Prioritising health service innovation investments using public preferences: a discrete choice experiment

Systematic Review of the Relative Social Value of Child and Adult Health

OBJECTIVES

We aimed to synthesise knowledge on the relative social value of child and adult health.

METHODS

Quantitative and qualitative studies that evaluated the willingness of the public to prioritise treatments for children over adults were included. A search to September 2023 was undertaken. Completeness of reporting was assessed using a checklist derived from Johnston et al. Findings were tabulated by study type (matching/person trade-off, discrete choice experiment, willingness to pay, opinion survey or qualitative). Evidence in favour of children was considered in total, by length or quality of life, methodology and respondent characteristics.

RESULTS

Eighty-eight studies were included; willingness to pay (n = 9), matching/person trade-off (n = 12), discrete choice experiments (n = 29), opinion surveys (n = 22) and qualitative (n = 16), with one study simultaneously included as an opinion survey. From 88 studies, 81 results could be ascertained. Across all studies irrespective of method or other characteristics, 42 findings supported prioritising children, while 12 provided evidence favouring adults in preference to children. The remainder supported equal prioritisation or found diverse or unclear views. Of those studies considering prioritisation within the under 18 years of age group, nine findings favoured older children over younger children (including for life saving interventions), six favoured younger children and five found diverse views.

CONCLUSIONS

The balance of evidence suggests the general public favours prioritising children over adults, but this view was not found across all studies. There are research gaps in understanding the public's views on the value of health gains to very young children and the motivation behind the public's views on the value of child relative to adult health gains.

CLINICAL TRIAL REGISTRATION

The review is registered at PROSPERO number: CRD42021244593. There were two amendments to the protocol: (1) some additional search terms were added to the search strategy prior to screening to ensure coverage and (2) a more formal quality assessment was added to the process at the data extraction stage. This assessment had not been identified at the protocol writing stage.

Public preferences for the allocation of societal resources over different healthcare purposes

OBJECTIVE

Increasing healthcare expenditures require governments to make difficult prioritization decisions. Considering public preferences can help raise citizens' support. Previous research has predominantly elicited preferences for the allocation of public resources towards specific treatments or patient groups and principles for resource allocation. This study contributes by examining public preferences for budget allocation over various healthcare purposes in the Netherlands.

METHODS

We conducted a Participatory Value Evaluation (PVE) choice experiment in which 1408 respondents were asked to allocate a hypothetical budget over eight healthcare purposes: general practice and other easily accessible healthcare, hospital care, elderly care, disability care, mental healthcare, preventive care by encouragement, preventive care by discouragement, and new and better medicines. A default expenditure was set for each healthcare purpose, based on current expenditures. Respondents could adjust these default expenditures using sliders and were presented with the implications of their adjustments on health and well-being outcomes, the economy, and the healthcare premium. As a constraint, the maximum increase in the mandatory healthcare premium for adult citizens was €600 per year. The data were analysed using descriptive statistics and a Latent Class Cluster Analysis (LCCA).

RESULTS

On average, respondents preferred to increase total expenditures on all healthcare purposes, but especially on elderly care, new and better medicines, and mental healthcare. Three preference clusters were identified. The largest cluster preferred modest increases in expenditures, the second a much higher increase of expenditures, and the smallest favouring a substantial reduction of the healthcare premium by decreasing the expenditure on all healthcare purposes. The analyses also demonstrated substantial preference heterogeneity between clusters for budget allocation over different healthcare purposes.

CONCLUSIONS

The results of this choice experiment show that most citizens in the Netherlands support increasing healthcare expenditures. However, substantial heterogeneity was identified in preferences for healthcare purposes to prioritize. Considering these preferences may increase public support for prioritization decisions.

Exploring patient treatment decision making for osteoarthritis in the UAE: a cross-sectional adaptive choice-based conjoint study

OBJECTIVE

To assess osteoarthritis (OA) patients' preferences for pharmaceutical treatment via Adaptive Choice-Based Conjoint (ACBC) method.

METHODS

A United Arab Emirates (UAE) based Patient and Public Involvement (PPI) group designed the ACBC questionnaire with 10 attributes and 34 levels. The questionnaire was developed using Sawtooth Software and analyzed through Hierarchical Bayesian (HB). Results were standardized using Z-score via SPSS.

RESULTS

Study participants were 1030 OA patients, 83.6% aged 50 or older and 83.4% female. The avoidance of medication's side effects accounted for 66% relative importance compared to 6% relative importance for the medication's benefits. The "way of taking the medicine" attribute had the highest coefficient of variation (70%) and the four side effect attributes "risk of gastric ulcer, addiction, kidney and liver impairment, and heart attacks and strokes" had a coefficient of variation from 18 to 21%.

CONCLUSIONS

Arab OA patients are similar to other ethnic groups in trading-off benefits and side effects and consistently prioritizing the avoidance of medications' side effects. Although the "Way of taking medicine" was the least important attribute it was associated with the highest variation amongst patients. OA patients also prefer prescribed medications to internet-purchased and over-the-counter options.

Societal Preferences for Meningococcal B Vaccination in Children: A Discrete Choice Experiment in Spain

INTRODUCTION

Immunization is the most effective strategy for the prevention of invasive meningococcal disease caused by Neisseria meningitidis serogroup B (MenB); however, parents need to weigh the risk-benefit and financial impact of immunizing their children against MenB in the absence of a national immunization program (NIP). This study aimed to explore societal preferences (of parents and pediatricians) regarding the attributes of a MenB vaccine in Spain.

METHODS

A discrete choice experiment (DCE) based on cross-sectional surveys was carried out to determine preferences. A literature review and scientific committee determined the six attributes related to the MenB vaccine included in the DCE: vaccination age, cost, duration, percentage of protection, adverse events probability, and expert/authority recommendation. Data were analyzed using a mixed logit model. Relative importance (RI) of attributes was calculated and compared between parents and pediatricians.

