Actual Health State Research Articles

The Health and Functional Age Trend Self-assessment of the Older Workers and Retirees in Ukraine from Online Data Human Health Passport in COVID-19 Pandemic

For a general health assessment of the workers in organized industries, and unorganized groups of the unemployed and retirees, systematic online health monitoring has been used. Health monitoring includes a scale assessment of functional tests. The actual state of population health is still not included in the registers of National Statistics of Ukraine for example workers, pensioners and unemployment. The purpose of the study was systemic online health monitoring, to assess the impact of professional, demographic, and socio-economic factors in the Covid-19 pandemic condition and the limited capacity of the health care system in Ukraine. Methods: The study used the intrinsic capacity assessment scales, the five-question scale to detect muscle dysfunction in older persons, as well as some questions regarding residual performance, psychophysiological methods, and characteristics of the functional age. The questionnaire Human Health Passport 1.1 includes 70 questions; 21 of them were assessed by 1 point for positive answer and zero for negative case. The screening was carried out online on social media to Internet users aged 31 to 90. In the study 377 respondents of which 83.83% were employees. The Human Health Passport screening showed reliability (Cronbach alfa = 0.737), and correlation relationships of the main estimated indicators are significant. So, 54% of respondents do need health care under a physician’s supervision. The 39% of respondents need should convey the proper individual schedule of workout and labour hours, as well as physical activity and breathing exercises because only 7% of respondents are healthy. Correlation analysis of the scale results showed a significant relationship between age, with static balancing, falls during the last 6 months, and the ability to 5 times sit to stand test within 14 seconds. The optimal management of Human Health Passport screening helps to reduce the harmful influence on the environment, and transport expenses for doctor visits and saves the doctor's time for patient examination and prevention of the exposure and the spread of COVID-19. The screening showed the existing reserves of training for health promotion and, the overstrain of the functional systems of the body in the working population - 76%—by the purpose. _________________________________________________________________________________________ Keywords: remote health screening; retirees; biological age; ageing; elderly workers

Laminated glass slabs design challenges: dynamic identification of a fractured pedestrian walkway

AbstractDesign codes provide requirements for traditional slabs to ensure both safety levels and in‐service functionality criteria. However, for existing structures, an initial diagnostic knowledge phase is essential to characterize the actual health state, e.g. based on structural health monitoring (SHM) with operational modal analysis (OMA) techniques. Considering recent advancements in glass as a structural material, laminated glass (LG) slabs are increasingly used in constructions and are mostly designed to account for the intrinsic brittleness and limited tensile resistance of glass. The latest pre‐code CEN/TS 19100:2021 is paving the road for an imminent future Eurocode 10 dedicated to providing new standards for the innovative use of structural glass. In the current study, a brief review of the design challenges of working with structural glass is presented. After that, the authors provided a simple experimental dynamic identification example of an LG pedestrian walkway located in the Basilica of Aquileia, Trieste (Italy). Specifically, the fundamental mode was compared to a healthy and a partially fractured LG slab. Functionality aspects related to comfort standards with respect to in‐service vibration issues are also detailed.

Evolutionary numerical model for cultural heritage structures via genetic algorithms: a case study in central Italy

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution.

