Evidence map›Paper›PMID 42835395›Full record

ArticleInternational journal of women's health2026

Assessment of the Burden of Maternal Hypertensive Disorders and the Quality of Care in China: A Systematic Analysis of the Global Burden of Disease (GBD) 2021 Study.

Weiwei Wang, Xun Jiang, Jiayao Long, Juanjuan Chen, Jingsi Chen, Yong Wang

Abstract read
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Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Weiwei WangDepartment of Obstetrics and Gynecology; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for 0bstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Materna1-Fetal Medicine; The Third Affiliated Hospital, Guangzhou, People's Republic of China.
Xun JiangThe Department of Obstetrics and Gynecology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, People's Republic of China.
Jiayao LongThe Department of Obstetrics and Gynecology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, People's Republic of China.
Juanjuan ChenThe Department of Obstetrics and Gynecology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, People's Republic of China.
Jingsi ChenThe Department of Obstetrics and Gynecology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510000, People's Republic of China.
Yong WangDepartment of Obstetrics and Gynecology; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for 0bstetrics and Gynecology; Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Materna1-Fetal Medicine; The Third Affiliated Hospital, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to analyze the disease burden and quality of care associated with maternal hypertensive disorders in China over time and across age groups from 1990 to 2021. Methods: This study employs statistical methods, including joinpoint regression analysis, decomposition analysis, age-period-cohort analysis, and the quality-of-care index, to assess the disease burden of maternal hypertensive disorders in China between 1990 and 2021. It uses an ARIMA model to forecast the disease burden for the period 2022-2035. Results: Compared with 1990, the decline in the number of deaths, death rate, number of DALYs, and DALYs rate in 2021 was over 80% in each case, at 89.66%, 87.50%, 84.97% and 82.97%, respectively; the decline in the number and rate of prevalence, as well as the number and rate of incidence, ranged between 35% and 45%. The burden of disease has shifted from the 20-24 age group to the 30-34 age group (peak in number of cases) or the 25-29 age group (peak in rates). ARIMA forecasts that these rates will continue to decline through to 2035, although the rate of decline in incidence and prevalence will slow. The quality-of-care index rose steadily from 2003 to 2021. Conclusion: The overall burden of maternal hypertensive disorders in China is declining. Based on the observed epidemiological trends: the quality of care is relatively low among both younger and older age groups; blood pressure management across the entire reproductive lifespan could be optimized in the future.

Indexed as

burden of diseaseChinaglobal burden of disease studymaternal hypertensive disordersquality of care index

Identifiers

PMID42835395
PMCPMC13635284

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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.