Tackling global inequalities in maternal hypertensive disorders: trends and the impact of public health emergencies, 1990–2021
Bibliographic record
Abstract
Background and aims: Despite progress made under the United Nations Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs), inequalities in global health persist, particularly in the maternal health. Public health emergencies also affect health equity. This research examined long-term disease burden trends (1990-2021) of maternal hypertensive disorders (MHD), with a focus on the influence of age and socio-demographic index (SDI) differences, as well as short-term disruptions during the COVID-19 pandemic, to inform more equitable maternal health policies. Methods: Using the Global Burden of Disease database (2021), this study conducted a systematic examination of indicators of MHD: incidence, maternal mortality ratio (MMR), and disability-adjusted life years (DALYs). All analyses were standardized for age-specific fertility rates (ASFR). Analyses included frontier analysis to identify achievable health outcomes, decomposition analysis to identify key factors, and age-period-cohort (APC) model to assess independent effects. Health inequalities were measured using the slope index of inequality (SII) and concentration curve. The secular trends were characterized using the average annual percentage change (AAPC), while the impact of the COVID-19 pandemic was assessed through the estimated annual percentage change (EAPC). Results: The APC model revealed elevated risks for adolescent and older pregnancies, with incidence fluctuating over 32 years but MMR steadily declining. Disease burden generally decreased with higher SDI levels. Decomposition analysis suggested that demographic factors increased the burden, while epidemiology mitigated it. Frontier analysis indicated effective maternal health control in Canada but Cameroon required progress. While the SII in 2021 declined [-2003.35 (95% CI: -2184.75 to -1821.96)], concentration curves revealed increased relative inequality in lower-SDI populations. Over 32 years, the global MHD burden declined, with a reduction in low-SDI regions [AAPC: -29.46 (95% CI: -30.06 to -28.85)] approximately 24 times that of high-SDI regions. However, the pandemic significantly slowed the decline in low- and low-middle SDI regions. Conclusion: This study highlights marked disparities in the disease burden among age groups across diverse SDI regions. Public health emergencies have intensified existing health inequalities and exposed gaps in healthcare resource distribution. Implementing targeted interventions and reinforcing maternal care during emergencies are critical for enhancing maternal health and advancing health equity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".