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Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state controlling the design of support structures. In this paper, the remaining fatigue life of the support structure for a GE Haliade 6 MW fixed-bottom jacket offshore wind turbine within the Block Island Wind Farm (BIWF) is assessed. The fatigue damage to the tower and the jacket support structure using stress time histories at instrumented and non-instrumented locations are processed. Two validated finite-element models are utilized for assessing the stress cycles. The modal expansion method and a simplified approach using static calculations of the responses are employed to estimate the stress at the non-instrumented locations—known as virtual sensors. It is found that the hotspots at the base of the tower have longer service lives than the jacket. The fatigue damage to the jacket leg joints is less than 20% and 40% of its fatigue capacity during the 25-year design lifetime of the BIWF OWT, using the modal expansion method and the simplified static approach, respectively.

With support from the US National Tsunami Hazard Mitigation Program (NTHMP), the authors have been developing tsunami inundation maps for the upper US East Coast (USEC), using high-resolution numerical modeling. These maps are envelopes of maximum elevations, velocity, or momentum flux, caused by the probable maximum tsunamis identified in the Atlantic oceanic basin, including from far-field coseismic or volcanic sources, and near-field Submarine mass failures (SMFs); the latter are the object of this work. Despite clear field evidence of past large-scale SMFs within our area of interest, such as the Currituck slide complex, their magnitude, pre-failed geometry, volume, and mode of rupture are poorly known. A screening analysis based on the Monte Carlo simulations (MCS) identified areas for possible tsunamigenic SMF sources along the USEC, indicating an increased level of tsunami hazard north of Virginia, potentially surpassing the inundation generated by a typical 100-year hurricane storm surge in the region, as well as that from the most extreme far-field coseismic sources in the Atlantic; to the south, the MCS indicated that SMF tsunami hazard significantly decreased. Subsequent geotechnical and geological analyses delimited four high-risk areas along the upper USEC where the potential for large tsunamigenic SMFs, identified in the MCS, was realistic on the basis of field data (i.e., sediment nature and volume/availability). In the absence of accurate site-specific field data, following NTHMP’s recommendation, for the purpose of simulating tsunami hazard from SMF PMTs, we parameterized an extreme SMF source in each of the four areas as a so-called Currituck proxy, i.e., a SMF having the same volume, dimensions, and geometry as the historical SMF. In this paper, after briefly describing our state-of-the-art SMF tsunami modeling methodology, in a second part, we parameterize and model the historical Currituck event, including: (1) a new reconstruction of the SMF geometry and kinematics; (2) the simulation of the resulting tsunami source generation; and (3) the propagation of the tsunami source over the shelf to the coastline, in a series of nested grids. A sensitivity analysis to model and grid parameters is performed on this case, to ensure convergence and accuracy of tsunami simulation results. Then, we model in greater detail and discuss the impact of the historical Currituck tsunami event along the nearest coastline where its energy was focused, off of Virginia Beach and Norfolk, as well as near the mouth of the Chesapeake Bay; our results are in qualitative agreement with an earlier modeling study. In a third part, following the same methodology, we model tsunami generation and propagation for SMF Currituck proxy sources sited in the four identified areas of the USEC. Finally, as an illustration of our SMF tsunami hazard assessment work, we present detailed tsunami inundation maps, as well as some other products, for one of the most impacted and vulnerable areas, near and around Ocean City, MD. We find that coastal inundation from near-field SMF tsunamis may be comparable to that caused by the largest far-field sources. Because of their short propagation time and, hence, warning times, SMF tsunamis may pose one of the highest coastal hazards for many highly populated and vulnerable communities along the upper USEC, certainly comparable to that from extreme hurricanes.

