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

Abstract

In large public health jurisdictions, only a 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 exposures and risks, focusing particularly on the feasibility of SMS (short message service)-based surveillance. Over five-years period we sequentially mailed letters, sent online surveys, and then text messages. The SMS approach was designed to assess the efficiency of a two-way personalized messaging model in gathering actionable public health data. The personalized SMS-follow-up model demonstrated the highest success: 56% of cases responded, enabling the identification and intervention of 10 distinct point-source outbreaks of Salmonella. 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.