Decision heuristic or preference? Attribute non-attendance in discrete choice problems

Elicitation of societal preferences for chronic lymphocytic leukemia's treatments: a discrete choice experiment

The overall value of treatments for chronic lymphocytic leukemia (CLL) depends on several factors, including preferences of the general population, who contributes to the financing of health systems. This study investigated societal preferences for attributes of CLL treatments in Italy. An online large-scale survey was designed using a discrete choice experiment (DCE) methodology and delivered to the Italian adult general population. Ten treatment attributes were identified, covering efficacy, safety, operational aspects and (hypothetical) out-of-pocket cost. DCE data were analyzed using a mixed logit regression model, estimating the willingness-to-pay for attribute levels' change. The general population significantly preferred more effective treatments, with shorter duration, administered orally rather than orally + intravenously. Changes in therapy duration, frequency of checkups and organ damage risk had the greatest impact on preferences. The integration of societal preferences in the value judgments of CLL therapies may help health authorities in establishing priority setting and taking pricing-reimbursement decisions.

Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis

OBJECTIVE

To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure.

METHODS

A best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred.

RESULTS

Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization.

CONCLUSION

Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

Two Methods, One Story? Comparing Results of a Choice Experiment and Multidimensional Thresholding From a Clinician Preference Study in Aneurysmal Subarachnoid Hemorrhage

OBJECTIVES

An increasing number of methods are used to elicit health preference information. It is unclear whether different elicitation methods produce similar results and policy advice. Here, we compared the results from a discrete choice experiment (DCE) and multidimensional thresholding (MDT) that were conducted in the same sample.

METHODS

Clinicians (N = 350) completed a DCE and MDT to elicit their preferences for 4 attributes related to the medical management of subarachnoid hemorrhage after aneurysm repair. Preference weights were compared between the DCE and MDT using a complete combinatorial convolution test. Additionally, data from the DCE and MDT were used to compute preference-based net treatment values for 16 hypothetical treatment profiles versus 1000 simulated comparators. The implied treatment recommendations were compared between the DCE and MDT.

RESULTS

Preference weight distributions and median weights did not differ significantly between the DCE and MDT for any attribute: likelihood of delayed cerebral ischemia (medians 0.48 vs 0.40; P = .41), risk of lung complications (medians 0.27 vs 0.30; P = .52), risk of hypotension (medians 0.10 vs 0.11; P = .55), and risk of anemia (medians 0.07 vs 0.07; P = .50). The DCE and MDT produced similar treatment net value distributions (P > .05) and implied the same treatment recommendations in 82.3% of cases.

CONCLUSIONS

The DCE and MDT elicited similar preference distributions and produced the same treatment recommendations for most tested cases. However, the share of people supporting the average treatment recommendation differed. More research is needed to determine how these findings would compare with those in other populations (in particular, patients) and applications.

Development of attributes and attribute levels for a discrete choice experiment on patients' and providers' choice for antiretroviral therapy service in Northwest Ethiopia

BACKGROUND

Discrete choice experiments (DCEs) are used to assess the strength of preferences and value of interventions. However, researchers using this approach have been criticized for not conducting or publishing rigorous studies to select the required attributes and levels. Proper specification of attributes and their levels determines the validity of DCE. Hence, our study aimed to identify and define attributes and levels for the design of a DCE to elicit patients' and providers' preferences for ART service in Northwest Ethiopia.

METHODS

Four stages were followed to derive the final list of attributes and levels: (1) a literature review to derive conceptual attributes; (2) key informant interviews of 17 providers and in-depth interviews of 15 adult stable patients to identify context-specific attributes and attribute levels; (3) ranking survey among 31 HIV/AIDS program implementers and rating survey among 35 adult stable patients and 42 health workers providing antiretroviral therapy (ART) service to indicate participants' preference of attributes; and (4) an expert opinion to reduce the list of attributes and levels.

RESULTS

First, a literature review identified 23 candidate attributes. Second, individual-level analysis of the qualitative transcripts confirmed 15 of these 23 attributes. Third, the ranking and rating surveys put the importance of the 23 ART service attributes in order of preference. Fourth, through discussions with eight experts, 17 attributes were discarded based on multiple criteria. The six retained attributes were: the location of ART refills, the frequency of receiving ART refills, the person providing ART refills, the participants/others seen at the same ART refill visit, medication refill pick-up/delivery times, and the total cost of the visit during antiretroviral (ARV) medication refill. Finally, levels were assigned to these 6 attributes based on data from the literature, transcripts, and knowledge of the Ethiopian context.

