Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data
Bibliographic record
Abstract
BACKGROUND: Low haemoglobin concentrations and anaemia are important risk factors for the health and development of women and children. We estimated trends in the distributions of haemoglobin concentration and in the prevalence of anaemia and severe anaemia in young children and pregnant and non-pregnant women between 1995 and 2011. METHODS: We obtained data about haemoglobin and anaemia for children aged 6-59 months and women of childbearing age (15-49 years) from 257 population-representative data sources from 107 countries worldwide. We used health, nutrition, and household surveys; summary statistics from WHO's Vitamin and Mineral Nutrition Information System; and summary statistics reported by other national and international agencies. We used a Bayesian hierarchical mixture model to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. We quantified the uncertainty of our estimates. FINDINGS: Global mean haemoglobin improved slightly between 1995 and 2011, from 125 g/L (95% credibility interval 123-126) to 126 g/L (124-128) in non-pregnant women, from 112 g/L (111-113) to 114 g/L (112-116) in pregnant women, and from 109 g/L (107-111) to 111 g/L (110-113) in children. Anaemia prevalence decreased from 33% (29-37) to 29% (24-35) in non-pregnant women, from 43% (39-47) to 38% (34-43) in pregnant women, and from 47% (43-51) to 43% (38-47) in children. These prevalences translated to 496 million (409-595 million) non-pregnant women, 32 million (28-36 million) pregnant women, and 273 million (242-304 million) children with anaemia in 2011. In 2011, concentrations of mean haemoglobin were lowest and anaemia prevalence was highest in south Asia and central and west Africa. INTERPRETATION: Children's and women's haemoglobin statuses improved in some regions where concentrations had been low in the 1990s, leading to a modest global increase in mean haemoglobin and a reduction in anaemia prevalence. Further improvements are needed in some regions, particularly south Asia and central and west Africa, to improve the health of women and children and achieve global targets for reducing anaemia. FUNDING: Bill & Melinda Gates Foundation, Grand Challenges Canada, and the UK Medical Research Council.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".