Cargando…

Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Islam, Tarikul, Akter, Noor Jahan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503329/
https://www.ncbi.nlm.nih.gov/pubmed/37709341
http://dx.doi.org/10.1136/bmjopen-2022-069512
_version_ 1785106503104987136
author Islam, Tarikul
Akter, Noor Jahan
author_facet Islam, Tarikul
Akter, Noor Jahan
author_sort Islam, Tarikul
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10503329
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-105033292023-09-16 Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability Islam, Tarikul Akter, Noor Jahan BMJ Open Public Health 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. BMJ Publishing Group 2023-09-13 /pmc/articles/PMC10503329/ /pubmed/37709341 http://dx.doi.org/10.1136/bmjopen-2022-069512 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Public Health
Islam, Tarikul
Akter, Noor Jahan
Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title_full Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title_fullStr Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title_full_unstemmed Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title_short Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
title_sort identifying regional disparities of infant mortality rates in bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503329/
https://www.ncbi.nlm.nih.gov/pubmed/37709341
http://dx.doi.org/10.1136/bmjopen-2022-069512
work_keys_str_mv AT islamtarikul identifyingregionaldisparitiesofinfantmortalityratesinbangladeshinsightsfromnationwidecrosssectionalstudiesandastatisticalmodellingapproachusinglinearmixedeffectsmodelwithtemporalvariability
AT akternoorjahan identifyingregionaldisparitiesofinfantmortalityratesinbangladeshinsightsfromnationwidecrosssectionalstudiesandastatisticalmodellingapproachusinglinearmixedeffectsmodelwithtemporalvariability