Enrollment or dropout: dynamics of social health insurance participation among Chinese children and their impact on health service utilization and medical expenses
Bibliographic record
Abstract
Since children's participation in social health insurance (SHI) in China is voluntary, fluctuations in enrollment or dropout are inevitable. Using data from the two waves of the China Family Panel Study in 2020 and 2022, this study aims to examine these participation dynamics and their impact on children's health service utilization and medical expenses. Specifically, a balanced panel of 1958 children under the age of 15 was constructed, first-difference and difference-in-difference models were employed to assess the factors influencing children's SHI enrollment or dropout, as well as the impact of these changes on health service utilization and medical expenses. Robustness checks were conducted after excluding new enrollees and dropouts separately. Our analysis showed that between 2020 and 2022, 263 children (13.4%) were newly enrolled in SHI, while 135 (6.9%) dropped out. Maternal SHI enrollment increased the likelihood of children's enrollment and reduced the probability of dropout. Children with commercial insurance were 34% less likely to enroll and 58% more likely to dropout. Compared to children with unchanged participation status, newly enrolled children were about 8% more likely to use outpatient services and had 77% higher medical expenses in the past year, whereas no significant changes were observed among those who dropped out. These findings highlight the dynamic nature of children's SHI participation in China and suggest that passive enrollment policies and parental participation could help promote universal coverage. Improving the reimbursement system, particularly for children's outpatient care, is also recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".