A direct comparison between discrete choice with duration and composite time trade-off methods: do they produce similar results?

OBJECTIVES: Discrete Choice Experiments (DCE) including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite Time-Trade-Off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. METHODS: An EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 DCE tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analysed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. RESULTS: The ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between -0.52 and -0.69, while this was -0.543 for the non-linear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. CONCLUSIONS: CTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for non-linear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the two methods measure the same construct and produce similar results.

The EQ-5D-5L valuation study for Trinidad and Tobago.

PURPOSE: The 2016 EQ-5D-3L value set for Trinidad and Tobago (T&T) allows for the calculation of EQ-5D-5L values via the crosswalk algorithm. The 2016 value set was based on methods predating the EQ-VT protocol, now considered the gold standard for developing EQ-5D value sets. Furthermore, direct elicitation of EQ-5D-5L is preferred over crosswalked values. This study aimed to produce an EQ-5D-5L value set for T&T. METHODS: A representative sample (age, sex, geography) of adults each completed 10 composite Time Trade-Off (cTTO) tasks and 12 Discrete Choice Experiment (DCE) tasks in face-to-face interviews. The cTTO data were analyzed using a Tobit model that corrects for heteroskedasticity. DCE data were analyzed using a mixed logit model. The cTTO and DCE data were combined in hybrid models. RESULTS: One thousand and seventy-nine adults completed the valuation interviews. Among the modelling approaches that were explored, the hybrid heteroskedastic Tobit model produced all internally consistent, statistically significant coefficients, and performed best in terms of out-of-sample predictivity for single states. Compared to the existing EQ-5D-5L crosswalk set, the new value set had a higher number of negative values (236 or 7.6% versus 21 or 0.7%). The mean absolute difference was 0.157 and the correlation coefficient between the two sets was 0.879. CONCLUSION: This study provides a value set for the EQ-5D-5L for T&T using the EQ-VT protocol. We recommend this value set for QALY computations relating to T&T.

Interaction Effects in Health State Valuation Studies: An Optimal Scaling Approach.

OBJECTIVES: This study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies. METHODS: Instead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents. RESULTS: Important interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits. CONCLUSIONS: The proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.

The Garbage Class Mixed Logit Model: Accounting for Low-Quality Response Patterns in Discrete Choice Experiments.

OBJECTIVES: To introduce the garbage class mixed logit (MIXL) model as a convenient alternative to manually screening and accounting for respondents with low data quality in discrete choice experiments. METHODS: Garbage classes are typically used in latent class logit analyses to designate or identify group(s) of respondents with low data quality. Yet, the same concept can be applied to MIXL models as well. RESULTS: Based on a reanalysis of 4 discrete choice experiments that were originally analyzed using a standard MIXL model, it is shown that garbage class MIXL models can achieve the same effect as manually screening for (and excluding) respondents with low data quality based on the more commonly used root likelihood test, but with less effort and ambiguity. CONCLUSIONS: Including a garbage class in MIXL models removes the influence of respondents with a random choice pattern from the MIXL model estimates, provides an estimate of the number of low-quality respondents in the dataset, and avoids having to manually screen for respondents with low data quality based on internal or statistical validity tests. Although less versatile than the combination of standard MIXL estimates with separate assessments of data quality and sensitivity analyses, the proposed garbage class MIXL model provides an attractive alternative.

The Sensitivity and Specificity of Repeated and Dominant Choice Tasks in Discrete Choice Experiments.

OBJECTIVES: This study aimed to identify the most commonly used internal validity tests in the discrete choice experiment (DCE) literature and establish their sensitivity and specificity. METHODS: A structured literature review of recent DCE articles (2018-2020Q1) published in the health, marketing, transport economics, and environmental science literature was used to identify commonly used internal validity tests. The 2 most frequently used internal validity tests were incorporated in 4 new data collections. Respondent preferences in each application were summarized using a mixed logit model, which served as the benchmark for the subsequent sensitivity and specificity calculations. The performance of the internal validity tests was also compared with that of the root likelihood (RLH) test, which is a likelihood-based statistical validity test that is commonly used in marketing applications. RESULTS: Dominant and repeated choice tasks were most commonly included in health-related DCE designs. Based on 4 applications, their specificity and sensitivity depend on the type of incorrect response pattern to be detected and on design characteristics such as the number of choice options per choice task and the number of internal validity tests as included in the experimental design. In all but one scenario, the performance of the dominant and repeated choice tasks was considerably worse than that of the RLH test. CONCLUSIONS: Dominant and repeated choice tasks are unreliable screening tests and costly in terms of statistical power. The RLH test, which is a statistical test that does not require additional choice tasks to be included in the DCE design, provides a more reliable alternative.

