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Stop antibiotics when you feel better? Opportunities, challenges and research directions.
Shortening standard antibiotic courses and stopping antibiotics when patients feel better are two ways to reduce exposure to antibiotics in the community, and decrease the risks of antimicrobial resistance and antibiotic side effects. While evidence shows that shorter antibiotic treatments are non-inferior to longer ones for infections that benefit from antibiotics, shorter courses still represent average treatment durations that might be suboptimal for some. In contrast, stopping antibiotics based on improvement or resolution of symptoms might help personalize antibiotic treatment to individual patients and help reduce unnecessary exposure. Yet, many challenges need addressing before we can consider this approach evidence-based and implement it in practice. In this viewpoint article, we set out the main evidence gaps and avenues for future research.
Uncertainty Quantification in Cost-effectiveness Analysis for Stochastic-based Infectious Disease Models: Insights from Surveillance on Lymphatic Filariasis.
Cost-effectiveness analyses (CEA) typically involve comparing effectiveness and costs of one or more interventions compared to standard of care, to determine which intervention should be optimally implemented to maximise population health within the constraints of the healthcare budget. Traditionally, cost-effectiveness evaluations are expressed using incremental cost-effectiveness ratios (ICERs), which are compared with a fixed willingness-to-pay (WTP) threshold. Due to the existing uncertainty in costs for interventions and the overall burden of disease, particularly with regard to diseases in populations that are difficult to study, it becomes important to consider uncertainty quantification whilst estimating ICERs. To tackle the challenges of uncertainty quantification in CEA, we propose an alternative paradigm utilizing the Linear Wasserstein framework combined with Linear Discriminant Analysis (LDA) using a demonstrative example of lymphatic filariasis (LF). This approach uses geometric embeddings of the overall costs for treatment and surveillance, disability-adjusted lifeyears (DALYs) averted for morbidity by quantifying the burden of disease due to the years lived with disability, and probabilities of local elimination over a time-horizon of 20 years to evaluate the cost-effectiveness of lowering the stopping thresholds for post-surveillance determination of LF elimination as a public health problem. Our findings suggest that reducing the stopping threshold from <1% to <0.5% microfilaria (mf) prevalence for adults aged 20 years and above, under various treatment coverages and baseline prevalences, is cost-effective. When validated on 20% of test data, for 65% treatment coverage, a government expenditure of WTP ranging from $500 to $3,000 per 1% increase in local elimination probability justifies the switch to the lower threshold as cost-effective. Stochastic model simulations often lead to parameter and structural uncertainty in CEA. Uncertainty may impact the decisions taken, and this study underscores the necessity of better uncertainty quantification techniques within CEA for making informed decisions.
Pregnant women as a sentinel population for genomic surveillance of malaria in the Democratic Republic of the Congo: a population-based study.
BACKGROUND: Genomic surveillance is a valuable tool for detecting changes in the drug susceptibility of malaria parasites, enabling timely adjustments to treatment strategies. However, implementation can be costly and challenging in high-burden countries, especially when targeting cohorts of children. To address these challenges, we investigated whether in the Democratic Republic of the Congo pregnant women attending antenatal care services could act as an effective sentinel population for children in the same area. METHODS: This population-based study aimed to target pregnant women in Kinshasa (Democratic Republic of the Congo), regardless of age, trimester of pregnancy, parity, or previous antenatal care centre attendance, and children younger than 14 years living in the same area. Women were invited to participate and enrolled during their routine antenatal care visit. For children, we originally planned to conduct standard school-based surveys, but implementation was affected by the COVID-19 pandemic and subsequent vaccination campaign. Therefore, we adopted an alternative approach, setting up screening posts in existing health centres and, with the support of community health workers, encouraging families to visit the posts at their convenience. The study was done in two areas of Kinshasa, urban (Binza) and semirural (Maluku), where malaria transmission is endemic and perennial. Blood samples from malaria-positive cases were genotyped using an amplicon sequencing platform, to allow comparisons of Plasmodium falciparum genomes between the two cohorts and estimations of drug resistance mutation prevalence. The study is registered with ClinicalTrials.gov, NCT05072613. FINDINGS: Between Nov 11, 2021, and June 21, 2023, 2794 children and 4001 pregnant women were recruited to the study. Malaria prevalence by rapid diagnostic test was 49·0% (95% CI 47·1-50·8) in children and 19·1% (17·9-20·3) in pregnant women. Parasite populations sampled from the two cohorts showed highly similar allele frequencies at all tested loci, including drug resistance markers potentially under selection. Pregnant women did not have higher frequencies of sulfadoxine-pyrimethamine resistant haplotypes, which undermine preventive treatments, than children and we did not find any kelch13 mutations at significant frequency. Although parasite densities were lower in pregnant women, the complexity of infection was similar to that in children. We found no evidence of Plasmodium vivax infections in the study. INTERPRETATION: A cohort of pregnant women produced highly similar results for antimalarial drug resistance surveillance as a cohort of children from the same area, through implementation of simple and efficient genomic surveillance systems integrated into routine antenatal care activities, while benefiting women with diagnosis and treatment. FUNDING: Bill & Melinda Gates Foundation and Wellcome Trust.
