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Travel-associated Illness Trends and Clusters, 2000–2010 - Vol. 19 No. 7 - July 2013 - Emerging Infectious Disease journal - CDC

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Travel-associated Illness Trends and Clusters, 2000–2010 - Vol. 19 No. 7 - July 2013 - Emerging Infectious Disease journal - CDC

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Volume 19, Number 7–July 2013

Volume 19, Number 7—July 2013


Travel-associated Illness Trends and Clusters, 2000–2010

Karin LederComments to Author , Joseph Torresi, John S. Brownstein, Mary E. Wilson, Jay S. Keystone, Elizabeth Barnett, Eli Schwartz, Patricia Schlagenhauf, Annelies Wilder-Smith, Francesco Castelli, Frank von Sonnenburg, David O. Freedman, and Allen C. Cheng, for the GeoSentinel Surveillance Network
Author affiliations: Royal Melbourne Hospital, Parkville, Victoria, Australia (K. Leder); Monash University, Melbourne, Victoria, Australia (K. Leder, A.C. Cheng); Austin Hospital, Heidelberg, Victoria, Australia (J. Torresi); University of Melbourne, Parkville (J. Torresi); Children’s Hospital, Boston, Massachusetts, USA (J.S. Brownstein); Harvard Medical School, Boston (J.S. Brownstein); Harvard School of Public Health, Boston (M.E. Wilson); Toronto General Hospital, Toronto, Ontario, Canada (J.S. Keystone); University of Toronto, Toronto (J.S. Keystone); Boston Medical Center, Boston (E. Barnett); Sheba Medical Center, Tel Hashomer, Isreal (E. Schwartz); Tel Aviv University, Tel Aviv, Israel (E. Schwartz); University of Zurich WHO Collaborating Centre for Travellers’ Health, Zurich, Switzerland (P. Schlagenhauf); Lee Kong Chian School of Medicine, Singapore (A. Wilder-Smith); University of Brescia, Brescia, Italy (F. Castelli); University of Munich, Munich, Germany (F. von Sonnenburg); University of Alabama at Birmingham, Birmingham, Alabama, USA (D.O. Freedman); Alfred Hospital, Melbourne (A.C. Cheng)
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Longitudinal data examining travel-associated illness patterns are lacking. To address this need and determine trends and clusters in travel-related illness, we examined data for 2000–2010, prospectively collected for 42,223 ill travelers by 18 GeoSentinel sites. The most common destinations from which ill travelers returned were sub-Saharan Africa (26%), Southeast Asia (17%), south-central Asia (15%), and South America (10%). The proportion who traveled for tourism decreased significantly, and the proportion who traveled to visit friends and relatives increased. Among travelers returning from malaria-endemic regions, the proportionate morbidity (PM) for malaria decreased; in contrast, the PM trends for enteric fever and dengue (excluding a 2002 peak) increased. Case clustering was detected for malaria (Africa 2000, 2007), dengue (Thailand 2002, India 2003), and enteric fever (Nepal 2009). This multisite longitudinal analysis highlights the utility of sentinel surveillance of travelers for contributing information on disease activity trends and an evidence base for travel medicine recommendations.
International travel is markedly increasing. In 2010, an estimated 940 million tourists arrived at international destinations, more than twice the 435 million in 1990 (1). Trips to developing regions have risen from 31% of all travel in 1990 to 47% in 2010, and trips to the Asia–Pacific region, Africa, and the Middle East have doubled in the past decade (1). Reasons for travel have also changed; from 1990 to 2010, trips for tourism decreased from 56% to 51%, and trips by those with ties to the destination country (travel for the purpose of visiting friends and relatives) increased from 20% to 27% (1,2).
More than half of international travelers to developing countries become ill during their trip, and ≈8% seek medical care for a travel-associated illness either during or after travel (3). Changes in travelers’ illnesses over time would be expected to reflect changing patterns of global travel destinations, changes in local disease epidemiology in regions visited, and/or availability of preventive measures such as vaccination and chemoprophylaxis. To examine illness trends and clusters among travelers, we analyzed multisite longitudinal data collected by GeoSentinel sites during 2000–2010.

Materials and Methods

Figure 1
Thumbnail of GeoSentinel regions. Figure 1. . GeoSentinel regions.
The GeoSentinel Surveillance Network ( is a global network of specialized travel and tropical medicine providers. It was established through the International Society of Travel Medicine and the US Centers for Disease Control and Prevention (CDC) (4). Since 1997, data have been collected on illnesses imported across international borders by travelers and immigrants. GeoSentinel sites have been added progressively over time; currently, 54 clinics in 26 countries contribute data (Figure 1). Information is recorded for demographics, travel history, reason for travel, clinical signs and symptoms, and diagnosis. All sites use the best available reference diagnostic tests and base the identification of country (or region) of illness acquisition on itinerary, known endemicity patterns, and incubation periods. GeoSentinel sites enter their de-identified questionnaire-based information into a central SQL (structured query language) database. The GeoSentinel data-collection protocol was reviewed by the institutional review board officer at the CDC and was classified as public health surveillance, not as human subjects research requiring submission to institutional review boards.
The mix of patients and diagnoses reported by individual GeoSentinel sites varies according to site location and clinic type (hospital or outpatient). To examine trends over time, we included only sites that consistently reported posttravel data throughout the 11-year period of interest. From these sites we examined inpatient and outpatient data for trends in demographics, reason for travel, and proportionate morbidity (PM) for certain key diagnoses. Specific infections were included in analyses on the basis of clinical relevance plus sufficient case numbers (Table 1). PM is expressed as number of cases/1,000 ill travelers returned from the region(s) of interest. Where model fit of the PM variation over time was adequate (assessed statistically as the proportion of variance explained by year and considered adequately fitted if the coefficient of determination [R2 statistic] was >50%), the rate of change in proportion was estimated by using linear regression with year as the independent variable and by using p value to assess the null hypothesis that there was no change over time. Statistical significance was set at p<0 .05.="" 10="" by="" college="" for="" lp="" p="" performed="" procedures="" stata="" station="" statistical="" tatacorp="" tx="" usa="" using="" were="" windows=""> Case clustering was assessed by using the scan statistic on georeferenced data. Cases were georeferenced to the centroid of the likely country of acquisition, and rate estimates were based on the total number of ill returned travelers from that country who visited GeoSentinel sites. Diseases examined—dengue, malaria, and enteric fever—were chosen because of clinical importance and relative frequency. The scan statistic uses a Poisson model to estimate the number of cases of each disease relative to the population returning from a given area. To encompass changes in season, the temporal window was set to 3 months. The spatial window was set to a 1,000-km radius. The scan statistic was calculated by using SatScan 9.1.1 (Kulldorff M; Information Management Services Inc., Boston, MA, USA), and significance was assessed by using 1,000 Monte Carlo simulations.

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