An IoT enabled smart healthcare system using deep reinforcement learning

AbstractSmart healthcare systems do exist with a variety of architectures. However, the hunt for better smart healthcare systems is more predominant. The cutting‐edge field of IoT (internet of things) and technological developments provide better solutions for smart healthcare systems using Sensor–Body Area Networks. Thus, the patient's sensor data can be collected, stored, analyzed, and suitable treatments can be offered, over the inter‐network, anytime, anywhere. The most complex part in such systems is the physician analysis of the huge volume of patient's data, to handle and prepare suitable diagnose and treatment for humanity. This article reveals a methodology of Deep Reinforcement Learning for smart healthcare decisions in an IoT interfaced Smart Healthcare–intelligent monitoring system. The system incorporates four layers, patient data collection, Edge computing, patient data transmission and Cloud computing. IoT is employed for automatic collection of Patient's data and for transmission of data, to data centers. Artificial intelligence techniques are used to analyze these data to provide suitable decisions, diagnosis, and treatment for those patients and humanity. Deep Reinforcement Learning provides the platform for smart decisions, diagnosis, and treatment. The investigation was experimented with synthetic simulated data of various BAN sensors. We developed a data set of size 286, which contains 21 different health parameters. After pre‐processing, these data were stored in the Amazon web services (AWS) cloud server using (message queue telemetry and transport) MQTT–IoT protocol. Initially, the Deep Q‐Network (DQN) was imposed to the training algorithm. The methodology was examined in PyTorch using a single GTX 1080 Ti X GPU with the training data sizes from 27 to 1536. The training time was about 10,000 to 90,000 s for training 500 epochs. In the high dimensional action space environment, the algorithm responded slowly to analyze, explore, and determine effective healthcare strategies. The systems convergence response of estimated hidden health state (g') and the actual health state (g) for the 21 different health parameters were estimated, whose values range from 0 to 1. The system responded with smart decisive interventions, which were good and close to that of a Physician's decision. The proposed methodology is definitely a promising solution for a smart and economic telemedicine.

Research on deterioration evolution trend of primary loop piping in nuclear power plant based on fusion health index

The perennial operation of nuclear power primary loop piping leads to deterioration of the piping and the potential risk of leakage. At present, there is no reliable technology to detect the leakage directly. This paper proposes a method to obtain the piping deterioration evolution trend based on the analysis of sets of data that can reflect real state of the piping. The specific method is to quantitatively construct a fusion health index model based on the improved Mahalanobis distance, which integrates the analytic hierarchy process and the entropy weight method. A prediction model combining convolutional neural networks and long short-term memory neural networks is established to make the trend analysis and prediction. The experimental results show that the method can better reflect the actual health state of the piping and effectively predict the deterioration evolution trend, which provides a specific reference value for ensuring the safe and stable operation of equipment.

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

ОСОБЕННОСТИ ОТКЛОНЯЮЩЕГОСЯ ПОВЕДЕНИЯ ДЕТЕЙ И ПОДРОСТКОВ С СИНДРОМОМ ДЕФИЦИТА ВНИМАНИЯ И ГИПЕРАКТИВНОСТИ С УЧЕТОМ СОЦИАЛЬНО-ГИГИЕНИЧЕСКИХ РАЗЛИЧИЙ

The peculiarities of the behavior of children and adolescents are the result of the influence on the body not only of hereditary predisposition and actual state of health, in particular mental, but also as a result of the influence of microsocial risk factors (family, in­school). The article presents the results of a comparative analysis of the prevalence of attention deficit hyperactivity disorder (ADHD) and the deviating behavior of children and adolescents of two groups raised in residential institutions and raised in families. A larger number of children with ADHD were identified among pupils of residential institutions, including those with a pronounced degree of severity of this pathology, with a higher tendency to implement aggressive and addictive behavior in comparison with children raised in families (37,2 ± 7,4 % and 11,1 ± 4,7 %, respectively; P < 0,05). In each group, children with a tendency to self­injurious and self­destructive behavior, a commitment to commit offenses and control behavioral manifestations of emotional reactions are found with equal frequency.

The intelligent monitoring and evaluation of the psychophysiological state of the workers employed on offshore oil and gas platforms

Oil and gas companies have an urgent need for technologies that provide complete and reliable information about the actual state of health and safety of personnel. To solve this problem, the article proposes a concept solution for the development of a system monitoring of the psychophysiological health of workers employed on offshore oil platforms. The concept is based on a person-centered approach and allows monitoring of health of employees simultaneously linking them to the context of the environment. The urgency of the problem is confirmed by statistical data, according to which workers in the oil and gas industry are 8 times more likely to get injured. The article analyzes the specific features of the professional activity of the workers employed on offshore oil platforms and shows that the deterioration of their health and psychological condition due to the long-term “sea environment” is unavoidable. It offers to develop an intelligent system for monitoring the psychophysiological condition of workers employed on offshore oil platforms and to assess its suitability for their position with the reference to the Cattell test and fuzzy patterns recognition. The development and systematic operation of such a system may timely detect undesirable consequences for the health status of workers employed on offshore oil platforms and prevent wrong decisions due to the “human factor”

Demographics Associated With Stress, Severe Mental Distress, and Anxiety Symptoms During the COVID-19 Pandemic in Japan: Nationwide Cross-sectional Web-Based Survey.

