Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community.

Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category's Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance.

Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy.

Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital. After data cleaning, 4,378 cases and 50 attributes are stored and collected for the data set. Through selecting the most feasible method, the cost parameter of CSHM is adapted to deal with imbalance of the dataset. In the experiment, 3940 samples are used for training and the rest 438 samples for testing. Although the accuracy of positive samples is barely acceptable (62.16%), the results suggest that the vast majority (98.4%) of those predicted positive instances are real positives. To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future.