Cannabis psychosis, gender matters

2016 ◽  
Vol 33 (S1) ◽  
pp. S379-S379
Author(s):  
I. Hamilton ◽  
P. Galdas ◽  
H. Essex
Keyword(s):  
Gender Differences ◽  
Epidemiological Studies ◽  
Hospital Episode Statistics ◽  
Psychiatric Services ◽  
Data Sets ◽  
Gender Ratio ◽  
National Data ◽  
Data Set ◽  
British Crime Survey ◽  
Competing Interest

IntroductionDespite recent findings pointing toward cannabis psychosis as one area where gender differences may exist, there has been a widespread lack of attention paid to gender as a determinant of health in both psychiatric services and within the field of addiction.ObjectivesTo explore gender differences in treatment presentations for people with cannabis psychosis.AimsTo use national data sets to investigate gender differences.MethodsAnalysis of British Crime Survey data and a Hospital Episode Statistics data set were used in combination with data from previously published epidemiological studies to compare gender differences.ResultsMale cannabis users outnumber female users by 2:1, a similar gender ratio is found for those admitted to hospital with a diagnosis of schizophrenia or psychosis. However this ratio increases significantly for those admitted to hospital with a diagnosis of cannabis psychosis, with males outnumbering females by 4:1.ConclusionsThis research brings into focus the marked gender differences in cannabis psychosis. Attending to gender is important for research and treatment with the aim of improving understanding and providing gender sensitive services.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Cannabis psychosis, gender matters

2015 ◽  
Vol 8 (3) ◽  
pp. 153-162 ◽  
Author(s):  
Ian Hamilton ◽  
Paul Galdas ◽  
Holly Essex
Keyword(s):  
Gender Differences ◽  
Design Methodology ◽  
Epidemiological Studies ◽  
Hospital Episode Statistics ◽  
Future Research ◽  
Gender Ratio ◽  
Content Type ◽  
Systemic Level ◽  
British Crime Survey ◽  
Practical Implications

Purpose – The purpose of this paper is to draw together key literature and analyses of data on admissions for cannabis psychosis in National Health Service hospitals in England (extracted from Hospital Episode Statistics (HES)) to highlight what is known about gender differences in cannabis psychosis and point towards suggestions for improving gender-sensitive treatment and future research. Design/methodology/approach – Analysis of British Crime Survey data and HES data were used in combination with data from previously published epidemiological studies to compare gender differences. Findings – Male cannabis users outnumber female users by 2:1, a similar gender ratio is found for those admitted to hospital with a diagnosis of schizophrenia or psychosis. However this ratio increases significantly for those admitted to hospital with a diagnosis of cannabis psychosis, with males outnumbering females by 4:1. Research limitations/implications – Consistent patterns in gender ratios for people admitted to hospital with cannabis psychosis over a period of 11 years are reported, it is not clear why this gender difference persists but it warrants further investigation which would be aided by improved gender recording at a systemic level. Practical implications – This review brings into focus the marked gender differences in cannabis psychosis. Attending to gender is important for research and treatment with the aim of improving understanding and providing gender-sensitive services. Originality/value – This paper adds to the literature on gender differences in cannabis psychosis.

Preparation of collected primary data for statistical analysis

2018 ◽  
pp. 145-154
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Statistical Analysis ◽  
Epidemiological Studies ◽  
Primary Data ◽  
Data Sets ◽  
Data Set ◽  
Patient Questionnaires ◽  
Detailed Statistical Analysis ◽  
Study Methodology ◽  
Existing Data

Although epidemiological studies are increasingly based on the analysis of existing data sets (including linked data sets), many studies still require primary data collection. Such data may come from patient questionnaires, interviews, abstraction from records, and/or the results of tests and measures such as weight or blood test results. The next stage is to analyse the data gathered from individual subjects to provide the answers required. Before commencing with the statistical analysis of any data set, the data themselves must be prepared in a format so that the detailed statistical analysis can achieve its goals. Items to be considered include the format the data are initially collected in and how they are transferred to an appropriate electronic form. This chapter explores how errors are minimized and the quality of the data set ensured. These tasks are not trivial and need to be planned as part of a detailed study methodology.

scholarly journals Nurse staffing levels and outcomes – mining the UK national data sets for insight

2017 ◽  
Vol 30 (3) ◽  
pp. 235-247 ◽  
Author(s):  
Alison Leary ◽  
Barbara Tomai ◽  
Adrian Swift ◽  
Andrew Woodward ◽  
Keith Hurst
Keyword(s):  
Data Collection ◽  
Nurse Staffing ◽  
Data Sets ◽  
National Data ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Routinely Collected Data ◽  
The Uk ◽  
The Impact

