The impact of ambient pollutants concentration on hospitalization costs for respiratory diseases

Abstract

Background

Evidence on the direct economic burden of air pollution for respiratory diseases remains limited in the Pearl River Delta region, China. This study quantifies the association between ambient pollutants and respiratory hospitalization costs in Pingshan District, Shenzhen, and compares pollutant-specific economic risks at equivalent Air Quality Index (AQI) levels.

Methods

Daily respiratory hospitalization costs, six criteria pollutants, including Particulate Matter (PM) with a diameter of less than or equal to 10 µm (PM₁₀), Particulate Matter with a diameter of less than or equal to 2.5 µm (PM_2.5), Nitrogen dioxide (NO₂), Ozone (O₃), Sulphur dioxide (SO₂), and Carbon monoxide (CO), and meteorological data (temperature, relative humidity, wind speed) were collected from May 13, 2014 to December 31, 2019. A distributed lag non-linear model (DLNM) integrated with a generalized additive model (GAM) was used to estimate exposure-lag-response associations over 14 days, adjusting for meteorology, long-term trends, and day-of-week effects. Single- and multi-pollutant models were applied to compute cumulative relative risks (RRs) at AQI 50 and 100.

Results

A total of 27,140 hospitalizations (mean 13 ± 5/day; mean per-patient cost ¥3,642 ± 2,448) were analyzed. PM₁₀, PM_2.5, and NO₂ were significantly associated with increased total costs. PM₁₀ and PM_2.5 showed acute effects (lag 0–2 days): cumulative RRs = 1.194 (95%CI:1.043–1.368) and 1.167 (95%CI:1.017–1.338). NO₂ had significant effects at both lag 0–2 days (RR = 1.311, 95%CI:1.039–1.652) and lag 3–7 days (RR = 1.460, 95%CI:1.022–2.086). At identical AQI levels, NO₂ caused the highest cost burden: at AQI 50, cost increases were 19.4% (PM₁₀), 16.7% (PM₂.₅), and 46.0% (NO₂); at AQI 100, they rose to 70.3%, 39.1%, and 113.2%. Multi-pollutant models expanded NO₂’s cumulative effect to 75.7%–93.3%. O₃ showed no significant association. No pollutant significantly affected per-patient costs.

Conclusion

PM₁₀, PM_2.5, and NO₂ drive respiratory hospitalization costs in Shenzhen, with NO₂ posing the highest unit economic risk per AQI increment. Policies should prioritize reducing traffic-related NO₂ emissions, and healthcare systems should leverage air quality forecasts for proactive resource allocation.