Preparing for avian influenza

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

Risks to healthcare workers with emerging diseases: lessons from MERS-CoV, Ebola, SARS, and avian flu

PURPOSE OF REVIEW

Several viral diseases have emerged and impacted healthcare systems worldwide. Healthcare personnels (HCPs) are at high risk of acquiring some emerging infections while caring for patients. We provide a review of risk factors, evidence of infection in HCPs, and prevention strategies with Middle East respiratory syndrome coronavirus, Ebola virus disease (Ebola), severe acute respiratory syndrome (SARS), and avian influenza.

RECENT FINDINGS

HCP-related infections with Middle East respiratory syndrome coronavirus, Ebola, and SARS have been reported among 1-27%, 2.5-12%, and 11-57% of total cases, respectively. The case fatality rate of Ebola in HCPs has been reported up to 73%. The WHO guidelines for the global surveillance of SARS were developed in 2004 and used as a template for other emerging diseases preparedness. Risks to HCPs with emerging diseases are related to inappropriate and insufficient infection control measures during an initial encounter, at the beginning of outbreak and with an overwhelming number of patient cases. To date, there are no reports of avian influenza transmission to HCPs from affected cases.

SUMMARY

Early and rapid detection of suspected infected patients with communicable diseases along with appropriate infection control practice, education, national and global preparedness guidelines would help to prevent disease transmission to HCPs.