Stroke prevention and control in Brazil: missed opportunities
Bibliographic record
Abstract
everal studies have shown that stroke has a huge global impact with a considerable financial cost both to health and care services and to patients and their families.Stroke is one of the leading causes of mortality and disability in Latin America and the Caribbean 1 .Brazil presents the fourth worst stroke mortality rate among these countries with an early in-hospital stroke mortality rate of 34.3% which is high when compared to the rates in Canada (6.9%) or the Netherlands (17.3%) 2 .Stroke mortality varies considerably according to social and economic development and about 85% of strokes occur in low-and middle-income countries (LMIC) with one third affecting the economically active population 3,4 .A recent publication from the Global Burden of Disease Collaborators has shown that stroke is among the three main causes of years of life lost in Brazil 5 .The demographic transition occurring in most developing countries towards an increase of the older population will amplify the impact of stroke.Understanding the epidemiology and risk factors for stroke is important to be able to identify people at risk and implement preventative interventions.Hypertension is probably the most important and well-documented risk factor 6 and there is overwhelming evidence that its control reduces the absolute and relative risk of stroke 7,8 .Population-based surveys are crucial for quantifying the disease burden, contributing to evidence-based healthcare planning, and evaluating the effectiveness and relative contribution of various primary and secondary/tertiary preventative measures for reducing the burden of diseases such as stroke 9 .In this issue of Arquivos de Neuropsiquiatria, Bensenor et al. 10 publish an important study which assesses the self-declared prevalence of stroke in the Brazilian population using the National Health Survey (Pesquisa Nacional de Saúde -PNS) undertaken in 2013 that covered both urban and rural areas and included information on gender, race/self-reported skin color, education level and occupation.This study estimated that there were 2.231.000people who had suffered a stroke and 568.000 stroke cases with severe disabilities and showed high stroke prevalence rates especially in older individuals without formal education and urban dwellers.The findings of this survey reveal the scale of the challenges in terms of stroke prevention and control in Brazil.Primary care offers the best opportunity for preventive interventions in people at risk of stroke.Evidence-based guidelines 11 for primary stroke prevention are easily available in many countries.Despite this, patients who present at hospital with first stroke have been found to have multiple untreated or undertreated risk factors 12 illustrating missed opportunities.Studies of the use and adherence to stroke prevention guidelines are few and limited.Cost-effective interventions are available for secondary prevention of stroke, and the potential gains associated with the consistent use of such interventions are very large.Making these interventions accessible to all patients with stroke could lead to substantial individual and public health benefits.Phase 1 of the WHO-PREMISE study 13 is aimed at assessing current practice patterns related to secondary prevention of cardio and cerebrovascular disease (CVD) and identifying barriers and opportunities for scaling up secondary prevention.It was conducted in areas of ten LMIC including Brazil (Porto Alegre).This study showed that several measures of well established secondary prevention interventions were far behind what would
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".