Michael Anthony Storey

Intolerance to enteral nutrition among infants in the neonatal intensive care unit (NICU) may lead to the use of prokinetic agents to improve gastric emptying and feeding tolerance. Two such oral prokinetic agents are erythromycin and amoxicillin-clavulanate that have been used in cases of dysmotility due to intestinal failure. However, dosing strategies vary and the recommended duration of therapy has not been determined. Moreover, the use of these oral antibiotics in the NICU may fall under the radar of neonatal antimicrobial stewardship programs. Our objective was to quantify their use in the Level 4 outborn NICU at Nationwide Children’s Hospital, Columbus, OH in order to inform antibiotic stewardship efforts. This was a retrospective cohort study of all infants who received enteral erythromycin or amoxicillin-clavulanate in the NICU. The number of doses of each antibiotic was obtained from the Nationwide Children’s Hospital Pharmacy database. Pertinent clinical, laboratory, and outcome data were obtained. From January 1, 2022 to April 30, 2023, 15 infants in the NICU received ≥1 dose of erythromycin ethylsuccinate and 19 infants received ≥1 dose of amoxicillin-clavulanate enterally. Erythromycin was always used to improve motility while amoxicillin-clavulanate served as a prokinetic agent in 53% of infants in whom it was prescribed. Demographics and reason for initiation are provided in Table 1. The median (IQR) doses per patient for erythromycin was 64 (28, 125; dosing interval varied from every 6 to every 8 hours) and for amoxicillin-clavulanate was 50 (9, 105) doses per patient (majority of doses were given on a BID schedule). Enteral erythromycin and amoxicillin-clavulanate have not been the target of antimicrobial stewardship programs. These antibiotics may contribute to dysbiosis and adverse events in NICU infants. An automatic stop time with a time-out when ordered in the electronic health record may be a viable strategy to reduce overutilization of these antibiotics. Pavel Prusakov, PharmD, Merck & Co: Grant/Research Support|Merck & Co: Employee Michael Storey, PharmD, MS, CSL Behring: Advisor/Consultant|Sarepta Therapeutics: Advisor/Consultant

Landscape fires emit smoke that contains particulate matter (PM) that can be harmful to human health. Prescribed fires or hazard reduction burns (HRBs) and wildfires can substantially reduce air quality in populated areas. While HRBs reduce the size and PM output of future wildfires, they also produce PM. There is a critical question of whether conducting HRBs adds to or reduces the total PM (prescribed + wildfire PM) exposure of populations, which has important community health implications. We modelled mean 24 h PM_2.5 in Sydney from fire type, 24 h active regional fire area and weather predictors. We compared HRB and wildfire area effects via an interaction between fire area and fire type, and we found that a non-linear effect of fire area was most appropriate. We conducted a trade-off analysis by using the model to predict increased HRB area scenarios and calculated the number of HRB and wildfire days with ‘Fair’ or worse air quality under each scenario. Regional HRBs and wildfire areas had similar effects on PM_2.5 in Sydney. Increasing regional HRB area produced substantial increases in HRB exceedance days (>12.5 µgm⁻³) in Sydney but only a small reduction in wildfire exceedance days. Our results indicate that small fires in the region have a higher per-hectare impact on PM levels in Sydney, and consequently, increasing regional HRB area would result in more poor air quality days overall (HRB + wildfire days) in Sydney.

Wildfires and hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm in diameter (PM_2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM_2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. Fire management agencies need to understand and predict PM_2.5 levels associated with a particular fire so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM_2.5 pollution. We modelled PM_2.5, measured at air quality stations in New South Wales (Australia) from ∼ 1400 d when individual fires were burning near air quality stations, as a function of fire and weather variables. Using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite hotspots, we identified days when one fire was burning within 150 km of at least 1 of 48 air quality stations. We extracted ERA5 gridded weather data and daily active fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM_2.5 levels to understand drivers of and predict PM_2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also being important. There was a strong increase in PM_2.5 with decreasing distance, with a sharp increase when the fire was within 20 km. The models improve our understanding of the drivers of PM_2.5 from individual fires and demonstrate a promising approach to PM_2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM_2.5 that mean further model development would be required for the models to be deployed operationally.

Wildfires and controlled hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm in diameter (PM_2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM_2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. It is important for forest-fire management agencies to understand and predict PM_2.5 levels associated with a particular fire, so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM_2.5 pollution. We modelled PM_2.5, measured at air quality stations in NSW Australia, from 1500 historical individual fires as a function of fire and weather variables. Using VIIRS satellite hotspots, we identified days where one fire was burning within 150 km of one of 48 air quality station. We extracted ERA5 gridded weather data and fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM_2.5 to understand drivers of and predict PM_2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also important. There was a strong increase in PM_2.5 modelled with decreasing distance, with a sharp increase when the fire was within 15 km. The models improve understanding of drivers of PM_2.5 from individual fires and demonstrate a promising approach to PM_2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM_2.5 that mean further model development would be required for the models to be deployed operationally.

