Pre-COVID life expectancy, mortality, and burden of diseases for adults 70 years and older in Australia: a systematic analysis for the Global Burden of Disease 2019 Study
Bibliographic record
Abstract
Background: The Australian population aged 70 and above is increasing and imposing new challenges for policy makers and providers to deliver accessible, appropriate and affordable health care. We examine pre-COVID patterns of health loss between 1990 and 2019 to inform policies and practices. Methods: Using the standardised methodology framework and analytical strategies from GBD 2019 methodologies, we estimated mortality, causes of death, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), life expectancy at age 70 and above (LE-70), and healthy life expectancy (HALE-70) in Australia comparing them globally and with high socio-demographic index (SDI) groups. Findings: DALY rates have been improving steadily over the past 30 years among Australians aged 70 and above. Decreases in DALY rates were primarily attributed to a fall in YLLs attributable to cardiovascular diseases (60%) and chronic respiratory disorders (30.2%) and transport injuries (56.9%), while the non-fatal burden remained stable from 1990 to 2019. According to the DALY rates, the top five leading causes are ischemic heart disease, Alzheimer's disease, COPD, stroke, and falls, where falls exhibited the largest increase since 1990. Interpretation: This study provides an in-depth report on the main causes of mortality and disability in Australia's population aged 70 and above. It sheds light on the shifts in burden over three decades, emphasising the need for the Australian health system to enhance its readiness in addressing the escalating demands of an ageing population. These findings establish pre-COVID baseline estimates for Australia's population aged 70 and above, informing healthcare preparedness. Funding: Bill & Melinda Gates Foundation.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".