Long Chain Omega-3 Fatty Acid Intervention in Ageing Adults at Risk of Dementia Following Repeated Head Trauma. Low-level Support or an Opportunity for an Unanswered Question?

Emerging evidence of brain injury on risk of neurodegenerative diseases such as Alzheimer's disease (AD) and chronic traumatic encephalopathy (CTE) have resulted in interest in therapeutic potential of omega-3 fatty acids (n-3FA). We conducted a systematic review of n-3FA therapeutic efficacy for ageing adults at risk of AD/CTE following a history of repeated head trauma. Databases for articles between 1980-June 2020 were examined for studies reporting on n-3 FAs in adults (≥ 45 years) with a history of repeated brain injury. Following an initial screen of 175 articles, 12 studies were considered but were eventually rejected, as they did not meet inclusion criteria. Our review could find no evidence to support, or disprove, effectiveness of n-3FA intervention in older adults with a history of head trauma. With animal studies showing neuro-restorative potential of n-3FA following brain injury, this review highlights the urgent need for human research in this area.

P1733MINERAL BONE DISEASE (MBD) IS THE POST KIDNEY TRANSPLANTATION POPULATION

Background and Aims

Patients with ESRD have a 4-fold higher risk of fracture compared to the general population. (1) Transplant bone disease is a combination of mineral bone disease sustained pre transplantation and damage incurred post transplantation, risk factors for which include immunosuppression and immobility in the early post transplantation period. Given the high morbidity, mortality and financial burden associated with fractures, the management and prevent of osteoporosis is fundamental. (2) We aimed to compare the management of MBD in our patient cohort to international nephrology and rheumatology guidelines. (1, 3)

Method

A retrospective review of all patients post kidney transplantation attending our department was carried out. Information pertaining to patient demographics, renal history, osteoporosis investigation and management was collected. The results were analysed with Microsoft Excel.

Results

154 patient records (92 male, 62 female) were reviewed. Mean age was 52 years (range 19-80). Mean eGFR was 54ml/min (range 9-90). 82% of patients were taking corticosteroids, the mean dose was 5mg. 9% were taking corticosteroids pre-transplantation. 11 patients (7%) had a history of parathyroidectomy. 19% of patients had a history of a fracture. 26% of patients had a DEXA scan performed which demonstrated 50% had osteopenia and 29% had osteoporosis. The mean fracture risk assessment tool (FRAX) score (risk of having an osteoporosis-related fracture in the next 10 years) for a major osteoporotic fracture was 10.2% (range 2.1-34%) and for a hip fracture was 3.1% (range 0.2-18). Based on these scores 19% and 26% of patients respectively met the criteria for treatment. Overall, 58% of patients were on treatment for either osteoporosis prevention or management or for mineral bone disease. 74% of patients with osteopenia were on treatment (32% calcium supplementation, 5% alfacalcidol, and 63% cholecalciferol). 82% of patients with osteoporosis were on treatment (45% cinacalcet, 36% cholecalciferol, 18% bisphosphonate and 9% denosumab). 27% of patients with osteoporosis were managed in keeping the guidelines.

Conclusion

Our cohort have significant risk factors for osteoporosis including maintenance corticosteroid treatment and previous fractures. There is a high prevalence of osteoporosis in our cohort compared to that reported internationally (4) and they had a high predicted rate of future fractures. Increased screening with targeted treatment is required to reduce the risk of fractures in our post renal transplant patients.

Supporting and maintaining the frail patient on long-term renal replacement therapy

The number of frail patients reaching end-stage kidney disease is increasing. They have a high level of comorbidity and symptom burden which need to be considered when making management plans. For those who choose renal replacement therapy, it is important to establish goals and ceilings of care and to provide holistic care focusing on optimising quality of life. Advance care planning is the process of documenting the patient's preferences for their treatment in the event they lose capacity to make decisions about their treatment.

