Context

In this project, you will learn how to fit a model for predicting resistance in bacteria, and see how different forms of cross-validation impact the interpretation of your performance results.

We will be focussing on a species called Neisseria gonorrhoeae, bacteria which cause gonorrhoea. Gonorrhoea is the second most common sexually transmitted infection (STI) in Europe, after chlamydia. Rates of gonorrhoea infection are on the rise, with a 26% increase reported from 2017-2018 in the UK.

Many people who are infected (especially women) experience no symptoms, helping its spread. However if the infection is left untreated, it can lead to infertility in women, and can occasionally spread to other parts of the body such as your joints, heart valves, brain or spinal cord.

Resistance of these bacteria to antibiotics is rising over time, making infections hard to treat. Below, you can see rates of resistance to different antibiotics. Image is from this paper: https://www.mdpi.com/2079-6382/7/3/60.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F359577%2F15abf2baad53ec5d984d43e5fd48000a%2FResistance.png?generation=1568108650228712&alt=media" alt="">

In the past, patients were treated with an antibiotic called ciprofloxaxcin. Doctors had to stop using this antibiotic because resistance to the drug became too common, causing treatments if infections to fail. Until very recently, the recommended treatment was two drugs - ceftriaxone and azithromycin. Azithromycin was removed from recommendations because of concern over rising resistance to the antibiotic. Currently in the UK, patients are only treated with ceftriaxone. In February 2018, the first ever reported case of resistance to treatment with ceftriaxone and azithromycin, as well as resistance to the last-resort treatment spectinomycin, was reported.

We will look at machine learning algorithms for predicting resistance to both ciprofloxacin and azithromycin.

Content

For this project, we will be working with "unitigs", which are segments of DNA shared by a subset of the strains in our collection. This dataset contains unitigs that are statistically associated with ciprofloxacin resistance.

Acknowledgements

The strains for this project have been gathered from the following sources:

Chisholm et al. (2016). An outbreak of high-level azithromycin resistant Neisseria gonorrhoeae in England. Sexually Transmitted Infections. Demczuk et al. (2015). Whole-Genome Phylogenomic Heterogeneity of Neisseria gonorrhoeae Isolates with Decreased Cephalosporin Susceptibility Collected in Canada between 1989 and 2013. Journal of Clinical Microbiology. Demczuk et al. (2016). Genomic Epidemiology and Molecular Resistance Mechanisms of Azithromycin-Resistant Neisseria gonorrhoeae in Canada from 1997 to 2014. Journal of Clinical Microbiology. Eyre et al. (2017). WGS to predict antibiotic MICs for Neisseria gonorrhoeae. The Journal of Antimicrobial Chemotherapy. Fifer et al. (2018). Sustained transmission of high-level azithromycin-resistant Neisseria gonorrhoeae in England: an observational study. The Lancet Infectious Diseases. Grad et al. (2014). Genomic epidemiology of Neisseria gonorrhoeae with reduced susceptibility to cefixime in the USA: a retrospective observational study. The Lancet Infectious Diseases. Grad et al. (2016). Genomic Epidemiology of Gonococcal Resistance to Extended-Spectrum Cephalosporins, Macrolides, and Fluoroquinolones in the United States, 2000-2013. The Journal of Infectious Diseases. Harris et al. (2018). Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey. The Lancet Infectious Diseases. Jacobsson et al. (2016). WGS analysis and molecular resistance mechanisms of azithromycin-resistant (MIC >2 mg/L) Neisseria gonorrhoeae isolates in Europe from 2009 to 2014. The Journal of Antimicrobial Chemotherapy. Lee et al. (2018). Genomic epidemiology and antimicrobial resistance of Neisseria gonorrhoeae in New Zealand. The Journal of Antimicrobial Chemotherapy. Sánchez-Busó et al. (2018). Antimicrobial exposure in sexual networks drives divergent evolution in modern gonococci. bioRxiv. Unemo et al. (2016). The novel 2016 WHO Neisseria gonorrhoeae reference strains for global quality assurance of laboratory investigations: phenotypic, genetic and reference genome characterization. The Journal of Antimicrobial Chemotherapy.

Inspiration

Antimicrobial resistance is becoming a serious concern— currently antimicrobial resistant infections kill over 700,000 people per year. By 2050 its estimated 10 million people will die from of antimicrobial resistant infections.

A lot of infections are treated “empirically”, meaning that if the doctor has an idea of what bacteria you’re infected with, they will prescribe a standard antibiotic to treat it. This means that if you have an infection that’s resistant to the standard antibiotic, while you’re taking your course of antibiotics, the bacteria are still living and replicating inside you. This can lead to a rise in the prevalence of antibiotic resistant bacteria over time.

There’s growing interest in testing for antibiotic resistance before a patient begins treatment. But, there are some practical limitations that get in the way of doing this routinely. A major problem is that when people go to their doctor or to the hospital for treatment, they expect to be given antibiotics to treat their infection straight away. Laboratory testing for resistance can take between 24 hours for infections like MRSA, to months for tuberculosis.

Whole genome sequencing is becoming cheaper over time, making it a more practical approach for detecting antibiotic resistance. Sequencing can, in theory, give you results in a matter of hours, rather than days. The mechanisms that drive resistance in these bacteria are coded in their DNA, meaning that a single test could tell us about resistance to a panel of antibiotics, and also give us other useful information, like whether the strain you’re infected with is on the rise, or is related to one that’s circulating in the hospital or community at the time. We know that the information we need to find is in the DNA of these bacteria, but we don’t always know how to find it. That’s where machine learning could come in.

