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Published on: December 12, 2013
Spatio-temporal evolution of stroke mortality in Minas Gerais, Brazil, 1980-2021
Daniel Hideki Bando1, Francisco Chiaravalloti Neto2, Alfredo Pereira de Queiroz3
1Universidade Federal de Alfenas, Instituto de Ciências da Natureza, Alfenas, MG, Brazil.
Objective:
To analyze spatio-temporal evolution of stroke mortality in Minas Gerais state, Brazil, 1980-2021.
Methods:
Ecological study with aggregated data by micro-region. Segmented linear regression was used for trend analysis; maps with rates per five-year period and scan statistics were used for spatial analysis.
Results:
There were 392,521 stroke-related deaths (rate of 52.6/100,000-year). All rates (crude, adjusted, by age group) showed a decreasing trend, less so in the crude rate (Annual Percent Change [APC] = -0.70) and a faster decrease in the 20-39 age group (APC = -4.48). A high-rate cluster was identified in the southern region (1980-1999; Relative Risk [RR] = 2.06), and a low-rate cluster in the northwest (2008-2021; RR = 0.59). The most significant decrease occurred in the south (APC = -3.64).
Conclusion:
Stroke mortality showed a decreasing trend. Clusters and areas with higher rates identified in the northeast in recent years require attention by service managers.
Main Results:
Decreasing trend in stroke mortality, a cluster was found in the south with high rates (1980-1999; relative risk [RR] = 2.06) and another in the northwest with low rates (2008-2021; RR = 0.59).
Implications For Services:
For health service managers, greater attention is suggested in the micro-regions and respective municipalities in the northeast that have presented a higher risk of mortality from stroke in recent years, with the aim of improving the regulation of health care service and network regulation, as well as optimizing access to these services.
Perspectives:
It is necessary to monitor the spatio-temporal evolution of stroke mortality and conduct new studies on different scales (for example, on the municipal scale) and methodologies (for example, multilevel analysis), to estimate risk factors and the social context of vulnerable populations.
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