BackgroundWith the spread of COVID-19, the deterioration of public mental health has become a major global and social problem.ObjectiveThe purpose of this study was to elucidate the relationship between the 3 mental health problems associated with COVID-19, that is, perceived stress, severe mental distress, and anxiety symptoms, and the various demographic factors, including occupation.MethodsA nationwide web-based questionnaire survey was conducted in Japan from August 4 to 31, 2020. In addition to sociodemographic data, the degrees of perceived stress, severe mental distress, and anxiety symptoms associated with COVID-19 were measured. After performing a descriptive statistical analysis, factors related to stress, severe mental distress, and anxiety symptoms were analyzed using logistic regression analysis.ResultsA total of 8203 respondents submitted survey responses, among whom 34.9% (2861/8203) felt intense stress associated with COVID-19, 17.1% (1403/8203) were depressed, and 13.5% (1110/8203) had severe anxiety symptoms. The logistic regression analysis showed that each of the 3 mental health problems were prevalent in females, nonbinary gender, people in their 50s, 60s and older, respondents who visited psychiatrists, and those currently in psychiatric care. Severe mental distress and anxiety symptoms were associated with the number of effective lifestyle coping strategies during the lockdown period. Severe mental distress was only prevalent in teenagers and respondents in their 20s, as students tended to develop stress and severe mental distress. With regard to occupation, working in nursing care and welfare, education and research, and medical and health sectors was associated with stress; however, working in these occupations was not associated with severe mental distress and anxiety symptoms. Unemployment was associated with severe mental distress and anxiety symptoms. All 3 mental health problems were prevalent in part-time workers and those working in entertainment and arts sectors.ConclusionsGender, age, occupation, history of psychiatric visits, and stress coping mechanisms were associated with mental health during the COVID-19 pandemic, but their associations with stress, severe mental distress, and anxiety symptoms differed. In addition, the actual state of mental health varied according to the respondents’ occupation. It is necessary to consider the impact of the COVID-19 pandemic on mental health not only at the individual level but also at the occupational level.

Discordance in prognostic ability between physician assessed NYHA classification and self-reported health status in patients with acute heart failure

Abstract Background Especially in patients with acute heart failure (AHF) the NYHA classification remains of uncertain representation of patients' actual health state. Alternatively, patient's subjective well-being, in terms of health-related quality of life (HRQL), showed to have an excellent prognostic ability in out clinic patients with chronic heart failure. Objectives It is unknown whether HRQL instruments can assess a more reliable prognostication in patients hospitalized due to AHF than the NYHA classification. Methods Goal Directed Afterload Reduction in Acute Congestive Cardiac Decompensation Study (GALACTIC) was a multicenter, randomized, open-label blinded-end-point trial that emphasized early intensive and sustained vasodilation in adult patients hospitalized due to AHF with NYHA functional class III/IV, however provided neutral findings. HRQL was assessed by the generic EQ-5D-3L which is a 3-leveled 5-item instrument and the disease-specific Kansas City Cardiomyopathy Questionnaire (KCCQ). Unadjusted and adjusted Cox regression models were performed after patients were grouped into low (EQ-5D −0.074<0.25; KCCQ 0<25), moderately low (0.25<0.5; 25<50), moderately high (0.5<0.75; 50<75) and high HRQL (0.75–1.0; 75–100). Results 781 patients were enrolled in 10 centres in 5 countries over 2 continents among which 536 (69%) patientshad completed theEQ-5D and 419 (54%) the KCCQ shortly after admission. Within 180 days of follow-up69 (13%) and 54 (13%) patients died and 151 (28%) and 122 (29%) died or were rehospitalized due to AHF, respectively. Cumulative incidence as well as HRs in patients grouped according to NYHA (n=536) indicated a comparable or significantly lower risk in patients with NYHA IV: e.g. for the combined outcome HR 1.07 (95% CI 0.777–1.473) and aHR 0.463 (95% CI 0.245–0.875). Whereas HRs in patients grouped according to both, EQ-5D (n=536) and KCCQ (n=419), increased from the group with highest to the group with the lowest HRQL: e.g. aHR for moderately high 1.11 (95% CI 0.718–1.715), for moderately low 1.721 (95% CI 1.102–2.688) and for low EQ-5D index 1.891 (95% CI 1.136–3.149) referenced to high HRQL (EQ-5D index 0.75–1.0). Conclusions These findings corroborate and extend previous work suggesting that NYHA classification poorly discriminates AHF patients' prognosis and challenge its' extensive application. HRQL might be a possible alternative to easily assess these patients' heath state. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Swiss National Science Foundation, the Swiss Heart Foundation A. 180-day mortality; B. composite outcome

