Search

Background: Hepatitis B virus (HBV) epidemiology in Europe differs by region and population risk group, and data are often incomplete. We estimated chronic HBV prevalence as measured by surface antigen (HBsAg) among general and key population groups for each country in the European Union, European Economic Area and the United Kingdom (EU/EEA/UK), including where data are currently unavailable. Methods: We combined data from a 2018 systematic review (updated in 2021), data gathered directly by the European Centre for Disease Control (ECDC) from EU/EEA countries and the UK and further country-level data. We included data on adults from the general population, pregnant women, first time blood donors (FTBD), men who have sex with men (MSM), prisoners, people who inject drugs (PWID), and migrants from 2001 to 2021, with three exceptions made for pre-2001 estimates. Finite Mixture Models (FMM) and Beta regression were used to predict country and population group HBsAg prevalence. A separate multiplier method was used to estimate HBsAg prevalence among the migrant populations within each country, due to biases in the data available. Results: There were 595 included studies from 31 countries (N = 41,955,969 people): 66 were among the general population (mean prevalence () 1.3% [range: 0.0-7.6%]), 52 among pregnant women ( 1.1% [0.1–5.3%]), 315 among FTBD ( 0.3% [0.0-6.2%]), 20 among MSM ( 1.7% [0.0-11.2%]), 34 among PWID ( 3.9% [0.0-16.9%]), 24 among prisoners ( 2.9% [0.0-10.7%]), and 84 among migrants ( 7.0% [0.2–37.3%]). The FMM grouped countries into 3 classes. We estimated HBsAg prevalence among the general population to be < 1% in 24/31 countries, although it was higher in 7 Eastern/Southern European countries. HBsAg prevalence among each population group was higher in most Eastern/Southern European than Western/Northern European countries, whilst prevalence among PWID and prisoners was estimated at > 1% for most countries. Portugal had the highest estimated prevalence of HBsAg among migrants (5.0%), with the other highest prevalences mostly seen in Southern Europe. Conclusions: We estimated HBV prevalence for each population group within each EU/EAA country and the UK, with general population HBV prevalence to be < 1% in most countries. Further evidence is required on the HBsAg prevalence of high-risk populations for future evidence synthesis. [ABSTRACT FROM AUTHOR]

Environmental effects on the transmission of many high burden infectious diseases are increasingly recognized, especially for transmission routes through water and vectors, or intermediate hosts, that live in the environment. Control of such diseases, which is currently largely based on treatment, is increasingly challenging due to climate-induced changes in infection patterns and the rapid emergence of drug resistance. Therefore, prevention through simulation of future disease risk and the devising of more comprehensive strategies, rather than exclusively relying on treatment, are becoming key. This requires mechanistic understanding of the link between disease transmission and underlying drivers, including heterogeneous on-the-ground environmental characteristics, which are those it may be possible to manipulate locally to reduce disease burdens. However, currently assumed drivers on the variability of disease risk are often only climatic, and environmental interventions to supplement drug treatment still under-recognised. In this work, we use the widespread parasitic disease of fasciolosis, and emerging zoonosis, as a case study. First, we apply the previously developed hydro-epidemiological model for liver fluke to investigate the variability of climatic and environmental drivers on disease risk across the UK. This allows exploring opportunities for environmental control strategies to complement treatment. Second, we evaluate how future risk of infection might respond to climate change across the country, including potential shifts in disease risk and its drivers, using climate projection data. Our results show shifts from modal to bimodal seasonality across the UK going forwards, and that environmental management may provide benefits in risk reduction in certain areas, offering a more sustainable and cost-effective approach in the long run. [ABSTRACT FROM AUTHOR]

Weather and climate patterns play an intrinsic role in societal health, yet a comprehensive synthesis of specific hazard-mortality causes does not currently exist. Country-level health burdens are thus highly uncertain, but harnessing collective expert knowledge can reduce this uncertainty, and help assess diverse mortality causes beyond what is explicitly quantified. Here, surveying 30 experts, we provide the first structured expert judgement of how weather and climate directly impact mortality, using the UK as an example. Current weather-related mortality is dominated by short-term exposure to hot and cold temperatures leading to cardiovascular and respiratory failure. We find additional underappreciated health outcomes, especially related to long-exposure hazards, including heat-related renal disease, cold-related musculoskeletal health, and infectious diseases from compound hazards. We show potential future worsening of cause-specific mortality, including mental health from flooding or heat, and changes in infectious diseases. Ultimately, this work could serve to develop an expert-based understanding of the climate-related health burden in other countries., Competing Interests: Declaration of interests YTEL was funded by a University of Bristol fellowship in Climate and Health. EB was funded by Policy Bristol. CR is funded by a UKRI Future Leaders Fellowship (MR/V021672). NSF received support of H2020 MSCA IF (grant ID 846824). AG was supported by the Medical Research Council-UK (MR/V034162/1). All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

BackgroundThe burden of chronic hepatitis B virus (HBV) varies across the European Union (EU) and European Economic Area (EEA).AimWe aimed to update the 2017 HBV prevalence estimates in EU/EEA countries and the United Kingdom for 2018 to 2021.MethodsWe undertook a systematic review, adding to HBV prevalence estimates from an existing (2005-2017) database. Databases were searched for original English-language research articles including HBV surface antigen prevalence estimates among the general population, pregnant women, first-time blood donors (FTB), men who have sex with men (MSM), migrants and people in prison. Country experts contributed grey literature data. Risk of bias was assessed using a quality assessment framework.FindingsThe update provided 147 new prevalence estimates across the region (updated total n = 579). Median HBV prevalence in the general population was 0.5% and the highest was 3.8% (Greece). Among FTB, the highest prevalence was 0.8% (Lithuania). Estimates among pregnant women were highest in Romania and Italy (5.1%). Among migrants, the highest estimate was 31.7% (Spain). Relative to 2017 estimates, median prevalence among pregnant women decreased by 0.5% (to 0.3%) and increased by 0.9% (to 5.8%) among migrants. Among MSM, the highest estimate was 3.4% (Croatia). Prevalence among people in prison was highest in Greece (8.3%) and the median prevalence increased by 0.6% (to 2.1%).ConclusionsThe HBV prevalence is low in the general population and confined to risk populations in most European countries with some exceptions. Screening and treatment should be targeted to people in prison and migrants.

University students have unique living, learning and social arrangements which may have implications for infectious disease transmission. To address this data gap, we created CONQUEST (COroNavirus QUESTionnaire), a longitudinal online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. Here, we analyse results from 740 students providing 1261 unique records from the start of the 2020/2021 academic year (14/09/2020-01/11/2020), where COVID-19 outbreaks led to the self-isolation of all students in some halls of residences. Although most students reported lower daily contacts than in pre-COVID-19 studies, there was heterogeneity, with some reporting many (median = 2, mean = 6.1, standard deviation = 15.0; 8% had ≥ 20 contacts). Around 40% of students' contacts were with individuals external to the university, indicating potential for transmission to non-students/staff. Only 61% of those reporting cardinal symptoms in the past week self-isolated, although 99% with a positive COVID-19 test during the 2 weeks before survey completion had self-isolated within the last week. Some students who self-isolated had many contacts (mean = 4.3, standard deviation = 10.6). Our results provide context to the COVID-19 outbreaks seen in universities and are available for modelling future outbreaks and informing policy.

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather-water-environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time-space patterns of infection, which will be valuable for decision support., (© 2018 The Authors.)