| id |
9319235d-4dff-4826-baec-54bcbed850b6 |
| user_id |
8684964a-bab1-4235-93a8-5fd5e24a1d0a |
| job_id |
sxocebzh-1504 |
| base_model_name |
xevyo |
| base_model_path |
/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf... |
| model_name |
increasing longevity |
| model_desc |
The Effects of increasing longevity |
| model_path |
/home/sid/tuning/finetune/backend/output/sxocebzh- /home/sid/tuning/finetune/backend/output/sxocebzh-1504/merged_fp16_hf... |
| source_model_name |
xevyo |
| source_model_path |
/home/sid/tuning/finetune/backend/output/xevyo-bas /home/sid/tuning/finetune/backend/output/xevyo-base-v1/merged_fp16_hf... |
| source_job_id |
xevyo-base-v1 |
| dataset_desc |
This research article introduces a new demographic This research article introduces a new demographic method to understand why lifetime risk of disease sometimes increases even when disease incidence is falling. The authors show that as people live longer, more of them survive into the ages where diseases typically occur. This can make the lifetime probability of developing a disease rise, even if age-specific incidence rates are decreasing. The paper proposes a decomposition technique that separates the influence of incidence changes from survival (longevity) changes, allowing researchers to determine what truly drives shifts in lifetime disease risk.
Using Swedish registry data, the authors apply their method to three conditions in men aged 60+:
Myocardial infarction (heart attack)
Hip fracture
Colorectal cancer
The analysis reveals how increasing longevity can hide improvements in disease prevention by pulling more people into higher-risk age ranges.
⭐ MAIN FINDINGS
⭐ 1. Lifetime risk is affected by two forces
The authors show that changes in lifetime disease risk come from:
Changing incidence (how many people get the disease at each age)
Changing survival (how many people live long enough to be at risk)
Their method cleanly separates these effects, which had previously been difficult to isolate.
⭐ 2. Longevity increases can mask declining incidence
For diseases that occur mainly at older ages, longer life expectancy creates a larger pool of people who reach the risky ages.
Examples from the study:
✔ Myocardial infarction (heart attack)
Incidence fell over time
But increased longevity created more survivors at risk
Net result: lifetime risk barely changed
Longevity canceled out the improvements.
✔ Hip fracture
Incidence declined
But longevity increased even more
Net result: lifetime risk increased
Sweden’s aging population drove hip-fracture risk upward despite fewer fractures per age group.
✔ Colorectal cancer
Incidence increased
Longevity had only a small effect (because colorectal cancer occurs earlier in life)
Net result: lifetime risk rose noticeably
Earlier age of onset means longevity plays a smaller role.
⭐ 3. Timing of disease matters
The effect of longevity depends on when a disease tends to occur:
Diseases of older ages (heart attack, hip fracture) are highly influenced by longevity increases.
Diseases that occur earlier (colorectal cancer) are less affected.
This explains why trends in lifetime risk can be misleading without decomposition.
⭐ 4. The method improves accuracy and clarity
The decomposition technique:
prevents false interpretations of rising or falling lifetime risk
quantifies exactly how much of the change is due to survival vs. incidence
avoids reliance on arbitrary standard populations
helps in forecasting healthcare needs
makes cross-country or cross-period comparisons more meaningful
⭐ OVERALL CONCLUSION
The paper concludes that lifetime risk statistics can be distorted by population aging. As life expectancy rises, more people survive to ages when diseases are more common, which can inflate lifetime risk even if actual incidence is improving. The authors’ decomposition method provides a powerful tool to uncover the true drivers behind lifetime risk changes separating improvements in disease prevention from demographic shifts.
This insight is crucial for public health planning, research, and interpreting long-term disease trends in ageing societies.... |
| dataset_meta |
{"num_examples": 55, "bad_lines": {"num_examples": 55, "bad_lines": 0}... |
| dataset_path |
/home/sid/tuning/finetune/backend/output/sxocebzh- /home/sid/tuning/finetune/backend/output/sxocebzh-1504/data/sxocebzh-1504.json... |
| training_output |
null |
| status |
completed |
| created_at |
1764446411 |
| updated_at |
1764446862 |
| source_adapter_path |
NULL |
| adapter_path |
/home/sid/tuning/finetune/backend/output/sxocebzh- /home/sid/tuning/finetune/backend/output/sxocebzh-1504/adapter... |
| plugged_in |
False |