Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Health Utility Estimates and Their Application to HIV Prevention in the United States: Implications for Cost-Effectiveness Modeling and Future Research Needs

Objectives. Health utility estimates from the current era of HIV treatment, critical for cost-effectiveness analyses (CEA) informing HIV health policy, are limited. We examined peer-reviewed literature to assess the appropriateness of commonly referenced utilities, present previously unreported quality-of-life data from two studies, and discuss future implications for HIV-related CEA. Methods. We searched a database of cost-effectiveness analyses specific to HIV prevention efforts from 1999 to 2016 to identify the most commonly referenced sources for health utilities and to examine practices around using and reporting health utility data. Additionally, we present new utility estimates from the Centers of Disease Control and Prevention’s Medical Monitoring Project (MMP) and the INSIGHT Strategies for Management of Anti-Retroviral Therapy (SMART) trial. We compare data collection time frames, sample characteristics, assessment methods, and key estimates. Results. Data collection for the most frequently cited utility estimates ranged from 1985 to 1997, predating modern HIV treatment. Reporting practices around utility weights are poor and lack details on participant characteristics, which may be important stratifying factors for CEA. More recent utility estimates derived from MMP and SMART were similar across CD4+ count strata and had a narrower range than pre–antiretroviral therapy (ART) utilities. Conclusions. Despite the widespread use of ART, cost-effectiveness analysis of HIV prevention interventions frequently apply pre-ART health utility weights. Use of utility weights reflecting the current state of the US epidemic are needed to best inform HIV research and public policy decisions. Improved practices around the selection, application, and reporting of health utility data used in HIV prevention CEA are needed to improve transparency.

'Mapping' Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases

BACKGROUND

The use of patient-reported outcome measures (PROMs) to monitor the effects of disease and treatment on patient symptomatology and daily life is increasing in rare diseases (RDs) (i.e. those affecting less than one in 2000 people); however, these instruments seldom yield health state utility values (HSUVs) for cost-utility analyses. In such a context, 'mapping' allows HSUVs to be obtained by establishing a statistical relationship between a 'source' (e.g. a disease-specific PROM) and a 'target' preference-based measure [e.g. the EuroQol-5 Dimension (EQ-5D) tool].

OBJECTIVE

This study aimed to systematically review all published studies using 'mapping' to derive HSUVs from non-preference-based measures in RDs, and identify any critical issues related to the main features of RDs, which are characterised by small, heterogeneous, and geographically dispersed patient populations.

METHODS

The following databases were searched during the first half of 2019 without time, study design, or language restrictions: MEDLINE (via PubMed), the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0). The keywords combined terms related to 'mapping' with Orphanet's list of RD indications (e.g. 'acromegaly') in addition to 'rare' and 'orphan'. 'Very rare' diseases (i.e. those with fewer than 1000 cases or families documented in the medical literature) were excluded from the searches. A predefined, pilot-tested extraction template (in Excel^®) was used to collect structured information from the studies.

RESULTS

Two groups of studies were identified in the review. The first group (n = 19) developed novel mapping algorithms in 13 different RDs. As a target measure, the majority used EQ-5D, and the others used the Short-Form Six-Dimension (SF-6D) and 15D; most studies adopted ordinary least squares (OLS) regression. The second group of studies (n = 9) applied previously published algorithms in non-RDs to comparable RDs, mainly in the field of cancer. The critical issues relating to 'mapping' in RDs included the availability of very few studies, the relatively high number of cancer studies, and the absence of research in paediatric RDs. Moreover, the reviewed studies recruited small samples, showed a limited overlap between RD-specific and generic PROMs, and highlighted the presence of cultural and linguistic factors influencing results in multi-country studies. Lastly, the application of existing algorithms developed in non-RDs tended to produce inaccuracies at the bottom of the EQ-5D scale, due to the greater severity of RDs.

CONCLUSIONS

More research is encouraged to develop algorithms for a broader spectrum of RDs (including those affecting young children), improve mapping study quality, test the generalisability of algorithms developed in non-RDs (e.g. HIV) to rare variants or evolutions of the same condition (e.g. AIDS wasting syndrome), and verify the robustness of results when mapped HSUVs are used in cost-utility models.

Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods

BACKGROUND

Cost-effectiveness analysis using quality-adjusted life-years as the measure of health benefit is commonly used to aid decision-makers. Clinical studies often do not include preference-based measures that allow the calculation of quality-adjusted life-years, or the data are insufficient. 'Mapping' can bridge this evidence gap; it entails estimating the relationship between outcomes measured in clinical studies and the required preference-based measures using a different data set. However, many methods for mapping yield biased results, distorting cost-effectiveness estimates.

OBJECTIVES

Develop existing and new methods for mapping; test their performance in case studies spanning different preference-based measures; and develop methods for mapping between preference-based measures.

DATA SOURCES

Fifteen data sets for mapping from non-preference-based measures to preference-based measures for patients with head injury, breast cancer, asthma, heart disease, knee surgery and varicose veins were used. Four preference-based measures were covered: the EuroQoL-5 Dimensions, three-level version (n = 11), EuroQoL-5 Dimensions, five-level version (n = 2), Short Form questionnaire-6 Dimensions (n = 1) and Health Utility Index Mark 3 (n = 1). Sample sizes ranged from 852 to 136,327. For mapping between generic preference-based measures, data from FORWARD, the National Databank for Rheumatic Diseases (which includes the EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, in its 2011 wave), were used.