PERSONALITY’S CORPOREALITY INTELLIGENCE INDICATORS RESEARCH

The article contains a personality’s corporeality intelligence phenomenon specifics theoretical analysis statement. The article presents the personality’s corporeality intelligence shaping factors - self-assessment, gender roles of the individual and his sexuality, regulatory, cognitive and emotional structures of the personality, assessment of their appearance and body image, health and subjective assessment – theoretical analysis results. The procedure and methodical bases of personality’s corporeality intelligence formation factors studying are described. The specifics of self- assessment, self-regulation, attitude to health and the severity of its emotional and social components empirical study results are presented. The results of empirical data set factor analysis were analyzed, which made it possible to identify individual’s body intelligence manifestation trends. It is established that the body image acts as a system-repeating factor in the self-identity system formation; corporeality intelligence acts as a combination of perception of the body, sexuality and actual state of health; loss of interest in oneself and one's body is a consequence of guilt or the need for self-restraint; emotional response to the sphere of the body depends on the success of human self-regulation; competence and self-regulation as components of corporeality intelligence form an inseparable unity; sexuality as an aspect of the individual’s corporeality intelligence is socially conditioned and depends on the system of personal relationships; independence and self-sufficiency are important factors in the social health of the individual, etc.

Off-line and on-line optimization of the energy management strategy in a Hybrid Electric Helicopter for urban air-mobility

This study addresses the optimization of the energy flows in a Hybrid Electric Helicopter for air-taxi operation in order to minimize the fuel consumption on four typical missions (defined in terms of power and altitude profiles) and contemporarily allowing electric back-up operation at any time during the mission. The proposed parallel hybrid electric powertrain includes a turboshaft engine, two electric machines, a lithium ion battery and all necessary control systems. The powertrain is modeled with an empirical but comprehensive approach in order to make possible the combination with numerical optimization algorithms. In particular, the battery is modeled with an electric equivalent circuit model that includes the effect of battery aging. The fuel consumption of the turboshaft engines is calculated with a mathematical function of power request, altitude and Mach number whose coefficients are fitted by means of comparison with the commercial code Gas-turbine Simulation Program (GSP). The optimization of the energy management of the hybrid powertrain is performed in two steps. The first one was the application of Dynamic Programming in order to obtain the optimal usage of the battery for a given mission (target values) and to provide insights into how to develop a suitable on-line optimizer to be applied during the real operation of the rotorcraft. For this second step analysis, the authors developed a version of the Equivalent Consumption Minimization Strategy opportunely adapted to the specific case of an aerial vehicle with turboshaft engine, in particular taking in to account the desired state of charge and the actual state of health of the battery. After an off-line optimization of its parameters with a multi-objective approach, the on-line optimizer guaranteed results similar to the target values and allowed a reduction of the fuel burn ranging between 10% and 22% with respect to using only the thermal engine to power the rotor shaft and without any need to adapt the parameters in case of aged battery.

