Antibiotic prescribing trends in primary care 2014-2022.

Antimicrobial resistance (AMR) is a global healthcare challenge that governments and health systems are tackling primarily through antimicrobial stewardship (AMS). This should, improve antibiotic use, avoid inappropriate prescribing, reduce prescription numbers, aligning with national/international AMS targets. In primary care in the United Kingdom (UK) antibiotics are mainly prescribed for patients with urinary and respiratory symptoms (22.7% and 46% of all antibiotic prescriptions respectively). This study aimed to capture the time-series trends (2014-2022) for commonly prescribed antibiotics for respiratory and urinary tract infections in primary care in England. Trends for Amoxicillin, Amoxicillin sodium, Trimethoprim, Clarithromycin, Erythromycin, Erythromycin ethylsuccinate, Erythromycin stearate, Doxycycline hyclate, Doxycycline monohydrate and Phenoxymethylpenicillin (Penicillin V) were determined. In doing so providing evidence regarding meeting UK antibiotic prescribing rate objectives (a 15% reduction in human antibiotic use 2019-2024). Time series trend analysis of 62,949,272 antibiotic prescriptions from 6,370 General Practices in England extracted from the National Health Service (NHS) Business Services Authority web portal were explored. With additional investigation of prescribing rate trends by quintiles of the Index of Multiple Deprivation (IMD). Overall, there is a downwards trend in antibiotic prescribing for those explored. There is an association between IMD, geographical location, and higher antibiotic prescribing levels (prescribing hot spots). England has a well-documented North-South divide of health inequalities, this is reflected in antibiotic prescribing. The corona virus pandemic (COVID-19) impacted on AMS, with a rise in doxycycline and trimethoprim prescriptions notable in higher IMD areas. Since then, prescribing appears to have returned to pre-pandemic levels in all IMDs and continued to decline. AMS efforts are being adhered to in primary care in England. This study provides further evidence of the link between locality and poorer health outcomes (reflected in higher antibiotic prescribing). Further work is required to address antibiotic use in hot spot areas.

Suicidal Crisis among Children and Young People: Associations with Adverse Childhood Experiences and Socio-Demographic Factors.

Suicide is a major public health issue and a leading cause of death among children and young people (CYP) worldwide. There is strong evidence linking adverse childhood experiences (ACEs) to an increased risk of suicidal behaviours in adults, but there is limited understanding regarding ACEs and suicidal crises in CYP. This study aims to examine the ACEs associated with CYP presenting at Emergency Departments for suicidal crises, and specifically the factors associated with repeat attendances. This is a case series study of CYP (aged 8-16) experiencing suicidal crisis who presented in a paediatric Emergency Department in England between March 2019 and March 2021 (n = 240). The dataset was subjected to conditional independence graphical analysis. Results revealed a significant association between suicidal crisis and several ACEs. Specifically, evidence of clusters of ACE variables suggests two distinct groups of CYP associated with experiencing a suicidal crisis: those experiencing "household risk" and those experiencing "parental risk". Female sex, history of self-harm, mental health difficulties, and previous input from mental health services were also associated with repeat hospital attendances. Findings have implications for early identification of and intervention with children who may be at a heightened risk for ACEs and associated suicidal crises.

Clinical Characteristics of COVID-19 Patients in a Regional Population With Diabetes Mellitus: The ACCREDIT Study.

Objective: To identify clinical and biochemical characteristics associated with 7- & 30-day mortality and intensive care admission amongst diabetes patients admitted with COVID-19. Research Design and Methods: We conducted a cohort study collecting data from medical notes of hospitalised people with diabetes and COVID-19 in 7 hospitals within the Mersey-Cheshire region from 1 January to 30 June 2020. We also explored the impact on inpatient diabetes team resources. Univariate and multivariate logistic regression analyses were performed and optimised by splitting the dataset into a training, test, and validation sets, developing a robust predictive model for the primary outcome. Results: We analyzed data from 1004 diabetes patients (mean age 74.1 (± 12.6) years, predominantly men 60.7%). 45% belonged to the most deprived population quintile in the UK. Median BMI was 27.6 (IQR 23.9-32.4) kg/m2. The primary outcome (7-day mortality) occurred in 24%, increasing to 33% by day 30. Approximately one in ten patients required insulin infusion (9.8%). In univariate analyses, patients with type 2 diabetes had a higher risk of 7-day mortality [p < 0.05, OR 2.52 (1.06, 5.98)]. Patients requiring insulin infusion had a lower risk of death [p = 0.02, OR 0.5 (0.28, 0.9)]. CKD in younger patients (<70 years) had a greater risk of death [OR 2.74 (1.31-5.76)]. BMI, microvascular and macrovascular complications, HbA1c, and random non-fasting blood glucose on admission were not associated with mortality. On multivariate analysis, CRP and age remained associated with the primary outcome [OR 3.44 (2.17, 5.44)] allowing for a validated predictive model for death by day 7. Conclusions: Higher CRP and advanced age were associated with and predictive of death by day 7. However, BMI, presence of diabetes complications, and glycaemic control were not. A high proportion of these patients required insulin infusion warranting increased input from the inpatient diabetes teams.