This is the dataset for the study of "Social dilemma in the excess use of antimicrobials incurring antimicrobial resistance". The emergence of antimicrobial resistance (AMR) caused by the excess use of antimicrobials has come to be recognized as a global threat to public health. There is a ‘tragedy of the commons’ type social dilemma behind this excessive use of antimicrobials, which should be recognized by all stakeholders. To address this global threat, we thus surveyed eight countries/areas to determine whether people recognize this dilemma and showed that although more than half of the population pays little, if any, attention to it, almost 20% recognize this social dilemma, and 15–30% of those have a positive attitude toward solving that dilemma. We suspect that increasing individual awareness of this social dilemma contributes to decreasing the frequency of AMR emergencies. Methods We designed a questionnaire to observe a social dilemma in the excess use of antimicrobials incurring antimicrobial resistance by placing two types of imaginary artificial-intelligence (AI) physicians who perform medical practice from either an individual or societal perspective. We assume two AI medical diagnosis systems: “Individual precedence AI” (abbreviated Individual-AI) and “World precedence AI” (abbreviated World-AI). Both AIs diagnose and prescribe medicine automatically. The Individual-AI system diagnoses patients and prescribes medicine to prevent infections based on an individual perspective, including all prophylactic prescriptions against rare accidental infections (not yet present and unlikely to occur). It does not consider the global risk of AMR in the decision. The World-AI system, instead, takes into account the global mortality rate of AMR, aiming to reduce the total number of all AMR-related deaths. Because of this, this AI system does not prescribe antimicrobials against rare and not-yet-present infections. This questionnaire design allows us to observe the social dilemma. For example, it shows a typical social dilemma caused by preferring the use of Individual-AI for diagnosing oneself but preferring the use of World-AI for diagnosing strangers.

The survey entitled “Survey on Medical Advancement” was administered to 8 countries/areas. The survey was conducted 4 times. For the two surveys in Japan, an internet survey company, Cross Marketing Inc. (https://www.cross-m.co.jp/en/), created the questionnaire webpages based on our study design. The company also collected the data. As of April 2020, Cross Marketing Inc. has 4.79 million people in an active panel (survey participants who registered in advance). Here, the definition of an active panel is a survey respondent who has been active within the last year. For the panels, the questionnaire and response column were displayed on the website through which the respondents could complete and submit their responses. We extracted 500 submissions for each gender and each age group by random sampling from all samples collected during the survey periods. The surveys in the 7 countries/areas (i.e., the United States, the United Kingdom, Sweden, Taiwan, Australia, Brazil, and Russia) are conducted by Cint (https://www.cint.com/). Cint is the world’s largest consumer network for digital survey-based research. The headquarters of the company is in Sweden. Cint maintains a survey platform that contained more than 100 million consumer monitors in over 80 countries as of May 2020. For surveys in the US, UK, Sweden, Taiwan, Australia, Brazil, and Russia, Cint Japan (https://jp.cint.com/), which is the Japanese distributor of Cint, created translated questionnaire webpages based on our study design. The company also collected the data. We extracted at least 500 (US, UK, SWE, BRA, RUS) or 250 (TWN, AUS) submissions for each gender (male and female) and each age group (20 s, 30 s, 40 s, 50 s, and 60 s) by random sampling from all samples collected between survey periods. Note that both companies eliminated inconsistent or apathetic respondents. For example, respondents with inconsistent responses (e.g., the registered age of the respondent differed from the reported age at the time of the survey.) were eliminated before reaching the authors. In addition, respondents with significantly short response times (i.e., shorter than 1 min) were eliminated because they may not have read the questions carefully.

Antibiotic resistance is a leading cause of hospitalization and death worldwide. Heavy metals such as arsenic have been shown to drive co-selection of antibiotic resistance, suggesting arsenic-contaminated drinking water is a risk factor for antibiotic resistance carriage. This study aimed to determine the prevalence and abundance of antibiotic-resistant Escherichia coli (AR-Ec) among people and drinking water in high (Hajiganj, >100 μg/L) and low arsenic-contaminated (Matlab,