CONCLUSIONS

This detailed description illuminates the attribute development process and provides the reader with a basis for evaluating the rigor of this phase of DCE construction. This paper contributes empirical evidence to the limited methodological literature on attributes and levels of development for DCE, thereby providing further empirical guidance on ART service preference, specifically among patients of low- and middle-income countries.

Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments

Health-related discrete choice experiments (DCEs) information can be used to inform decision-making on the development, authorisation, reimbursement and marketing of drugs and devices as well as treatments in clinical practice. Discrete choice experiment is a stated preference method based on random utility theory (RUT), which imposes strong assumptions on respondent choice behaviour. However, respondents may use choice processes that do not adhere to the normative rationality assumptions implied by RUT, applying simplifying decision rules that are more selective in the amount and type of processed information (i.e., simplifying heuristics). An overview of commonly detected simplifying heuristics in health-related DCEs is lacking, making it unclear how to identify and deal with these heuristics; more specifically, how researchers might alter DCE design and modelling strategies to accommodate for the effects of heuristics. Therefore, the aim of this paper is three-fold: (1) provide an overview of common simplifying heuristics in health-related DCEs, (2) describe how choice task design and context as well as target population selection might impact the use of heuristics, (3) outline DCE design strategies that recognise the use of simplifying heuristics and develop modelling strategies to demonstrate the detection and impact of simplifying heuristics in DCE study outcomes.

The stated preferences of community-based volunteers for roles in the prevention of violence against women and girls in Ghana: A discrete choice analysis

Violence against women and girls (VAWG) is a human rights violation with substantial health-related consequences. Interventions to prevent VAWG, often implemented at the community level by volunteers, have been proven effective and cost-effective. One such intervention is the Rural Response System in Ghana, a volunteer-run program which hires community based action teams (COMBATs) to sensitise the community about VAWG and to provide counselling services in rural areas. To increase programmatic impact and maximise the retention of these volunteers, it is important to understand their preferences for incentives. We conducted a discrete choice experiment (DCE) among 107 COMBAT volunteers, in two Ghanaian districts in 2018, to examine their stated preferences for financial and non-financial incentives that could be offered in their roles. Each respondent answered 12 choice tasks, and each task comprised four hypothetical volunteering positions. The first three positions included different levels of five role attributes. The fourth option was to cease volunteering as a COMBAT volunteer (opt-out). We found that, overall, COMBAT volunteers cared most for receiving training in volunteering skills and three-monthly supervisions. These results were consistent between multinomial logit, and mixed multinomial logit models. A three-class latent class model fitted our data best, identifying subgroups of COMBAT workers with distinct preferences for incentives: The younger 'go getters'; older 'veterans', and the 'balanced bunch' encompassing the majority of the sample. The opt-out was chosen only 4 (0.3%) times. Only one other study quantitatively examined the preferences for incentives of VAWG-prevention volunteers using a DCE (Kasteng et al., 2016). Understanding preferences and how they vary between sub-groups can be leveraged by programme managers to improve volunteer motivation and retention. As effective VAWG-prevention programmes are scaled up from small pilots to the national level, data on volunteer preferences may be useful in improving volunteer retention.

Preferences for COVID-19 epidemic control measures among French adults: a discrete choice experiment

In this stated preferences study, we describe for the first time French citizens' preferences for various epidemic control measures, to inform longer-term strategies and future epidemics. We used a discrete choice experiment in a representative sample of 908 adults in November 2020 (before vaccination was available) to quantify the trade-off they were willing to make between restrictions on the social, cultural, and economic life, school closing, targeted lockdown of high-incidence areas, constraints to directly protect vulnerable persons (e.g., self-isolation), and measures to overcome the risk of hospital overload. The estimation of mixed logit models with correlated random effects shows that some trade-offs exist to avoid overload of hospitals and intensive care units, at the expense of stricter control measures with the potential to reduce individuals' welfare. The willingness to accept restrictions was shared to a large extent across subgroups according to age, gender, education, vulnerability to the COVID-19 epidemic, and other socio-demographic or economic variables. However, individuals who felt at greater risk from COVID-19, and individuals expressing high confidence in the governmental management of the health and economic crisis, more easily accepted all these restrictions. Finally, we compared the welfare impact of alternative strategies combining different epidemic control measures. Our results suggest that policies close to a targeted lockdown or with medically prescribed self-isolation were those satisfying the largest share of the population and achieving high gain in average welfare, while average welfare was maximized by the combination of all highly restrictive measures. This illustrates the difficulty in making preference-based decisions on restrictions.

