The temporal association between different noninfluenza respiratory viruses and invasive streptococcal disease is poorly understood. We sought to investigate the temporal association between invasive group A streptococcal disease (iGAS) and invasive pneumococcal disease (IPD) with respiratory viral infections from 2017 to 2022 in children from Hamilton County, Ohio.
Invasive streptococcal disease cases were identified by microbiological cultures and notifiable disease reports at Cincinnati Children’s Hospital Medical Center. Respiratory viral infections were detected using prospective, active surveillance of children with acute respiratory illness and respiratory virus molecular testing through the New Vaccine Surveillance Network. Poisson time-series regression was used to model weekly counts of invasive streptococcal disease by season and cumulative counts of respiratory virus infections by genus over the previous 2 and 4 weeks.
Overall, there were 47 cases of iGAS, 49 cases of IPD, and 5355 respiratory virus infections identified between 2017 and 2022. For invasive streptococcal disease, the most common culture sources were blood (41%), bronchoalveolar lavage or pleural fluid (35%), and tissue or wound (11%). Most cases of invasive streptococcal disease occurred in spring (n = 34) and winter (n = 24). Influenza virus detections in the prior 2 or 4 weeks were temporally associated with iGAS, whereas rhinovirus/enterovirus detections were negatively associated with iGAS.
In addition to the known temporal association with influenza viruses, we found a negative temporal association between rhinoviruses/enteroviruses and iGAS, which has not been previously described. Further understanding of these specific viral-bacterial interactions may help inform public health interventions to mitigate invasive streptococcal disease risk.
Seasonal respiratory viruses are thought to increase the risk of invasive streptococcal disease, with prior studies demonstrating a temporal relationship between influenza viruses and invasive streptococcal disease.
Our study examines the temporal relationship between noninfluenza respiratory viruses including nonseasonal viruses such as adenovirus, bocavirus, and rhinovirus/enteroviruses with invasive group A streptococcal disease and invasive pneumococcal disease.
Introduction
Streptococcus pyogenes and Streptococcus pneumoniae colonize the respiratory tract and/or skin and can also cause a wide range of clinical syndromes, including sepsis, necrotizing fasciitis, meningitis, osteomyelitis, endocarditis, and pneumonia.1 Invasive group A streptococcal disease (iGAS) and invasive pneumococcal disease (IPD) occur year-round but typically peak during the winter and early spring months in temperate climates, coinciding with the respiratory viral season.2 Although the pneumococcal vaccination program has reduced rates of IPD, invasive streptococcal diseases continue to cause thousands of pediatric hospitalizations and hundreds of pediatric deaths each year.3–5
Although cases of invasive streptococcal disease declined at the onset of the COVID-19 pandemic,2,6 many pediatric hospitals noted an increase in incidence of invasive streptococcal disease in the fall of 2022.7,8 This occurred in the context of a resurgence of respiratory syncytial virus (RSV), influenza viruses, and SARS-CoV-2 infections, leading some investigators to hypothesize that respiratory viruses contributed to the rise in invasive streptococcal disease.8
Several pre– and post–COVID-19 pandemic studies have assessed the relationship between influenza viruses and iGAS as well as influenza viruses and RSV with IPD in individual patients, but few have examined the temporal associations in a well-defined population.9–16 In addition, studies examining the relationship between both iGAS and IPD with noninfluenza viruses at a population level, including other seasonal and nonseasonal respiratory viruses such as rhinoviruses and adenoviruses, are limited.
In this analysis, we captured cases of invasive streptococcal disease and respiratory viral detections by using 2 surveillance programs of children from Hamilton County, Ohio, between 2017 and 2022. By leveraging population-based data from Hamilton County, we examined the temporal association between invasive streptococcal disease hospitalizations and respiratory viruses from an ecological perspective.
Methods
For invasive streptococcal disease and respiratory virus detection, all children aged younger than 18 years with a primary residence in Hamilton County, Ohio, from January 1, 2017, to December 31, 2022, who were cared for at Cincinnati Children’s Hospital Medical Center (CCHMC) were eligible for the study. CCHMC is a quaternary children’s hospital that serves more than 97% of Hamilton County children and is the only children’s hospital within a 50-mile radius.
