Cardiovascular hospitalization dynamics in Iran’s second-largest city: A spatial and temporal perspective - Shahab MohammadEbrahimi; Tahereh Samimi; Mohammad Dehghan; Munazza Fatima; Elahe Zare; Atieh Sedghian; Saeid Eslami; Behzad Kiani (PLoS ONE, 2026)
Introduction Cardiovascular disease (CVD) is a leading cause of mortality worldwide, with an especially high burden in developing countries such as Iran. Understanding spatial disparities and temporal trends in CVD hospitalizations can guide targeted public health interventions in high-risk regions. Methods Data was sourced from Mashhad University of Medical Sciences for five years (2016–2020) and included CVD cases classified by ICD-10 codes, excluding records with non-resident status or incomplete addresses. Temporal trends were assessed through a monthly classical decomposition, ARIMA modeling for forecasting, and Joinpoint regression (JR) to detect shifts over time. Spatial analyses included calculating relative risks, examining spatial autocorrelation, identifying hotspots, and applying flexible spatial scan statistics (FSSS) to detect clusters with irregular shapes. Results The study included 52,132 CVD cases, with a median age of 64 years (IQR: 53_76) and a male predominance (54.44%). Temporal analysis showed significant fluctuations, with the highest hospitalization rate in 2019 and the seasonal pattern was repeated annually. ARIMA(0,0,0)(0,0,1) 12 modeling revealed a seasonal moving average of −0.722 and a mean of 844.95 hospitalizations/month, capturing seasonal fluctuations with a stable trend reflecting cardiovascular health risks. The JR analysis showed a rising hospitalization trend with a monthly percent change of 1.69 [95% CI: 1.44_2.29; p = 0.001] for males and 1.99 [95% CI: 1.73_2.68; p = 0.001] for females, followed by a sharp decline after November 2019. After a positive spatial autocorrelation (Global Moran’s I of 0.176 ( p < 0.001)), hot spot classification revealed central Mashhad as a high-risk zone with concentrated clusters of CVD hospitalizations. The Flexible Spatial Scan Statistics (FSSS) analysis indicated that the Most Likely Cluster (MLC) was situated in the city center, presenting an approximate 10-fold relative risk [RR: 9.804, LLR: 2328.3, p < 0.001]. Conclusions This study provides the first extensive spatial and temporal analysis of CVD hospitalizations in Mashhad, underscoring critical high-risk areas in need of focused public health interventions. Addressing these spatial disparities can significantly improve cardiovascular health outcomes and bolster resilience within the local populations.