Do pregnant people have opportunities to participate in clinical trials? an exploratory survey of NIHR HTA-funded trialists
Abstract Background Pregnant people are often excluded from clinical trials, primarily due to safety concerns. However, exclusion causes population-level harms as well as sometimes providing individual protection. Harms caused to pregnant people by exclusion from clinical trials have been clearly evidenced and highlighted during the COVID pandemic. The National Institute for Health and Care Research (NIHR) has since provided guidance on improving inclusion of under-served groups, which includes pregnant people, in clinical research. Appropriate inclusion and active facilitation to participate are required to provide equitable evidence-based healthcare during pregnancy and to comply with ethical principles for research. Methods We carried out an exploratory, online, cross-sectional survey of trialists to assess whether, why, and how pregnant people are included or excluded from clinical trials funded by the NIHR Health Technology Assessment (HTA) Programme, with awards starting in 2022–2023. Trialists were the respondents, with trials the primary focus of this survey. Invitations were sent to trialists between October 2023 and March 2024. Summary statistics were calculated to describe the characteristics of the trials and respondents, to describe eligibility of pregnant people, reasons for this, and how this eligibility is documented and implemented. Results We identified 120 trials of which 88 were eligible for this survey. Responses were received for 81 trials. Pregnant people are excluded from 34 of these 81 trials. Pregnant people are eligible for inclusion in 40 of the 81 trials, including four which partially exclude people during pregnancy. Eligibility is unclear for seven trials. Exclusions are mostly for safety reasons. Sponsors and regulatory authorities are unnecessary barriers to inclusion in some trials. Eight trials of 40 trials make explicit or deliberate attempts to include people during pregnancy. Conclusions A minority of the 120 trials include people during pregnancy. Most trials for which pregnant people are eligible do not report explicitly including people during pregnancy or facilitating their inclusion. A small number of trials, different in setting, clinical area, and intervention type, are intentionally designed and conducted in a way that include people during pregnancy. There are clear opportunities to improve the inclusion of pregnant people in clinical trials in the NIHR HTA Programme.
Health impact and economic evaluation of the Expanded Program on Immunization in China from 1974 to 2024: a modelling study.
BACKGROUND: The Expanded Program on Immunization (EPI), initiated by WHO in 1974, is a cornerstone of public health. China's EPI covers more than a sixth of the world's population and includes eight routine vaccines with high coverage rates. This study aimed to estimate health and economic impacts of China's EPI over the past 50 years (1974-2024). METHODS: This study mathematically modelled the impact of all eight routine vaccines in China's EPI against eight pathogens (measles, pertussis, hepatitis B, tuberculosis, hepatitis A, Japanese encephalitis, meningitis A, and poliomyelitis) based on data availability and their substantial disease burden, particularly accounting for non-linearities in vaccine impact. Health and economic outcomes were determined using mathematical models between a counterfactual scenario without vaccination (vaccine coverage set to zero) and the current vaccination scenario (routine vaccination scheduled at age 0-6 years), based on calendar year and birth cohort approaches. The health impact of China's EPI from 1974 to 2024 was measured in the number of cases, deaths, and disability-adjusted life-years (DALYs) averted. FINDINGS: We estimated that China's EPI averted 703·02 million cases (95% credible interval 699·51-722·80) and 2·48 million deaths (2·14-2·97) in 1974-2024 based on the calendar year approach, equivalent to averting an estimated 160·22 million DALYs (145·05-196·99). Using the birth cohort approach, we predicted 707·41 million cases (703·93-727·03) and 7·01 million deaths (6·95-7·87) averted over the lifetime, corresponding to 279·02 million DALYs (265·78-316·12). From a societal perspective, the aggregated cost of vaccination was estimated to be US$124·06 billion (120·49-127·49), although the benefits amounted to $2417·85 billion (2359·38-2710·35). China's EPI yielded an aggregate benefit-cost ratio of 19·48 (18·82-22·08) from the societal perspective and 8·02 (7·64-8·80) from the provider's perspective. INTERPRETATION: China's EPI has shown remarkable health and economic achievements, contributing to worldwide EPI success in the past 50 years. Further investment in EPI is warranted to sustain coverage and expand vaccine inclusion in China and globally. FUNDING: Beijing Natural Science Foundation. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
General practice antibiotic prescriptions attributable to respiratory syncytial virus by age and antibiotic class: an ecological analysis of the English population.
