リスクの可視化: リスクグラフが患者の理解と選択に与える影響
Stella M Marceta1, Esther W de Bekker-Grob1, Jorien Veldwijk1
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, the Netherlands; Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, the Netherlands; Erasmus Centre for Health Economics Rotterdam, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, the Netherlands.
Social science & medicine (1982)
|August 31, 2025
まとめ
Icon ArraysとStacked Column Chartは,ディスクレート選択実験 (DCE) の健康リスクの伝達に有効です. これらのグラフは,患者の結果や予測の正確性を大幅に変更することなく,理解と信頼性を向上させます.
科学分野:
- 健康 経済
- リスクコミュニケーション
- 患者の好みに関する研究
背景:
- リスク偏好を誘発することは,特にディスクリート・チョイス・エクスペリメント (DCE) のような偏好を明らかにした研究において,医療研究において極めて重要です.
- DCE内の回答者の理解を支援する様々なリスクグラフの有効性に関する経験的証拠は限られている.
- グラフィック表現がリスクの認識と意思決定にどのように影響するかを理解することは,健康研究方法論の改善に不可欠です.
研究 の 目的:
- 離散選択実験 (DCE) を利用した患者の好みの研究で最も適切なリスクグラフを特定する.
- 回答者の理解,妥当性,信頼性,および健康関連の意思決定における選択結果に対する様々なグラフの影響を評価する.
- 予測の正確性に関して,異なるリスクグラフのパフォーマンスを比較します.
主な方法:
- 文献調査,構造化された認知面接,および異なる数の研究アームを持つ2つのランダム化制御DCEを含む混合方法のアプローチが採用されました.
- オランダでのインフルエンザワクチン接種に関するケーススタディが用いられ,ベンダー企業と全般医療従事者を通じて参加者を募った.
- この研究では,異なるグラフィックタイプ (アイコン配列,コラムグラフ,スタックコラムグラフ,バーグラフ,リスクレッダー) が理解,有効性,信頼性,選択結果,予測精度,ヒューリスティックの使用に与える影響を評価した.
主要な成果:
- 認知面接では,アイコンアレイ (100アイコン),コラムチャート,スタックされたコラムチャートが他のテストされたグラフィックよりもよく理解されたことが明らかになりました.
- ランダム化制御されたDCEの発見は,アイコンアレイとスタックされたコラムチャートがコラムチャートと比較して優れた理解,有効性,および信頼性を示したことを示した.
- 理解の違いにもかかわらず,Icon ArraysとStacked Column Chartの間では,臨床的に重要な結果または予測の正確性 (明示された好みと明らかにされた好み) の違いは観察されなかった.
結論:
- Icon ArraysとStacked Column Chartsは,ディスクリート選択実験 (DCE) で比較可能なパフォーマンスを示し,同様の結果が得られます.
- これらのグラフィックタイプは,将来のDCEで使用することを推奨し,リスクコミュニケーションを強化し,患者の好みを提起することを改善します.
- 研究結果は,リスクグラフの選択のための証拠に基づいた勧告を提供することで,健康偏好研究の設計を最適化するのに寄与します.
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