The estimated prevalence and incidence of late stage age related macular degeneration in the UK

BACKGROUND: UK estimates of age related macular degeneration (AMD) occurrence vary. AIMS: To estimate prevalence, number and incidence of AMD by type in the UK population aged ≥50 years. METHODS: Age-specific prevalence rates of AMD obtained from a Bayesian meta-analysis of AMD prevalence were applied to UK 2007-2009 population data. Incidence was estimated from modelled age-specific prevalence. RESULTS: Overall prevalence of late AMD was 2.4% (95% credible interval (CrI) 1.7% to 3.3%), equivalent to 513 000 cases (95% CrI 363 000 to 699 000); estimated to increase to 679 000 cases by 2020. Prevalences were 4.8% aged ≥65 years, 12.2% aged ≥80 years. Geographical atrophy (GA) prevalence rates were 1.3% (95% CrI 0.9% to 1.9%), 2.6% (95% CrI 1.8% to 3.7%) and 6.7% (95% CrI 4.6% to 9.6%); neovascular AMD (NVAMD) 1.2% (95% CrI 0.9% to 1.7%), 2.5% (95% CrI 1.8% to 3.4%) and 6.3% (95% CrI 4.5% to 8.6%), respectively. The estimated number of prevalent cases of late AMD were 60% higher in women versus men (314 000 cases in women, 192 000 men). Annual incidence of late AMD, GA and NVAMD per 1000 women was 4.1 (95% CrI 2.4% to 6.8%), 2.4 (95% CrI 1.5% to 3.9%) and 2.3 (95% CrI 1.4% to 4.0%); in men 2.6 (95% CrI 1.5% to 4.4%), 1.7 (95% CrI 1.0% to 2.8%) and 1.4 (95% CrI 0.8% to 2.4%), respectively. 71 000 new cases of late AMD were estimated per year. CONCLUSIONS: These estimates will guide health and social service provision for those with late AMD and enable estimation of the cost of introducing new treatments

Travel to School and Physical Activity Levels in 9-10 Year-Old UK Children of Different Ethnic Origin; Child Heart and Health Study in England (CHASE)

BACKGROUND: Travel to school may offer a convenient way to increase physical activity levels in childhood. We examined the association between method of travel to school and physical activity levels in urban multi-ethnic children. METHODS AND FINDINGS: 2035 children (aged 9-10 years in 2006-7) provided data on their usual method of travel to school and wore an Actigraph-GT1M activity monitor during waking hours. Associations between method of travel and mean level of physical activity (counts per minute [CPM], steps, time spent in light, moderate or vigorous activity per day) were examined in models adjusted for confounding variables. 1393 children (69%) walked or cycled to school; 161 (8%) used public transport and 481 (24%) travelled by car. White European children were more likely to walk/cycle, black African Caribbeans to travel by public transport and South Asian children to travel by car. Children travelling by car spent less time in moderate to vigorous physical activity (-7 mins, 95%CI-9,-5), and had lower CPM (-32 CPM, 95%CI-44,-19) and steps per day (-813 steps, 95%CI,-1043,-582) than walkers/cyclists. Pupils travelling by public transport had similar activity levels to walkers/cyclists. Lower physical activity levels amongst car travellers' were especially marked at travelling times (school days between 8-9 am, 3-5 pm), but were also evident on weekdays at other times and at weekends; they did not differ by gender or ethnic group. CONCLUSION: Active travel to school is associated with higher levels of objectively measured physical activity, particularly during periods of travel but also at other times. If children travelling by car were to achieve physical activity levels (steps) similar to children using active travel, they would increase their physical activity levels by 9%. However, the population increase would be a modest 2%, because of the low proportion of car travellers in this urban population

Complexity of Bradley-Manna-Sipma Lexicographic Ranking Functions

In this paper we turn the spotlight on a class of lexicographic ranking functions introduced by Bradley, Manna and Sipma in a seminal CAV 2005 paper, and establish for the first time the complexity of some problems involving the inference of such functions for linear-constraint loops (without precondition). We show that finding such a function, if one exists, can be done in polynomial time in a way which is sound and complete when the variables range over the rationals (or reals). We show that when variables range over the integers, the problem is harder -- deciding the existence of a ranking function is coNP-complete. Next, we study the problem of minimizing the number of components in the ranking function (a.k.a. the dimension). This number is interesting in contexts like computing iteration bounds and loop parallelization. Surprisingly, and unlike the situation for some other classes of lexicographic ranking functions, we find that even deciding whether a two-component ranking function exists is harder than the unrestricted problem: NP-complete over the rationals and $\Sigma^P_2$-complete over the integers.Comment: Technical report for a corresponding CAV'15 pape