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

Parent's experiences of their child's withdrawal syndrome: a driver for reciprocal nurse-parent partnership in withdrawal assessment.

Withdrawal assessment in critically ill children is complicated by the reliance on non-specific behaviours and compounded when the child's typical behaviours are unknown. The existing approach to withdrawal assessment assumes that nurses elicit the parents' view of the child's behaviours. OBJECTIVE AND RESEARCH METHODOLOGY: This qualitative study explored parents' perspectives of their child's withdrawal and preferences for involvement and participation in withdrawal assessment. Parents of eleven children were interviewed after their child had completed sedation weaning during recovery from critical illness. Data were analysed using thematic analysis. SETTING: A large children's hospital in the Northwest of England. FINDINGS: Parents experienced varying degrees of partnership in the context of withdrawal assessment and identified information deficits which contributed to their distress of parenting a child with withdrawal syndrome. Most parents were eager to participate in withdrawal assessment and reported instances where their knowledge enabled a personalised interpretation of their child's behaviours. Reflecting on the reciprocal nature of the information deficits resulted in the development of a model for nurse-parent collaboration in withdrawal assessment. CONCLUSION: Facilitating nurse-parent collaboration in withdrawal assessment may have reciprocal benefits by moderating parental stress and aiding the assessment and management of withdrawal syndrome.

Systems Toxicology Approach to Identifying Paracetamol Overdose.

Paracetamol (acetaminophen (APAP)) is one of the most commonly used analgesics in the United Kingdom and the United States. However, exceeding the maximum recommended dose can cause serious liver injury and even death. Promising APAP toxicity biomarkers are thought to add value to those used currently and clarification of the functional relationships between these biomarkers and liver injury would aid clinical implementation of an improved APAP toxicity identification framework. The framework currently used to define an APAP overdose is highly dependent upon time since ingestion and initial dose; information that is often highly unpredictable. A pharmacokinetic/pharmacodynamic (PK/PD) APAP model has been built in order to understand the relationships between a panel of biomarkers and APAP dose. Visualization and statistical tools have been used to predict initial APAP dose and time since administration. Additionally, logistic regression analysis has been applied to histology data to provide a prediction of the probability of liver injury.

Nursing judgement and decision-making using the Sedation Withdrawal Score (SWS) in children.

AIMS: The aim of the study was to evaluate registered children's nurses' approaches to the assessment and management of withdrawal syndrome in children. BACKGROUND: Assessment of withdrawal syndrome is undertaken following critical illness when the child's condition may be unstable with competing differential diagnoses. Assessment tools aim to standardize and improve recognition of withdrawal syndrome. Making the right decisions in complex clinical situations requires a degree of mental effort and it is not known how nurses make decisions when undertaking withdrawal assessments. DESIGN: Cognitive interviews with clinical vignettes. METHODS: Interviews were undertaken with 12 nurses to explore the cognitive processes they used when assessing children using the Sedation Withdrawal Score (SWS) tool. Interviews took place in Autumn 2013. FINDINGS: Each stage of decision-making-noticing, interpreting and responding-presented cognitive challenges for nurses. When defining withdrawal behaviours nurses tended to blur the boundaries between Sedation Withdrawal Score signs. Challenges in interpreting behaviours arose from not knowing if the patient's behaviour was a result of withdrawal or other co-morbidities. Nurses gave a range of diagnoses when interpreting the vignettes, despite being provided with identical information. Treatment responses corresponded to definite withdrawal diagnoses, but varied when nurses were unsure of the diagnosis. CONCLUSION: Cognitive interviews with vignettes provided insight into nurses' judgement and decision-making. The SWS does not standardize the assessment of withdrawal due to the complexity of the context where assessments take place and the difficulties of determining the cause of equivocal behaviours in children recovering from critical illness.

Match Physical Performance of Elite Female Soccer Players During International Competition.

