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. 2024 Feb 14;11(3):ofae081.
doi: 10.1093/ofid/ofae081. eCollection 2024 Mar.

Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students

Diya M Uthappa  1   2 , Micah T McClain  3   4 , Bradly P Nicholson  5 , Lawrence P Park  2   3   4 , Ilya Zhbannikov  6 , Sunil Suchindran  3 , Monica Jimenez  5 , Florica J Constantine  3 , Marshall Nichols  7 , Daphne C Jones  4 , Lori L Hudson  8 , L Brett Jaggers  3 , Timothy Veldman  2 , Thomas W Burke  3 , Ephraim L Tsalik  3   4 , Geoffrey S Ginsburg  3 , Christopher W Woods  2   3   4
Affiliations

Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students

Diya M Uthappa et al. Open Forum Infect Dis. .

Abstract

Background: Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population.

Methods: We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group.

Results: We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group.

Conclusions: Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

Keywords: college health; disease transmission; respiratory viral illness; surveillance.

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Conflict of interest statement

Potential conflicts of interest. M. T. M. reports grants from the Defense Advanced Research Projects Agency (DARPA) and the National Institutes of Health (NIH), and has a patent pending on “Methods to diagnose and treat acute respiratory infections.” T. W. B. reports grants from DARPA and NIH; reports owning equity in and serving as a consultant for Biomeme; and has a patent pending on Methods to diagnose and treat acute respiratory infections. E. L. T. reports consultancy fees and equity from Biomeme; has patents pending on Biomarkers for the molecular classification of bacterial infection and Methods to diagnose and treat acute respiratory infections; and is currently an employee of Danaher Diagnostics. C. W. W. and G. S. G. have patents pending on Molecular classification of bacterial infection and gene expression signatures useful to predict or diagnose sepsis and methods of using the same, and have patents issued on Methods to diagnose and treat acute respiratory disease and Methods of identifying infectious disease and assays for identifying infectious disease. C. W. W. reports owning equity in and consulting for Biomeme; reports grants from DARPA, NIH, Antibacterial Resistance Leadership Group, and Sanofi; and has received consultancy fees from bioMérieux, Roche, Biofire, Giner, and Biomeme. All other authors report no potential conflicts.

Figures

Figure 1.
Figure 1.
Comparison of symptomatic illness between the whole cohort and the close contact (CC) group using logistic regression analysis. The CC sampling group is represented in green, and whole cohort sampling group is represented in blue. Weekly point estimates for the proportion of self-reported illness have been graphed along with the corresponding 95% confidence intervals. The sparse reporting midyear corresponds to winter break.
Figure 2.
Figure 2.
Sensitivity analysis: comparison of symptomatic illness between the whole cohort and the close contact (CC) group, with and without imputation of missing symptom reports as an absence of symptoms. Green dots represent the attack rate among the CCs. Blue line represents the attack rate among those in the whole cohort who completed at least 25% of symptom logs without imputation. Red line represents the attack rate using the imputed sensitivity analysis data set—the attack rate among those in the whole cohort who completed at least 25% of symptom logs where the lack of a symptom report was imputed as a symptom score of 0.
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