Focus on the impact of social factors and lifestyle on the disease burden of low back pain: findings from the global burden of disease study 2019
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the leading causes of disability worldwide. Differences in social backgrounds and lifestyles in various regions and countries may contribute to the discrepancies in the disease burden of LBP. METHODS: Based on the GBD 2019, we collected and analyzed numbers and age-standardized rates (ASR) of LBP disability-adjusted life years (DALYs). Temporal trends in ASR were also analyzed using estimated annual percentage change (EAPC). The Age-period-cohort (APC) model was used to estimate age, period and cohort trends in DALYs of LBP. An autoregressive integrated moving average (ARIMA) model was used to forecast DALYs of LBP trends from 2020 to 2035. RESULTS: The DALYs due to LBP increased from 1990 to 2019. The APC model showed that the risk of DALYs for global LBP increased with age and year and that the risk of DALYs was lower in the later-born cohort than in the earlier-born cohort. The main risk factors which GBD estimates were available for DALYs of LBP include smoking, occupational ergonomic factors and high BMI. It is expected that DALYs of LBP will continue to rise until 2035. CONCLUSION: From 1990 to 2019, the global disease burden of LBP remained high. It is necessary to pay attention to the influence of social factors and lifestyle on LBP. Focusing on the impact of social factors as well as lifestyle on the prognosis of LBP and targeting interventions may further reduce the disease burden of LBP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".