Acceptability, Feasibility, and Validity of Detecting Respiratory Pathogens During Acute Respiratory Illness in Participant-Collected Swabs in a Low-Income, Community Sample

Abstract

Background

Community surveillance for acute respiratory illness (ARI) can include unsupervised participant-collected nasal swabs. Little is known about use of self-swabs in low-income populations or among households including extended family members and the validity of self-collected swabs. We assessed the acceptability, feasibility, and validity of unsupervised participant-collected nasal swabs in a low-income, community sample.

Methods

This was a substudy of a larger prospective community-based ARI surveillance study in 405 households in New York City. Participating household members self-collected swabs on the day of a research home visit for an index case, and for 3–6 subsequent days. Demographics associated with agreement to participate and swab collection were assessed, and index case self-collected versus research staff–collected swab results were compared.

Results

Most households (n = 292 [89.6%]) agreed to participate, including 1310 members. Being <18 years old, female, and the household reporter or member of the nuclear family (parents and children) were associated with both agreement to participate and self-swab collection. Being born in the United States or immigrating ≥10 years ago was associated with participation, and being Spanish-speaking and having less than a high school education were associated with swab collection. In all, 84.4% collected at least 1 self-swabbed specimen; self-swabbing rates were highest during the first 4 collection days. Concordance between research staff–collected swabs and self-swabs was 88.4% for negative swabs, 75.0% for influenza, and 69.4% for noninfluenza pathogens.

Conclusions

Self-swabbing was acceptable, feasible, and valid in this low-income, minoritized population. Some differences in participation and swab collection were identified that could be noted by future researchers and modelers.