Estimating prevalence of immunocompromising conditions in Canada in 2022 using an administrative hospital database
Bibliographic record
Abstract
Abstract Background Immunocompromising conditions (ICs) are conditions that may be present at different moments in a person’s life and affect their health. In individuals with ICs, the body’s ability to effectively fight infections or mount a robust response to vaccination is weakened. It is thus important to understand the burden of ICs in part to inform public health policies on the procurement of vaccines for this population. However, no estimates of prevalence rates of ICs exist in Canada. Objective Our objective was to address this gap. Intervention We defined our list of ICs using different published sources and expert opinion. Using diagnoses coded with ICD-10 over a period of 14 years in the Discharge Abstract Database, we were able to estimate prevalence rates of ICs. Outcome For all ICs combined and conservatively assuming a hospitalization rate of 0.1, general population prevalence rates is estimated at 1.8% for females and 1.9% for males. Inflammatory bowel disease, severe liver disease and severe cancers are among the most frequent ICs at all age groups and for both genders. Functional asplenia and Primary Immunodeficient conditions are mostly present at young ages. Transplantation and renal dialysis are also among the most frequent ICs. Conclusion The prevalence of ICs estimated using DAD aligns with estimates obtained using other methods from other countries (2.7–6.6%) under certain assumptions for hospitalization rates. This work serves as the foundation for future investigation into obtaining more precise estimates of prevalence rates of ICs in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".