Statistical analysis
COPD
REGRESSION, COPD
Continuous variables were described by mean [standard deviation (SD)] or median (lower quartile, upper quartile) and were compared by The 1:1 propensity score matching (PSM), with the caliper of 0.02 and in the nearest method, was implemented to balance the populations' characteristics between low and high altitudes. Subsequently, logistic regression models estimated odd ratios (Ors) and 95% CI to evaluate the association between high altitude and COPD risk (Furthermore, the participants were randomly divided into the training and validation sets at the ratio of 6:4. In the training set, the univariable and stepwise logistic regression models were performed to screen the related factors to construct a nomogram for COPD (GOLD criterion). The nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), which was reassessed in the validation set for internal validation.Statistical analysis was performed by SPSS 26.0 and R 4.13. The result was considered statistically significant only when the two-sided 