PM2.5 Impact on Catastrophic Health Expenditure in China, 24,004 Participants

A 2026 study by Huan He investigates the relationship between PM2.5 air pollution and catastrophic health expenditure among middle-aged and older adults in China. The dataset contains analysis results from four waves of the China Health and Retirement Longitudinal Study, matched with PM2.5 and meteorological data, covering 24,004 participants aged 45 and above. It employs random effects logit models and instrumental variable estimation to examine the effect of annual average PM2.5 concentration.

Use Cases

• Modeling the relationship between PM2.5 exposure and catastrophic health expenditure based on the described random effects logit model.
• Analyzing the mitigating effect of social health insurance types on pollution-related financial risk based on the interaction effects tested.
• Investigating subgroup heterogeneity in susceptibility to PM2.5 based on urban/rural location, age, education, and chronic conditions mentioned in the results.
• Examining the mechanisms linking air pollution to financial burden, such as increased disease costs and diminished labor supply, as outlined in the study.

Strengths

• Dataset is based on a large longitudinal study with 24,004 participants.
• Analysis integrates health survey data with external PM2.5 and meteorological data.
• Employs advanced econometric methods including instrumental variable estimation to address endogeneity.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size suggests the dataset likely contains summary or model results, not the underlying raw panel data.

Provenance

Source

Huan He, via figshare.

Collection Method

Analysis of four waves of the China Health and Retirement Longitudinal Study (CHARLS) matched with external air pollution and weather data.

Time Range

Temporal coverage of the underlying CHARLS waves is not specified.

Freshness

Last updated 2026-04-21 17:48:47; freshness should be verified.

Geography

China.