BACKGROUND: Respiratory syncytial virus (RSV) may contribute to a substantial volume of antibiotic prescriptions in primary care. However, data on the type of antibiotics prescribed for such infections are only available for children <5 years in the UK. Understanding the contribution of RSV to antibiotic prescribing would facilitate predicting the impact of RSV preventative measures on antibiotic use and resistance. The objective of this study was to estimate the proportion of antibiotic prescriptions in English general practice attributable to RSV by age and antibiotic class. METHODS: Generalized additive models examined associations between weekly counts of general practice antibiotic prescriptions and laboratory-confirmed respiratory infections from 2015 to 2018, adjusting for temperature, practice holidays and remaining seasonal confounders. We used general practice records from the Clinical Practice Research Datalink and microbiology tests for RSV, influenza, rhinovirus, adenovirus, parainfluenza, human metapneumovirus, Mycoplasma pneumoniae and Streptococcus pneumoniae from England's Second Generation Surveillance System. RESULTS: An estimated 2.1% of antibiotics were attributable to RSV, equating to an average of 640 000 prescriptions annually. Of these, adults ≥75 years contributed to the greatest volume, with an annual average of 149 078 (95% credible interval: 93 733-206 045). Infants 6-23 months had the highest average annual rate at 6580 prescriptions per 100 000 individuals (95% credible interval: 4522-8651). Most RSV-attributable antibiotic prescriptions were penicillins, macrolides or tetracyclines. Adults ≥65 years had a wider range of antibiotic classes associated with RSV compared with younger age groups. CONCLUSIONS: Interventions to reduce the burden of RSV, particularly in older adults, could complement current strategies to reduce antibiotic use in England.
Global, regional, and national prevalence of adult overweight and obesity, 1990-2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021.
BACKGROUND: Overweight and obesity is a global epidemic. Forecasting future trajectories of the epidemic is crucial for providing an evidence base for policy change. In this study, we examine the historical trends of the global, regional, and national prevalence of adult overweight and obesity from 1990 to 2021 and forecast the future trajectories to 2050. METHODS: Leveraging established methodology from the Global Burden of Diseases, Injuries, and Risk Factors Study, we estimated the prevalence of overweight and obesity among individuals aged 25 years and older by age and sex for 204 countries and territories from 1990 to 2050. Retrospective and current prevalence trends were derived based on both self-reported and measured anthropometric data extracted from 1350 unique sources, which include survey microdata and reports, as well as published literature. Specific adjustment was applied to correct for self-report bias. Spatiotemporal Gaussian process regression models were used to synthesise data, leveraging both spatial and temporal correlation in epidemiological trends, to optimise the comparability of results across time and geographies. To generate forecast estimates, we used forecasts of the Socio-demographic Index and temporal correlation patterns presented as annualised rate of change to inform future trajectories. We considered a reference scenario assuming the continuation of historical trends. FINDINGS: Rates of overweight and obesity increased at the global and regional levels, and in all nations, between 1990 and 2021. In 2021, an estimated 1·00 billion (95% uncertainty interval [UI] 0·989-1·01) adult males and 1·11 billion (1·10-1·12) adult females had overweight and obesity. China had the largest population of adults with overweight and obesity (402 million [397-407] individuals), followed by India (180 million [167-194]) and the USA (172 million [169-174]). The highest age-standardised prevalence of overweight and obesity was observed in countries in Oceania and north Africa and the Middle East, with many of these countries reporting prevalence of more than 80% in adults. Compared with 1990, the global prevalence of obesity had increased by 155·1% (149·8-160·3) in males and 104·9% (95% UI 100·9-108·8) in females. The most rapid rise in obesity prevalence was observed in the north Africa and the Middle East super-region, where age-standardised prevalence rates in males more than tripled and in females more than doubled. Assuming the continuation of historical trends, by 2050, we forecast that the total number of adults living with overweight and obesity will reach 3·80 billion (95% UI 3·39-4·04), over half of the likely global adult population at that time. While China, India, and the USA will continue to constitute a large proportion of the global population with overweight and obesity, the number in the sub-Saharan Africa super-region is forecasted to increase by 254·8% (234·4-269·5). In Nigeria specifically, the number of adults with overweight and obesity is forecasted to rise to 141 million (121-162) by 2050, making it the country with the fourth-largest population with overweight and obesity. INTERPRETATION: No country to date has successfully curbed the rising rates of adult overweight and obesity. Without immediate and effective intervention, overweight and obesity will continue to increase globally. Particularly in Asia and Africa, driven by growing populations, the number of individuals with overweight and obesity is forecast to rise substantially. These regions will face a considerable increase in obesity-related disease burden. Merely acknowledging obesity as a global health issue would be negligent on the part of global health and public health practitioners; more aggressive and targeted measures are required to address this crisis, as obesity is one of the foremost avertible risks to health now and in the future and poses an unparalleled threat of premature disease and death at local, national, and global levels. FUNDING: Bill & Melinda Gates Foundation.
