Increased Incidence Of Inhalation Burn Injury During The Covid-19 Pandemic: A National Database Study.

The COVID-19 pandemic has forced many Americans to adapt their daily routines. In 2020, there was a significant increase in house fires according to the National Fire Prevention Association (NFPA). The objective of this study was to characterize the changes in suspected smoke inhalations during the first year of the pandemic in the National Emergency Medical Services Information System (NEMSIS). The NEMSIS database was queried for all EMS transports captured between 2017-2020. Differences in the incidences of suspected smoke inhalations and fire dispatches in 2020 were estimated using Poisson regression models. There was a 13.4% increase in the incidence of fire dispatches and a 15% increase in suspected smoke inhalations transported in 2020 compared to the previous 3 years. The IRR of both fire dispatches (1.271; 95% CI: 1.254-1.288; p<0.001) and suspected smoke inhalation (1.152; 95% CI: 1.070-1.241; p<0.001) was significantly elevated in 2020. The increases in fire dispatches and suspected smoke inhalations observed in the NEMSIS database are in concordance with other literature indicating the increase in fire incidence and morbidity observed during the pandemic. These results should inform fire prevention outreach efforts and resource allocation in burn centers in the event of future pandemic.

Pandemic Puppies: Man's Best Friend or Public Health Problem? A Multidatabase Study.

INTRODUCTION: The public health implications of the COVID-19 pandemic reach beyond those of the disease itself. Various centers have anecdotally reported increases in the incidence of dog bite injuries which predominate in pediatric populations. The reasons for this increase are likely multifactorial and include an increase in canine adoptions, remote learning, and psychosocial stressors induced by lockdowns. We hypothesized that there was a significant increase in the proportion of dog bite injuries at our institution and within a nationally representative cohort. METHODS: We queried our electronic health record and the National Electronic Injury Surveillance System (NEISS) for all records pertaining to dog bites between 2015 and 2020, and the annual incidence was calculated. Poisson regression was then used to estimate whether there was a significant difference in the adjusted risk ratio for each year. RESULTS: The institutional and national cohorts revealed relative increases in the incidence of dog bite injury of 243 and 147.9 per 100,000 over the study period, respectively. Both cohorts observed significant increases of 44% and 25% in the annual incidence relative to 2019, respectively. Poisson regression revealed a significantly elevated adjusted relative risk in the institutional cohort for 2020 (2.664, CI: 2.076-3.419, P < 0.001). The national cohort also revealed an increase (1.129, CI: 1.091-1.169, P < 0.001). CONCLUSIONS: A nationwide increase in the incidence of dog bite injuries among children was observed during COVID-19 in 2020. These findings suggest that dog bites remain a public health problem that must be addressed by public health agencies.

Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

BACKGROUND: Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. RESEARCH QUESTION: Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? METHODS: This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). RESULTS: The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. CONCLUSIONS: This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.