| Objective To provide an effective and practical method for prediction and prevention of abnormal Bone mineral density(BMD)in elderly people,through the investigation of bone health status and its influencing factors,a risk prediction model was established.Methods1.In this Cross-sectional study,Subjects over 40 years old were selected by stratified sampling in several communities in Wuhan and Fuyang City,Hubei Province.2.The Osteoporosis Questionnaire survey,body measurement and BMD testing were conducted in these subjects.Diagnosis of osteoporosis among subjects using the WHO osteoporosis diagnostic criteria.3.Data was entered into the database established by Epidata3.0.SPSS19.0 statistical software was adopted and P<0.05 indicated that difference was statistically significant;enumeration data were expressed by rate(%)and compared using 2 test;measurement data was presented as s and were compared by ANOVA.Logistic regression was used in multivariate analysis.According to the regression coefficient and odds ratio of the influencing factors,the corresponding scores were established.The risk prediction model for the high-risk population of elderly people in Hubei Province was initially established.4.The screening value of this risk prediction model was Evaluated by receiver operating characteristic(ROC)curve and Hosmer-lemeshow goodness-of-fit test and the cut-off point of the model was determined by the Yoden index.10% of the subjects were randomly selected for testing and evaluating efficacy of the model.Results1.A total of 614 subjects were included in the study.The prevalence of osteoporosis,osteopenia,and normal BMD were 38.76%,35.67%,and 25.57%,respectively.2.According to the univariate analysis,age,BMI,awareness of osteoporosis and osteoporotic fracture,education,passive smoking,drinking,tea drinking,carbonated drinks,fractures history,taking calcium tablets and vitamin D,and menopause were significantly different(P<0.05);according to the multivariate analysis,age,BMI,education,and fractures history were significantly different for the elderly(P<0.05);BMI and fracture history were significantly different for elderly male(P<0.05);Age,fracture history and menopause were significantly different for elderly female(P<0.05).3.The..risk..prediction..model..was:Logit P=1.794+0.062X1+-0.163X2+1.176X4+0.265X51-0.571X52,with a sensitivity of 63.50% and a specificity of 79.60% in the elderly(AUC=0.782,95% CI:0.742-0.822);Logit P=6.286-0.260X2+1.037X4,with a sensitivity of 54.80% and a specificity of 89.60% in elderly male(AUC=0.754,95%CI:0.673-0.834);Logit P=-1.254+0.039X1+1.557X3+1.109X4,with a sensitivity of 81.30% and a specificity of 65.10% in elderly female(AUC=0.806,95%CI: 0.758-0.854).4.10% of subjects(62 elderly,15 males,46 females)randomly extracted by SPSS19.0were used to validate these models and the accuracy of models was calculated by cut-off values.Risk prediction models for the elderly,men,and women were established,and the sensitivity and specificity were 88.89% and 58.82%,81.82% and50.00%,100.00% and 41.67%,respectively.Conclusion1.The elderly were at high risk of osteoporosis.Age,BMI,education,fracture history,and menopause were related to the prevalence of abnormal BMD.Among them,BMI,education(college degree and above)were independent protection factors;age,education(secondary school and below),fracture history,and menopause were independent risk factors2.The risk prediction models established in this study were simple and effective clinical prediction tools,theses could be used for preliminary screening of osteoporosis in elderly people in Hubei Province. |
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