Hannah Haylea

Infectious disease outbreaks, such as the Ebola outbreak in West Africa, highlight the need for surveillance systems to quickly detect outbreaks and provide data to prevent future pandemics. The World Health Organization (WHO) developed the Joint External Evaluation (JEE) tool to conduct country-level assessments of surveillance capacity. However, considering that outbreaks begin and are first detected at the local level, national-level evaluations may fail to identify capacity improvements for outbreak detection. The gaps in local surveillance system processes illuminate a need for investment in on-the-ground surveillance improvements that may be lower cost than traditional surveillance improvement initiatives, such as enhanced training or strengthening data transfer mechanisms before building new laboratory facilities. To explore this premise, we developed a methodology for assessing surveillance systems with special attention to the local level and applied this methodology to the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.

Objective: To conduct a field-based assessment of the malaria outbreak surveillance system in Mashonaland East, Zimbabwe.

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.

Objective: To develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.