Health Informatics to Model the Scott County HIV Outbreak

健康信息学对斯科特县艾滋病毒爆发进行建模

• 批准号: 10017931
• 负责人: Timothy Glenn Heckman
• 金额: $ 19.81万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017931
• 关键词: Accident and Emergency department; Accounting; Admission activity; Affect; Alcohol or Other Drugs use; Algorithms; Area; Collection; Communicable Diseases; Communities; Contact Tracing; Counseling; County; Data; Data Sources; Development; Disease; Disease Outbreaks; Doctor of Medicine; Drug abuse; Emergency response; Epidemiology; Future; Geographic Locations; HIV; HIV Infections; HIV/STD; Health; Health Personnel; Healthcare; Hepatitis C Incidence; Human immunodeficiency virus test; Indiana; Infection; Informatics; Information and Media; Intervention; Investigation; Law Enforcement; Letters; Machine Learning; Medical; Modality; Modeling; Needle-Exchange Programs; Opioid; Oxymorphone; Persons; Population; Prophylactic treatment; Public Health; Public Health Informatics; Recording of previous events; Records; Research; Research Personnel; Resources; Risk; Rural; Series; Signal Transduction; Site; Social Network; Soft Tissue Infections; Source; Specialist; Syringes; System; Techniques; Time; Twitter; United States; austin; cost; data archive; data mining; digital; diverse data; electronic data; epidemiology study; experience; frontier; innovation; opioid injection; opioid overdose; opioid use; pre-exposure prophylaxis; prescription opioid; public health emergency; response; rural counties; search engine; social media; surveillance data; text searching; time use; transmission process; trend

ABSTRACT Many regions of the United States, particularly rural and frontier areas, lack the resources to proactively identify isolated infectious disease outbreaks. Scott County Indiana experienced an HIV outbreak in 2014- 2015, resulting in approximately 200 new HIV infections. If not for a vigilant Disease Intervention Specialist (DIS) who noticed an escalating number of HIV infections in Scott County over a brief period of time, the outbreak could have been much worse. An isolated outbreak in resource-limited settings such as Scott County underscores the need for more innovative, automated, and real-time HIV biosurveillance systems in non-urban areas. To date, digital HIV epidemiologic research has relied almost exclusively on Twitter; however, this approach is likely too restrictive and has not yet yielded a promising approach to predicting HIV outbreaks. More heterogeneous sources of data--in addition to social media--may more efficiently predict the arrival of HIV in a community with limited surveillance resources. The proposed health informatics research will analyze historical time series data collected from 2014 through 2016 to identify predictor variables that model the Scott County HIV outbreak. Data to be analyzed include: (1) emergency room (ER) admissions and discharges related to opioid use and soft tissue infections related to drug abuse; (2) HIV testing surveillance data; (3) HCV incidence data; (4) search engine inquires of relevant topics, such as Google or Bing searches for “HIV testing” and “Opana”; (5) law enforcement arrest records (particularly those related to opioid possession and distribution); and (6) electronic healthcare reimbursement data of HIV-related treatments (e.g., post-exposure prophylaxis). Automated data/text mining and machine learning techniques will also be applied to (7) social media data (i.e., Twitter tweets and Reddit forum posts) that make reference to HIV, Opana, substance use, and other terms to determine if trends in social media data could have predicted HIV's arrival in, and transmission throughout, Scott County. Using the diverse data sources listed above, our team will correlate the time series of key predictor variables to identify the data source (or sources) most predictive of a known HIV outbreak. If our team develops a health informatics approach and algorithm(s) identifying trends in social media and other electronic data indicating an imminent HIV outbreak, state and county health departments can use these “signals” to increase the number of HIV testing and counseling sites in the affected area, health care providers can more aggressively screen for HIV/STI infection, syringe-service programs can be mobilized rapidly and targeted more efficiently, contact tracing activities can be initiated, and PEP and PrEP can be prescribed to those at risk for HIV infection in the geographic region of concern. This study will also examine feasibility issues related to the collection and analysis of electronic health information and social media data in HIV biosurveillance efforts, such as data accessibility, costs, and generalizability.

抽象的 美国的许多地区，特别是农村和边境地区，缺乏资源来积极主动地开展工作 识别孤立的传染病暴发。印第安纳州斯科特县在 2014 年爆发了艾滋病毒疫情 - 2015 年，导致大约 200 例新的艾滋病毒感染者。如果没有警惕的疾病干预专家 (DIS) 注意到斯科特县的艾滋病毒感染人数在短时间内不断增加， 疫情可能会更严重。在斯科特县等资源有限的地区爆发孤立的疫情 强调非城市地区需要更加创新、自动化和实时的艾滋病毒生物监测系统 地区。迄今为止，数字艾滋病毒流行病学研究几乎完全依赖 Twitter；然而，这 这种方法可能过于严格，并且尚未产生一种有前景的方法来预测艾滋病毒的爆发。 除了社交媒体之外，更多异构数据源可能会更有效地预测艾滋病毒的到来 在监视资源有限的社区中。拟议的健康信息学研究将分析 从 2014 年到 2016 年收集的历史时间序列数据，用于识别对斯科特建模的预测变量 县艾滋病毒爆发。需要分析的数据包括：（1）急诊室（ER）入院和出院情况 与阿片类药物使用和与药物滥用相关的软组织感染有关； （二）艾滋病毒检测监测数据； (3)丙肝病毒 发病率数据； (4)搜索引擎查询相关主题，例如Google或Bing搜索“HIV检测” 和“奥帕纳”； (5) 执法逮捕记录（特别是与持有阿片类药物和 分配）; (6) HIV 相关治疗的电子医疗报销数据（例如暴露后治疗） 预防）。自动数据/文本挖掘和机器学习技术也将应用于（7）社交 提及 HIV、Opana、药物使用的媒体数据（即 Twitter 推文和 Reddit 论坛帖子）， 和其他术语来确定社交媒体数据的趋势是否可以预测艾滋病毒的到来，以及 整个斯科特县的传输。使用上面列出的不同数据源，我们的团队将关联 关键预测变量的时间序列，用于识别最能预测已知数据的数据源（或多个数据源） 艾滋病毒爆发。如果我们的团队开发出一种健康信息学方法和算法来识别社交趋势 媒体和其他电子数据表明艾滋病毒即将爆发，州和县卫生部门可以 利用这些“信号”增加受影响地区艾滋病毒检测和咨询站点的数量、医疗保健 提供者可以更积极地筛查艾滋病毒/性传播感染，可以动员注射器服务计划 可以更快速、更有针对性地启动接触者追踪活动，并可以开展 PEP 和 PrEP 为所在地理区域内有艾滋病毒感染风险的人开具处方。这项研究还将考察 与收集和分析电子健康信息和社交媒体数据有关的可行性问题 HIV 生物监测工作，例如数据可获取性、成本和普遍性。