Spatial epidemiological analysis of chronic obstructive pulmonary disease in Qingdao City, China

Existing studies on Chronic Obstructive Pulmonary Disease (COPD) relying on voluntary questionnaire surveys have inherent limitations, while spatial epidemiological research in Qingdao remains scarce. This population census study aimed to eliminate selection bias through systematic population coverage, identify high-risk clusters using spatial analysis, and provide geospatial evidence for precision public health strategies by concurrently analyzing COPD prevalence. Data were obtained from the 2023 Qingdao COPD High-Risk Population Screening Program. Using a Geographic Information System (GIS) framework, streets and towns in Qingdao City were designated as the basic spatial units for analysis. Global Moran’s I and Local Moran’s I spatial autocorrelation statistics were employed to characterize global and local spatial clustering patterns of COPD high-risk individuals and confirmed patients across the city. The 2023 screening project identified 503,119 individuals with positive COPD Population Screener Questionnaire (COPD-PS) results (high-risk population) and 55,533 confirmed COPD patients. Gender, age, and Body Mass Index (BMI) were identified as risk factors for COPD prevalence among adults aged 40 and above in Qingdao City. Global spatial autocorrelation analysis revealed significant clustering of COPD-PS positive rates in Qingdao City (Global Moran’s I = 0.13, Z = 4.27, P < 0.001), whereas spatial autocorrelation of COPD prevalence did not reach statistical significance. Local spatial autocorrelation analysis indicated that there were significant spatial clusters of COPD high-risk populations in Shibei District and Badaxia Road Subdistrict, while no obvious clusters of COPD patients were observed. This large-scale census provides the first comprehensive COPD spatial epidemiology dataset for Qingdao City. GIS-derived cluster maps enable prioritization of high-burden areas for resource allocation and targeted interventions, supporting the transition to precision public health approaches.