Heterogeneity in general practitioners' preferences for quality improvement programs: a choice experiment and policy simulation in France

Public Heterogeneous Preferences for Low-Dose Computed Tomography Lung Cancer Screening Service Delivery in Western China: A Discrete Choice Experiment

BACKGROUND

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) is an efficient method that can reduce lung cancer mortality in high-risk individuals. However, few studies have attempted to measure the preferences for LDCT LCS service delivery. This study aimed to generate quantitative information on the Chinese population's preferences for LDCT LCS service delivery.

METHODS

The general population aged 40 to 74 in the Sichuan province of China was invited to complete an online discrete choice experiment (DCE). The DCE required participants to answer 14 discrete choice questions comprising five attributes: facility levels, facility ownership, travel mode, travel time, and out-of-pocket cost. Choice data were analyzed using mixed logit and latent class logit (LCL) models.

RESULTS

The study included 2529 respondents, with 746 (29.5%) identified as being at risk for lung cancer. Mixed logit model (MLM) analysis revealed that all five attributes significantly influenced respondents' choices. Facility levels had the highest relative importance (44.4%), followed by facility ownership (28.1%), while out-of-pocket cost had the lowest importance (6.4%). The at-risk group placed relatively more importance on price and facility ownership compared to the non-risk group. LCL model identified five distinct classes with varying preferences.

CONCLUSION

This study revealed significant heterogeneity in preferences for LCS service attributes among the Chinese population, with facility level and facility ownership being the most important factors. The findings underscore the need for tailored strategies targeting different subgroup preferences to increase screening participation rates and improve early detection outcomes.

Heterogeneity in physician's job preferences in a dual practice context - Evidence from a DCE

Many countries are facing challenges in recruiting and retaining physicians, particularly in regions where the public and private sectors compete for doctors. Understanding the factors influencing physicians' job choices can help inform policies aimed at attracting and retaining this valuable workforce. This study aims to elicit the strength of physicians' preferences regarding various job-related aspects, including earnings, time flexibility, discussion of clinical cases, frequency of facilities and equipment updates, training opportunities and autonomy in decision making. To achieve this, a Discrete Choice Experiment (DCE) was administered to 697 physicians. Each participant completed a series of eight choice tasks, where they had to choose between two hypothetical jobs differing in these attributes with levels mirroring positions in the public and private sectors in Portugal. The resulting choices were analysed using mixed logit, generalized multinomial logit and latent classes models to account for diverse unobserved variations in physicians' preferences and to explore preference heterogeneity across different observable characteristics. Jobs that offered more autonomy and training opportunities were strongly preferred, as physicians would require additional compensation to work with reduced autonomy (equivalent to 28.62% of gross income) or less frequent training (equivalent to 22.75%). This study also shows that the ranking of the job characteristics is similar between physicians working exclusively in the public sector and those engaged in dual practice. Nevertheless, public sector physicians place more emphasis on the availability of frequent training possibilities and frequent updates of facilities and equipment compared to their counterparts in dual practice. These findings contribute to existing knowledge by highlighting the significance of non-monetary attributes and shedding light on the preferences of physicians across various employment scenarios. They offer valuable insights for policy development aimed at influencing physicians' allocation of time between sectors.

Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review

BACKGROUND

Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices.

OBJECTIVES

Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare.

METHODS

This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both.

RESULTS

Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity.

CONCLUSIONS

Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.

Assessing Payers' Preferences for Real-World Evidence in the United States: A Discrete Choice Experiment

OBJECTIVES

To rank the US payers' preferences for attributes of real-world evidence (RWE) studies in the context of chronic disease and to quantify trade-offs among them.

METHODS

We conducted a discrete choice experiment in which 180 employees from payer organizations were tasked to choose between 2 RWE studies assuming they were assessing evidence to inform formulary decisions for chronic disease treatment. Each RWE study was characterized by 7 attributes with 3 levels each: very informative, moderately informative, and not measured. We used a D-optimal main-effects design. Survey data were fitted to a conditional logit model to obtain a relative measure of the ranking of importance for each attribute.

RESULTS

Clinical outcomes were the most preferred attribute. It was 4.68 times as important as productivity outcomes-the least preferred attribute. It was followed by health-related quality of life (2.78), methodologic rigor (2.09), resource utilization (1.71), and external validity (1.56).

CONCLUSIONS

This study provides a quantification of the value payers place on key RWE attributes. Across attributes, payers have higher preferences for clinical and health-related quality of life outcomes than the other attributes. Between attributes' levels, payers prefer high levels of information in clinical outcomes and methodologic rigor but are indifferent in other attributes. Our results bridge the gap between the information that payers seek and the attributes that RWE studies prioritize and effectively guide future research design.

Public Preferences for Exit Strategies From COVID-19 Lockdown in Germany-A Discrete Choice Experiment

Objectives: To decrease the rapid growth of SARS-CoV-2 in Germany, a stepped lockdown was conducted. Acceptance and compliance regarding entering and exiting lockdown measures are key for their success. The aim of the present study was to analyse the population's preferences for exiting lockdown measures. Methods: To evaluate population's preferences and identify trade-offs between different exit strategies, a discrete choice experiment was conducted on 28-29 April (n = 1,020). Overall, six attributes and 16 choice sets (fractional-factorial design) without an opt-out were chosen. Conditional logit and latent class models were conducted. Results: Most attributes proved to be significant. Two attributes dominated all others: Avoiding a mandatory tracing app, and providing sufficient intensive care capacities. Preventing a high long-term unemployment rate and avoiding the isolation of persons aged 70+, were relevant, though utilities were comparatively lower. We identified subgroups (elderly persons and persons with school children) with different utilities, which indicates specific attributes affecting them dissimilarly. Conclusions: The population prefers cautious re-opening strategies and is at least sceptical about the adoption of severe protection measures. Government should balance interests between subgroups.

