Background: Fungal sensitization is strongly associated with asthma severity and increased outdoor spore exposure correlates with higher asthmarelated hospitalization. However, data on outdoor airborne fungal spores in lower northern Thailand are limited. Objective: To assess changes in outdoor airborne mold spore patterns and counts over two distinct periods, ten years apart, in Phitsanulok, lower northern Thailand, to optimize allergic disease management. Materials and Methods: Mold spores were collected from August 2013 to July 2014 and from August 2022 to July 2023, using a Burkard seven-day volumetric spore trap positioned 15 meters above ground. Exposed slides were stained for microscopic analysis, and meteorological data were recorded. Results: Monthly outdoor airborne mold spore levels ranged from 336.5 to 939.6 spores/m³ (2013-2014) and 257.7 to 821.8 spores/m³ (2022-2023), with peaks in June (2013-2014) and May to June (2022-2023). Cladosporium spp. was the most abundant spore type in both periods. Rankings for the sixth to eleventh spore types were consistent, while the top five varied. Cladosporium spp. peaked in July 2014 and May to June 2023, approaching 500 spores/m³, a threshold for allergic risk. Overall, total spore concentrations were highest in the early rainy season, as rainfall increased from late April through June. In both periods, many spore types exhibited lower concentrations during months with the lowest relative humidity (March to April) and during peak rainfall (September). Conclusion: Cladosporium spp. accounted for the majority of outdoor mold spores. Many spore types increased at the onset of the rainy season and decreased during months with the lowest humidity.

Background/Objectives: Preventing recurrent anaphylaxis is crucial for patient safety. This study aimed to identify predictive factors and develop a prediction model to estimate recurrence risk, thereby enhancing targeted preventive strategies. Methods: This prognostic prediction study used a retrospective observational cohort design, analyzing medical records from an anaphylaxis registry at Naresuan University Hospital, Phitsanulok, Thailand, between March 2011 and February 2021. We developed a prediction model using multivariable Cox proportional hazards regression analysis. Statistically significant and clinically relevant predictors were weighted into a risk score derived from hazard ratio regression coefficients. Model performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration metrics, and decision curve analysis. Results: Over the 10-year period, 381 patients experienced 439 anaphylaxis episodes, including 58 recurrences (13.2%). The final model comprised six predictors: history of food, insect, and drug allergies; asthma; chest discomfort; and severe anaphylaxis. Corresponding risk scores were 4, 5, 5.5, 1, 2.5, and 1.5 points, respectively. Total scores ranged from 0 to 19.5 and were categorized into low (9.0) risk groups. The high-risk group had a likelihood ratio positive (LHR+) of 4.65. The model demonstrated acceptable discrimination (AuROC 0.773 (95% CI: 0.714–0.832)) and good calibration. Bootstrap validation showed consistent performance (AuROC 0.773 (95% CI: 0.714–0.831)). Decision curve analysis indicated clinical utility across relevant threshold probabilities. Conclusions: This prediction model provides a simple, clinically applicable tool for estimating the risk of recurrent anaphylaxis and may support improved prevention and management strategies.