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Intersections of menstruation, gender-based violence and contraceptive use: qualitative insights from girls and young women’s experiences in western Kenyan family planning clinics
Get PDFObjective: To examine how menstruation, contraceptive use and gender-based violence intersect to shape the sexual and reproductive health and autonomy of girls and young women in Kenya. Design: Qualitative study exploring girls and young women’s experiences with contraceptive use and menstrual management, using in-depth interviews and focus group discussions analysed through a reflexive thematic approach. Setting: Four county-run family planning clinics in Uasin Gishu County, Kenya. Participants: 77 girls and young women aged 15–19 years (via 35 in-depth interviews and 7 focus group discussions) and 27 family planning clinic providers (via 5 focus group discussions). Results: Interviewees’ contributions suggest that covert contraceptive use, when discovered through menstrual monitoring, provoked intimate partner violence. Heavy menstrual bleeding, whether related to contraceptive use or not, was viewed as a sexual restriction and also fuelled intimate partner violence. Furthermore, the inability to afford sanitary pads, combined with the stigma surrounding menstruation, drove some girls and young women into exploitative sexual relationships, often resulting in unwanted or unintended pregnancies. Conclusions: Menstrual bleeding and contraceptive use, both independently and in combination, affect girls and young women’s reproductive autonomy and overall health and well-being, particularly in relation to gender-based violence. Improving menstrual hygiene management, challenging the stigma and harmful norms tied to menstruation and contraception and ensuring safe contraceptive use are integral to improving sexual and reproductive health and autonomy and are requisite for preventing and eradicating gender-based violence
Species functional traits affect regional and local dominance across western Amazonian forests
Get PDFSeveral studies have documented dominance by few species in Amazonian forests. Dominant species tend to be either locally abundant (local dominants) or regionally frequent (widespread dominants) but rarely both (oligarchs). Here, we explore relationships between dominance and functional traits. We ask whether: (i) dominance is associated with specific functional profiles and (ii) dominance patterns (local vs. widespread dominants) are associated with different functional traits. We combined census data from 503 forest inventory plots across four lowland forest habitats in western Amazonia with trait information for ~2600 tree species, encompassing data collected in the focal plots and data from published sources. We considered traits that relate to leaf, wood, seed and whole‐plant strategies: specific leaf area (SLA), leaf area (LA), N content per unit leaf mass (LN), wood density (WD), seed mass (SM) and maximum diameter at breast height (DBHmax). Our results reveal that dominant species display different trait combinations depending on the habitat type. Taller dominant species exhibit higher regional frequency, associated with higher dispersal ability and lower local abundance, likely due to negative density dependence. Greater SM contributes to higher regional frequency of dominant species via greater dispersal by birds and mammals and seedling survival. Finally, traits related to resource conservation strategies, such as lower SLA, LA, LN and greater WD, favour higher local densities across most habitats, while the opposite pattern was linked to higher regional frequency. Synthesis. Our findings reveal that (i) dominance is associated with different functional traits depending on the habitat type, and (ii) different functional trait values define distinct dominance patterns. Our study exemplifies the potential of trait‐based approaches to illuminate the ecological mechanisms that may underlie dominance in tropical forests. Finally, accounting for both local abundance and regional frequency when studying dominance is likely to improve our understanding and forecasting of how different species will respond to global change drivers in western Amazonia
The impact of different antimicrobial exposures on the gut microbiome in the ARMORD observational study
Get PDFBetter metrics to compare the impact of different antimicrobials on the gut microbiome would aid efforts to control antimicrobial resistance (AMR). The Antibiotic Resistance in the Microbiome – Oxford (ARMORD) study recruited inpatients, outpatients, and healthy volunteers in Oxfordshire, UK, who provided stool samples for metagenomic sequencing. Data on previous antimicrobial use and potential confounders were recorded. Exposures to each antimicrobial were considered as factors in a multivariable linear regression, also adjusted for demographics, with separate analyses for those contributing samples cross-sectionally or longitudinally. Outcomes were Shannon diversity and relative abundance of specific bacterial taxa (Enterobacteriaceae, Enterococcus, and major anaerobic groups) and antimicrobial resistance genes (targeting beta-lactams, tetracyclines, aminoglycosides, macrolides, and glycopeptides). 225 adults were included in the cross-sectional analysis, and a subset of 79 patients undergoing haematopoietic cell transplant provided serial samples for longitudinal analysis. Results were largely consistent between the two sampling frames. Recent use of piperacillin-tazobactam, meropenem, intravenous co-amoxiclav, and clindamycin was associated with large reductions in microbiome diversity and reduced abundance of anaerobes. Exposure to piperacillin-tazobactam and meropenem was associated with a decreased abundance of Enterobacteriaceae and an increased abundance of Enterococcus and major AMR genes, but there was no evidence that these antibiotics had a greater impact on microbiome diversity than iv co-amoxiclav or oral clindamycin. In contrast, co-trimoxazole, doxycycline, antifungals, and antivirals had less impact on microbiome diversity and selection of AMR genes. Simultaneous estimation of the impact of over 20 antimicrobials on the gut microbiome and AMR gene abundance highlighted important differences between individual drugs. Some drugs in the WHO Access group (co-amoxiclav, clindamycin) had similar magnitude impact on microbiome diversity to those in the Watch group (meropenem, piperacillin-tazobactam) with potential implications for acquisition of resistant organisms. Metagenomic sequencing can be used to compare the impact of different antimicrobial agents and treatment strategies on the commensal flora
