Trading-off privacy and utility: the Washington State experience assessing the performance of a public health digital exposure notification system for coronavirus disease 2019

ObjectiveDigital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State’s experience utilizing these constructs to design data-driven evaluation approaches.MethodsWe conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs.ResultsWe identified 8 useful performance indicators within reach, engagement, and effectiveness constructs.DiscussionWe use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure.ConclusionContinued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.     

The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.     

Design and implementation of a privacy preserving electronic health record linkage tool in Chicago

Objective To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.Methods The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.Results The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.Conclusions Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.     

Dobbs and the future of health data privacy for patients and healthcare organizations

The Supreme Court recently overturned settled case law that affirmed a pregnant individual’s Constitutional right to an abortion. While many states will commit to protect this right, a large number of others have enacted laws that limit or outright ban abortion within their borders. Additional efforts are underway to prevent pregnant individuals from seeking care outside their home state. These changes have significant implications for delivery of healthcare as well as for patient-provider confidentiality. In particular, these laws will influence how information is documented in and accessed via electronic health records and how personal health applications are utilized in the consumer domain. We discuss how these changes may lead to confusion and conflict regarding use of health information, both within and across state lines, why current health information security practices may need to be reconsidered, and what policy options may be possible to protect individuals’ health information.     

健康医疗大数据时代的隐私保护探析

以基因数据为例,全面分析健康大数据隐私面临的挑战,从联盟数据分析、同态加密、硬件加密、差分隐私几方面探讨隐私数据保护策略,阐述有关数据安全和隐私保护法律建设,以期为相关研究提供参考。     

区块链技术在电子健康档案中的应用与存在的问题

我国电子健康档案建设还处于初级阶段,数据的交互与利用和数据隐私安全问题之间的矛盾尚未能得到很好解决。区块链技术的出现,为数据共享与隐私之间的矛盾问题提供了新的解决方案。通过总结区块链技术在国内外电子健康档案中的应用及存在的问题,为区块链技术在电子健康档案建设中的发展提供借鉴。     

Electronic case reporting (eCR) of COVID-19 to public health: implementation perspectives from the Minnesota Department of Health

Electronic case reporting (eCR) is the automated generation and transmission of case reports from electronic health records to public health for review and action. These reports (electronic initial case reports: eICRs) adhere to recommended exchange and terminology standards. eCR is a partnership of the Centers for Disease Control and Prevention (CDC), Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE). The Minnesota Department of Health (MDH) received eICRs for COVID-19 from April 2020 (3 sites, manual process), automated eCR implementation in August 2020 (7 sites), and on-boarded ∼1780 clinical units in 460 sites across 6 integrated healthcare systems (through March 2022). Approximately 20 000 eICRs/month were reported to MDH during high-volume timeframes. With increasing provider/health system implementation, the proportion of COVID-19 cases with an eICR increased to 30% (March 2022). Evaluation of data quality for select demographic variables (gender, race, ethnicity, email, phone, language) across the 6 reporting health systems revealed a high proportion of completeness (>80%) for half of variables and less complete data for rest (ethnicity, email, language) along with low ethnicity data (<50%) for one health system. Presently eCR implementation at MDH includes only one EHR vendor. Next steps will focus on onboarding other EHRs, additional eICR data extraction/utilization, detailed analysis, outreach to address data quality issues, and expanding to other reportable conditions.     

Codesigning a community-based participatory research project to assess tribal perspectives on privacy and health data sharing: A report from the Strong Heart Study

Broad health data sharing raises myriad ethical issues related to data protection and privacy. These issues are of particular relevance to Native Americans, who reserve distinct individual and collective rights to control data about their communities. We sought to gather input from tribal community leaders on how best to understand health data privacy and sharing preferences in this population. We conducted a workshop with 14 tribal leaders connected to the Strong Heart Study to codesign a research study to assess preferences concerning health data privacy for biomedical research. Workshop participants provided specific recommendations regarding who should be consulted, what questions should be posed, and what methods should be used, underscoring the importance of relationship-building between researchers and tribal communities. Biomedical researchers and informaticians who collect and analyze health information from Native communities have a unique responsibility to safeguard these data in ways that align to the preferences of specific communities.     