Not all respondents use a multiplicative utility function in choice experiments for health state valuations, which should be reflected in the elicitation format (or statistical analysis).

Discrete choice experiments (DCEs) that include health states and duration are becoming a common method for estimating quality-adjusted life year (QALY) tariffs. These DCEs need to be analyzed under the assumption that respondents treat health and duration multiplicatively. However, in the most commonly used DCE duration format there is no guarantee that respondents actually do so; in fact, respondents can easily simplify the choice tasks by considering health and duration separately. This would result in valid DCE responses but preclude subsequent QALY tariff calculations. Using a Bayesian latent class model and data from two existing valuation studies, our analyses confirm that in both datasets the majority of respondents do not appear to have used a multiplicative utility function. Moreover, a statistical correction for respondents who used an incorrect function changes the range of the QALY weights. Hence our results imply that one can neither assume that respondents use the theoretically required multiplicative utility function nor assume that the type of utility function that respondents use does not affect the estimated QALY weights. As a solution, we advise researchers to use an alternative, more constrained DCE elicitation format that avoids these behavioral problems.

Heterogeneity in preferences for outcomes of integrated care for persons with multiple chronic diseases: a latent class analysis of a discrete choice experiment.

PURPOSE: For an integrated care programme to be successful, preferences of the stakeholders involved should be aligned. The aim of this study is to investigate to which extent outcomes beyond health are valued and to study the heterogeneity of preferences of those involved in integrated care. METHODS: A discrete choice experiment (DCE) was conducted to elicit preferences for eight Triple Aim outcomes, i.e., physical functioning, psychological well-being, social relationships & participation, enjoyment of life, resilience, person-centeredness, continuity of care and total health and social care costs. Stakeholders were recruited among Dutch persons with multi-morbidity, informal caregivers, professionals, payers, and policymakers. A Bayesian mixed-logit model was used to analyse the data. Subsequently, a latent class analysis was performed to identify stakeholders with similar preferences. RESULTS: 739 stakeholders completed the DCE. Enjoyment of life was perceived as the most important outcome (relative importance: 0.221) across stakeholders, while total health and social care costs were perceived as least important (0.063). The latent class analysis identified four classes. The first class (19.9%) put most weight on experience with care outcomes. The second class (39%) favoured enjoyment of life. The third class (18%) focused relatively more on physical health. The fourth class (24%) had the least consistent preferences. CONCLUSION: This study has highlighted the heterogeneity in views of stakeholders in integrated care on what is important in health(care) for persons with multi-morbidity. To accurately value integrated care a variety of outcomes beyond health-e.g., enjoyment of life and experience with care-should be taken into account.

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences.

OBJECTIVES: This article explains how to optimize Bayesian D-efficient discrete choice experiment (DCE) designs for the estimation of quality-adjusted life year (QALY) tariffs that are unconfounded by respondents' time preferences. METHODS: The calculation of Bayesian D-errors is explained for DCE designs that allow for the disentanglement of respondents' time and health-state preferences. Time preferences are modelled via an exponential, hyperbolic, or power discount function and the performance of the proposed DCE designs is compared with that of several conventional DCE designs that do not take nonlinear time preferences into account. RESULTS: Based on the achieved D-error, asymptotic standard error, and estimated sample size to obtain statistically significant estimates of the discount rate parameters, the proposed designs outperform the conventional DCE designs. CONCLUSIONS: We recommend that applied researchers use appropriately optimized DCE designs for the estimation of QALY tariffs that are corrected for time preferences. The TPC-QALY software package that accompanies this article makes the recommended designs easily accessible for health-state valuation researchers.

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models.

BACKGROUND: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. OBJECTIVES: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. METHODS: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. RESULTS: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). CONCLUSIONS: Our study shows that DCEs are able to predict choices-mimicking real-world decisions-if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.

Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments.

