title: Educational level and osteoporosis risk in postmenopausal Moroccan women: a classification tree analysis.
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.
authors.affiliation: Department of Rheumatology, El Ayachi University Hospital, Rabat-Salé, Morocco.
authors.forename: Fadoua
authors.initials: F
authors.lastname: Allali
authors.forename: Samira
authors.initials: S
authors.lastname: Rostom
authors.forename: Loubna
authors.initials: L
authors.lastname: Bennani
authors.forename: Redouane
authors.initials: R
authors.lastname: Abouqal
authors.forename: Najia
authors.initials: N
authors.lastname: Hajjaj-Hassouni
mesh_terms: D000368:Aged; D000886:Anthropometry / Q000379:methods; D015519:Bone Density; D002118:Calcium / Q000378:metabolism; D004522:Educational Status; D005260:Female; D006801:Humans; D008875:Middle Aged; D009018:Morocco; D015999:Multivariate Analysis; D015663:Osteoporosis, Postmenopausal / Q000175:diagnosis / Q000453:epidemiology*; D017698:Postmenopause; D012306:Risk; D011795:Surveys and Questionnaires
chemical_list: D002118:Calcium