Heterogeneous Preferences for Colorectal Cancer Screening in Germany: Results of a Discrete Choice Experiment

OBJECTIVES

Colorectal cancer (CRC) screening tests differ in benefits, harms, and processes, making individual informed decisions preference based. The objective was to analyze the preferences of insurees in Germany for characteristics of CRC screening modalities.

METHODS

A generic discrete choice experiment with 2-alternative choice sets and 6 attributes (CRC mortality, CRC incidence, complications, preparation, need for transportation, and follow-up; 3 levels each) depicting characteristics of fecal testing, sigmoidoscopy, and colonoscopy was generated. Participants completed 8 choice tasks. Internal validity was tested using a within-set dominated pair. Between June and October 2020, written questionnaires were sent to a stratified random sample (n = 5000) of 50-, 55-, and 60-year-old insurees of the AOK (Allgemeine Ortskrankenkasse) Lower Saxony, who had previously received an invitation to participate in the organized screening program including evidence-based information. Preferences were analyzed using conditional logit, mixed logit, and latent-class model.

RESULTS

From 1282 questionnaires received (26% [1282 of 4945]), 1142 were included in the analysis. Approximately 42% of the respondents chose the dominated alternative in the internal validity test. Three heterogeneous preference classes were identified. Most important attributes were preparation (class 1; n = 505, 44%), CRC mortality (class 2; n = 347, 30%), and CRC incidence (class 3; n = 290, 25%). Contrary to a priori expectations, a higher effort was preferred for bowel cleansing (class 1) and accompaniment home (classes 1 and 2).

CONCLUSION

Internal validity issues of choice data need further research and warrant attention in future discrete choice experiment surveys. The observed preference heterogeneity suggests different informational needs, although the underlying reasons remained unclear.

Dyadic Discrete Choice Experiments Enable Persons with Dementia and Informal Caregivers to Participate in Health Care Decision Making: A Mixed Methods Study

BACKGROUND

Discrete choice experiments (DCEs) may facilitate persons with dementia and informal caregivers to state care preferences. DCEs can be cognitively challenging for persons with dementia.

OBJECTIVE

This study aims to design a dementia friendly dyadic DCE that enables persons with dementia and informal caregivers to provide input individually and jointly, by testing the number of attributes and choice tasks persons with dementia can complete and providing insight in their DCE decision-making process.

METHODS

This study included three DCE rounds: 1) persons with dementia, 2) informal caregivers, and 3) persons with dementia and informal caregivers together. A flexible DCE design was employed, with increasing choice task complexity to explore cognitive limitations in decision-making. Summary statistics and bivariate comparisons were calculated. A qualitative think-aloud approach was used to gain insight in the DCE decision-making processes. Transcripts were analyzed using thematic analysis.

RESULTS

Fifteen person with dementia, 15 informal caregiver, and 14 dyadic DCEs were conducted. In the individual DCE, persons with dementia completed six choice tasks (median), and 80% could complete a choice task with least three attributes. In the dyadic DCE persons with dementia completed eight choice tasks (median) and could handle slightly more attributes. Qualitative results included themes of core components in DCE decision-making such as: understanding the choice task, attribute and level perception, option attractiveness evaluation, decision rule selection, and preference adaptation.

CONCLUSION

Persons with dementia can use simple DCE designs. The dyadic DCE was promising for dyads to identify overlapping and discrepant care preferences while reaching consensus.

Discrete choice analysis of health worker job preferences in Ethiopia: Separating attribute non-attendance from taste heterogeneity

When measuring preferences, discrete choice experiments (DCEs) typically assume that respondents consider all available information before making decisions. However, many respondents often only consider a subset of the choice characteristics, a heuristic called attribute non-attendance (ANA). Failure to account for ANA can bias DCE results, potentially leading to flawed policy recommendations. While conventional latent class logit models have most commonly been used to assess ANA in choices, these models are often not flexible enough to separate non-attendance from respondents' low valuation of certain attributes, resulting in inflated rates of ANA. In this paper, we show that semi-parametric mixtures of latent class models can be used to disentangle successfully inferred non-attendance from respondent's "weaker" taste sensitivities for certain attributes. In a DCE on the job preferences of health workers in Ethiopia, we demonstrate that such models provide more reliable estimates of inferred non-attendance than the alternative methods currently used. Moreover, since we find statistically significant variation in the rates of ANA exhibited by different health worker cadres, we highlight the need for well-defined attributes in a DCE, to ensure that ANA does not result from a weak experimental design.