On Multiphase-Linear Ranking Functions

Multiphase ranking functions ($\mathit{M{\Phi}RFs}$) were proposed as a means to prove the termination of a loop in which the computation progresses through a number of "phases", and the progress of each phase is described by a different linear ranking function. Our work provides new insights regarding such functions for loops described by a conjunction of linear constraints (single-path loops). We provide a complete polynomial-time solution to the problem of existence and of synthesis of $\mathit{M{\Phi}RF}$ of bounded depth (number of phases), when variables range over rational or real numbers; a complete solution for the (harder) case that variables are integer, with a matching lower-bound proof, showing that the problem is coNP-complete; and a new theorem which bounds the number of iterations for loops with $\mathit{M{\Phi}RFs}$. Surprisingly, the bound is linear, even when the variables involved change in non-linear way. We also consider a type of lexicographic ranking functions, $\mathit{LLRFs}$, more expressive than types of lexicographic functions for which complete solutions have been given so far. We prove that for the above type of loops, lexicographic functions can be reduced to $\mathit{M{\Phi}RFs}$, and thus the questions of complexity of detection and synthesis, and of resulting iteration bounds, are also answered for this class.Comment: typos correcte

The impact of El Niño-Southern Oscillation (ENSO) on winter and early spring U.S. tornado outbreaks

© 2017 American Meteorological Society. In recent years, the potential of seasonal outlooks for tornadoes has attracted the attention of researchers. Previous studies on this topic have focused mainly on the influence of global circulation patterns [e.g., El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation, or Pacific decadal oscillation] on spring tornadoes. However, these studies have yielded conflicting results of the roles of these climate drivers on tornado intensity and frequency. The present study seeks to establish linkages between ENSO and tornado outbreaks over the United States during winter and early spring. These linkages are established in two ways: 1) statistically, by relating raw counts of tornadoes in outbreaks (defined as six or more tornadoes in a 24-h period in the United States east of the Rocky Mountains), and their destructive potential, to sea surface temperature anomalies observed in the Niño-3.4 region, and 2) qualitatively, by relating ENSO to shifts in synoptic-scale atmospheric phenomena that contribute to tornado outbreaks. The latter approach is critical for interpreting the statistical relationships, thereby avoiding the deficiencies in a few of the previous studies that did not provide physical explanations relating ENSO to shifts in tornado activity. The results suggest that shifts in tornado occurrence are clearly related to ENSO. In particular, La Niña conditions consistently foster more frequent and intense tornado activity in comparison with El Niño, particularly at higher latitudes. Furthermore, it is found that tornado activity changes are tied not only to the location and intensity of the subtropical jet during individual outbreaks but also to the positions of surface cyclones, low-level jet streams, and instability axes

Partisanship and positionality in qualitative research: Exploring the influences of the researcher’s experiences of serious crime on the research process

Debates on positionality and partisanship in the research process are long-standing, complex and often highly contentious. Engaging with, and contributing to both these key debates, this paper introduces the concept of victim-as-researcher. There is a growing body of literature which engages with participatory research undertaken with victims of crime. Nevertheless, there is a paucity of literature which speaks to the experiences of victims of serious, violent crime who subsequently engage in qualitative research with offenders who have been convicted of offences similar to the ones experienced by the researcher and which, therefore, can elicit trauma whilst in the field and through analysis. In reflecting on the dilemmas and challenges navigated by the victim-as-researcher seeking to produce an authentic, appreciative account that foregrounds the voices of offenders and seeks to humanise them, this paper engages with a number of key issues including, emotional labour, victimology and reflexivity. Following this discussion of relevant concepts and ideas, the second part of the paper focusses on practical tips to help others who share a positionality as victim-as-researcher. Thus, this paper aims to extend the literature on positionality and victimology by foregrounding the victim-as-researcher as an important category in reflexive sociological and criminological research