MAIN METHODS DEVELOPED

Mixture-model-based approaches for direct mapping, in which the dependent variable is the health utility value, including adaptations of methods developed to model the EuroQoL-5 Dimensions, three-level version, and beta regression mixtures, were developed, as were indirect methods, in which responses to the descriptive systems are modelled, for consistent multidirectional mapping between preference-based measures. A highly flexible approach was designed, using copulas to specify the bivariate distribution of each pair of EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, responses.

RESULTS

A range of criteria for assessing model performance is proposed. Theoretically, linear regression is inappropriate for mapping. Case studies confirm this. Flexible, direct mapping methods, based on different variants of mixture models with appropriate underlying distributions, perform very well for all preference-based measures. The precise form is important. Case studies show that a minimum of three components are required. Covariates representing disease severity are required as predictors of component membership. Beta-based mixtures perform similarly to the bespoke mixture approaches but necessitate detailed consideration of the number and location of probability masses. The flexible, bi-directional indirect approach performs well for testing differences between preference-based measures.

LIMITATIONS

Case studies drew heavily on EuroQoL-5 Dimensions. Indirect methods could not be undertaken for several case studies because of a lack of coverage. These methods will often be unfeasible for preference-based measures with complex descriptive systems.

CONCLUSIONS

Mapping requires appropriate methods to yield reliable results. Evidence shows that widely used methods such as linear regression are inappropriate. More flexible methods developed specifically for mapping show that close-fitting results can be achieved. Approaches based on mixture models are appropriate for all preference-based measures. Some features are universally required (such as the minimum number of components) but others must be assessed on a case-by-case basis (such as the location and number of probability mass points).

FUTURE RESEARCH PRIORITIES

Further research is recommended on (1) the use of the monotonicity concept, (2) the mismatch of trial and mapping distributions and measurement error and (3) the development of indirect methods drawing on methods developed for mapping between preference-based measures.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 34. See the NIHR Journals Library website for further project information. This project was also funded by a Medical Research Council grant (MR/L022575/1).

Development of algorithms to estimate EQ-5D and derive health utilities from WHOQOL-HIV Bref: a mapping study

PURPOSE

This study aimed to develop and evaluate different families of applicable models available for utility mapping between World Health Organization Quality of Life for HIV-abbreviated version (WHOQOL-HIV Bref) and EQ-5D-3L and to propose an optimised algorithm to estimate health utilities of people living with HIV.

METHODS

Estimation dataset was collected between July 2014 and September 2016 in a cross-sectional study including 1526 people living with HIV/Aids (PLWH) under care at the Instituto Nacional de Infectologia Evandro Chagas-FIOCRUZ, in Brazil. Data of WHOQOL-HIV Bref and EQ-5D-3L questionnaires were collected. Fisher's exact tests were used for testing WHOQOL-HIV Bref response frequencies among groups of responses to each of the five EQ-5D-3L domains. Multiple correspondence analyses (MCA) were used to inspect the relationships between both instrument responses. Different families of applicable models available for utility mapping between WHOQOL-HIV Bref and EQ-5D-3L were adjusted for the prediction of disutility.

RESULTS

Candidate models' performances using mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were similarly good, which was evidenced by the overlapping of its 95% confidence intervals of the mean tenfold cross-validation or estimated generalisation errors. However, the Hurdle Logistic-Log-Normal model was better on average according to generalisation errors both in the prediction of Brazilian utility values (MAE = 0.1037, MSE = 0.0178, and RMSE = 0.1332) and for those of the UK (MAE = 0.1476, MSE = 0.0443, and RMSE = 0.2099).

CONCLUSIONS

Mapping EQ-5D-3L responses or deriving health utilities directly from WHOQOL-HIV Bref responses can be a valid alternative for settings with no preference-based health utility data.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

The implementation and evaluation of HIV symptom management guidelines: A preliminary study in China

Objective

The overarching objective of this study was to examine the effectiveness of HIV symptom management guidelines in China in reducing the incidence and severity of symptoms and improving patients' quality of life.

Methods

We conducted a controlled, pre- and post-implementation design in the HIV/AIDS inpatient unit in Shanghai. Patients recruited from November 2014 to February 2015 were in the intervention group and those from October 2013 to February 2014 were in the control group. There were 74 patients in each group. Participants in the intervention group received interventions based on the HIV symptom management guidelines. Overall symptom severity, depression, and quality of life were measured in two groups at baseline, week 4, and week 8.

Results

Totally 126 patients completed the research, 65 in the intervention group and 61 in the control group. The total symptom severity scores showed a statistically significant difference between groups across time (P < 0.05). It showed that frequencies of fatigue (36.9% vs. 44.3%), fever (6.2% vs. 11.5%), loss in weight (9.2% vs. 16.4%), mouth ulcers (12.3% vs. 16.4%), headaches (9.2% vs. 19.7%) and depression (F = 1.09, P > 0.05) in the intervention group were lower than those in the control group in week 8 without statistical significance. The multilevel growth mixture model indicated a greater increase in the total score of quality of life for the group treated according to the symptom management guidelines (P = 0.04).

Conclusion

The evidence-based HIV symptom management guidelines can improve a patient's quality of life and relieve negative symptoms. The guidelines can be applied in a similar context to other HIV/AIDS units or clinics.