@article{Berry_Fagliano_Tsai_McGreevy_Walsh_Hamby_2013, title={Evaluation of Heat-related Illness Surveillance Based on Chief Complaint Data from New Jersey Hospital Emergency Rooms}, volume={5}, url={https://ojphi.org/ojs/index.php/ojphi/article/view/4438}, DOI={10.5210/ojphi.v5i1.4438}, abstractNote={The NJ Department of Health‰Ûªs syndromic surveillance system developed an algorithm to categorize heat-related illness (HRI) based on a patient‰Ûªs chief complaint during an emergency room visit, then matched these data with subsequent Uniform Billing (UB) diagnosis data. The overall sensitivity of the algorithm was 16% and the positive predictive value was 40%. Evaluation of a major heat event found both the sensitivity and positive predictive value increased to about 23% and 60%, respectively. While the HRI algorithm was relatively insensitive, sensitivity improved during major heat events and all excursions in HRI were identified using chief complaint data.}, number={1}, journal={Online Journal of Public Health Informatics}, author={Berry, Michael and Fagliano, Jerald and Tsai, Stella and McGreevy, Katharine and Walsh, Andrew and Hamby, Teresa}, year={2013}, month={Mar.} }