Date of Award

2016

Degree Name

PhD Nursing

Dissertation Committee

Mary Barger, PhD, MPH, RN, CNM, FACNM, Chairperson; Joseph Burkard, DNSc, MSNA, CRNA, Committee Member; John Arnold, MD, CAPT, MC, USN, Committee Member

Keywords

coronavirus, influenza-like illness, psychometrics, symptom experience, unsupervised machine learning

Abstract

AIMS: The primary objective of this study was to identify if symptom presentation expressed over the course of an influenza-like illness (ILI) can predict virus type by use of unsupervised machine learning. The secondary objective was to describe clinical characteristics of strain specific coronavirus. Finally, examine the psychometric properties of the Canadian Acute Respiratory Illness and Flu Scale (CARIFS).

BACKGROUND: ILI outbreaks have been a significant source of non-battle injury among military personnel. Many different viruses cause ILI, and it is difficult to determine which virus is causing the illness. Recent studies have examined the etiology and epidemiology of ILIs. Other studies have examined influenza virus symptom severity either a dichotomous or liner-sum analysis. No studies to the researcher’s knowledge have examined ILI symptoms through an unsupervised learning analysis, and few studies have examined self-reported outpatient ILI reported symptoms over an extended time frame.

METHODS: This is a secondary analysis of data collected over a four year period by the Acute Respiratory Infection Consortium (ARIC), from an otherwise healthy military population. The symptom data was captured on visit days and by a symptom diary patients filled out at home using a symptom severity instrument designed for this study.

FINDINGS: Clustering by unsupervised machine learning was unable to predict virus type based on physical symptom presentation over the course of ILI. It did identify patient attributes, like sex and age that caused patients to experience symptoms differently. Additionally, clinical similarities and differences were noted between the four common human coronavirus strains. The strain HKU1 tended to have higher systemic symptom scores and higher gastrointestinal symptom severity score over the course of illness when compared to the other strains. Finally, the psychometric properties of CARIFS revealed many strengths and limitations for its use in research. The CARIFS should be reexamined using current knowledge of symptom management to increase the validity of the instrument.

IMPLICATIONS: The results demonstrated how individuals experience physical symptoms differently making it difficult to predict the viral strain causing ILI. Future research should focus on the development of symptom instruments using the theoretical underpinnings of the symptom management theory.

Document Type

Dissertation: Open Access

Department

Nursing

Available for download on Wednesday, March 13, 2019

Included in

Nursing Commons

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