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Suarez EA, Nguyen M, Zhang D, Zhao Y, Stojanovic D, Munoz M, Liedtka J, Anderson A, Liu W, Dashevsky I, Cole D, DeLuccia S, Menzin T, Noble J, Maro JC. Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System. Pharmacoepidemiol Drug Saf 2023; 32:126-136. [PMID: 35871766 DOI: 10.1002/pds.5512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.
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Affiliation(s)
- Elizabeth A Suarez
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Di Zhang
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yueqin Zhao
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Danijela Stojanovic
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Monica Munoz
- Division of Pharmacovigilance, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jane Liedtka
- Division of Pediatric and Maternal Health, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Abby Anderson
- Division of Urology, Obstetrics and Gynecology, Center for Drug and Evaluation Research, US Food and Drug Administration, Beltsville, Maryland, USA
| | - Wei Liu
- Division of Epidemiology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Inna Dashevsky
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - David Cole
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Sandra DeLuccia
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Talia Menzin
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Noble
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
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Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs. Epidemiology 2023; 34:90-98. [PMID: 36252086 DOI: 10.1097/ede.0000000000001561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BACKGROUND Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. METHODS We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. RESULTS The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. CONCLUSIONS Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.
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Liu Z, Meng R, Yang Y, Li K, Yin Z, Ren J, Shen C, Feng Z, Zhan S. Active Vaccine Safety Surveillance: Global Trends and Challenges in China. HEALTH DATA SCIENCE 2021; 2021:9851067. [PMID: 38487501 PMCID: PMC10880162 DOI: 10.34133/2021/9851067] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/03/2021] [Indexed: 03/17/2024]
Abstract
Importance. The great success in vaccine-preventable diseases has been accompanied by vaccine safety concerns. This has caused vaccine hesitancy to be the top 10 in threats to global health. The comprehensive understanding of adverse events following immunization should be entirely based on clinical trials and postapproval surveillance. It has increasingly been recognized worldwide that the active surveillance of vaccine safety should be an essential part of immunization programs due to its complementary advantages to passive surveillance and clinical trials.Highlights. In the present study, the framework of vaccine safety surveillance was summarized to illustrate the importance of active surveillance and address vaccine hesitancy or safety concerns. Then, the global progress of active surveillance systems was reviewed, mainly focusing on population-based or hospital-based active surveillance. With these successful paradigms, the practical and reliable ways to create robust and similar systems in China were discussed and presented from the perspective of available databases, methodology challenges, policy supports, and ethical considerations.Conclusion. In the inevitable trend of the global vaccine safety ecosystem, the establishment of an active surveillance system for vaccine safety in China is urgent and feasible. This process can be accelerated with the consensus and cooperation of regulatory departments, research institutions, and data owners.
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Affiliation(s)
- Zhike Liu
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China
| | - Ruogu Meng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Yu Yang
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Keli Li
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zundong Yin
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingtian Ren
- Center for Drug Reevaluation, National Medical Products Administration, BeijingChina
| | - Chuanyong Shen
- Center for Drug Reevaluation, National Medical Products Administration, BeijingChina
| | - Zijian Feng
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Siyan Zhan
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China
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Maro JC, Eworuke E, Hou L, Welch EC, Goulding MR, Izem R, Lee JY, Toh S, Fireman B, Nguyen MD. Conducting prospective sequential surveillance in real-world dynamic distributed databases. Pharmacoepidemiol Drug Saf 2020; 29:1331-1335. [PMID: 32449261 DOI: 10.1002/pds.5002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 03/24/2020] [Accepted: 03/26/2020] [Indexed: 11/07/2022]
Affiliation(s)
- Judith C Maro
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Efe Eworuke
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Laura Hou
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Emily C Welch
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Margie R Goulding
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Rima Izem
- Office of Biostatistics and Study Methodology, Department of Pediatrics, George Washington University and Children's National Research Institute, Washington, DC, USA
| | - Joo-Yeon Lee
- Kaiser Permanente Northern California, Oakland, CA, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Bruce Fireman
- Kaiser Permanente Northern California, Oakland, CA, USA
| | - Michael D Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
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Workshop on the Italian Pharmacovigilance System in the International Context: Critical Issues and Perspectives. Drug Saf 2018; 42:683-687. [PMID: 30565019 DOI: 10.1007/s40264-018-0768-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Stergiopoulos S, Brown CA, Felix T, Grampp G, Getz KA. A Survey of Adverse Event Reporting Practices Among US Healthcare Professionals. Drug Saf 2017; 39:1117-1127. [PMID: 27638657 PMCID: PMC5045838 DOI: 10.1007/s40264-016-0455-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Introduction The under-reporting of adverse drug events (ADEs) is an international health concern. A number of studies have assessed the root causes but, to our knowledge, little information exists relating under-reporting to practices and systems used for the recording and tracking of drug‐related adverse event observations in ambulatory settings, institutional settings, and retail pharmacies. Objectives Our objective was to explore the process for reporting ADEs in US hospitals, ambulatory settings, and retail pharmacies; to explore gaps and inconsistencies in the reporting process; and to identify the causes of under-reporting ADEs in these settings. Methods The Tufts Center for the Study of Drug Development (Tufts CSDD) interviewed 11 thought leaders and conducted a survey between May and August 2014 among US-based healthcare providers (HCPs) in diverse settings to assess their experiences with, and processes for, reporting ADEs. Results A total of 123 individuals completed the survey (42 % were pharmacists; 27 % were nurses; 15 % were physicians; and 16 % were classified as ‘other’). HCPs indicated that the main reasons for under-reporting were difficulty in determining the cause of the ADE, given that most patients receive multiple therapies simultaneously (66 % of respondents); that HCPs lack sufficient time to report ADEs (63 % of respondents); poor integration of ADE-reporting systems (53 % of respondents); and uncertainty about reporting procedures (52 % of respondents). Discussion The results of this pilot study identify that key factors contributing to the under-reporting of ADEs relate to a lack of standardized process, a lack of training and education, and a lack of integrated health information technologies. Electronic supplementary material The online version of this article (doi:10.1007/s40264-016-0455-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Stella Stergiopoulos
- Tufts Center for the Study of Drug Development, Tufts Medical School, 75 Kneeland Street, Ste 1100, Boston, MA, 02111, USA.
