自动化开放队列的发病和患病分析。

Automating incidence and prevalence analysis in open cohorts.

机构信息

Institute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK.

NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK.

出版信息

BMC Med Res Methodol. 2024 Jul 4;24(1):144. doi: 10.1186/s12874-024-02266-7.

DOI:10.1186/s12874-024-02266-7

PMID:38965539

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11223317/

Abstract

MOTIVATION

Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses.

IMPLEMENTATION

We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later.

GENERAL FEATURES

The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence.

AVAILABILITY

The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication .

摘要

动机

数据越来越多地用于公共卫生的改进和研究，特别是诸如电子健康记录中收集的行政数据。患者以非均匀的方式进入和退出这些通常是开放队列数据集；这可能会使关于发病率和患病率的简单问题变得耗时且分析之间存在不必要的差异。因此，我们开发了方法来自动化分析开放队列数据集中的发病率和患病率，以提高分析的透明度、效率和可重复性。