Prevalence of Iron Deficiency in Pregnancy: A Single Centre Canadian Study
Bibliographic record
Abstract
Abstract Background Iron deficiency (ID) is the most common and widespread nutritional deficiency in both developing and developed countries (WHO, 2001; Mei et al., 2011). Women of child bearing age are at the highest risk, but this risk increases even more during pregnancy. The expansion of blood volume, growth of the fetus and placenta increase demand for iron to approximately 5.0mg/day by the third trimester (Met et al., 2011). Common symptoms of ID during pregnancy include fatigue, shortness of breath, and difficulty concentrating (WHO, 2001). Poor prenatal iron status is associated with diminished cognitive performance, language ability, and motor functions in the child (Tamura et al, 2002). For the mother, it is associated with risk of blood transfusion and post-partum depression. Despite international recommendations and guidelines on the management of ID in pregnancy, it remains a problem of epidemic proportions and is often unrecognized and left untreated. To increase awareness of ID, a quality improvement project, IRON Deficiency project in Pregnancy: Maternal Iron Optimization (IRON MOM) was implemented January 1st, 2017 at St. Michael's Hospital (SMH), in Toronto, Canada. Phase 1 of the project involved adapting lab requisitions and workflow in the obstetrics clinic to incorporate routine measurement of ferritin in week 12, 16 and 28 of pregnancy. As part of the IRON MOM, laboratory requisitions were modified to include ferritin as part of routine screening for all pregnant women. Objective The primary objective of this study was to assess the prevalence of ID in pregnant women consistently screened for ID after the implementation of the IRON MOM quality improvement project at a tertiary hospital in Toronto, Canada. Methods Administrative laboratory data was collected from the electronic medical record system at SMH, Toronto, Canada between January 1 and December 31, 2017. Suboptimal iron stores was defined as serum ferritin between 30-50μg/L. ID was defined as serum ferritin between 15-29μg/L, and severe ID was defined as <15μg/L. Significant anemia was defined as hemoglobin levels <100 g/L. Descriptive statistics were used to calculate proportions. SAS version 9.4 was used to perform the analyses. Results In 2017, 2400 ferritin tests were completed on pregnant women at our institution. A total of 76.8% (1844/2400) of tests demonstrated iron deficiency with a ferritin <30μg/L. Of those, 30.2% (726/2400) had ferritin between 15-29μg/L, and 46.6% (1118/2400) were severely iron deficient with a ferritin <15μg/L (Figure 1). 3282 hemoglobin checks, at delivery, occurred in this same one-year period and 10.5% (345/3282) were significantly anemic (<100 g/L). Of those, 6.2% (204/3282) had hemoglobin levels between 90-99g/L, 2.6% (85/3282) had hemoglobin levels between 80-89g/L, and 1.7% (56/3282) had hemoglobin levels <80g/L. Conclusion We found an extremely high prevalence of ID in our pregnant patient population. This confirms that ID remains an underappreciated problem, even at a tertiary care centre. Our findings highlight a tremendous gap in awareness, which demands strategies to improve knowledge translation. Future directions include the simplification and digitization of IRON MOM to empower pregnant patients to advocate for their care. Figure 1. Figure 1. Disclosures Lausman: Ferring: Other: gave a talk.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".