Using Nomogram to Predict the Hospitalization Forgone Among Internal Migrants in China: A Nationally Representative Cross-Sectional Secondary Data Analysis

BACKGROUND: Migrants are one of the most vulnerable populations facing many health issues. Inadequate health care access and unequal insurance are the most challenging. This study aimed to construct a nomogram to predict the risk of hospitalization forgone among internal migrants in China. METHODS: We analyzed the 2014 Mobile Population Social Integration and Mental Health Survey (MPSIMHS) launched by National Health and Family Planning Commission. Using the Probability Proportionate to Size Sampling method (PPS), MPSIMHS sampled from eight cities (districts) with a total sample size of 15,999. Of total 589 patients who were diagnosed with hospitalization requirement, 116 forwent their hospitalization, 473 had no forgone. Demographics, socioeconomic status, and health conditions were analyzed using univariate analysis and multivariate logistic regression. A nomogram was built and validated by applying bootstrap resampling. RESULTS: After model selection, gender, age group, marital status, migration range, insurance (having NRMI), and self-evaluated health were chosen into the nomogram to predict the risk of hospitalization forgone. The nomogram that predicted the risk of hospitalization forgone was validated for discrimination and calibration using bootstrap resampling. The calibration curves illustrated optimal agreement between the actual and predicted probabilities of the nomogram. The value of C-index from bootstrap was 0.80 (95% CI: 0.76–0.85). CONCLUSION: This study identified some possible factors contributing to migrant’s hospitalization forgone: being single, male and middle-aged, having fixed health insurance, and having bad or great self-evaluated health. By integrating significant and easy-to-get prognostic factors, a nomogram was developed to estimate an individual patient’s risk of hospitalization forgone, which might have practical utility and the potential to assist clinicians in making hospitalization recommendations.