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; Xiaoju Li; Yunhua Hu; Jiaolong Ma; Qiang Niu; Yizhong Yan

doi:10.3389/fnut.2024.1343772

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

Front Nutr. 2024 Feb 15:11:1343772. doi: 10.3389/fnut.2024.1343772. eCollection 2024.

Authors

Tongtong Xu^#¹, Chenxian Dong^#¹, Jianjiang Shao^#¹, Chaojing Huo¹, Zuhai Chen¹, Zhengyang Shi¹, Teng Yao¹, Chenyang Gu¹, Wanting Wei¹, Dongsheng Rui^{1

2

3

4}, Xiaoju Li^{1

2

3

4}, Yunhua Hu^{1

2

3

4}, Jiaolong Ma^{1

2

3

4}, Qiang Niu^{1

2

3

4}, Yizhong Yan^{1

2

3

4}

Affiliations

¹ Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
² Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Shihezi, Xinjiang, China.
³ Key Laboratory of Preventive Medicine, Shihezi University, Shihezi, Xinjiang, China.
⁴ Key Laboratory of Xinjiang Endemic and Ethnic Diseases (Ministry of Education), School of Medicine, Shihezi University, Xinjiang, Shihezi, China.

^# Contributed equally.

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.

Keywords: epidemiology; global burden; malnutrition; maternal disorders; prediction.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The Shihezi University High-level Talents Program (RCZK202368), the Shihezi University High-level Talents Program (RCZK2021B28), the Shihezi University self-funded project (ZZZC202125), the Shihezi University 2023 College Student Research and Training Program SRP Project (SRP2023085), the Shihezi University College students’ innovation and entrepreneurship training program (SRP2022084).