Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis

Mapping Kansas City cardiomyopathy, Seattle Angina, and minnesota living with heart failure to the MacNew-7D in patients with heart disease

INTRODUCTION

The Kansas City Cardiomyopathy Questionnaire (KCCQ), Seattle Angina Questionnaire (SAQ), and Minnesota Living with Heart Failure Questionnaire (MLHFQ) are widely used non-preference-based instruments that measure health-related quality of life (QOL) in people with heart disease. However, currently it is not possible to estimate quality-adjusted life-years (QALYs) for economic evaluation using these instruments as the summary scores produced are not preference-based. The MacNew-7D is a heart disease-specific preference-based instrument. This study provides different mapping algorithms for allocating utility scores to KCCQ, MLHFQ, and SAQ from MacNew-7D to calculate QALYs for economic evaluations.

METHODS

The study included 493 participants with heart failure or angina who completed the KCCQ, MLHFQ, SAQ, and MacNew-7D questionnaires. Regression techniques, namely, Gamma Generalized Linear Model (GLM), Bayesian GLM, Linear regression with stepwise selection and Random Forest were used to develop direct mapping algorithms. Cross-validation was employed due to the absence of an external validation dataset. The study followed the Mapping onto Preference-based measures reporting Standards checklist.

RESULTS

The best models to predict MacNew-7D utility scores were determined using KCCQ, MLHFQ, and SAQ item and domain scores. Random Forest performed well for item scores for all questionnaires and domain score for KCCQ, while Bayesian GLM and Linear Regression were best for MLHFQ and SAQ domain scores. However, models tended to over-predict severe health states.

CONCLUSION

The three cardiac-specific non-preference-based QOL instruments can be mapped onto MacNew-7D utilities with good predictive accuracy using both direct response mapping techniques. The reported mapping algorithms may facilitate estimation of health utility for economic evaluations that have used these QOL instruments.

Health-related quality of life in Parkinson's disease: systematic review and meta-analysis of EuroQol (EQ-5D) utility scores

INTRODUCTION

Evaluating the Health-related quality of life (HRQoL) of individuals with Parkinson's disease (PD) holds significant importance in clinical and research settings. The EQ-5D is a widely recognized tool for comprehensive measurement of HRQoL using utility values. This study aims to systematically review and synthesize EQ-5D utility values from existing literature on patients with PD and their caregivers.

METHODS

We conducted a systematic search for studies that provided EQ-5D utility scores for patients with PD, using PubMed-Medline, Scopus, and Embase and selected the studies. The selected studies underwent systematic review, including an assessment of their quality. We performed a meta-analysis using a random-effect model and conducted a meta-regression analysis to investigate sources of heterogeneity among the studies.

RESULTS

The search result of 13,417 articles that were reviewed, 130 studies with 33,914 participants were selected for systematic review, and 79 studies were included for meta-analysis. The pooled EQ-5D utility values and visual analog score (VAS) among PD were 62.72% (60.53-64.93, I2 = 99.56%) and 0.60 (0.55-0.65, I2 = 99.81%), respectively. The pooled scores for caregivers' EQ-VAS and EQ-5D utility were 70.10% (63.99-76.20, I2 = 98.25%) and 0.71 (0.61-0.81, I2 = 94.88%), respectively. Disease duration (P < 0.05) showed a negative correlation with EQ-5D utility values on meta-regression.

CONCLUSION

The pooled utility values of PD and their caregivers help to understand their HRQoL and aid in conducting health economics research. The negative association between disease duration and utility values highlights the evolving nature of HRQoL challenges, suggesting the need for appropriate long-term disease management.

Cost-effectiveness analysis of insecticide ban aimed at preventing Parkinson's disease in Central California

BACKGROUND

Our study assessed whether banning specific insecticides to reduce the PD burden in three Central California (CA) counties is cost-effective.

METHOD

We applied a cost-effectiveness analysis using a cohort-based Markov model to estimate the impact and costs of banning seven insecticides that were previously associated with PD in these counties as well as mixture exposures to some of these pesticides. We relied for our estimations on the cohort of 65- and 66-year-olds living in these counties who were unaffected by PD at baseline in 2020 and projected their incidence, costs, and reduction in quality-adjusted-life-years (QALY) loss due to developing PD over a 20-year period. We included a shiny app for modeling different scenarios (https://sherlockli.shinyapps.io/pesticide_pd_economics_part_2/).

