A number of studies have shown an association between health-related quality of life (HRQL) and socioeconomic status (SES). Indicators of SES usually serve as potential confounders; associations between SES and HRQL are rarely discussed in their own right. Also, few studies assess the association between HRQL and SES among those with a chronic disease. The study focuses on the question of whether people with the same state of health judge their HRQL differently according to their SES, and whether a bias could be introduced by ignoring these differences.

Background: Little is known as to how health-related quality of life (HRQoL) when measured by generic instruments such as EQ-5D differ across smokers, ex-smokers and never-smokers in the general population; whether the overall pattern of this difference remain consistent in each domain of HRQoL; and what implications this variation, if any, would have for economic evaluations of tobacco control interventions.Methods: Using the 2006 round of Health Survey for England data (n = 13,241), this paper aims to examine the impact of smoking status on health-related quality of life in English population. Depending upon the nature of the EQ-5D data (i.e. tariff or domains), linear or logistic regression models were fitted to control for biology, clinical conditions, socio-economic background and lifestyle factors that an individual may have regardless of their smoking status. Age- and gender-specific predicted values according to smoking status are offered as the potential 'utility' values to be used in future economic evaluation models.Results: The observed difference of 0.1100 in EQ-5D scores between never-smokers (0.8839) and heavy-smokers (0.7739) reduced to 0.0516 after adjusting for biological, clinical, lifestyle and socioeconomic conditions. Heavy-smokers, when compared with never-smokers, were significantly more likely to report some/severe problems in all five domains - mobility (67%), self-care (70%), usual activity (42%), pain/discomfort (46%) and anxiety/depression (86%) -. 'Utility' values by age and gender for each category of smoking are provided to be used in the future economic evaluations.Conclusion: Smoking is significantly and negatively associated with health-related quality of life in English general population and the magnitude of this association is determined by the number of cigarettes smoked. The varying degree of this association, captured through instruments such as EQ-5D, may need to be fed into the design of future economic evaluations where the intervention being evaluated affects (e.g. tobacco control) or is affected (e.g. treatment for lung cancer) by individual's (or patients') smoking status.

Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery. NHS PRO data (130,945 observations) from April 2015 to April 2017 were used to train and test eight classifiers to predict binary postoperative improvement based on minimal important differences. Area under the receiver operating characteristic, J-statistic and several other metrics were calculated. The dependent outcomes were generic and disease-specific improvement based on the EQ-5D-3L visual analogue scale (VAS) as well as the Oxford Hip and Knee Score (Q score). The area under the receiver operating characteristic of the best training models was around 0.87 (VAS) and 0.78 (Q score) for hip replacement, while it was around 0.86 (VAS) and 0.70 (Q score) for knee replacement surgery. Extreme gradient boosting, random forests, multistep elastic net and linear model provided the highest overall J-statistics. Based on variable importance, the most important predictors for post-operative outcomes were preoperative VAS, Q score and single Q score dimensions. Sensitivity analysis for hip replacement VAS evaluated the influence of minimal important difference, patient selection criteria as well as additional data years. Together with a small benchmark of the NHS prediction model, robustness of our results was confirmed. Supervised machine-learning implementations, like extreme gradient boosting, can provide better performance than linear models and should be considered, when high predictive performance is needed. Preoperative VAS, Q score and specific dimensions like limping are the most important predictors for postoperative hip and knee PROMs.

The concept of complexity is used in palliative care (PC) to describe the nature of patients’ situations and the extent of resulting needs and care demands. However, the term or concept is not clearly defined and operationalised with respect to its particular application in PC. As a complex problem, a care situation in PC is characterized by reciprocal, nonlinear relations and uncertainties. Dealing with complex problems necessitates problem-solving methods tailored to specific situations. The theory of complex adaptive systems (CAS) provides a framework for locating problems and solutions. This study aims to describe criteria contributing to complexity of PC situations from the professionals’ view and to develop a conceptual framework to improve understanding of the concept of “complexity” and related elements of a PC situation by locating the complex problem “PC situation” in a CAS. Qualitative interview study with 42 semi-structured expert (clinical/economical/political) interviews. Data was analysed using the framework method. The thematic framework was developed inductively. Categories were reviewed, subsumed and connected considering CAS theory. The CAS of a PC situation consists of three subsystems: patient, social system, and team. Agents in the "system patient" are allocated to further subsystems on patient level: physical, psycho-spiritual, and socio-cultural. The "social system" and the "system team" are composed of social agents, who affect the CAS as carriers of characteristics, roles, and relationships. Environmental factors interact with the care situation from outside the system. Agents within subsystems and subsystems themselves interact on all hierarchical system levels and shape the system behaviour of a PC situation. This paper provides a conceptual framework and comprehensive understanding of complexity in PC. The systemic view can help to understand and shape situations and dynamics of individual care situations; on higher hierarchical level, it can support an understanding and framework for the development of care structures and concepts. The framework provides a foundation for the development of a model to differentiate PC situations by complexity of patients and care needs. To enable an operationalisation and classification of complexity, relevant outcome measures mirroring the identified system elements should be identified and implemented in clinical practice.