状态空间模型及其在传染病发病率预测中的应用

目的 :研究状态空间模型的建立及其在传染病发病预测中的应用,并探讨提高模型准确性和实用性的途径。方法 :以2005年1月~2010年12月我国肺结核发病资料建立模型,以2011年的发病资料作为模型预测效果的考核样本。首先采用移动平均比率法和HP滤波对资料进行初步分析,然后根据分析的结果进行定阶、初始化并估计参数,建立状态空间模型,最后对预测结果进行检验和分析。结果:状态空间模型可以将发病率变化过程中的各种特征成分分解出来,其年内逐月发病率的预测精度在90%以上。结论:状态空间模型对我国肺结核发病情况的拟合度较高,预测效果良好。     

A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States

BackgroundPrivacy-related concerns can prevent equitable participation in health research by US Indigenous communities. However, studies focused on these communities'' views regarding health data privacy, including systematic reviews, are lacking.MethodsWe conducted a systematic literature review analyzing empirical, US-based studies involving American Indian/Alaska Native (AI/AN) and Native Hawaiian or other Pacific Islander (NHPI) perspectives on health data privacy, which we define as the practice of maintaining the security and confidentiality of an individual’s personal health records and/or biological samples (including data derived from biological specimens, such as personal genetic information), as well as the secure and approved use of those data.ResultsTwenty-one studies involving 3234 AI/AN and NHPI participants were eligible for review. The results of this review suggest that concerns about the privacy of health data are both prevalent and complex in AI/AN and NHPI communities. Many respondents raised concerns about the potential for misuse of their health data, including discrimination or stigma, confidentiality breaches, and undesirable or unknown uses of biological specimens.ConclusionsParticipants cited a variety of individual and community-level concerns about the privacy of their health data, and indicated that these deter their willingness to participate in health research. Future investigations should explore in more depth which health data privacy concerns are most salient to specific AI/AN and NHPI communities, and identify the practices that will make the collection and use of health data more trustworthy and transparent for participants.     

公共卫生中的知情权和信息公开制度初探

知情权与政府信息公开制度发展较晚，但作用日益重要。我国法律中的相关规定较为零散，亟需加强研究。卫生行政部门的信息公开关系到普通民众的生命健康安全，因而更值得从制度建设和实践操作上进一步完善。     

卫生政策学案例教学实践与思考

基于卫生政策学课程特点,分析实施案例教学的重要性。并从总体思路、教学内容、实施流程、考核评价等方面介绍卫生政策学案例教学的设计与具体实施过程。通过总结教学实践情况。提出案例教学中应注意的问题及相关建议。     

名老中医救治重大传染病经验数据仓库及智能挖掘平台的实现

为保存和有效利用名老中医救治重大传染病的经验,通过分析名老中医经验整理的研究方法,提出构建名老中医救治重大传染病经验数据仓库及智能挖掘平台的设想,并分析了其可行性、技术条件及现实意义,为今后研究名老中医防治传染病经验开创一条新的途径。     

迁西县自然疫源性和虫媒传染病流行因素分析

目的通过分析迁西县2005年1月-2011年6月迁西县自然疫源性和虫媒传染病的发病情况和影响因素,为有效预防和控制自然疫源性和虫媒传染病的发生和蔓延提供科学依据。方法应用描述性流行病学方法对资料进行分析,数据采用Excel2003软件进行统计。结果迁西县2005年1月～2011年6月通过网络直报报告自然疫源性和虫媒传染病包括狂犬病(2例)、肾综合症出血热(25例)、布鲁氏菌病(简称布病5例)、斑疹伤寒(8例)、流行性乙型脑炎(1例)、疟疾(1例)6种传染病共42例病例,死亡2例,年平均发病率为1.61/10万,病死率4.76%。气候变暖、生态环境变化、畜牧养殖业快速发展、防治宣传教育不足等因素是影响迁西县自然疫源性和虫媒传染病发病与流行的重要影响因素。结论在开展自然疫源性和虫媒传染病监测、宿主动物和病媒生物带毒情况监测和人群免疫状况监测的同时应加强气候和生态环境变化的监测,各相关部门加强合作,开展综合防治。     

新冠病毒肺炎疫情下社区医院公共卫生人力资源配置

目前在新型冠状病毒肺炎疫情的影响下,社区医院的公共卫生人力资源是疫情防控紧急状态下的主力军之一,公共卫生人力资源的数量、质量和分布是疫情防控工作的影响因素。建立信息共享机制,对公共卫生人力资源合理安排,进行网格化管理,必要时申请调查是每一个社区医院需要思考的问题。通过调查具有代表性的9家社区医院公共卫生人力资源情况,分析普遍存在的问题及原因,并提出相应的建议。