Datson, N, Drust, B, Weston, M, Jarman, IH, Lisboa, P, and Gregson, W. Match physical performance of elite female soccer players during international competition. J Strength Cond Res 31(9): 2379-2387, 2017-The purpose of this study was to provide a detailed analysis of the physical demands of competitive international female soccer match play. A total of 148 individual match observations were undertaken on 107 outfield players competing in competitive international matches during the 2011-2012 and 2012-2013 seasons, using a computerized tracking system (Prozone Sports Ltd., Leeds, England). Total distance and total high-speed running distances were influenced by playing position, with central midfielders completing the highest (10,985 ± 706 m and 2,882 ± 500 m) and central defenders the lowest (9,489 ± 562 m and 1,901 ± 268 m) distances, respectively. Greater total very high-speed running distances were completed when a team was without (399 ± 143 m) compared to with (313 ± 210 m) possession of the ball. Most sprints were over short distances with 76% and 95% being less than 5 and 10 m, respectively. Between half reductions in physical performance were present for all variables, independent of playing position. This study provides novel findings regarding the physical demands of different playing positions in competitive international female match play and provides important insights for physical coaches preparing elite female players for competition.

Causal and mediating factors for anxiety, depression and well-being.

Background The relationship between well-being and mental ill health is complex; people may experience very low levels of well-being even in the absence of overt mental health problems. Aims This study tested the hypothesis that anxiety, depression and well-being have different causal determinants and psychological mediating mechanisms. Method The influence of causal and mediating factors on anxiety, depression and well-being were investigated in a cross-sectional online questionnaire survey hosted on a UK national broadcasting website. Results Multivariate conditional independence analysis of data from 27 397 participants revealed different association pathways for the two constructs. Anxiety and depression were associated with negative life events mediated by rumination; low levels of subjective well-being were associated with material deprivation and social isolation, mediated by adaptive coping style. Conclusions Our findings support the 'two continua' model of the relationship between psychological well-being and mental health problems, with implications for both treatment and prevention.

Neutrophil to lymphocyte count ratio as an early indicator of blood stream infection in the emergency department.

OBJECTIVES: Early identification of patients with blood stream infection (BSI), especially bacteraemia, is important as prompt treatment improves outcome. The initial stages of severe infection may be characterised by increased numbers of neutrophils in the peripheral blood and depression of the lymphocyte count (LC). The neutrophil to LC ratio (NLCR) has previously been compared with conventional tests, such as C-reactive protein (CRP) and white cell count (WCC), and has been proposed as a useful marker in the timely diagnosis of bacteraemia. METHODS: Data on consecutive adult patients presenting to the emergency department with pyrexial illness during the study period, November 2009 to October 2010, were analysed. The main outcome measure was positive blood cultures (bacteraemia). Sensitivity, specificity, positive and negative predictive values and likelihood ratios were determined for NLCR, CRP, WCC, neutrophil count and LC. RESULTS: 1954 patients met the inclusion criteria. Blood cultures were positive in 270 patients, hence the prevalence of bacteraemia was 13.8%. With the exception of WCC, there were significant differences in the mean value for each marker between bacteraemic and non-bacteraemic patients (p<0.001). The area under the receiver operating characteristic curve was highest for NLCR (0.72; 95% CI 0.69 to 0.75) and LC (0.71; 0.68 to 0.74) and lowest for WCC (0.54; 0.40 to 0.57). The sensitivity and specificity of NLCR for predicting bacteraemia were 70% (64% to 75%) and 57% (55% to 60%), respectively. Positive and negative predictive values for NLCR were 0.20 (0.18 to 0.23) and 0.92 (0.91 to 0.94), respectively. The positive likelihood ratio was 1.63 (1.48 to 1.79) and the negative likelihood ratio was 0.53 (0.44 to 0.64). CONCLUSIONS: Although NLCR outperforms conventional markers of infection, it is insufficient in itself to guide clinical management of patients with suspected BSI, and it offers no advantage over LC. However, it may offer some diagnostic utility when taken into account as part of the overall assessment.

Conditional independence mapping of DIGE data reveals PDIA3 protein species as key nodes associated with muscle aerobic capacity.

