Evidence map›Paper›PMID 32678015›Full record

ArticleBMC public health2020

Trends and seasonality in cause-specific mortality among children under 15 years in Guangzhou, China, 2008-2018.

Xiao-Han Xu, Hang Dong, Li Li, Wen-Hui Liu, Guo-Zhen Lin, Chun-Quan Ou

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiao-Han XuState Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Hang DongGuangzhou Center for Disease Control and Prevention, Guangzhou, 510440, Guangdong, China.
Li LiState Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Wen-Hui LiuGuangzhou Center for Disease Control and Prevention, Guangzhou, 510440, Guangdong, China.
Guo-Zhen LinGuangzhou Center for Disease Control and Prevention, Guangzhou, 510440, Guangdong, China. xwkgzcdc@126.com.
Chun-Quan OuState Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China. ouchunquan@hotmail.com.ORCID http://orcid.org/0000-0001-6866-7213

Funding

National Nature Science Foundation of China 81973140
6 · The paper itself

Abstract

backgroundThis study analyzed the trends and seasonality in mortality among children aged 0-14 years in Guangzhou, China during 2008-2018. Understanding the epidemiology of this public health problem can guide policy development for children mortality prevention.

methodsA population-based epidemiological retrospective study was conducted. Seven thousand two hundred sixty-five individual data of children mortality were obtained from the Guangzhou Center for Disease Control and Prevention (CDC). The Poisson regression was used to quantify the annual average reduction rate and the difference in mortality rate between sex and age groups. Incidence ratio with 95% confidence interval (CI) was estimated to determine the temperaol variations in mortality by month, season, school term, day of the week and between holidays and other days.

resultsBetween 2008 and 2018, the children mortality rate in Guangzhou decreased from 54.0 to 34.3 per 100,000 children, with an annual reduction rate of 4.6% (95% CI: 1.1%-8.1%), especially the under-5 mortality rate decreased by 8.3% (95% CI: 4.8%-11.6%) per year. Decline trends varied by causes of death, even with an upward trend for the mortality of asphyxia and neurological diseases. The risk of death among males children was 1.33 times (95% CI: 1.20-1.47) of that of females. The distribution of causes of death differed by age group. Maternal and perinatal, congenital and pneumonia were the top three causes of death in infants and cancer accounted for 17% of deaths in children aged 1-14 years. Moreover, the injury-related mortality showed significant temporal variations with higher risk during the weekend. And there was a summer peak for drowning and a winter peak for asphyxia.

conclusionsGuangzhou has made considerable progress in reducing mortality over the last decade. The findings of characteristics of children mortality would provide important information for the development and implementation of integrated interventions targeted specific age groups and causes of death.

Indexed as

AdolescentCause of DeathChildChild MortalityChild, PreschoolChinaFemaleHumansIncidenceInfantInfant, NewbornMaleRetrospective StudiesSeasonsChildrenChinaMortalityPoisson regression modelSeasonality

Identifiers

PMID32678015
PMCPMC7364532

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