RESULTS

A total of 278 parents [55.8% female, mean age 40.4 (standard deviation, SD 7.3) years] and 200 pediatricians [73.0% female, mean age 45.8 (SD 12.9) years] answered the DCE. For parents, the highest RI was attributed to vaccine cost, expert/authority recommendation, and percentage of protection (26.4%, 26.1%, and 22.9%, respectively), while for pediatricians the highest RI was assigned to percentage of protection, expert/authority recommendation, and vaccination age (27.2%, 23.7%, and 22.6%, respectively). Significant differences between parents and pediatricians were found in the RI assigned to all attributes (p < 0.001), except for vaccine recommendation.

CONCLUSION

In the decision regarding MenB vaccination, cost was a driver in parental decision-making but had a low RI for pediatricians and, conversely, vaccination age was highly valued by pediatricians but was the attribute with least importance for parents. Despite these differences, expert/authority recommendation and percentage of protection were essential criteria for both groups. These results provide relevant information about MenB vaccination, highlighting the importance of considering societal preferences for NIP inclusion.

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.

Willingness-to-pay for cancer treatment and outcome: a systematic review

BACKGROUND

Understanding patient preferences in cancer management is essential for shared decision-making. Patient or societal willingness-to-pay (WTP) for desired outcomes in cancer management represents their preferences and values of these outcomes.

OBJECTIVE

The aim of this systematic review is to critically evaluate how current literature has addressed WTP in relation to cancer treatment and achievement of outcomes.

METHODS

Seven databases were searched from inception until 2 March 2021 to include studies with primary data of WTP values for cancer treatments or achievement of outcomes that were elicited using stated preference methods.

RESULTS

Fifty-four studies were included in this review. All studies were published after year 2000 and more than 90% of the studies were conducted in high-income countries. Sample size of the studies ranged from 35 to 2040, with patient being the most studied population. There was a near even distribution between studies using contingent valuation and discrete choice experiment. Based on the included studies, the highest WTP values were for a quality-adjusted life year (QALY) ($11,498-$589,822), followed by 1-year survival ($3-$198,576), quality of life (QoL) improvement ($5531-$139,499), and pain reduction ($79-$94,662). Current empirical evidence suggested that improvement in QoL and pain reduction had comparable weights to survival in cancer management.

CONCLUSION

This systematic review provides a summary on stated preference studies that elicited patient preferences via WTP and summarised their respective values. Respondents in this review had comparable WTP for 1-year survival and QoL, suggesting that improvement in QoL should be emphasised together with survival in cancer management.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Preferences for Renal Cell Carcinoma Pharmacological Treatment: A Discrete Choice Experiment in Patients and Oncologists

Introduction

The purpose of this investigation was to explore patients’ and oncologists’ preferences for the characteristics of a pharmacological regimen for patients with advanced renal cell carcinoma (aRCC).

Material and Methods

Cross-sectional observational study based on a discrete choice experiment (DCE) conducted in Spain. A literature review, a focus group with oncologists and interviews with patients informed the DCE design. Five attributes were included: progression survival gain, risk of serious adverse events (SAEs), health-related quality of life (HRQoL), administration mode, and treatment cost. Preferences were analyzed using a mixed-logit model to estimate relative importance (RI) of attributes (importance of an attribute in relation to all others), which was compared between aRCC patients and oncologists treating aRCC. Willingness to pay (WTP, payer: health system) for a benefit in survival or in risk reduction and maximum acceptable risk (MAR) in SAEs for improving survival were estimated from the DCE. Subgroup analyses were performed to identify factors that influence preference.

Results

A total of 105 patients with aRCC (77.1% male, mean age 65.9 years [SD: 10.4], mean time since RCC diagnosis 6.3 years [SD: 6.1]) and 67 oncologists (52.2% male, mean age 41.9 years [SD: 8.4], mean duration of experience in RCC 10.2 years [SD: 7.5]) participated in the study. The most important attribute for patients and oncologists was survival gain (RI: 43.6% vs. 54.7% respectively, p<0.05), followed by HRQoL (RI: 35.5% vs. 18.0%, respectively, p<0.05). MAR for SAEs was higher among oncologists than patients, while WTP (for the health system) was higher for patients. Differences in preferences were found according to time since diagnosis and education level (patients) or length of professional experience (oncologists).

Conclusion

Patients’ and oncologists’ preferences for aRCC treatment are determined mainly by the efficacy (survival gain) but also by the HRQoL provided. The results of the study can help to inform decision-making in the selection of appropriate aRCC treatment.

Using discrete-choice experiments to elicit preferences for digital wearable health technology for self-management of chronic kidney disease

Objectives

Wearable digital health technologies (DHTs) have the potential to improve chronic kidney disease (CKD) management through patient engagement. This study aimed to investigate and elicit preferences of individuals with CKD toward wearable DHTs designed to support self-management of their condition.

Methods

Using the results of our review of the published literature and after conducting qualitative patient interviews, five-choice attributes were identified and included in a discrete-choice experiment. The design consisted of 10-choice tasks, each comprising two hypothetical technologies and one opt-out scenario. We collected data from 113 adult patients with CKD stages 3–5 not on dialysis and analyzed their responses via a latent class model to explore preference heterogeneity.

Results

Two patient segments were identified. In all preference segments, the most important attributes were the device appearance, format, and type of information provided. Patients within the largest preference class (70 percent) favored information provided in any format except the audio, while individuals in the other class preferred information in text format. In terms of the style of engagement with the device, both classes wanted a device that provides options rather than telling them what to do.

Conclusions

Our analysis indicates that user preferences differ between patient subgroups, supporting the case for offering a different design of the device for different patients’ strata, thus moving away from a one-size-fits-all service provision. Furthermore, we showed how to leverage the information from user preferences early in the R&D process to inform and support the provision of nuanced person-centered wearable DHTs.

What Aspects of Illness Influence Public Preferences for Healthcare Priority Setting? A Discrete Choice Experiment in the UK

BACKGROUND

Decisions on funding new healthcare technologies assume that all health improvements are valued equally. However, public reaction to health technology assessment (HTA) decisions suggests there are health attributes that matter deeply to them but are not currently accounted for in the assessment process. We aimed to determine the relative importance of attributes of illness that influence the value placed on alleviating that illness.

