A continuous quality improvement strategy to strengthen screening practices and facilitate the routine use of intravenous iron for treating anaemia in pregnant and postpartum women in Nigeria: a study protocol
Bibliographic record
Abstract
BACKGROUND: Pregnancy-related anaemia is a public health challenge across Africa. Over 50% of pregnant women in Africa get diagnosed with this condition, and up to 75% of these are caused by iron deficiency. The condition is a significant contributor to the high maternal deaths across the continent and, in particular, Nigeria, which accounts for about 34% of global maternal deaths. Whereas oral iron is the mainstay treatment for pregnancy-related anaemia in Nigeria, this treatment is not very effective given the slow absorption of the medication, and its gastrointestinal adverse effects which lead to poor compliance by women. Intravenous iron is an alternative therapy which can rapidly replenish iron stores, but fears of anaphylactic reactions, as well as several misconceptions, have inhibited its routine use. Newer and safer intravenous iron formulations, such as ferric carboxymaltose, present an opportunity to overcome some concerns relating to adherence. Routine use of this formulation will, however, require addressing misconceptions and systemic barriers to adoption in the continuum of care of obstetric women from screening to treatment. This study aims to test the options to strengthen routine screening for anaemia during and immediately after pregnancy, as well as evaluate and improve conditions necessary to deliver ferric carboxymaltose to pregnant and postpartum women with moderate to severe anaemia. METHODS: This study will be conducted in a cluster of six health facilities in Lagos State, Nigeria. The study will employ continuous quality improvement through the Diagnose-Intervene-Verify-Adjust framework and Tanahashi's model for health system evaluation to identify and improve systemic bottlenecks to the adoption and implementation of the intervention. Participatory Action Research will be employed to engage health system actors, health services users, and other stakeholders to facilitate change. Evaluation will be guided by the consolidated framework for implementation research and the normalisation process theory. DISCUSSION: We expect the study to evolve transferable knowledge on barriers and facilitators to the routine use of intravenous iron that will inform scale-up across Nigeria, as well as the adoption of the intervention and strategies in other countries across Africa.
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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 | 0.058 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".