Attribute nonattendance in COVID-19 vaccine choice: A discrete choice experiment based on Chinese public preference

Objectives

The global coronavirus disease 2019 (COVID‐19) pandemic has not been well controlled, and vaccination could be an effective way to prevent this pandemic. By accommodating attribute nonattendance (ANA) in a discrete choice experiment (DCE), this paper aimed to examine Chinese public preferences and willingness to pay (WTP) for COVID‐19 vaccine attributes, especially the influence of ANA on the estimated results.

Methods

A DCE was designed with four attributes: effectiveness, protection period, adverse reactions and price. A random parameter logit model with an error component (RPL‐EC) was used to analyse the heterogeneity of respondents' preferences for COVID‐19 vaccine attributes. Two equality constraint latent class (ECLC) models were used to consider the influence of ANA on the estimated results in which the ECLC‐homogeneity model considered only ANA and the ECLC‐heterogeneity model considered both ANA and preference heterogeneity.

Results

Data from 1,576 samples were included in the analyses. Effectiveness had the highest relative importance, followed by adverse reactions and protection period, which were determined by the attributes and levels presented in this study. The ECLC‐heterogeneity model improved the goodness of fit of the model and obtained a lower probability of ANA. In the ECLC‐heterogeneity model, only a small number of respondents (29.09%) considered all attributes, and price was the most easily ignored attribute (64.23%). Compared with the RPL‐EC model, the ECLC‐homogeneity model obtained lower WTPs for COVID‐19 vaccine attributes, and the ECLC‐heterogeneity model obtained mixed WTP results. In the ECLC‐heterogeneity model, preference group 1 obtained higher WTPs, and preference groups 2 and 3 obtained lower WTPs.

Conclusions

The RPL‐EC, ECLC‐homogeneity and ECLC‐heterogeneity models obtained inconsistent WTPs for COVID‐19 vaccine attributes. The study found that the results of the ECLC‐heterogeneity model considering both ANA and preference heterogeneity may be more plausible because ANA and low preference may be confused in the ECLC‐homogeneity model and the RPL‐EC model. The results showed that the probability of ANA was still high in the ECLC‐heterogeneity model, although it was lower than that in the ECLC‐homogeneity model. Therefore, in future research on DCE (such as the field of vaccines), ANA should be considered as an essential issue.

Public Contribution

Chinese adults from 31 provinces in mainland China participated in the study. All participants completed the COVID‐19 vaccine choice questions generated through the DCE design.

Weighting or aggregating? Investigating information processing in multi-attribute choices

Multi-attribute choices are commonly analyzed in economics to value goods and services. Analysis assumes individuals consider all attributes, making trade-offs between them. Such decision-making is cognitively demanding, often triggering alternative decision rules. We develop a new model where individuals aggregate multi-attribute information into meta-attributes. Applying our model to a choice experiment (CE) dataset, accounting for attribute aggregation (AA) improves model fit. The probability of adopting AA is greater for: homogenous attribute information; participants who had shorter response time and failed the dominance test; and for later located choices. Accounting for AA has implications for welfare estimates. Our results underline the importance of accounting for information processing rules when modelling multi-attribute choices.

An Exploration on Attribute Non-attendance Using Discrete Choice Experiment Data from the Irish EQ-5D-5L National Valuation Study

BACKGROUND

Generic measures of health-related quality of life (HRQoL) permit comparisons of competing demands for healthcare resources using outcomes that reflect the preferences of tax payers. EQ-5D instruments are the most commonly used generic, preference-based measures of HRQoL. The EQ-5D-5L enables respondents to describe their health state using five dimensions of health, each with five response levels. The standardised protocol for the valuation of EQ-5D-5L health states comprises use of the composite time trade-off valuation technique, supplemented by a discrete choice experiment (DCE).