ObjectiveTo evaluate the clinical and cost-effectiveness of a new blended dental contract incentivising improved oral health compared with a traditional dental contract based on units of dental activity (UDAs).DesignNon-randomised controlled study.SettingSix UK primary care dental practices, three working under a new blended dental contract; three matched practices under a traditional contract.Participants550 new adult patients.InterventionsA new blended/incentive-driven primary care dentistry contract and service delivery model versus the traditional contract based on UDAs.Main outcome measuresPrimary outcome was as follows: percentage of sites with gingival bleeding on probing. Secondary outcomes were as follows: extracted and filled teeth (%), caries (International Caries Detection and Assessment System (ICDAS)), oral health-related quality of life (Oral Health Impact Profile-14 (OHIP-14)). Incremental cost-effective ratios used OHIP-14 and quality adjusted life years (QALYs) derived from the EQ-5D-3L.ResultsAt 24 months, 291/550 (53%) patients returned for final assessment; those lost to follow-up attended 6.46 appointments on average (SD 4.80). The primary outcome favoured patients in the blended contract group. Extractions and fillings were more frequent in this group. Blended contracts were financially attractive for the dental provider but carried a higher cost for the service commissioner. Differences in generic health-related quality of life were negligible. Positive changes over time in oral health-related quality of life in both groups were statistically significant.ConclusionsThis is the first UK study to assess the clinical and cost-effectiveness of a blended contract in primary care dentistry. Although the primary outcome favoured the blended contract, the results are limited because 47% patients did not attend at 24 months. This is consistent with 39% of adults not being regular attenders and 27% only visiting their dentist when they have a problem. Promotion of appropriate attendance, especially among those with high need, necessitates being factored into recruitment strategies of future studies.

Objective Acute heart failure syndrome (AHFS) is a major cause of hospitalisation and imparts a substantial burden on patients and healthcare systems. Tools to define risk of AHFS hospitalisation are lacking. Methods A prospective cohort study (n=628) of patients with stable chronic heart failure (CHF) secondary to left ventricular systolic dysfunction was used to derive an AHFS prediction model which was then assessed in a prospectively recruited validation cohort (n=462). Results Within the derivation cohort, 44 (7%) patients were hospitalised as a result of AHFS during 1 year of follow-up. Predictors of AHFS hospitalisation included furosemide equivalent dose, the presence of type 2 diabetes mellitus, AHFS hospitalisation within the previous year and pulmonary congestion on chest radiograph, all assessed at baseline. A multivariable model containing these four variables exhibited good calibration (Hosmer–Lemeshow p=0.38) and discrimination (C-statistic 0.77; 95% CI 0.71 to 0.84). Using a 2.5% risk cut-off for predicted AHFS, the model defined 38.5% of patients as low risk, with negative predictive value of 99.1%; this low risk cohort exhibited <1% excess all-cause mortality per annum when compared with contemporaneous actuarial data. Within the validation cohort, an identically applied model derived comparable performance parameters (C-statistic 0.81 (95% CI 0.74 to 0.87), Hosmer–Lemeshow p=0.15, negative predictive value 100%). Conclusions A prospectively derived and validated model using simply obtained clinical data can identify patients with CHF at low risk of hospitalisation due to AHFS in the year following assessment. This may guide the design of future strategies allocating resources to the management of CHF.

