Self‐reported hypertension as a public health surveillance tool: Don't throw out the baby with the bathwater
Notice bibliographique
Résumé
Based on a thorough systematic review of epidemiological studies comparing the identification of hypertension by self-reporting with measured blood pressure, Gonçalves et al1 showed that self-reported hypertension would have a low sensitivity for the identification of hypertensive individuals. More precisely, they showed that, on average, less than half of patients with hypertension would be identified by self-reporting.1 Nevertheless, the author did not consider, first, that most studies based on measured blood pressure overestimate the prevalence of hypertension and, second, that self-reported hypertension entails important advantages as a public health surveillance tool. Hypertension is a state of sustained elevated blood pressure and it is well known that an individual with elevated blood pressure at an initial visit will often have a much lower blood pressure at subsequent visits, because of habituation and regression to the mean phenomena.2 Therefore, in practice, hypertension diagnosis is based on multiple blood pressure measurements, ideally gathered at 3 or more separated visits.3 However, in most epidemiological studies, blood pressure is measured at a limited number of visits and often at only 1. For instance, in the meta-analysis of Gonçalves et al,1 10 studies have measured blood pressure at 1 visit, 4 at 2 visits, and none at 3 visits. The way blood pressure is measured in epidemiological studies allows assessing the prevalence of elevated blood pressure but not the prevalence of hypertension. Self-reported hypertension is less exposed to this bias because participants are asked if they are taking hypertensive drugs or if they had been diagnosed with hypertension by a physician or another healthcare professional. In both situations, we can assume that blood pressure has been measured more than once. Of course, measuring blood pressure has unique advantages compared with self-reporting. Hence, when the goal is to identify individuals with hypertension in order to make treatment decisions, the measurement method needs to be highly sensitive and to provide accurate blood pressure estimates, and using self-reported hypertension is not conceivable.4 Studies designed to tackle the etiology of hypertension should also use measured blood pressure. However, when the goal is to identify prevalence and evolution of hypertension at a population level, surveys using self-report can be sufficiently informative.5 Although estimates based on self-report can lead to an under- or overestimation of the true prevalence of hypertension, depending on age, sex, culture, education, and proximity to health care,6 this method is simple, low cost, and easy to apply to representative and large samples of a country.5 Further, if the degree of bias is relatively stable across time,7 surveillance organisms can correctly assess hypertension trends over time and draw conclusions and forecasts on the evolution of hypertension and hypertension-related complications among a population. Hence, self-reported hypertension is an imperfect proxy for the identification of hypertension and has a potential for bias. Nevertheless, it entails other important features, such as access to high shares of the population at low cost. By acknowledging the risk for bias and being aware of potential underlying causes of these biases, surveillance organisms can generate relevant estimates of hypertension trends to, in fine, guide hypertension management programs at a country or a regional level.8 The authors report no conflicts of interest to disclose.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,007 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».