Resident and Facility-Level Predictors of Influenza PCR Positivity in Nursing Home Residents Open Access

Abstract

Introduction: Nursing home residents experience disproportionate morbidity and

mortality from influenza compared to older adults living in the community. While

molecular tests like RT-PCR are the gold standard for diagnosis, point-of-care (POC)

antigen tests are frequently used for rapid results despite lower sensitivity. This study

estimated the likelihood of residents testing positive for influenza given different

characteristics and outbreak scenarios while measuring the real-world accuracy of POC

tests.

Methods: This study used data from a prospective sentinel surveillance project

involving 50 nursing homes. Analysis was limited to 382 respiratory illness events

where residents received both an antigen POC test and a PCR test. Multivariable

logistic regression using a forward stepwise selection procedure identified primary

predictors of influenza positivity. Diagnostic performance was evaluated using RT-PCR

as the gold standard.

Results: Of the 382 events, 37 (9.7%) were confirmed positive via RT-PCR.

Multivariable analysis showed the strongest independent predictors of influenza PCR

positivity were the presence of at least one prior antigen-positive resident in the same

facility during that month (aOR: 2.78, 95% CI: 2.00–3.95) and the combined symptom of

cough or wheezing (aOR: 8.49, 95% CI: 1.55–160.84). POC tests demonstrated high

specificity (99%) but low sensitivity (38%). The positive predictive value (PPV) reached

100% among residents with a cough or wheezing. However, the negative predictive

value (NPV) dropped to 68% in facilities with more than one positive POC test.

Conclusion: While positive POC results are highly reliable indicators of true infection

in this population, low sensitivity results in a high rate of false negatives. During peak

influenza season or an outbreak, a negative POC test is unreliable and should be

confirmed with a PCR-based test to prevent undetected viral spread. Integrating this

diagnostic algorithm can improve detection and potentially reduce influenza-related

morbidity and mortality.

Table of Contents

Table of Contents

Introduction …………………………………………………………………………………………………………………………….. 1

Methods ………………………………………………………………………………………………………………………………….. 3

Results …………………………………………………………………………………………………………………………………….. 8

Discussion ……………………………………………………………………………………………………………………………… 15

References …………………………………………………………………………………………………………………………….. 19

 

About this Master's Thesis

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School	Rollins School of Public Health
Department	Epidemiology
Subfield / Discipline	Epidemiology - MPH & MSPH
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Epidemiology
Keyword	Nursing home Predictors Influenza Specificity Sensitivty
Committee Chair / Thesis Advisor	Scott Fridkin, Emory University
Committee Members	Paulina Rebolledo, Emory University

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