Preferences of Recent Mums in Remote and Rural Areas for Type of Intrapartum Care: A Discrete Choice Experiment.

BACKGROUND AND OBJECTIVES: Pregnant women living in rural areas considering their preferred place of birth may have to 'trade-off' travel time/distance and other attributes of care (e.g. the full choice of birthplace options is rarely available locally). This study assesses the preferences and trade-offs of recent mothers who live in remote and rural areas of Great Britain. METHODS: An online survey, informed by qualitative research, was administered to women living in rural areas who had given birth in the preceding 3 years. The survey included a discrete choice experiment (DCE) to elicit women's preferences and trade-offs for place of birth. The DCE presented women with a series of eight choice tasks in which place of birth was defined by four attributes: (1) type of facility, (2) familiarity with staff, (3) understanding options and feel relaxed and reassured and (4) the travel time to the place of intrapartum care. DCE data were analysed using an error components logit model to identify preferences. RESULTS: Across 251 survey responses, holding everything else equal, respondents preferred: intrapartum care in locations with more specialist staff and equipment, locations where they understood their options and felt reassured and where travel time was minimal. Women were willing to travel (92-183 min) to a well-staffed and equipped facility if they understood their options and felt relaxed and reassured. Willingness to travel was reduced if the care received at the specialist facility was such that they did not understand their options and felt tense and powerless (41-132 min). CONCLUSION: These insights into the preferences of recent mums from remote and rural areas could inform future planning of rural intrapartum care.

The Perception of Women in Rural and Remote Scotland About Intrapartum Care: A Qualitative Study.

BACKGROUND: The views of mothers are important in shaping policy and practice regarding options for intrapartum care. Mothers in rural and remote areas face unique challenges accessing services, and these need to be well understood. Therefore, our aim was to understand the compromises that women who live in remote and rural settings, more than 1 h from a maternity unit, face regarding intrapartum care. METHODS: Qualitative semi-structured telephone interviews (n = 14) were undertaken in rural Scotland with 13 women who had young children and one who was pregnant. Interviews were transcribed and thematically analysed by two researchers. RESULTS: Key themes identified were women's perceptions about risk and the safety of different pathways of maternity care and birth locations; the actual and perceived distance between home and the place of birth, and the type of maternity care available at a place of birth. Mothers in rural and remote areas face particular challenges in choosing where to have their babies. In addition to clinical decisions about 'place of birth' agreed with healthcare professionals, they have to mentally juggle the implications of giving birth when at a distance from family support and away from familiar surroundings. It was clear that many women from rural communities have a strong sense of 'place' and that giving birth in a geographical location, community and culture that feels familiar is important to many of them. CONCLUSIONS: Health care staff need to appreciate the impact of non-clinical factors that are important to mothers in remote and rural areas and acknowledge these, even when they cannot be accommodated. Local and national policy also needs to reflect and respond to the practical challenges faced by rurality.

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.

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.

The influence of mortality reminders on cultural in-group versus out-group takeaway food safety perceptions during the COVID-19 pandemic.

During the early stages of the COVID-19 pandemic takeaway food orders generally increased, yet sales of Chinese and Italian food declined. At this time, news sources ran stories on the safety of cuisine from these countries, frequently juxtaposed with communications on mortality-related information related to the virus. Terror management theory suggests mortality concerns can lead people to defend against the psychological threat of death by bolstering positive evaluations of products and values of their own culture, and by disparaging products and values of other cultures. This translates to food preferences, with death reminders heightening consumption of food from one's own (vs. others') culture. However, whether this extends to food safety perceptions has not yet been probed. In the present experimental study, we examine whether death reminders (vs. a control topic) led U.S. participants to view American takeaway food as safer to consume, relative to Chinese and Italian food. Results indicate that across conditions, American food was evaluated as safer relative to Chinese and Italian takeout. Further, American takeaway was seen as safer after mortality reminders (vs. a control topic), with no differences in safety evaluations for Chinese or Italian takeout. Results are discussed in relation to the COVID-19 pandemic.

Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider.

Providing an opt-out alternative in discrete choice experiments can often be considered to be important for presenting real-life choice situations in different contexts, including health. However, insufficient attention has been given to how best to address choice behaviours relating to this opt-out alternative when modelling discrete choice experiments, particularly in health studies. The objective of this paper is to demonstrate how to account for different opt-out effects in choice models. We aim to contribute to a better understanding of how to model opt-out choices and show the consequences of addressing the effects in an incorrect fashion. We present our code written in the R statistical language so that others can explore these issues in their own data. In this practical guideline, we generate synthetic data on medication choice and use Monte Carlo simulation. We consider three different definitions for the opt-out alternative and four candidate models for each definition. We apply a frequentist-based multimodel inference approach and use performance indicators to assess the relative suitability of each candidate model in a range of settings. We show that misspecifying the opt-out effect has repercussions for marginal willingness to pay estimation and the forecasting of market shares. Our findings also suggest a number of key recommendations for DCE practitioners interested in exploring these issues. There is no unique best way to analyse data collected from discrete choice experiments. Researchers should consider several models so that the relative support for different hypotheses of opt-out effects can be explored.