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

The AMIS Project Thailand is an anthropological research project aimed at better understanding the role of antimicrobials in daily life in Thai society in diverse contexts and with different actors, including health care providers, day wage urban workers, scientists and policy-makers. The research focuses on the ways in which antimicrobials enable particular ways of life, livelihoods and institutional practices, and explores the context and the wider motivations for antimicrobial use in Thailand. Specifically, it focusses on access to antimicrobials in community pharmacies in Thailand, the transmission of antimicrobial resistant infection in bedridden patients, and the impact of rational drug use policies.Our use of antibiotics has escalated. We are often most aware of antibiotic use when we treat infections - for people, and animals. However, their use is more widespread. We use them routinely to reduce risks of infection amongst people with vulnerable immune systems, in farming livestock, to manage infection and to promote growth and even in crop farming. This widespread use is linked to a rise in antibiotic resistance (AMR). The amount of antibiotic chemicals in circulation is held responsible for driving selection pressure amongst bacteria such that some infections become untreatable with previously effective drugs. This can have dramatic consequences for both health and economics. And yet, scientists have emphasised the lack of evidence for using antibiotics in many scenarios. For example, it is estimated that at least 50% of human antibiotic usage has no clinical benefit. Policy makers are agreed that we must reduce our reliance on these medicines. But how? Efforts to change end user behaviour are often called for but thus far have not had the widespread impacts required to curb the emergence and spread of resistance. In this research, we propose that antibiotics are embedded within our socioeconomic infrastructure in such a deep way that attempts simply to change behaviour of patients, physicians or farmers are peripheral to our underlying dependency on their use. We suggest that by understanding the ways in which antibiotics are intertwined with our lives, institutions and infrastructures today we may identify ways to replace their use while minimising unintended consequences. For example, attempts to reduce use of one antibiotic often increases the use of another. Removing antibiotics all together may require a more systemic intervention such as the promotion of recovery time. In so-doing, however, other consequences could occur for the workforce and for economies tied up with pharmaceutical production and sales. We need to look closely at measures that have already been taken to reduce antibiotic use and understand what their consequences have been, as well as play out potential new interventions in different settings. If we are able to identify effective measures to reduce reliance on antibiotics in different scenarios, this will be more cost effective and timely than one-size-fits-all efforts to change end user behaviour. The issue of AMR is global, and is expected to have most severe consequences for low and middle income countries (LMICs). The need to reduce use of antibiotics in these settings presents a particular challenge, where markets fill the gaps of fractured health systems. An access-excess balance is described whereby many in need of antimicrobials remain untreated while these medicines are commonly used unnecessarily. This scenario persists despite decades of research and programmes into the rational use of medicines in LMICs. New approaches are needed to uncover the significance of antibiotics in our societies, to understand why the imperative to target their use so difficult to enact. This project presents a fresh approach beyond the traditionally delineated domains of social, biological or clinical sciences. The project aims to launch the AMIS Hub, an internationally recognised centre of excellence for developing, implementing and disseminating high quality research on antimicrobials in society, and two initial studies in Thailand and Uganda with the following objectives: 1) To understand the roles of antibiotics in every-day life and infrastructure 2) To evaluate the impact on care of imperatives to restrict antibiotics 3) To identify and rehearse counterfactuals to antibiotic use. This project will generate a critical mass of researchers undertaking high quality research into AMR, high quality comparative evidence indicating the nature of our reliance on antibiotics and recommendations for alternatives to antibiotics that will minimise unintended consequences. Ethnographic fieldwork was conducted between August 2018-November 2020 and included qualitative interviews, focus groups, participant observation, a household drug survey, and health facility mapping. A total of 89 semi-structured and informal interviews were conducted with 47 informants. Informants were recruited using a purposive sampling strategy designed to include stakeholders with a range of roles and perspectives on antimicrobial use in Thailand. Interviews were conducted face-to-face or remotely online or via telephone and lasted between 40-60 minutes. Three focus group discussions were conducted with community health volunteers, working age and elderly residents, recruited via the sub-district health promoting hospital. Participant observation was conducted in two privately-owned community pharmacies and with 27 bedridden patients and their relatives. A household drug survey was conducted in 95 households across three communities in the sub-district. The survey included four sections: demographic information; health status; household medicines; medicine storage. Follow-up questions were used to elicit further details about antibiotic access and use. In addition to ethnographic and survey data, key legislation and policy documents were reviewed to contextualise ethnographic fieldwork.

Hospital Associated Pneumonia (HAP) is a common healthcare associated infection, thought to affect 1-2% of all UK hospital admissions. Patients with HAP are more likely to need intensive care support and have increased length of stay and mortality rates. Unlike in community-acquired pneumonia, tools to stratify risk or severity are lacking. While there is some understanding of risk-factors that predispose people to HAP, prognostic factors are less well defined.  Treatment guidelines suggest broad spectrum antibiotics but there is little understanding of the causative organisms which cause HAP. 

​To explore HAP, PIONEER, with the NIHR Birmingham BRC Infection and Acute Care theme, have curated a highly granular dataset of 22,580 hospital acquired pneumonia spells. The data includes demography, co-morbidities including Charlson comorbidity index, symptoms, serial physiology and acuity, investigations including microbiology, imaging, medications (dose and route), ward locations including intensive care details and outcomes. The current dataset includes admissions from 01-01-2018 to 31-12-2022 but can be expanded to assess other timelines of interest.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains

155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

The AMIS Project Uganda is an anthropological research project aimed at better understanding the use of antimicrobials in Ugandan society. The study used a mixed-methods approach to investigate the roles and context of antimicrobials in daily life in Tororo, Wakiso and Kampala districts from the perspective of health care providers, farmers and day wage urban workers. The data collection includes field-note and contact summaries, in-depth interview transcripts, key informant interview transcripts, medicines surveys, oral histories, and participant feedback dialogues.Our use of antibiotics has escalated. We are often most aware of antibiotic use when we treat infections - for people, and animals. However, their use is more widespread. We use them routinely to reduce risks of infection amongst people with vulnerable immune systems, in farming livestock, to manage infection and to promote growth and even in crop farming. This widespread use is linked to a rise in antibiotic resistance (AMR). The amount of antibiotic chemicals in circulation is held responsible for driving selection pressure amongst bacteria such that some infections become untreatable with previously effective drugs. This can have dramatic consequences for both health and economics. And yet, scientists have emphasised the lack of evidence for using antibiotics in many scenarios. For example, it is estimated that at least 50% of human antibiotic usage has no clinical benefit. Policy makers are agreed that we must reduce our reliance on these medicines. But how? Efforts to change end user behaviour are often called for but thus far have not had the widespread impacts required to curb the emergence and spread of resistance. In this research, we propose that antibiotics are embedded within our socioeconomic infrastructure in such a deep way that attempts simply to change behaviour of patients, physicians of farmers are peripheral to our underlying dependency on their use. We suggest that by understanding the ways in which antibiotics are intertwined with our lives, institutions and infrastructures today we may identify ways to replace their use while minimising unintended consequences. For example, attempts to reduce use of one antibiotic often increases the use of another. Removing antibiotics all together may require a more systemic intervention such as the promotion of recovery time. In so-doing, however, other consequences could occur for the workforce and for economies tied up with pharmaceutical production and sales. We need to look closely at measures that have already been taken to reduce antibiotic use and understand what their consequences have been, as well as play out potential new interventions in different settings. If we are able to identify effective measures to reduce reliance on antibiotics in different scenarios, this will be more cost effective and timely than one-size-fits-all efforts to change end user behaviour. The issue of AMR is global, and is expected to have most severe consequences for low and middle income countries (LMICs). The need to reduce use of antibiotics in these settings presents a particular challenge, where markets fill the gaps of fractured health systems. An access-excess balance is described whereby many in need of antimicrobials remain untreated while these medicines are commonly used unnecessarily. This scenario persists despite decades of research and programmes into the rational use of medicines in LMICs. New approaches are needed to uncover the significance of antibiotics in our societies, to understand why the imperative to target their use so difficult to enact. This project presents a fresh approach beyond the traditionally delineated domains of social, biological or clinical sciences. The project aims to launch the AMIS Hub, an internationally recognised centre of excellence for developing, implementing and disseminating high quality research on antimicrobials in society, and two initial studies in Thailand and Uganda with the following objectives: 1. To understand the roles of antibiotics in every-day life and infrastructure 2. To evaluate the impact on care of imperatives to restrict antibiotics 3. To identify and rehearse counterfactuals to antibiotic use This project will generate a critical mass of researchers undertaking high quality research into AMR, high quality comparative evidence indicating the nature of our reliance on antibiotics and recommendations for alternatives to antibiotics that will minimise unintended consequences. This study used an observational mixed-methods research design. Fieldwork was conducted in blocks of 6-10 weeks over a period of 24 months between 2018 and 2021. This included: 1) 51 semi-structured interviews with key informants, including healthcare providers, farmers and day-wage workers to understand how antibiotics are intertwined in people’s lives; 2) 12 stakeholder interviews with policy makers, public health practitioners, regulators, consumer groups, NGOs, and researchers to better understand AMR policies and initiatives; 3) long-term participant observation with 73 individuals; 4) Medicines surveys involving 609 participants to capture and explore patterns of antibiotic use for humans and animals. In total, 733 individuals were engaged during fieldwork across the three sites. The semi-structured interviews were based on pre-defined topic guides to investigate the ways in which antimicrobials enable particular ways of life and livelihoods, as well as the wider reasons for antimicrobial use in Uganda. Participants were identified using a combination of purposive and snowball sampling techniques to enrol a wide range of stakeholders related to antimicrobial use. Following informed consent, the interviews were audio recorded and later transcribed and translated into English. In addition to ethnographic fieldwork, documentary analysis was conducted to identify relevant public health literature, policy documents, and media materials, to contextualise ethnographic findings. Participatory research activities were also conducted to elicit preliminary feedback on findings from research participants. This included 10 feedback dialogue events, engaging 123 individuals.

Dataset Description:This dataset originates from the transposon mutant library of Acinetobacter baumannii (A. baumannii), and aims to explore genes related to mucosal phenotype, antibiotic resistance, oxidative stress response, and pathogenicity. The dataset includes experimental results of different mutant strains under various conditions, such as growth, antibiotic resistance, serum bactericidal activity, acid tolerance, hydrogen peroxide tolerance, and mouse infection models. A detailed description is provided below:Data Generation and Processing:Data Generation Process: The transposon mutant library of A. baumannii was constructed using the transposon insertion mutagenesis technique. Each mutant strain corresponds to a specific gene knockout or insertion (e.g., ydcR, rna, omp38, etc.). The mutants were cultured and tested under various experimental conditions, covering aspects such as antibiotic resistance, serum tolerance, and acid resistance.Data Processing Methods: Data were processed using standard laboratory analytical methods, with software such as GraphPad Prism employed for data analysis and visualization, particularly for survival curve plotting and statistical analysis.Temporal and Spatial Information:This study does not involve specific temporal or spatial resolution. All experimental data were generated under controlled laboratory conditions, with no geographic or time-related spatial data included.Tabular Data:Record Count: The dataset contains various experimental data, with the number of records varying by experimental type. For example, the antibiotic resistance testing data table contains 24 records, while the mouse infection model data table includes information on survival rates and time of death for each mutant strain.Row and Column Labels: Row labels generally correspond to the different mutant strain identifiers (e.g., ∆rna, omp38∷Kana, etc.). Column labels include experimental parameters (e.g., antibiotic resistance, serum tolerance, acid tolerance) and their corresponding values (e.g., OD values, mortality rate, survival time).Units of Measurement: The units of measurement used in the data include CFU/mL (colony-forming units per milliliter), % (percentage), hours (h), mg/mL, etc.Missing Data Statement:There are no missing data points in the dataset.Error Statement:All experiments were performed under strictly controlled laboratory conditions to ensure reproducibility and accuracy. Therefore, no experimental errors were observed.Data File Contents and Description:The data files contain records of various experimental results, such as antibiotic resistance data, hydrogen peroxide tolerance tests, and serum bactericidal assays. Each data file is accompanied by a corresponding documentation file that describes the specific content of the data, experimental methods, and interpretation of results.Data File Format and Applicable Software:The data files are typically stored in common formats such as Excel. Some files may also utilize specialized statistical analysis software (e.g., GraphPad Prism), which can be downloaded and used from the official GraphPad website.

