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Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module
Incidence and prevalence are key epidemiological determinants characterizing the quantum of a disease. We compared incidence and prevalence estimates derived automatically from the first ever online, essentially real-time, healthcare analytics platform—Livingstone—against findings from comparable pe...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368254/ https://www.ncbi.nlm.nih.gov/pubmed/37490430 http://dx.doi.org/10.1371/journal.pdig.0000310 |
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author | Heywood, Benjamin R. Morgan, Christopher Ll. Berni, Thomas R. Summers, Darren R. Jones, Bethan I. Jenkins-Jones, Sara Holden, Sarah E. Riddick, Lauren D. Fisher, Harry Bateman, James D. Bannister, Christian A. Threlfall, John Buxton, Aron Shepherd, Christopher P. Mathias, Elgan R. Thomason, Rhiannon K. Hubbuck, Ellen Currie, Craig J. |
author_facet | Heywood, Benjamin R. Morgan, Christopher Ll. Berni, Thomas R. Summers, Darren R. Jones, Bethan I. Jenkins-Jones, Sara Holden, Sarah E. Riddick, Lauren D. Fisher, Harry Bateman, James D. Bannister, Christian A. Threlfall, John Buxton, Aron Shepherd, Christopher P. Mathias, Elgan R. Thomason, Rhiannon K. Hubbuck, Ellen Currie, Craig J. |
author_sort | Heywood, Benjamin R. |
collection | PubMed |
description | Incidence and prevalence are key epidemiological determinants characterizing the quantum of a disease. We compared incidence and prevalence estimates derived automatically from the first ever online, essentially real-time, healthcare analytics platform—Livingstone—against findings from comparable peer-reviewed studies in order to validate the descriptive epidemiology module. The source of routine NHS data for Livingstone was the Clinical Practice Research Datalink (CPRD). After applying a general search strategy looking for any disease or condition, 76 relevant studies were first retrieved, of which 10 met pre-specified inclusion and exclusion criteria. Findings reported in these studies were compared with estimates produced automatically by Livingstone. The published reports described elements of the epidemiology of 14 diseases or conditions. Lin’s concordance correlation coefficient (CCC) was used to evaluate the concordance between findings from Livingstone and those detailed in the published studies. The concordance of incidence values in the final year reported by each study versus Livingstone was 0.96 (95% CI: 0.89–0.98), whilst for all annual incidence values the concordance was 0.93 (0.91–0.94). For prevalence, concordance for the final annual prevalence reported in each study versus Livingstone was 1.00 (0.99–1.00) and for all reported annual prevalence values, the concordance was 0.93 (0.90–0.95). The concordance between Livingstone and the latest published findings was near perfect for prevalence and substantial for incidence. For the first time, it is now possible to automatically generate reliable descriptive epidemiology from routine health records, and in near-real time. Livingstone provides the first mechanism to rapidly generate standardised, descriptive epidemiology for all clinical events from real world data. |
format | Online Article Text |
id | pubmed-10368254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103682542023-07-26 Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module Heywood, Benjamin R. Morgan, Christopher Ll. Berni, Thomas R. Summers, Darren R. Jones, Bethan I. Jenkins-Jones, Sara Holden, Sarah E. Riddick, Lauren D. Fisher, Harry Bateman, James D. Bannister, Christian A. Threlfall, John Buxton, Aron Shepherd, Christopher P. Mathias, Elgan R. Thomason, Rhiannon K. Hubbuck, Ellen Currie, Craig J. PLOS Digit Health Research Article Incidence and prevalence are key epidemiological determinants characterizing the quantum of a disease. We compared incidence and prevalence estimates derived automatically from the first ever online, essentially real-time, healthcare analytics platform—Livingstone—against findings from comparable peer-reviewed studies in order to validate the descriptive epidemiology module. The source of routine NHS data for Livingstone was the Clinical Practice Research Datalink (CPRD). After applying a general search strategy looking for any disease or condition, 76 relevant studies were first retrieved, of which 10 met pre-specified inclusion and exclusion criteria. Findings reported in these studies were compared with estimates produced automatically by Livingstone. The published reports described elements of the epidemiology of 14 diseases or conditions. Lin’s concordance correlation coefficient (CCC) was used to evaluate the concordance between findings from Livingstone and those detailed in the published studies. The concordance of incidence values in the final year reported by each study versus Livingstone was 0.96 (95% CI: 0.89–0.98), whilst for all annual incidence values the concordance was 0.93 (0.91–0.94). For prevalence, concordance for the final annual prevalence reported in each study versus Livingstone was 1.00 (0.99–1.00) and for all reported annual prevalence values, the concordance was 0.93 (0.90–0.95). The concordance between Livingstone and the latest published findings was near perfect for prevalence and substantial for incidence. For the first time, it is now possible to automatically generate reliable descriptive epidemiology from routine health records, and in near-real time. Livingstone provides the first mechanism to rapidly generate standardised, descriptive epidemiology for all clinical events from real world data. Public Library of Science 2023-07-25 /pmc/articles/PMC10368254/ /pubmed/37490430 http://dx.doi.org/10.1371/journal.pdig.0000310 Text en © 2023 Heywood et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Heywood, Benjamin R. Morgan, Christopher Ll. Berni, Thomas R. Summers, Darren R. Jones, Bethan I. Jenkins-Jones, Sara Holden, Sarah E. Riddick, Lauren D. Fisher, Harry Bateman, James D. Bannister, Christian A. Threlfall, John Buxton, Aron Shepherd, Christopher P. Mathias, Elgan R. Thomason, Rhiannon K. Hubbuck, Ellen Currie, Craig J. Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title | Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title_full | Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title_fullStr | Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title_full_unstemmed | Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title_short | Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module |
title_sort | real-world evidence from the first online healthcare analytics platform—livingstone. validation of its descriptive epidemiology module |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368254/ https://www.ncbi.nlm.nih.gov/pubmed/37490430 http://dx.doi.org/10.1371/journal.pdig.0000310 |
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