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.

Accounting for Attribute-Level Non-Attendance in a Health Choice Experiment: Does it Matter?

An extensive literature has established that it is common for respondents to ignore attributes of the alternatives within choice experiments. In most of the studies on attribute non-attendance, it is assumed that respondents consciously (or unconsciously) ignore one or more attributes of the alternatives, regardless of their levels. In this paper, we present a new line of enquiry and approach for modelling non-attendance in the context of investigating preferences for health service innovations. This approach recognises that non-attendance may not just be associated with attributes but may also apply to the attribute's levels. Our results show that respondents process each level of an attribute differently: while attending to the attribute, they ignore a subset of the attribute's levels. In such cases, the usual approach of assuming that respondents either attend to the attribute or not, irrespective of its levels, is erroneous and could lead to misguided policy recommendations. Our results indicate that allowing for attribute-level non-attendance leads to substantial improvements in the model fit and has an impact on estimated marginal willingness to pay and choice predictions.

Elimination and selection by aspects in health choice experiments: prioritising health service innovations.

Priorities for public health innovations are typically not considered equally by all members of the public. When faced with a choice between various innovation options, it is, therefore, possible that some respondents eliminate and/or select innovations based on certain characteristics. This paper proposes a flexible method for exploring and accommodating situations where respondents exhibit such behaviours, whilst addressing preference heterogeneity. We present an empirical case study on the public's preferences for health service innovations. We show that allowing for elimination-by-aspects and/or selection-by-aspects behavioural rules leads to substantial improvements in model fit and, importantly, has implications for willingness to pay estimates and scenario analysis.

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

BACKGROUND: Prioritising scarce resources for investment in innovation by publically funded health systems is unavoidable. Many healthcare systems wish to foster transparency and accountability in the decisions they make by incorporating the public in decision-making processes. This paper presents a unique conceptual approach exploring the public's preferences for health service innovations by viewing healthcare innovations as 'bundles' of characteristics. This decompositional approach allows policy-makers to compare numerous competing health service innovations without repeatedly administering surveys for specific innovation choices. METHODS: A Discrete Choice Experiment (DCE) was used to elicit preferences. Individuals chose from presented innovation options that they believe the UK National Health Service (NHS) should invest the most in. Innovations differed according to: (i) target population; (ii) target age; (iii) implementation time; (iv) uncertainty associated with their likely effects; (v) potential health benefits; and, (vi) cost to a taxpayer. This approach fosters multidimensional decision-making, rather than imposing a single decision criterion (e.g., cost, target age) in prioritisation. Choice data was then analysed using scale-adjusted Latent Class models to investigate variability in preferences and scale and valuations amongst respondents. RESULTS: Three latent classes with considerable heterogeneity in the preferences were present. Each latent class is composed of two consumer subgroups varying in the level of certainty in their choices. All groups preferred scientifically proven innovations, those with potential health benefits that cost less. There were, however, some important differences in their preferences for innovation investment choices: Class-1 (54%) prefers innovations benefitting adults and young people and does not prefer innovations targeting people with 'drug addiction' and 'obesity'. Class- 2 (34%) prefers innovations targeting 'cancer' patients only and has negative preferences for innovations targeting elderly, and Class-3 (12%) prefers spending on elderly and cancer patients the most. CONCLUSIONS: DCE can help policy-makers incorporate public preferences for health service innovation investment choices into decision making. The findings provide useful information on the public's valuation and acceptability of potential health service innovations. Such information can be used to guide innovation prioritisation decisions by comparing competing innovation options. The approach in this paper makes, these often implicit and opaque decisions, more transparent and explicit.

Prosthetic rehabilitation of a patient with a unilateral cleft palate: a clinical report.

Cleft palate is a congenital disorder characterized by maxillary growth defect and dental anomalies. Its correction requires an interdisciplinary approach, which includes surgical, orthodontic, and prosthetic treatments. This clinical report describes the prosthetic management of a 19-year-old woman with a unilateral cleft palate defect that had not been closed completely with surgical repair. The deficient maxillary residual anterior ridge was restored with a tooth-supported overdenture that improved her facial appearance, speech, and masticatory functions.

Investigating heterogeneity in the characterization of risks using best worst scaling.

This research proposes and implements a new approach to the elicitation and analysis of perceptions of risk. We use best worst scaling (BWS) to elicit the levels of control respondents believe they have over risks and the level of concern those risks prompt. The approach seeks perceptions of control and concern over a large risk set and the elicitation method is structured so as to reduce the cognitive burden typically associated with ranking over large sets. The BWS approach is designed to yield strong discrimination over items. Further, the approach permits derivation of individual-level values, in this case of perceptions of control and worry, and analysis of how these vary over observable characteristics, through estimation of random parameter logit models. The approach is implemented for a set of 20 food and nonfood risks. The results show considerable heterogeneity in perceptions of control and worry, that the degree of heterogeneity varies across the risks, and that women systematically consider themselves to have less control over the risks than men.