The AMIS Project Thailand (Study 2, MOPH) is an anthropological research project aimed at better understanding the role of antimicrobials in daily life in Thai society in diverse contexts and with different actors, including health care providers, farmers, day wage urban workers, scientists and policy-makers. The research focuses on the ways in which antimicrobials enable particular ways of life, livelihoods and institutional practices, and explores the context and the wider motivations for antimicrobial use in Thailand.Our use of antibiotics has escalated. We are often most aware of antibiotic use when we treat infections - for people, and animals. However, their use is more widespread. We use them routinely to reduce risks of infection amongst people with vulnerable immune systems, in farming livestock, to manage infection and to promote growth and even in crop farming. This widespread use is linked to a rise in antibiotic resistance (AMR). The amount of antibiotic chemicals in circulation is held responsible for driving selection pressure amongst bacteria such that some infections become untreatable with previously effective drugs. This can have dramatic consequences for both health and economics. And yet, scientists have emphasised the lack of evidence for using antibiotics in many scenarios. For example, it is estimated that at least 50% of human antibiotic usage has no clinical benefit. Policy makers are agreed that we must reduce our reliance on these medicines. But how? Efforts to change end user behaviour are often called for but thus far have not had the widespread impacts required to curb the emergence and spread of resistance. In this research, we propose that antibiotics are embedded within our socioeconomic infrastructure in such a deep way that attempts simply to change behaviour of patients, physicians of farmers are peripheral to our underlying dependency on their use. We suggest that by understanding the ways in which antibiotics are intertwined with our lives, institutions and infrastructures today we may identify ways to replace their use while minimising unintended consequences. For example, attempts to reduce use of one antibiotic often increases the use of another. Removing antibiotics all together may require a more systemic intervention such as the promotion of recovery time. In so-doing, however, other consequences could occur for the workforce and for economies tied up with pharmaceutical production and sales. We need to look closely at measures that have already been taken to reduce antibiotic use and understand what their consequences have been, as well as play out potential new interventions in different settings. If we are able to identify effective measures to reduce reliance on antibiotics in different scenarios, this will be more cost effective and timely than one-size-fits-all efforts to change end user behaviour. The issue of AMR is global, and is expected to have most severe consequences for low and middle income countries (LMICs). The need to reduce use of antibiotics in these settings presents a particular challenge, where markets fill the gaps of fractured health systems. An access-excess balance is described whereby many in need of antimicrobials remain untreated while these medicines are commonly used unnecessarily. This scenario persists despite decades of research and programmes into the rational use of medicines in LMICs. New approaches are needed to uncover the significance of antibiotics in our societies, to understand why the imperative to target their use so difficult to enact. This project presents a fresh approach beyond the traditionally delineated domains of social, biological or clinical sciences. The project aims to launch the AMIS Hub, an internationally recognised centre of excellence for developing, implementing and disseminating high quality research on antimicrobials in society, and two initial studies in Thailand and Uganda with the following objectives: 1) To understand the roles of antibiotics in every-day life and infrastructure 2) To evaluate the impact on care of imperatives to restrict antibiotics 3) To identify and rehearse counterfactuals to antibiotic use. This project will generate a critical mass of researchers undertaking high quality research into AMR, high quality comparative evidence indicating the nature of our reliance on antibiotics and recommendations for alternatives to antibiotics that will minimise unintended consequences. Ethnographic methods were used in order to gain in depth understandings in each setting. This involved 35 weeks of ethnographic fieldwork (including participant observation and informal interviews) and 53 in depth interviews implemented over a period of 24 months. There were four main research sites/topics: Antimicrobial/AMR policy and implementation; Pig farming; Orange production and consumption and Migrants and TB care. Participants were sampled from each site using convenience, purposive and snowballing techniques. Participants included: pig farmers, AMR policy makers, citrus orchard owners and workers, plant scientists, health care professionals, data entry clerks, TB and other patients, poor urban residents.

This Special Eurobarometer on Antimicrobial Resistance seeks to measure the use of antibiotics among EU citizens in the 28 Member States and their level of knowledge about the nature and effectiveness of antibiotics and the risks associated with unnecessary use. Furthermore, it seeks to determine the impact of antibiotic awareness campaigns on the knowledge and behaviour in the area. The results reveal a modest positive evolution since the first survey taken in 2009: 35% of respondents say they have taken antibiotics in the past year, which represents a 5% decrease since the 2009 survey. 2% fewer people took antibiotics against flu in 2013 (18% vs 20%), and more people are aware that antibiotics do not kill viruses than in 2009 (40% vs 36%). The current tracker survey uses the same questions as in 2009, and the report addresses the same key objectives: - to identify the use of antibiotics among the EU public: the frequency with which they take antibiotics, how they obtained them, and for what reason they took them; - to measure the levels of public knowledge about the nature and effectiveness of antibiotics and the risks associated with their unnecessary use; - to determine the impact of antibiotic awareness campaigns on the knowledge and actions of Europeans.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Data collection

Literature research strategy

We systematically searched all peer-reviewed journal articles and theses using Web of Science and Google scholar up to March 1st, 2019 that investigated 1) mortalities from cultured aquatic animals due bacterial infections (dataset 1) and 2) AMR from aquaculture environments (dataset 2). Since AMR changes over time, we only retained articles on this subject published within the last 10 years. We performed two independent literature searches for each of the subjects following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Supplementary Figure 2, 3). The following keyword combinations were used: 1) (aquaculture* OR farm* OR rear*) AND (fish OR shrimp OR shellfish) AND (mortality OR outbreak OR infection) AND (Aeromonas OR Edwardsiella OR Flavobacterium OR Streptococc* OR Vibrio OR Yersinia) and 2) (antimicrobial or antibiotic) AND (resistance OR susceptibil*) AND (aquaculture OR fish OR shrimp OR ...