Simulation-based Bayesian inference for epidemic models

This is the author pre-print version. The final version is available from the publisher via the DOI in this record.A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for large-scale systems. In addition, designing efficient proposal distributions for the missing data is typically challenging. Pseudo-marginal methods instead integrate across the missing data using a Monte Carlo estimate for the likelihood, generated from multiple independent simulations from the model. These techniques can avoid the high memory requirements of DA-MCMC, and under certain conditions produce the exact marginal posterior distribution for parameters. A novel method is presented for implementing importance sampling for dynamic epidemic models, by conditioning the simulations on sets of validity criteria (based on the model structure) as well as the observed data. The flexibility of these techniques is illustrated using both removal time and final size data from an outbreak of smallpox. It is shown that these approaches can circumvent the need for reversible-jump MCMC, and can allow inference in situations where DA-MCMC is impossible due to computationally infeasible likelihoods. © 2013 Elsevier B.V. All rights reserved.T. J. M. was in part supported by Department for the Environment, Food and Rural Affairs/Higher Education Funding Council of England, grant number VT0105 and BBSRC grant (BB/I012192/1). J. V. R was in part supported by Australian Research Council’s Discovery Projects funding scheme (project number DP110102893). R. D. was in part supported by Natural Sciences and Engineering Research Council (NSERC) of Canada’s Discovery Grants Program. A. R. C. was in part supported by National Medical Research Council (NMRC/HINIR/005/2009) and NUS Initiative to Improve Health in Asia. The authors would like to thank Andrew Conlan and Theo Kypraios for useful discussions

Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes.

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics.FMN gratefully acknowledges financial support from BBSRC, USDA-ARS, USDA-Aphis PPQ, Citrus Research and Development Foundation. CAG gratefully acknowledges the support of a BBSRC Professorial Fellowship, with additional support from USDA and Defra. ARC was supported by BBSRC, USDA, the National University of Singapore, and NMRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on mode of travel (ENABLE London study, a natural experiment)

Background Interventions to encourage active modes of travel (walking, cycling) may improve physical activity levels, but longitudinal evidence is limited and major change in the built environment / travel infrastructure may be needed. East Village (the former London 2012 Olympic Games Athletes Village) has been repurposed on active design principles with improved walkability, open space and public transport and restrictions on residential car parking. We examined the effect of moving to East Village on adult travel patterns. Methods One thousand two hundred seventy-eight adults (16+ years) seeking to move into social, intermediate, and market-rent East Village accommodation were recruited in 2013–2015, and followed up after 2 years. Individual objective measures of physical activity using accelerometry (ActiGraph GT3X+) and geographic location using GPS travel recorders (QStarz) were time-matched and a validated algorithm assigned four travel modes (walking, cycling, motorised vehicle, train). We examined change in time spent in different travel modes, using multilevel linear regresssion models adjusting for sex, age group, ethnicity, housing group (fixed effects) and household (random effect), comparing those who had moved to East Village at follow-up with those who did not. Results Of 877 adults (69%) followed-up, 578 (66%) provided valid accelerometry and GPS data for at least 1 day (≥540 min) at both time points; half had moved to East Village. Despite no overall effects on physical activity levels, sizeable improvements in walkability and access to public transport in East Village resulted in decreased daily vehicle travel (8.3 mins, 95%CI 2.5,14.0), particularly in the intermediate housing group (9.6 mins, 95%CI 2.2,16.9), and increased underground travel (3.9 mins, 95%CI 1.2,6.5), more so in the market-rent group (11.5 mins, 95%CI 4.4,18.6). However, there were no effects on time spent walking or cycling

Quantifying childhood fat mass: comparison of a novel height-and-weight-based prediction approach with DXA and bioelectrical impedance

Accurate assessment of childhood adiposity is important both for individuals and populations. We compared fat mass (FM) predictions from a novel prediction model based on height, weight and demographic factors (height–weight equation) with FM from bioelectrical impedance (BIA) and dual-energy X-ray absorptiometry (DXA), using the deuterium dilution method as a reference standard. FM data from all four methods were available for 174 ALSPAC Study participants, seen 2002–2003, aged 11–12-years. FM predictions from the three approaches were compared to the reference standard using; R2, calibration (slope and intercept) and root mean square error (RMSE). R2 values were high from ‘height–weight equation’ (90%) but lower than from DXA (95%) and BIA (91%). Whilst calibration intercepts from all three approaches were close to the ideal of 0, the calibration slope from the ‘height–weight equation’ (slope = 1.02) was closer to the ideal of 1 than DXA (slope = 0.88) and BIA (slope = 0.87) assessments. The ‘height–weight equation’ provided more accurate individual predictions with a smaller RMSE value (2.6 kg) than BIA (3.1 kg) or DXA (3.4 kg). Predictions from the ‘height–weight equation’ were at least as accurate as DXA and BIA and were based on simpler measurements and open-source equation, emphasising its potential for both individual and population-level FM assessments