Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh.
Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
BMJ Open. 2023 Sep 13;13(9):e069512. doi: 10.1136/bmjopen-2022-069512.
The major objective of this project is to find the best suitable model for district-wise infant mortality rate (IMR) data of Bangladesh over the period 2014-2020 that captures the regional variability and overtime variability of the data.
DESIGN, SETTING AND PARTICIPANTS: Data from seven consecutive cross-sectional surveys that were conducted in Bangladesh between 2014 and 2020 as a part of the Sample Vital Registration System (SVRS) were used in this study. The study included a total of 13 173 (with 390 infant deaths), 17 675 (with 512 infant deaths), 17 965 (with 501 infant deaths), 23 205 (with 556 infant deaths), 23 094 (with 498 infant deaths), 23 090 (with 497 infant deaths) and 23 297 (with 495 infant deaths) complete cases from SVRS datasets for each respective year.
A linear mixed effects model (LMM) with a quadratic trend over time in the fixed effects part and a nested random intercept, as well as a nested random slope for a linear trend over time in the part of the random effect, was implemented to describe the situation. This model was selected based on two popular selection criteria: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).
The LMMs analysis results demonstrated statistically significant variations in IMR across different districts and over time. Examining the district-specific area under the logarithm of the IMR curves yielded valuable insights into the disparities in IMR among different districts and regions. Furthermore, a significant inverse relationship was observed between IMR and life expectancy at birth, underscoring the significance of mitigating IMR as a means to enhance population health outcomes.
This study accentuates district-wise and temporal variability when modelling IMR data and highlights regional heterogeneity in infant mortality rates in Bangladesh. Area-based programmes should be created for mothers residing in locations with a higher risk of IMR. Further research can examine socioeconomic elements generating these discrepancies.
本项目的主要目标是找到最适合孟加拉国 2014-2020 年各地区婴儿死亡率(IMR)数据的模型,以捕捉数据的区域变异性和随时间的变异性。
设计、设置和参与者:本研究使用了孟加拉国在 2014 年至 2020 年期间作为样本生命登记系统(SVRS)的七个连续横断面调查的数据。该研究共包括来自 SVRS 数据集的 13173 名(390 名婴儿死亡)、17675 名(512 名婴儿死亡)、17965 名(501 名婴儿死亡)、23205 名(556 名婴儿死亡)、23094 名(498 名婴儿死亡)、23090 名(497 名婴儿死亡)和 23297 名(495 名婴儿死亡)完整案例,分别来自每个年份的 SVRS 数据集。
采用固定效应部分随时间呈二次趋势的线性混合效应模型(LMM)和嵌套随机截距,以及随时间呈线性趋势的随机效应部分嵌套随机斜率,来描述这种情况。该模型是基于两个流行的选择标准(Akaike 信息准则(AIC)和贝叶斯信息准则(BIC))选择的。
LMMs 分析结果表明,IMR 在不同地区和随时间存在统计学显著差异。检查对数 IMR 曲线下的特定地区结果,深入了解了不同地区和区域之间 IMR 的差异。此外,IMR 与出生时预期寿命之间呈显著负相关,这强调了降低 IMR 以提高人口健康结果的重要性。
本研究强调了建模 IMR 数据时的地区和时间变异性,并突出了孟加拉国婴儿死亡率的区域异质性。应针对居住在 IMR 风险较高地区的母亲制定基于地区的方案。进一步的研究可以研究产生这些差异的社会经济因素。