Infectious disease is the single biggest cause of death worldwide. New infectious agents, such as the SARS, MERS and other novel coronavirus, novel influenza viruses, viruses causing viral haemorrhagic fever (e.g. Ebola), and viruses that affect the central nervous system (CNS) such as TBEV & Nipah require investigation to understand pathogen biology and pathogenesis in the host. Even for known infections, resistance to antimicrobial therapies is widespread, and treatments to control potentially deleterious host responses are lacking.

In order to develop a mechanistic understanding of disease processes, such that risk factors for severe illness can be identified and treatments can be developed, it is necessary to understand pathogen characteristics associated with virulence, the replication dynamics and in-host evolution of the pathogen, the dynamics of the host response, the pharmacology of antimicrobial or host-directed therapies, the transmission dynamics, and factors underlying individual susceptibility.

The work proposed here may require sampling that will not immediately benefit the participants. It may also require analysis of the host genome, which may reveal other information about disease susceptibility or other aspects of health status.

Community engagement approaches that have impacted on health outcomes are often time intensive, small-scale and require high levels of financial and human resources. They can be difficult to sustain and scale-up in low resource settings. Given the reach of health services into communities in low income countries, the health system provides a valuable and potentially sustainable entry point that would allow for scale-up of community engagement interventions. This study explored the process of developing an embedded approach to community engagement taking the global challenge of antibiotic resistance as an example. We undertook detailed formative research through a qualitative study to explore the most appropriate mechanisms through which to embed the intervention within the existing health system and community infrastructure, and via a quantitative survey to understand patterns of knowledge, attitudes and practices regarding antibiotic usage in humans.We plan to develop and test the "community dialogue" approach for preventing and controlling antibiotic resistance in Bangladesh. The emergence and spread of antibiotic resistance is especially problematic in settings where antibiotics can be bought without a prescription and where they are over-prescribed by health workers and over-used by the public. The World Health Organisation recommends that the general public can help combat antibiotic resistance by preventing infections, using antibiotics only when prescribed by a health professional, completing the full prescription, never using leftover antibiotics and never sharing antibiotics. The Ministry of Health and Family Welfare has established the Revitalization of Community Health Care Initiative in Bangladesh. They have done so in order to improve access, utilisation and equity of healthcare. This initiative aims to enable community clinics in rural areas to deliver an essential service package to the approximately 6000 people in their catchments areas. So far, around 13,300 community clinics have been built across the country. Members of this research team have already collaborated with MOHFW on developing and evaluating an intervention to improve the quality of essential services provided within the community clinics in Comilla district. A key part of this package was training community health care providers to prescribe antibiotics correctly and this was very successful. Improving provider capacity to prescribe antibiotics correctly is one important component within a range of behavioural issues that impact on the ways in which antibiotics are used. We intend to build on our existing partnership and develop an intervention that improves knowledge and behaviour in relation preventing and controlling antibiotic resistance within communities in Bangladesh, where antibiotics are available not only from community clinics but also from drug stores. The project has five objectives: to conduct research to inform the content of and processes for delivering community dialogues; to adapt the community dialogues approach to the setting; to pilot-test the approach in the catchment areas of five community clinics; to evaluate the feasibility of the pilot intervention in terms of the number of people it reaches, the extent to which it is delivered as intended, and whether or not is it acceptable to a range of stakeholders; and to engage with key stakeholders, such as policy makers, district health officials, community clinic staff and communities to ensure that the intervention is appropriate. We will ensure that the community dialogues are "embedded" within the infrastructure of the community support groups of the community clinics. This means that they will be delivered through existing mechanisms and will, therefore, be replicable across Bangladesh. This intervention has the potential to a. contribute to a body of urgent action recommended by WHO to prevent a post-antibiotic era, in which common infections and minor injuries will kill; b. build health system capacity in Bangladesh to deliver community-based interventions; and c. empower communities in Bangladesh to build cohesion and social capital, thus enabling them to contribute further to the economic and social welfare of the country. Furthermore, it has the potential to be adapted for implementation in other national health systems that support similar infrastructures, and to be adapted to address other areas of ARM, including behaviours that contribute to resistance to drugs to treat infections caused by parasites (e.g. malaria) and viruses (e.g. HIV). Qualitative: interviews were conducted with four community health care providers (CHCPs) who work in community clinics. Ten focus group discussions (FGDs) each with 6-8 participants were held with community members. Quantitative: a household survey was conducted in five rural clusters of villages, where we interviewed women (aged 18-49 who had at least one child) about their knowledge, attitudes and practices regarding antibiotic usage in humans, and that of their husbands' and children.

