic emergence. CONCLUSIONS:Two types of vaccines are available: inactivated and live attenuated, only live attenuated vaccines can induce heterosubtypic immunity. Current vaccines are effective in controlling circulating strains; they cannot always help restrain pandemic emergence because of the uncertainty of the oncoming reassortant strains, however. This urges the development of universal vaccines for prevention of pandemic influenza.