Invasive Streptococcal Disease Case Surveillance
Invasive S pyogenes and S pneumoniae infections were identified by the Department of Infection Control and Prevention at CCHMC through microbiological cultures and notifiable disease reports created for the Ohio Department of Health. Both confirmed and probable cases of invasive streptococcal disease were included based on the following definitions17:
Confirmed case: microbiological isolation of S pyogenes or S pneumoniae from any sterile site (including blood, cerebrospinal fluid, pleural fluid, peritoneal fluid, bone, joint fluid, and other internal body sites obtained sterilely) or positive polymerase chain reaction (PCR) test results for S pneumoniae from cerebrospinal fluid
Probable case (adapted from Miller et al to include additional nonsterile sites)17: microbiological isolation of S pyogenes and S pneumoniae from a nonsterile site (including lower respiratory tract, sinus, ear, abscess, epiglottis) with compatible clinically invasive infection such as orbital cellulitis, mastoiditis, or pneumonia. Compatible clinically invasive infections were determined through independent chart review by 2 physician investigators (Z.T. and N.P.) with a third physician adjudicator (F.S.H.) for any disagreements.
Respiratory Viral Infections
To ascertain weekly trends in respiratory viral infections at the county level, we used existing data collected for the New Vaccine Surveillance Network (NVSN), a Centers for Disease Control and Prevention–funded, prospective, population-based, active surveillance network of children with acute respiratory illness (ARI).18 For this study, we used data from the Cincinnati site and included Hamilton County children who were evaluated in the emergency department or hospitalized with a diagnosis of ARI, defined as at least 1 of the following signs or symptoms: fever, cough, earache, nasal congestion, rhinorrhea, shortness of breath, rapid or shallow breathing, apnea, or altered life-threatening event/brief resolved unexplained event. Respiratory viral detections from emergency department visits and hospitalizations were chosen to focus on medically attended and symptomatic ARI episodes.
A respiratory midturbinate and/or oropharyngeal swab was obtained from enrolled children and was tested using a multiplex PCR respiratory viral panel (NxTAG Respiratory Pathogen panel, Luminex Molecular Diagnostics), which includes 18 virus types/subtypes. Inpatient surveillance occurred year-round at least 5 days per week from Monday through Friday, whereas emergency department surveillance occurred year-round at least 4 times per week.
Data Collection
For invasive streptococcal disease cases, clinical, microbiological, and demographic data, including self-reported race and ethnicity, were collected via manual and automated chart abstraction of the electronic medical record (Epic Hyperspace, Epic Systems Corporation) and the enterprise intelligence resource (VigiLanz Corp). The Pediatric Health Information System administrative database was used to obtain history of complex chronic conditions and insurance status. Immunization records at the time of culture positivity were abstracted from the state vaccine registry (Ohio Impact Statewide Immunization Information System) web application and the electronic medical record. S pneumoniae stereotypes were submitted for positive sterile-site cultures to the Ohio Department of Health, which determined serotypes using whole-genome sequencing. Both invasive streptococcal disease data and NVSN data were entered and managed using REDCap electronic data capture tools hosted at the Center for Clinical & Translational Science & Training at the University of Cincinnati/CCHMC using standardized case instruments.
Statistical Methods
Descriptive statistics were computed for patient demographics and clinical variables, including the median and IQR for continuous variables as well as counts and percentages for categorical variables. Poisson regression models were used to assess the relationship between weekly counts of iGAS and IPD as separate dependent variables and cumulative counts of respiratory viral infections by genus. Two separate models were performed using counts of respiratory viruses summed over 2 or 4 weeks before infection. Season (winter, spring, summer, and fall) was included as a categorical covariate. Initially, zero-inflated Poisson (ZIP) models were fit. Goodness-of-fit statistics and ZIP parameter tests showed that a ZIP model was not required. Model parameter estimates (and P values) from the ZIP and Poisson models were also similar. Goodness-of-fit statistics (scaled Pearson χ2) demonstrated that null hypothesis of no overdispersion was valid. All statistical analyses were performed using SAS software, version 9.4M5, specifically PROC GENMOD for the Poisson model. The surveillance protocol was reviewed and approved by the institutional review boards at the Centers for Disease Control and Prevention and CCHMC (45 C.F.R. part 46; 21 C.F.R. part 56).19,20
Results
From 2017 to 2022, 96 cases of invasive streptococcal disease were identified in Hamilton County children; 47 were iGAS (27 confirmed and 20 probable), and 49 were IPD (33 confirmed and 16 probable). Compared with the general county population, there was a higher proportion of children with invasive streptococcal disease who are Black (44% vs 25.4% in Hamilton County), Hispanic (10% vs 3.7% in Hamilton County), and using public insurance (57% vs 27.9% in Hamilton County).
