Effects of the Implementation of Transport-Driven Poverty Alleviation Policy on Health Care–Seeking Behavior and Medical Expenditure Among Older People in Rural Areas: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Improving the rural residents' accessibility to and affordability of health care is recognized as a common target globally. The Health in All Policies approach, from the Declaration of Helsinki to the United Nations' Decade Of Healthy Ageing, strengthened the far-reaching effect of large-scale public policies on health care-seeking behavior; however, the effects of national transport policy on health care-seeking behavior is unclear. OBJECTIVE: This quasi-experimental study aimed to examine the effects of the implementation of transport-driven poverty alleviation (TPA) policy on health care-seeking behavior and medical expenditure among older adults in rural areas and the mechanism underlying these effects. METHODS: We designed a quasi-experiment to estimate the effects of TPA policy implementation on health care-seeking behavior and medical expenditure among older adults in rural areas through a difference-in-differences (DID) analysis based on data from the China Health and Retirement Longitudinal Study in 2011, 2013, 2015, and 2018. The underlying mechanism was analyzed and effect modification patterns were further investigated by poor households, health status, and age. RESULTS: Our findings validated a positive contribution of TPA policy on health care-seeking behavior among older adults in rural areas. After the implementation of TPA policy, the number of inpatient visits increased by annually 0.35 times per person, outpatient medical expenditure increased by 192% per month, and inpatient medical expenditure increased by 57% annually compared with those of older adults in rural areas without the implementation of TPA policy. Further, there was a significant modification effect, with a positive effect among poor households, healthier older adults, and those aged 60-80 years. Additionally, the policy improved the patients' capabilities to seek long-distance care (β=23.16, 95% CI -0.99 to 45.31) and high-level hospitals (β=.08, 95% CI -0.02 to 0.13), and increased individual income to acquire more medical services (β=4.57, 95% CI -4.46 to 4.68). CONCLUSIONS: These findings validate the positive contribution of TPA policy on health care-seeking behavior among older adults in rural areas; however, the medical expenditure incurred was also high. Concerted efforts are needed to address health care-seeking dilemmas in rural areas, and attention must be paid to curbing medical expenditure growth for older adults in rural areas during TPA policy implementation.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".