Cancers Attributable to Obesity in Indonesia: A Prevalence Based Study Using Disability Adjusted Life Years
Bibliographic record
Abstract
The prevalence of obesity in the world has nearly tripled since 1975. Obesity clearly known as risk factor for various diseases, including many types of cancer. This study aims to determine the obesity attributable fraction (OAF) of seven cancers based on the relative risk of esophageal cancer, colorectal, pancreatic, endometrial, ovarian, prostate and kidney cancer and also to estimate the burden of cancer caused by obesity with disability adjusted life years (DALY) indicator. This study is a descriptive epidemiological study with prevalence-based method, which the prevalence data obtained from Indonesian National Health Insurance (BPJS) 2016. The OAF is calculated by combining both data of obesity prevalence and relative risk and the DALY indicator is calculated as the sum of years of life lost due to premature mortality (YLL) and the equivalent healthy years lost due to disability (YLD). Based on OAF calculation, three highest proportions OAF in men were in colorectal cancer (6.02%), kidney (4.91%) and pancreatic cancer (4.55%), while in women were from kidney cancer (13.92%), endometrial (12.63%) and colorectal cancer (7.49%). Meanwhile the burden priorities of cancer by obesity in Indonesia were come from colon cancer (23,051), ovarian cancer (21.911), and pancreatic cancer (4,564). Burden of cancer attributable to obesity in Indonesia mostly related to digestive organ and high prevalence in female population. It is the impact of life changes and less activity due to globalization. All cancers attributable to obesity should be considered and have to controlled by the government through health programs and policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".