During the same period, there were 5355 respiratory virus infections detected through NVSN in Hamilton County children with ARI cared for in the emergency department or hospital; rhinoviruses/enteroviruses (n = 1786, 33%), RSV (n = 1156, 22%), and influenza viruses (n = 588, 11%) were most commonly detected.
The seasonality and monthly case count of invasive streptococcal disease and respiratory virus detection are shown in Figure 1. Most cases of invasive streptococcal disease occurred in the winter (n = 24) and spring (n = 34) with 70% of all cases occurring between November and May. The highest annual number of cases of iGAS occurred in the years 2022 and 2017, whereas the highest annual number of cases of IPD occurred in 2022 (Supplemental Table 2).
Seasonality and monthly case count of invasive streptococcal disease and respiratory viruses by type, Hamilton County, Ohio, 2017–2022.
Seasonality and monthly case count of invasive streptococcal disease and respiratory viruses by type, Hamilton County, Ohio, 2017–2022.
Most children (54%) had a prior complex chronic condition (Table 1). The most common source of culture isolation was bronchoalveolar lavage or pleural fluid (38%) and blood (30%) for iGAS and blood (51%) and bronchoalveolar lavage or pleural fluid (33%) for IPD. Approximately one-third of iGAS and IPD cases required admission to the intensive care unit, of which 14 (6 iGAS and 8 IPD) needed mechanical ventilation and 12 (6 iGAS and 6 IPD) needed vasopressor support (Table 2). Three deaths occurred, all in children with chronic medical conditions and S pneumoniae isolated from blood. Among the 49 cases of IPD, 24 were typeable, of which 13 (54%) were serotypes not covered by currently available pneumococcal vaccines (Supplemental Table 1). Most children (88%) had received at least 1 dose of a pneumococcal vaccine.
Demographic Characteristics of Children (n = 96) With Invasive Streptococcal or Pneumococcal Disease, Hamilton County, Ohio, 2017–2022
Category . | All Invasive Streptococcal Disease (n = 96) . | Invasive Group A Streptococcal Disease (n = 47) . | Invasive Pneumococcal Disease (n = 49) . |
---|---|---|---|
Age, median (IQR), y | 4 (1–9) | 4 (1–9) | 4 (1–9) |
Sex, n (%) | |||
Female | 42 (44) | 16 (34) | 26 (53) |
Male | 54 (56) | 31 (66) | 23 (47) |
Race, n (%) | |||
Asian, Native Hawaiian, and Other Pacific Islander | 5 (5) | 4 (9) | 1 (2) |
Black | 40 (42) | 20 (43) | 20 (41) |
Othera | 9 (9) | 4 (9) | 5 (10) |
White | 42 (44) | 19 (40) | 23 (47) |
Ethnicity, n (%) | |||
Hispanic | 10 (10) | 3 (6) | 7 (14) |
Non-Hispanic | 86 (90) | 44 (94) | 42 (86) |
Insurance type, n (%) | |||
Private | 33 (34) | 15 (32) | 18 (37) |
Public | 55 (57) | 29 (62) | 26 (53) |
Other | 8 (8) | 3 (6) | 5 (10) |
Pneumococcal vaccine status, n (%) | |||
Unvaccinated | 8 (8) | 6 (13) | 2 (4) |
Unknown vaccine history | 11 (11) | 7 (15) | 4 (8) |
Partially vaccinatedb | 6 (6) | 1 (2) | 5 (10) |
Fully vaccinatedb | 71 (74) | 33 (70) | 38 (78) |
Influenza vaccine status, n (%) | |||
Unvaccinated | 18 (19) | 10 (21) | 8 (16) |
Unknown vaccine history | 11 (11) | 7 (15) | 4 (8) |
≥1 dose | 67 (70) | 30 (64) | 37 (76) |
Prior medical conditions | |||
No chronic medical history, n (%) | 44 (46) | 23 (49) | 21 (43) |
Cardiac history, n | 10 | 5 | 5 |
Gastrointestinal history, n | 21 | 9 | 12 |
Hematological history, n | 11 | 3 | 8 |
Oncological history, n | 4 | 0 | 4 |
Metabolic history, n | 7 | 4 | 3 |
Neurological history, n | 17 | 8 | 9 |
Congenital history, n | 7 | 3 | 4 |
Renal history, n | 12 | 6 | 6 |
