The use of online obituaries as a tool for public health surveillance

使用在线讣告作为公共卫生监测工具

• 批准号: 10452934
• 负责人: Maria Liliana Alva
• 金额: $ 7.8万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452934
• 关键词: Acquired Immunodeficiency Syndrome; African American; African American population; Age; Age Distribution; Attention; Awareness; COVID-19; COVID-19 monitoring; COVID-19 mortality; Cancer Patient; Cause of Death; Centers for Disease Control and Prevention (U.S.); Cessation of life; Containment; Data; Data Analyses; Data Collection; Data Linkages; Data Science; Data Set; Death Certificates; Death Records; Disease; Disease Marker; Disease Outbreaks; Effectiveness; Elderly; Emergency Care; Emergency Situation; Ethnic Origin; Event; Excess Mortality; Exercise; Exhibits; Fatality rate; Federal Government; Foundations; Future; Geography; Health; Health Policy; Home; Hospitals; Infectious Disease Epidemiology; Latinx; Life Expectancy; Link; Location; Measurement; Measures; Medical Records; Minority Men; Modeling; Monitor; Newspapers; Not Hispanic or Latino; Online Systems; Persons; Policies; Population Surveillance; Property; Public Health; Race; Records; Registries; Research; Resources; Risk Factors; Severity of illness; Sex Distribution; Stroke; Structure; Symptoms; Testing; Time; Training; Variant; Virus; Work; age group; application programming interface; base; caucasian American; coronavirus disease; cost; cost effective; data access; disadvantaged population; health disparity; hispanic community; innovation; mortality; open data; open source; opioid overdose; pandemic disease; premature; pressure; prospective; racial minority; response; sex; statistics; supervised learning; text searching; tool; web page; web site

Project Summary This study aims to (1) establish the degree of representativeness across age, sex, and race of obituary data by comparing that information with death certificate records to understand open-source data's reliability and measurement properties. (2) Build a model that uses online obituary data to predict administrative records. During health care emergencies, it is essential to monitor all-cause mortality and not just cause-specific deaths and to calculate the number of excess deaths for several reasons: (1) official statistics on cause-specific deaths might undercount people who did not test positive before dying; (2) hospitals and civil registries may not process death certificates for several days, or even weeks, which creates lags in the data; (3) the person completing the death certificate does not have access to the complete medical record or otherwise know about a positive test or symptoms; (4) pandemic and health emergencies divert attention and resources away from other conditions (e.g., cancer patients have seen delays and postponing treatment) and discouraged people from going to the hospital when needed (e.g., strokes), which may have indirectly caused an increase in fatalities from diseases other than COVID-19. Automated data collection from text mining of openly available online obituaries could allow us to derive quick predictions of age and sex distribution of death by location in a cost-effective way. Currently, publicly available datasets have a two-year lag. From the moment death records are captured to the time these are released, this delay hampers monitoring efforts. Providing information on sex, age, and race is critical because health emergencies might directly or indirectly cause a disproportionate increase in fatalities among certain groups. In places where mortality is exceptionally high (or low) based on obituary data, this form of monitoring can inform the policy response's effectiveness. This work can also be foundational for disease monitoring should future pandemics arise because online death records are easier and cheaper to access than administrative data.

项目概要 本研究旨在 (1) 通过以下方式确定讣告数据在年龄、性别和种族方面的代表性程度： 将该信息与死亡证明记录进行比较，以了解开源数据的可靠性和 测量属性。 (2) 建立一个使用在线讣告数据来预测行政记录的模型。 在医疗紧急情况期间，必须监测全因死亡率，而不仅仅是特定原因的死亡 并计算超额死亡人数，原因如下：（1）针对特定原因的官方统计数据 死亡人数可能会低估死前检测结果未呈阳性的人数； (2) 医院和民事登记处不得 处理死亡证明需要几天甚至几周的时间，这会造成数据滞后； (3) 人 填写死亡证明无法获得完整的医疗记录或以其他方式了解 阳性测试或症状； (4) 流行病和突发卫生事件转移注意力和资源 其他情况（例如，癌症患者出现延误和推迟治疗）和灰心丧气的人 避免在需要时（例如中风）去医院，这可能间接导致了 COVID-19 以外的疾病造成的死亡。从公开可用的文本挖掘中自动收集数据 在线讣告可以让我们快速预测死亡地点的年龄和性别分布 具有成本效益的方式。目前，公开可用的数据集有两年的滞后。从死亡记录的那一刻起 被捕获到被释放的时间，这种延迟妨碍了监测工作。提供有关以下方面的信息 性别、年龄和种族至关重要，因为突发卫生事件可能直接或间接导致不成比例的 某些群体的死亡人数增加。在死亡率异常高（或低）的地方 讣告数据，这种形式的监测可以告知政策反应的有效性。这个作品还可以 由于在线死亡记录更容易，因此在未来出现流行病时是疾病监测的基础 并且比管理数据的访问成本更低。