Using discrete choice experiments to value benefits and risks in primary care
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Notice bibliographique
Résumé
Discrete choice experiments (DCEs) are a stated preference valuation method. As a ubiquitous component of healthcare delivery, risk is increasingly used as an attribute in DCEs. Risk is a complex concept that is open to misinterpretation; potentially undermining the robustness of DCEs as a valuation method. This thesis employed quantitative, qualitative and eye-tracking methods to understand if and how risk communication formats affected individuals’ choices when completing a DCE and the valuations derived. This thesis used a case study focussing on the elicitation of women’s preferences for a national breast screening programme. Breast screening was chosen because of its relevance to primary care and potential contribution to the ongoing debate about the benefits and harms of mammograms. A DCE containing three attributes (probability of detecting a cancer; risk of unnecessary follow-up; and cost of screening) was designed. Women were randomised to one of two risk communication formats: i) percentages only; or ii) icon arrays and percentages (identified from a structured review of risk communication literature in health).Traditional quantitative analysis of the discrete choices made by 1,000 women recruited via an internet panel revealed the risk communication format made no difference in terms of either preferences or the consistency of choices. However, latent class analysis indicated that women’s preferences for breast screening were highly heterogeneous; with some women acquiring large non-health benefits from screening, regardless of the risks, and others expressing complete intolerance for unnecessary follow-ups, regardless of the benefits. The think-aloud method, identified as a potential method from a systematic review of qualitative research alongside DCEs, was used to reveal more about DCE respondents’ decision-making. Nineteen face-to-face cognitive interviews identified that respondents felt more engaged with the task when risk was presented with an additional icon array. Eye-tracking methods were used to understand respondents’ choice making behaviour and attention to attributes. The method was successfully used alongside a DCE and provided valid data. The results of the eye-tracking study found attributes were visually attended to by respondents most of the time. For researchers seeking to use DCEs for eliciting individuals’ preferences for benefit-risk trade-offs, respondents were more receptive to risk communicated via an icon array suggesting this format is preferable. Policy-makers should acknowledge preference heterogeneity, and its drivers, in their appraisal of the benefits of breast screening programmes. Future research is required to test alternative risk communication formats and explore the robustness of eye-tracking and qualitative research methods alongside DCEs.<br/>
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle