Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleSilva L, Gogoi M, Lal Z, et al., 2026,
Antibiotic knowledge among ethnic minority groups in high-income countries: A mixed-methods systematic review.
, Public Health Pract (Oxf), Vol: 11OBJECTIVES: Antimicrobial resistance (AMR) is a major global public health concern. Although low-income countries are disproportionately affected by AMR, certain underserved groups in high-income countries (HICs), such as migrants and ethnic minorities, disproportionately bear the burden of AMR. This may be driven by socio-cultural factors including differences in health literacy. This review aimed to investigate the level of antibiotic knowledge amongst different ethnic minority groups in HICs. STUDY DESIGN: This was a mixed-methods systematic literature review. METHODS: We searched four databases (MEDLINE, EMBASE, the Cochrane library, CINAHL) to May 5, 2023, for primary studies on knowledge of antibiotics in different ethnic groups in HICs. We included studies in English using qualitative, quantitative and/or mixed-methods approaches and reporting on antibiotic knowledge by ethnicity. We used the convergent integrated approach for data synthesis and the Mixed-Methods Appraisal tool for quality assessment. RESULTS: 3935 articles were screened and 24 studies (17 quantitative, 5 qualitative, and 2 mixed-methods) were included, comprising 52778 participants from 8 countries (USA, UK, Australia, New Zealand, Netherlands, Greece, Sweden, Germany). Overall, participants from ethnic minority groups were able to identify common names of antibiotics and were aware of risks of antibiotics and side effects. However, participants thought antibiotics would treat viral-type illnesses. Ethnic minority groups generally had lower levels of knowledge compared to ethnic majority groups. CONCLUSIONS: Although ethnic minority communities possessed good levels of knowledge on certain aspects of antibiotics (e.g. being able to identify names of antibiotics), there were gaps in other areas (e.g. misperception that antibiotics are used for viral infections). The lower level of knowledge in ethnic minority groups compared to majority groups may be a contributing factor to health inequaliti
-
Journal articleKoemen S, Faria NR, Bastos LS, et al., 2026,
Fast and trustworthy nowcasting of dengue fever: a case study using attention-based probabilistic neural networks in São Paulo, Brazil
, Epidemics, Vol: 54, ISSN: 1755-4365Nowcasting methods are crucial in infectious disease surveillance, as reporting delays often lead to underestimation of recent incidence and can impair timely public health decision-making. Accurate real-time estimates of case counts are essential for resource allocation, policy responses, and communication with the public. In this paper, we propose a novel probabilistic neural network (PNN) architecture, named NowcastPNN, to estimate occurred-but-not-yet-reported cases of infectious diseases, demonstrated here using dengue fever incidence in São Paulo, Brazil. The proposed model combines statistical modelling of the true number of cases, assuming a Negative Binomial (NB) distribution, with recent advances in machine learning and deep learning, such as the attention mechanism. Uncertainty intervals are obtained by sampling from the predicted NB distribution and using Monte Carlo (MC) Dropout. Using proper scoring rules for the prediction intervals, NowcastPNN achieves nearly a 30% reduction in losses compared to the second-best model among other state-of-the-art approaches. While our model requires a large training dataset (equivalent to two to four years of incidence counts) to outperform benchmarks, it is computationally cheap and outperforms alternative methods even with significantly fewer observations as input. These features make the NowcastPNN model a promising tool for nowcasting in epidemiological surveillance of arboviral threats and other domains involving right-truncated data.
-
Journal articleMcCain K, Vicco A, Morgenstern C, et al., 2026,
A systematic review and meta-analysis of Zika virus epidemiology
, Nature Health, ISSN: 3005-0693Zika virus (ZIKV), classified as a priority pathogen by the World Health Organization, is an Aedes-borne arbovirus that can cause neurological complications and birth defects in newborns of mothers infected during pregnancy. We conducted a systematic review of peer-reviewed studies reporting ZIKV epidemiological parameters, transmission models and outbreaks (PROSPERO CRD42023393345) to characterize its transmissibility, seroprevalence, risk factors, disease sequelae and natural history. We performed meta-analyses of the proportions of congenital Zika syndrome, pregnancy loss among ZIKV-infected mothers and symptomatic cases. We extracted information from 574 studies. Across 418 included studies assigned a high-quality score, we extracted 969 parameters, 127 outbreak records and 154 models. Using random-effects models, we estimated proportions of congenital Zika syndrome (4.65%, 95% confidence interval (CI): 3.38–6.67%), pregnancy loss (2.48%, 95% CI: 1.62–3.78%) and symptomatic cases (51.20%, 95% CI: 38.00–64.23%). Seroprevalence estimates (n = 354) were retrieved beyond South America and French Polynesia. Basic reproduction number estimates (n = 77) ranged between 1.12 and 7.4. We found 66 human epidemiological delay estimates, including the intrinsic incubation period (n = 11, range: 4–12.1 days), infectious period (n = 15, range: 3–50 days), extrinsic incubation period (n = 22, range: 5.1–24.2 days) and serial interval (n = 27, range: 7.4–32.9 days). These data are available in the R package ‘epireview’ (version 1.4.5). We provide a comprehensive systematic summary of ZIKV epidemiology, revealing large heterogeneities and inconsistencies in the reporting of parameter estimates, study designs and parameter definitions and underscoring the need for standardized epidemiological definitions.
