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利用推特识别和应对食物中毒:食品安全圣路易斯项目。

Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project.

作者信息

Harris Jenine K, Hawkins Jared B, Nguyen Leila, Nsoesie Elaine O, Tuli Gaurav, Mansour Raed, Brownstein John S

机构信息

Brown School, Washington University in St Louis, St Louis, Missouri (Dr Harris); Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts (Drs Hawkins, Tuli, and Brownstein); Department of Pediatrics, Harvard Medical School, Boston, Massachusetts (Drs Hawkins and Brownstein); City of St Louis Department of Health, St Louis, Missouri (Ms Nguyen); Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington (Dr Nsoesie); and Chicago Department of Public Health, Chicago, Illinois (Mr Mansour).

出版信息

J Public Health Manag Pract. 2017 Nov/Dec;23(6):577-580. doi: 10.1097/PHH.0000000000000516.

Abstract

CONTEXT

Foodborne illness affects 1 in 4 US residents each year. Few of those sickened seek medical care or report the illness to public health authorities, complicating prevention efforts. Citizens who report illness identify food establishments with more serious and critical violations than found by regular inspections. New media sources, including online restaurant reviews and social media postings, have the potential to improve reporting.

OBJECTIVE

We implemented a Web-based Dashboard (HealthMap Foodborne Dashboard) to identify and respond to tweets about food poisoning from St Louis City residents.

DESIGN AND SETTING

This report examines the performance of the Dashboard in its first 7 months after implementation in the City of St Louis Department of Health.

MAIN OUTCOME MEASURES

We examined the number of relevant tweets captured and replied to, the number of foodborne illness reports received as a result of the new process, and the results of restaurant inspections following each report.

RESULTS

In its first 7 months (October 2015-May 2016), the Dashboard captured 193 relevant tweets. Our replies to relevant tweets resulted in more filed reports than several previously existing foodborne illness reporting mechanisms in St Louis during the same time frame. The proportion of restaurants with food safety violations was not statistically different (P = .60) in restaurants inspected after reports from the Dashboard compared with those inspected following reports through other mechanisms.

CONCLUSION

The Dashboard differs from other citizen engagement mechanisms in its use of current data, allowing direct interaction with constituents on issues when relevant to the constituent to provide time-sensitive education and mobilizing information. In doing so, the Dashboard technology has potential for improving foodborne illness reporting and can be implemented in other areas to improve response to public health issues such as suicidality, spread of Zika virus infection, and hospital quality.

摘要

背景

食源性疾病每年影响四分之一的美国居民。很少有患病者寻求医疗护理或向公共卫生当局报告病情,这使得预防工作变得复杂。报告病情的公民指出的食品经营场所存在比常规检查发现的更严重和关键的违规行为。包括在线餐厅评论和社交媒体帖子在内的新媒体来源有改善报告情况的潜力。

目的

我们实施了一个基于网络的仪表板(健康地图食源性疾病仪表板),以识别和回应来自圣路易斯市居民关于食物中毒的推文。

设计与背景

本报告考察了仪表板在圣路易斯市卫生部实施后的前7个月的表现。

主要观察指标

我们检查了捕获和回复的相关推文数量、因新流程收到的食源性疾病报告数量以及每次报告后餐厅检查的结果。

结果

在其运行的前7个月(2015年10月至2016年5月),仪表板捕获了193条相关推文。我们对相关推文的回复导致在同一时间段内提交的报告比圣路易斯市其他几种现有的食源性疾病报告机制更多。与通过其他机制报告后接受检查的餐厅相比,仪表板报告后接受检查的餐厅中存在食品安全违规行为的比例在统计学上没有差异(P = 0.60)。

结论

仪表板在使用当前数据方面不同于其他公民参与机制,它允许在与选民相关的问题上与选民直接互动,以提供时效性强的教育和动员信息。通过这样做,仪表板技术有改善食源性疾病报告的潜力,并且可以在其他领域实施,以改善对诸如自杀倾向、寨卡病毒感染传播和医院质量等公共卫生问题的应对。

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本文引用的文献

1
Measuring patient-perceived quality of care in US hospitals using Twitter.
BMJ Qual Saf. 2016 Jun;25(6):404-13. doi: 10.1136/bmjqs-2015-004309. Epub 2015 Oct 13.
4
Reporting of foodborne illness by U.S. consumers and healthcare professionals.
Int J Environ Res Public Health. 2013 Aug 19;10(8):3684-714. doi: 10.3390/ijerph10083684.
5
Evaluation of a statewide foodborne illness complaint surveillance system in Minnesota, 2000 through 2006.
J Food Prot. 2010 Nov;73(11):2059-64. doi: 10.4315/0362-028x-73.11.2059.
6
Food safety: emerging trends in foodborne illness surveillance and prevention.
J Am Diet Assoc. 2004 Nov;104(11):1708-17. doi: 10.1016/j.jada.2004.08.028.
7
Food-related illness and death in the United States.
Emerg Infect Dis. 1999 Sep-Oct;5(5):607-25. doi: 10.3201/eid0505.990502.

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