Measuring the Masses: A Proposed Template for Post-Event Medical Reporting (Paper 4).

case reporting data modeling mass gathering mass-gathering health mass-gathering medicine

Journal

Prehospital and disaster medicine
ISSN: 1945-1938
Titre abrégé: Prehosp Disaster Med
Pays: United States
ID NLM: 8918173

Informations de publication

Date de publication:
Apr 2021
Historique:
pubmed: 20 2 2021
medline: 26 11 2021
entrez: 19 2 2021
Statut: ppublish

Résumé

Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting. Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources. Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain. The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as "overlays," depending on the event category and type. This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.

Sections du résumé

BACKGROUND BACKGROUND
Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting.
METHODS METHODS
Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources.
FINDINGS RESULTS
Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain.
DISCUSSION CONCLUSIONS
The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as "overlays," depending on the event category and type.
CONCLUSIONS CONCLUSIONS
This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.

Identifiants

pubmed: 33602353
pii: S1049023X21000091
doi: 10.1017/S1049023X21000091
doi:

Types de publication

Journal Article

Langues

eng

Pagination

218-226

Auteurs

Sheila Turris (S)

Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.

Haddon Rabb (H)

Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.

Elizabeth Chasmar (E)

Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.

Matthew Brendan Munn (MB)

Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.

Christopher W Callaghan (CW)

Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.

Alison Hutton (A)

University of Newcastle, Newcastle, Australia.

Jamie Ranse (J)

Department of Emergency Medicine, Gold Coast Health, Southport, Queensland, Australia.
Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia.

Adam Lund (A)

Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Mass Gathering Medicine Interest Group, Department of Emergency Medicine, University of British Columbia, Canada.
School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada.

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