Unsettling Events: Understanding Migrants’ Responses to Geopolitical Transformative Episodes through a Life-Course Lens

Migration under the European Union’s (EU) Freedom of Movement is constructed as temporary and circular, implying that migrants respond to changing circumstances by returning home or moving elsewhere. This construction underpins predictions of an exodus of EU migrants from the United Kingdom (UK) in the context of Brexit. While migration data indicate an increase in outflows since the vote to leave the EU, the scale does not constitute a “Brexodus.” Moreover, EU migrants’ applications for UK citizenship have been increasing. The data, though, are not sufficiently detailed to reveal who is responding to Brexit in which way. This article aims to offer a deeper understanding of how migrants experience and respond to changing geopolitical episodes such as Brexit. Introducing the term “unsettling events,” we analyze data collected longitudinally, in the context of three moments of significant change: 2004 EU enlargement, 2008–09 economic recession, and Brexit. Examining our data, mainly on Polish migrants, through a life-course lens, our findings highlight the need to account for the situatedness of migrant experiences as lived in particular times (both personal and historical), places, and relationships. In so doing, we reveal various factors informing migrants’ experiences of and reactions to unsettling events and the ways in which their experiences and reactions potentially impact migration projects.

Urinary and serum soluble CD25 complements urinary soluble CD163 to detect active renal anti-neutrophil cytoplasmic autoantibody-associated vasculitis: a cohort study

Background

Early detection of renal involvement in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) is of major clinical importance to allow prompt initiation of treatment and limit renal damage. Urinary soluble cluster of differentiation 163 (usCD163) has recently been identified as a potential biomarker for active renal vasculitis. However, a significant number of patients with active renal vasculitis test negative using usCD163. We therefore studied whether soluble CD25 (sCD25), a T cell activation marker, could improve the detection of renal flares in AAV.

Methods

sCD25 and sCD163 levels in serum and urine were measured by enzyme-linked immunosorbent assay in 72 patients with active renal AAV, 20 with active extrarenal disease, 62 patients in remission and 18 healthy controls. Urinary and blood CD4+ T and CD4+ T effector memory (TEM) cell counts were measured in 22 patients with active renal vasculitis. Receiver operating characteristics (ROC) curves were generated and recursive partitioning was used to calculate whether usCD25 and serum soluble CD25 (ssCD25) add utility to usCD163.

Results

usCD25, ssCD25 and usCD163 levels were significantly higher during active renal disease and significantly decreased after induction of remission. A combination of usCD25, usCD163 and ssCD25 outperformed all individual markers (sensitivity 84.7%, specificity 95.1%). Patients positive for sCD25 but negative for usCD163 (n = 10) had significantly higher C-reactive protein levels and significantly lower serum creatinine and proteinuria levels compared with the usCD163-positive patients. usCD25 correlated positively with urinary CD4+ T and CD4+ TEM cell numbers, whereas ssCD25 correlated negatively with circulating CD4+ T and CD4+ TEM cells.

Conclusion

Measurement of usCD25 and ssCD25 complements usCD163 in the detection of active renal vasculitis.

Statistical strategies for the analysis of massive data sets

The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.

Comparing predictive abilities of longitudinal child growth models

The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well-being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave-one-out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.

‘Drunken Tans’: Representations of Sex and Violence in the Anglo-Irish War (1919–21)

War is a highly gendered experience which is both informed by and informs constructions of masculinity and femininity. The dominant depiction of masculine heroes and feminine victims simplifies the complex intersections of militarism, nationalism and gendered roles and identities. Focusing on a case study of the Anglo-Irish War or War of Independence (1919–1921), this paper examines how violence against women, especially sexual violence, was written about and reported in ways which framed representations of Irish and British masculinity and Irish femininity.

In addition, by analysing a range of varied sources including newspapers, autobiographical accounts and recorded testimonies, this paper attempts to assess the extent to which violence against women formed a key aspect of military practice in the war. In conclusion, I engage with some of the difficulties faced by researchers today in exploring evidence of gendered violence in specific historical, cultural and militarized contexts.

Antimicrobial resistance and molecular epidemiology using whole-genome sequencing of Neisseria gonorrhoeae in Ireland, 2014-2016: focus on extended-spectrum cephalosporins and azithromycin

High-level resistance and treatment failures with ceftriaxone and azithromycin, the first-line agents for gonorrhoea treatment are reported and antimicrobial-resistant Neisseria gonorrhoeae is an urgent public health threat. Our aims were to determine antimicrobial resistance rates, resistance determinants and phylogeny of N. gonorrhoeae in Ireland, 2014-2016. Overall, 609 isolates from four University Hospitals were tested for susceptibility to extended-spectrum cephalosporins (ESCs) and azithromycin by the MIC Test Strips. Forty-three isolates were whole-genome sequenced based on elevated MICs. The resistance rate to ceftriaxone, cefixime, cefotaxime and azithromycin was 0, 1, 2.1 and 19%, respectively. Seven high-level azithromycin-resistant (HLAzi-R) isolates were identified, all susceptible to ceftriaxone. Mosaic penA alleles XXXIV, X and non-mosaic XIII, and G120K plus A121N/D/G (PorB1b), H105Y (MtrR) and A deletion (mtrR promoter) mutations, were associated with elevated ESC MICs. A2059G and C2611T mutations in 23S rRNA were associated with HLAzi-R and azithromycin MICs of 4-32 mg/L, respectively. The 43 whole-genome sequenced isolates belonged to 31 NG-MAST STs. All HLAzi-R isolates belonged to MLST ST1580 and some clonal clustering was observed; however, the isolates differed significantly from the published HLAzi-R isolates from the ongoing UK outbreak. There is good correlation between previously described genetic antimicrobial resistance determinants and phenotypic susceptibility categories for ESCs and azithromycin in N. gonorrhoeae. This work highlights the advantages and potential of whole-genome sequencing to be applied at scale in the surveillance of antibiotic resistant strains of N. gonorrhoeae, both locally and internationally.

Smooth individual level covariates adjustment in disease mapping

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia.

Non-ignorable missingness in logistic regression

Nonresponses and missing data are common in observational studies. Ignoring or inadequately handling missing data may lead to biased parameter estimation, incorrect standard errors and, as a consequence, incorrect statistical inference and conclusions. We present a strategy for modelling non-ignorable missingness where the probability of nonresponse depends on the outcome. Using a simple case of logistic regression, we quantify the bias in regression estimates and show the observed likelihood is non-identifiable under non-ignorable missing data mechanism. We then adopt a selection model factorisation of the joint distribution as the basis for a sensitivity analysis to study changes in estimated parameters and the robustness of study conclusions against different assumptions. A Bayesian framework for model estimation is used as it provides a flexible approach for incorporating different missing data assumptions and conducting sensitivity analysis. Using simulated data, we explore the performance of the Bayesian selection model in correcting for bias in a logistic regression. We then implement our strategy using survey data from the 45 and Up Study to investigate factors associated with worsening health from the baseline to follow-up survey. Our findings have practical implications for the use of the 45 and Up Study data to answer important research questions relating to health and quality-of-life. Copyright © 2017 John Wiley & Sons, Ltd.

Antimicrobial-resistant Faecal Organisms in Algae Products Marketed as Health Supplements

Dietary supplements are increasingly popular in Irish society. One of these is blue-green algae which is used with a variety health benefits in mind. A batch of Chlorella powder was found to be contaminated with Salmonella species in Ireland in 2015. This prompted additional testing of a total of 8 samples of three different products (Chlorella, Spirulina and Super Greens), for other faecal flora and antimicrobial resistance in any bacteria isolated. All 8 samples cultured enteric flora such as Enterococci, Enterobacteriaceae and Clostridium species. Antimicrobial susceptibility testing revealed one isolate with extended-spectrum ?-lactamase (ESBL) activity and one with carbapenemase activity. Clinicians caring for vulnerable patients should be aware of the potential risk of exposure to antimicrobial resistant bacteria associated with these products.