COVID-19 and EQ-5D-5L health state valuation

Background We investigate whether and how general population health state values were influenced by the initial stages of the COVID-19 pandemic. Changes could have important implications, as general population values are used in health resource allocation. Data In Spring 2020, participants in a UK general population survey rated 2 EQ-5D-5L states, 11111 and 55555, as well as dead, using a visual analogue scale (VAS) from 100 = best imaginable health to 0 = worst imaginable health. Participants answered questions about their pandemic experiences, including COVID-19’s effect on their health and quality of life, and their subjective risk/worry about infection. Analysis VAS ratings for 55555 were transformed to the full health = 1, dead = 0 scale. Tobit models were used to analyse VAS responses, as well as multinomial propensity score matching (MNPS) to create samples balanced according to participant characteristics. Results Of 3021 respondents, 2599 were used for analysis. There were statistically significant, but complex associations between experiences of COVID-19 and VAS ratings. For example, in the MNPS analysis, greater subjective risk of infection implied higher VAS ratings for dead, yet worry about infection implied lower ratings. In the Tobit analysis, people whose health was affected by COVID-19 rated 55555 higher, whether the effect on health was positive or negative. Conclusion The results complement previous findings that the onset of the COVID-19 pandemic may have impacted EQ-5D-5L health state valuation, and different aspects of the pandemic had different effects

Measuring commissioners’ willingness-to-pay for community based childhood obesity prevention programmes using a discrete choice experiment

Background: In the UK, rates of childhood obesity remain high. Community based programmes for child obesity prevention are available to be commissioned by local authorities. However, there is a lack of evidence regarding how programmes are commissioned and which attributes of programmes are valued most by commissioners. The aim of this study was to determine the factors that decision-makers prioritise when commissioning programmes that target childhood obesity prevention. Methods: An online discrete choice experiment (DCE) was used to survey commissioners and decision makers in the UK to assess their willingness-to-pay for childhood obesity programmes. Results: A total of 64 commissioners and other decision makers completed the DCE. The impact of programmes on behavioural outcomes was prioritised, with participants willing to pay an extra £16,600/year if average daily fruit and vegetable intake increased for each child by one additional portion. Participants also prioritised programmes that had greater number of parents fully completing them, and were willing to pay an extra £4810/year for every additional parent completing a programme. The number of parents enrolling in a programme (holding the number completing fixed) and hours of staff time required did not significantly influence choices. Conclusions: Emphasis on high programme completion rates and success increasing children’s fruit and vegetable intake has potential to increase commissioning of community based obesity prevention programmes

Patient perspective on decisions to switch disease-modifying treatments in relapsing-remitting multiple sclerosis

Background: There are now large cohorts of people with relapsing-remitting multiple sclerosis (pwRRMS) who have taken several Disease-Modifying Treatments (DMTs). Studies about switching DMTs mostly focus on clinical outcomes rather than patients' decision-making. Neurologists are now required to support decisions at various times during the relapsing disease course and they do so with concerns about DMTs risks. This qualitative study investigates how pwRRMS weigh up the pros and cons of DMTs, focusing on perceptions of effectiveness and risks when new treatments are considered. / Objective: To increase understanding of people's experiences of decision-making when switching DMTs. / Methods: 30 semi-structured interviews were conducted with pwRRMS in England. 16 participants had switched DMT and their experiences were compared with those who had only taken one DMT. Interviews were analysed thematically to answer: what main factors influence people's decision-making to switch DMTs and why? / Results: Of the 16 participants with experience of switching DMT, eight had taken two or more DMTs; eight had taken three or more. Two was the DMT median. This study demonstrated that despite the term "switching" implying that similar treatments are inter-changeable, for pwRRMS taking new treatments involves different emotions, routines, risks, prognosis and communication experiences. Two meta themes identified were: 1) A distinctive, rapid and emotional decision-making process where old emotions related to MS prognosis are revisited. 2) Switching has a different impact on communication for escalation or de-escalation processes. / Conclusion: Switching DMT involves different routines, risks, prognosis and communication experiences. These decisions are emotionally difficult because of the fear about transitioning to secondary progressive MS, and DMT effectiveness uncertainty. Patient centred decision aids should include information about first and consecutive treatment decisions

Finding the best fit: examining the decision-making of augmentative and alternative communication professionals in the UK using a discrete choice experiment