| | - Carrie A Brown
- Tufts Center for the Study of Drug Development, Tufts Medical School, 75 Kneeland Street, Ste 1100, Boston, MA, 02111, USA
| | | | | | - Kenneth A Getz
- Tufts Center for the Study of Drug Development, Tufts Medical School, 75 Kneeland Street, Ste 1100, Boston, MA, 02111, USA
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Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J, Menius JA, Painter JL, Thomas M, Pierce CE, Rodriguez HW, Brownstein JS, Freifeld CC, Bell HG, Dasgupta N. Social Media Listening for Routine Post-Marketing Safety Surveillance. Drug Saf 2016; 39:443-54. [PMID: 26798054 DOI: 10.1007/s40264-015-0385-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources. OBJECTIVES The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance. METHODS A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information. RESULTS In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information. CONCLUSION Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.
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Affiliation(s)
- Gregory E Powell
- GlaxoSmithKline, 5 Moore Dr., Research Triangle Park, NC, 27709, USA.
| | | | | | | | - James Blowers
- GlaxoSmithKline, 5 Moore Dr., Research Triangle Park, NC, 27709, USA
| | - J Alan Menius
- GlaxoSmithKline, 5 Moore Dr., Research Triangle Park, NC, 27709, USA
| | - Jeffery L Painter
- GlaxoSmithKline, 5 Moore Dr., Research Triangle Park, NC, 27709, USA
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Richesson RL, Sun J, Pathak J, Kho AN, Denny JC. Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods. Artif Intell Med 2016; 71:57-61. [PMID: 27506131 PMCID: PMC5480212 DOI: 10.1016/j.artmed.2016.05.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 05/30/2016] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale. METHODS Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review. RESULTS The practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources. CONCLUSIONS Machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.
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Affiliation(s)
- Rachel L Richesson
- Duke University School of Nursing, 311 Trent Drive, Durham, NC 27710 USA.
| | - Jimeng Sun
- School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30313, USA.
| | - Jyotishman Pathak
- Department of Health Sciences Research, 200 1st Street SW, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Abel N Kho
- Departments of Medicine and Preventive Medicine, Northwestern University, 633 N St. Clair St. 20th floor. Chicago IL 60611, USA.
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, 2525 West End Ave, Suite 672, Nashville, TN 37203, USA.
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Lash TL, Fox MP, Cooney D, Lu Y, Forshee RA. Quantitative Bias Analysis in Regulatory Settings. Am J Public Health 2016; 106:1227-30. [PMID: 27196652 PMCID: PMC4984770 DOI: 10.2105/ajph.2016.303199] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2016] [Indexed: 11/04/2022]
Abstract
Nonrandomized studies are essential in the postmarket activities of the US Food and Drug Administration, which, however, must often act on the basis of imperfect data. Systematic errors can lead to inaccurate inferences, so it is critical to develop analytic methods that quantify uncertainty and bias and ensure that these methods are implemented when needed. "Quantitative bias analysis" is an overarching term for methods that estimate quantitatively the direction, magnitude, and uncertainty associated with systematic errors influencing measures of associations. The Food and Drug Administration sponsored a collaborative project to develop tools to better quantify the uncertainties associated with postmarket surveillance studies used in regulatory decision making. We have described the rationale, progress, and future directions of this project.
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Affiliation(s)
- Timothy L Lash
- Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Matthew P Fox
- Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Darryl Cooney
- Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Yun Lu
- Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Richard A Forshee
- Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD
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Gini R, Schuemie M, Brown J, Ryan P, Vacchi E, Coppola M, Cazzola W, Coloma P, Berni R, Diallo G, Oliveira JL, Avillach P, Trifirò G, Rijnbeek P, Bellentani M, van Der Lei J, Klazinga N, Sturkenboom M. Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies. EGEMS 2016; 4:1189. [PMID: 27014709 PMCID: PMC4780748 DOI: 10.13063/2327-9214.1189] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Introduction: We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing. Case Descriptions and Variation Among Sites: We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE). Findings: National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++. Conclusion: Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.
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Affiliation(s)
- Rosa Gini
- Agenzia Regionale di Sanità della Toscana; Erasmus MC University Medical Center
| | - Martijn Schuemie
- Janssen Research & Development, Epidemiology; Observational Health Data Sciences and Informatics (OHDSI)
| | | | - Patrick Ryan
- Janssen Research & Development, Epidemiology; Observational Health Data Sciences and Informatics (OHDSI)
| | - Edoardo Vacchi
- Università degli Studi di Milano, Dipartimento di Informatica
| | - Massimo Coppola
- Consiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell'Informazione
| | - Walter Cazzola
- Università degli Studi di Milano, Dipartimento di Informatica
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