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Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030

BACKGROUND: Sustainable Development Goal 3.2 (SDG 3.2) is to reduce Under-5 and neonatal mortality rates (U5MR and NMR), two major health systems’ performance indicators, globally by 2030. We aimed to report Iran’s U5MR and NMR status during 2010–2017 and its achievement of SDG 3.2 by 2030, using sc...

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Autores principales: Ebrahimi, Narges, Shahin, Sarvenaz, Koolaji, Sogol, Ghanbari, Ali, Mehdipour, Parinaz, Masinaei, Masoud, Moghaddam, Sahar Saeedi, Rezaei, Negar, Ghamari, Azin, Malekpour, Mohammad-Reza, Rezaei, Nazila, Jamshidi, Hamidreza, Larijani, Bagher, Khosravi, Ardeshir, Farzadfar, Farshad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079046/
https://www.ncbi.nlm.nih.gov/pubmed/37023033
http://dx.doi.org/10.1371/journal.pone.0283784
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author Ebrahimi, Narges
Shahin, Sarvenaz
Koolaji, Sogol
Ghanbari, Ali
Mehdipour, Parinaz
Masinaei, Masoud
Moghaddam, Sahar Saeedi
Rezaei, Negar
Ghamari, Azin
Malekpour, Mohammad-Reza
Rezaei, Nazila
Jamshidi, Hamidreza
Larijani, Bagher
Khosravi, Ardeshir
Farzadfar, Farshad
author_facet Ebrahimi, Narges
Shahin, Sarvenaz
Koolaji, Sogol
Ghanbari, Ali
Mehdipour, Parinaz
Masinaei, Masoud
Moghaddam, Sahar Saeedi
Rezaei, Negar
Ghamari, Azin
Malekpour, Mohammad-Reza
Rezaei, Nazila
Jamshidi, Hamidreza
Larijani, Bagher
Khosravi, Ardeshir
Farzadfar, Farshad
author_sort Ebrahimi, Narges
collection PubMed
description BACKGROUND: Sustainable Development Goal 3.2 (SDG 3.2) is to reduce Under-5 and neonatal mortality rates (U5MR and NMR), two major health systems’ performance indicators, globally by 2030. We aimed to report Iran’s U5MR and NMR status during 2010–2017 and its achievement of SDG 3.2 by 2030, using scenario-based projection. STUDY DESIGN: To estimate the national and subnational levels of U5MR and NMR, we applied an Ensemble Bayesian Model Averaging (EBMA) with Gaussian Process Regression (GPR) and Spatio_temporal models. We used all available data sources including: 12-year data from the Death Registration System (DRS), two censuses, and a demographic and health surveys (DHS). This study employed two approaches, Maternal Age Cohort (MAC) and Maternal Age Period (MAP), to analyze summary birth history data obtained from censuses and DHS. In addition, we calculated the child mortality rate directly from DHS using the complete birth history method. National and subnational NMR was projected up to 2030 with a scenario-based method using average Annual Rate of Reduction (ARR) introduced by UN-IGME. RESULTS: In 2017, national U5MR and NMR were 15·2 (12·4–18·0) and 11·8 (10·4–13·2), with an average ARR of 5·1% (2·1–8·9) and 3·1% (0·9–5·8) during 2010–2017, respectively. According to our projection scenarios, 17 provinces have not fulfilled SDG 3.2 for NMR yet, and the current trend (the current trend of NMR improvement in Iran) will not result in reaching SDG for some provinces by 2030; However, if each province has the same neonatal mortality annual reduction rate as the best-performing province in the same region, besides achieving SDG, the national NMR will be reduced to 5·2, and almost 92,000 newborn lives will be saved. CONCLUSIONS: Iran has achieved SDG3.2 regarding U5MR and NMR; however, there are provincial inequalities. For all provinces to reach SDG3.2, health policies should focus on reducing provincial inequalities by precise planning for neonatal health care.
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spelling pubmed-100790462023-04-07 Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030 Ebrahimi, Narges Shahin, Sarvenaz Koolaji, Sogol Ghanbari, Ali Mehdipour, Parinaz Masinaei, Masoud Moghaddam, Sahar Saeedi Rezaei, Negar Ghamari, Azin Malekpour, Mohammad-Reza Rezaei, Nazila Jamshidi, Hamidreza Larijani, Bagher Khosravi, Ardeshir Farzadfar, Farshad PLoS One Research Article BACKGROUND: Sustainable Development Goal 3.2 (SDG 3.2) is to reduce Under-5 and neonatal mortality rates (U5MR and NMR), two major health systems’ performance indicators, globally by 2030. We aimed to report Iran’s U5MR and NMR status during 2010–2017 and its achievement of SDG 3.2 by 2030, using scenario-based projection. STUDY DESIGN: To estimate the national and subnational levels of U5MR and NMR, we applied an Ensemble Bayesian Model Averaging (EBMA) with Gaussian Process Regression (GPR) and Spatio_temporal models. We used all available data sources including: 12-year data from the Death Registration System (DRS), two censuses, and a demographic and health surveys (DHS). This study employed two approaches, Maternal Age Cohort (MAC) and Maternal Age Period (MAP), to analyze summary birth history data obtained from censuses and DHS. In addition, we calculated the child mortality rate directly from DHS using the complete birth history method. National and subnational NMR was projected up to 2030 with a scenario-based method using average Annual Rate of Reduction (ARR) introduced by UN-IGME. RESULTS: In 2017, national U5MR and NMR were 15·2 (12·4–18·0) and 11·8 (10·4–13·2), with an average ARR of 5·1% (2·1–8·9) and 3·1% (0·9–5·8) during 2010–2017, respectively. According to our projection scenarios, 17 provinces have not fulfilled SDG 3.2 for NMR yet, and the current trend (the current trend of NMR improvement in Iran) will not result in reaching SDG for some provinces by 2030; However, if each province has the same neonatal mortality annual reduction rate as the best-performing province in the same region, besides achieving SDG, the national NMR will be reduced to 5·2, and almost 92,000 newborn lives will be saved. CONCLUSIONS: Iran has achieved SDG3.2 regarding U5MR and NMR; however, there are provincial inequalities. For all provinces to reach SDG3.2, health policies should focus on reducing provincial inequalities by precise planning for neonatal health care. Public Library of Science 2023-04-06 /pmc/articles/PMC10079046/ /pubmed/37023033 http://dx.doi.org/10.1371/journal.pone.0283784 Text en © 2023 Ebrahimi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ebrahimi, Narges
Shahin, Sarvenaz
Koolaji, Sogol
Ghanbari, Ali
Mehdipour, Parinaz
Masinaei, Masoud
Moghaddam, Sahar Saeedi
Rezaei, Negar
Ghamari, Azin
Malekpour, Mohammad-Reza
Rezaei, Nazila
Jamshidi, Hamidreza
Larijani, Bagher
Khosravi, Ardeshir
Farzadfar, Farshad
Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title_full Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title_fullStr Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title_full_unstemmed Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title_short Predicting Iran’s achievement to Sustainable Development Goal 3.2: A systematic analysis of neonatal mortality with scenario-based projections to 2030
title_sort predicting iran’s achievement to sustainable development goal 3.2: a systematic analysis of neonatal mortality with scenario-based projections to 2030
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079046/
https://www.ncbi.nlm.nih.gov/pubmed/37023033
http://dx.doi.org/10.1371/journal.pone.0283784
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