The Online Elicitation of Personal Utility Functions (OPUF) tool: a new method for valuing health states

Introduction Standard valuation methods, such as TTO and DCE are inefficient. They require data from hundreds if not thousands of participants to generate value sets. Here, we present the Online elicitation of Personal Utility Functions (OPUF) tool; a new type of online survey for valuing EQ-5D-5L health states using more efficient, compositional elicitation methods, which even allow estimating value sets on the individual level. The aims of this study are to report on the development of the tool, and to test the feasibility of using it to obtain individual-level value sets for the EQ-5D-5L. Methods We applied an iterative design approach to adapt the PUF method, previously developed by Devlin et al., for use as a standalone online tool. Five rounds of qualitative interviews, and one quantitative pre-pilot were conducted to get feedback on the different tasks. After each round, the tool was refined and re-evaluated. The final version was piloted in a sample of 50 participants from the UK. A demo of the EQ-5D-5L OPUF survey is available at: https://eq5d5l.me Results On average, it took participants about seven minutes to complete the OPUF Tool. Based on the responses, we were able to construct a personal EQ-5D-5L value set for each of the 50 participants. These value sets predicted a participants' choices in a discrete choice experiment with an accuracy of 80%. Overall, the results revealed that health state preferences vary considerably on the individual-level. Nevertheless, we were able to estimate a group-level value set for all 50 participants with reasonable precision. Discussion We successfully piloted the OPUF Tool and showed that it can be used to derive a group-level as well as personal value sets for the EQ-5D-5L. Although the development of the online tool is still in an early stage, there are multiple potential avenues for further research.

Determinants of quality of life among COVID-19 patients in Southwestern region of Bangladesh

Objectives: The outbreak of COVID-19 has caused an unprecedented health crisis and dramatically changed human lives. This study aims to identify risk factors related to health-related quality of life (HRQoL) among COVID-19 patients who were discharged from the hospital. Material and Methods: A total of 557 COVID-19 patients of Jhenaidah district of Bangladesh who had tested positive before February 1 of 2021 were selected for this cross-sectional study. The EuroQol 5-dimensional-5 level questionnaire was used to measure the HRQoL. Thai value set was used to assess the full health status. Chi-square test was used to find out the association of HRQoL with sociodemographic and clinical factors. Finally, logistic regression was used to find out the predictors of the dimensions of HRQoL. Results: Using the Thai value set, it is observed that 57.27% of participants had reported that they had experienced moderate or severe health problems. About 40.57% of the respondent reported anxiety or depression, whereas 39.14% of the participants had experienced moderate or severe pain or discomfort. The result of the logistic regression showed that age, gender, occupation, place of care, heart problems, and diabetes significantly affect various dimensions of the HRQoL. Conclusion: The COVID-19 significantly depletes the health condition of the patients in both mental and physical aspects. Therefore, the policy-makers and government should need to come with comprehensive strategies to reduce the psychological and physical health woe of COVID-19 patients.

A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids

Background Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors. Objectives The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets. Methods We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors. Results Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets—while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients. Conclusion The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

Developing the EQ-5D-5L Value Set for Uganda Using the ‘Lite’ Protocol

Objective A ‘lite’ version of the EQ-5D-5L valuation protocol, which requires a smaller sample by collecting more data from each participant, was proposed and used to develop an EQ-5D-5L value set for Uganda. Methods Adult respondents from the general Ugandan population were quota sampled based on age and sex. Eligible participants were asked to complete 20 composite time trade-off tasks in the tablet-assisted personal interviews using the offline EuroQol Portable Valuation Technology software under routine quality control. No discrete choice experiment task was administered. The composite time trade-off data were modelled using four additive and two multiplicative regression models. Model performance was evaluated based on face validity, prediction accuracy in cross-validation and in predicting mild health states. The final value set was generated using the best-performing model. Results A representative sample (N = 545) participated in this study. Responses to composite time trade-off tasks from 492 participants were included in the primary analysis. All models showed face validity and generated comparable prediction accuracy. The Tobit model with constrained intercepts and corrected for heteroscedasticity was considered the preferred model for the value set on the basis of better performance. The value set ranges from − 1.116 (state 55555) to 1 (state 11111) with ‘pain/discomfort’ as the most important dimension. Conclusions This is the first EQ-5D-5L valuation study using a ‘lite’ protocol involving composite time trade-off data only. Our results suggest its feasibility in resource-constrained settings. The established EQ-5D-5L value set for Uganda is expected to be used for economic evaluations and decision making in Uganda and the East Africa region.

