The prevalence of low bone mineral density in women aged 55 years or older and the association with socioeconomic factors across the Globe
Bibliographic record
Abstract
BACKGROUND: Previous epidemiological studies have reported significant regional variations in the global prevalence of osteoporosis. However, these variations cannot be fully explained by known risk factors. AIM: This study aims to explore the regional variations in the global prevalence of low bone mineral density (LBMD) among women aged 55 years or older and its association of socioeconomic factors. METHODS: We used data from the 2019 Global Burden of Disease (GBD 2019) to highlight the regional differences in the prevalence of LBMD among women aged 55 or older worldwide. We then examined the correlations between LBMD in this demographic and four socioeconomic factors: GDP, urbanization ratio, prevalence of undernourishment (sourced from the World Bank), and current health expenditure (CHE) (obtained from the World Health Organization). To investigate the relationships between LBMD in women aged 55 and older and the urbanization ratio, prevalence of undernourishment, and CHE, we utilized linear mixed models. RESULTS: The age-standardized summary exposure value (ASSEV) of LBMD in women aged 55 or older was highest in Western Sub-Saharan Africa (42.88, 95% UI, 33.43-53.04 in 1990 and 39.68, 95% UI, 30.42-49.66 in 2019), followed by Eastern Sub-Saharan Africa, Central Sub-Saharan Africa, and Southeast Asia. The lowest ASSEV was found in Central Asia (20.21, 95% UI, 13.74-27.39 in 1990 and 18.14, 95% UI, 12.03-25.67 in 2019), followed by Western Europe. The ASSEV of LBMD in women aged 55 or older was negatively correlated with CHE (β =-2.39, P < 0.001) and positively correlated with the prevalence of undernourishment (β = 1.76, P < 0.001). No significant correlation was found between the ASSEV of LBMD in women aged 55 or older and the urbanization ratio. CONCLUSIONS: Socioeconomic factors have close relationship with LBMD. The imbalances of socioeconomic development might be the reason for variations of LBMD in women aged 55 or older worldwide. Reduction of undernourishment and enhancement of health expenditure might contribute to preventing LBMD. A limited increase in health investment could greatly decrease the prevalence of LBMD, especially in regions with low health expenditure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".