BMJ Open. 2023 Sep 13;13(9):e069512. doi: 10.1136/bmjopen-2022-069512.
ABSTRACT
OBJECTIVE: 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.
METHOD: 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).
RESULTS: 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.
CONCLUSION: 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.
PMID:37709341 | DOI:10.1136/bmjopen-2022-069512