OBJECTIVE

This paper presents the first exploration on attribute non-attendance (ANA) to the dimensions of the EQ-5D-5L using DCE data collected following the standardised protocol.

METHOD

This paper uses the equality constrained latent class model and the endogenous attribute attendance model to examine ANA to the dimensions of the EQ-5D-5L.

RESULTS

The results suggest that respondents are less likely to consider the physical dimensions of the EQ-5D-5L (such as self-care and usual activities) when evaluating the health states. The effects of ANA on utility scores depends on the interpretation of the underlying reasons for ANA.

CONCLUSIONS

We recommend that future value sets based in whole or in part on DCE data examine the impact of and reasons for non-attendance in national valuation studies.

Partial attribute attendance in environmental choice experiments: A comparative case study between Guangzhou (China) and Brussels (Belgium)

Attribute non-attendance (ANA) in discrete choice experiment (DCE) exercises has attracted increasing, yet limited, scholarly attention. This paper attempts to investigate ANA in a comparative case study, with a focus on its patterns and their association with socioeconomic, behavioral and perceptual factors, as well as its impacts on willingness-to-pay (WTP) estimates. We deploy a four-level polytomous scale (always, often, seldom, and never considered) for respondents to state their various degrees of attribute attendance (SANA) in an identical DCE questionnaire about urban river restoration initiatives in two global cities with contrast socioeconomic contexts, yet similar request for restoring polluted and modified urban rivers, Guangzhou (south China) and Brussels (Belgium). The survey results reveal the existence of large proportions of partial attendance in two sampled cities. We use an extended mixed logit model, which incorporates separate parameters delineating each attribute's different attendance groups, to estimate respondents' average WTP values. We find that accounting for SANA could improve the goodness-of-fit of the model and affect the magnitude of mean WTP estimates. Respondents' attribute attendance level pertaining to various attributes is mainly associated with their perceived importance of urban rivers' ecosystem services, but may not be necessarily correlated with the strength of their preference for corresponding attributes as indicated by the mean WTP estimates. Whether this discontinuity between respondents' stated ANA levels and WTP estimates within Guangzhou sample questions the ability of DCEs to generate unbiased welfare estimation and policy guidance in developing countries calls for further studies.

To pay or not to pay? Cost information processing in the valuation of publicly funded healthcare

Discrete choice experiments (DCEs) commonly include a monetary attribute. This enables willingness to pay (WTP), a monetary measure of benefit, to be estimated for non-monetary attributes. There has been concern that the inclusion of a cost attribute challenges the credibility of the experiment when valuing publicly funded healthcare systems. However, very little research has explored this issue. Using a UK sample, we allocated participants across two versions of a DCE: one including a cost attribute and the other excluding a cost attribute. The DCE was identical in all other respects. We find no significant difference in response time across the two surveys, monotonicity was higher for the COST DCE and cost was stated as the most commonly ignored attribute in the COST DCE. Whilst the inclusion of a cost attribute did not alter the structure of preferences, it resulted in a lower level of choice consistency. Using an unrestricted latent class model, we find evidence of a credibility effect: respondents with experience of paying for health services and who perceive the choices as realistic are less likely to ignore cost. Further, respondents with a higher response time are less likely to be cost minimisers. Results are robust across different model specifications and choice formats. DCE practitioners should give due consideration to cost credibility when including a cost attribute, ensuring participants engage with the cost attribute. Ways to do this are suggested, including careful motivation of the cost attribute, consideration to the appropriate payment vehicle and careful consideration to the cost attribute when developing and piloting the survey. Failure to do this will result in an invalid willingness to pay estimates and thus policy recommendations.

Opening the 'Black Box': An Overview of Methods to Investigate the Decision-Making Process in Choice-Based Surveys

The desire to understand the preferences of patients, healthcare professionals and the public continues to grow. Health valuation studies, often in the form of discrete choice experiments, a choice based survey approach, proliferate as a result. A variety of methods of pre-choice process analysis have been developed to investigate how and why people make their decisions in such experiments and surveys. These techniques have been developed to investigate how people acquire and process information and make choices. These techniques offer the potential to test and improve theories of choice and/or associated empirical models. This paper provides an overview of such methods, with the focus on their use in stated choice-based healthcare studies. The methods reviewed are eye tracking, mouse tracing, brain imaging, deliberation time analysis and think aloud. For each method, we summarise the rationale, implementation, type of results generated and associated challenges, along with a discussion of possible future developments.