Rice bacterial leaf blight, also known as white leaf blast, grass blast, ground fire and so on. Rice bacterial blight is one of the important diseases in rice production, one of the main diseases in rice, and a quarantine disease. Rice bacterial leaf blight mainly damages leaves and causes plant wilting to death. Once it occurs, the yield of rice bacterial leaf blight can generally be reduced by about 10%, and the serious yield can be reduced by 50-60%, or even 90%. Generally, indica rice is heavier than japonica glutinous rice, and late rice is heavier than early rice. It mainly occurs in leaves and sheaths. At first, translucent yellow spots appeared on the leaf margin, then developed into ripple yellow-green or grey-green spots along one or both sides of the leaf margin or along the middle vein; the boundary between the disease and the healthy part was obvious; after a few days, the disease spots turned grey-white and curled inward, looking far into a faded color, so it was called bacterial blight. [Control methods] Based on the selection of disease-resistant varieties and on the premise of reducing bacterial sources, we should pay close attention to fertilizer and water management, supplemented by chemical control, focusing on slurry management and chemical control in seedling stage. [Seed treatment] Soak seeds with formalin 50 times liquid for 3 hours before sowing, then stuff seeds for 12 hours, and then germinate after washing. It can also be used to soak rice seeds 6-8 kg after mixing with 2 milliliters of Zaoshiling EC and 10-12 liters of water. After 36 hours of soaking, it can accelerate germination and sowing. [Agricultural control] Selection of disease-resistant varieties is the most economical and effective way to control bacterial blight, such as Huaan 2, Zhong9A/838, Wandao 44, Jinliangyou 36, Teyou 813 and Youyou 128. Clean up the residue of diseased rice straw, diseased rice straw is not directly returned to the field, as far as possible to prevent pathogenic bacteria on diseased rice straw into the rice seedling and Honda. Good management of seedling fields and cultivation of disease-free seedlings. Select the position of seedling field and strictly prevent flooding seedlings. The seedling field should be high, disease-free, easy to drain and irrigate, far away from the straw heap, threshing and drying fields, and the late rice seedling field should also be far away from the early rice disease field. Prevent cross irrigation, straw irrigation and long-term deep water irrigation. To prevent excessive partial application of nitrogen fertilizer, we should also apply phosphorus and potassium fertilizer. [Pharmaceutical control] Before or after the arrival of typhoon rainstorm, the old sick areas sprayed the diseased fields or susceptible varieties once, especially the flooded fields. According to the development of the disease and climatic conditions, the number of medications is usually sprayed once every 7-10 days, twice early onset and once late onset. Every 667 square meters, 86.2% cuprous oxide water granules, sterile pesticides (Menshen) 50 mL, 70% Yekujing (also known as Xikujing) suspension 100-150 g, or 25% Yekuning wettable powder 100 g, or Gaoke 20% fluorosilazole imidazole 30-50 mL, or 10% chloramphenicol wettable powder 100 g, or 50% Daisen ammonium 100 g (not available after heading), or 25% Xiaokulin wettable powder 40 g, or 32% nucleus. Glucoside morphine guanidine (anti viral disease) 600 times liquid, or 15% carbendazim 200 grams, or Print-Rite 3 (50% chlorbromo isocyanuric acid, registered rice leaf blight) above medicament and water 50 liter spray. [Pest control and disease control] This is a general measure to control persistent insect-borne virus disease, and also a first-aid measure to control stripe blight. Preliminary experiments show that it has good effect, but if the control is not proper, it will still cause serious harm. In order to ensure the effect of pest control, we should pay attention to the use of medicines in conjunction with tablets at the same time. Because of the rapid transmission of the virus and the particularity of the virus disease itself, the effect of insecticide treatment alone can not reach 100%. It is better to combine with resistant varieties to achieve better results.

This dataset contains data from WHO's data portal covering the following categories:

Air pollution, Antimicrobial resistance (AMR), Assistive technology, Child mortality, Dementia diagnosis, treatment and care, Dementia policy and legislation, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, HIV, Health Inequality Monitor, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, International Health Regulations (2005) monitoring framework, Malaria, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence against women, Violence prevention, Water, sanitation and hygiene (WASH), Women and health, World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Lung adenocarcinoma cells harboring epidermal growth factor receptor (EGFR) mutations are sensitive to EGFR tyrosine kinase inhibitors (TKIs), including gefitinib. Acquired resistance to EGFR-TKIs develops after prolonged treatments. Known mechanisms for EGFR-TKI resistance, including KRAS mutation, HER2 mutation, EGFR T790M mutation and MET gene amplification did not observe in the resistant cells, PC9/gef. The study was prompt to explore effective strategies against resistance to EGFR-TKIs in PC9/gef cells. Here, we used label-free quantitative mass spectrometry to globally profile the basal phosphoproteome and proteome of a panel of TKI sensitive PC9, TKI resistant PC9/gef and TKI dose-dependent PC9/gef NSCLC cell lines. For phosphorylation level, we identified 5844 phosphorylation sites from 4612 phosphopeptides of 1548 proteins. For protein level, we identified 3835 proteins. Most of the quantitatively change is from phosphorylation whereas most of the protein level is unchanged. Among these big datasets, there is a phosphopattern of phosphopeptides presented up-regulated in resistant cells but no response to further gefitinib treatment; we proposed this group could regulate drug resistance. By motif analysis, these phosphopeptides mapped to the corresponding kinases, CK2, as the drug resistant kinase. Network analysis showed that CK2 directed interacting with 10 proteins. Among these proteins, we found that HMGA1 is the substrate protein to CK2. By biochemical evidence, we discovered that CK2 could regulate cell death in TKI-resistant cells. Furthermore, we found that HMGA1 for the first time could be the potential drug resistant target to reverse the drug resistance in PC9/gef cells. The results provide new insights into HMGA1 as the drug resistant target through the cellular signaling networks associated with the TKI-induced drug resistant NSCLCs.

