Evidence map›Paper›PMID 38425484›Full record

ArticleFrontiers in nutrition2024

Global burden of maternal disorders attributable to malnutrition from 1990 to 2019 and predictions to 2035: worsening or improving?

Tongtong Xu, Chenxian Dong, Jianjiang Shao, Chaojing Huo, Zuhai Chen, Zhengyang Shi, Teng Yao, Chenyang Gu, Wanting Wei, Dongsheng Rui and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 1 country.

Tongtong Xu *Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Chenxian Dong *Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Jianjiang Shao *Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Chaojing HuoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Zuhai ChenDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Zhengyang ShiDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Teng YaoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Chenyang GuDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Wanting WeiDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Dongsheng RuiDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Xiaoju LiDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Yunhua HuDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Jiaolong MaDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Qiang NiuDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Yizhong YanDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Shihezi University · CNXinjiang Production and Construction Corps · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Maternal malnutrition is a major global public health problem that can lead to serious maternal diseases. This study aimed to analyze and predict the spatio-temporal trends in the burden of maternal disorders attributable to malnutrition, and to provide a basis for scientific improvement of maternal malnutrition and targeted prevention of maternal disorders. Methods: Data on maternal disorders attributable to malnutrition, including number of deaths, disability-adjusted life years (DALYs), population attributable fractions (PAFs), age-standardized mortality rates (ASMRs), and age-standardized DALY rates (ASDRs) were obtained from the Global Burden of Disease Study 2019 to describe their epidemiological characteristics by age, region, year, and type of disease. A log-linear regression model was used to calculate the annual percentage change (AAPC) of ASMR or ASDR to reflect their temporal trends. Bayesian age-period-cohort model was used to predict the number of deaths and mortality rates to 2035. Results: Global number of deaths and DALYs for maternal disorders attributable to malnutrition declined by 42.35 and 41.61% from 1990 to 2019, with an AAPC of -3.09 (95% CI: -3.31, -2.88) and -2.98 (95% CI: -3.20, -2.77) for ASMR and ASDR, respectively. The burden was higher among younger pregnant women (20-29 years) in low and low-middle socio-demographic index (SDI) regions, whereas it was higher among older pregnant women (30-39 years) in high SDI region. Both ASMR and ASDR showed a significant decreasing trend with increasing SDI. Maternal hemorrhage had the highest burden of all diseases. Global deaths are predicted to decline from 42,350 in 2019 to 38,461 in 2035, with the ASMR declining from 1.08 (95% UI: 0.38, 1.79) to 0.89 (95% UI: 0.47, 1.31). Conclusion: Maternal malnutrition is improving globally, but in the context of the global food crisis, attention needs to be paid to malnutrition in low SDI regions, especially among young pregnant women, and corresponding measures need to be taken to effectively reduce the burden of disease.

Indexed as

epidemiologyglobal burdenmalnutritionmaternal disordersprediction

Identifiers

PMID38425484
PMCPMC10902107
OpenAlexW4391841743

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.