Human rhinovirus C: Age, season, and lower respiratory illness over the past 3 decades

Abstract

Background: Human rhinoviruses (HRVs) cause common colds, and the recently discovered HRV-C is increasingly associated with lower respiratory illness among populations such as children and asthmatic patients.

Objective: To determine how HRV-C is associated with respiratory illness and to evaluate changes in prevalence and species over 2 decades.

Methods: A prospective study of children younger than 5 years was performed at the Vanderbilt Vaccine Clinic over a 21-year period. Nasal-wash specimens from children presenting with upper or lower respiratory illness at acute care visits were tested for HRV and HRV-positives genotyped. Demographic and clinical features were compared between children with or without HRV, and with different HRV species.

Results: HRV was detected in 190 of 527 (36%) specimens from a population of 2009 children from 1982 through 2003. Of these, 36% were HRV-C. Age (P = .039) and month of illness (P < .001) were associated with HRV infection and HRV species. HRV-C was significantly associated with lower respiratory illness, compared with HRV-A (P = .014). HRV-A and HRV-C prevalence fluctuated throughout the 21-year period; HRV-C was more prevalent during winter (P = .058).

Conclusions: HRV-C is not a new virus but has been significantly associated with childhood lower respiratory illness in this population for several decades. Temporal changes in virus prevalence occur, and season may predict virus species. Our findings have implications for diagnostic, preventive, and treatment strategies due to the variation in disease season and severity based on species of HRV infection.

Fig 1

Age and month are significantly correlated with HRV infection. A, HRV is more common in younger children in fall and spring months. Winter, solid line; spring, dashed line; summer, gray dot; fall, gray dash/dot. Graph shown for white males. B, In September, children had a 2.47-fold increased risk of positive HRV diagnosis than in July. Lines represent modeled negative binomial regression.

Fig 2

Older females in the winter months were more likely to present with HRV-C. A, Age and season predicted HRV species (shown for white males). The negative binomial regression modeled here predicts that HRV-C is more common with increasing age. B, Sex was also a significant factor in the likelihood of HRV-C (shown for 11.2-month-old white child in the fall).

Fig 3

HRV-A rate varies from year to year. HRV-A fluctuated significantly over the years; recently, there was more HRV-A present in children with respiratory illness. Lines represent the negative binomial regression model based on the data.

Fig 4

LRI diagnosis is more often associated with HRV-C and URI with HRV-A. Percentage of patients diagnosed with infection type: LRI (bronchiolitis, croup, pneumonia, and asthma) and URI (coryza, pharyngitis, and acute otitis media). White bars represent HRV-C, and gray bars represent HRV-A.

Fig 5

Phylogenetic tree of HRV species depicting HRV species and URI (red circle) or LRI (blue square) diagnosis. Grouping URI and LRI diagnoses suggests that HRV species is associated with certain clinical phenotypes (or respiratory disease severity). Novel sequences are designated by “VU” followed by the sample number, and sequences matching published HRV strains are marked as HRV followed by the strain number.

Fig E1

Number of samples analyzed for HRV by year. Black bars indicate the number of positive samples.

Fig E2

The probability of HMPV varies significantly over month and year. Lines represent negative binomial regression model based on data.

Fig E3

The probability of parainfluenza virus varies significantly over month and year. January and February were least likely to be associated with positive PIV. Lines represent negative binomial regression model based on data.

Fig E4

The probability of RSV infection varies by month. December, January, and February were more likely to be associated with virus detection. Lines represent negative binomial regression model based on data.

Fig E5

The probability of influenza varies by month. Because of low sample size, data were binned into 2-month intervals. January/February was most likely to be associated with positive influenza diagnosis. Lines represent negative binomial regression model based on data.

Fig E6

The probability of adenovirus varies by both month and year. September was least likely to be associated with adenovirus, and May was most often associated with adenovirus. Lines represent negative binomial regression model based on data.