Systematic review and validity assessment of methods used in discrete choice experiments of primary healthcare professionals

INTRODUCTION

Discrete choice experiments (DCEs) have been used to measure patient and healthcare professionals preferences in a range of settings internationally. Using DCEs in primary care is valuable for determining how to improve rational shared decision making. The purpose of this systematic review is to assess the validity of the methods used for DCEs assessing the decision making of healthcare professionals in primary care.

MAIN BODY

A systematic search was conducted to identify articles with original data from a discrete choice experiment where the population was primary healthcare professionals. All publication dates from database inception to 29th February 2020 were included. A data extraction and validity assessment template based on guidelines was used. After screening, 34 studies met the eligibility criteria and were included in the systematic review. The sample sizes of the DCEs ranged from 10 to 3727. The published DCEs often provided insufficient detail about the process of determining the attributes and levels. The majority of the studies did not involve primary care healthcare professionals outside of the research team in attribute identification and selection. Less than 80% of the DCEs were piloted and few papers investigated internal or external validity.

CONCLUSIONS

For findings to translate into improvements in rational shared decision making in primary care DCEs need to be internally and externally valid and the findings need to be able to be communicated to stakeholders in a way that is understandable and relevant.

Public preferences for primary care provision in Germany - a discrete choice experiment

Background

Primary care is a central element of healthcare and addresses the main health problems of the population. While primary care gains in importance due to an aging population, there is an ongoing debate on physician shortages in German rural regions. The study aims on analyzing the population’s preferences on primary healthcare and, therefore, on helping policy makers to make care delivery more responsive to patients’ needs when planning political reforms of primary care.

Methods

A paper-based discrete choice experiment (DCE) was used to assess preferences of the population of eight rural regions in Germany. Based on literature search and qualitative research, six attributes were selected and included in the choice experiment. The survey presented participants with eight choice sets in which they had to choose between two possible scenarios of care. A conditional logistic regression as well as a latent class model (LCM) were used to analyze preferences for primary healthcare.

Results

Nine hundred four participants completed the survey (response rate 46.1%). The conditional logistic regression showed significant impact of the attributes “home visits”, “distance to practice”, “number of healthcare providers”, “opening hours of the practice”, and “diagnostic facilities” on the respondents’ choices of primary healthcare alternatives. Moreover, the LCM identified four classes that can be characterized by preference homogeneity within and heterogeneity between the classes.

Conclusion

Although the study revealed heterogeneous preferences among the latent classes, several similarities in preferences for primary care could be detected. The knowledge on these public preferences may help policy makers when implementing new models of primary care and, thus, raise the populations’ acceptance of future primary care provision and innovative care models.

Electronic supplementary material

The online version of this article (10.1186/s12875-019-0967-y) contains supplementary material, which is available to authorized users.

Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review

BACKGROUND

Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood.

OBJECTIVE

We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications.

METHODS

We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist.

RESULTS

We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences between classes. Less than half of the studies reported that individual characteristics were included in the segmentation models, and 30% reported that post-estimation analyses were conducted to examine class characteristics. While a higher percentage of studies discussed clinical implications of the segmentation results, an increasing number of studies proposed policy recommendations based on segmentation results since 2010.

CONCLUSIONS

LCA is increasingly used to study preference heterogeneity in health and support decision-making. However, there is little consensus on best practices as its application in health is relatively new. With an increasing demand to study preference heterogeneity, guidance is needed to improve the quality of applications of segmentation methods in health to support policy development and clinical practice.

Application of discrete choice experiments to enhance stakeholder engagement as a strategy for advancing implementation: a systematic review

Background

One of the key strategies to successful implementation of effective health-related interventions is targeting improvements in stakeholder engagement. The discrete choice experiment (DCE) is a stated preference technique for eliciting individual preferences over hypothetical alternative scenarios that is increasingly being used in health-related applications. DCEs are a dynamic approach to systematically measure health preferences which can be applied in enhancing stakeholder engagement. However, a knowledge gap exists in characterizing the extent to which DCEs are used in implementation science.

Methods

We conducted a systematic literature search (up to December 2016) of the English literature to identify and describe the use of DCEs in engaging stakeholders as an implementation strategy. We searched the following electronic databases: MEDLINE, Econlit, PsychINFO, and the CINAHL using mesh terms. Studies were categorized according to application type, stakeholder(s), healthcare setting, and implementation outcome.

Results

Seventy-five publications were selected for analysis in this systematic review. Studies were categorized by application type: (1) characterizing demand for therapies and treatment technologies (n = 32), (2) comparing implementation strategies (n = 22), (3) incentivizing workforce participation (n = 11), and (4) prioritizing interventions (n = 10). Stakeholders included providers (n = 27), patients (n = 25), caregivers (n = 5), and administrators (n = 2). The remaining studies (n = 16) engaged multiple stakeholders (i.e., combination of patients, caregivers, providers, and/or administrators). The following implementation outcomes were discussed: acceptability (n = 75), appropriateness (n = 34), adoption (n = 19), feasibility (n = 16), and fidelity (n = 3).

Conclusions

The number of DCE studies engaging stakeholders as an implementation strategy has been increasing over the past decade. As DCEs are more widely used as a healthcare assessment tool, there is a wide range of applications for them in stakeholder engagement. The DCE approach could serve as a tool for engaging stakeholders in implementation science.

Electronic supplementary material

The online version of this article (10.1186/s13012-017-0675-8) contains supplementary material, which is available to authorized users.