Objectives: Many children with varied disabilities, for example, cerebral palsy, autism, can benefit from augmentative and alternative communication (AAC) systems. However, little is known about professionals’ decision-making when recommending symbol based AAC systems for children. This study examines AAC professionals’ preferences for attributes of AAC systems and how they interact with child characteristics. Design: AAC professionals answered a discrete choice experiment survey with AAC system and child-related attributes, where participants chose an AAC system for a child vignette. Setting: The survey was administered online in the UK. Participants 155 UK-based AAC professionals were recruited between 20 October 2017 and 4 March 2018. Outcomes: The study outcomes were the preferences of AAC professionals’ as quantified using a mixed logit model, with model selection performed using a step-wise procedure and the Bayesian Information Criterion. Results Significant differences were observed in preferences for AAC system attributes, and large interactions were seen between child attributes included in the child vignettes, for example, participants made more ambitious choices for children who were motivated to communicate using AAC, and predicted to progress in skills and abilities. These characteristics were perceived as relatively more important than language ability and previous AAC experience. Conclusions: AAC professionals make trade-offs between attributes of AAC systems, and these trade-offs change depending on the characteristics of the child for whom the system is being provided

The cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.Background: Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. Objectives: Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. Methods: Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a \ua320000/QALY threshold. Uncertainty was characterized using bootstrapping. Results: People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups’ 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (\ua39830 versus \ua310 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. Conclusions: Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty

An Item-Response Mapping from General Health Questionnaire Responses to EQ-5D-3L Using a General Population Sample from England

Background The 12-item General Health Questionnaire (GHQ-12) is widely used to measure mental health and well-being; however, it is not possible to estimate values on the full health = 1, dead = 0 scale used to construct quality-adjusted life-years (QALYs) from GHQ-12 responses as it is not preference-based. Objective The aim of this study was to create an item-response mapping between GHQ-12 and EQ-5D-3L health states, for which several value sets exist. Methods Data from the 2012 Health Survey for England with complete GHQ-12 and EQ-5D-3L descriptive system responses were used for analysis. Data were split 70/30 into estimation/test samples. Four modelling approaches, with EQ-5D-3L levels on each dimension as dependent variables and GHQ-12 responses as independent variables were assessed: non-parametric, simple ordered logit (OL), extended OL, and least absolute shrinkage and selection operator (LASSO). Approaches were assessed using Akaike and Bayesian information criteria, predictive accuracy measured using root mean squared error (RMSE), and simplicity. Results A total of 8114 responses became 6924 after discarding missing values, with 4847 used in estimation and 2077 used for testing. LASSO had a better model fit on the pain/discomfort dimension, but no model had markedly superior predictive accuracy. The non-parametric approach was chosen for the mapping algorithm based on simplicity. Predicted and observed EQ-5D-3L values for the test sample had a correlation of 0.488. Prediction accuracy was better for GHQ-12 scores below 20 than scores above 20. Conclusion The mapping allows EQ-5D-3L responses to be predicted using GHQ-12 responses, which may be useful in estimating utility values and QALYs. An R script and Microsoft Excel spreadsheet are provided to facilitate calculations

Joint modelling of choice and rating data: Theory and examples

In many cases, ordinal data, for example rating objects on a scale from 1 to 5, is observed only for those objects that have been chosen from a set of discrete alternatives, with no ratings for unchosen objects. An example is customer ratings of goods sold by online retailers. The joint modelling of choice and rating is made difficult by the missing ratings for unchosen alternatives. A method of jointly modelling choice and rating data termed a choice-ordered logit (COL) model is presented. Two types of COL model are defined: two-step, which places a positive probability on the chosen alternative not having the highest rating, and one-step, where the highest rated alternative is always chosen. Three case studies exemplifying the use of COL models are given. One uses simulated data and two use data from discrete choice experiments. It is shown that COL models can produce robust estimates. Two-step models provided a better fit than one-step, and most participants seemed to use two-step decision-making. However, a sizeable minority used one-step decision-making in one case study. It is argued that COL models have benefits over standard approaches, in particular adding information on strength-of-preference to discrete choices

Does a health crisis change how we value health?

General population health state values are used in healthcare resource allocation, including health technology assessment. We examine whether UK general population health valuations changed during the COVID-19 pandemic. Ratings of EQ-5D-5L health states 11111 (no problems), 55555 (extreme problems), and dead were collected in a UK general population survey during the pandemic (April–May 2020) using the 0 = worst imaginable health, 100 = best imaginable health visual analog scale (EQ-VAS). Ratings for 55555 were transformed to a full health = 1, dead = 0 scale. Responses were compared to similar data collected pre-pandemic (2018). After propensity score matching to minimize sample differences, EQ-VAS responses were analyzed using Tobit regressions. On the 0–100 scale, 11111 was rated on average 8.67 points lower, 55555 rated 9.56 points higher, and dead rated 7.45 points lower post-pandemic onset compared to pre-pandemic. On the full health = 1, dead = 0 scale, 55555 values were 0.09 higher post-pandemic onset. There was evidence of differential impacts of COVID-19 by gender, age, and ethnicity, although only age impacted values on the 1–0 scale. COVID-19 may have affected how people value health. It is unknown whether the effect is large enough to have policy relevance, but caution should be taken in assuming pre-COVID-19 values are unchanged