A Health Index Construction Framework for Prognostics Based on Feature Fusion and Constrained Optimization

The actual health state of a system is difficult to measure in online applications. Hence, features extracted from the sensor data are usually fused as a composite health index (HI) to represent the system status. If the HI shows explicit increasing or decreasing trend, prognostic methods are applied to extrapolate the future degradation and predict the remaining useful life (RUL). However, the practical requirements of prognostics for HI construction are often not considered. Thus, the advanced index may not capture the fault progression well, which would lead to inaccurate predictions. In this article, an HI construction frame-work based on feature fusion and constrained optimization is proposed. Multiple features are fused with weighted sum and nonlinear fusion functions. Three desired properties of HI for prognostics are considered to build an optimization model of feature weights. To solve this multi-variable model effectively, a self-adaptive differential evolution (SADE) algorithm is proposed. Then, a state-space model based on Wiener process is applied to the constructed HIs for online RUL prediction. To evaluate the performance of the HIs, a consistency-based metric is developed. Illustrative examples using two industrial datasets demonstrate the efficiency and adaptability of the proposed methods.

Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

Data-driven vibration-based bearing fault diagnosis using non-steady-state training data

Abstract. This paper presents the extension of an empirical study in which a universally applicable fault diagnosis method is used to analyse vibration data of bearings measured with accelerometers. The motivation for extending the previously published results was to provide a profound analysis of the proposed approach with regard to a more feasible training scenario for real applications. For a detailed assessment of the method, data were acquired on two different test beds: a gearbox test bed equipped with various bearings at different health states and an accelerated lifetime (ALT) test bed to degrade a bearing and introduce an operational fault. Features were extracted from the raw data of two different accelerometers and used to monitor the actual health state of the bearings. For that purpose, feature selection and classifier training are performed in a supervised-learning approach. The accuracy is estimated using an independent test dataset. The results of the gearbox test bed data show that the training of the method can be performed with non-steady-state data and that the same feature set can be used for different revolution speeds if a small decrease in accuracy is acceptable. The results of the ALT test bed show that the same features that were identified in the gearbox test start to change significantly when the bearing starts to degrade. Thus, it is possible to observe the identified features for applying predictive maintenance.

The influence of the anode overhang effect on the capacity of lithium-ion cells – a 0D-modeling approach

Abstract In this work, the behavior of the anode overhang effect in lithium-ion cells is experimentally investigated and simulated for experimental three electrode cells. An empirical method is presented to transfer the simple 0D-model from the experimental cell level to pouch cells. An anode overhang model will help to correctly interpret cyclic and calendaric aging experiments e.g. in terms of a more reliable and detailed separation of reversible and irreversible capacity losses as well as evaluating the actual state of health of a lithium-ion battery. For the three electrode cells different cathode diameters are used to modulate different sized anode overhang areas. Moreover, the influence of different state of charges on the reversible capacity effect is investigated. To examine the influence of the separator on the evolution of usable cell capacity different separator thicknesses are applied. It is found that the properties of the separator significantly influence the rate at which balancing processes between overhang and active region take place. The results show good accordance of the experimental results to the simulation.

Condition monitoring of electric-cam mechanisms based on Model-of-Signals of the drive current higher-order differences

Condition monitoring of electric motor driven mechanisms is of great importance in industrial machines. The knowledge of the actual health state of such components permits to address maintenance policies which results in better exploitation of their actual operational life span and consequently in maintenance cost reduction. In this paper, we exploit the way electric cams are implemented on the vast majority of PLC/Motion controllers to develop a suitable condition monitoring procedure. This technique relies on computing the higher-order differences of the current absorbed by slave motors to get signals that do not depend on a priori knowledge of the cam trajectory and of the mechanism nominal model. Subsequently, we will use these data in the Model-of-Signals framework, to gather information on the mechanism’s health condition, which in turn can be used to perform predictive maintenance policies. The differenced signal is modelled as an ARMA process and the model capabilities in condition monitoring are then shown in simulation and experimental application. Besides, this framework allows exploiting the edge-computing capabilities of the machinery controllers by implementing recursive estimation algorithms.

