Chile’s public healthcare sector hypertension control rates before and during the pandemic and HEARTS implementation
Bibliographic record
Abstract
Hypertension (arterial blood pressure ≥ 140/90 mmHg) is a risk factor for cardiovascular diseases, with the greatest burden of attributable deaths in Chile, having a national prevalence of 27.6%. In 2018, the implementation of HEARTS begun in primary health care centers of the Public Health System, with the aim of achieving increase in control rates, by raising the proportion of hypertensive individuals who meet blood pressure goals (< 140/90 mmHg for individuals 15-79 years old and of 150/90 mmHg for individuals 80 years and older), and thus contributing to reduce cardiovascular morbidity and mortality associated with this condition. This is a descriptive study that follows average treatment and control rates from the Public Health System between 2017-2021 obtained from health centers statistics reports during HEARTS implementation. Treatment and control rates remained at 57% and 39% respectively between 2017-2019. Between 2020 and 2021, in the context of the SARS-CoV-2 pandemic, treatment and control rates decreased very significantly, reaching 46% and 26%, respectively, in December 2021, even though the number of centers reporting the implementation of HEARTS increased from 227 to 387 in this same period. Prior to the pandemic, during the last quarter of 2019, a decrease in cardiovascular health controls was already observed as a result of social protests. In light of the results, the technical pillars of the HEARTS Initiative have an important role in helping to recover the population control rates reached in 2019 and increasing the speed to achieve better hypertension control rates.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".