Background and Objective Although value sets for the five-level version of the generic health-related quality-of-life instrument EQ-5D are emerging, there is still no value set available in the literature based on time trade-off valuations made by individuals experiencing the valued health states. The aim of this study was to estimate experience-based value sets for the EQ-5D-5L for Sweden using time trade-off and visual analogue scale valuation methods. Methods In a large, cross-sectional, population-based, self-administered postal health survey, the EQ-5D-5L descriptive system, EQ visual analogue scale and a time trade-off question were included. Time trade-off and visual analogue scale valuations of the respondent’s current health status were used in statistical modelling to estimate a single-index value of health for each of the 3125 health states. Ordinary least-squares and generalised linear models were estimated with the main effect within each of the five dimensions represented by 20 dummy variables reflecting the additional decrement in value for levels 2–5 when the severity increases by one level sequentially beginning from having no problem. Interaction variables representing the occurrence of severity levels in at least one of the dimensions were tested: severity level 2 or worse (N2); severity level 3 or worse (N3); severity level 4 or worse (N4); severity level 5 (N5). Results A total of 896 health states (28.7% of the 3125 possible EQ-5D-5L health states) were reported by the 25,867 respondents. Visual analogue scale (n = 23,899) and time trade-off (n = 13,381) responders reported valuations of their currently experienced health state. The preferred regression models used ordinary least-squares estimation for both time trade-off and visual analogue scale values and showed consistency in all coefficients after combining certain levels. Levels 4 and 5 for the dimensions of mobility, self-care and usual activities were combined in the time trade-off model. Including the interaction variable N5, indicating severity level 5 in at least one of the five dimensions, made it possible to distinguish between the two worst severity levels where no other dimension is at level 5 as this coefficient is applied only once. In the visual analogue scale regression model, levels 4 and 5 of the mobility dimension were combined. The interaction variables N2–N4 were included, indicating that each of these terms reflect a statistically significant decrement in visual analogue scale value if any of the dimensions is at severity level 2, 3 or 4, respectively. Conclusions Time trade-off and visual analogue scale value sets for the EQ-5D-5L are now available for Sweden. The time trade-off value set is the first such value set based on experience-based time trade-off valuation. For decision makers with a preference for experience-based valuations of health states from a representative population-based sample, the reported value sets may be considered fit for purpose to support resource allocation decision as well as evaluating population health and healthcare performance.

The EQ-5D-5L is a widely used generic instrument to measure health-related quality of life. This study evaluates health perception in a representative sample of the general German population from 2015. To compare results over time, a component analysis technique was used that separates changes in the description and valuation of health states. The whole sample and also subgroups, stratified by sociodemographic parameters as well as disease affliction, were analyzed. In total, 2040 questionnaires (48.4% male, mean age 47.3 year) were included. The dimension with the lowest number of reported problems was self-care (93.0% without problems), and the dimension with the highest proportion of impairment was pain/discomfort (71.2% without problems). Some 64.3% of the study population were identified as problem-free. The visual analog scale (VAS) mean for all participants was 85.1. Low education was connected with significantly lower VAS scores, but the effect was small. Depression, heart disease, and diabetes had a strong significant negative effect on reported VAS means. Results were slightly better than those in a similar 2012 survey; the most important driver was the increase in the share of the study population that reported to be problem-free. In international comparisons, health perception of the general German population is relatively high and, compared with previous German studies, fairly stable over recent years. Elderly and sick people continue to report significant reductions in perceived health states.