Background

Many internal migrants during the urbanization process in China are Migrant Parents, the aging group who move to urban areas to support their family involuntarily. They are more vulnerable economically and physically than the younger migrants. However, the fragmentation of rural and urban health insurance schemes divided by "hukou" household registration system limit migrant's access to healthcare services in their resident location. Some counties have started to consolidate the Urban Resident Basic Medical Insurance (URBMI) and the New Rural Cooperative Medical Scheme (NRCMS) as one Integrated Medical Insurance Schemes (IMIS) from 2008. The consolidation aimed to reduce the disparity between different schemes and increase the health care utilization of migrants.

Results

Using the inpatient sample of migrant parents from China Migrants Dynamic Survey in 2015, we used Ordinary Least Squares (OLS) for regression models. We found that the migrant parents covered by the IMIS are more likely to choose inpatient services and seek medical treatment in the migrant destination. We further subdivide Non-IMISs into NCMSs and URBMIs in the regression to alleviate the doubt about endogenous. The results revealed that the migrant parents in IMIS use more local medical services than both of them in URBMI and NCMS.

Conclusions

The potential mechanisms of our results could be that IMIS alleviates the difficulty of seeking medical care in migrant destinations by improving the convenience of medical expense reimbursement and enhancing health insurance benefits.

INTRODUCTION: Childhood circumstances impact senior health, prompting the introduction of machine learning methods to assess their individual and collective contributions to senior health. METHODS: Using health and retirement study (HRS) and China Health and Retirement Longitudinal Study (CHARLS), we analyzed 2,434 American and 5,612 Chinese participants aged 60 and above. Conditional inference trees and forests were employed to estimate the influence of childhood circumstances on self-rated health (SRH). RESULTS: The conventional method estimated higher inequality of opportunity (IOP) values in both China (0.039, accounting for 22.67% of the total Gini coefficient 0.172) and the US (0.067, accounting for 35.08% of the total Gini coefficient 0.191). In contrast, the conditional inference tree yielded lower estimates (China: 0.022, accounting for 12.79% of 0.172; US: 0.044, accounting for 23.04% of 0.191), as did the forest (China: 0.035, accounting for 20.35% of 0.172; US: 0.054, accounting for 28.27% of 0.191). Childhood health, financial status, and regional differences were key determinants of senior health. The conditional inference forest consistently outperformed others in predictive accuracy, as demonstrated by lower out-of-sample mean squared error (MSE). DISCUSSION: The findings emphasize the need for early-life interventions to promote health equity in aging populations. Machine learning showcases the potential in identifying contributing factors.

OBJECTIVE: Schools play an essential role in providing mental health care for adolescents. School closures during COVID-19, as well as re-opening to remote-only instruction in Fall 2020, may indirectly affect the utilization of emergency psychiatric care. We examine COVID-19-related changes in emergency psychiatric care among youth during the school closure and after school reopening (with remote instruction). METHODS: We use Box-Jenkins interrupted time series methods to analyze psychiatric emergency department (ED) visits among patients 10-19 years at LAC + USC Medical Center (LAC + USC) between January 5th, 2018, and December 31st, 2020. We control for the 1st societal shutdown in LA County (i.e., the nine weeks from March 13 to May 14, 2020) when analyzing the potential return to remote school shock. RESULTS: Youth psychiatric ED visits fell by 15.3 per week during the Spring 2020 school closure (p < .05). The return to remote school coefficient (i.e., August 14th to September 10, 2020), by contrast, is positive but does not reach statistical detection above expected values (p = .11). However, the proportion of psychiatric ED visits rises 38% among youth during the return to remote school period (p = 0.006). CONCLUSION: The initiation of Fall 2020 remote instruction corresponded with a greater proportion of youth ED visits that are classified as psychiatric.

Primary care providers (PCPs) are increasingly called upon to screen for and treat depression. However, PCPs often lack the training to diagnose and treat depression. We designed an innovative 12-month evidence and mentorship-based primary care psychiatric training program entitled the University of California, Irvine (UCI) School of Medicine Train New Trainers Primary Care Psychiatry (TNT PCP) Fellowship and examined whether this training impacted clinician prescription rates for antidepressants. We retrieved information on 18,844 patients and 192 PCPs from a publicly insured health program in Southern California receiving care between 2017 and 2021. Of the 192 PCPs, 42 received TNT training and 150 did not. We considered a patient as exposed to the providers TNT treatment if they received care from a provider after the provider completed the 1-year fellowship. We utilized the number of antidepressant prescriptions per patient, per quarter-year as the dependent variable. Linear regression models controlled for provider characteristics and time trends. Robustness checks included clustering patients by provider identification. After PCPs completed TNT training, exposed patients received 0.154 more antidepressant prescriptions per quarter-year relative to expected levels (p < 0.01). Clustering of standard errors by provider characteristics reduced precision of the estimate (p < 0.10) but the direction and magnitude of the results were unchanged. Early results from the UCI TNT PCP Fellowship demonstrate enhanced antidepressant prescription behavior in PCPs who have undergone TNT training. A novel, and relatively low-cost, clinician training program holds the potential to empower PCPs to optimally deliver depression treatment.