Menu choice stated preference tasks to capture demand complementarity in health.

Health programs/services are often bundled, allowing for both substitution and complementarity. We adapt Discrete Choice Experiments to capture bundling, with application to a case study of exercise and nutrition; complementarity arises due to the goal of improving health. Our contributions are (1) to present a menu-based choice experiment to explore bundling; (2) to analyse the menu-based data using an extension of the choice set generation model (GenL) to account for correlations between bundles and component singles. A nationally representative sample of 333 Australians chose between a nutrition program only; exercise program only; both nutrition and exercise programs; or their status quo. Overall, we show that by incorporating the menu choice task and introducing the combined alternative, we capture a significant portion of the population seeking both exercise and nutrition components. We estimate a latent class GenL model, and identify two latent classes: Class 1 preferred to choose programs on offer, and Class 2 was more price sensitive and had a stronger preference for staying with their status quo. We show in the post-estimation analysis that heterogeneity in preferences translates into heterogeneity in the way alternatives are bundled, indicating that the combined offering is appealing to specific classes of individuals who prefer bundling. By implementing the menu choice task, researchers and policymakers can effectively identify, cater to and influence the demand for combined exercise and nutrition options, leading to more targeted and impactful interventions in promoting healthier lifestyle choices.

Preparing for future pandemics: A multi-national comparison of health and economic trade-offs.

Government investment in preparing for pandemics has never been more relevant. The COVID-19 pandemic has stimulated debate regarding the trade-offs societies are prepared to make between health and economic activity. What is not known is: (1) how much the public in different countries are prepared to pay in forgone GDP to avoid mortality from future pandemics; and (2) which health and economic policies the public in different countries want their government to invest in to prepare for and respond to the next pandemic. Using a future-focused, multi-national discrete choice experiment, we quantify these trade-offs and find that the tax-paying public is prepared to pay $3.92 million USD (Canada), $4.39 million USD (UK), $5.57 million USD (US) and $7.19 million USD (Australia) in forgone GDP per death avoided in the next pandemic. We find the health policies that taxpayers want to invest in before the next pandemic and the economic policies they want activated once the next pandemic hits are relatively consistent across the countries, with some exceptions. Such results can inform economic policy responses and government investment in health policies to reduce the adverse impacts of the next pandemic.

Choice Set Formation in Residential Mobility and Its Implications for Segregation Dynamics.

We develop and estimate a statistical model of neighborhood choice that draws on insights from cognitive science and decision theory as well as qualitative studies of housing search. The model allows for a sequential decision process and the possibility that people consider a small and selective subset of all potential destinations. When combined with data from the Los Angeles Family and Neighborhood Survey, our model reveals that affordability constraints and households' tendency toward short-distance moves lead blacks and Hispanics to have racially stratified choice sets in which their own group is disproportionately represented. We use an agent-based model to assess how racially stratified choice sets contribute to segregation outcomes. Our results show that cognitive decision strategies can amplify patterns of segregation and inequality.

Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm.

In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilised ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11-17 years (N = 1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose 'best' attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of 'worst' attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations.

Reconceptualising the external validity of discrete choice experiments.

External validity is a crucial but under-researched topic when considering using discrete choice experiment (DCE) results to inform decision making in clinical, commercial or policy contexts. We present the theory and tests traditionally used to explore external validity that focus on a comparison of final outcomes and review how this traditional definition has been empirically tested in health economics and other sectors (such as transport, environment and marketing) in which DCE methods are applied. While an important component, we argue that the investigation of external validity should be much broader than a comparison of final outcomes. In doing so, we introduce a new and more comprehensive conceptualisation of external validity, closely linked to process validity, that moves us from the simple characterisation of a model as being or not being externally valid on the basis of predictive performance, to the concept that external validity should be an objective pursued from the initial conceptualisation and design of any DCE. We discuss how such a broader definition of external validity can be fruitfully used and suggest innovative ways in which it can be explored in practice.

Citizen participation in patient prioritization policy decisions: an empirical and experimental study on patients' characteristics.

Health systems worldwide are grappling with the need to control costs to maintain system viability. With the combination of worsening economic conditions, an aging population and reductions in tax revenues, the pressures to make structural changes are expected to continue growing. Common cost control mechanisms, e.g. curtailment of patient access and treatment prioritization, are likely to be adversely viewed by citizens. It seems therefore wise to include them in the decision making processes that lead up to policy changes. In the context of a multilevel iterative mixed-method design a quantitative survey representative of the German population (N = 2031) was conducted to probe the acceptance of priority setting in medicine and to explore the practicability of direct public involvement. Here we focus on preferences for patients' characteristics (medical aspects, lifestyle and socio-economic status) as possible criteria for prioritizing medical services. A questionnaire with closed response options was fielded to gain insight into attitudes toward broad prioritization criteria of patient groups. Furthermore, a discrete choice experiment was used as a rigorous approach to investigate citizens' preferences toward specific criteria level in context of other criteria. Both the questionnaire and the discrete choice experiment were performed with the same sample. The citizens' own health and social situation are included as explanatory variables. Data were evaluated using corresponding analysis, contingency analysis, logistic regression and a multinomial exploded logit model. The results show that some medical criteria are highly accepted for prioritizing patients whereas socio-economic criteria are rejected.

Using stated preference and revealed preference modeling to evaluate prescribing decisions.

The use of stated preference analyses to evaluate choice of health care products has been growing in recent years. This paper shows how revealed preference data can be enriched with stated preference data and highlights the relative advantages of revealed and stated preference data. The techniques were applied to a study of determinants of physicians' prescriptions of alcoholism medications. Analyses were conducted on the relationship between physicians' perceptions of existing alcoholism medication attributes and their prescribing rates of those medications. Analyses were also conducted on physicians' decisions to prescribe hypothetical alcoholism medications with varying attributes such as efficacy, side effects, compliance, mode of action, and price. Finally, analyses were conducted on the combined stated and revealed preference data. Joint estimation suggests that parameters from the revealed and stated preference data are equal, up to scale. Joint analyses highlight how stated preference data can be used to estimate parameters for attributes that are not observed in the marketplace, that do not vary in the marketplace, or that are highly collinear with other attributes in actual markets.

Using stated preference modeling to forecast the effect of medication attributes on prescriptions of alcoholism medications.

OBJECTIVE: The objective of this study was to forecast physicians' preferred rate of prescriptions of alcoholism medications given different medications attributes (i.e., efficacy, side effects, compliance, price, mode of administration, method of action). METHODS: Stated preference modeling was used. Data came from a survey of 1388 physicians specializing in addiction medication (65% response rate). Physicians were given four hypothetical scenarios, each in which they were asked to choose between prescribing one of two hypothetical alcoholism medications with given attributes or prescribing no medication. RESULTS: Prescribing decisions were elastic with respect to the efficacy of the medication (1.35 and 1.65 using two efficacy measures). A 10% increase in the percentage of patients who would remain abstinent on the medication would lead to a 13.5% increase in the percentage of patients prescribed the medication. Prescribing decisions were inelastic with respect of nonserious side effects (-0.24), compliance (0.80), and price (-0.25). The market share of alcoholism medications with extremely favorable characteristics (i.e., 80% abstinence rate, a 95% no heavy drinking rate, a 10% side effect rate, a 80% compliance rate, and a price of US dollars 0.25) was predicted to be 53%, and 47% of the population would not be prescribed a medication to prevent alcoholism. CONCLUSIONS: The market share of new medications to treat alcoholism among addiction specialists could surpass the low usage rates of existing medications if those medications have better attributes. However, prescription levels may not reach that expected for treatment of other diseases.