Respiratory history, n | 22 | 12 | 10 |
Prematurity history, n | 6 | 3 | 3 |
Category . | All Invasive Streptococcal Disease (n = 96) . | Invasive Group A Streptococcal Disease (n = 47) . | Invasive Pneumococcal Disease (n = 49) . |
---|---|---|---|
Age, median (IQR), y | 4 (1–9) | 4 (1–9) | 4 (1–9) |
Sex, n (%) | |||
Female | 42 (44) | 16 (34) | 26 (53) |
Male | 54 (56) | 31 (66) | 23 (47) |
Race, n (%) | |||
Asian, Native Hawaiian, and Other Pacific Islander | 5 (5) | 4 (9) | 1 (2) |
Black | 40 (42) | 20 (43) | 20 (41) |
Othera | 9 (9) | 4 (9) | 5 (10) |
White | 42 (44) | 19 (40) | 23 (47) |
Ethnicity, n (%) | |||
Hispanic | 10 (10) | 3 (6) | 7 (14) |
Non-Hispanic | 86 (90) | 44 (94) | 42 (86) |
Insurance type, n (%) | |||
Private | 33 (34) | 15 (32) | 18 (37) |
Public | 55 (57) | 29 (62) | 26 (53) |
Other | 8 (8) | 3 (6) | 5 (10) |
Pneumococcal vaccine status, n (%) | |||
Unvaccinated | 8 (8) | 6 (13) | 2 (4) |
Unknown vaccine history | 11 (11) | 7 (15) | 4 (8) |
Partially vaccinatedb | 6 (6) | 1 (2) | 5 (10) |
Fully vaccinatedb | 71 (74) | 33 (70) | 38 (78) |
Influenza vaccine status, n (%) | |||
Unvaccinated | 18 (19) | 10 (21) | 8 (16) |
Unknown vaccine history | 11 (11) | 7 (15) | 4 (8) |
≥1 dose | 67 (70) | 30 (64) | 37 (76) |
Prior medical conditions | |||
No chronic medical history, n (%) | 44 (46) | 23 (49) | 21 (43) |
Cardiac history, n | 10 | 5 | 5 |
Gastrointestinal history, n | 21 | 9 | 12 |
Hematological history, n | 11 | 3 | 8 |
Oncological history, n | 4 | 0 | 4 |
Metabolic history, n | 7 | 4 | 3 |
Neurological history, n | 17 | 8 | 9 |
Congenital history, n | 7 | 3 | 4 |
Renal history, n | 12 | 6 | 6 |
Respiratory history, n | 22 | 12 | 10 |
Prematurity history, n | 6 | 3 | 3 |
Includes American Indian and Alaskan Native (n = 1) and patient refused to report race (n = 8).
As defined by age at time of presentation: Fully vaccinated refers to children who have received all age-appropriate doses, whereas partially vaccinated refers to children who have received at least 1 but not all age-appropriate doses.
Clinical Characteristics and Outcomes of Children With Invasive Streptococcal Disease and Source of Positive Culture, Hamilton County, Ohio, 2017–2022
Category . | Invasive Group A Streptococcal Disease (n = 47), n (%) . | Invasive Pneumococcal Disease (n = 49), n (%) . |
---|---|---|
Culture source | ||
Blood | 14 (30) | 25 (51) |
Bone or synovial fluid | 1 (2) | 2 (4) |
Bronchoalveolar fluid | 17 (36) | 14 (29) |
Pleural fluid | 1 (2) | 2 (4) |
Cerebrospinal fluid | 0 (0) | 4 (8) |
Othera | 5 (11) | 0 (0) |
Tissue or wound | 9 (19) | 2 (4) |
Intensive care unit admission | 15 (32) | 16 (33) |
Mechanical ventilation | 6 (13) | 8 (16) |
Vasopressor support | 6 (13) | 6 (12) |
Remained hospitalized after 30 d | 3 (6) | 5 (10) |
Death | 0 (0) | 3 (6) |
Category . | Invasive Group A Streptococcal Disease (n = 47), n (%) . | Invasive Pneumococcal Disease (n = 49), n (%) . |
---|---|---|
Culture source | ||
Blood | 14 (30) | 25 (51) |
Bone or synovial fluid | 1 (2) | 2 (4) |
Bronchoalveolar fluid | 17 (36) | 14 (29) |
Pleural fluid | 1 (2) | 2 (4) |
Cerebrospinal fluid | 0 (0) | 4 (8) |
Othera | 5 (11) | 0 (0) |
Tissue or wound | 9 (19) | 2 (4) |
Intensive care unit admission | 15 (32) | 16 (33) |
Mechanical ventilation | 6 (13) | 8 (16) |
Vasopressor support | 6 (13) | 6 (12) |
Remained hospitalized after 30 d | 3 (6) | 5 (10) |
Death | 0 (0) | 3 (6) |
Other culture source includes body fluid (n = 3), bone marrow (n = 1), and retropharyngeal surgical fluid (n = 1).
