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Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020

OBJECTIVE: The present study investigated the province-level distribution and drivers of infant mortality rate (IMR) in mainland China. DESIGN: Ecological analysis based on publicly available data for all 31 provinces in mainland China. DATA SOURCES: Data on province-level IMRs in 2020 were obtained...

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Autores principales: Zhang, Xiao, Tang, Yuwen, Zhang, Beibei, Zhang, Yongjing, Dai, Jifeng, Zhang, Junhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583042/
https://www.ncbi.nlm.nih.gov/pubmed/37827731
http://dx.doi.org/10.1136/bmjopen-2022-070444
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author Zhang, Xiao
Tang, Yuwen
Zhang, Beibei
Zhang, Yongjing
Dai, Jifeng
Zhang, Junhui
author_facet Zhang, Xiao
Tang, Yuwen
Zhang, Beibei
Zhang, Yongjing
Dai, Jifeng
Zhang, Junhui
author_sort Zhang, Xiao
collection PubMed
description OBJECTIVE: The present study investigated the province-level distribution and drivers of infant mortality rate (IMR) in mainland China. DESIGN: Ecological analysis based on publicly available data for all 31 provinces in mainland China. DATA SOURCES: Data on province-level IMRs in 2020 were obtained from the official websites of the healthcare commissions within each province and from the China Health Statistics Yearbook 2021. Data on potential IMR drivers were retrieved from the China Statistical Yearbook 2021. DATA ANALYSIS: GeoDa V.1.12.1 and ArcMap V.10.2 software were used to examine province-level distribution of IMR. Global and local spatial autocorrelations were performed, and Getis-ord G* hotspots and coldspots were identified. Geodetector was used to analyse the individual and joint influence of drivers on IMR. RESULTS: IMRs in 2020 varied from 1.91 to 7.60 per 1000 live births across provinces. The following statistically significant drivers with q values >0.5 were identified: health literacy of the population (0.6673), male illiteracy rate (0.6433), proportion of the population older than >65 years (0.6369), per capita government health expenditure (0.6216), forest coverage rate (0.5820), per capita disposable income (0.5785), per capita number of hospitals (0.5592), per capita gross regional product (0.5410) and sulfur dioxide concentration in the atmosphere (0.5158). The following three interactions among these drivers emerged as strongest influences on province-level IMR: proportion of population >65 years ∩ per capita gross regional product (q=0.9653), forest coverage rate ∩ per capita gross regional product (0.9610) and per capita government health expenditure ∩ sulfur dioxide (0.9295). CONCLUSION: IMR in mainland China varies substantially across the country, being generally high-west and low-east. Several factors, on their own and interacting together, contribute to IMR. Policies and programmes to reduce IMR should be formulated according to local conditions and should focus on western provinces of the country.
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spelling pubmed-105830422023-10-19 Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020 Zhang, Xiao Tang, Yuwen Zhang, Beibei Zhang, Yongjing Dai, Jifeng Zhang, Junhui BMJ Open Public Health OBJECTIVE: The present study investigated the province-level distribution and drivers of infant mortality rate (IMR) in mainland China. DESIGN: Ecological analysis based on publicly available data for all 31 provinces in mainland China. DATA SOURCES: Data on province-level IMRs in 2020 were obtained from the official websites of the healthcare commissions within each province and from the China Health Statistics Yearbook 2021. Data on potential IMR drivers were retrieved from the China Statistical Yearbook 2021. DATA ANALYSIS: GeoDa V.1.12.1 and ArcMap V.10.2 software were used to examine province-level distribution of IMR. Global and local spatial autocorrelations were performed, and Getis-ord G* hotspots and coldspots were identified. Geodetector was used to analyse the individual and joint influence of drivers on IMR. RESULTS: IMRs in 2020 varied from 1.91 to 7.60 per 1000 live births across provinces. The following statistically significant drivers with q values >0.5 were identified: health literacy of the population (0.6673), male illiteracy rate (0.6433), proportion of the population older than >65 years (0.6369), per capita government health expenditure (0.6216), forest coverage rate (0.5820), per capita disposable income (0.5785), per capita number of hospitals (0.5592), per capita gross regional product (0.5410) and sulfur dioxide concentration in the atmosphere (0.5158). The following three interactions among these drivers emerged as strongest influences on province-level IMR: proportion of population >65 years ∩ per capita gross regional product (q=0.9653), forest coverage rate ∩ per capita gross regional product (0.9610) and per capita government health expenditure ∩ sulfur dioxide (0.9295). CONCLUSION: IMR in mainland China varies substantially across the country, being generally high-west and low-east. Several factors, on their own and interacting together, contribute to IMR. Policies and programmes to reduce IMR should be formulated according to local conditions and should focus on western provinces of the country. BMJ Publishing Group 2023-10-12 /pmc/articles/PMC10583042/ /pubmed/37827731 http://dx.doi.org/10.1136/bmjopen-2022-070444 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
Zhang, Xiao
Tang, Yuwen
Zhang, Beibei
Zhang, Yongjing
Dai, Jifeng
Zhang, Junhui
Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title_full Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title_fullStr Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title_full_unstemmed Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title_short Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020
title_sort province-level distribution and drivers of infant mortality in mainland china: a geodetector-based analysis of data from 2020
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583042/
https://www.ncbi.nlm.nih.gov/pubmed/37827731
http://dx.doi.org/10.1136/bmjopen-2022-070444
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