A longitudinal study of European students' alcohol use and related behaviours as they travel abroad to study

Background: Travelling away from home can be associated with fewer limits on behavior, particularly for students who participate in exchange programs. Aims: To examine the effects of eight moderators on change in alcohol use and related negative outcomes, drug use and unprotected sexual behavior in European study abroad students before, during, and after their time abroad. Methods: A three wave (before departure, while abroad, and after their return) longitudinal design collecting data on the frequency and volume of alcohol consumed, heavy episodic drinking, alcohol-related outcomes, drug use, and unprotected casual sex. Results: The baseline survey was completed by 1145 students participating in one or two semester exchange programs (67.5% spent up to a semester abroad), of which 906 participated in two or more waves, representing 42 and 33 countries of origin and destination, respectively. Mean age was 22.2 years (SD = 2.28) and 72.7% were female. Students increased the amount of alcohol consumed by 35% (B = 0.32; 95% CI 0.287–0.349) and experienced more alcohol-related consequences (B = 0.15; 95% CI 0.089–0.219) during the study abroad experience, though levels fell below pre-departure levels when they returned home. Factors related to greater alcohol use while abroad include pre-departure expectations about alcohol use during the study abroad experience, psychological adjustment to the host country, academic involvement, and host country living costs. No statistically meaningful change in drug use and unprotected sexual behavior was observed. Conclusions: Studying abroad exposes European students to additional time-limited alcohol-related health risks

Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

RNA-seq technology has been widely used as an alternative approach to traditional microarrays in transcript analysis. Sometimes gene expression by sequencing, which generates RNA-seq data set, may have missing read counts. These missing values can adversely affect downstream analyses. Most of the methods for analysing the RNA-seq data sets require a complete matrix of RNA-seq data. In the past few years, researchers have been putting a great deal of effort into presenting evaluations of the different imputation algorithms in microarray gene expression data sets, However, these are limited works for RNA-seq data sets and a comparative study for investigating the performance of the missing value imputation for RNA-seq data is essential. In this paper, we propose the use of some parametric models such as Regression imputation, Bayesian generalized linear model, Poisson mixture model, EM approach , Bayesian Poisson regression, Bayesian quasi-Poisson regression and the Bootstrap version of two latter for single imputation of missing values in RNA-seq count data sets. The approaches are also applied for identifying differentially expressed genes in the presence of missing values. Multiple imputation, proposed by Rubin (1978), is also used for multiple imputation of missing RNA-seq counts. This approach allows appropriate assessment of imputation uncertainty for missing values. The performance of the single and multiple imputations are investigated using some simulation studies. Also, some real data sets are analyzed using the proposed approaches

Multi-morbidity using General Practice drug chapters and the relationship with secondary healthcare utilisation in Wales, UK

Introduction Multi-morbidity and polypharmacy are increasing but are under investigated. Data linkage has much to offer in understanding trends in prevalence, inter-relationships between variables and impact on healthcare activity. We created Welsh population e-cohorts in 2000 and 2014 to study these issues, using the Secure Anonymised Information Linkage (SAIL) Databank. Objectives and Approach The aim of this study was to measure changing prevalences of multimorbidity, initially through disease chapter prescribing and then to explore the relationship between the number of morbidities recorded in primary care and use of different hospital based outpatient services. Data linkage was used to create cohorts of Welsh residents registered to SAIL providing General Practices (GPs) for at least 360 days in 2000 and 2014. The 13 Read code drug chapters were used to calculate morbidity scores between 0 and 13. Proportional odds or cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns. Results The GP cohorts included 1.6 million and 2.1 million population with 56.6% and 73.4% having at least one recorded morbidity for 2000 and 2014 respectively. In 2014, 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least common (2.7%) in 5-9 year olds. Some 35% of individuals attended at least one outpatient specialty in 2014, varying from 22.4% for 5-9 year olds and 63.2% for 80-84 year olds. Preliminary modelling results show the number of GP recorded morbidity chapters was strongly related to increasing outpatient attendances at different specialties, e.g. OR 15.3 (95%CI: 15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group. Increasing age and female gender were associated with increased numbers of specialists attended whilst deprivation had a more modest impact. Conclusion/Implications There has been a large increase in recorded multimorbidity across all age groups in Wales. In this exploratory cross-sectional design, multimorbidity was strongly related to increasing use of outpatient services. Further work is ongoing to define and utilise more refined multimorbidity metrics and incorporate longitudinal designs in analysis

The relationship between General Practice metrics of multi-morbidity and secondary healthcare utilisation in Wales, UK

Objectives Multi-morbidity and polypharmacy are increasing and interrelated phenomena but are poorly understood. The aim of this study is to contribute to the understanding of these issues, measure the changing prevalence’s of multimorbidity/ polypharmacy and explore the relationship between multimorbidity as recorded in primary care and the use of outpatient services. Methods The Secure Anonymised Information Linkage (SAIL) Databank facilitated linkage techniques to create population based e-cohorts of de-identified Welsh residents. Individuals were registered to a SAIL providing General Practice (GP) for at least 360 days in 2000 and 2014. Categories of morbidity were created using the 13 Read drug code chapters. In an initial cross sectional exploratory analysis proportional odds and cumulative logit models were used to relate GP recorded morbidities to outpatient attendance patterns in the same year. Findings The GP e-cohorts included 1.6 million (2000) and 2.1 million (2001) people, with 56.6% and 73.4% having ≥1 recorded morbidity for 2000 and 2014, respectively. In 2014, groups with 5+ morbidities were most prevalent (61.3%) in 85+ year olds and least (2.7%) in 5-9 year olds. Some 35% of individuals attended ≥1 outpatient specialty in 2014; 22.4% in 5-9 year olds and 63.2% for 80-84 year olds. Results from preliminary models showed the number of GP recorded morbidities was strongly related to increasing outpatient attendances at different specialties, OR=15.3 (95%CI:15.1-15.4) of being in a higher outpatient attendance category for the 5+ morbidity group relative to the zero morbidity group. Conclusion Preliminary analysis has shown large increases in GP recorded multimorbidity across Wales over fifteen years and strong relationships and NHS service utilisation in cross-sectional analyses. Further work will include creating more refined definitions for multimorbidity metrics, linkage to hospital admission data, comparisons across healthcare settings and the development of longitudinal models

Do doctors’ attachment styles and emotional intelligence influence patients’ emotional expressions in primary care consultations? An exploratory study using multilevel analysis

Objective: To investigate whether and how doctors’ attachment styles and emotional intelligence (EI) might influence patients’ emotional expressions in general practice consultations. Methods: Video recordings of 26 junior doctors consulting with 173 patients were coded using the Verona Coding Definition of Emotional Sequences (VR-CoDES). Doctors’ attachment style was scored across two dimensions, avoidance and anxiety, using the Experiences in Close Relationships: Short Form questionnaire. EI was assessed with the Mayer-Salovey-Caruso Emotional Intelligence Test. Multilevel Poisson regressions modelled the probability of patients’ expressing emotional distress, considering doctors’ attachment styles and EI and demographic and contextual factors. Results: Both attachment styles and EI were significantly associated with frequency of patients’ cues, with patient- and doctor-level explanatory variables accounting for 42% of the variance in patients’ cues. The relative contribution of attachment styles and EI varied depending on whether patients’ presenting complaints were physical or psychosocial in nature. Conclusion: Doctors’ attachment styles and levels of EI are associated with patients’ emotional expressions in primary care consultations. Further research is needed to investigate how these two variables interact and influence provider responses and patient outcomes. Practice Implications: Understanding how doctors’ psychological characteristics influence PPC may help to optimise undergraduate and postgraduate medical education

Reusable, set-based selection algorithm for matched control groups

ABSTRACT Aims The wealth of data available in linked administrative datasets offers great potential for research, but researchers face methodological and computational challenges in data preparation, due to the size and complexity of the data. The creation of matched control groups in the Secure Anonymised Information Linkage (SAIL) Databank illustrates this point: SAIL contains multiple health datasets describing millions of individuals in Wales. The volume of data creates the potential for more precise matching, but only if an appropriate algorithm can be applied. We aimed to create such an algorithm for reuse by many research projects. Methods We developed set-based code in SQL that efficiently selects matches from millions of potential combinations in a relational database environment. It is parameterized to allow different matching criteria to be employed as needed, including follow-up time around an index event. A combinatorial optimisation problem occurs when a potential control could match more than one subject, which we solved by ranking potential match pairs first by subject with the fewest potential matches, then by closeness of the match. Results One example of the algorithm’s use was the Suicide Information Database Cymru, an electronic case-control study on suicide in Wales between 2003 and 2011. Subjects who had a cause of death recorded as self-harm were each matched to twenty controls who were alive at the subject’s date of death and had the same gender and similar birth week. The rate of matching success was >99.9%, with all subjects but one matching the full twenty controls. >99.99% of the matched controls had a week of birth that was identical to the subject. The second example was a matched cohort study looking at hospital admissions and type 1 diabetes, using the Brecon register of childhood diabetes in Wales, with matching based on week of birth within two weeks, gender, county of residence, deprivation quintile, and residence in Wales at time of diagnosis. This study had a matching rate of 98.9%; 97.5% of subjects matched to five controls, and 69.8% of matches had the same week of birth. Conclusions This algorithm provides good matching performance while executing efficiently and scalably on large datasets. Its implementation as reusable code will facilitate more efficient, high-quality research in SAIL. Instead of spending many hours developing a custom solution, analysts can execute parameterized code in a few minutes. We hope it to be useful more widely beyond SAIL as well

Osteoporosis and fracture risk - a linked data study in Wales

Introduction Osteoporosis is a global disease with a 30-40% lifetime fracture risk according to the World Health Organisation. Over half a million people receive treatment for fragility fractures annually in the UK. Osteoporosis incidence is rising with aging populations; however, medical secondary prevention treatment may reduce fracture risk. Objectives and Approach Primary aims were to investigate if secondary medical prevention treatment following an index fracture was associated with survival and subsequent fracture risk, evaluated using a pseudonymised population based e-cohort study design. Patients aged ≥60 years with an index fragility fracture at any anatomical location were identified from the Secure Anonymised Information Linkage (SAIL) databank. Fracture data were identified from secondary care datasets (emergency department and inpatient) and the National Hip Fracture Register data. In addition linkages were made to primary care datasets for medical prescription and Office for National Statistics records for mortality, supplementing data on demographic characteristics and co-morbidity. Results The cohort comprised 81,252 cases between April 2009 and December 2016 of median age 78 years (range 60-109) and 22,896 (28%) males. Medical secondary prevention treatment was received by 29,393 cases (36%). Subsequent fractures were reported for 10,907 cases (14%) and 29,026 cases (36%) died during the study period. For those that received medical prevention, the subsequent fracture and mortality rates were 15% and 28% respectively compared to 12% and 31% for those that did not receive the prevention treatment. Further analyses will include a discrete time competing risks model. Conclusion/Implications A population based e-cohort was successfully created by linking data across multiple datasets. Preliminary findings identified that <50% of eligible patients receive secondary medical prevention treatment after an index fragility fracture. These findings may help inform and unify treatment pathways for those at risk of fragility fractures