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Educational level and osteoporosis risk in postmenopausal Moroccan women: a classification tree analysis

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Abstract

The objectives of this study are (1) to evaluate whether the prevalence of osteoporosis and peripheral fractures might be influenced by the educational level and (2) to develop a simple algorithm using a tree-based approach with education level and other easily collected clinical data that allow clinicians to classify women into varying levels of osteoporosis risk. A total number of 356 women with a mean age of 58.9 ± 7.7 years were included in this study. Patients were separated into four groups according to school educational level; group 1, no education (n = 98 patients); group 2, elementary level (n = 57 patients); group 3, secondary level (n = 138 patients) and group 4, university level (n = 66 patients). We observed dose–response linear relations between educational level and mean bone mineral density (BMD). The mean BMDs of education group 1 (10.39% (lumbar spine), 10.8% (trochanter), 16.8% (wrist), and 8.8% (femoral neck)) were lower compared with those of group IV (p < 0.05). Twelve percent of patient had peripheral fractures. The prevalence of peripheral fractures increased with lowered educational levels. Logistic regression analysis revealed a significant independent increase in the risk of peripheral fracture in patients with no formal education (odds ratio, 5.68; 95% , 1.16–27.64) after adjustment for age, BMI and spine BMD. Using the classification tree, four predictors were identified as the most important determinant for osteoporosis risk: the level of education, physical activity, age >62 years and BMI <30 kg/m2. This algorithm correctly classified 74% of the women with osteoporosis. Based on the area under the receiver–operator characteristic curves, the accuracy of the Classification and Regression Tree (CART) model was 0.79. Our findings suggested that a lower level of education was associated with significantly lower BMDs at the lumbar spine and the hip sites, and with higher prevalence of osteoporosis at these sites in a dose–response manner, even after controlling for the strong confounders. On the other hand, our CART algorithm based on four clinical variables may help to estimate the risk of osteoporosis in a health care system with limited resources.

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Acknowledgments

This work was supported by grants from the University Mohammed V, Souissi, Rabat-Morocco.

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Authors and Affiliations

  1. Department of Rheumatology, El Ayachi University Hospital, Rabat-Salé, Morocco

    Fadoua Allali, Samira Rostom, Loubna Bennani & Najia Hajjaj-Hassouni

  2. Laboratory of Biostatistical, Clinical and Epidemiological Research, Faculty of Medicine and Pharmacy, Rabat, Morocco

    Fadoua Allali, Redouane Abouqal & Najia Hajjaj-Hassouni

Authors
  1. Fadoua Allali
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  2. Samira Rostom
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  3. Loubna Bennani
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  4. Redouane Abouqal
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  5. Najia Hajjaj-Hassouni
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Correspondence to Samira Rostom.

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Allali, F., Rostom, S., Bennani, L. et al. Educational level and osteoporosis risk in postmenopausal Moroccan women: a classification tree analysis. Clin Rheumatol 29, 1269–1275 (2010). https://doi.org/10.1007/s10067-010-1535-y

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  • DOI: https://doi.org/10.1007/s10067-010-1535-y

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