Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.
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ABSTRACT:
SUBMITTER: Kim J
PROVIDER: S-EPMC7523159 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.
| S-EPMC8194878 | biostudies-literature
Project description:OBJECTIVE:Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. MATERIALS AND METHODS:We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model. RESULTS:We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. DISCUSSION:The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications. CONCLUSION:The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.
| S-EPMC7025359 | biostudies-literature
Project description:IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.
| S-EPMC7580065 | biostudies-literature
Project description:PURPOSE:Privacy-protecting analytic and data-sharing methods that minimize the disclosure risk of sensitive information are increasingly important due to the growing interest in utilizing data across multiple sources. We conducted a simulation study to examine how avoiding sharing individual-level data in a distributed data network can affect analytic results. METHODS:The base scenario had four sites of varying sizes with 5% outcome incidence, 50% treatment prevalence, and seven confounders. We varied treatment prevalence, outcome incidence, treatment effect, site size, number of sites, and covariate distribution. Confounding adjustment was conducted using propensity score or disease risk score. We compared analyses of three types of aggregate-level data requested from sites: risk-set, summary-table, or effect-estimate data (meta-analysis) with benchmark results of analysis of pooled individual-level data. We assessed bias and precision of hazard ratio estimates as well as the accuracy of standard error estimates. RESULTS:All the aggregate-level data-sharing approaches, regardless of confounding adjustment methods, successfully approximated pooled individual-level data analysis in most simulation scenarios. Meta-analysis showed minor bias when using inverse probability of treatment weights (IPTW) in infrequent exposure (5%), rare outcome (0.01%), and small site (5,000 patients) settings. SE estimates became less accurate for IPTW risk-set approach with less frequent exposure and for propensity score-matching meta-analysis approach with rare outcomes. CONCLUSIONS:Overall, we found that we can avoid sharing individual-level data and obtain valid results in many settings, although care must be taken with meta-analysis approach in infrequent exposure and rare outcome scenarios, particularly when confounding adjustment is performed with IPTW.
| S-EPMC6135666 | biostudies-literature
Project description:Use of deceased subject Electronic Health Records can be an important piloting platform for informatics or biomedical research. Existing legal framework allows such research under less strict de-identification criteria; however, privacy of non-decedent must be protected. We report on creation of the decease subject Integrated Data Repository (dsIDR) at National Institutes of Health, Clinical Center and a pilot methodology to remove secondary protected health information or identifiable information (secondary PxI; information about persons other than the primary patient). We characterize available structured coded data in dsIDR and report the estimated frequencies of secondary PxI, ranging from 12.9% (sensitive token presence) to 1.1% (using stricter criteria). Federating decedent EHR data from multiple institutions can address sample size limitations and our pilot study provides lessons learned and methodology that can be adopted by other institutions.
| S-EPMC4420001 | biostudies-literature
Project description:As elective surgery slowly reopens across the country, it is paramount that surgeons recognize and take responsibility for their roles in protecting patient safety during the coronavirus disease 2019 pandemic. Namely, these include (1) to prevent further spread of the severe acute respiratory syndrome-CoV-2 virus, (2) to understand the shift in injuries that has occurred as a result of altered lifestyles led by our patients, and (3) to leverage our platforms to disseminate information regarding how individuals can maintain musculoskeletal health during the pandemic. Efforts taken to reduce the spread of severe acute respiratory syndrome-CoV-2 virus can be focused on 3 broad categories of provider-patient interaction: preoperative and clinic visits, surgical encounters, and postoperative care.
| S-EPMC7508518 | biostudies-literature
Project description:Due to recent developments in technologies associated with the Internet of Things (IoT), a large number of people now regularly use smart devices, such as smartwatches and smartphones. However, these devices are prone to data leaks because of security vulnerabilities. In particular, Android devices use permission-based security, which allows users to directly approve permissions requested by an app when installing it. As a result, many malicious apps can obtain and leak private user data by requesting more permissions than are needed. However, it is difficult to identify malicious apps based solely on the requested permissions. A system is hence needed to accurately identify malicious apps and protect private data from them. In this paper, we propose a system for hiding data related to a user's contacts or providing virtual data according to preconfigured policies when an Android app requests access to them. By hiding data related to the contacts, the proposed system can protect them from malicious apps. By using virtual data, it can even detect malicious apps that leak private data. The system requires less storage and provides faster access to user contacts than prevalent solutions to similar problems.
| S-EPMC6040689 | biostudies-literature
Project description:We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications.
| S-EPMC4151119 | biostudies-literature
Project description:We sought to define the host immune response, a.k.a, the “cytokine storm” that has been implicated in fatal COVID-19 using an AI-based approach. Over 45,000 publicly available transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a ‘seed’ gene; ACE2 was rationalized because the receptor it encodes enables the virus that causes Covid-19, SARS-CoV-2, to enter host cells. The signature was surprisingly conserved in all viral pandemics, including COVID-19, inspiring the nomenclature ViP-signature. A subset of 20-genes classified disease severity in respiratory pandemics. The ViP signatures pinpointed airway epithelial and myeloid cells as the major contributors of an IL-15 cytokine storm, and epithelial and NK cell destruction as determinants of severity/fatality. They also helped formulate precise therapeutic goals to reduce disease symptoms and severity. Thus, the ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool in our armamentarium to rapidly assess disease severity and vet candidate drugs.
2020-12-07 | GSE157059 | GEO
Project description:We sought to define the host immune response, a.k.a, the “cytokine storm” that has been implicated in fatal COVID-19 using an AI-based approach. Over 45,000 publicly available transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a ‘seed’ gene; ACE2 was rationalized because the receptor it encodes enables the virus that causes Covid-19, SARS-CoV-2, to enter host cells. The signature was surprisingly conserved in all viral pandemics, including COVID-19, inspiring the nomenclature ViP-signature. A subset of 20-genes classified disease severity in respiratory pandemics. The ViP signatures pinpointed airway epithelial and myeloid cells as the major contributors of an IL-15 cytokine storm, and epithelial and NK cell destruction as determinants of severity/fatality. They also helped formulate precise therapeutic goals to reduce disease symptoms and severity. Thus, the ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool in our armamentarium to rapidly assess disease severity and vet candidate drugs.
2020-12-07 | GSE157058 | GEO