Alert System to Detect Possible School-based Outbreaks of Influenza-like Illness

To evaluate the usefulness of school absentee data in identifying outbreaks as part of syndromic surveillance, we examined data collected from public schools in Miami-Dade County, Florida, USA. An innovative automated alert system captured information about school-specific absenteeism to detect and provide real-time notification of possible outbreaks of influenza-like illness

Evaluation of school absenteeism data for early outbreak detection, New York City

BACKGROUND: School absenteeism data may have utility as an early indicator of disease outbreaks, however their value should be critically examined. This paper describes an evaluation of the utility of school absenteeism data for early outbreak detection in New York City (NYC). METHODS: To assess citywide temporal trends in absenteeism, we downloaded three years (2001–02, 2002–03, 2003–04) of daily school attendance data from the NYC Department of Education (DOE) website. We applied the CuSum method to identify aberrations in the adjusted daily percent absent. A spatial scan statistic was used to assess geographic clustering in absenteeism for the 2001–02 academic year. RESULTS: Moderate increases in absenteeism were observed among children during peak influenza season. Spatial analysis detected 790 significant clusters of absenteeism among elementary school children (p < 0.01), two of which occurred during a previously reported outbreak. CONCLUSION: Monitoring school absenteeism may be moderately useful for detecting large citywide epidemics, however, school-level data were noisy and we were unable to demonstrate any practical value in using cluster analysis to detect localized outbreaks. Based on these results, we will not implement prospective monitoring of school absenteeism data, but are evaluating the utility of more specific school-based data for outbreak detection

Optimizing Use of Multistream Influenza Sentinel Surveillance Data

We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998–2007. Our study demonstrates that simultaneous monitoring of multiple streams of influenza surveillance data can improve the accuracy and timeliness of alerts compared with monitoring of aggregate data or of any single stream alone

Reinventing ‘Towel City USA’: Textiles, Tourism and the Future of the Southeastern Mill Town

Throughout the southeastern region of the United States it is common to find towns that were built by textile companies in the late 19th century with the express purpose of manufacturing textile products. Textile companies sought rural areas within this region to build factories and then surrounded the factories with homes, schools, and stores designed to attract and keep workers (Suggs, 2002)

Is a previous unplanned pregnancy a risk factor for a subsequent unplanned pregnancy?

Objective: The objective of the study was to determine whether a history of unplanned pregnancy was a risk factor for a subsequent unplanned pregnancy. Study Design: We analyzed 542 women aged 14-35 years, enrolled in Project PROTECT, a randomized clinical trial to promote dual-method contraception use to prevent sexually transmitted diseases and unplanned pregnancy. Predictors of unplanned pregnancy were assessed by comparing women with and without a history of unplanned pregnancy. Results: More than 1 in 5 women (22.5%) experienced an unintended pregnancy. History of an unintended pregnancy was a predictor of unintended pregnancy (adjusted odds ratio, 1.91; 95% confidence interval, 1.09-3.34). Other factors that were significantly associated with unplanned pregnancy included young age and low educational status. Conclusions: Future efforts should focus on bridging the gap between identifying risk factors for unplanned pregnancy and interventions aimed at reducing the incidence in high-risk groups

The New School Absentees Reporting System for Pandemic Influenza A/H1N1 2009 Infection in Japan

Objective: To evaluate the new Japanese School Absentees Reporting System for Infectious Disease (SARSID) for pandemic influenza A/H1N1 2009 infection in comparison with the National epidemiological Surveillance of Infectious Disease (NESID). Methods:We used data of 53,223 students (97.7%) in Takamatsu city Japan. Data regarding school absentees in SARSID was compared with that in NESID from Oct 13, 2009 to Jan 12, 2010. Results: Similar trends were observed both in SARSID and NESID. However, the epidemic trend for influenza in SARSID was thought to be more sensitive than that in NESID. Conclusion: The epidemic trend for influenza among school-aged children could be easily and rapidly assessed by SARSID compared to NESID. SARSID might be useful for detecting the epidemic trend of influenza

Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

A variety of obstacles, including bureaucracy and lack of resources, delay detection and reporting of dengue and exist in many countries where the disease is a major public health threat. Surveillance efforts have turned to modern data sources such as Internet usage data. People often seek health-related information online and it has been found that the frequency of, for example, influenza-related web searches as a whole rises as the number of people sick with influenza rises. Tools have been developed to help track influenza epidemics by finding patterns in certain web search activity. However, few have evaluated whether this approach would also be effective for other diseases, especially those that affect many people, that have severe consequences, or for which there is no vaccine. In this study, we found that aggregated, anonymized Google search query data were also capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after a long delay, web search query data is available for analysis within a day. Therefore, because it could potentially provide earlier warnings, these data represent a valuable complement to traditional dengue surveillance