Evidence map›Paper›PMID 38312999›Full record

ArticleGlobal heart2024

Mortality in Women with Coronary Artery Disease in Paraná State, Brazil: A Bayesian Spatiotemporal Analysis.

Marcelo Puzzi, Miyoko Massago, Júlia Loverde Gabella, Samile Bonfim de Oliveira, Daniel Augusto Message Dos Santos, Fernanda Shizue Nishida Carignano, Sandra Marisa Pelloso, Lincoln Luis Silva, Oscar Kenji Nihei, Maria Dalva de Barros Carvalho and 2 more

Abstract read
In one paragraph

Article in Global heart, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Marcelo PuzziPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0003-0857-0932
Miyoko MassagoPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0001-6805-5399
Júlia Loverde GabellaStudy Group on Digital Technologies and Geoprocessing in Health (GETS), State University of Maringá, Maringá, Brazil.ORCID 0000-0003-2244-3278
Samile Bonfim de OliveiraPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0002-4094-6410
Daniel Augusto Message Dos SantosPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0001-6903-176X
Fernanda Shizue Nishida CarignanoPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0002-5078-9017
Sandra Marisa PellosoPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0001-8455-6839
Lincoln Luis SilvaStudy Group on Digital Technologies and Geoprocessing in Health (GETS), State University of Maringá, Maringá, Brazil.ORCID 0000-0001-8445-0743
Oscar Kenji NiheiStudy Group on Digital Technologies and Geoprocessing in Health (GETS), State University of Maringá, Maringá, Brazil.ORCID 0000-0002-9156-7787
Maria Dalva de Barros CarvalhoPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0002-1377-3331
Amanda de Carvalho DutraPost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0002-2372-7275
Luciano de AndradePost-Graduation Program in Health Sciences, State University of Maringá, Maringá, Brazil.ORCID 0000-0003-2077-1518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mortality resulting from coronary artery disease (CAD) among women is a complex issue influenced by many factors that encompass not only biological distinctions but also sociocultural, economic, and healthcare-related components. Understanding these factors is crucial to enhance healthcare provisions. Therefore, this study seeks to identify the social and clinical variables related to the risk of mortality caused by CAD in women aged 50 to 79 years old in Paraná state, Brazil, between 2010 and 2019. Methods: This is an ecological study based on secondary data sourced from E-Gestor, IPARDES, and DATASUS. We developed a model that integrates both raw and standardized coronary artery disease (CAD) mortality rates, along with sociodemographic and healthcare service variables. We employed Bayesian spatiotemporal analysis with Markov Chain Monte Carlo simulations to assess the relative risk of CAD mortality, focusing specifically on women across the state of Paraná. Results: A total of 14,603 deaths from CAD occurred between 2010 and 2019. Overall, temporal analysis indicates that the risk of CAD mortality decreased by around 22.6% between 2010 (RR of 1.06) and 2019 (RR of 0.82). This decline was most prominent after 2014. The exercise stress testing rate, accessibility of cardiology centers, and IPARDES municipal performance index contributed to the reduction of CAD mortality by approximately 4%, 8%, and 34%, respectively. However, locally, regions in the Central-West, Central-South, Central-East, and Southern regions of the Central-North parts of the state exhibited risks higher-than-expected. Conclusion: In the last decade, CAD-related deaths among women in Paraná state decreased. This was influenced by more exercise stress testing, better access to cardiology centers, improved municipal performance index. Yet, elevated risks of deaths persist in certain regions due to medical disparities and varying municipal development. Therefore, prioritizing strategies to enhance women's access to cardiovascular healthcare in less developed regions is crucial.

Indexed as

Coronary Artery DiseaseAgedBayes TheoremBrazilFemaleHumansMiddle AgedRisk FactorsSpatio-Temporal Analysiscoronary artery diseaseepidemiologyhealth disparitiesspatiotemporal analysiswomen’s health

Identifiers

PMID38312999
PMCPMC10836177

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.