Ability to pay and equity in access to Italian and British National Health Services

Background: Equity in delivery and distribution of health care is an important determinant of health and a cornerstone in the long way to social justice. We performed a comparative analysis of the prevalence of Italian and British residents who have fully paid out-of-pocket for health services which they could have obtained free of charge or at a lower cost from their respective National Health Services. Methods: Cross-sectional study based on a standardized questionnaire survey carried out in autumn 2006 among two representative samples (n = 1000) of the general population aged 20-74 years in each of the two countries. Results: 78% (OR 19.9; 95% CI 15.5-25.6) of Italian residents have fully paid out-of-pocket for at least one access to health services in their lives, and 45% (OR 18.1; 95% CI 12.9-25.5) for more than five accesses. Considering only the last 2 years, 61% (OR 16.5; 95% CI 12.6-21.5) of Italians have fully paid out-of-pocket for at least one access. The corresponding pattern for British residents is 20 and 4% for lifelong prevalence, and 10% for the last 2 years. Conclusions: Opening the public health facilities to a privileged private access to all hospital physicians based on patient's ability to pay, as Italy does, could be a source of social inequality in access to care and could probably represent a major obstacle to decreasing waiting times for patients in the standard formal ‘free of charge' way of acces

Thermal detection of single e-h pairs in a biased silicon crystal detector

We demonstrate that individual electron-hole pairs are resolved in a 1 cm$^2$ by 4 mm thick silicon crystal (0.93 g) operated at $\sim$35 mK. One side of the detector is patterned with two quasiparticle-trap-assisted electro-thermal-feedback transition edge sensor (QET) arrays held near ground potential. The other side contains a bias grid with 20\% coverage. Bias potentials up to $\pm$ 160 V were used in the work reported here. A fiber optic provides 650~nm (1.9 eV) photons that each produce an electron-hole ($e^{-} h^{+}$) pair in the crystal near the grid. The energy of the drifting charges is measured with a phonon sensor noise $\sigma$ $\sim$0.09 $e^{-} h^{+}$ pair. The observed charge quantization is nearly identical for $h^+$'s or $e^-$'s transported across the crystal.Comment: 4 journal pages, 5 figure

Síndrome metabólica e disfunção eréctil - avaliação de parâmetros clínicos e hemodinâmicos

Objetivos Calcular a prevalência de fatores de risco cardiovascular, incluindo a síndrome metabólica (SM), numa série de doentes portugueses com disfunção erétil (DE) e quantificar o impacto individual e agregado dos mesmos, nos parâmetros hemodinâmicos e no grau de severidade reportada. Material e métodos Estudo de uma série de 408 doentes com DE seguidos em consulta de Urologia, no período 2008-2010. A SM foi definida pelos critérios propostos pela National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III), tendo sido quantificadas as variáveis hipertensão arterial (HTA), intolerância a glicose (IG), hipertrigliceridemia (HTG), diminuição de HDL e obesidade central, sob a forma dicotómica. O estudo hemodinâmico foi efetuado por eco-doppler peniano dinâmico (D-PCDU) e a avaliação da severidade da DE recorrendo ao questionário International Index of Erectile Function 5-questions (IIEF-5). Resultados Verificou-se uma prevalência elevada de HTA (46,3%), IG (36,0%), HTG (24,8%), diminuição de HDL (22,3%) e obesidade central (41,2%). A prevalência de SM foi de 26,5%. O IIEF-5 e o pico de velocidade sistólica (PSV) apresentaram medianas de 12,0 e 34,0 cm/s, respetivamente. As análises multivariadas revelaram a HTA e a IG como fatores independentes influenciando negativamente o valor do PSV (p = 0,002) e o score do IEEF-5 (p = 0,010), respetivamente. Conclusão Enfatiza-se a elevada prevalência de fatores de risco cardiovascular numa população de doentes com DE, assim como a forte associação independente da HTA ao agravamento dos parâmetros hemodinâmicos da função erétil.Objectives To estimate the prevalence of cardiovascular risk factors, including metabolic syndrome (MS), in a series of Portuguese patients with erectile dysfunction (ED) and to quantify their individual and aggregate role regarding penile hemodynamics and degree of ED severity. Material and methods A cross-sectional study of 408 patients seen in the Urology Department of Hospital Sao João (Portugal) within the period 2008-2010 was performed. MS was defined in accordance with the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. For this purpose, we used the dichotomized variables: hypertension (HTA), glucose intolerance (GI), hypertriglyceridemia (HTG), decreased HDL cholesterol and central obesity. Penile hemodynamics were measured using the dynamic penile color Doppler ultrasound (D-PCDU) and ED severity was assessed with the International Index of Erectile Function-5 questions (IIEF-5). Results There was high prevalence of HTA (46.3%), GI (36.0%), HTG (24.8%), decreased HDL (22.3%) and central obesity (41.2%). Prevalence of MS was 26.5%. The median of IIEF-5 and peak systolic velocity (PSV) was 12.0 and 34.0 cms, respectively. Multivariate analysis revealed HTA and GI as independent factors decreasing the value of PSV (p = .002) and the score IEEF-5 (p = .010), respectively. Conclusion We emphasize the high prevalence of cardiovascular risk factors in a population of patients with ED as well as the strong independent association between AHT and hemodynamic worsening of erectile function