BackgroundHealthcare provider performance is commonly assessed using patient outcomes, e.g. survival rates. Patient characteristics that may affect outcomes in the absence of genuine provider-level differences must therefore be balanced across providers to ensure a fair comparison. There are many methods that can accommodate this patient ‘casemix’ but none that also allow the assessment of provider-level covariate effects, i.e. the potential causes of performance differences. We aim to demonstrate the utility of multilevel latent class (MLC) modelling to identify causal provider-level covariate effects after accommodating patient differences.MethodsWe simulated data for patients and providers, based on a previously utilised real-world dataset of patients diagnosed with colorectal cancer. Age at diagnosis, sex and socioeconomic status were included at the patient level, and we explored a continuous outcome. We included both binary and continuous effects at the provider level, to reflect organisational features such as surgeon speciality or available beds, although these were analysed separately to demonstrate proof-of-principle. We simulated unique sets of 100 datasets using a range of coefficient effect values and error variances. Interest lies in the ability of the MLC model to recover these simulated provider-level coefficient effects.ResultsModels contained one patient-level latent class and up to five provider-level latent classes. For the binary provider-level covariate, median recovered values were almost identical to simulated effects throughout, e.g. for the simulated coefficient value 0.500 at 33% error variance, the median recovered value was 0.499 (95% CI 0.489–0.509) across all models. For the continuous provider-level covariate, median recovered values improved as the number of provider-level latent classes were increased, e.g. for the simulated coefficient value 0.200 at 33% error variance, the median recovered value was 0.153 (95% CI 0.113–0.184) for two provider-level classes and 0.191 (95% CI 0.168–0.210) for five provider-level classes.DiscussionThe MLC modelling approach achieved successful recovery of simulated coefficient values, within credible intervals for at least three provider-level latent classes. Very small simulated coefficient values were not recovered as well as higher values, which may be due to the variability introduced during simulation dominating the coefficient effect. There is also some attenuation of effect seen for the continuous provider-level covariate. We have demonstrated the utility of this approach to separate modelling for prediction (to accommodate patient casemix) and for causal inference (to explore provider-level effects) across a data hierarchy. There is much scope to extend the assessment of upper-level causal effects by consideration of a multivariable DAG.

We evaluated clinicians’ current practice for giving advice to patients with obstructive sleep apnoea syndrome. Clinicians were invited to complete a web-based survey and indicate the advice they would give to patients in a number of scenarios about driving; they were also asked what they considered to be residual drowsiness and adequate compliance following CPAP treatment. In the least contentious scenario, 94% of clinicians would allow driving; in the most contentious a patient had a 50% chance of being allowed to drive. Following treatment with CPAP, clinicians’ interpretation of what constituted residual drowsiness was inconsistent. In each vignette the same clinician was more likely to say ‘yes’ to ‘excessive’ than to ‘irresistible’ (71%±12% vs 42%±10%, p=0.0045). There was also a lack of consensus regarding ‘adequate CPAP compliance’; ‘yes’ responses ranged from 13% to 64%. There is a need for clearer guidance; a recent update to the Driver and Vehicle Licensing Agency guidance, and a statement from the British Thoracic Society, making it clear that sleepiness while driving is the key issue, may help.

IntroductionCurrently advice about an OSAS patient's fitness to drive is based upon the severity of the condition, with or without objective measure of daytime sleepiness and their account of their driving. Although there is a trend towards increased likelihood of accidents with more severe OSAS, this is not sufficiently robust data. There are conflicting data about the relationship between perceived sleepiness and the likelihood of being involved in an accident. Recently we have established that it is possible to identify with high degree of certainty a group of OSAS sufferers who perform significantly worse than others using specific simulator parameters on an advanced office based driving simulator (miniSim). We now explore the impact of patients' perception of problem driving, demographic, clinical, and polysomnographic characteristics on the outcomes of the simulator test.Methods133 (52±10 yrs, ESS 12±5, AHI 29±21) patients completed a detailed driving related questionnaire and performed a 90 km motorway driving scenario on the miniSim. Two events were programmed to trigger evasive actions, one subtle (Veer event) where an alert driver should not crash, while with the other (Brake event) even a fully alert driver might crash. There were three possible outcomes of the simulator runs; “fail”, “indeterminate” and “pass”. The questionnaire responses, demographic, clinical and polysomnographic characteristics were compared between the three outcome categories using one way ANOVA. Logistic regression was performed to explore whether a “fail” could be predicted from any of these data.ResultsThe results of one way ANOVA are described in Abstract P20 table 1. Patients who fail the simulator test tend to report more sleepiness while driving with a higher ESS & ODI. They also have more, but statistically insignificant, near misses and history of accidents. None of this information could predict a “fail” accurately in the logistic regression analysis.Abstract P20 Table 1Distribution and outcomes of one way ANOVA of clinical parameters and scores for questionnaire categoriesFail (n=32)Indeterminate (n=47)Pass (n=54)One way ANOVAMean (SD)Mean (SD)Mean (SD)p ValueClinical parameters Age (yrs)50 (11)50 (10)55 (10)0.05 BMI (kg/m2)34 (6)35 (8)34 (5)0.33 ESS13 (6)12 (5)10 (5)0.03 AHI (events/h)34 (24)30 (23)25 (16)0.2 ODI (events/h)39 (27)35 (28)23 (15)0.01Scores for different questionnaire categories Sleepiness while driving12 (11)7 (8.5)7 (8.3)0.03 Nods/rumble1.22 (1.47)0.78 (1.19)0.77 (1.34)0.27 Accidents/near misses0.75 (1.39)0.78 (1.69)0.46 (0.86)0.42 Coping strategies7.1 (4.3)6.7 (4.9)6.6 (4.5)0.69AHI, Apnoea Hypopnoea Index; BMI, Body Mass Index; ESS, Epworth Sleepiness Score; ODI, Oxygen Desaturation Index.ConclusionsThese data confirm that patients' accounts and perception of their own driving and the severity of their OSAS may not be reliable predictors of safe driving. Whether poor performance on an advanced driving simulator is predictive of poor on road performance needs to be established.