METHOD

We conducted a discrete choice experiment survey that presented general public respondents with 15 funding decisions between hypothetical health conditions. The conditions were defined by five attributes that characterise serious illnesses, plus the health gain from treatment. Respondent preferences were modelled using conditional logistic regression and latent class analysis.

RESULTS

905 members of the UK public completed the survey in November 2017. Respondents generally preferred to provide treatments for conditions with 'better' characteristics. The exception was treatment availability, where respondents preferred to provide treatments for conditions where there is no current treatment, and were prepared to accept lower overall health gain to do so. A subgroup of respondents preferred to prioritise 'worse' health states.

CONCLUSION

This study suggests a preference among the UK public for treating an unmet need; however, it does not suggest a preference for prioritising other distressing aspects of health conditions, such as limited life expectancy, or where patients are reliant on care. Our results are not consistent with the features currently prioritised in UK HTA processes, and the preference heterogeneity we identify presents a major challenge for developing broadly acceptable policy.

Public preferences for delayed or immediate antibiotic prescriptions in UK primary care: A choice experiment

BACKGROUND

Delayed (or "backup") antibiotic prescription, where the patient is given a prescription but advised to delay initiating antibiotics, has been shown to be effective in reducing antibiotic use in primary care. However, this strategy is not widely used in the United Kingdom. This study aimed to identify factors influencing preferences among the UK public for delayed prescription, and understand their relative importance, to help increase appropriate use of this prescribing option.

METHODS AND FINDINGS

We conducted an online choice experiment in 2 UK general population samples: adults and parents of children under 18 years. Respondents were presented with 12 scenarios in which they, or their child, might need antibiotics for a respiratory tract infection (RTI) and asked to choose either an immediate or a delayed prescription. Scenarios were described by 7 attributes. Data were collected between November 2018 and February 2019. Respondent preferences were modelled using mixed-effects logistic regression. The survey was completed by 802 adults and 801 parents (75% of those who opened the survey). The samples reflected the UK population in age, sex, ethnicity, and country of residence. The most important determinant of respondent choice was symptom severity, especially for cough-related symptoms. In the adult sample, the probability of choosing delayed prescription was 0.53 (95% confidence interval (CI) 0.50 to 0.56, p < 0.001) for a chesty cough and runny nose compared to 0.30 (0.28 to 0.33, p < 0.001) for a chesty cough with fever, 0.47 (0.44 to 0.50, p < 0.001) for sore throat with swollen glands, and 0.37 (0.34 to 0.39, p < 0.001) for sore throat, swollen glands, and fever. Respondents were less likely to choose delayed prescription with increasing duration of illness (odds ratio (OR) 0.94 (0.92 to 0.96, p < 0.001)). Probabilities of choosing delayed prescription were similar for parents considering treatment for a child (44% of choices versus 42% for adults, p = 0.04). However, parents differed from the adult sample in showing a more marked reduction in choice of the delayed prescription with increasing duration of illness (OR 0.83 (0.80 to 0.87) versus 0.94 (0.92 to 0.96) for adults, p for heterogeneity p < 0.001) and a smaller effect of disruption of usual activities (OR 0.96 (0.95 to 0.97) versus 0.93 (0.92 to 0.94) for adults, p for heterogeneity p < 0.001). Females were more likely to choose a delayed prescription than males for minor symptoms, particularly minor cough (probability 0.62 (0.58 to 0.66, p < 0.001) for females and 0.45 (0.41 to 0.48, p < 0.001) for males). Older people, those with a good understanding of antibiotics, and those who had not used antibiotics recently showed similar patterns of preferences. Study limitations include its hypothetical nature, which may not reflect real-life behaviour; the absence of a "no prescription" option; and the possibility that study respondents may not represent the views of population groups who are typically underrepresented in online surveys.

CONCLUSIONS

This study found that delayed prescription appears to be an acceptable approach to reducing antibiotic consumption. Certain groups appear to be more amenable to delayed prescription, suggesting particular opportunities for increased use of this strategy. Prescribing choices for sore throat may need additional explanation to ensure patient acceptance, and parents in particular may benefit from reassurance about the usual duration of these illnesses.

Do You Prefer Safety to Social Participation? Finnish Population-Based Preference Weights for the Adult Social Care Outcomes Toolkit (ASCOT) for Service Users

Introduction. The Adult Social Care Outcomes Toolkit (ASCOT) was developed in England to measure people’s social care–related quality of life (SCRQoL). Objectives. The aim of this article is to estimate preference weights for the Finnish ASCOT for service users (ASCOT). In addition, we tested for learning and fatigue effects in the choice experiment used to elicit the preference weights. Methods. The analysis data (n = 1000 individuals) were obtained from an online survey sample of the Finnish adult general population using gender, age, and region as quotas. The questionnaire included a best-worst scaling (BWS) experiment using ASCOT. Each respondent sequentially selected four alternatives (best, worst; second-best, second-worst) for eight BWS tasks (n = 32,000 choice observations). A scale multinomial logit model was used to estimate the preference parameters and to test for fatigue and learning. Results. The most and least preferred attribute-levels were “I have as much control over my daily life as I want” and “I have no control over my daily life.” The preference weights were not on a cardinal scale. The ordering effect was related to the second-best choices. Learning effect was in the last four tasks. Conclusions. This study has developed a set of preference weights for the ASCOT instrument in Finland, which can be used for investigating outcomes of social care interventions on adult populations. The learning effect calls for the development of study designs that reduce possible bias relating to preference uncertainty at the beginning of sequential BWS tasks. It also supports the adaptation of a modelling strategy in which the sequence of tasks is explicitly modelled as a scale factor.

