Council tax valuation bands, socio-economic status and health outcome: a cross-sectional analysis from the Caerphilly Health and Social Needs Study

Council tax valuation bands (CTVBs) are a categorisation of household property value in Great Britain. The aim of the study was to assess the CTVB as a measure of socio-economic status by comparing the strength of the associations between selected health and lifestyle outcomes and CTVBs with two measures of socio-economic status: the National Statistics Socio-Economic Classification (NS-SEC) and the 2001 UK census-based Townsend deprivation index. METHODS: Cross-sectional analysis of data on 12,092 respondents (adjusted response 62.7%) to the Caerphilly Health and Social Needs Study, a postal questionnaire survey undertaken in Caerphilly county borough, south-east Wales, UK. The CTVB was assigned to each individual by matching the sampling frame to the local authority council tax register. Crude and age-gender adjusted odds ratios for each category of CTVB, NS-SEC and fifth of the ward distribution of Townsend scores were estimated for smoking, poor diet, obesity, and limiting long-term illness using logistic regression. Mean mental (MCS) and physical (PCS) component summary scores of the Short-Form SF-36 health status questionnaire were estimated in general linear models. RESULTS: There were significant trends in odds ratios across the CTVB categories for all outcomes, most marked for smoking and mental and physical health status. The adjusted odds ratio for being a smoker in the lowest versus highest CTVB category was 3.80 (95% CI: 3.06, 4.71), compared to 3.00 (95% CI: 2.30, 3.90) for the NS-SEC 'never worked and long-term unemployed' versus 'higher managerial and professional' categories, and 1.61 (95% CI: 1.42, 1.83) for the most deprived versus the least deprived Townsend fifth. The difference in adjusted mean MCS scores was 5.9 points on the scale for CTVB, 9.2 for NS-SEC and 3.2 for the Townsend score. The values for the adjusted mean PCS scores were 6.3 points for CTVB, 11.3 for NS-SEC, and 2.5 for the Townsend score. CONCLUSION: CTVBs assigned to individuals were strongly associated with the health and lifestyle outcomes modelled in this study. CTVBs are readily available for all residential properties and deserve further consideration as a proxy for socio-economic status in epidemiological studies in Great Britain

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available

An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring.

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.This work was supported by a UK Engineering and Physical Sciences Research Council (EPSRC) Impact Acceleration Award awarded to PHC; the EPSRC [EP/H019944/1]; the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]; the Oxford and King’s College London Centres of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC under grants [WT88877/Z/09/Z] and [WT088641/Z/09/Z]; the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s & St Thomas’ NHS Foundation Trust and King’s College London; the NIHR Oxford Biomedical Research Centre Programme; a Royal Academy of Engineering Research Fellowship (RAEng) awarded to DAC; and EPSRC grants EP/P009824/1 and EP/N020774/1 to DAC

Predicting Clinical Deteriorations using Wearable Sensors

Introduction Acutely-ill hospitalised patients are at risk of clinical deteriorations such as cardiac arrest, admission to intensive care, or unexpected death. Currently, patients are manually assessed every 4-6 hours to determine the likelihood of subsequent deterioration. However, this is limited to intermittent assessments, delaying time-sensitive interventions. Wearable sensors, combined with an alerting system, could provide continuous automated assessments of the likelihood of deteriorations. To be suitable for hospital use, wearable sensors must be unobtrusive and provide reliable measurements of key vital signs including breathing rate (BR), a key predictor of deteriorations. The aims of this work were: (i) to develop a technique for monitoring BR unobtrusively using wearable sensors, and (ii) to assess whether wearable sensors provide reliable predictions of deteriorations when using this technique. Monitoring breathing rate (BR) unobtrusively Current methods for monitoring BR using wearable sensors are obtrusive. An alternative approach is to estimate BR from electrocardiogram or pulse oximeter signals, which are already acquired by wearable sensors to monitor heart rate and blood oxygen levels. Both signals are subtly modulated by breathing, providing opportunity to use them to monitor BR. I assessed the performance of previously proposed signal processing techniques for estimating BR from these signals in both healthy and hospitalised subjects. Although some techniques were precise enough for use with healthy subjects in the laboratory, they were imprecise when used with hospital patients. Therefore, I developed a novel technique, combining the strengths of time- and frequency-domain techniques. Its performance was assessed on data from 264 subjects. In hospital patients, the technique provided highly precise BRs 86% of the time, which exceeds the performance of manual observation, the current clinical standard. Assessing the reliability of wearable sensors for predicting deteriorations I implemented methods for rejecting unreliable sensor data, and for fusing continuous multiparametric data, to predict deteriorations. These were used alongside the novel technique for monitoring BR to predict deteriorations using wearable sensors. The system was assessed in a clinical trial of 184 hospital patients, conducted in collaboration with clinicians. The reliability of the system was assessed by comparing its predictions against documented deteriorations. Its predictive value was similar to that of the routine manual assessments (AUROCs of 0.78 vs 0.79). Crucially it provided continuous assessment, potentially providing predictions of deteriorations hours earlier than routine practice. Conclusion This work has demonstrated the potential for wearable sensors to reliably and unobtrusively predict deteriorations, when coupled with a novel technique for monitoring BR. This could improve patient outcomes, and reduce costs. Further work should investigate which patients would benefit most from this technology, and whether it could reduce clinical workload. In the future the technology could potentially be used with consumer wearables to improve patient safety in the community, where clinical expertise is less readily available.This poster was displayed at the STEM for Britain event, held in the Houses of Parliament (London, UK) on 12th March 2018