Measurement Of Quasiparticle Transport In Aluminum Films Using Tungsten Transition-Edge Sensors

We report new experimental studies to understand the physics of phonon sensors which utilize quasiparticle diffusion in thin aluminum films into tungsten transition-edge-sensors (TESs) operated at 35 mK. We show that basic TES physics and a simple physical model of the overlap region between the W and Al films in our devices enables us to accurately reproduce the experimentally observed pulse shapes from x-rays absorbed in the Al films. We further estimate quasiparticle loss in Al films using a simple diffusion equation approach.Comment: 5 pages, 6 figures, PRA

The influence of metabolic profile of obese men on the severity of erectile dysfunction: Are metabolically healthy obese individuals protected?

Objective: To determine the prevalence of erectile dysfunction (ED) in metabolically healthy obese (MHO) individuals, and to compare ED severity and hypogonadism prevalence in MHO, metabolically unhealthy obese (MUO) and metabolically healthy non-obese individuals.Material and methods: ED patients (n=460) were evaluated by standardized protocol, that included clinical evaluation, abridged 5-item version of the International Index of Erectile Function (IIEF-5) questionnaire survey, and Penile Duplex Doppler Ultrasound (PDDU) exam. Patients were classified as obese [body mass index (BMI) =30.0 kg/m2] and non-obese (BMI <30.0 kg/m2), and metabolic health status was defined by National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) criteria. Statistical analysis was performed and statistical significance was considered at p-level <0.05.Results: The mean age of the subjects was 56.2±10.5 years. MHO was present in 40% of obese individuals (n=37). MUO had lower mean peak systolic velocity (mPSV) compared to MHO (28.1 cm/s vs. 36.9 cm/s; p=0.005), and IIEF-5 scores were also lower in MUO compared to MHO patients (10.2 vs. 13.1; p=0.018). No statistical differences in IIEF-5 score, mPSV and hypogonadism prevalence between MHO and metabolically healthy non-obese (MHNO) patients were observed.Conclusion: Our results lead us to conclude that healthy metabolic profile protects obese individuals from severity of ED. The strong association between obesity and ED may be otherwise attributed to metabolic abnormalities present in the obese