Prioritizing guideline recommendations for implementation: a systematic, consumer-inclusive process with a case study using the Australian Clinical Guidelines for Stroke Management

BACKGROUND

Implementation of evidence-based care remains a key challenge in clinical practice. Determining "what" to implement can guide implementation efforts. This paper describes a process developed to identify priority recommendations from clinical guidelines for implementation, incorporating the perspectives of both consumers and health professionals. A case study is presented where the process was used to prioritize recommendations for implementation from the Australian Stroke Clinical Guidelines.

METHODS

The process was developed by a multidisciplinary group of researchers following consultation with experts in the field of implementation and stroke care in Australia. Use of the process incorporated surveys and facilitated workshops. Survey data were analysed descriptively; responses to ranking exercises were analysed via a graph theory-based voting system.

RESULTS

The four-step process to identify high-priority recommendations for implementation comprised the following: (1) identifying key implementation criteria, which included (a) reliability of the evidence underpinning the recommendation, (b) capacity to measure change in practice, (c) a recommendation-practice gap, (d) clinical importance and (e) feasibility of making the recommended changes; (2) shortlisting recommendations; (3) ranking shortlisted recommendations and (4) reaching consensus on top priorities. The process was applied to the Australian Stroke Clinical Guidelines between February 2019 and February 2020. Seventy-five health professionals and 16 consumers participated. Use of the process was feasible. Three recommendations were identified as priorities for implementation from over 400 recommendations.

CONCLUSION

It is possible to implement a robust process which involves consumers, clinicians and researchers to systematically prioritize guideline recommendations for implementation. The process is generalizable and could be applied in clinical areas other than stroke and in different geographical regions to identify implementation priorities. The identification of three clear priority recommendations for implementation from the Australian Stroke Clinical Guidelines will directly inform the development and delivery of national implementation strategies.

Does Controlling for Scale Heterogeneity Better Explain Respondents' Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents' randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

People's preferences for self-management support

Objective

To identify and assess the preferences of people with long‐term health conditions toward generalizable characteristics of self‐management support interventions, with the objective to inform the design of more person‐centered support services.

Data Sources

Primary qualitative and quantitative data collected on a representative sample of individuals with at least one of the fifteen most prevalent long‐term conditions in the UK.

Study Design

Targeted literature review followed by a series of one‐to‐one qualitative semistructured interviews and a large‐scale discrete choice experiment.

Data Collection

Digital recording of one‐to‐one qualitative interviews, one‐to‐one cognitive interviews, and a series of online quantitative surveys, including two best‐worst scaling and one discrete choice experiment, with individuals with long‐term conditions.

Principal Findings

On average, patients preferred a self‐management support intervention that (a) discusses the options available to the patient and make her choose, (b) is individual‐based, (c) face to face (d) with doctor or nurse, (e) at the GP practice, (f) sessions shorter than 1 hour, and (g) occurring annually for two‐third of the sample and monthly for the rest. We found heterogeneity in preferences via three latent classes, with class sizes of 41% (C1), 30% (C2), and 29% (C3). The individuals’ gender [P < 0.05(C1), P < 0.01(C3)], age [P < 0.05(C1), P < 0.05(C2)], type of long‐term condition [P < 0.05(C1), P < 0.01(C3)], and presence of comorbidity [P < 0.01(C1), P < 0.01(C3), P < 0.01(C3)] were able to characterize differences between these latent classes and help understand the heterogeneity of preferences toward the above mentioned features of self‐management support interventions. These findings were then used to profile individuals into different preference groups, for each of whom the most desirable form of self‐management support, one that was more likely to be adopted by the recipient, could be designed.

Conclusions

We identified several factors that could be used to inform a more nuanced self‐management support service design and provision that take into account the recipient's characteristics and preferences.

Exorcising the positivist ghost in the priority-setting machine: NICE and the demise of the 'social value judgement'

The National Institute for Health and Care Excellence (NICE), the UK's primary health care priority-setting body, has traditionally described its decisions as being informed by 'social value judgements' about how resources should be allocated across society. This paper traces the intellectual history of this term and suggests that, in NICE's adoption of the idea of the 'social value judgement', we are hearing the echoes of welfare economics at a particular stage of its development, when logical positivism provided the basis for thinking about public policy choice. As such, it is argued that the term offers an overly simplistic conceptualisation of NICE's normative approach and contributes to a situation in which NICE finds itself without the necessary language fully and accurately to articulate its basis for decision-making. It is suggested that the notion of practical public reasoning, based on reflection about coherent principles of action, might provide a better characterisation of the enterprise in which NICE is, or hopes to be, engaged.

Community views on factors affecting medicines resource allocation: cross-sectional survey of 3080 adults in Australia

Objective The aim of the present study was to determine Australian community views on factors that influence the distribution of health spending in relation to medicines. Methods A cross-sectional web-based survey was performed of 3080 adults aged ≥18 years. Participants were asked to rank, in order of importance, 12 criteria according to which medicines funding decisions may be made. Results Of all respondents, 1213 (39.4%) considered disease severity to be the most important prioritisation criterion for funding a new medicine. This was followed by medicines treating a disease affecting children (13.2%) and medicines for cancer patients (9.1%). Medicines targeting a disease for which there is no alternative treatment available received highest priority from 8.6% of respondents. The remaining eight prioritisation criteria were each assigned a top ranking from 6.6% to 1.7% of respondents. Medicines targeting a disease for which there is no alternative treatment available were ranked least important by 7.7% of respondents, compared with 2.4%, 1.9% and 1.0% for medicines treating severe diseases, diseases affecting children and cancer respectively. 'End-of-life treatments' and 'rare disease therapies' received the least number of highest priority rankings (2.0% and 1.7% respectively). Conclusions These results provide useful information about public preferences for government spending on prescribed medicines. Understanding of public preferences on the funding of new medicines will help the Pharmaceutical Benefits Advisory Committee and government determine circumstances where greater emphasis on equity is required and help inform medicines funding policy that best meets the needs of the Australian population. What is known about this topic? There is increased recognition of the importance of taking into account public preferences in the heath technology assessment (HTA) decision-making process. What does this paper add? The Australian public view the severity of disease to be the most important funding prioritisation criterion for medicines, followed by medicines used to treat children or to treat cancer. What are the implications for practitioners? The general public are capable of giving opinions on distributional preferences. This information can help inform medicines funding policy and ensure that it is consistent with the values of the Australian population.

