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
Human Data Sciences, Cardiff, United Kingdom.
School of Mathematics, Cardiff University, Cardiff, United Kingdom.
PLOS Digit Health. 2023 Jul 25;2(7):e0000310. doi: 10.1371/journal.pdig.0000310. eCollection 2023 Jul.
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.
发病率和患病率是表征疾病数量的关键流行病学决定因素。我们将首个在线、基本实时的医疗分析平台——利文斯通自动得出的发病率和患病率估计值,与同类同行评审研究的结果进行了比较,以验证描述性流行病学模块。利文斯通常规国民保健服务数据的来源是临床实践研究数据链(CPRD)。在应用寻找任何疾病或病症的通用搜索策略后,首先检索到76项相关研究,其中10项符合预先设定的纳入和排除标准。将这些研究报告的结果与利文斯通自动生成的估计值进行比较。已发表的报告描述了14种疾病或病症的流行病学要素。使用林氏一致性相关系数(CCC)来评估利文斯通的结果与已发表研究中详细描述的结果之间的一致性。每项研究报告的最后一年发病率值与利文斯通的一致性为0.96(95%可信区间:0.89 - 0.98),而所有年度发病率值的一致性为0.93(0.91 - 0.94)。对于患病率,每项研究报告的最后一年患病率与利文斯通的一致性为1.00(0.99 - 1.00),所有报告的年度患病率值的一致性为0.93(0.90 - 0.95)。利文斯通与最新发表结果之间的一致性对于患病率近乎完美,对于发病率则较为显著。首次能够从常规健康记录中自动生成可靠的描述性流行病学,而且几乎是实时生成。利文斯通提供了首个机制,可从真实世界数据中快速生成所有临床事件的标准化描述性流行病学。