Citation

Lund A, Turris S, Rabb H, Munn MB, Chasmar E, Ranse J, Hutton A. Prehosp. Disaster Med. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Cambridge University Press)

DOI

10.1017/S1049023X21000108

PMID

unavailable

Abstract

INTRODUCTION: Without a robust evidence base to support recommendations for first aid, health, and medical services at mass gatherings (MGs), levels of care will continue to vary. Streamlining and standardizing post-event reporting for MG medical services could improve inter-event comparability, and prospectively influence event safety and planning through the application of a research template, thereby supporting and promoting growth of the evidence base and the operational safety of this discipline. Understanding the relationships between categories of variables is key. The present paper is focused on theory building, providing an evolving conceptual model, laying the groundwork for exploring the relationships between categories of variables pertaining the health outcomes of MGs.

METHODS: A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.

FINDINGS: A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into "inputs," "modifiers," "actuals," and "outputs" and organized temporally into pre-, during, post-event, and reporting phases.

DISCUSSION: Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.

CONCLUSION: Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

Language: en

Keywords

case reporting; conceptual modeling; mass gathering; mass-gathering health; mass-gathering medicine