Graduate Program in Demography, Universidade Federal do Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil.
Graduate Program in Demography, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil.
Popul Health Metr. 2024 May 27;22(1):9. doi: 10.1186/s12963-024-00329-x.
Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030.
We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030.
The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential.
Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning.
由于事件/暴露(即随机误差)数量较少,小区域的死亡率估计可能较为困难。如果死亡记录不完整,会给死亡率估计带来系统不确定性。巴西之前的研究已经结合了人口统计学和统计方法来部分解决这些问题。我们估计了 2010 年所有 5565 个巴西城市的年龄和性别特定死亡率,并预测了 2010 年至 2030 年期间的概率死亡率和预期寿命。
我们结合了使用线性样条的年龄特定死亡率预测工具(TOPALS)、贝叶斯模型、空间平滑模型和一个特定的程序,来估计 2010 年所有巴西城市的年龄和性别特定死亡率。然后,我们调整了 Lee-Carter 模型,以预测 2010 年至 2030 年期间所有城市的年龄和性别死亡率。
2010 年所有巴西城市调整后的性别和年龄特定死亡率显示出明显的区域模式,与未调整的估计相比,社会经济发展程度较低的城市的预期寿命有所下降。预测的死亡率表明了不同的区域改善,导致巴西小地区的出生时预期寿命趋同。因此,观察到巴西各城市的死亡年龄的变异性降低,同时存在持续的性别差异。
成功地估计和预测了小区域的死亡率,并为未来的生命表生成了相关的不确定性估计。我们的方法可以应用于存在数据质量问题的国家,以改善公共政策规划。