What Matters to Potential Patients in Chemotherapy Service Delivery? A Discrete Choice Experiment

Hierarchical Medical: What are the Factors Driving ESKD Patients to Choose Community Hemodialysis Centers in China? A Labelled Discrete Choice Experiment

Purpose

In China, secondary and tertiary hospital-based dialysis facilities had been the most prominent provider of hemodialysis treatment. Developing community hemodialysis centers was the key to constructing hierarchical hemodialysis system. Thus, the aim of this study was to explore end-stage kidney disease (ESKD) patients' preferences for hemodialysis services and attract patients with stable condition to choose community hemodialysis services.

Patients and Methods

The study used a labelled discrete choice experiment with ESKD patients in Wuhan, Hubei Province in China. Patients were asked to make a choice between hospital-based hemodialysis facilities and community hemodialysis centers with different attribute levels. Mixed logit model was used to measure their preferences and heterogeneity for hemodialysis services. The marginal utility was measured to predict the change of patients' choice probability of community hemodialysis centers.

Results

A total of 420 ESKD patients consented to complete the questionnaires and 408 were included in the analysis after excluding responses that did not pass the consistency test. All attributes were significantly influencing respondents' choice of hemodialysis service. Patients were more inclined to hemodialysis services with smooth and effective referral, regular doctors, 20 minutes of travel time, and home-based offline follow-up. Gender, age, income, hemodialysis year, and hemodialysis times weekly were found to influence the preferences. When the community hemodialysis service attributes gradually meet the patients' preferences, as many as 58.39% of patients will choose community hemodialysis centers.

Conclusion

A better understanding of ESKD patients' preferences for hemodialysis service is a crucial step for the future policy implementations. Although patients tended to choose hospital-based hemodialysis facilities, patients' preferences for hemodialysis institutions would reverse with the change of service attribute. Establishing a smooth and effective referral is the most important attribute to improve patients' acceptance of community hemodialysis centers. Strengthening the integration of service can facilitate hierarchical hemodialysis service system.

What is the economic and social return on investment for telephone cancer information and support services in Australia? An evaluative social return on investment study protocol

INTRODUCTION

Over 50% of people affected by cancer report unmet support needs. To address unmet information and psychological needs, non-government organisations such as Cancer Councils (Australia) have developed state-based telephone cancer information and support services. Due to competing demands, evidence of the value of these services is needed to ensure that future investment makes the best use of scarce resources. This research aims to determine the costs and broader economic and social value of a telephone support service, to inform future funding and service provision.

METHODS AND ANALYSIS

A codesigned, evaluative social return on investment analysis (SROI) will be conducted to estimate and compare the costs and monetised benefits of Cancer Council Victoria's (CCV) telephone support line, 13 11 20, over 1-year and 3-year benefit periods. Nine studies will empirically estimate the parameters to inform the SROI and calculate the ratio (economic and social value to value invested): step 1 mapping outcomes (in-depth analysis of CCV's 13 11 20 recorded call data; focus groups and interviews); step 2 providing evidence of outcomes (comparative survey of people affected by cancer who do and do not call CCV's 13 11 20; general public survey); step 3 valuing the outcomes (financial proxies, value games); step 4 establishing the impact (Delphi); step 5 calculating the net benefit and step 6 service improvement (discrete choice experiment (DCE), 'what if' analysis). Qualitative (focus groups, interviews) and quantitative studies (natural language processing, cross-sectional studies, Delphi) and economic techniques (willingness-to-pay, financial proxies, value games, DCE) will be applied.

ETHICS AND DISSEMINATION

Ethics approval for each of the studies will be sought independently as the project progresses. So far, ethics approval has been granted for the first two studies. As each study analysis is completed, results will be disseminated through presentation, conferences, publications and reports to the partner organisations.

A Systematic Review of Discrete Choice Experiments in Oncology Treatments

BACKGROUND

As the number and type of cancer treatments available rises and patients live with the consequences of their disease and treatments for longer, understanding preferences for cancer care can help inform decisions about optimal treatment development, access, and care provision. Discrete choice experiments (DCEs) are commonly used as a tool to elicit stakeholder preferences; however, their implementation in oncology may be challenging if burdensome trade-offs (e.g. length of life versus quality of life) are involved and/or target populations are small.

OBJECTIVES

The aim of this review was to characterise DCEs relating to cancer treatments that were conducted between 1990 and March 2020.

DATA SOURCES

EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews were searched for relevant studies.

STUDY ELIGIBILITY CRITERIA

Studies were included if they implemented a DCE and reported outcomes of interest (i.e. quantitative outputs on participants' preferences for cancer treatments), but were excluded if they were not focused on pharmacological, radiological or surgical treatments (e.g. cancer screening or counselling services), were non-English, or were a secondary analysis of an included study.

ANALYSIS METHODS

Analysis followed a narrative synthesis, and quantitative data were summarised using descriptive statistics, including rankings of attribute importance.

RESULT

Seventy-nine studies were included in the review. The number of published DCEs relating to oncology grew over the review period. Studies were conducted in a range of indications (n = 19), most commonly breast (n =10, 13%) and prostate (n = 9, 11%) cancer, and most studies elicited preferences of patients (n = 59, 75%). Across reviewed studies, survival attributes were commonly ranked as most important, with overall survival (OS) and progression-free survival (PFS) ranked most important in 58% and 28% of models, respectively. Preferences varied between stakeholder groups, with patients and clinicians placing greater importance on survival outcomes, and general population samples valuing health-related quality of life (HRQoL). Despite the emphasis of guidelines on the importance of using qualitative research to inform attribute selection and DCE designs, reporting on instrument development was mixed.

LIMITATIONS

No formal assessment of bias was conducted, with the scope of the paper instead providing a descriptive characterisation. The review only included DCEs relating to cancer treatments, and no insight is provided into other health technologies such as cancer screening. Only DCEs were included.

CONCLUSIONS AND IMPLICATIONS

Although there was variation in attribute importance between responder types, survival attributes were consistently ranked as important by both patients and clinicians. Observed challenges included the risk of attribute dominance for survival outcomes, limited sample sizes in some indications, and a lack of reporting about instrument development processes.

PROTOCOL REGISTRATION

PROSPERO 2020 CRD42020184232.