Delayed Antibiotic Prescription by General Practitioners in the UK: A Stated-Choice Study

Delayed antibiotic prescription in primary care has been shown to reduce antibiotic consumption, without increasing risk of complications, yet is not widely used in the UK. We sought to quantify the relative importance of factors affecting the decision to give a delayed prescription, using a stated-choice survey among UK general practitioners. Respondents were asked whether they would provide a delayed or immediate prescription in fifteen hypothetical consultations, described by eight attributes. They were also asked if they would prefer not to prescribe antibiotics. The most important determinants of choice between immediate and delayed prescription were symptoms, duration of illness, and the presence of multiple comorbidities. Respondents were more likely to choose a delayed prescription if the patient preferred not to have antibiotics, but consultation length had little effect. When given the option, respondents chose not to prescribe antibiotics in 51% of cases, with delayed prescription chosen in 21%. Clinical features remained important. Patient preference did not affect the decision to give no antibiotics. We suggest that broader dissemination of the clinical evidence supporting use of delayed prescription for specific presentations may help increase appropriate use. Establishing patient preferences regarding antibiotics may help to overcome concerns about patient acceptance. Increasing consultation length appears unlikely to affect the use of delayed prescription.

Factors influencing patients' preferences for their treating physician

OBJECTIVES

To explore preferences of the patients attending King Abdulaziz Medical City (KAMC), Jeddah, Saudi Arabia, in their treating physicians and to determine demographic and personal factors influencing their preferences.

METHODS

Through a cross sectional design, 366 patients attending KAMC were systematically selected, and were requested to a pre-designed valid questionnaire. The study conducted between November 2017 and January 2018. The Statistical Package for Social Sciences was used for statistical analysis which included independent sample t-test, ANOVA and Pearson correlation. A p-value less than 0.05 was considered significant.

RESULTS

The study showed that females were significantly more preferring to get information, careful examination and receiving attention. Older respondents were interested in nding out how serious is their problem and having enough time for talking about their condition. Higher income respondents focused on necessity of physician caring for their personal situation. Highly educated individuals were concerned about getting enough information, and patients with chronic diseases were much interested by the truth of their condition. Each dimension of the personality traits was significantly correlated with different package of preferences in their treating physician.

CONCLUSION

Respondents' preferences in their treating physician are significantly influenced by their demographic characteristics as well as their personality traits. It is highly recommended that physicians, generally, should be attentive listener, informative while discussion, maintaining eye contact and showing interest and empathy for adult middle-aged patient. Also, to consider other preferable features based on patients' demographics and personalities.

Value judgment of new medical treatments: Societal and patient perspectives to inform priority setting in The Netherlands

BACKGROUND

In many countries, medical interventions are reimbursed on the basis of recommendations made by advisory boards and committees that apply multiple criteria in their assessment procedures. Given the diversity of these criteria, it is difficult to find common ground to determine what information is required for setting priorities.

OBJECTIVE

To investigate whether society and patients share the same interests and views concerning healthcare priorities.

METHODS

We applied a framework of discrete choice models in which respondents were presented with judgmental tasks to elicit their preferences. They were asked to choose between two hypothetical scenarios of patients receiving a new treatment. The scenarios graphically presented treatment outcomes and patient characteristics. Responses were collected through an online survey administered among respondents from the general population (N = 1,253) and patients (N = 1,389) and were analyzed using conditional logit and mixed logit models.

RESULTS

The respondents' preferences regarding new medical treatments revealed that they attached the most relative importance to additional survival years, age at treatment, initial health condition, and the cause of disease. Minor differences in the relative importance assigned to three criteria: age at treatment, initial health, and cause of disease were found between the general population and patient samples. Health scenarios in which patients had higher initial health-related quality of life (i.e., a lower burden of disease) were favored over those in which patients' initial health-related quality of life was lower.

CONCLUSIONS

Overall, respondents within the general population expressed preferences that were similar to those of the patients. Therefore, priority-setting studies that are based on the perspectives of the general population may be useful for informing decisions on reimbursement and other types of priority-setting processes in health care. Incorporating the preferences of the general population may simultaneously increase public acceptance of these decisions.

Tailoring chemotherapy supply according to patients' preferences: a quantitative method in colorectal cancer care

Objectives: The aim of this study was to conduct a discrete choice experiment with patients affected by colorectal cancer to understand their preferences for different attributes of the chemotherapy supply. Our overall goal is to provide evidence on the relative importance of each attribute in order to tailor chemotherapy supply according to patients' priorities in the design or reorganization processes of cancer services.Methods: Focus groups were used to identify the attributes and levels for the discrete choice experiment. The attributes were: continuity of care, understanding, information, treatment choice, and time for therapy. Respondents were asked to choose between two mutually exclusive hypothetical alternatives of chemotherapy supply. Patients completed the discrete choice experiment along with the health-related quality of life and patients' satisfaction questions. Conditional and mixed logistic models were used to analyses the data.Results: Patients with colorectal cancer treated with chemotherapy (n = 76) completed the survey. The most important aspects of chemotherapy supply were: "Providing detailed and complete information" and "High ability in understanding" patients. Preferences were also influenced by the availability of a trusted doctor. Except for one attribute (waiting time for therapy), all other characteristics significantly influenced respondents' preferences.Conclusions: Results should support a policy of strengthening medical doctors' capabilities to communicate with patients, providing them complete information and involving them in the clinical decisions. Specifically, the findings should be used to improve the current provision of cancer care by identifying areas of preferred intervention from the perspectives of patients in order to tailor the service supply accordingly.