Confirmatory factor analysis of competing PANSS negative symptom models: data from OPTiMiSE first-episode schizophrenia study
Get PDFBackground: The negative symptoms of psychosis are heterogeneous, which complicates efforts to understand their pathophysiology and develop effective treatments. Factor analytic studies of the Positive and Negative Syndrome Scale (PANSS) have reported two factorial negative symptom models, expressive deficit and social amotivation, albeit with different compositions. Although models derived from other assessment scales have been directly compared, no study has previously applied this approach to PANSS. Aims: Our objectives were to (a) to establish which negative PANSS-derived factorial model provided the best fit to our data, (b) test its stability and (c) determine its clinical and demographic correlates. Method: A cohort of medication naive or minimally treated patients with first-episode schizophrenia (n = 446) were assessed using the PANSS scale before and 4 weeks after amisulpride treatment. Confirmatory factor analysis was performed to test five PANSS models. Hierarchical multiple regression was conducted to examine the associations between identified dimensions and clinical and demographic variables. Results: A nine-item PANSS model comprising social amotivation and expressive deficit dimensions outperformed the other models: comparative fit index = 0.98, goodness of fit index = 0.97, Tucker–Lewis index = 0.97, root mean square error of approximation = 0.06 (CI 90%: 0.04–0.08), Bayesian information criterion = 191.9, Akaike information criterion = 101.7. At baseline, the social amotivation dimension was associated with more severe depression whereas the expressive deficit dimension was associated with younger age. Both dimensions at baseline were associated with poor functioning, but expressive deficit to a lesser extent. Conclusions: A nine-item PANSS model incorporating social amotivation and expressive deficit dimensions appeared to best reflect the underlying structure of negative symptoms in our sample
Understanding the mental health of adolescents and young adults in rural South Africa through participatory research
Get PDFMost mental health problems in adulthood begin during adolescence. Factors such as poverty, violence, and unemployment contribute to the high burden of mental health problems. In Africa, the lack of resources for diagnosis, treatment, and management exacerbates the situation. Research on adolescent mental health has mostly focused on negative psychological effects, while positive attributes like coping, resilience, and emotional growth have been overlooked. This study explored factors affecting the mental health of adolescents and young adults and to collaboratively identify potential interventions using a participatory research approach. After reviewing the literature, we developed scenarios on issues such as unemployment, violence, poverty and HIV, which were adapted by peer supervisors into role-plays that reflect their local realities. We conducted workshops with ten peer navigators and eight young people, who listed problems affecting their mental health and identified possible interventions. Data from the workshops were synthesized to create a preliminary conceptual framework, which was then refined with input from three professional nurses who work in adolescent-friendly clinics. Young people and peer navigators identified romantic relationships and alcohol use as both contributors to mental health problems and coping strategies for stressors like poverty and violence. These behaviours were also linked to sexual and reproductive health (SRH) risks including unprotected sex. Cultural and spiritual experiences, such as the ancestral calling to become a traditional healer were also described as common in the community and were linked to emotional distress. Participants recommended family-strengthening and community-based interventions to build resilience and promote positive parenting, while respecting existing traditional practices. This study enhances our understanding of mental health challenges among young people and emphasizes the importance of strengthening protective factors such as resilience. It offers guidance for developing culturally appropriate mental health interventions. Further research is needed to examine causal pathways and explore how SRH services and traditional healing practices can be integrated into mental health strategies
Clinical Criteria to Guide Antineuronal Antibody Testing for People With Early and Persistent Psychosis Attending Mental Health Services
