Influenza immunization in Canada’s low-income population
Bibliographic record
Abstract
BACKGROUND: Immunization offers the best protection from influenza infection. Little evidence describes disparities in immunization uptake among low-income individuals. Higher rates of chronic disease put this population at increased risk of influenza-related complications. This analysis examines if the type of main source of household income in low-income groups affects influenza immunization uptake. We hypothesized that individuals on social assistance have less access to immunization compared to those with employment earnings or seniors' benefits. METHODS: Data was obtained from the Canadian Community Health Survey annual component 2009-2010. A total of 10,373 low-income respondents (<20,000$ Canadian per annum) were included. Logistic regression, stratified according to type of provincial publicly funded immunization program, was used to examine the association between influenza immunization (in the last 12 months) and main source of household income (employment earnings; social assistance as a combination of employment insurance or worker's compensation or welfare; or seniors' benefits). RESULTS: Overall, 32.5% of respondents reported receiving influenza immunization. In multivariable analysis of universal publicly funded influenza immunization programs, those reporting social assistance (AOR 1.24, 95% CI 1.02-1.51) or seniors' benefits (AOR 1.56, 95% CI 1.23-1.98) were more likely to be immunized compared to those reporting employment earnings. Similar results were observed for high-risk programs. CONCLUSIONS: Among the low-income sample, overall influenza immunization coverage is low. Those receiving social assistance or seniors' benefits may have been targeted due to higher rates of chronic disease. Programs reaching the workforce may be important to attain broader coverage. However, CCHS data was collected during the H1N1 pandemic influenza, thus results may not be generalizable to influenza immunization in non-pandemic years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".