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

项目摘要 这项研究的目的是(1)通过以下方式建立不同年龄、性别和种族的讣告数据的代表性程度 将这些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)建立利用网上讣告数据预测行政记录的模型。 在卫生保健紧急情况下，必须监测所有原因的死亡，而不仅仅是特定原因的死亡。 计算超额死亡人数的原因有以下几个：(1)按具体原因统计的官方数据 死亡可能会低估那些在死亡前没有检测出阳性的人；(2)医院和民事登记处可能不会 处理几天甚至几周的死亡证明，这会在数据中造成滞后；(3)人 填写死亡证明不能获得完整的医疗记录或以其他方式知道 阳性检测或症状；(4)大流行和突发卫生事件转移了人们的注意力和资源 其他情况(例如，癌症患者看到延迟和推迟治疗)和气馁的人 在需要的时候去医院(例如中风)，这可能间接导致了 新冠肺炎以外的其他疾病造成的死亡。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 性价比高的方式。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 在被捕获到释放这些武器之前，这一延迟阻碍了监测工作。提供有关以下方面的信息 性别、年龄和种族是至关重要的，因为突发卫生事件可能直接或间接地导致不成比例的 某些群体的死亡人数有所增加。在死亡率特别高(或很低)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。这项工作也可以 为疾病监测奠定基础，以防未来出现大流行，因为在线死亡记录更容易 而且访问成本比管理数据更低。

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.