Objective: Emergency department (ED)–based syndromic surveillance systems are being used by public health departments to monitor for outbreaks of infectious diseases, including bioterrorism; however, few systems have been validated. The authors evaluated a “drop‐in” syndromic surveillance system by comparing syndrome categorization in the ED with chief complaints and ED discharge diagnoses from medical record review. Methods: A surveillance form was completed for each ED visit at 15 participating Arizona hospitals between October 27 and November 18, 2001. Each patient visit was assigned one of ten clinical syndromes or “none.” For six of 15 EDs, κ statistics were used to compare syndrome agreement between surveillance forms and syndrome categorization with chief complaint and ED discharge diagnosis from medical record review. Results: Overall, agreement between surveillance forms and ED discharge diagnoses (κ= 0.55; 95% confidence interval [CI] = 0.52 to 0.59) was significantly higher than between surveillance forms and chief complaints (κ= 0.48; 95% CI = 0.44 to 0.52). Agreement between chief complaints and ED discharge diagnoses was poor for respiratory tract infection with fever (κ= 0.33; 95% CI = 0.27 to 0.39). Furthermore, pediatric chief complaints showed lower agreement for respiratory tract infection with fever when compared with adults (κ= 0.34 [95% CI = 0.20 to 0.47] vs. κ= 0.44 [95% CI = 0.28 to 0.59], respectively). Conclusions: In general, this syndromic surveillance system classified patients into appropriate syndrome categories with fair to good agreement compared with chief complaints and discharge diagnoses. The present findings suggest that use of ED discharge diagnoses, in addition to or instead of chief complaints, may increase surveillance validity for both automated and drop‐in syndromic surveillance systems.
Reports of coccidioidomycosis cases in Arizona have increased substantially. We investigated factors associated with the increase.We analyzed the National Electronic Telecommunications System for Surveillance (NETSS) data from 1998 to 2001 and used Geographic Information Systems (GIS) to map high-incidence areas in Maricopa County. Poisson regression analysis was performed to assess the effect of climatic and environmental factors on the number of monthly cases; a model was developed and tested to predict outbreaks.The overall incidence in 2001 was 43 cases/100,000 population, a significant (P<.01, test for trend) increase from 1998 (33 cases/100,000 population); the highest age-specific rate was in persons > or =65 years old (79 cases/100,000 population in 2001). Analysis of NETSS data by season indicated high-incidence periods during the winter (November-February). GIS analysis showed that the highest-incidence areas were in the periphery of Phoenix. Multivariable Poisson regression modeling revealed that a combination of certain climatic and environmental factors were highly correlated with seasonal outbreaks (R2=0.75).Coccidioidomycosis in Arizona has increased. Its incidence is driven by seasonal outbreaks associated with environmental and climatic changes. Our study may allow public-health officials to predict seasonal outbreaks in Arizona and to alert the public and physicians early, so that appropriate preventive measures can be implemented.
A seroepidemiologic survey of cattle diseases was undertaken in Suriname in 1985 to help assess the livestock disease situation in that country. The six diseases covered by the survey were bovine coronavirus infection, bovine rhinotracheitis, bovine virus diarrhea, brucellosis, parainfluenza-3 infection, and respiratory syncytial virus infection. The results indicated relatively low prevalences of these diseases compared to the prevalences found in most developed countries. The reasons for this are uncertain, but the finding suggests that the cattle population in Suriname could lack extensive exposure to these diseases and so could be highly susceptible to them. In addition, the evident need for more thoroughgoing survey data points up the need to establish a continuous animal data health monitoring system in Suriname--as well as in other developing countries where there is a need to objectively assess the livestock disease picture.
