Global and regional estimates of clinical and economic burden of low back pain in high-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Low back pain (LBP) is a common health problem, and the leading cause of activity limitation and work absence among people of all ages and socioeconomic strata. This study aimed to analyse the clinical and economic burden of LBP in high income countries (HICs) via systematic review and meta-analysis. Methods: A literature search was carried out on PubMed, Medline, CINAHL, PsycINFO, AMED, and Scopus databases was from inception to March 15th, 2023. Studies that assessed the clinical and economic burden of LBP in HICs and published in English language were reviewed. The methodological quality of the included studies was assessed using the Newcastle-Ottawa quality assessment scale (NOS) for cohort studies. Two reviewers, using a predefined data extraction form, independently extracted data. Meta-analyses were conducted for clinical and economic outcomes. Results: = 4). The average annual direct and indirect costs estimate per population for LBP ranged from € 2.3 billion to € 2.6 billion; and € 0.24 billion to $8.15 billion, respectively. In the random effects meta-analysis, the pooled annual rate of hospitalization for LBP was 3.2% (95% confidence interval 0.6%-5.7%). The pooled direct costs and total costs of LBP per patients were USD 9,231 (95% confidence interval -7,126.71-25,588.9) and USD 10,143.1 (95% confidence interval 6,083.59-14,202.6), respectively. Discussion: Low back pain led to high clinical and economic burden in HICs that varied significantly across the geographical contexts. The results of our analysis can be used by clinicians, and policymakers to better allocate resources for prevention and management strategies for LBP to improve health outcomes and reduce the substantial burden associated with the condition. Systematic review registration: https://www.crd.york.ac.uk/prospero/#recordDetails?, PROSPERO [CRD42020196335].
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.055 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".