Profiling of protein species is important because gene polymorphisms, splice variations and post-translational modifications may combine and give rise to multiple protein species that have different effects on cellular function. Two-dimensional gel electrophoresis is one of the most robust methods for differential analysis of protein species, but bioinformatic interrogation is challenging because the consequences of changes in the abundance of individual protein species on cell function are unknown and cannot be predicted. We conducted DIGE of soleus muscle from male and female rats artificially selected as either high- or low-capacity runners (HCR and LCR, respectively). In total 696 protein species were resolved and LC-MS/MS identified proteins in 337 spots. Forty protein species were differentially (P<0.05, FDR<10%) expressed between HCR and LCR and conditional independence mapping found distinct networks within these data, which brought insight beyond that achieved by functional annotation. Protein disulphide isomerase A3 emerged as a key node segregating with differences in aerobic capacity and unsupervised bibliometric analysis highlighted further links to signal transducer and activator of transcription 3, which were confirmed by western blotting. Thus, conditional independence mapping is a useful technique for interrogating DIGE data that is capable of highlighting latent features. BIOLOGICAL SIGNIFICANCE: Quantitative proteome profiling revealed that there is little or no sexual dimorphism in the skeletal muscle response to artificial selection on running capacity. Instead we found that noncanonical STAT3 signalling may be associated with low exercise capacity and skeletal muscle insulin resistance. Importantly, this discovery was made using unsupervised multivariate association mapping and bibliometric network analyses. This allowed our interpretation of the findings to be guided by patterns within the data rather than our preconceptions about which proteins or processes are of greatest interest. Moreover, we demonstrate that this novel approach can be applied to 2D gel analysis, which is unsurpassed in its ability to profile protein species but currently has few dedicated bioinformatic tools.

A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.

BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. METHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. CONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing.

Finding reproducible cluster partitions for the k-means algorithm.

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

The effects of deprivation and relative deprivation on self-reported morbidity in England: an area-level ecological study.

BACKGROUND: Socioeconomic status gradients in health outcomes are well recognised and may operate in part through the psychological effect of observing disparities in affluence. At an area-level, we explored whether the deprivation differential between neighbouring areas influenced self-reported morbidity over and above the known effect of the deprivation of the area itself. METHODS: Deprivation differentials between small areas (population size approximately 1,500) and their immediate neighbours were derived (from the Index of Multiple Deprivation (IMD)) for Lower Super Output Area (LSOA) in the whole of England (n=32482). Outcome variables were self-reported from the 2001 UK Census: the proportion of the population suffering Limiting Long-Term Illness (LLTI) and 'not good health'. Linear regression was used to identify the effect of the deprivation differential on morbidity in different segments of the population, controlling for the absolute deprivation. The population was segmented using IMD tertiles and P2 People and Places geodemographic classification. P2 is a commercial market segmentation tool, which classifies small areas according to the characteristics of the population. The classifications range in deprivation, with the most affluent type being 'Mature Oaks' and the least being 'Urban Challenge'. RESULTS: Areas that were deprived compared to their immediate neighbours suffered higher rates of 'not good health' (ß=0.312, p<0.001) and LLTI (ß=0.278, p<0.001), after controlling for the deprivation of the area itself ('not good health'-ß=0.655, p<0.001; LLTI-ß=0.548, p<0.001). The effect of the deprivation differential relative to the effect of deprivation was strongest in least deprived segments (e.g., for 'not good health', P2 segments 'Mature Oaks'-ß=0.638; 'Rooted Households'-ß=0.555). CONCLUSIONS: Living in an area that is surrounded by areas of greater affluence has a negative impact on health in England. A possible explanation for this phenomenon is that negative social comparisons between areas cause ill-health. This 'psychosocial effect' is greater still in least deprived segments of the population, supporting the notion that psychosocial effects become more important when material (absolute) deprivation is less relevant.

Using clustering techniques to identify localities with multiple health and social needs.

Development of health promoting policies requires an understanding not just of the interplay between different measures of health but also their relationship with broader education, criminal justice and other social issues. Methods to better utilise multi-sectoral data to inform policy are needed. Applying clustering techniques to 30 health and social metrics we identify 5 distinct local authority types, with poor outcomes for the majority of metrics concentrated in the same cluster. Clusters were distinguished especially by levels of: child poverty; breastfeeding initiation; children's tooth decay; teenage pregnancy; healthy eating; mental illness; tuberculosis and smoking deaths. Membership of the worst cluster (C5) was focused in Northern England which contains 15.7% of authorities analysed (n=324), but 63.0% of those in C5. The concentration of challenges in certain areas creates disproportionate pressures that may exceed the cumulative effects of individual challenges. Such distinct health clusters also raise issues of transferability of effective policies between areas with different cluster membership.

Area effects on health inequalities: the impact of neighbouring deprivation on mortality.

The exact nature of the association between the context of the local area and local health outcomes is unknown. We investigated whether areas geographically close but divergent in terms of deprivation have greater inequality in health than those where deprivation is similar across neighbouring localities. In order to disaggregate the strong correlation between the deprivation of a target area and that of its surrounding areas, we used principal component analysis to create a measure of relative deprivation. Both deprivation (ß=0.183, p<0.001) and relative deprivation were positively associated with mortality (ß=0.099, p<0.001), and the effect of relative deprivation was shown to be most pronounced in more affluent segments of the population.