IntroductionRoad traffic accidents (RTA) are known to peak at certain times of the day especially early afternoons. OSAS patients are at higher risk of being involved in RTA. Recently we have established that it is possible to identify with high degree of certainty a group of OSAS sufferers who perform significantly worse than others using specific simulator parameters on our advanced office based driving simulator (miniSim). We now explore whether the time of day when the study is performed affects simulator outcomes.Methods205 (52±10 yrs, ESS 12±5, AHI 33±22) patients performed a 90 km motorway driving scenario on the miniSim. Two events were programmed to trigger evasive actions, one subtle (Veer event) where an alert driver should not crash, while with the other (Brake event) even a fully alert driver might crash. There were three possible outcomes of the simulator runs; “fail”, “indeterminate” and “pass”. “Fail” was defined by any crash other than at the brake event and/or inability to complete the test. Comparisons were made between the patient populations performing the test before & after 12:00 in terms of demographics, symptoms & severity of OSAS. Outcomes on the simulator, lane position & reaction times were also compared between these groups.ResultsThere were no differences between the patients performing at the different time slots in terms of age, BMI, ESS & AHI (Abstract P21 table 1). The number of “fails”, “indeterminates” & “passes” during morning & afternoon runs were: 16/26/70 (n=112) & 22/30/41 (n=93). Patients performing in the afternoon were no more likely to fail the test than those doing it in the morning (Fisher's exact test p=0.1). There were no differences in terms of lane position or reaction times (p=0.38, 0.65).Abstract P21 Table 1Comparing patients performing before and after 12:00 hParametersPatients performing before 12:00 h (n=112)Patients performing after 12:00 h (n=93)p Values (t tests)Mean (SD)Age (years)52.7 (10.5)52.2 (10.5)0.74BMI (kg/m2)34 (6.3)35 (7)0.25ESS11 (6)12 (5)0.15AHI (events/hour)32.6 (23.3)32.7 (20)0.96ODI (events/hour)32.6 (22.5)35.4 (24)0.41SDLP (metres)0.42 (0.15)0.44 (0.13)0.38VeerRT (sec)1.63 (0.54)1.59 (0.47)0.65AHI, Apnoea Hypopnoea Index; BMI, Body mass index; ESS, Epworth Sleepiness Scale; ODI, Oxygen Destauration index; SDLP, Mean of SD of lane position; VeerRT, Reaction time at the Veer event.ConclusionThe results indicate that the time of day the study is performed is unlikely to affect outcomes on this driving simulator. It has implications for its clinical use as the test can performed at any time of the day.

A knowledge of rain-fade-slope is essential in the design of fade countermeasures. The principal method of estimation, proposed by the European Space Agency, may be inadequate for terrestrial microwave links. A revised method based on the discrete Fourier transform is examined.

In recent literature, exploratory and confirmatory factor analyses have been used to test the latent structure amongst MetS components and regression modelling is used to test the relation between chronic diseases and MetS components.