Suppr超能文献

用于人群级别的心血管代谢疾病监测和预防的非传统电子数据集的识别:系统评价方案。

Identifying non-traditional electronic datasets for population-level surveillance and prevention of cardiometabolic diseases: a scoping review protocol.

机构信息

Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada

Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.

出版信息

BMJ Open. 2021 Aug 18;11(8):e053485. doi: 10.1136/bmjopen-2021-053485.

Abstract

INTRODUCTION

Cardiometabolic diseases, including cardiovascular disease, obesity and diabetes, are leading causes of death and disability worldwide. Modern advances in population-level disease surveillance are necessary and may inform novel opportunities for precision public health approaches to disease prevention. Electronic data sources, such as social media and consumer rewards points systems, have expanded dramatically in recent decades. These non-traditional datasets may enhance traditional clinical and public health datasets and inform cardiometabolic disease surveillance and population health interventions. However, the scope of non-traditional electronic datasets and their use for cardiometabolic disease surveillance and population health interventions has not been previously reviewed. The primary objective of this review is to describe the scope of non-traditional electronic datasets, and how they are being used for cardiometabolic disease surveillance and to inform interventions. The secondary objective is to describe the methods, such as machine learning and natural language processing, that have been applied to leverage these datasets.

METHODS AND ANALYSIS

We will conduct a scoping review following recommended methodology. Search terms will be based on the three central concepts of non-traditional electronic datasets, cardiometabolic diseases and population health. We will search EMBASE, MEDLINE, CINAHL, Scopus, Web of Science and Cochrane Library peer-reviewed databases and will also conduct a grey literature search. Articles published from 2000 to present will be independently screened by two reviewers for inclusion at abstract and full-text stages, and conflicts will be resolved by a separate reviewer. We will report this data as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

ETHICS AND DISSEMINATION

No ethics approval is required for this protocol and scoping review, as data will be used only from published studies with appropriate ethics approval. Results will be disseminated in a peer-reviewed publication.

摘要

简介

心血管疾病、肥胖症和糖尿病等心脏代谢疾病是全球范围内导致死亡和残疾的主要原因。在人群层面进行疾病监测的现代进展是必要的,并且可能为预防疾病的精准公共卫生方法提供新的机会。社交媒体和消费者奖励积分系统等电子数据源在最近几十年中得到了巨大的扩展。这些非传统数据集可能会增强传统的临床和公共卫生数据集,并为心脏代谢疾病监测和人口健康干预措施提供信息。然而,非传统电子数据集的范围及其在心脏代谢疾病监测和人口健康干预中的应用尚未得到审查。本综述的主要目的是描述非传统电子数据集的范围,以及它们如何用于心脏代谢疾病监测,并为干预措施提供信息。次要目的是描述已应用于利用这些数据集的方法,如机器学习和自然语言处理。

方法和分析

我们将按照推荐的方法进行范围综述。搜索词将基于非传统电子数据集、心脏代谢疾病和人口健康这三个核心概念。我们将搜索 EMBASE、MEDLINE、CINAHL、Scopus、Web of Science 和 Cochrane Library 同行评审数据库,并将进行灰色文献搜索。从 2000 年至今发表的文章将由两位评审员独立筛选,以在摘要和全文阶段进行纳入,并由另一位评审员解决冲突。我们将按照系统评价和荟萃分析扩展的首选报告项目为范围综述报告该数据。

伦理和传播

本方案和范围综述不需要伦理批准,因为数据将仅从具有适当伦理批准的已发表研究中使用。结果将在同行评议的出版物中公布。

相似文献

4
Quality indicators for substance use disorder care: a scoping review protocol.
BMJ Open. 2025 Mar 29;15(3):e085216. doi: 10.1136/bmjopen-2024-085216.
5
7
Methods for the health technology assessment of complex interventions: a protocol for a scoping review.
BMJ Open. 2020 Nov 30;10(11):e039263. doi: 10.1136/bmjopen-2020-039263.

本文引用的文献

2
Predicting population health with machine learning: a scoping review.
BMJ Open. 2020 Oct 27;10(10):e037860. doi: 10.1136/bmjopen-2020-037860.
4
Machine learning prediction in cardiovascular diseases: a meta-analysis.
Sci Rep. 2020 Sep 29;10(1):16057. doi: 10.1038/s41598-020-72685-1.
5
A Review of Obesity, Physical Activity, and Cardiovascular Disease.
Curr Obes Rep. 2020 Dec;9(4):571-581. doi: 10.1007/s13679-020-00403-z.
9
Understanding the rise of cardiometabolic diseases in low- and middle-income countries.
Nat Med. 2019 Nov;25(11):1667-1679. doi: 10.1038/s41591-019-0644-7. Epub 2019 Nov 7.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验