-
Journal articleWilliams TJ, Griffiths JS, Gonzales-Huerta LE, et al., 2026,
Selective Targeting of IL-1RAP-Dependent Eosinophilic Inflammation in Allergic Fungal Airway Disease.
, Allergy -
Journal articleHowes A, Stringer A, Flaxman SR, et al., 2026,
Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
, Journal of Theoretical Biology, Vol: 618, ISSN: 0022-5193Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, inference has been conducted using empirical Bayes and a Gaussian approximation, implemented via the TMB R package. We propose a new inference method based on an extension of adaptive Gauss-Hermite quadrature to deal with more than 20 hyperparameters. Using data from Malawi, our method improves the accuracy of inferences for model parameters, while being substantially faster to run than Hamiltonian Monte Carlo with the No-U-Turn sampler. Our implementation leverages the existing TMB C++ template for the model’s log-posterior, and is compatible with any model with such a template.
-
Journal articleDixon-Zegeye M, Walker M, Ramani A, et al., 2026,
HISTONCHO: A dataset of intervention histories for onchocerciasis control & elimination in sub-Saharan Africa
, Scientific Data, ISSN: 2052-4463In sub-Saharan Africa (SSA), onchocerciasis control has been implemented for many decades, beginning in 1974 under the Onchocerciasis Control Programme in West Africa (OCP) and in 1995 in Central and East Africa (plus Liberia) under the African Programme for Onchocerciasis Control (APOC). Since the establishment of the Expanded Special Project for Elimination of Neglected Tropical Diseases (ESPEN) in 2016, data on mass drug administration (MDA) with ivermectin has been centrally compiled for all endemic countries at implementation unit (IU) level, beginning in 2013. This paper presents HISTONCHO, a dataset collating detailed information on interventions, including vector control, from 1975 through to 2022, using the ESPEN portal (2013-2022), regional and country reports, implementation partners’ records, and published literature. Reconstructing such intervention histories is crucial for an understanding of their evolution, modelling their impact, and tailoring future interventions. We discuss strengths and limitations associated with the ESPEN database, and how HISTONCHO can be improved to support modelling of intervention strategies as well as onchocerciasis control and elimination efforts by endemic country programmes.
-
Journal articleEbi KL, Haines A, Andrade RFS, et al., 2026,
Correction to: The attribution of human health outcomes to climate change: transdisciplinary practical guidance (Climatic Change, (2025), 178, 8, (143), 10.1007/s10584-025-03976-7)
, Climatic Change, Vol: 179, ISSN: 0165-0009The original article has been corrected. In this article Kathryn Bowen at affiliation ‘Melbourne Climate Futures; and Environment, Climate, and Global Health, University of Melbourne, Melbourne, Australia’ was missing from the author list. The section “Conflicts of Interest” was also missing and should have read: “Select authors declare potential interests arising from funding from Wellcome, NIH, NIHR, Oak Foundation, CDC, CSTE, WHO, Green Climate Fund, World Bank, Asia Development Bank, CIHR, SSHRC, NSF, NovoNordisc (sponsored travel), and honoraria for academic engagement from US universities. One author is a Wellcome employee. One author (KLE) is a Deputy Editor for Climatic Change.”
-
Journal articleGrégoire V, Zhu AW, Brown CM, et al., 2026,
Public reporting guidelines for outbreak data: Enabling accountability for effective outbreak response by developing standards for transparency and uniformity.
, Public Health, Vol: 251OBJECTIVES: There are few standards for what information about an infectious disease outbreak should be reported to the public and when. To address this problem, we undertook a consensus process to develop recommendations for what epidemiological information public health authorities should report to the public during an outbreak. STUDY DESIGN: We conducted a Delphi study following the steps outlined in the ACcurate COnsensus Reporting Document (ACCORD) for health-related activities or research. METHODS: We assembled a steering committee of nine experts representing federal and state public health, academia, and international partners to develop a candidate list of reporting items. We then invited 45 experts, 35 of whom agreed to participate in a Delphi panel. Of those, 25 participated in voting in the first round, 25 in the second round, and 25 in the third round, demonstrating consistent engagement in the consensus-building process. The final stage of the Delphi process consisted of a hybrid consensus meeting to finalize the voting items. RESULTS: The Delphi process yielded nine core reporting items representing a minimum standard for public outbreak reporting: numbers of new confirmed cases, new hospital admissions, new deaths, cumulative confirmed cases, cumulative hospital admissions, and cumulative deaths, each reported weekly and at Administrative Level 1 (typically state or province), and stratified by sex, age group, and race/ethnicity. CONCLUSIONS: This minimum reporting standard creates a strong framework for uniform sharing of outbreak information and promotes consistency of data between jurisdictions, enabling effective response by promoting access to information about an unfolding epidemic.
-
Journal articleSangkaew S, Daniels BC, Ming DK, et al., 2026,
Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.
, PLOS Digit Health, Vol: 5Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in outpatient settings. This study developed models using clinical and laboratory data from patients in outpatient settings during the febrile phase. Data from two cohort studies in Vietnam and Thailand were used to develop and validate six models: logistic regression with warning signs, Lasso-selected logistic regression, random forest, extreme gradient boosted classification, support vector machine, and artificial neural network. Models predicted dengue shock syndrome (DSS) as the primary endpoint and moderate plasma leakage and/or DSS as the secondary endpoint. We assessed model performance, discrimination, and calibration, using sensitivity, specificity, accuracy, Brier score, AUROC, CITL, calibration slope, calibration plots, and decision curve analysis. The optimal model was the Lasso-selected logistic regression for predicting DSS and the combined endpoint of moderate plasma leakage and/or DSS (Brier score: 0.044 [95% CI 0.043, 0.044] and 0.104 [95% CI 0.104, 0.105]; AUROC: 0.789 [95% CI 0.787, 0.791] and 0.741 [95% CI 0.740, 0.742]). We identified hematocrit, platelet count, lymphocyte count, and aspartate aminotransferase as predictors for DSS, and abdominal pain or tenderness, vomiting, mucosal bleeding, white blood cell count, lymphocyte count, platelet count, aspartate aminotransferase, and serum albumin as predictors for the secondary endpoint. Logistic regression and machine learning models using clinical and laboratory data during the febrile phase can support early prediction of severe disease in outpatient settings. Integrating risk prediction models into a decision support system could improve triage and optimize healthcare and resource allocation in endemic and resource-limited areas.
-
Journal articleBerden J, Hanley-Cook GT, Chimera B, et al., 2026,
Synergies between food biodiversity, processing levels, and the EAT-Lancet diet for nutrient adequacy and environmental sustainability: a multiobjective optimization using the European Prospective Investigation into Cancer and Nutrition cohort.
, Am J Clin Nutr, Vol: 123BACKGROUND: Diets have become increasingly monotonous and high in ultraprocessed foods (UPFs), contributing to poor health outcomes and environmental degradation. Although sustainable diets, food biodiversity, and food processing levels have each been linked to nutritional and environmental outcomes, their combined impact has not been assessed. OBJECTIVES: This study aims to examine whether food biodiversity, intakes of UPFs, and adherence to the EAT-Lancet diet can simultaneously optimize nutrient adequacy while reducing environmental impacts. METHODS: Using data from 368,733 adults in the European Prospective Investigation into Cancer and Nutrition, we assessed associations and interactions between dietary species richness (DSR) (disaggregated into DSRPlant and DSRAnimal), food processing levels (Nova categories; % g/d), and adherence to EAT-Lancet recommendations [healthy reference diet (HRD) score; 0-140 points] with the Probability of Adequate Nutrient Intake Diet (PANDiet) score, dietary greenhouse gas emissions (GHGe; kg CO2-eq/d), and land use (m2/d). Regression models subsequently informed multiobjective optimization to identify optimal dietary patterns balancing nutritional and environmental outcomes. RESULTS: Compared with observed diets, optimal diets showed a mean HRD score increase of 13.91 (95% confidence interval: 13.89, 13.93) points; DSRPlant increased by mean of 1.36 (1.35, 1.37) species, and a mean substitution of 12.44 (12.40, 12.49) percentage points of UPFs with unprocessed or minimally processed foods. Correspondingly, the mean PANDiet score increased by 4.12 (4.10, 4.14) percentage points, whereas GHGe and land use reduced by 1.07 (1.05, 1.09) kg CO2-eq/d and 1.43 (1.41, 1.45) m2/d, respectively. CONCLUSIONS: Diets that adhere to the EAT-Lancet diet, are more biodiverse, and prioritize unprocessed and minimally processed foods over UPFs, have the potential to synergistically enhance nutrient adequacy while minimizing environmental impacts. T
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
Contact us
For any enquiries related to the MRC Centre please contact:
Scientific Manager
Susannah Fisher
mrc.gida@imperial.ac.uk
External Relationships and Communications Manager
Dr Sabine van Elsland
s.van-elsland@imperial.ac.uk