RESULTS

According to our scenarios, banning insecticides to reduce the occurrence of PD in three Central CA counties was cost-effective relative to not banning insecticides. In the worst-case scenario of exposure to a single pesticide, methomyl, versus none would result in an estimated 205 (95 % CI: 75, 348) additional PD cases or 12 % (95 % CI: 4 %, 20 %) increase in PD cases over a 20-year period based on residential proximity to pesticide applications. The increase in PD cases due to methomyl would increase health-related costs by $72.0 million (95 % CI: $5.5 million, $187.4 million). Each additional PD patient due to methomyl exposure would incur $109,327 (95 % CI, $5554, $347,757) in costs per QALY loss due to PD. Exposure to methomyl based on workplace proximity to pesticide applications generated similar estimates. The highest PD burden and associated costs would be incurred from exposure to multiple pesticides simultaneously.

CONCLUSION

Our study provides an assessment of the cost-effectiveness of banning specific insecticides to reduce PD burden in terms of health-related QALYs and related costs. This information may help policymakers and stakeholders to make decisions concerning the regulation of pesticides.

Mapping the Food Allergy Quality of Life Questionnaire Parent Form onto the Short-Form Six-Dimensions version 2

BACKGROUND

The Food Allergy Quality of Life Questionnaire Parent Form (FAQLQ-PF) is the most widely used quality of life questionnaire in food allergy. The objective of this study was to develop a mapping algorithm to convert FAQLQ-PF scores into health state utilities.

METHODS

The Short-Form Six-Dimensions version 2 (SF-6Dv2) and FAQLQ-PF questionnaires were collected from an academic center oral immunotherapy referral cohort. Utility estimates were derived from the SF-6Dv2 using the food allergy preference set. Candidate mapping algorithm models were developed using seven regression methods starting from either the total average score, the average scores of each of the three domains or the individual item scores of FAQLQ-PF. The process was repeated twice, including only section A, common to all age groups, or including all age-applicable sections of the FAQLQ-PF. The mean absolute error (MAE) and root mean squared error (RMSE) were used to select the best fitting model. An independent cohort from a previous national online survey was used for external validation.

RESULTS

In the index cohort, 1000 of 1257 respondents had completed both questionnaires. The lowest MAE (0.0791) and RMSE (0.1020) were recorded when entering individual item scores in a categorical regression model. The model including only FAQLQ-PF section A was found to be most consistent when tested in the external validation cohort (n = 248) (MAE of 0.0898).

CONCLUSION

The FAQLQ-PF was mapped onto SF-6Dv2 utilities with good predictive accuracy in two independent cohorts. This will enable calculation of health utility for cost-effectiveness analyses in food allergy.

Deriving health utility indices from a food allergy quality-of-life questionnaire

BACKGROUND

The Food Allergy Quality-of-Life Questionnaire-Parent Form (FAQLQ-PF) is widely used to assess food allergy-specific health-related quality of life (FAQL), but cannot be used directly in cost-utility analyses, which require health state utility (HSU) scores. Currently, limited evidence is available regarding the HSU of food-allergic children/adolescents. This study aimed to develop mapping algorithms from the FAQLQ-PF onto HSU scores generated by generic, preference-based, health-related quality-of-life (HRQL) instruments.

METHODS

Caregivers of children aged 7 to 17 years with a clinician diagnosis of IgE-mediated food allergy, recruited via Allergy & Anaphylaxis Australia, completed an online FAQLQ-PF questionnaire and proxy generic preference-based pediatric instruments (Assessment of Quality of Life [AQoL]-6D and Child Health Utility 9D [CHU9D]). Optimal statistical methods were based on series of goodness-of-fit statistics.

RESULTS

Mean FAQLQ-PF total score, AQoL-6D, and CHU9D utility scores of 238 food-allergic children/adolescents were 3.49 (SD: 1.41), 0.78 (SD: 0.22), and 0.74 (SD: 0.22), respectively. The Spearman correlation coefficients of FAQLQ-PF with AQoL-6D and CHU9D were rho = -0.56 and rho = -0.45, respectively. Optimal mapping algorithms were generated from selected FAQLQ-PF items, mapped onto AQoL-6D or CHU9D utility scores, with AQoL-6D demonstrating better performance.

CONCLUSIONS

This study generated mapping algorithms to help facilitate the use of FAQLQ-PF for cost-utility analyses, which are essential for health economic evaluation. External validation of the reported mapping algorithms is warranted.

Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique

BACKGROUND

This study aims to derive country-specific EQ-5D-5L health status utility (HSU) from the MacNew Heart Disease Health-related Quality of Life questionnaire (MacNew) using both traditional regression analyses, as well as a machine learning technique.

