Global, regional and national burden of low back pain 1990–2019: A systematic analysis of the Global Burden of Disease study 2019
Bibliographic record
Abstract
BACKGROUND: To comprehensively analyze the global level and trends of prevalence, incidence and years lived with disability (YLDs) for low back pain (LBP) from 1990 to 2019 by age, sex and sociodemographic index (SDI). METHODS: Publicly available modelled data and methods were obtained from the Global Burden of Diseases (GBD) study 2019, and used to evaluate the global burden of LBP through a systematic analysis. RESULTS: Globally, the age-standardized prevalence, incidence and YLDs rate of LBP were slightly decreased from 1990 to 2019, but the number of the prevalent cases, incident cases and YLDs had substantially increased, and LBP remains the leading cause of YLDs in 2019 worldwide. The number of prevalent cases was increased with age and peaked at the age of 45-54 years for both sexes, and the global prevalence rate was higher in females than in males and increased with age, peaking at the 80-84 age group in both sexes in 2019. Overall, a positive association between the age-standardized YLD rate and SDI was observed over the past thirty years. At the national revel, the United States, Denmark and Switzerland had the three highest levels of age-standardized prevalence, while Zambia, Zimbabwe and Canada showed the highest increase in the age-standardized prevalence during 1990-2019. CONCLUSIONS: LBP is a major public health issue globally, and its burden remains high. Increasing population awareness about its risk factors and preventive measures for LBP are needed to reduce the future burden of this condition. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: Due to the high prevalence and heavy burden of LBP globally, it is important to update its epidemiological data. This systematic analysis provides researchers and healthcare policy makers with up-to-date, comprehensive and comparable information on global LBP burden, which is of clinical translational significance.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".