Wildfires and controlled hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm in diameter (PM_2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM_2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. It is important for forest-fire management agencies to understand and predict PM_2.5 levels associated with a particular fire, so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM_2.5 pollution. We modelled PM_2.5, measured at air quality stations in NSW Australia, from 1500 historical individual fires as a function of fire and weather variables. Using VIIRS satellite hotspots, we identified days where one fire was burning within 150 km of one of 48 air quality station. We extracted ERA5 gridded weather data and fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM_2.5 to understand drivers of and predict PM_2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also important. There was a strong increase in PM_2.5 modelled with decreasing distance, with a sharp increase when the fire was within 15 km. The models improve understanding of drivers of PM_2.5 from individual fires and demonstrate a promising approach to PM_2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM_2.5 that mean further model development would be required for the models to be deployed operationally.

Wildfires and controlled hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm in diameter (PM_2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM_2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. It is important for forest-fire management agencies to understand and predict PM_2.5 levels associated with a particular fire, so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM_2.5 pollution. We modelled PM_2.5, measured at air quality stations in NSW Australia, from 1500 historical individual fires as a function of fire and weather variables. Using VIIRS satellite hotspots, we identified days where one fire was burning within 150 km of one of 48 air quality station. We extracted ERA5 gridded weather data and fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM_2.5 to understand drivers of and predict PM_2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also important. There was a strong increase in PM_2.5 modelled with decreasing distance, with a sharp increase when the fire was within 15 km. The models improve understanding of drivers of PM_2.5 from individual fires and demonstrate a promising approach to PM_2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM_2.5 that mean further model development would be required for the models to be deployed operationally.

Smoke from Hazard Reduction Burns (HRBs) and wildfires contains pollutants that are harmful to human health. This includes particulate matter less than 2.5 μm in diameter (PM_2.5), which affects human cardiovascular and respiratory systems and can lead to increased hospitalisations and premature deaths. Better models are needed to predict PM_2.5 levels associated with HRBs so that agencies can properly assess smoke pollution risk and balance smoke risk with the wildfire mitigation benefits of HRBs. Given this need, our aim was to develop a probabilistic model of daily PM_2.5 using Bayesian regression. We focused on the region around Sydney, Australia, which regularly has hazard reduction burning, wildfires and associated smoke. We developed two regional models (mean daily and maximum daily) from observed PM_2.5, weather reanalysis and satellite fire hotspot data. The models predict that the worst PM_2.5 in Sydney occurs when PM_2.5 was high the previous day, there is low ventilation index (i.e. the product of wind speed and planetary boundary layer height), low temperature, west to northwest winds in the Blue Mountains, an afternoon sea breeze and large areas of HRBs are being conducted, particularly to the west and north of Sydney. A major benefit of our approach is that models are fast to run, require simple inputs and Bayesian predictions convey both predicted PM_2.5 and associated prediction uncertainty. Future research could include the application of similar methods to other regions, collecting more data to improve model precision and developing Bayesian PM_2.5 models for wildfires.

Wildfires and controlled hazard reduction burns produce smoke that contains pollutants including particulate matter. Particulate matter less than 2.5 µm in diameter (PM_2.5) is harmful to human health, potentially causing cardiovascular and respiratory issues that can lead to premature deaths. PM_2.5 levels depend on environmental conditions, fire behaviour and smoke dispersal patterns. It is important for forest-fire management agencies to understand and predict PM_2.5 levels associated with a particular fire, so that pollution warnings can be sent to communities and/or hazard reduction burns can be timed to avoid the worst conditions for PM_2.5 pollution. We modelled PM_2.5, measured at air quality stations in NSW Australia, from 1500 historical individual fires as a function of fire and weather variables. Using VIIRS satellite hotspots, we identified days where one fire was burning within 150 km of one of 48 air quality station. We extracted ERA5 gridded weather data and fire area estimates from the hotspots for our modelling. We created random forest models for afternoon, night and morning PM_2.5 to understand drivers of and predict PM_2.5. Fire area and boundary layer height were important predictors across the models, with temperature, wind speed and relative humidity also important. There was a strong increase in PM_2.5 modelled with decreasing distance, with a sharp increase when the fire was within 15 km. The models improve understanding of drivers of PM_2.5 from individual fires and demonstrate a promising approach to PM_2.5 model development. However, although the models predicted well overall, there were several large under-predictions of PM_2.5 that mean further model development would be required for the models to be deployed operationally.

Spotting is thought to increase wildfire rate of spread (ROS) and in some cases become the main mechanism for spread. The role of spotting in wildfire spread is controlled by many factors including fire intensity, number of and distance between spot fires, weather, fuel characteristics and topography. Through a set of 30 laboratory fire experiments on a 3 m x 4 m fuel bed, subject to air flow, we explored the influence of manually ignited spot fires (0, 1 or 2), the presence or absence of a model hill and their interaction on combined fire ROS (i.e. ROS incorporating main fire and merged spot fires). During experiments conducted on a flat fuel bed, spot fires (whether 1 or 2) had only a small influence on combined ROS. Slowest combined ROS was recorded when a hill was present and no spot fires were ignited, because the fires crept very slowly downslope and downwind of the hill. This was up to, depending on measurement interval, 5 times slower than ROS in the flat fuel bed experiments. However, ignition of 1 or 2 spot fires (with hill present) greatly increased combined ROS to similar levels as those recorded in the flat fuel bed experiments (depending on spread interval). The effect was strongest on the head fire, where spot fires merged directly with the main fire, but significant increases in off-centre ROS were also detected. Our findings suggest that under certain topographic conditions, spot fires can allow a fire to overcome the low spread potential of downslopes. Current models may underestimate wildfire ROS and fire arrival time in hilly terrain if the influence of spot fires on ROS is not incorporated into predictions.