Temporal trends and regional disparities in breast cancer mortality in Brazil, 2000-2023.
OBJECTIVE To analyze temporal trends in breast cancer mortality in Brazil, its major geographic regions, and the 27 Federative Units from 2000 to 2023. METHODS This ecological time-trend study examined breast cancer mortality among women in Brazil using nationwide mortality data. To improve data quality, mortality rates were corrected through redistribution of ill-defined causes of death, incomplete cancer diagnoses, and adjustments for broader mortality data quality issues, including underreporting, population under-enumeration, and age misreporting. Age-standardized mortality rates were calculated using the World Health Organization (WHO) world standard population. Temporal trends were assessed using Prais-Winsten regression models applied to corrected and uncorrected rates, with annual percent change (APC) estimates and 95% confidence intervals (95% CI). RESULTS Mortality data correction increased the number of breast cancer deaths by 15.9% in Brazil during 2000-2023, with substantially larger corrections in regions with historically lower data quality, particularly in the North (67.2%) and Northeast (54.8%) during 2000-2007. The magnitude of correction progressively decreased over time in all regions. After correction, age-standardized breast cancer mortality rates in Brazil increased from 22.2 deaths per 100,000 women in 2000-24.2 in 2023. Prais-Winsten regression identified a significant increasing national trend (APC: 0.28%; 95% CI: 0.19-0.38). Increasing trends predominated in the North (0.73%; 95% CI: 0.45-1.02), Northeast (0.82%; 95% CI: 0.63-1.00), and Midwest (1.18%; 95% CI: 0.72-1.63), whereas the Southeast showed a stationary trend (0.14%; 95% CI: -0.04-0.32) and the South a decreasing trend (-0.21%; 95% CI: -0.40 to -0.02). CONCLUSIONS Breast cancer mortality in Brazil remains marked by regional inequalities, with increasing trends concentrated in historically underserved regions. Mortality data correction substantially influenced trend estimates, highlighting the importance of reliable data for guiding policies aimed at expanding screening, improving access to timely treatment, and reducing regional disparities.