A randomized controlled discrete choice experiment (DCE) with 3,320 participating respondents was used to investigate the individual and combined impact of level overlap and color coding on task complexity, choice consistency, survey satisfaction scores, and dropout rates. The systematic differences between the study arms allowed for a direct comparison of dropout rates and cognitive debriefing scores and accommodated the quantitative comparison of respondents' choice consistency using a heteroskedastic mixed logit model. Our results indicate that the introduction of level overlap made it significantly easier for respondents to identify the differences and choose between the choice options. As a stand-alone design strategy, attribute level overlap reduced the dropout rate by 30%, increased the level of choice consistency by 30%, and avoided learning effects in the initial choice tasks of the DCE. The combination of level overlap and color coding was even more effective: It reduced the dropout rate by 40% to 50% and increased the level of choice consistency by more than 60%. Hence, we can recommend attribute level overlap, with color coding to amplify its impact, as a standard design strategy in DCEs.

Effect of Level Overlap and Color Coding on Attribute Non-Attendance in Discrete Choice Experiments.

OBJECTIVE: The aim of this study was to test the hypothesis that level overlap and color coding can mitigate or even preclude the occurrence of attribute nonattendance in discrete choice experiments. METHODS: A randomized controlled experiment with five experimental study arms was designed to investigate the independent and combined impact of level overlap and color coding on respondents' attribute nonattendance. The systematic differences between the study arms allowed for a direct comparison of observed dropout rates and estimates of the average number of attributes attended to by respondents, which were obtained by using augmented mixed logit models that explicitly incorporated attribute non-attendance. RESULTS: In the base-case study arm without level overlap or color coding, the observed dropout rate was 14%, and respondents attended, on average, only two out of five attributes. The independent introduction of both level overlap and color coding reduced the dropout rate to 10% and increased attribute attendance to three attributes. The combination of level overlap and color coding, however, was most effective: it reduced the dropout rate to 8% and improved attribute attendance to four out of five attributes. The latter essentially removes the need to explicitly accommodate for attribute non-attendance when analyzing the choice data. CONCLUSIONS: On the basis of the presented results, the use of level overlap and color coding are recommendable strategies to reduce the dropout rate and improve attribute attendance in discrete choice experiments.

Advocating a Paradigm Shift in Health-State Valuations: The Estimation of Time-Preference Corrected QALY Tariffs.

BACKGROUND: Despite evidence of nonproportional trade-offs in time trade-off exercises and the explicit incorporation of exponential discounting in health technology assessment calculations, quality-adjusted life-year (QALY) tariffs are currently still established under the assumption of linear time preferences. OBJECTIVES: The aim of this study was to introduce a general method of accommodating for nonlinear time preferences in discrete choice experiment (DCE) duration studies and to evaluate its impact on estimated QALY tariffs. METHODS: A parsimonious utility function is proposed that accommodates any discounting function and preserves linear time preferences as a special case. Based on an efficient DCE design and 1775 respondents from a nationally representative scientific household panel, preferences and QALY tariffs for the Dutch SF-6D were estimated while accommodating for nonlinear time preferences via exponential and hyperbolic discounting functions. RESULTS: When the discount rate was estimated directly, we found strong evidence of nonlinear time preferences (with an exponential and hyperbolic discount rate of 5.7% and 16.5%, respectively). When the discount rate was estimated as a function of health state severity, we found that years lived in better health states are discounted minus years lived in impaired health states. Finally, the best statistical fit was obtained when using a hyperbolic discount function, which resulted in smaller QALY decrements and fewer health states classified as worse than immediate death. CONCLUSIONS: Our results highlight the relevance and even necessity of a paradigm shift in health valuation studies in favor of time-preference corrected QALY tariffs, with potentially important implications for health technology assessment calculations and regulatory decisions.

SPECT/CT-guided lymph drainage mapping for the planning of unilateral elective nodal irradiation in head and neck squamous cell carcinoma.

PURPOSE: To investigate the feasibility of lymph drainage mapping (LDM) using SPECT/CT to help select head and neck cancer (HNSCC) patients for unilateral elective neck irradiation (ENI). Patients with lateralized HNSCC treated with radiotherapy routinely undergo bilateral ENI, despite the incidence of contralateral regional failure being relatively low even after unilateral ENI. We hypothesized that patients with a lateralized tumor without visible lymph drainage to the contralateral neck have an extremely low risk of contralateral involved nodes. Excluding the contralateral neck from elective irradiation will reduce radiation-induced toxicity and improve quality-of-life. METHODS: Fifty-five patients with lateralized cT1-3N0-2bM0 HNSCC not crossing the midline underwent LDM. Radiolabeled 99mTc-nanocolloid was injected in 4-5 depots around and in the primary tumor. Lymph drainage patterns were visualized using planar scintigraphy and SPECT/CT after 4 h. We report on the incidence of contralateral drainage, the location of draining areas, and the size of underlying nodes. RESULTS: Lymphatic drainage was successfully visualized in 54 patients (98%). In 11 patients (20%) with visible contralateral drainage, 14 draining areas (16 nodes; median volume 0.50 cc, diameter 8.0 mm) were identified. Neck levels with contralateral drainage were level II (88%), III (25%), and IV (13%). Contralateral drainage was significantly higher in T3 compared to T1-2 tumors (45 and 14%, respectively, P = 0.035). CONCLUSION: SPECT/CT-guided LDM is feasible and can be used to guide unilateral ENI in HNSCC patients in prospective studies. In addition, the anatomical confidence in visualization of contralateral drainage indicates a potential for ENI limited to draining levels alone.

Are Health State Valuations from the General Public Biased? A Test of Health State Reference Dependency Using Self-assessed Health and an Efficient Discrete Choice Experiment.

Health state valuations of patients and non-patients are not the same, whereas health state values obtained from general population samples are a weighted average of both. The latter constitutes an often-overlooked source of bias. This study investigates the resulting bias and tests for the impact of reference dependency on health state valuations using an efficient discrete choice experiment administered to a Dutch nationally representative sample of 788 respondents. A Bayesian discrete choice experiment design consisting of eight sets of 24 (matched pairwise) choice tasks was developed, with each set providing full identification of the included parameters. Mixed logit models were used to estimate health state preferences with respondents' own health included as an additional predictor. Our results indicate that respondents with impaired health worse than or equal to the health state levels under evaluation have approximately 30% smaller health state decrements. This confirms that reference dependency can be observed in general population samples and affirms the relevance of prospect theory in health state valuations. At the same time, the limited number of respondents with severe health impairments does not appear to bias social tariffs as obtained from general population samples. Copyright © 2016 John Wiley & Sons, Ltd.

The effect of regional politics on regional life expectancy in Italy (1980-2010).

BACKGROUND: The evidence on the association between politics and health is scarce considering the importance of this topic for population health. Studies that investigated the effect of different political regimes on health outcomes show inconsistent results. METHODS: Bayesian time-series cross-section analyses are used to examine the overall impact of regional politics on variations in Italian regional life expectancy (LE) at birth during the period 1980-2010. Our analyses control for trends in and unobserved determinants of regional LE, correct for temporal as well as spatial autocorrelation, and employ a flexible specification for the timing of the political effects. RESULTS: In the period from 1980 to 1995, we find no evidence that the communist, left-oriented coalitions and Christian Democratic, centre-oriented coalitions have had an effect on regional LE. In the period from 1995 onwards, after a major reconfiguration of Italy's political regimes and a major healthcare reform, we again find no evidence that the Centre-Left and Centre-Right coalitions have had a significant impact on regional LE. CONCLUSION: The presented results provide no support for the notion that different regional political regimes in Italy have had a differential effect on regional LE, even though Italian regions have had considerable and increasing autonomy over healthcare and health-related policies and expenditures.

Estimating the Impact of Health-related Behaviors on Geographic Variation in Cardiovascular Mortality: A New Approach Based on the Synthesis of Ecological and Individual-level Data.

BACKGROUND: Incidence of and mortality from cardiovascular disease (CVD) exhibit a strong geographical pattern, with inhabitants of more affluent neighborhoods showing a substantially lower risk of CVD mortality than inhabitants of deprived neighborhoods. Thus far, there is insufficient evidence as to what extent these differences can be attributed to differences in health-related behaviors. METHODS: Using a Hierarchical Related Regression approach, we combined individual and aggregate (ecological) data to investigate the extent to which small-area variation in CVD mortality in Dutch neighborhoods can be explained by several behavioral risk factors (i.e., smoking, drinking, overweight, and physical inactivity). The proposed approach combines the benefits of both an ecological analysis (in terms of data availability and statistical power) and an individual-level analysis (in terms of identification of the parameters and interpretation of the results). RESULTS: After correcting for differences in age and sex, accounting for differences in the behavioral risk factors reduces income-related inequalities in CVD mortality by approximately 30%. CONCLUSIONS: Direct targeting of the excess prevalence of unhealthy behaviors in deprived neighborhoods is identified as a relevant strategy to reduce inequalities in CVD mortality. Our results also show that the proposed Hierarchical Related Regression approach provides a powerful method for the investigation of small-area variation in health outcomes.

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL).

