Exploring Iron Deficiency in Alberta: Following up on Clinical Observations
Bibliographic record
Abstract
Context: Iron deficiency (ID) is a common and preventable micronutrient deficiency, affecting cognitive development, immune function, and well-being of individuals, and leading to substantial health care costs. Objective: Our study investigates ID prevalence in Alberta from 2010 to 2022 and its association with patient and provider characteristics. We hypothesize that the prevalence of ID in Alberta has been increasing over the past decade based on clinical observations and anecdotes. We also explore anemia prevalence to see if results converge as these two health conditions are closely related. Study Design: Retrospective analysis of electronic medical data obtained from the Canadian Primary Care Research Network, Southern Alberta Primary Care Research Network (SAPCReN-CPCSSN). Population/Participants: Cohort of 94,264 individuals aged six and older residing in Alberta. These individuals had at least one ferritin test between 2010-2022. ID was defined in accordance with the World Health Organization (WHO) guidelines (i.e., serum ferritin below 15 μg/mL). Additionally, anemia was assessed using WHO guidelines based on hemoglobin levels. Instrument: Secondary data were extracted from de-identified SAPCReN-CPCSSN electronic medical records. Outcome/Evaluation: We examined the prevalence of ID over the study period, focusing on trends and correlations to patient and provider demographics. We examined the prevalence of anemia over the same time period. Results: Contrary to our hypothesis, our findings demonstrated a trend of decreasing ID in Alberta, particularly during the pandemic years (i.e., 2020-2022). Within this overall decline, ID remained higher among women of reproductive age and individuals with higher material deprivation. Notably, providers who were female and located in urban sites were more likely to perform ferritin testing. Our analysis uncovered a paradoxical trend: while ID is decreasing, anemia is trending upward, underscoring the need to better understand ID and its implications. Conclusions: The decreasing trend in ID prevalence is encouraging; however, ID remains high among vulnerable populations, which highlights the importance of targeted interventions to address ID effectively. Further investigation is needed to better understand the underlying factors that contribute to our paradoxical finding, which also underscores the importance of assessing multiple clinical indicators to understand patients’ full experiences.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".