Burden of anemia and its underlying causes in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019
Bibliographic record
Abstract
BACKGROUND: Anemia is a common disease which affects around 40% of children and 30% of reproductive age women and can have major health consequences. The present study reports the global, regional and national burden of anemia and its underlying causes between 1990 and 2019, by age, sex and socio-demographic index (SDI). METHODS: Publicly available data on the point prevalence and years lived with disability (YLDs) were retrieved from the global burden of disease (GBD) 2019 study for 204 countries and territories between 1990 and 2019. The point prevalence, YLD counts and rates per 100,000 population were presented, along with their corresponding 95% uncertainty intervals. RESULTS: In 2019, the global age-standardized point prevalence and YLD rates for anemia were 23,176.2 (22,943.5-23,418.6) and 672.4 (447.2-981.5) per 100,000 population, respectively. Moreover, the global age-standardized point prevalence and YLD rate decreased by 13.4% (12.1-14.5%) and 18.8% (16.9-20.8%), respectively, over the period 1990-2019. The highest national point prevalences of anemia were found in Zambia [49327.1 (95% UI: 46,838.5-51,700.1)], Mali [46890.1 (95% UI: 44,301.1-49,389.8)], and Burkina Faso [46117.2 (95% UI: 43,640.7-48,319.2)]. In 2019, the global point prevalence of anemia was highest in the 15-19 and 95+ age groups in females and males, respectively. Also, the burden of anemia was lower in regions with higher socio-economic development. Globally, most of the prevalent cases were attributable to dietary iron deficiency, as well as hemoglobinopathies and hemolytic anemias. CONCLUSIONS: Anemia remains a major health problem, especially among females in less developed countries. The implementation of preventive programs with a focus on improving access to iron supplements, early diagnosis and the treatment of hemoglobinopathies should be taken into consideration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".