Using Eye-Tracking Technology with Older People in Memory Clinics to Investigate the Impact of Mild Cognitive Impairment on Choices for EQ-5D-5L Health States Preferences

BACKGROUND

Population ageing is a phenomenon taking place in almost every global region. Current estimates indicate that 10-20% of older people in developed countries have mild cognitive impairment (MCI), with these percentages predicted to rise markedly by 2050.

OBJECTIVE

Our objective was to apply eye-tracking technology to investigate the information processes adopted by older people with and without MCI in determining preferences for health states in the five-level EuroQol-5 Dimensions (EQ-5D-5L) instrument.

METHODS

Older people (aged ≥ 65 years; including both patients and family carers) attending outpatient memory clinics in Southern Adelaide between July 2017 and June 2018, competent to read and converse in English and with a Mini-Mental State Examination (MMSE) cognition score of ≥ 19 were invited to participate. In total, 52 people met the inclusion criteria, of whom 20 (38%) provided informed consent and fully participated. Participants were categorised into two subgroups (each n = 10) for comparison based upon established MMSE cognition thresholds (19-23, lower MMSE indicative of MCI; ≥ 24, higher MMSE indicative of good cognition). A discrete-choice experiment (DCE) comprising a series of pairwise choices between alternative EQ-5D-5L health states of varying survival duration with differential levels of task complexity (approximated by the degree of attribute level overlap in each choice), was administered as a face-to-face interview with the participant wearing an eye-tracking device.

RESULTS

Attribute non-attendance (ANA) was higher for the lower MMSE subgroup than for the higher MMSE subgroup, although these differences were generally not statistically significant. ANA remained relatively low and consistent for participants with good cognition regardless of task complexity. In contrast, ANA increased notably in participants exhibiting MCI, increasing from 10% on average per participant in the lower MMSE subgroup with five attribute level overlap to 23% on average per participant in the lower MMSE subgroup with zero attribute level overlap.

CONCLUSIONS

This exploratory study provided important insights into the information processes adopted by older people with varying levels of cognitive functioning when choosing between alternative EQ-5D-5L health states of varying survival duration and specifically the relationships between cognitive capacity, task complexity and the extent of ANA. Recent advances in econometric modelling of health state valuation data have demonstrated the added value of capturing ANA information as this can be accounted for in the DCE data analysis, thereby improving the precision of model estimates. Eye-tracking technology can usefully inform the design, conduct and econometric modelling of DCEs, driving the inclusion of this rapidly growing population traditionally excluded from preference-elicitation studies of this nature.

Public preferences for ecological indicators used in Everglades restoration

The Everglades is one of the largest wetland ecosystems in the world covering almost 18,000 square miles from central Florida southward to Florida Bay. Over the 20th century, efforts to drain the Everglades for agriculture and development severely damaged the ecosystem so that today roughly 50% of the historic flow of water through the Everglades has been diverted elsewhere. In an attempt to restore the Everglades, the U.S. Congress authorized the Comprehensive Everglades Restoration Plan (CERP) in 2000, expected to cost over $16 billion and to take several decades to complete. We used the results from a stated preference choice experiment (SPCE) survey of Florida households to estimate the willingness to pay for several ecological attributes related to CERP performance indicators likely to be impacted by Everglades restoration. We also used a latent class model (LCM) to explore preference heterogeneity among respondents. On average, survey respondents were willing to pay for improvements in all of the attributes included in the survey, namely increased populations of wading birds, American alligators, endangered snail kites, and spotted seatrout, and reduced polluted discharges from Lake Okeechobee to the Caloosahatchee and St. Lucie rivers. Willingness to pay was highest for reduced polluted discharges from Lake Okeechobee.

Health shock and preference instability: assessing health-state dependency of willingness-to-pay for corrective eyeglasses

BACKGROUND

Differences in contingent valuation (CV) estimates for identical healthcare goods can cast considerable doubt on the true economic measures of consumer preferences. Hypothetical nature of CV methods can potentially depend on the salience, context and perceived relevance of the good or service under consideration. Thus, the high demand elasticity for healthcare goods warrants careful selection of study population as the contexts of valuation significantly changes after experiencing health shock.

