A deviation bar chart for detecting dengue outbreaks in Puerto Rico

• Published in: American journal of public health (1971) 1999-03, Vol.89 (3), p.374-378
• Main Authors: Rigau-Perez, J G, Millard, P S, Walker, D R, Deseda, C C, Casta-Velez, A
• Format: Article
• Language: English
• Subjects: Arboviroses; Biological and medical sciences; Data Interpretation, Statistical; Dengue - epidemiology; Dengue fever; Dengue fevers; Dengue haemorrhagic fever; Disease; Disease control; Disease Outbreaks - statistics & numerical data; Epidemics; Epidemiologic Measurements; Epidemiology; Health surveillance; Human viral diseases; Humans; Identification; Incidence; Infectious diseases; Medical laboratories; Medical sciences; Mortality; Municipalities; Outbreaks; Population Surveillance - methods; Public health; Public health medicine; Puerto Rico; Puerto Rico - epidemiology; Reproducibility of Results; Residence Characteristics; Retrospective Studies; Seasons; Sensitivity and Specificity; Software; Tropical viral diseases; Viral diseases
• ISSN: 0090-0036; 1541-0048
• DOI: 10.2105/AJPH.89.3.374

A Centers for Disease Control and Prevention deviation bar chart (Statistical Software for Public Health Surveillance) and laboratory-based surveillance data were evaluated for their utility in detecting dengue outbreaks in Puerto Rico. A significant increase in dengue incidence was defined as an excess of suspected cases of more than 2 SDs beyond the mean for all 4-week periods from April through June (the period of lowest seasonal incidence), 1989 through 1993. An outbreak was defined as a cumulative annual rate of reported dengue greater than 3 per 1000 population. Retrospective application of the system to 1994 data showed agreement with previous analyses. In 1995 and 1996, 36.4% and 27.3%, respectively, of municipalities with a significant increase in reports for 2 or more consecutive weeks before the first week of September had an outbreak, compared with 9.0% (in 1995, P = .042) and 6.0% (in 1996, P = .054) of towns without a significant increase. The system showed sensitivity near 40%, specificity near 89%, and accuracy in classifying municipalities near 84%. This method provides a statistically based, visually striking, specific, and timely signal for dengue control efforts.