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Mortality Due to Acute Myocardial Infarction in Brazil from 1996 to 2016: 21 Years of Disparities in Brazilian Regions

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DataCite Commons2021-03-25 更新2024-07-28 收录
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https://scielo.figshare.com/articles/dataset/Mortality_Due_to_Acute_Myocardial_Infarction_in_Brazil_from_1996_to_2016_21_Years_of_Disparities_in_Brazilian_Regions/14277850/1
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Abstract Background: Acute myocardial infarction (AMI), the leading cause of death in Brazil, has presented regional disparities in mortality rate time trends in recent years. Previous time trend studies did not correct for cause-of-death garbage codes, which may have skewed the estimates. Objective: To analyze regional and gender-based inequalities in the AMI mortality trend in Brazil from 1996-2016. Methods: A 21-year time series study (1996-2016). Data are from the Mortality Information System and population estimates from the Brazilian Institute of Geography and Statistics. Corrections of deaths due to ill-defined causes of death, garbage codes, and underreporting were made. The time series broken down by major geographic regions, gender, capital cities, and other municipalities was analyzed using the linear regression technique segmented by Jointpoint. Statistical significance level was set at 5%. Results: In the period, mortality decreased more sharply in women (−2.2%; 95% CI: −2.5; −1.9) than in men (−1.7%; 95% CI: - 1.9; −1.4) and more in the capital cities (−3.8%; 95% CI: - 4.3; −3.3) than in other municipalities (−1.5%; 95% CI: - 1.8; −1.3). Regional inequalities were observed, with an increase for men living in other municipalities of the North (3.3; 95% CI: 1.3; 5.4) and Northeast (1.3%; 95% CI: 1.0; 1.6). Statistical significance level was set at 5%. Mortality rates after corrections showed a significant difference in relation to the estimates without corrections, mainly due to the redistribution of garbage codes. Conclusions: Although AMI-related mortality has decreased in Brazil in recent years, this trend is uneven by region and gender. Correcting the numbers of deaths is essential to obtaining more reliable estimates.
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SciELO journals
创建时间:
2021-03-24
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