Syndemics of stigma, minority-stress, maladaptive coping, risk environments and littoral spaces among men who have sex with men using chemsex

There has been a steep rise in the use of drugs during sex by some men who have sex with men in economically developed countries, with associated increases in sexual risk for HIV and other STIs. This paper presents data from telephone interviews with 15 men attending sexual health clinics for post-exposure prophylaxis (PEP) following a chemsex-related risk for HIV, and discusses some of the theoretical approaches that have been employed to understand chemsex and inform interventions. Interviews were conducted as part of a larger intervention study, which used an adapted version of motivational Interviewing to explore risk behaviour and support change. Participants conceptualised their chemsex and HIV-related risks in a psycho-social context, highlighting the influences of psycho-socio-cultural challenges of homophobic marginalisation and the ‘gay scene’ on behaviour. Multiple influences of stigma, marginalisation, minority stress and maladaptive coping (including drug-use) contribute to syndemic ‘risk-environments’ and ‘littoral spaces’ in which chemsex and risk behaviours are played out

Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis.

This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographic and remotely sensed coastal datasets, as demonstrated here using open-access UK Environment Agency datasets. Two core methodologies, used to generate the novel reference dataset, are presented. Firstly, we establish a robust approach to extracting shorelines from vertical aerial photography, validated against LiDAR (Light Detection and Ranging) and coastal topography surveys. Secondly, we present a standard methodology for quantifying sediment volume change from spatially continuous LiDAR elevation datasets. As coastal systems are monitored at greater spatial resolution and temporal frequency there is an unprecedented opportunity to determine how and why coastal systems have changed in the past with a view to informing future forecasting. With revelation of trends that suggest increasing coastal risk, coastal change research is needed to inform the management and protection of coasts

In the Name of Fairness: Assessing the Bias in Clinical Record De-identification

Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic health records. This process, known as de-identification, is often achieved through the use of machine learning algorithms by many commercial and open-source systems. While these systems have shown compelling results on average, the variation in their performance across different demographic groups has not been thoroughly examined. In this work, we investigate the bias of de-identification systems on names in clinical notes via a large-scale empirical analysis. To achieve this, we create 16 name sets that vary along four demographic dimensions: gender, race, name popularity, and the decade of popularity. We insert these names into 100 manually curated clinical templates and evaluate the performance of nine public and private de-identification methods. Our findings reveal that there are statistically significant performance gaps along a majority of the demographic dimensions in most methods. We further illustrate that de-identification quality is affected by polysemy in names, gender context, and clinical note characteristics. To mitigate the identified gaps, we propose a simple and method-agnostic solution by fine-tuning de-identification methods with clinical context and diverse names. Overall, it is imperative to address the bias in existing methods immediately so that downstream stakeholders can build high-quality systems to serve all demographic parties fairly.Comment: Accepted by FAccT 2023; updated appendix with the de-identification performance of GPT-

Adventures in data citation: sorghum genome data exemplifies the new gold standard

Scientific progress is driven by the availability of information, which makes it essential that data be broadly, easily and rapidly accessible to researchers in every field. In addition to being good scientific practice, provision of supporting data in a convenient way increases experimental transparency and improves research efficiency by reducing unnecessary duplication of experiments. There are, however, serious constraints that limit extensive data dissemination. One such constraint is that, despite providing a major foundation of data to the advantage of entire community, data producers rarely receive the credit they deserve for the substantial amount of time and effort they spend creating these resources. In this regard, a formal system that provides recognition for data producers would serve to incentivize them to share more of their data. The process of data citation, in which the data themselves are cited and referenced in journal articles as persistently identifiable bibliographic entities, is a potential way to properly acknowledge data output. The recent publication of several sorghum genomes in Genome Biology is a notable first example of good data citation practice in the field of genomics and demonstrates the practicalities and formatting required for doing so. It also illustrates how effective use of persistent identifiers can augment the submission of data to the current standard scientific repositories

Will nature work with us? Erosion and flooding impacts on a UK barrier

‘Barrier island’ refers to a diverse collection of coastal landforms that often support substantial human populations, critical infrastructures, and ecosystems. Globally, many coastal barriers are experiencing climatically altered environmental forcing coupled with increasing anthropogenic pressures. This paper undertakes high resolution shoreline change analysis to reveal how Blakeney Point, a mixed sandy-gravel barrier located on the UK’s East Coast, has evolved over centennial, decadal and event timescales. We seek to establish the implications of barrier evolution, under contrasting management regimes, for present erosion and flooding hazards. Interrogating a series of alternative shoreline proxies reveals a series of interdependent behaviors. Over the 130-year period of study, Blakeney Point is shown to be rolling landward at a mean rate of 0.60 m a-1. Assuming continued landward retreat over the coming decades, future flood-generating storm events will encounter more landward shoreline positions than today. Superimposed on this trend, we observe the presence of alongshore migrating erosional hotspots which give rise to unpredictable morphologies at any given location on the spit. Finally, we find that instances of barrier setback are driven by individual storm events, which makes barrier retreat both highly variable and discontinuous in time and space. This is illustrated by the presence of overwash, particularly along stretches of the barrier that have experienced a recent shift in management regime towards a non-interventionist approach.This work was funded by the NERC/ESRC Data, Risk and Environmental Analytical Methods (DREAM) Centre, Grant/Award Number: NE/M009009/1. It is also a contribution to the NERC-funded project “Physical and Biological dynamic coastal processes and their role in coastal recovery” (BLUEcoast), Grant Award Number: NE/N015924/1

Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit

Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling body of evidence in population health studies showing that a wide range of SDOH is strongly correlated with health outcomes. Yet, a majority of the risk prediction models based on electronic health records (EHR) do not incorporate a comprehensive set of SDOH features as they are often noisy or simply unavailable. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features. We investigate the impact of such features on common EHR prediction tasks across different patient populations. We find that community-level SDOH features do not improve model performance for a general patient population, but can improve data-limited model fairness for specific subpopulations. We also demonstrate that SDOH features are vital for conducting thorough audits of algorithmic biases beyond protective attributes. We hope the new integrated EHR-SDOH database will enable studies on the relationship between community health and individual outcomes and provide new benchmarks to study algorithmic biases beyond race, gender, and age