Measuring public preferences in France for potential consequences stemming from re-allocation of healthcare resources

When deciding which new programme to implement and where the additional resources, if needed, will come from, the decision makers need to accommodate the uncertainty of the potential changes in population health and medical expenditures that can occur. They also need to determine the value of these potential changes. The objective of this study is to identify a public valuation function measuring how the public values changes in population health and medical expenditures when healthcare resources are re-allocated. We report the results of a choice experiment conducted in March 2016 in a representative sample of the population living in France (N = 1008). The main results indicate that the public is more sensitive to changes in population health than changes in the level of medical expenditures. There is a non-linear valuation of these changes with evidence of asymmetric preferences and non-constant marginal sensitivity. The public gives 1.4 times more weight to decrease in population health than for the same-size increase. The public becomes less sensitive to marginal changes in population health as the level of changes increases. In a simulation study of 5000 resource allocation decisions, we show that non-linearities in public valuation of population health and medical expenditures matters. The linear and non-linear public valuation functions were associated with respectively 50.1% and 28.1% of situations of acceptable outcome of the reallocation of resources. The level of agreement between these two functions was moderate with a Kappa coefficient of 0.56, and the probability of agreement was mainly driven by the distribution of net changes in population health. This study provides a method and an estimation of a public valuation function that describes the preferences (or values attributed) for every potential outcome stemming from the reallocation of healthcare resources. The results show the importance of measuring such function rather than assuming one.

Using discrete choice model to elicit preference for health-care priority setting

BACKGROUND

Regarding lack of resources in the health-care sector, prioritization of these resources is inevitable. The objective of the current study was to elicit public preference in prioritizing and allocating health resources using a discrete choice experiment technique, which is currently the most commonly applied method in this field of researches.

METHODS

In this discrete choice study, five attributes were selected through interview with 25 health experts to elicit people preferences in Tehran (Iran) in 2017. Eighteen choice tasks were arranged within 3 blocks, and this would be achieved with a sample size of 579. Choice data were modeled using generalized estimating equation method and STATA 14 software.

RESULTS

Five attributes including level of emergency, severity of disease, communicable, benefit from treatment, and age are the most important attributes in the prioritizing health resources from the expert's point of view. As well as among these attributes, communicable (odds ratio = 2.81) is the most important attributes from the public's point of view.

CONCLUSION

The results of this study could be very useful for prioritizing resources which is one of the most challenging measurements of the health system. By identifying the importance of each patient's characteristic, patients can be categorized in groups with different priorities, as well as the diagnosis-related group system, based on which resources are allocated.

Treatment decision-making in chronic lymphocytic leukaemia: Key factors for healthcare professionals. PRELIC study

OBJECTIVE

To explore the preferences of Spanish healthcare professionals (haematologists and hospital pharmacists) for the treatment selection of active Chronic Lymphocytic Leukaemia (CLL) patients at first relapse, condition that mainly afflicts older adults.

METHODS

A discrete choice experiment (DCE) was conducted among haematologists and hospital pharmacists. A literature review and a focus group informed the DCE design. CLL treatment settings were defined by seven attributes: four patient/disease-related attributes (age, functional status, comorbidities, and risk of the disease) and three treatment-related attributes (efficacy [hazard ratio of progression-free survival, HR-PFS], rate of discontinuations due to adverse events and cost). A mixed-logit model was used to determine choice-based preferences. Relative importance (RI) of attributes was calculated and compared between stakeholders. Willingness-to-pay (WTP) was estimated through the DCE. Besides, nine ad-hoc questions were posed, to explore more in depth CLL treatment decision making.

RESULTS

A total of 130 participants (72 haematologists and 58 hospital pharmacists) answered the DCE. All attributes were significant predictors of preferences (p < 0.05) in the multinomial model. Higher RI was obtained for treatment-related attributes: the highest rated being 'cost' (23.8%) followed by 'efficacy' (20.9%). Regarding patient-related attributes, the highest RI was obtained for 'age' (18.1%). No significant differences (p > 0.05) in RI between haematologists and pharmacists were found. WTP for the treatment was higher for younger CLL patients. Ad-hoc questions showed that patient age and functional status influence treatment decisions.

CONCLUSIONS

For healthcare professionals, 'cost' and 'efficacy' (treatment-related attributes) and age (patient-related attribute) are the main factors that determine CLL treatment selection at first relapse. WTP decreases as patient's age increases.

Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated Preferences: A Systematic Review

BACKGROUND

Scale heterogeneity, or differences in the error variance of choices, may account for a significant amount of the observed variation in the results of discrete choice experiments (DCEs) when comparing preferences between different groups of respondents.

OBJECTIVE

The aim of this study was to identify if, and how, scale heterogeneity has been addressed in healthcare DCEs that compare the preferences of different groups.

METHODS

A systematic review identified all healthcare DCEs published between 1990 and February 2016. The full-text of each DCE was then screened to identify studies that compared preferences using data generated from multiple groups. Data were extracted and tabulated on year of publication, samples compared, tests for scale heterogeneity, and analytical methods to account for scale heterogeneity. Narrative analysis was used to describe if, and how, scale heterogeneity was accounted for when preferences were compared.

RESULTS

A total of 626 healthcare DCEs were identified. Of these 199 (32%) aimed to compare the preferences of different groups specified at the design stage, while 79 (13%) compared the preferences of groups identified at the analysis stage. Of the 278 included papers, 49 (18%) discussed potential scale issues, 18 (7%) used a formal method of analysis to account for scale between groups, and 2 (1%) accounted for scale differences between preference groups at the analysis stage. Scale heterogeneity was present in 65% (n = 13) of studies that tested for it. Analytical methods to test for scale heterogeneity included coefficient plots (n = 5, 2%), heteroscedastic conditional logit models (n = 6, 2%), Swait and Louviere tests (n = 4, 1%), generalised multinomial logit models (n = 5, 2%), and scale-adjusted latent class analysis (n = 2, 1%).