The cost of emergency hospital admissions for falls on snow and ice in England during winter 2009/10: a cross sectional analysis.

BACKGROUND: In the UK, the 2009/10 winter was characterised by sustained low temperatures; grit stocks became depleted and surfaces left untreated. We describe the relationship between temperature and emergency hospital admissions for falls on snow and ice in England, identify the age and gender of those most likely to be admitted, and estimate the inpatient costs of these admissions during the 2009/10 winter. METHODS: Hospital Episode Statistics were used to identify episodes of emergency admissions for falls on snow and ice during winters 2005/06 to 2009/10; these were plotted against mean winter temperature. By region, the logs of the rates of weekly emergency admissions for falls on snow and ice were plotted against the mean weekly temperature for winters 2005/06 to 2009/10 and a linear regression analysis undertaken. For the 2009/10 winter the number of emergency hospital admissions for falls on snow and ice were plotted by age and gender. The inpatient costs of admissions in the 2009/10 winter for falls on snow and ice were calculated using Healthcare Resource Group costs and Admitted Patient Care 2009/10 National Tariff Information. RESULTS: The number of emergency hospital admissions due to falls on snow and ice varied considerably across years; the number was 18 times greater in 2009/10 (N = 16,064) than in 2007/08 (N = 890). There is an exponential increase [Ln(rate of admissions) = 0.456 - 0.463*(mean weekly temperature)] in the rate of emergency hospital admissions for falls on snow and ice as temperature falls. The rate of admissions in 2009/10 was highest among the elderly and particularly men aged 80 and over. The total inpatient cost of falls on snow and ice in the 2009/10 winter was 42 million GBP. CONCLUSIONS: Emergency hospital admissions for falls on snow and ice vary greatly across winters, and according to temperature, age and gender. The cost of these admissions in England in 2009/10 was considerable. With responsibility for health improvement moving to local councils, they will have to balance the cost of public health measures like gritting with the healthcare costs associated with falls. The economic burden of falls on snow and ice is substantial; keeping surfaces clear of snow and ice is a public health priority.

Cross-sectional measures and modelled estimates of blood alcohol levels in UK nightlife and their relationships with drinking behaviours and observed signs of inebriation.

BACKGROUND: Management of nightlife in UK cities focuses on creating safe places for individuals to drink. Little is known about intoxication levels as measuring total alcohol consumption on nights out is complicated by early evening interviews missing subsequent consumption and later interviews risking individuals being too drunk to recall consumption or participate at all. Here we assess mixed survey and modelling techniques as a methodological approach to examining these issues. METHODS: Interviews with a cross sectional sample of nightlife patrons (n = 214) recruited at different locations in three cities established alcohol consumption patterns up to the point of interview, self-assessed drunkenness and intended drinking patterns throughout the remaining night out. Researchers observed individuals' behaviours to independently assess drunkenness. Breath alcohol tests and general linear modelling were used to model blood alcohol levels at participants' expected time of leaving nightlife settings. RESULTS: At interview 49.53% of individuals regarded themselves as drunk and 79.43% intended to consume more alcohol before returning home, with around one in ten individuals (15.38% males; 4.35% females) intending to consume >40 units (equal to 400 mls of pure alcohol). Self-assessed drunkenness, researcher observed measures of sobriety and blood alcohol levels all correlated well. Modelled estimates for blood alcohol at time of going home suggested that 71.68% of males would be over 0.15%BAC (gms alcohol/100 mls blood). Higher blood alcohol levels were related to drinking later into the night. CONCLUSIONS: UK nightlife has used substantive health and judicial resources with the aim of creating safer and later drinking environments. Survey and modelling techniques together can help characterise the condition of drinkers when using and leaving these settings. Here such methods identified patrons as routinely getting drunk, with risks of drunkenness increasing over later nights. Without preventing drunkenness and sales to intoxicated individuals, extended drinking hours can simply act as havens for drunks. A public health approach to nightlife is needed to better understand and take into account the chronic effects of drunkenness, the damages arising after drunk individuals leave city centres and the costs of people avoiding drunken city centres at night.

Partial logistic artificial neural network for competing risks regularized with automatic relevance determination.

Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995).

How to find simple and accurate rules for viral protease cleavage specificities.

BACKGROUND: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way. RESULTS: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods. CONCLUSION: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.