Abstract
BACKGROUND: Osteoporotic fractures pose a significant public health burden, particularly in resource-constrained settings where diagnostic tools like DXA scans are unavailable. This study aimed to develop and validate a simple, community-based osteoporosis risk scoring system that incorporates demographic, clinical, and radiographic parameters to identify high-risk individuals for early intervention. METHODS: A cross-sectional study was conducted in Karachi, Pakistan, involving 750 participants aged 40 years and above. Data on demographic characteristics, clinical risk factors, and lifestyle habits were collected using a structured questionnaire. Radiographic assessments identified vertebral compression fractures, generalized osteopenia, and trabecular bone loss. Participants were stratified into four risk categories: low, moderate, high, and very high risk. The predictive validity of the scoring system was evaluated using logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: The developed tool classified participants into low (38 %), moderate (32 %), high (20 %), and very high (10 %) risk groups. Fracture incidence ranged from 11.29 % in the low-risk group to 28.23 % in the very high-risk group. The scoring system demonstrated strong predictive accuracy, with a sensitivity of 83 %, specificity of 75 %, and an area under the curve (AUC) of 0.82. Odds ratios for fractures progressively increased with higher risk categories, confirming the model's validity. CONCLUSION: This Muzzammil's osteoporosis risk scoring system is a cost-effective and practical tool for early identification of high-risk individuals in low-resource settings. Its implementation could aid in targeted prevention strategies, reducing osteoporotic fracture incidence and improving public health outcomes. Further validation in diverse populations is recommended to optimize its utility.