Low back pain trends attributable to high body mass index over the period 1990–2021 and projections up to 2036
Bibliographic record
Abstract
Background: High body mass index (BMI) is a crucial determinant in low back pain (LBP) incidence and progression. However, the effect of increased BMI on LBP has been largely overlooked at the global, regional, and national levels. This research aimed to use data from the 2021 global burden of disease (GBD) study to determine trends associated with LBP due to high BMI from 1990 to 2021, thereby providing evidence for developing targeted policies. Methods: Epidemiological data on the association between high BMI and LBP is obtained from the GBD 2021. Disability-adjusted life-years (DALYs) attributable to high BMI-related LBP are stratified by year, age, country, and socio-demographic index (SDI). The estimated annual percentage change (EAPC) was calculated to evaluate the trends from 1990 to 2021. A Bayesian age-period cohort (BAPC) model was used to assess the corresponding trends from 2022 to 2036. Additionally, statistical models, such as decomposition analysis and frontier analysis, were used. Results: According to the GBD 2021, the number of DALYs caused by LBP attributed to high BMI reached 8,363,759 in 2021, which is an increase of 170.97% since 1990. The age-standardized rate of disability-adjusted life years (ASDR) for LBP caused by high BMI has been increasing from 1990 to 2021, with an EAPC of 1.14%. Among the five SDI regions, ASDR has increased. High-income North Americans exhibited the highest risk of LBP caused by high BMI, with Hungary being the most affected. Frontier analysis highlights the urgent need for intervention in countries such as the Netherlands, Germany, and Canada. Finally, the burden of LBP related to high BMI will continue to rise from 2022 to 2036. Conclusion: Between 1990 and 2021, there was a global increase in lower back pain due to high BMI, with a projected continuation of this trend. Monitoring BMI is crucial for developing region-specific and national strategies, and research emphasizes the urgency of reducing the health burden of high BMI and improving the quality of life for the global population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".