Barriers to Access to Treatment for Hypertensive Patients in Primary Health Care of Less Developed Northwest China: A Predictive Nomogram

Figures

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  Figure 1. The flow chart of inclusion and screening of surveyed subjects.

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  Figure 2. Demographic features, socioeconomic status, live setting, health-related behaviors, and anthropometric value selection using the LASSO binary logistic regression model. (a) Optimal parameter (lambda) selection in the LASSO model used fivefold cross-validation via minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted versus log (lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1-SE of the minimum criteria (the 1-SE criteria). (b) LASSO coefficient profiles of the 17 features. A coefficient profile plot was produced against the log (lambda) sequence. Vertical line was drawn at the value selected using fivefold cross-validation, where optimal lambda resulted in five features with nonzero coefficients. LASSO: least absolute shrinkage and selection operator; SE: standard error.

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  Figure 3. Developed medication nontreatment nomogram.

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  Figure 4. Receiver operating characteristic curve of the untreated nomogram prediction in the study.

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  Figure 5. Calibration curves of the nomogram in the training set (a) and validation set (b). The x-axis represents the predicted untreated risk. The y-axis represents the actual identified untreated patients. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.

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  Figure 6. Decision curve analysis for the untreated nomogram. The y-axis measures the net benefit. The red line represents the medication untreated risk nomogram. The thin solid line represents the assumption that all patients are nontreatment to medication. Thin thick solid line represents the assumption that no patients are nontreatment to medication. The decision curve showed that if the threshold probability of a patient ranges from 7% to 91%, using this untreated nomogram in the current study to predict medication nontreatment risk adds more benefit than the intervention-all-patients scheme or the intervention-none scheme.