Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland; Travel Clinic, University of Zurich, 8001 Zurich, Switzerland.
Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; University of Basel, 4003 Basel, Switzerland.
Travel Med Infect Dis. 2018 Jan-Feb;21:36-42. doi: 10.1016/j.tmaid.2017.11.005. Epub 2017 Nov 15.
New research methods offer opportunities to investigate the influence of environment on health during travel. Our study uses data from a smartphone application to describe spatial and environmental patterns in health among travellers.
A prospective cohort of travellers to Thailand used a smartphone application during their trips to 1) answer a daily questionnaire about health behaviours and events, and 2) collect streaming data on environment, itinerary, and weather. Incidence of health events was described by region and trip type. The relationship between environmental factors and health events was modelled using a logistic mixed model.
The 75/101 (74.3%) travellers that completed the study answered 940 questionnaires, 796 (84.7%) of which were geolocated to Southeast Asia. Accidents occurred to 20.0% of participants and were mainly in the Thai islands, while self-rated "severe" mental health events (21.3%) were centred in Bangkok. The odds of a health event were higher in Chiang Mai (2.34, 95% CI: 1.08, 5.08) and on rainy days (1.86, 95% CI: 1.03, 3.36).
Distinct patterns in spatial and environmental risk factors emerged in travellers to Thailand. Location based tracking could identify "hotspots" for health problems and update travel advice to target specific risk groups and regions.
新的研究方法为研究旅行期间环境对健康的影响提供了机会。我们的研究使用智能手机应用程序中的数据来描述旅行者健康的空间和环境模式。
一项针对前往泰国旅行者的前瞻性队列研究使用智能手机应用程序，1）回答有关健康行为和事件的每日问卷，2）收集环境、行程和天气的流媒体数据。健康事件的发生率按地区和旅行类型描述。使用逻辑混合模型对环境因素与健康事件之间的关系进行建模。
完成研究的 101 名旅行者中有 75 名（74.3%）回答了 940 份问卷，其中 796 份（84.7%）在东南亚进行了地理位置定位。事故发生率为 20.0%，主要发生在泰国岛屿上，而自评“严重”心理健康事件（21.3%）则集中在曼谷。在清迈（2.34，95%CI：1.08，5.08）和雨天（1.86，95%CI：1.03，3.36）发生健康事件的可能性更高。
前往泰国的旅行者中出现了不同的空间和环境风险因素模式。基于位置的跟踪可以识别健康问题的“热点”，并更新旅行建议，以针对特定的风险群体和地区。
