26 Defining and measuring overdiagnosis when there is no diagnosis of disease: the experience of the canadian task force on preventive health care with overdiagnosis in the context of fracture risk assessment
Notice bibliographique
Résumé
<h3></h3> ‘Overdiagnosis means making people patients unnecessarily, by identifying problems that were never going to cause harm or by medicalising ordinary life experiences through expanded definitions of diseases’ (Broderson et al). That definition seems intuitive and for certain diseases (like cancers), measurement of the extent of this phenomenon has been described. Measurement relies sometimes on data from randomized trials, e.g. the comparison of the number of incident cases in a screened group versus a non-screened group. In high-quality randomized screening trials with sufficient follow-up time and little or no contamination of the control group, the excess number of diagnosed cases in the screened group represents the degree of overdiagnosis. But, what about when there is no disease, when we are identifying problems defined by an estimated risk of a future event? That was the question faced by members of the Canadian Task Force on Preventive Health Care when developing a protocol for an evidence synthesis to support a guideline on screening to prevent fragility fractures. In that setting, the screened group is given a risk of a future event, not a diagnosis that could become apparent in the non-screened group. In fact, there are no symptoms to diagnose before somebody experiences a fracture, so these individuals would experience the outcome, not the ‘disease’ hereby defined as a risk. For the Task Force, in the setting of screening to prevent fragility fractures, overdiagnosed individuals are those who are deemed to be at excess risk of fracture – either according to a set threshold or based on shared decision-making –but who would have never known they were at risk because, without screening, they would not have experienced a fracture. We will explain the process that lead us to this definition and will give our perspective on how to calculate the degree of overdiagnosis when it is not possible to compare the occurrence of disease in screened and not screened groups. We believe this extension of the definition and the proposed way of calculating overdiagnosis in the setting of risk assessment is a way forward in the conceptualization of the overdiagnosis phenomenon. We will suggest that this could be applied to other chronic diseases, including, for example hypercholesterolemia and the risk of cardiovascular disease, where the value of cholesterol is also used (with other factors) to estimate risk and inform decisions. To our knowledge, it is the first time that overdiagnosis in risk assessment has been defined in this manner. <h3>Objectives</h3> Propose a way to conceptualize overdiagnosis and calculate its extent in the context of risk assessment. <h3>Method</h3> This is the result of a group reflection on the topic that started while trying to define outcomes for a systematic review on the prevention of fragility fractures. <h3>Results</h3> Starting from more common ways of understanding and calculating overdiagnosis we will present how we propose to achieve this in the setting of risk assessment. We will share the logic we followed and some graphical representation of our idea. <h3>Conclusions</h3> Overdiagnosis is not an easy concept to grasp when there is no disease. At times this has been simplified by labeling a risk as a disease (ex: osteoporosis is not a disease in itself; it confers a certain amount of risk of fractures). We will share our thoughts about a way to further understanding of overdiagnosis in the context of risk assessment.
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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