METHODS

In this study, using triple-bounded dichotomous choice (TBDC) experiments, we test how negative health shock (namely, being diagnosed with refractive errors), can alter preference over a common health good (namely, corrective eyeglasses). We compared elicited WTP of diagnosed patients with a synthetically constructed comparable cohort without the same health shock, controlling for the possible self-selection using a number of matching techniques based on the observable socio-demographic characteristics.

RESULTS

The consumers diagnosed with vision problems exhibit a rightward shift in their demand curve compared to observationally identical consumers without such problems resulting in about 17% higher consumer surplus. The consumers without the health shock are willing to pay about BDT 762.4 [95% CI: BDT 709.9 - BDT 814.9] for corrective eyeglasses, which gets 15-30% higher for the matched with-health-shock consumers. Multivariable analyses suggest more educated and wealthier individuals are willing to pay respectively BDT 208 and BDT 119 more for corrective eyeglasses. We have tested the models for different matching protocols. Our results are fairly robust to alternate specifications and various matching techniques.

CONCLUSION

The preferences for healthcare goods, such as eyeglasses, can significantly depend upon the respondent being diagnosed with refractive errors. Our findings have implications for general cost-benefit analyses often relying on WTP, which can vary depending on the contexts. There are also increasing interests in cost recovery models, which require understanding the demand for healthcare goods and services. We find eliciting the demand needs to consider the health status of the population from which the respondents are sampled.

Attribute development and level selection for a discrete choice experiment to elicit the preferences of health care providers for capitation payment mechanism in Kenya

BACKGROUND

Stated preference elicitation methods such as discrete choice experiments (DCEs) are now widely used in the health domain. However, the "quality" of health-related DCEs has come under criticism due to the lack of rigour in conducting and reporting some aspects of the design process such as attribute and level development. Superficially selecting attributes and levels and vaguely reporting the process might result in misspecification of attributes which may, in turn, bias the study and misinform policy. To address these concerns, we meticulously conducted and report our systematic attribute development and level selection process for a DCE to elicit the preferences of health care providers for the attributes of a capitation payment mechanism in Kenya.

METHODOLOGY

We used a four-stage process proposed by Helter and Boehler to conduct and report the attribute development and level selection process. The process entailed raw data collection, data reduction, removing inappropriate attributes, and wording of attributes. Raw data was collected through a literature review and a qualitative study. Data was reduced to a long list of attributes which were then screened for appropriateness by a panel of experts. The resulting attributes and levels were worded and pretested in a pilot study. Revisions were made and a final list of attributes and levels decided.

RESULTS

The literature review unearthed seven attributes of provider payment mechanisms while the qualitative study uncovered 10 capitation attributes. Then, inappropriate attributes were removed using criteria such as salience, correlation, plausibility, and capability of being traded. The resulting five attributes were worded appropriately and pretested in a pilot study with 31 respondents. The pilot study results were used to make revisions. Finally, four attributes were established for the DCE, namely, payment schedule, timeliness of payments, capitation rate per individual per year, and services to be paid by the capitation rate.

CONCLUSION

By rigorously conducting and reporting the process of attribute development and level selection of our DCE,we improved transparency and helped researchers judge the quality.

What can discrete choice experiments do for you?

CONTEXT

In everyday life, the choices we make are influenced by our preferences for the alternatives available to us. The same is true when choosing medical education, training and jobs. More often than not, those alternatives comprise multiple attributes and our ultimate choice will be guided by the value we place on each attribute relative to the others. In education, for example, choice of university is likely to be influenced by preferences for institutional reputation, location, cost and course content; but which of these attributes is the most influential? An understanding of what is valued by applicants, students, trainees and colleagues is of increasing importance in the higher education and medical job marketplaces because it will help us to develop options that meet their needs and preferences.

METHODS

In this article, we describe the discrete choice experiment (DCE), a survey method borrowed from economics that allows us to quantify the values respondents place on the attributes of goods and services, and to explore whether and to what extent they are willing to trade less of one attribute for more of another.

CONCLUSIONS

To date, DCEs have been used to look at medical workforce issues but relatively little in the field of medical education. However, many outstanding questions within medical education could be usefully addressed using DCEs. A better understanding of which attributes have most influence on, for example, staff or student satisfaction, choice of university and choice of career, and the extent to which stakeholders are prepared to trade one attribute against another is required. Such knowledge will allow us to tailor the way medical education is provided to better meet the needs of key stakeholders within the available resources.