非致命性伤害数据：监测和研究需考虑的特征。

Non-fatal injury data: characteristics to consider for surveillance and research.

机构信息

Oak Ridge Associated Universities (ORAU), Division of Injury Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.

出版信息

Inj Prev. 2022 Jun;28(3):262-268. doi: 10.1136/injuryprev-2021-044397. Epub 2022 Feb 24.

DOI:10.1136/injuryprev-2021-044397

PMID:35210312

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9133150/

Abstract

BACKGROUND

All data systems used for non-fatal injury surveillance and research have strengths and limitations that influence their utility in understanding non-fatal injury burden. The objective of this paper was to compare characteristics of major data systems that capture non-fatal injuries in the USA.

METHODS

By applying specific inclusion criteria (eg, non-fatal and non-occupational) to well-referenced injury data systems, we created a list of commonly used non-fatal injury data systems for this study. Data system characteristics were compiled for 2018: institutional support, years of data available, access, format, sample, sampling method, injury definition/coding, geographical representation, demographic variables, timeliness (lag) and further considerations for analysis.

RESULTS

Eighteen data systems ultimately fit the inclusion criteria. Most data systems were supported by a federal institution, produced national estimates and were available starting in 1999 or earlier. Data source and injury case coding varied between the data systems. Redesigns of sampling frameworks and the use of International Classification of Diseases, 9th Revision, Clinical Modification/International Classification of Diseases, 10th Revision, Clinical Modification coding for some data systems can make longitudinal analyses complicated for injury surveillance and research. Few data systems could produce state-level estimates.

CONCLUSION

Thoughtful consideration of strengths and limitations should be exercised when selecting a data system to answer injury-related research questions. Comparisons between estimates of various data systems should be interpreted with caution, given fundamental system differences in purpose and population capture. This research provides the scientific community with an updated starting point to assist in matching the data system to surveillance and research questions and can improve the efficiency and quality of injury analyses.

摘要

背景

所有用于非致命性伤害监测和研究的数据系统都有其优势和局限性，这会影响到它们在了解非致命性伤害负担方面的实用性。本文的目的是比较美国主要数据系统在捕获非致命性伤害方面的特点。

方法

通过对参考资料充分的伤害数据系统应用特定的纳入标准（例如非致命性和非职业性），我们为这项研究创建了一个常用非致命性伤害数据系统列表。对 2018 年的数据系统特征进行了汇编：机构支持、数据可用年限、获取途径、格式、样本、抽样方法、伤害定义/编码、地理代表性、人口统计学变量、及时性（滞后）以及进一步分析的考虑因素。

结果

最终有 18 个数据系统符合纳入标准。大多数数据系统都得到了联邦机构的支持，提供了全国性的估计值，并且从 1999 年或更早开始就可以获取。数据来源和伤害病例编码在数据系统之间存在差异。一些数据系统的抽样框架进行了重新设计，并且使用了国际疾病分类，第 9 修订版，临床修正/国际疾病分类，第 10 修订版，临床修正编码，这使得伤害监测和研究的纵向分析变得复杂。很少有数据系统能够生成州级估计值。