Global, regional, and national prevalence of child and adolescent overweight and obesity, 1990-2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021.
BACKGROUND: Despite the well documented consequences of obesity during childhood and adolescence and future risks of excess body mass on non-communicable diseases in adulthood, coordinated global action on excess body mass in early life is still insufficient. Inconsistent measurement and reporting are a barrier to specific targets, resource allocation, and interventions. In this Article we report current estimates of overweight and obesity across childhood and adolescence, progress over time, and forecasts to inform specific actions. METHODS: Using established methodology from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021, we modelled overweight and obesity across childhood and adolescence from 1990 to 2021, and then forecasted to 2050. Primary data for our models included 1321 unique measured and self-reported anthropometric data sources from 180 countries and territories from survey microdata, reports, and published literature. These data were used to estimate age-standardised global, regional, and national overweight prevalence and obesity prevalence (separately) for children and young adolescents (aged 5-14 years, typically in school and cared for by child health services) and older adolescents (aged 15-24 years, increasingly out of school and cared for by adult services) by sex for 204 countries and territories from 1990 to 2021. Prevalence estimates from 1990 to 2021 were generated using spatiotemporal Gaussian process regression models, which leveraged temporal and spatial correlation in epidemiological trends to ensure comparability of results across time and geography. Prevalence forecasts from 2022 to 2050 were generated using a generalised ensemble modelling approach assuming continuation of current trends. For every age-sex-location population across time (1990-2050), we estimated obesity (vs overweight) predominance using the log ratio of obesity percentage to overweight percentage. FINDINGS: Between 1990 and 2021, the combined prevalence of overweight and obesity in children and adolescents doubled, and that of obesity alone tripled. By 2021, 93·1 million (95% uncertainty interval 89·6-96·6) individuals aged 5-14 years and 80·6 million (78·2-83·3) aged 15-24 years had obesity. At the super-region level in 2021, the prevalence of overweight and of obesity was highest in north Africa and the Middle East (eg, United Arab Emirates and Kuwait), and the greatest increase from 1990 to 2021 was seen in southeast Asia, east Asia, and Oceania (eg, Taiwan [province of China], Maldives, and China). By 2021, for females in both age groups, many countries in Australasia (eg, Australia) and in high-income North America (eg, Canada) had already transitioned to obesity predominance, as had males and females in a number of countries in north Africa and the Middle East (eg, United Arab Emirates and Qatar) and Oceania (eg, Cook Islands and American Samoa). From 2022 to 2050, global increases in overweight (not obesity) prevalence are forecasted to stabilise, yet the increase in the absolute proportion of the global population with obesity is forecasted to be greater than between 1990 and 2021, with substantial increases forecast between 2022 and 2030, which continue between 2031 and 2050. By 2050, super-region obesity prevalence is forecasted to remain highest in north Africa and the Middle East (eg, United Arab Emirates and Kuwait), and forecasted increases in obesity are still expected to be largest across southeast Asia, east Asia, and Oceania (eg, Timor-Leste and North Korea), but also in south Asia (eg, Nepal and Bangladesh). Compared with those aged 15-24 years, in most super-regions (except Latin America and the Caribbean and the high-income super-region) a greater proportion of those aged 5-14 years are forecasted to have obesity than overweight by 2050. Globally, 15·6% (12·7-17·2) of those aged 5-14 years are forecasted to have obesity by 2050 (186 million [141-221]), compared with 14·2% (11·4-15·7) of those aged 15-24 years (175 million [136-203]). We forecasted that by 2050, there will be more young males (aged 5-14 years) living with obesity (16·5% [13·3-18·3]) than overweight (12·9% [12·2-13·6]); while for females (aged 5-24 years) and older males (aged 15-24 years), overweight will remain more prevalent than obesity. At a regional level, the following populations are forecast to have transitioned to obesity (vs overweight) predominance before 2041-50: children and adolescents (males and females aged 5-24 years) in north Africa and the Middle East and Tropical Latin America; males aged 5-14 years in east Asia, central and southern sub-Saharan Africa, and central Latin America; females aged 5-14 years in Australasia; females aged 15-24 years in Australasia, high-income North America, and southern sub-Saharan Africa; and males aged 15-24 years in high-income North America. INTERPRETATION: Both overweight and obesity increased substantially in every world region between 1990 and 2021, suggesting that current approaches to curbing increases in overweight and obesity have failed a generation of children and adolescents. Beyond 2021, overweight during childhood and adolescence is forecast to stabilise due to further increases in the population who have obesity. Increases in obesity are expected to continue for all populations in all world regions. Because substantial change is forecasted to occur between 2022 and 2030, immediate actions are needed to address this public health crisis. FUNDING: Bill & Melinda Gates Foundation and Australian National Health and Medical Research Council.
