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SCHUEMIE M, REPS J, BLACK A, DeFALCO F, EVANS L, FRIDGEIRSSON E, GILBERT JP, KNOLL C, LAVALLEE M, RAO GA, RIJNBEEK P, SADOWSKI K, SENA A, SWERDEL J, WILLIAMS RD, SUCHARD M. Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research. Stud Health Technol Inform 2024; 310:966-970. [PMID: 38269952 PMCID: PMC10868467 DOI: 10.3233/shti231108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.
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Affiliation(s)
- Martijn SCHUEMIE
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Biostatistics, UCLA, Los Angeles, CA, USA
| | - Jenna REPS
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Adam BLACK
- Observational Health Data Science and Informatics, New York, NY, USA
- Odysseus Data Services Inc., Cambridge, MA, USA
| | - Frank DeFALCO
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Lee EVANS
- Observational Health Data Science and Informatics, New York, NY, USA
- LTS Computing LLC, West Chester, PA, USA
| | - Egill FRIDGEIRSSON
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - James P. GILBERT
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Chris KNOLL
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Martin LAVALLEE
- Observational Health Data Science and Informatics, New York, NY, USA
- Virginia Commonwealth University, Richmond, VA, USA
| | - Gowtham A. RAO
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Peter RIJNBEEK
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Katy SADOWSKI
- Observational Health Data Science and Informatics, New York, NY, USA
- TrialSpark Inc., New York, NY, USA
| | - Anthony SENA
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joel SWERDEL
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Ross D. WILLIAMS
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marc SUCHARD
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Biostatistics, UCLA, Los Angeles, CA, USA
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City, UT, USA
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DABKE G, LE MENACH A, BLACK A, GAMBLIN J, PALMER M, BOXALL N, BOOTH L. Duration of shedding of Verocytotoxin-producing Escherichia coli in children and risk of transmission in childcare facilities in England. Epidemiol Infect 2014; 142:327-34. [PMID: 23672954 PMCID: PMC9151086 DOI: 10.1017/s095026881300109x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Revised: 03/28/2013] [Accepted: 04/17/2013] [Indexed: 11/06/2022] Open
Abstract
Exclusion of children with presumptive Verocytotoxin-producing Escherichia coli (VTEC) from childcare facilities until negative stool specimens are obtained is routine practice that disrupts families. We estimated the shedding and exclusion duration and transmission risk in such facilities. The study population comprised 225 children aged <6 years attending 201 childcare facilities in England with microbiologically confirmed VTEC in 2010-2011. We estimated the interval from onset to first negative specimen, and identified transmission events with secondary cases linked to facilities. The median duration of shedding was 31 days, and median period of exclusion was 39·5 days. Cases attending facilities while shedding VTEC did so for a median of 2 days before exclusion. Secondary cases occurred in 6/83 facilities (7%) attended by infectious cases. Despite evidence of VTEC shedding at facilities, transmission is relatively low. Revised control guidelines could consider supervised return for prolonged asymptomatic shedders.
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Affiliation(s)
- G. DABKE
- Hampshire and Isle of Wight Health Protection Unit, Hampshire, UK
| | - A. LE MENACH
- Health Protection Agency, London and South East Regional Epidemiology Units, London, UK
- European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Control and Prevention, Tomtebodavägen, Solna, Stockholm, Sweden
| | - A. BLACK
- Hampshire and Isle of Wight Health Protection Unit, Hampshire, UK
| | - J. GAMBLIN
- Hampshire and Isle of Wight Health Protection Unit, Hampshire, UK
| | - M. PALMER
- Hampshire and Isle of Wight Health Protection Unit, Hampshire, UK
| | - N. BOXALL
- Health Protection Agency, London and South East Regional Epidemiology Units, London, UK
| | - L. BOOTH
- Hampshire and Isle of Wight Health Protection Unit, Hampshire, UK
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