Sociodemographic determinants of health inequities in low back pain: a narrative review
Bibliographic record
Abstract
Background: Health equity is defined as the absence of unjust and avoidable disparities in access to healthcare, quality of care, or health outcomes. The World Health Organization (WHO) has developed a conceptual framework that outlines the main causes of health inequalities and how these contribute to health inequities within a population. Despite the WHO implementing health equity policies to ensure accessibility and quality of healthcare services, disparities persist in the management of patients suffering from low back pain (LBP). The objective of this study was to review the existing evidence on the impact of health inequities on the care trajectories and treatments provided to individuals with LBP. Methods: A narrative review was performed, which included a literature search without language and study design restrictions in MEDLINE Ovid database, from January 1, 2000, to May 15, 2023. Search terms included free-text words for the key concepts of "low back pain," "health inequities," "care pathways," and "sociodemographic factors." Results: Studies have revealed a statistically significant association between the prevalence of consultations for LBP and increasing age. Additionally, a significant association between healthcare utilization and gender was found, revealing that women were more likely to seek medical attention for LBP compared to men. Furthermore, notable disparities related to race and ethnicity were identified, more specifically in opioid prescriptions, spinal surgery recommendations, and access to complementary and alternative medical approaches for LBP. A cross-sectional analysis found that non-Hispanic White individuals with chronic LBP were more likely to be prescribed one or more pharmacological treatments. Lower socioeconomic status and level of education, as well as living in lower-income areas were also found to be associated with greater risks of receiving non-guideline concordant care, including opioid and MRI prescriptions, before undergoing any conservative treatments. Conclusion: Persistent inequalities related to sociodemographic determinants significantly influence access to care and care pathways of patients suffering from LBP, underscoring the need for additional measures to achieve equitable health outcomes. Efforts are needed to better understand the needs and expectations of patients suffering from LBP and how their individual characteristics may affect their utilization of healthcare services.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".