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Record W2808451685 · doi:10.1111/jch.13326

Self‐reported hypertension as a public health surveillance tool: Don't throw out the baby with the bathwater

2018· article· en· W2808451685 on OpenAlexaff

Bibliographic record

VenueJournal of Clinical Hypertension · 2018
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBlood pressureMasked HypertensionEpidemiologyPublic healthPrehypertensionHabituation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.169
GPT teacher head0.376
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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