Development of an oral regimen of unithiol for the treatment of snakebite envenoming: a phase 1 open-label dose-escalation safety trial and pharmacokinetic analysis in healthy Kenyan adults.
BACKGROUND: Viperidae snakes are responsible for many of the 94,000 deaths caused by snakebite envenoming each year. The most pathological venom component of this globally diverse family of snakes are the zinc-dependent snake venom metalloproteinase (SVMP) enzymes, which can be inhibited by the metal chelator, unithiol. A short-course oral regimen, readily available and rapidly deployed ahead of hospital admission is needed. METHODS: This open-label, phase 1 clinical trial assessed the safety of single ascending oral, multiple ascending oral, and single ascending intravenous doses of unithiol in 64 healthy adult volunteers from Kilifi County, Kenya. The multiple dose stage was informed by an interim safety and pharmacokinetic analysis, and predefined target plasma concentrations. Plasma concentrations of unithiol were measured using high-performance liquid chromatography-mass spectrometry, and safety was described by full adverse event reporting. FINDINGS: 175 individuals were screened, and 64 (median age 30 years, IQR 25-38 years) received the study drug. There were no dose limiting toxicities or serious adverse events. There were 61 solicited adverse events, 17 related unsolicited adverse events, and 53 laboratory adverse events, all of mild or moderate severity. The maximum oral dose of 1500 mg was well tolerated and associated with the following pharmacokinetic parameters: Cmax 14.7 μg/mL, Tmax 2.9 h, T1/2 18.4 h, and AUC0-∞ 204.5 μg.h/mL. INTERPRETATION: The phase 2 recommended dose (1500 mg loading dose, followed by 900 mg doses at 6-h and 24-h) has no safety concerns, and has promising pharmacokinetic properties for clinical use. Unithiol is affordable, stable at room temperature, and has the potential to be given orally in remote rural clinics. Its further development for snakebite indication is warranted. FUNDING: Wellcome Trust, Bloomsbury Set, and Cures Within Reach.
Uncertainty-Inspired Multi-Task Learning in Arbitrary Scenarios of ECG Monitoring.
As the scenarios for electrocardiogram (ECG) monitoring become increasingly diverse, particularly with the development of wearable ECG, the influence of ambiguous factors in diagnosis has been amplified. Reliable ECG information must be extracted from abundant noises and confusing artifacts. To address this issue, we suggest an uncertainty-inspired model for beat-level diagnosis (UI-Beat). The base architecture of UI-Beat separates heartbeat localization and event diagnosis in two branches to address the problem of heterogeneous data sources. To disentangle the epistemic and aleatoric uncertainty within one stage in a deterministic neural network, we propose a new method derived from uncertainty formulation and realize it by introducing the class-biased transformation. Then the disentangled uncertainty can be utilized to screen out noise and identify ambiguous heartbeat synchronously. The results indicate that UI-Beat can significantly improve the performance of noise detection (from 91.60% to 97.50% for real-world noise detection and from 61.40% to 82.41% for real-world artifact detection). For multi-lead ECG analysis, UI-Beat is approaching the performance upper bound in heartbeat localization (only 15 false positives and 9 false negatives out of the 175,907 heartbeats in the INCART database) and achieving a significant performance improvement in heartbeat classification through uncertainty-based cross-lead fusion compared to single-lead prediction and other state-of-the-art methods (an average improvement of 14.28% for detecting heartbeats of S and 3.37% for detecting heartbeats of V). Considering the characteristic of one-stage ECG analysis within one model, it is suggested that the proposed UI-Beat has the potential to be employed as a general model for arbitrary scenarios of ECG monitoring, with the capacity to remove invalid episodes, and realize heartbeat-level diagnosis with confidence provided.
Refined matrix completion for spectrum estimation of heart rate variability.
Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.