This dataset contains data from WHO's data portal covering the following categories:

Air pollution, Antimicrobial resistance (AMR), Assistive technology, Child mortality, Dementia diagnosis, treatment and care, Dementia policy and legislation, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, HIV, Health Inequality Monitor, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, International Health Regulations (2005) monitoring framework, Malaria, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence against women, Violence prevention, Water, sanitation and hygiene (WASH), Women and health, World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

Background: The introduction of new and often shorter tuberculosis (TB) drug regimens affects the cost of TB programmes. Methods: We modelled drug purchase and import costs for 20-month, 9-month and 4- to 6-month TB drug regimens based on 2016–2020 treatment numbers from a TB programme in Karakalpakstan, Uzbekistan, and 2021 Global Drug Facility prices. Results: On average, 2225±374 (±sd) people per year started TB treatment, 30±2.1% of whom were diagnosed with drug-resistant forms of TB. Transitioning from a 6-month to a 4-month drug-susceptible (DS)-TB drug regimen increased the TB programme's annual DS-TB drug cost from USD 65±10 K to USD 357±56 K (p<0.001) and its drug import cost from USD 6.4±1.0 K to USD 9.3±1.4 K (p=0.008). Transitioning from a 20-month all-oral multidrug-resistant (MDR)-TB drug regimen to a 9-month MDR-TB drug regimen with an injectable antibiotic decreased the TB programme's annual MDR-TB drug cost from USD 1336±265 K to USD 266±53 K (p<0.001) and had no significant effect on the drug import cost (USD 28±5.5 K versus USD 27±5.4 K; p=0.88). Purchasing (USD 577±114 K) and importing (USD 3.0±0.59 K) the 6-month all-oral MDR-TB drug regimen cost more than procuring the 9-month MDR-TB drug regimen but less than the 20-month all-oral MDR-TB drug regimen (both p<0.01). Conclusion: Introducing new and shorter TB drug regimens could increase the cost of TB programmes with low drug resistance rates and decrease the cost of TB programmes with high drug resistance rates.

This study investigates the factors influencing preferences for delayed (or “backup”) antibiotic prescription: a strategy that can be used in primary care as part of antibiotic stewardship. The data are generated from discrete choice experiments performed in the UK, among both general practitioners (GPs), and members of the public (two samples: adults, and parents of child(ren) under 18). Respondents were presented with prescribing scenarios described by 6 attributes, and made a choice of their preferred type of antibiotic prescription. Respondents were recruited by commercial providers from existing on-line panels, and were sampled to be representative of the UK population in terms of sex, age, ethnicity, and country of residence within the UK. The survey was completed on-line. The dataset consists of anonymised survey responses, including the choice questions, and respondent characteristics such as age and gender.Bacteria are increasingly resistant to the antibiotics we use to kill them. This is a major problem for us all. The more we use antibiotics, the more resistant the bacteria become. In England, GPs prescribe about 3/4 of antibiotics used. Most of these prescriptions (about 80%) will not actually benefit the patient but all will help to increase antibiotic resistance. Many ways to help GPs prescribe fewer antibiotics to patients who are unlikely to benefit have been tested. These have included training and strategies like 'back-up' prescriptions for patients to use if symptoms get worse. Many have worked well and GPs using them have prescribed fewer antibiotics. The problem is that GPs not taking part in the research have not started using them, so very little has changed overall. Government policies have also been introduced to give general practices more money if doctors prescribe fewer antibiotics to patients who are unlikely to benefit. This cost millions of pounds, and reduced antibiotic use, but it is not clear how promising extra money actually makes changes happen. When researchers test a new diagnostic tool or training package, they don't just ask whether it works, but how much it would cost the NHS to use widely. Working out 'value for money' balances costs now against benefits and risks in the future. Antibiotics are so cheap (usually under £1 for a course) that most analyses suggest everyone should always get antibiotics. The problem is that we don't have good ways to include the threat that resistance poses in the future in these cost-benefit calculations. This means that we spend time and money looking for new 'magic bullets' to reduce antibiotic prescribing, without getting the most from what we already know can work, but is not used. We need to know why effective strategies aren't used and improve them so they are used regularly in every-day care. This project aims first to understand in more detail how the strategies that previous studies have shown reduce antibiotic prescribing worked. We need to do this because 'common sense' ideas are often wrong: different people react in different ways to the same situation and what researchers think is going on is often not the case. This will help us come up with ways to make existing strategies more attractive and easier to use every day. These will increase their uptake across England, meaning we can continue to reduce unnecessary antibiotic use and the threat of antibiotic resistance. We will investigate 4 different, carefully chosen, areas. - Incentivising general practices: evaluating an England-wide policy to pay general practices to reduce unnecessary antibiotic use - Changes to the system: exploring 'delayed prescribing' where GPs give a prescription that patients can 'cash-in' if their symptoms get worse - Improving GPs communication skills: investigating ways to help GPs explain to patients when antibiotics are not needed - Better-informing decision-makers: assessing how we balance the health and cost impact of antibiotic use now vs resistance in the future, and how we determine cost-effectiveness of changes in antibiotic use In each area, we will run detailed studies to find out what the barriers are to using effective strategies, and work out ways to overcome them. These will include studies with clinical commissioning groups, with GPs, nurse prescribers, practice managers and patients. We will then develop ways to improve strategies, in partnership with different types of healthcare professionals and patients. We will test these enhancements in an implementation study. We will also develop and apply new economic evaluation research methods to assess value for money. Our team covers expertise in health psychology, health economics, biostatistics, primary care, pharmacy and clinical epidemiology. It also provides the opportunity for younger researchers to take leadership roles whilst being supported by more experienced colleagues. Discrete Choice Experiment. Data collected via an on-line survey via existing online panels, and analysed by researchers at the University of Oxford.