Modeling SF-6D Health Utilities: Is Bayesian Approach Appropriate?

Background: Valuation studies of preference-based health measures like SF6D have been conducted in many countries. However, the cost of conducting such studies in countries with small populations or low- and middle-income countries (LMICs) can be prohibitive. There is potential to use results from readily available countries’ valuations to produce better valuation estimates. Methods: Data from Lebanon and UK SF-6D value sets were analyzed, where values for 49 and 249 health states were extracted from samples of Lebanon and UK populations, respectively, using standard gamble techniques. A nonparametric Bayesian model was used to estimate a Lebanon value set using the UK data as informative priors. The resulting estimates were then compared to a Lebanon value set obtained using Lebanon data by itself via various prediction criterions. Results: The findings permit the UK evidence to contribute potential prior information to the Lebanon analysis by producing more precise valuation estimates than analyzing Lebanon data only under all criterions used. Conclusions: The positive findings suggest that existing valuation studies can be merged with a small valuation set in another country to produce value sets, thereby making own country value sets more attainable for LMICs.

Rural Population’s Preferences Matter: A Value Set For The EQ-5D-3L Health States For China’s Rural Population

Purpose: To develop an EQ-5D-3L social value set based on Chinese rural population’s preferences using the time trade-off method, and to compare the differences in health states preferences between China urban and rural population.Methods: Between Sep 2013 and Nov 2013, a total of 1201 participants were recruited from rural areas of five Chinese cities (Beijing, Chengdu, Guiyang, Nanjing, and Shenyang) using a quota sampling method. A total of 97 EQ-5D-3L health states were valued for estimating the value set. Various models with different specifications were explored at both aggregate and individual levels. The final model was determined by a set of predefined selection criteria. Findings: An ordinary least square model at the aggregate level included 10 dummy variables for specifying the level 2 and 3 for each dimension and a N3 term presenting any dimension on level 3 was selected was selected as the final model. The final model provides a value set ranges from -0.218 to 0.859. The predicted utility values were highly correlated with but consistently lower than that of the published Chinese EQ-5D-3L value set (for urban population).Conclusion: The availability of the China rural value set provides a set of social preferences weights for researchers and policy decision-makers for use in China rural area.

Estimating an EQ-5D-3L Value Set for Romania Using Time Trade-Off

Objective: To provide health-related quality of life (HRQoL) data to support health technology assessment (HTA) and reimbursement decisions in Romania, by developing a country-specific value set for the EQ-5D-3L questionnaire. Methods: We used the cTTO method to elicit health state values using a computer-assisted personal interviewing approach. Interviews were standardized following the most recent version of the EQ-VT protocol developed by the EuroQoL Foundation. Thirty EQ-5D-3L health states were randomly assigned to respondents in blocks of three. Econometric modeling was used to estimate values for all 243 states described by the EQ-5D-3L. Results: Data from 1556 non-institutionalized adults aged 18 years and older, selected from a national representative sample, were used to build the value set. All tested models were logically consistent; the final model chosen to generate the value set was an interval regression model. The predicted EQ-5D-3L values ranged from 0.969 to 0.399, and the relative importance of EQ-5D-3L dimensions was in the following order: mobility, pain/discomfort, self-care, anxiety/depression, and usual activities. Conclusions: These results can support reimbursement decisions and allow regional cross-country comparisons between health technologies. This study lays a stepping stone in the development of a health technology assessment process more driven by locally relevant data in Romania.