Respiratory rate monitoring to detect deteriorations using wearable sensors

This poster provides an overview of the work described in: P. H. Charlton, "Continuous respiratory rate monitoring to detect clinical deteriorations using wearable sensors," Ph.D. Thesis, King’s College London, 2017.This poster was first presented at the Bioengenuity Keynotes Conference, held on Monday 6th March at the University of Oxford

GSFC OSTM, Jason-l and TOPEX POD Update

The OSTM (Jason-2) has been in orbit for three years (since June 2008), and the full suite of altimeter data from TOPEX/Poseidon, Jason-I and Jason-2 now span nearly twenty years since the launch of TOPEX in 1992. Issues that affect the stability of the orbits through time and the orbit accuracy include the reference frame, the radiation pressure models for the altimeter satellites and the fidelity of the dynamic force model, including time-variable gravity, as well as the performance of the individual tracking systems. We have conducted detailed analyses of the new ITRF2008 reference frame and find only a small effect on global mean sea level compared to ITRF2005, although we note an improvement in POD quality over the most recent time periods for Jason-2. In the past year we have developed a new time series of orbits for TOPEX/Poseidon, Jason-I, and Jason-2 based on the ITRF2008 reference frame using SLR and DORIS data and for Jason-2 using GPS data. In addition, we have continued to experiment with improvements to the radiation pressure model for the altimeter satellites in particular the Jason satellites since these nonconservative force model errors now rank as the most outstanding source of error on altimeter satellite POD. In the previous (ITRF2005-based) and current (ITRF2008-based) orbits we have relied on a simplified time-variable gravity (TVG) model, derived from GRACE solutions. We have recently experimented with improvements using higher fidelity TVG models (both temporally and spatially) and report on the results. We have computed a time series of GPS-only reduced-dynamic orbits at GSFC, and used these in conjunction with the SLR-DORIS dynamic and reduced-dynamic orbits to assess reference fiame stability with respect to the different tracking systems for both ITRF2005 and ITRF2008. We show through internal (GSFConly) and external comparisons (with other analysis centers) that the radial orbit accuracy for Jason-2 remains at I cm

Decline of a rare moth at its last known English site : causes and lessons for conservation

The conditions required by rare species are often only approximately known. Monitoring such species over time can help refine management of their protected areas. We report population trends of a rare moth, the Dark Bordered Beauty Epione vespertaria (Linnaeus, 1767) (Lepidoptera: Geometridae) at its last known English site on a protected lowland heath, and those of its host-plant, Salix repens (L.) (Malpighiales: Salicaceae). Between 2007 and 2014, adult moth density reduced by an average of 30-35% annually over the monitored area, and its range over the monitored area contracted in concert. By comparing data from before this decline (2005) with data taken in 2013, we show that the density of host-plants over the monitored area reduced three-fold overall, and ten-fold in the areas of highest host-plant density. In addition, plants were significantly smaller in 2013. In 2005, moth larvae tended to be found on plants that were significantly larger than average at the time. By 2013, far fewer plants were of an equivalent size. This suggests that the rapid decline of the moth population coincides with, and is likely driven by, changes in the hostplant population. Why the host-plant population has changed remains less certain, but fire, frost damage and grazing damage have probably contributed. It is likely that a reduction in grazing pressure in parts of the site would aid host-plant recovery, although grazing remains an important site management activity. Our work confirms the value of constant monitoring of rare or priority insect species, of the risks posed to species with few populations even when their populations are large, of the potential conflict between bespoke management for species and generic management of habitats, and hence the value of refining our knowledge of rare species' requirements so that their needs can be incorporated into the management of protected areas

Investigating the beneficial traits of Trichoderma hamatum GD12 for sustainable agriculture : insights from genomics

Trichoderma hamatum strain GD12 is unique in that it can promote plant growth, activate biocontrol against pre- and post-emergence soil pathogens and can induce systemic resistance to foliar pathogens. This study extends previous work in lettuce to demonstrate that GD12 can confer beneficial agronomic traits to other plants, providing examples of plant growth promotion in the model dicot, Arabidopsis thaliana and induced foliar resistance to Magnaporthe oryzae in the model monocot rice. We further characterize the lettuce-T. hamatum interaction to show that bran extracts from GD12 and an N-acetyl-β-D-glucosamindase-deficient mutant differentially promote growth in a concentration dependent manner, and these differences correlate with differences in the small molecule secretome. We show that GD12 mycoparasitises a range of isolates of the pre-emergence soil pathogen Sclerotinia sclerotiorum and that this interaction induces a further increase in plant growth promotion above that conferred by GD12. To understand the genetic potential encoded by T. hamatum GD12 and to facilitate its use as a model beneficial organism to study plant growth promotion, induced systemic resistance and mycoparasitism we present de novo genome sequence data. We compare GD12 with other published Trichoderma genomes and show that T. hamatum GD12 contains unique genomic regions with the potential to encode novel bioactive metabolites that may contribute to GD12's agrochemically important traits. Read Full Tex

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Global well-posedness of the 3-D full water wave problem

We consider the problem of global in time existence and uniqueness of solutions of the 3-D infinite depth full water wave problem. We show that the nature of the nonlinearity of the water wave equation is essentially of cubic and higher orders. For any initial interface that is sufficiently small in its steepness and velocity, we show that there exists a unique smooth solution of the full water wave problem for all time, and the solution decays at the rate $1/t$.Comment: 60 page