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Record W6991906951

Iniciativas para a redução do consumo de sódio no Brasil: avaliação e análise de impacto

2023· article· pt· W6991906951 on OpenAlexfundno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2023
Typearticle
Languagept
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
FundersUniversity of OxfordInternational Development Research CentreEuropean Food Safety Authority
KeywordsSodiumPublic healthConsumption (sociology)MicrosimulationHealthcare systemDisease burdenDisease
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Inadequate diets are important preventable risk factors for non-communicable diseases (NCDs) in the world. Among critical nutrients associated with NCDs, excessive sodium consumption is the largest risk factor for cardiovascular diseases, mediated by hypertension. Different dietary sources contribute to sodium intake; therefore, sodium reduction depends on multiple parallel and complementary strategies, which can be evaluated previously to implementation through macro and microsimulations. Objectives: The objectives of this study were to develop and apply macro and microsimulation methodologies to evaluate the impact of excessive sodium intake and of the national voluntary targets for sodium reduction on morbimortality and costs of disease in Brazil. Methods: Four manuscripts were produced based on data from national surveys, national statistics and health information systems of the National Health System (SUS). The first manuscript evaluated the impact of the national voluntary sodium targets on the sodium content of priority food categories. The second detailed the development and application of a cost of disease macrosimulation methodology for estimating attributable costs to sodium. The third manuscript used macrosimulations to estimate the attributable deaths and costs to excessive sodium intake in Brazil. The last manuscript used microsimulation models to estimate the projected a 20-year impact of the voluntary sodium targets on morbimortality and direct and indirect health costs. Results: Excessive sodium intake represents a large health burden to Brazilians, and an economic burden to the National Health System and to society. In 2017, it was estimated that 47,017 deaths from all cardiovascular diseases (CVD) mediated by hypertension (equivalent to 585 thousand years of lofe lost), US$ 195 million in expenditures to the National Health System and US$ 800 million in productivity losses to premature deaths were attributable to excessive sodium intake. The voluntary sodium reduction targets for processed and ultraprocessed foods have reduced the average sodium content of foods in 5% to 28% and the average salt intake of the population in 0.25 g/day, from 2011 to 2017. The continuity of the voluntary targets over 20 years would prevent or postpone 112 thousand CVD cases and 2,524 deaths from coronary heart disease and stroke, which represent US$ 292.5 in direct and indirect treatment costs. Conclusions: The results highlight the burden of excessive sodium intake to health and its costs to the National Health System and to the Brazilian society, which support the need for prioritizing sodium reduction in the health agenda. Besides, considering the multiple dietary sources of sodium and the limited impact of the voluntary targets on the incidence, deaths and costs of cardiovascular diseases, it is necessary to expand the impact of food reformulation and strengthen other strategies addressed to the other dietary sources of sodium. Therefore, modeling the impact of dietary factors on NCDs on related morbimortality and costs is an important tool to formulate and implement more cost-effective policies in Brazil and in other countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.313
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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