Get PDFBackgroundEarly detection of autoimmune psychosis (AP) mediated by antineuronal antibodies (Abs) is critical for achieving optimal clinical outcomes. However, evidence remains limited regarding who should be tested and how Ab-positive cases should be managed. In this large-scale study, we evaluated proposed clinical criteria for targeted Ab testing in psychiatric services and described the clinical course of seropositive patients.MethodsIndividuals with early psychosis (EP) or persistent psychosis (PP) were prospectively assessed with clinical criteria to determine high- or low-risk status for AP. Blood samples were collected for Ab testing using a fixed cell-based assay. Seropositive individuals were invited for detailed review, including clinical, functional, and cognitive assessments at baseline and a 12-month follow-up. Blood samples were collected from 754 individuals (EP: n = 352, PP: n = 402).ResultsAbs were present in 2.3% (17/754), including 3.4% (12/352) of patients with EP and 1.2% (5/402) of patients with PP. AP was confirmed in 2 cerebrospinal fluid (CSF)-positive high-risk individuals (total: 2/754, 0.3%; EP: 1/352, 0.3%; PP: 1/402, 0.2%). Both improved with immunotherapy. Although some low-risk patients were seropositive, none were diagnosed clinically with AP.ConclusionsAP prevalence was low in this cohort. Targeted testing informed by clinical high-risk criteria successfully identified 2 immunotherapy-responsive AP cases. This approach appears feasible but requires further validation. People with psychosis and high-risk AP features should be considered for Ab testing in sera and CSF where indicated. Further research is required to embed targeted Ab testing into mental health services
Early analysis of data from the British & Irish Brain Arteriovenous Malformations Registry (BIBAR)
Get PDFBackground and objectivesIt is important to establish a platform that allows methodical recording of treatments provided for brain arteriovenous malformations (bAVMs) which is a complex and heterogenous disease. In this preliminary report, the authors present the early analysis of the treatment of bAVMs from the British & Irish Brain AVM Registry (BIBAR).Research questionCan a multicenter registry effectively capture bAVMs presentation and treatment data?Materials & methodsThe British Neurovascular Group (BNVG) set up a bAVMs registry working group in November 2018, with the primary aim of trying to ascertain the number and types of treatments provided for bAVMs across the United Kingdom.ResultsBetween January 1, 2019 to December 31, 2023, treatment decisions were recorded for 1969 registered patients with bAVMs, of which 1713 patients received treatment at the time of the analysis. 56.28 % (964) patients had no evidence of rupture at the time of the initial treatment decision, whilst 43.72 % (749) presented with evidence of rupture at initial presentation. Of these, 83.31 % (624) were treated with radiosurgery, 13.62 % (102) with surgery and 0.93 % (7) underwent embolization. Age was negatively correlated with likelihood of surgical treatment. Patients who did not receive any treatment at the time of this analysis were not included.Discussion and conclusionWe have shown that with a collective, collaborative effort, a national bAVM registry is feasible and as data capture becomes more complete, can provide valuable data on treatment types and volunes and provide an insight into the decision making underlying those treatments
Interdisciplinary modelling and analysis of infectious diseases
Get PDFInfectious diseases pose substantial and continuous threats to global health. From the Black Death to the influenza pandemics, and more recently COVID-19, the impacts of infectious disease outbreaks have long highlighted the need for a quantitative understanding of epidemic dynamics. Epidemiological modelling is about providing such an understanding. As data are always imperfect, models are also imperfect, yet models remain useful tools that create manageable abstractions of reality to quantitatively describe why, when, and where infectious diseases spread. These modelling objectives are grounded in practical aims -- to inform policy, advance scientific research, and benefit public health. Public health authorities face considerable uncertainty and time pressures when making difficult and consequential decisions about epidemic control. This necessitates timely and reliable information about the past, current, and likely future epidemic dynamics in order to respond appropriately with targeted strategies for short-term outbreak responses and longer-term planning. As argued in the current thesis, the power of our modelling lies in the ability to provide such information, developing methods that can i) learn from the past, ii) monitor the present, and iii) plan for the future. Despite an increasing adoption of multi-model and interdisciplinary approaches, epidemiological modelling can sometimes be hampered by narrow perspectives that isolate models, data, or branches of science. Here, we will describe how we can often improve upon restrictive modelling approaches, even in resource-limited settings. We will i) allow the research questions to determine the methodology and ii) reach beyond single modelling disciplines, data streams, and branches of science to answer the questions about epidemic dynamics and drivers. While still dependent on data collection efforts and well-developed, public-health-relevant models, we aim to show that this multi-model, interdisciplinary perspective can often provide a greater volume and variety of insights to reliably inform public health policy and hopefully benefit society. The COVID-19 pandemic, caused by the spread of SARS-CoV-2, had devastating consequences in societies globally. The urgent need to understand and control the spread led to a large volume and variety of data streams being available for monitoring epidemic dynamics and drivers. Two such data streams came from wastewater-based epidemiology (WBE) and community infection prevalence surveys, which offered potential avenues to complement costly and typically incomplete disease case surveillance. In Chapter 3, we performed the first nationwide analysis of the utility of WBE for monitoring community prevalence of SARS-CoV-2 at high spatial