Physics-informed machine learning methods for reduced order modeling

LAUREA MAGISTRALELa necessità di calcolare in maniera rapida e accurata la soluzione di equazioni alle derivate parziali (PDEs) parametrizzate si traduce tradizionalmente nello sviluppo di modelli di ordine ridotto di natura lineare. Questi approcci data-driven, come la Proper Orthogonal Decomposition (POD), sono oggigiorno diffusi e già implementati all'interno delle più famose librerie di algebra lineare per qualsiasi linguaggio di programmazione scientifico. Mentre il grado di accuratezza che in genere essi raggiungono quando applicati a PDEs di tipo lineare e stazionario è soddisfacente, sia le loro prestazioni che la loro capacità di ridurre la dimensionalità del problema calano una volta che lo stesso sia instazionario o eventualmente non lineare. Per rispondere all'insorgere di questa problematica, nel campo del Deep Learning sono stati recentemente sviluppati nuovi approcci. Nel presente lavoro vengono presentate due strategie completamente differenti. La prima consiste nel calcolo della soluzione parametrizzata in un unico step mediante l'impiego di Physics Informed Neural Networks (PINNs), le quali vengono allenate allo scopo di minimizzare il residuo della PDE soddisfacendo allo stesso tempo le condizioni al contorno (ed eventualmente iniziali) prescritte, per ogni valore del parametro all'interno del suo dominio. Il principale vantaggio di questa strategia, chiamata PINNs-FOM, rispetto ai solutori di PDEs tradizionali risiede nel fatto che le PINNs sono degli algoritmi completamente mesh-free. Dall'altro lato, comparate con i classici ROMs, le PINNs sono di particolare interesse in quanto non richiedono la generazione di soluzioni high-fidelity, chiamate snapshots, particolarmente costose dal punto di vista computazionale. La seconda idea consiste nello sviluppo di un ROM non lineare basato sull'implementazione di un convolutional autoencoder. Sebbene questo approccio, di seguito chiamato PDNNs-Autoencoder (Projection Driven Neural Network-Autoencoder), si basi ancora sulla proiezione di costosi snapshots sullo spazio in cui cercare le soluzioni candidate, la capacità di questo metodo di estrarre le informazioni più importanti dai dati è superiore rispetto ai ROM di tipo lineare. Per questo motivo i PDNNs-Autoencoder offrono un'accuratezza superiore, nei casi sopracitati in cui i ROM lineari perdono efficacia. L'accuratezza e l'efficienza di queste due strategie verranno valutate su 4 differenti casi test. I risultati ottenuti saranno confrontati con quelli derivanti da ROM di tipo lineare, ma che impiegano anch'essi reti neurali artificiali per la predizione rapida della soluzione in tempo reale.The need for a fast and accurate solution of parameterized Partial Differential Equations (PDEs) traditionally translates into the development of linear Reduced Order Models (ROMs). These data-driven approaches, such as the Proper Orthogonal Decomposition (POD), are nowadays widespread and embedded in the most famous linear-algebra libraries of any scientific programming language. While their accuracy is in general satisfactory when dealing with linear and steady parameterized PDEs, both their performances and dimensionality reduction capabilities decay in the case of time-dependent and possibly non-linear problems. To mitigate this issue, novel approaches have been devised in the Deep Learning context. In this work, two completely different strategies are presented. The first one consists of the computation of the parameterized solution in one step by means of Physics Informed Neural Networks (PINNs), which are trained to minimize the PDE residual while satisfying the boundary and initial conditions, for any instance of the parameter inside its domain. The main advantage of this strategy, named PINNs-FOM, compared with traditional PDE solvers lies in the fact that they are completely mesh-free algorithms. On the other hand, compared to classical ROMs, PINNs are of particular interest because they do not require the generation of high-fidelity solutions, called snapshots, via computationally expensive Full Order Models (FOMs) solvers. The second idea consists of the development of a convolutional-autoencoder based non-linear ROM. Despite this approach, named PDNNs-Autoencoder (Projection Driven Neural Network-Autoencoder) still relies on the projection of costly snapshots on a lower dimensional manifold, the feature extractor capabilities of the autoencoder are superior to the linear ROM ones, leading to an improvement in accuracy, especially in the above mentioned cases in which linear ROMs are less effective. The accuracy and efficiency of these two strategies will be assessed on 4 different parameterized PDEs test problems. The results will be compared with linear ROM based approaches, which in turn rely on Artificial Neural Networks too for the fast online prediction of the solution