Prevalence of winter migration to warmer destinations among Ontarians (“snowbirds”) and patterns of their use of health care services: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Older Canadians frequently migrate to warmer destinations for the winter season (snowbirds). Our aim was to examine the prevalence of migration to warmer destinations among Ontarians, and to compare the characteristics and use of health care services of snowbirds to those of older Ontarians who did not migrate for the winter. METHODS: We conducted a population-based analysis using health administrative databases from Ontario. We compiled 10 seasonal cohorts (2009/10 to 2018/19) of adults aged 65 or more who filled a travel supply of medications under the Ontario Drug Benefits program (snowbirds) between September and January (snowbird season). We calculated the seasonal prevalence of snowbirds per 100 Ontarians aged 65 or more. We matched each snowbird in the 2018/19 season to 2 nonsnowbirds on age and sex, and compared their characteristics and patterns of use of government-funded health care services. RESULTS: Over the 10-year period, 53 431 to 70 863 Ontarians aged 65 or more were identified as snowbirds (seasonal prevalence 2.6%-3.3%). Compared to nonsnowbirds, snowbirds were more likely to be recent migrants, live in higher-income neighbourhoods, have fewer comorbidities and make more visits to primary care physicians. From January to March 2019, snowbirds accessed government-funded health care services for a median of 0 days (interquartile range [IQR] 0-1 d), compared to 4 days (IQR 2-8 d) among nonsnowbirds. INTERPRETATION: About 3% of older Ontarians migrate to warmer destinations for the winter each season. Since few access health care services in Ontario from January to March, researchers are encouraged to consider the snowbird population and the impact of their absence on evaluations that assume continuous observation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".