Purpose Despite the generation of mass data by the nursing workforce, determining the impact of the contribution to patient safety remains challenging. Several cross-sectional studies have indicated a relationship between staffing and safety. The purpose of this paper is to uncover possible associations and explore if a deeper understanding of relationships between staffing and other factors such as safety could be revealed within routinely collected national data sets. Design/methodology/approach Two longitudinal routinely collected data sets consisting of 30 years of UK nurse staffing data and seven years of National Health Service (NHS) benchmark data such as survey results, safety and other indicators were used. A correlation matrix was built and a linear correlation operation was applied (Pearson product-moment correlation coefficient). Findings A number of associations were revealed within both the UK staffing data set and the NHS benchmarking data set. However, the challenges of using these data sets soon became apparent. Practical implications Staff time and effort are required to collect these data. The limitations of these data sets include inconsistent data collection and quality. The mode of data collection and the itemset collected should be reviewed to generate a data set with robust clinical application. Originality/value This paper revealed that relationships are likely to be complex and non-linear; however, the main contribution of the paper is the identification of the limitations of routinely collected data. Much time and effort is expended in collecting this data; however, its validity, usefulness and method of routine national data collection appear to require re-examination.

Personality and insomnia: The role of gender

2017 ◽  
Vol 41 (S1) ◽  
pp. s855-s855
Author(s):  
A.P. Amaral ◽  
M.J. Soares ◽  
A.T. Pereira ◽  
S. Bos ◽  
C. Roque ◽  
...  
Keyword(s):  
Gender Differences ◽  
General Population ◽  
Epidemiological Studies ◽  
High Prevalence ◽  
Competing Interest ◽  
Concern Over Mistakes ◽  
The Relationship ◽  
Insomnia Symptoms ◽  
Good Sleep

IntroductionSeveral epidemiological studies have been conducted to document the prevalence and correlates of insomnia. Most of them confirm their high prevalence in the general population, and a gender difference in the risk for insomnia.AimsTo study the role of gender in the relationship between personality (perfectionism and neuroticism) and insomnia ([IG] insomnia group, [ISG] insomnia symptoms group, and [GSG] good sleepers group).MethodsA total of 549 college students (80.1% females) filled in the MPS (Frost et al., 1990; Hewitt and Flett, 1991), EPI (Barton et al., 1992, 1995), and a self-reported questionnaire to assess insomnia symptoms.ResultsNo differences were found between female and male samples, concerning the dimension of perfectionism – doubts about actions. The IG and the ISG showed higher levels of doubts about actions than the GSG. However, only in female sample the IG and the ISG showed higher levels of concern over mistakes in comparison with the GSG. In males, no significant differences between the sleep groups were found, in which respects concern over mistakes. The level of extroversion was higher in the GSG, but only in male sample. In females, there were no significant differences between the sleep groups in relation to extroversion.ConclusionsNo gender differences were found for the role of doubts about actions in insomnia. Only in females, the dimension – concern over mistakes is important in insomnia, and only in males the dimension – extroversion is important to have a good sleep. These results warrant further research.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Population genetics of multi-host parasites – the case for molecular epidemiological studies of Schistosoma japonicum using larval stages from naturally infected hosts

Parasitology ◽  
2005 ◽  
Vol 131 (5) ◽  
pp. 617-626 ◽  
Author(s):  
J. SHRIVASTAVA ◽  
C. M. GOWER ◽  
E. BALOLONG ◽  
T. P. WANG ◽  
B. Z. QIAN ◽  
...  
Keyword(s):  
Population Genetics ◽  
Schistosoma Japonicum ◽  
Population Genetic ◽  
Epidemiological Studies ◽  
Sampling Procedure ◽  
Data Sets ◽  
Data Set ◽  
Larval Stages ◽  
Life Cycle Stages ◽  
Laboratory Maintenance

Population genetics of multi-host pathogens offers great potential for the understanding of their complex epidemiology but care must be taken to ensure that the sampling procedure does not bias estimates of population indices. The transfer of material to laboratory passage, in particular, runs the risk of bottlenecking and imposing non-random host-induced selection pressures according to the hosts used in passage. We present a novel technique allowing single-locus microsatellite genotyping of the naturally sampled larval stages, enabling unbiased population genetic studies of the multi-host zoonotic parasite Schistosoma japonicum. The utility of these larval genotyping methods for molecular epidemiological studies are illustrated in results from 3 separate data sets. In the first data set, potential loss of alleles based on the definitive host species used for laboratory maintenance was identified by comparing adult worm populations derived from mice and rabbits infected with cercarial populations originating from the same set of snails. In the second data set, bottlenecking was demonstrated by the loss of alleles in adult worms derived within a single generation of laboratory maintenance compared to their parent field-collected cercarial samples. In the final data set, comparison of miracidia and adult worms recovered from naturally infected animals demonstrated that larval analyses can provide stage-specific epidemiological information and that population genetics of schistosomes can be well described by analysis of larval stages. Our results thus advocate the use of natural life-cycle stages to obtain an accurate and ethical representation of the population genetic structure of S. japonicum and other multi-host pathogens.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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