Using web data to improve surveillance for heat sensitive health outcomes

Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, a lack of resources to support time- and labor-intensive diagnostic and reporting processes makes it difficult to establish region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people’s individual and subjective thoughts and feelings during heat waves. Focusing on the state of Florida, this study aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes.

The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, the authors found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases.

Findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This paper also shows that activity patterns for reducing heat stress are associated with several health outcomes. Based on the results, the authors believe web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective.