Text message surveillance for rapid Salmonella outbreak detection: a novel public health approach

Objectives: In public health agencies covering a large population, only a very small proportion of people infected with Salmonella are interviewed due to resource constraints. As such, sources of illness are rarely found, and preventative action not implemented. We trialled alternative methods to contact notified salmonellosis cases to collect information on potential exposures and risks with a particular focus on the feasibility of SMS (short message service) -based surveillance. 

Methods: The project was conducted over a five-year period to include phases of mailed letters, online surveys, and text messages. The SMS approach was designed to assess the efficiency of a two-way personalized messaging model in gathering actionable public health data 

Results: The personalized SMS-based follow-up model demonstrated the highest success, with a response rate of 56% from cases. The information gathered enabled the identification and intervention in ten distinct point-source outbreaks of Salmonella. 

Conclusions: SMS-based surveillance offers a novel, efficient and acceptable method for collecting critical food exposure data in Salmonella cases. In settings where resources are constrained, SMS can complement traditional case follow-up methods, enhancing both the timeliness and effectiveness of outbreak detection. Integrating this follow-up with routine clinical care could further enhance the acceptance and success of this method. This study highlights the promise of SMS in streamlining surveillance efforts and warrants further exploration for application to other infectious diseases.