Admission to hospital for pneumonia and influenza attributable to 2009 pandemic A/H1N1 Influenza in First Nations communities in three provinces of Canada
Bibliographic record
Abstract
BACKGROUND: Early reports of the 2009 A/H1N1 influenza pandemic (pH1N1) indicated that a disproportionate burden of illness fell on First Nations reserve communities. In addition, the impact of the pandemic on different communities may have been influenced by differing provincial policies. We compared hospitalization rates for pneumonia and influenza (P&I) attributable to pH1N1 influenza between residents of First Nations reserve communities and the general population in three Canadian provinces. METHODS: Hospital admissions were geocoded using administrative claims data from three Canadian provincial data centres to identify residents of First Nations communities. Hospitalizations for P&I during both waves of pH1N1 were compared to the same time periods for the four previous years to establish pH1N1-attributable rates. RESULTS: Residents of First Nations communities were more likely than other residents to have a pH1N1-attributable P&I hospitalization (rate ratio [RR] 2.8-9.1). Hospitalization rates for P&I were also elevated during the baseline period (RR 1.5-2.1) compared to the general population. There was an average increase of 45% over the baseline in P&I admissions for First Nations in all 3 provinces. In contrast, admissions overall increased by approximately 10% or less in British Columbia and Manitoba and by 33% in Ontario. Subgroup analysis showed no additional risk for remote or isolated First Nations compared to other First Nations communities in Ontario or Manitoba, with similar rates noted in Manitoba and a reduction in P&I admissions during the pandemic period in remote and isolated First Nations communities in Ontario. CONCLUSIONS: We found an increased risk for pH1N1-related hospital admissions for First Nations communities in all 3 provinces. Interprovincial differences may be partly explained by differences in age structure and socioeconomic status. We were unable to confirm the assumption that remote communities were at higher risk for pH1N1-associated hospitalizations. The aggressive approach to influenza control in remote and isolated First Nations communities in Ontario may have played a role in limiting the impact of pH1N1 on residents of those communities.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".