Abstract Background Autumn and winter weather fluctuations, particularly increases in wind speed and temperature variability, may significantly contribute to the development of ACS. Nevertheless, comprehensive investigations into correlation analyses, exposure-response relationships, and delayed effects remain scarce in the current literature. Methods This study employed a retrospective epidemiological design, gathering 2445 cases of ACS from a tertiary hospital in Beijing City between August 2016 and July 2021. Meteorological data from the corresponding period were utilized. Spearman correlation analysis, logistic regression, RCS curve fitting, and case cross-matching were conducted to investigate the influence of meteorological factors on ACS incidence during the autumn and winter seasons. Results (1) Elevated wind speed and low temperature significantly elevated the risk of ACS during autumn and winter: Specifically, in autumn, the OR for wind speed was 1.324 (95%CI: 1.051–1.609, P < 0.05) and in winter, it was 1.414 (95%CI: 1.127–1.871, P < 0.05). For air temperature, the OR was 0.763 in autumn (95%CI: 0.251–0.957, P < 0.05) and 0.737 in winter (95%CI: 0.469–0.986, P < 0.05). (2) The relationship between wind speed, temperature, and ACS risk exhibited distinct patterns in autumn and winter: Wind speed demonstrated a linear positive correlation with ACS risk in both seasons, whereas temperature displayed a nonlinear association. In autumn, there was an inverted U-shaped relationship between average daily temperature and ACS incidence, with a threshold of approximately 7.5°C (Pnon-line = 0.022). Below this threshold, the risk of ACS increased with rising temperatures. In winter, the relationship between the lowest temperature and ACS incidence followed a "barb" type pattern, with a threshold of around − 5°C (Pnon-line = 0.013), below which the risk escalated rapidly. (3)Both fall and winter wind speed and temperature exhibited significant lag effects on the onset of ACS. Wind speed was found to significantly increase the risk of ACS onset with a lag of 2 days (OR = 1.111, 95%CI: 1.057–1.274, P < 0.05), demonstrating a consistent positive association in both fall (OR = 1.237, 95%CI: 1.093–1.393, P < 0.05) and winter (OR = 1.395, 95%CI: 1.188–1.673, P < 0.05). Fall daily mean temperature (OR = 0.956, 95%CI: 0.904–0.991, P < 0.05) and winter minimum temperature (OR = 0.866, 95%CI: 0.745–0.996, P < 0.05) exhibited protective effects at lag days 5 and 2, respectively. Conclusion Elevated wind speed and exceptionally low temperatures during autumn and winter emerge as significant autonomous risk factors for ACS, exhibiting distinct seasonal patterns, critical thresholds, and lag effects. These findings underscore the necessity of integrating crucial meteorological variables into ACS early warning systems and enhancing targeted health interventions and management strategies for high-risk populations during extreme weather conditions.
Qiu et al. (Sun,) studied this question.