BACKGROUND: Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health-related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses. METHODS: The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned. RESULTS: The obtained coefficients indicated that "physical health,""mood," and "relationships" were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension "future." The predicted values for the MHQoL ranged from -0.741 for the worst state to 1 for the best state. CONCLUSIONS: This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions. HIGHLIGHTS: A discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from -0.741 for the worst state to 1 for the best state.

An investigation of age dependency in Dutch and Chinese values for EQ-5D-Y.

Aims: The primary aim was to explore the age dependency of health state values derived via trade-offs between health-related quality of life (HRQoL) and life years in a discrete choice experiment (DCE). The secondary aim was to explore if people weigh life years and HRQoL differently for children, adolescents, adults, and older adults. Methods: Participants from the general population of the Netherlands and China first completed a series of choice tasks offering choices between two EQ-5D-Y states with a given lifespan. The choice model captured the value of a year in full health, disutility determined by EQ-5D-Y, and a discount rate. Next, they received a slightly different choice task, offering choices between two lives that differed in HRQoL and life expectancy but produced the same number of quality-adjusted life years (QALYs). Participants were randomly assigned to fill out the survey for three or four age frames: a hypothetical person of 10, 15, 40, and 70 years (the last one only applicable to China) to allow the age dependency of the responses to be explored. Results: A total of 1,234 Dutch and 1,818 Chinese people administered the survey. Controlling for time preferences, we found that the agreement of health state values for different age frames was generally stronger in the Netherlands than in China. We found no clear pattern of differences in the QALY composition in both samples. The probability distribution over response options varied most when levels for lifespan or severity were at the extremes of the spectrum. Conclusion/discussion: The magnitude and direction of age effects on values seemed dimension- and country specific. In the Netherlands, we found a few differences in dimension-specific weights elicited for 10- and 15-year-olds compared to 40-year-olds, but the overall age dependency of values was limited. A stronger age dependency of values was observed in China, where values for 70-year-olds differed strongly from the values for other ages. The appropriateness of using existing values beyond the age range for which they were measured needs to be evaluated in the local context.

Comparison of Bayesian random-effects and traditional life expectancy estimations in small-area applications.

There are several measures that summarize the mortality experience of a population. Of these measures, life expectancies are generally preferred based on their simpler interpretation and direct age standardization, which makes them directly comparable between different populations. However, traditional life expectancy estimations are highly inaccurate for smaller populations and consequently are seldom used in small-area applications. In this paper, the authors compare the relative performance of traditional life expectancy estimation with a Bayesian random-effects approach that uses correlations (i.e., borrows strength) between different age groups, geographic areas, and sexes to improve the small-area life expectancy estimations. In the presented Monte Carlo simulations, the Bayesian random-effects approach outperforms the traditional approach in terms of bias, root mean square error, and coverage of the 95% confidence intervals. Moreover, the Bayesian random-effects approach is found to be usable for populations as small as 2,000 person-years at risk, which is considerably smaller than the minimum of 5,000 person-years at risk recommended for the traditional approach. As such, the proposed Bayesian random-effects approach is well-suited for estimation of life expectancies in small areas.

Estimating an anchored utility tariff for the well-being of older people measure (WOOP) for the Netherlands.

OBJECTIVE: Health economic evaluations using common health-related quality of life measures may fall short in adequately incorporating all relevant benefits of health and social care interventions targeted at older people. The Well-being of Older People measure (WOOP) is a broader well-being measure that comprises nine well-being domains. The objective of this study was to estimate a utility tariff for the WOOP, to facilitate its application in cost-utility analyses. METHODS: A discrete choice experiment (DCE) with duration approach was set up and fielded among 2,012 individuals from the Netherlands aged 65 years and above. Matched pairwise choice tasks, colour-coding and level overlap were used to reduce the cognitive burden of the DCE. The choice tasks were created using a Bayesian heterogeneous D-efficient design. The estimation procedure accommodated for nonlinear time preferences via an exponential discounting function. RESULTS: The estimation results showed that 'physical health', 'mental health', and 'making ends meet' were the most important well-being domains for older people, followed by 'independence' and 'living situation'. Of somewhat lesser importance were domains like 'social life', 'receiving support' and 'feeling useful'. The generated utility tariffs can be used to translate well-being states described with the WOOP to a utility score between -0.616 and 1. CONCLUSIONS: This study established a tariff for the WOOP, which will facilitate its use in economic evaluations of health and social care interventions targeted at older people, first of all in the Netherlands.