Assessment of health and safety culture maturity in the construction industry in developing economies

Purpose The degree to which accidents happen or are prevented in any organisation is the function of both the health and safety culture and the safety culture maturity level of the organisation. Therefore, this paper aims to determine the state of health and safety culture in the construction industry in developing economies and to assess their category on the safety maturity ladder using the Ghanaian construction industry as an example. This is to help construction companies in developing countries become conscious of the state of health and safety in the industry so they can be motivated to improve along the ladder. Design/methodology/approach In total, 250 contractors made up of 155 building contractor,s and 95 road contractors took part in the survey. The sample size was determined by Yamane’s (1967) formula with stratified simple random sampling technique adopted in selecting the companies in the survey. This paper also uses (Guttman Scale) Scalogram analysis to measure the state of health and safety culture in the Ghanaian construction industry. Findings The results show that health and safety culture of the Ghanaian construction industry is at the first level, the pathological stage. Even though Ghanaian contractors have health and safety policies and codes of conduct in place, safety is not seen as a key business risk. Consequently, management and most frontline staff do not emphasise the importance of integration of safety measures in the various activities on the site. Thus, safety is not seen as unavoidable and a part of the construction activity. Practical implications The findings of this study inform state authorities, consultants and contractors of areas that they need to focus more on improving health and safety culture in developing countries. This would go a long way in protecting construction workers in the industry. Originality/value This study, to the best of the authors’ current knowledge, is the first of its kind in the Ghanaian construction industry. The study brings to the fore the actual state of health and safety in the construction industry in developing countries such as Ghana. The value of the findings lies in the fact that it will provide the motivation for construction companies in developing countries to develop a commitment to safety, and to provide appropriate and effective safety improvement techniques to progress to the subsequent stages of the safety culture maturity ladder.

Valuing Health States in Russia: A First Feasibility Study

ObjectiveThe aim of this study was to explore the feasibility of different health-related quality-of-life valuation methods in a new setting. Based on a small feasibility study of 100 young Russians, we trialed different methodologies and identified key differences that have implications for the development of health technology assessment in Russia. MethodsIn face-to-face interviews, respondents completed a series of health self-assessments based on a modified version of the EQ-5D-3L, visual analogue scale, time tradeoff, standard gamble, and best–worst scaling methodologies, covering actual and hypothetical health states. ResultsWe found that (1) the visual analogue scale produced lower health valuations and fewer logical inconsistencies than either time trade-off or standard gamble methodologies; (2) initial health states can be decisive in determining values assigned to health improvements; (3) respondents evaluate abstract health states more positively than their own actual health states; (4) there is evidence consistent with the hypothesis that actual and hypothetical health state valuation, using EQ-5D-3L, is an artifact of understanding rather than preference and that the incorporation of additional levels may therefore be no panacea if the dimensions themselves overlook important attributes; and (5) the country context is important in determining how respondents relate to the survey tools and how those survey tools are translated and delivered. ConclusionsRussia is commencing its health technology assessment journey and should proceed cautiously as it moves toward the valuation of health benefits. These results suggest a useful framework for a more in-depth development of health valuation methodologies in Russia.

Manipulating the 5 Dimensions of the EuroQol Instrument: The Effects on Self-Reporting Actual Health and Valuing Hypothetical Health States.

Background. The EQ-5D instrument has 5 dimensions. This article reports on the effects of manipulating a) the order in which the 5 dimensions are presented (appearing first v. last), b) splitting of the composite dimensions ("pain or discomfort" and "anxiety or depression"), and c) removing or "bolting off" 1 of the 5 EQ-5D dimensions at a time. The effects were examined in 2 contexts: 1) self-reporting health and 2) health state valuations. Methods. Three different types of discrete choice experiments (DCE) including a duration attribute were designed. An online survey with 12 subtypes, each with 10 DCE tasks, was designed and completed by 2494 members of the UK general public. Results. Of the 3 manipulations in the self-reporting context, only b) splitting anxiety or depression had a significant effect. In the health state valuation context, b) splitting level 5 pain or discomfort (relative to pain) and splitting level 5 anxiety or depression (relative to anxiety) had significant effects as did c) bolting off dimensions. Conclusions. We find that the values given to certain health dimensions are sensitive to the way in which it is described and the other health dimensions presented. Of particular interest is the effect of splitting composite dimensions: a given EQ-5D(-5L) profile may mean different things depending on whether the profile is used to self-report one's health or to value hypothetical states, so that the health state values of EQ-5D(-5L) in population tariffs may not correspond to the states that patients self-report themselves in.