resolution throughout a nation across the COVID-19 pandemic. Introducing a new geospatial mapping approach and shedding metric, we found that i) higher vaccination coverage, rising immunity levels, and Omicron SARS-CoV-2 variants all induced lower faecal shedding over time and ii) WBE could complement, but not replace, prevalence surveys. The main focus of the thesis is dengue, the world’s most widespread and fastest expanding mosquito-borne disease. Dengue virus transmission is driven by the coalescence of environmental, human, and biological influences. In Chapter 4, we introduced a multi-model workflow that integrates heterogeneous data streams in an approach that unifies and extends techniques for climate-informed modelling and forecasting of dengue epidemics. Applying our workflow to dengue epidemics in northern Peru, we used wavelets and Bayesian hierarchical models to quantify both spatiotemporal dynamics and climatic drivers of epidemics at varying temporal and spatial resolutions. Our ensemble models for forecasting also blended the strengths of different modelling disciplines to produce statistically rigorous and public-health-focused outputs. Continuing this theme of multi-model approaches, in Chapter 5, we investigated the relative importance of human movement in shaping urban dengue outbreaks in the neighbourhoods of Fortaleza, Brazil. Here, our new evidence synthesis approach melded Bayesian hierarchical and deep learning models with new theory, which allowed us to unveil a consistently high importance of human movement within and across dengue seasons and a relatively constant spatial profile for source-sink dynamics of dengue epidemics in neighbourhoods. Adopting a mechanistic modelling perspective, in Chapter 6, we worked to address the lack of methods for monitoring how fast vector-borne pathogens are spreading over time. To this end, we introduced new theory to extend the popular renewal equations that form the basis of most methods for estimating time-varying reproduction numbers, R(t), for directly transmitted diseases. Starting from age-structured models of dengue transmission across humans and mosquitoes, we derived renewal equations that control how past human infections generate new human infections. This work provides theoretical foundations for inference frameworks that estimate R(t) for vector-borne pathogens and, ultimately, inform epidemic control strategies. To better understand how to simulate and parameterise these models, in Chapter 7, we developed new theory that extends the seminal Gillespie algorithms to exactly simulate stochastic systems which experience external variability and time-varying propensities between events. These algorithms, illustrated for a model of dengue transmission, are generalisable to simulating stochastic reaction processes that are used across many branches of science, from chemistry to biology. Chapter 8 captures the simultaneous theoretical and public health focus of this thesis, as we provide a new evaluation framework for forecasters and forecast evaluators. This framework shifts the perspective of forecast evaluation to the decision-maker. Translating metrics and ideas from many branches of science to the context of epidemics, we provide methods to measure the value of forecasts by their ability to inform the difficult and uncertain epidemic-control-related decisions faced by public health authorities. This evaluation framework connects statistical concepts with principles from decision theory and information theory to promote more useful and reliable infectious disease forecasts for improved public health responses. In Chapter 9, we briefly summarise two published manuscripts: i) our research on the future of artificial intelligence for modelling infectious diseases, and ii) our systematic review of literature from the COVID-19 pandemic on the effectiveness of social distancing measures for epidemic control. Finally, in Chapter 10, we summarise our methods and analyses and discuss how these address our stated objectives, how they fit in the wider epidemiological context, and how they could be extended to address limitations and explore new policy and research questions in resource-limited settings. As public health authorities combat persistent infectious disease threats, tackle the related challenges of climate change and conspiracy theories, and prepare for future epidemics and pandemics, our interdisciplinary methods will support authorities by providing robust, timely, and actionable information to improve global public health
Emerging semantic segmentation from positive and negative coarse label learning
Get PDFLarge annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especially in medical imaging. In contrast, coarse annotations are quicker, cheaper, and easier to produce, even by non-experts. In this paper, we propose to use coarse drawings from both positive (target) and negative (background) classes in the image, even with noisy pixels, to train a convolutional neural network (CNN) for semantic segmentation. We present a method for learning the true segmentation label distributions from purely noisy coarse annotations using two coupled CNNs. The separation of the two CNNs is achieved by high fidelity with the characters of the noisy training annotations. We propose to add a complementary label learning that encourages estimating negative label distribution. To illustrate the properties of our method, we first use a toy segmentation dataset based on MNIST. We then present the quantitative results of experiments using publicly available datasets: Cityscapes dataset for multi-class segmentation, and retinal images for medical applications. In all experiments, our method outperforms state-of-the-art methods, particularly in the cases where the ratio of coarse annotations is small compared to the given dense annotations