Exploring the impact of ambient temperature on respiratory diseases admissions, length of Stay, and hospitalization costs in Lanzhou City, based on distributed lag non-linear model

Abstract

This study was to explore the relationships between daily mean temperature and hospital admissions, length of stay and hospitalization costs for respiratory diseases, and to estimate the risk effects and burden of disease. A time-series analysis was conducted by distributed lag non-linear model (DLNM) to explore the exposure-lag-response relationships between daily mean temperature and hospital admissions, length of stay, and hospitalization costs for respiratory diseases. The total cumulative exposure between the daily admissions, length of stay and hospitalization costs of respiratory diseases and the daily mean temperature showed significant nonlinear relationships, all with a shape approximately “W”. Extremely low temperature presented the greatest risk to respiratory diseases of admissions, length of stay and hospitalization costs, with the relative risks of 1.66 (95 % CI:1.32–2.09), 1.71 (95 % CI:1.33–2.20), 2.09 (95 % CI:1.53–2.84), respectively. The risks caused by low temperatures have delayed effect, capable of generating higher risks within lag 21 days. In contrast, the effects of high temperatures on the three outcomes only in the short term. The relative risks of exposure to extremely cold weather for elderly patients were the greatest, which were 2.47 (95 % CI:1.89–3.24), 2.11 (95 % CI:1.58–2.81) and 2.59 (95 % CI:1.81–3.70), respectively. In Lanzhou city, both low and high temperatures posed a certain risk to the hospital admissions, length of stay and hospitalization costs of respiratory diseases. Cold temperature exposure is the main risk factor to increase the risks of the three outcomes, and its risks have significant lag effect. Elderly patients are vulnerable to cold temperature exposure.