METHODS

Data were drawn from the Multi-Instrument Comparison (MIC) survey. The EQ-5D-5L was scored using 4 country-specific tariffs (United States, United Kingdom, Germany, and Canada). The traditional regression techniques, as well as a machine learning technique, deep neural network (DNN), were adopted to directly predict country-specific EQ-5D-5L HSUs (i.e. a direct mapping approach). An indirect response mapping was undertaken additionally. The optimal algorithm was identified based on three goodness-of-fit tests, namely, the mean absolute error (MAE), mean error (ME) and root mean square error (RMSE), with the first being the primary criteria. Internal validation was undertaken.

RESULTS

Indirect response mapping and direct mapping (via betamix with MacNew items as the key predictors) were found to produce the optimal mapping algorithms with the lowest MAE when EQ-5D-5L were scored using three country-specific tariffs (United Kingdom, Canada, and Germany for the former and United Kingdom, United States, Canada and Germany for the latter approach). DNN approach generated the lowest MAE and RMSE when using the Germany-specific tariff.

CONCLUSIONS

Among different approaches been explored, there is not a conclusive conclusion regarding the optimal method for developing mapping algorithms. A machine learning approach represents an alternative mapping approach that should be explored further. The reported algorithms from response mapping have the potential to be more widely used; however, the performance needs to be externally validated.

Mapping the Chinese Version of the EORTC QLQ-BR53 Onto the EQ-5D-5L and SF-6D Utility Scores

OBJECTIVE

This study aimed to develop mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-BR53, including EORTC QLQ-C30 and QLQ-BR23) onto the 5-level EQ-5D (EQ-5D-5L) and Short Form 6D (SF-6D) utility scores.

METHODS

The data were taken from 607 breast cancer patients in mainland China. The EQ-5D-5L and SF-6D instruments were scored using Chinese-specific tariffs. Three model specifications and seven statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit, censored least absolute deviation (CLAD) model, generalized linear model (GLM), robust MM-estimator, finite mixtures of beta regression model for directly estimating health utility, and using ordered logit regression (OLOGIT) to predict response levels. A five-fold cross-validation approach was conducted to test the generalizability of each model. Two key goodness-of-fit statistics (mean absolute error and mean squared error) and three secondary statistics were employed to choose the optimal models.

RESULTS

Participants had a mean ± standard deviation (SD) age of 49.0 ± 9.8 years. The mean ± SD health state utility scores were 0.828 ± 0.184 (EQ-5D-5L) and 0.646 ± 0.125 (SF-6D). Mapping performance was better when both the QLQ-C30 and QLQ-BR23 dimensions were considered rather than when either of these dimensions were used alone. The mapping functions from the optimal direct mapping and indirect mapping approaches were reported.

CONCLUSIONS

The algorithms reported in this paper enable EORTC QLQ-BR53 breast cancer data to be mapped into utilities predicted from the EQ-5D-5L and SF-6D. The algorithms allow for the calculation of quality-adjusted life years for use in breast cancer cost-effectiveness analyses studies.

Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure

BACKGROUND

Mapping algorithms can be used to convert scores from a non-preference based instrument to health state utilities. The objective of this study was to develop mapping algorithms which will enable the Minnesota Living with Heart Failure Questionnaire (MLHFQ) scores to be converted into EQ-5D-5L utility scores that can be used in heart failure related cost utility studies.

METHOD

Patients diagnosed with heart failure were recruited from Australia. Mapping algorithms were developed using both direct and indirect response mapping approach. Three model specifications were considered to predict the EQ-5D-5 L utility score using MLHFQ total score (Model 1), MLHFQ domain scores (Model 2), or MLHFQ item scores (Model 3). Six regression techniques, each of which has the capability to cope with either skewness, heteroscedasticity, ceiling effects and/or the potential presence of outliers in the data set were used to identify the optimal mapping functions for each of the three models. Goodness-of-fit of the models were assessed using six indicators. In the absence of an external validation dataset, predictive performance of was assessed using three-fold cross validation method. In the indirect response mapping, EQ. 5D 5 L responses were predicted separately using the MLHFQ item scores using ordered logit model.

RESULTS

A total of 141 patients participated in the study. The lowest mean absolute error (MAE) was recorded from the multivariable fractional polynomials (MFP) model in all three-model specifications. Regarding the indirect response mapping, results showed that the performance was comparable with the direct mapping approach based on root mean squared error (RMSE) but was worse based on MAE.

CONCLUSION

The MLHFQ can be mapped onto EQ-5D-5 L utilities with good predictive accuracy using both direct and indirect response mapping techniques. The reported mapping algorithms would facilitate calculation of health utility for economic evaluations related to heart failure.