Life expectancy and geographic variation in mortality: an observational comparison study of six high-income Anglophone countries
Bibliographic record
Abstract
OBJECTIVE: To compare life expectancy levels and within-country geographic variation in life expectancy across six high-income Anglophone countries between 1990 and 2018. DESIGN: Demographic analysis using aggregated mortality data. SETTING: Six high-income Anglophone countries (USA, UK, Canada, Australia, Ireland and New Zealand), by sex, including an analysis of subnational geographic inequality in mortality within each country. POPULATION: Data come from the Human Mortality Database, the WHO Mortality Database and the vital statistics agencies of six high-income Anglophone countries. MAIN OUTCOME MEASURES: Life expectancy at birth and age 65; age and cause of death contributions to life expectancy differences between countries; index of dissimilarity for within-country geographic variation in mortality. RESULTS: Among six high-income Anglophone countries, Australia is the clear best performer in life expectancy at birth, leading its peer countries by 1.26-3.95 years for women and by 0.97-4.88 years for men in 2018. While Australians experience lower mortality across the age range, most of their life expectancy advantage accrues between ages 45 and 84. Australia performs particularly well in terms of mortality from external causes (including drug- and alcohol-related deaths), screenable/treatable cancers, cardiovascular disease and influenza/pneumonia and other respiratory diseases compared with other countries. Considering life expectancy differences across geographic regions within each country, Australia tends to experience the lowest levels of inequality, while Ireland, New Zealand and the USA tend to experience the highest levels. CONCLUSIONS: Australia has achieved the highest life expectancy among Anglophone countries and tends to rank well in international comparisons of life expectancy overall. It serves as a potential model for lower-performing countries to follow to reduce premature mortality and inequalities in life expectancy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".