CONCLUSIONS

Scale heterogeneity is a prevalent issue in healthcare DCEs. Despite this, few published DCEs have discussed such issues, and fewer still have used formal methods to identify and account for the impact of scale heterogeneity. The use of formal methods to test for scale heterogeneity should be used, otherwise the results of DCEs potentially risk producing biased and potentially misleading conclusions regarding preferences for aspects of healthcare.

Measuring Public Preferences for Health Outcomes and Expenditures in a Context of Healthcare Resource Re-Allocation

BACKGROUND

The final outcome of any resource allocation decision in healthcare cannot be determined in advance. Thus, decision makers, in deciding which new program to implement (or not), need to accommodate the uncertainty of different potential outcomes (i.e., change in both health and costs) that can occur, the size and nature (i.e., 'bad' or 'good') of these outcomes, and how they are being valued. Using the decision-making plane, which explicitly incorporates opportunity costs and relaxes the assumptions of perfect divisibility and constant returns to scale of the cost-effectiveness plane, all the potential outcomes of each resource allocation decision can be described.

OBJECTIVE

In this study, we describe the development and testing of an instrument, using a discrete choice experiment methodology, allowing the measurement of public preferences for potential outcomes falling in different quadrants of the decision-making plane.

METHOD

In a sample of 200 participants providing 4200 observations, we compared four versions of the preference-elicitation instrument using a range of indicators.

RESULTS

We identified one version that was well accepted by the participants and with good measurement properties.

CONCLUSION

This validated instrument can now be used in a larger representative sample to study the preferences of the public for potential outcomes stemming from re-allocation of healthcare resources.

Societal Preferences for Funding Orphan Drugs in the United Kingdom: An Application of Person Trade-Off and Discrete Choice Experiment Methods

BACKGROUND

It is unclear whether UK National Health Service (NHS) policies for orphan drugs, which permit funding of non-cost-effective treatments, reflect societal preferences.

METHODS

We conducted person trade-off (PTO) and discrete choice experiment (DCE) among 3950 adults selected to be representative of the UK general population. Experimental design was informed by surveys of patients affected by rare diseases, their caregivers, health care staff, and policymakers. Societal preferences were estimated in relation to treating a common disease, increases in waiting lists, or filling of vacant NHS posts. Results of the DCE were applied to recently licensed orphan drugs.

RESULTS

On the basis of equal cost, the majority of respondents to the PTO (54%; 95% confidence interval [CI] 50-59) chose to allocate funds equally between patients treated for rare diseases and those treated for common diseases, with 32% (95% CI 28-36) favoring treating rare diseases over treating common diseases (14%; 95% CI 11-17), which this reduced to 23% (95% CI 20-27) when rare disease treatments were 10 times more expensive. When framed differently, more respondents prioritized not increasing waiting list size (43%; 95% CI 39-48) than to treat rare disease patients (34%; 95% CI 30-38).

DISCUSSION

The DCE indicated a greater preference for treating a common disease over a rare disease. Respondents agreed with five of 12 positive appraisal recommendations for orphan drugs, even if their list price was higher, but preferred the NHS not to fund the remainder.

CONCLUSIONS

The general public does not value rarity as a sufficient reason to justify special consideration for additional NHS funding of orphan drugs. This has implications regarding the appropriateness of operating higher thresholds of cost-effectiveness.

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.

Preferences for public involvement in health service decisions: a comparison between best-worst scaling and trio-wise stated preference elicitation techniques

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public's choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as 'trio-wise'. We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize best-worst scaling Case 2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our trio-wise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.

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.

Does the Public Prefer Health Gain for Cancer Patients? A Systematic Review of Public Views on Cancer and its Characteristics

BACKGROUND

Policies such as the Cancer Drugs Fund in England assumed a societal preference to fund cancer care relative to other conditions, even if that resulted in lower health gain for the population overall.

OBJECTIVE

The aim of this study was to investigate the evidence for such a preference among the UK public.

METHODS

The MEDLINE, PubMed and Econlit electronic databases were searched for studies relating to preferences for prioritising cancer treatment, as well as studies relating to preferences for the characteristics of cancer (severity of disease, end-of-life). The searches were run in November 2015 and updated in March 2017. Empirical preference studies, studies of public views, and studies in English were included.

RESULTS

We identified 24 studies relating to cancer preferences. Two directly addressed health trade-offs in the UK-one showed a preference for health gain in cancer, while the other found no such preference but provided results consistent with population health maximisation. Other studies mostly showed support for cancer but did not require a direct health trade-off. Severity and end-of-life searches identified 12 and 6 papers, respectively, which were additional to existing reviews. There is consistent evidence that people give priority to severe illness, while results for end-of-life are mixed.

CONCLUSION

We did not find consistent support for a preference for health gains to cancer patients in the context of health maximisation. The evidence base is small and the results are highly sensitive to study design. There remains a contradiction between these findings and the popular view of cancer, and further work is required to determine the features of cancer which contribute to that view.

Developing a decision support system to link health technology assessment (HTA) reports to the health system policies in Iran

The recent increase of 'Health Technology Assessment' (HTA)-related activities in Iran has necessitated the clarification of policy-making process based on the HTA reports. This study aimed to develop a Decision Support System (DSS) in order to adopt evidence-informed policies regarding health technologies in Iran. The study can be classified as Health Policy and Systems Research. A core panel of seven experts conducted two separate reviews of relevant literature for: 1- Determining the potential technology-related policies. 2- Listing the criteria influencing those policy decisions. The policies and criteria were separately discussed and subsequently rated for appropriateness and necessity during two expert meetings in 2013. In the next step, The 'Discrete Choice Experiment' (DCE) method was employed to develop the DSS for the final technology-related policies. Accordingly, the core panel members independently rated the appropriateness of each policy for 30 virtual technologies based on the random values assigned to all the criteria for each technology. The obtained data for each policy were separately analysed using stepwise regression model, resulting in a minimal set of independent and statistically significant criteria contributing in the experts' judgments about the appropriateness of that policy. The obtained regression coefficients were used as the relative weights of the different levels of the final criteria of any policy statement, shaping the decision support scoring tool for each policy. The study has outlined 64 policy decisions under 7 macro policy areas concerning a health technology. Also, 34 criteria used for making those policy decisions have been organized within a portfolio. DCE, using stepwise regression, resulted in 64 scoring tools shaping the DSS for all HTA-related policies. Both the results and methodology of the study may serve as a guide for policy makers (researchers), particularly in low and middle income countries, in developing decision aids for their own context-specific HTA-related policies.

Stated and Revealed Preferences for Funding New High-Cost Cancer Drugs: A Critical Review of the Evidence from Patients, the Public and Payers

BACKGROUND

The growing focus on patient-centred care has encouraged the inclusion of patient and public input into payer drug reimbursement decisions. Yet, little is known about patient/public priorities for funding high-cost medicines, and how they compare to payer priorities applied in public funding decisions for new cancer drugs.

OBJECTIVES

The aim was to identify and compare the funding preferences of cancer patients and the general public against the criteria used by payers making cancer drug funding decisions.

METHODS

A thorough review of the empirical, peer-reviewed English literature was conducted. Information sources were PubMed, EMBASE, MEDLINE, Web of Science, Business Source Complete, and EconLit. Eligible studies (1) assessed the cancer drug funding preferences of patients, the general public or payers, (2) had pre-defined measures of funding preference, and (3) had outcomes with attributes or measures of 'value'. The quality of included studies was evaluated using a health technology assessment-based assessment tool, followed by extraction of general study characteristics and funding preferences, which were categorized using an established WHO-based framework.

RESULTS

Twenty-five preference studies were retrieved (11 quantitative, seven qualitative, seven mixed-methods). Most studies were published from 2005 onward, with the oldest dating back to 1997. Two studies evaluated both patient and public perspectives, giving 27 total funding perspectives (41 % payer, 33 % public, 26 % patients). Of 41 identified funding criteria, payers consider the most (35), the general public considers fewer (23), and patients consider the fewest (12). We identify four unique patient criteria: financial protection, access to medical information, autonomy in treatment decision making, and the 'value of hope'. Sixteen countries/jurisdictions were represented.

CONCLUSIONS

Our results suggest that (1) payers prioritize efficiency (health gains per dollar), while citizens (patients and the general public) prioritize equity (equal access to cancer medicines independent of cost or effectiveness), (2) citizens prioritize few criteria relevant to payers, and (3) citizens prioritize several criteria not considered by payers. This can explain why payer and citizen priorities clash when new cancer medicines are denied public funding.

The health systems' priority setting criteria for selecting health technologies: A systematic review of the current evidence

BACKGROUND

In the recent years, using health technologies to diagnose and treat diseases has had a considerable and accelerated growth. The proper use of these technologies may considerably help in the diagnosis and treatment of different diseases. On the other hand, unlimited and unrestricted entry of these technologies may result in induced demand by service providers. The aim of this study was to determine the appropriate criteria used in health technologies priority-setting models in the world.

METHODS

Using MESH and free text, we sought and retrieved the relevant articles from the most appropriate medical databases (the Cochrane Library, PubMed and Scopus) through three separate search strategies up to March 2015. The inclusion criteria were as follows: 1) Studies with specific criteria; 2) Articles written in English; 3) Those articles conducted in compliance with priority setting of health technologies. Data were analyzed qualitatively using a thematic synthesis technique.

RESULTS

After screening the retrieved papers via PRISMA framework, from the 7,012 papers, 40 studies were included in the final phase. Criteria for selecting health technologies (in pre assessment and in the assessment phase) were categorized into six main themes: 1) Health outcomes; 2) Disease and target population; 3) Technology alternatives; 4) Economic aspects; 5) Evidence; 6) and other factors. "Health effects/benefits" had the maximum frequency in health outcomes (8 studies); "disease severity" had the maximum frequency in disease and target population (12 studies); "the number of alternatives" had the maximum frequency in alternatives (2 studies); "cost-effectiveness" had the maximum frequency in economic aspects (15 studies); "quality of evidence" had the maximum frequency in evidence (4 studies); and "issues concerning the health system" had the maximum frequency in other factors (10 studies).

CONCLUSION

The results revealed an increase in the number of studies on health technologies priority setting around the world, and emphasized the necessity of application of a multi- criteria approach for appropriate decision making about healthcare technologies in the health systems.

Prioritising patients for bariatric surgery: building public preferences from a discrete choice experiment into public policy

OBJECTIVES

To derive priority weights for access to bariatric surgery for obese adults, from the perspective of the public.

SETTING

Australian public hospital system.

PARTICIPANTS

Adults (N=1994), reflecting the age and gender distribution of Queensland and South Australia.

PRIMARY AND SECONDARY OUTCOME MEASURES

A discrete choice experiment in which respondents indicated which of two individuals with different characteristics should be prioritised for surgery in repeated hypothetical choices. Potential surgery recipients were described by seven key characteristics or attributes: body mass index (BMI), presence of comorbid conditions, age, family history, commitment to lifestyle change, time on the surgical wait list and chance of maintaining weight loss following surgery. A multinomial logit model was used to evaluate preferences and derive priority weights (primary analysis), with a latent class model used to explore respondent characteristics that were associated with variation in preference across the sample (see online supplementary analysis).

RESULTS

A preference was observed to prioritise individuals who demonstrated a strong commitment to maintaining a healthy lifestyle as well as individuals categorised with very severe (BMI≥50 kg/m2) or (to a lesser extent) severe (BMI≥40 kg/m2) obesity, those who already have obesity-related comorbidity, with a family history of obesity, with a greater chance of maintaining weight loss or who had spent a longer time on the wait list. Lifestyle commitment was considered to be more than twice as important as any other criterion. There was little tendency to prioritise according to the age of the recipient. Respondent preferences were dependent on their BMI, previous experience with weight management surgery, current health state and education level.

CONCLUSIONS

This study extends our understanding of the publics' preferences for priority setting to the context of bariatric surgery, and derives priority weights that could be used to assist bodies responsible for commissioning bariatric services.