A Poisson regression was used to examine the temporal association between invasive streptococcal disease and respiratory virus cases that occurred cumulatively in the prior 2 or 4 weeks. For iGAS cases, prior (2 or 4 weeks) influenza virus detection was found to be temporally associated with iGAS, whereas prior (2 or 4 weeks) rhinovirus/enterovirus detection was negatively associated with the occurrence of iGAS (Table 3). Human metapneumovirus (HMPV) detection was temporally associated with iGAS only in the 4-week lagged effects model.
β Estimate of Association Between Respiratory Virus Detection, Cumulative Over 2 and 4 Weeks Before Infection, and Invasive Streptococcal Infection Using Poisson Regression in Hamilton County Children, 2017–2022
. | Invasive Group A Streptococcal Disease (n = 47) . | Invasive Pneumococcal Disease (n = 49) . | ||||
---|---|---|---|---|---|---|
Two-Week Lagged Effects Model . | ||||||
. | β Estimatea . | 95% CI . | P Value . | β Estimate . | 95% CI . | P Value . |
Adenoviruses (n = 356) | 0.087 | −0.062 to 0.235 | .25 | 0.190b | 0.036–0.344b | .02b |
Bocavirus (n = 361) | −0.158 | −0.332 to 0.016 | .07 | 0.070 | −0.056 to 0.195 | .28 |
Endemic coronaviruses (n = 200) | −0.011 | −0.161 to 0.140 | .89 | 0.097 | −0.032 to 0.227 | .14 |
Influenza viruses (n = 558) | 0.040b | 0.007 to 0.073b | .02b | 0.024 | −0.016 to 0.063 | .24 |
HMPV (n = 260) | 0.108 | −0.015 to 0.231 | .09 | 0.025 | −0.127 to 0.176 | .75 |
Parainfluenza viruses (n = 477) | −0.031 | −0.130 to 0.069 | .54 | 0.012 | −0.061 to 0.085 | .75 |
Rhinoviruses/enteroviruses (n = 1786) | −0.056b | −0.111 to −0.0002b | .049b | −0.036 | −0.086 to 0.014 | .15 |
RSVs (n = 1156) | 0.006 | −0.021 to 0.032 | .67 | 0.020 | −0.004 to 0.044 | .11 |
SARS-CoV-2 (n = 201) | −0.022 | −0.141 to 0.096 | .71 | 0.018 | −0.103 to 0.139 | .77 |
All respiratory viruses (n = 5355) | 0.004 | −0.003 to 0.010 | .28 | 0.006 | −0.0002 to 0.013 | .06 |
. | Invasive Group A Streptococcal Disease (n = 47) . | Invasive Pneumococcal Disease (n = 49) . | ||||
---|---|---|---|---|---|---|
Two-Week Lagged Effects Model . | ||||||
. | β Estimatea . | 95% CI . | P Value . | β Estimate . | 95% CI . | P Value . |
Adenoviruses (n = 356) | 0.087 | −0.062 to 0.235 | .25 | 0.190b | 0.036–0.344b | .02b |
Bocavirus (n = 361) | −0.158 | −0.332 to 0.016 | .07 | 0.070 | −0.056 to 0.195 | .28 |
Endemic coronaviruses (n = 200) | −0.011 | −0.161 to 0.140 | .89 | 0.097 | −0.032 to 0.227 | .14 |
Influenza viruses (n = 558) | 0.040b | 0.007 to 0.073b | .02b | 0.024 | −0.016 to 0.063 | .24 |
HMPV (n = 260) | 0.108 | −0.015 to 0.231 | .09 | 0.025 | −0.127 to 0.176 | .75 |
Parainfluenza viruses (n = 477) | −0.031 | −0.130 to 0.069 | .54 | 0.012 | −0.061 to 0.085 | .75 |
Rhinoviruses/enteroviruses (n = 1786) | −0.056b | −0.111 to −0.0002b | .049b | −0.036 | −0.086 to 0.014 | .15 |
RSVs (n = 1156) | 0.006 | −0.021 to 0.032 | .67 | 0.020 | −0.004 to 0.044 | .11 |
SARS-CoV-2 (n = 201) | −0.022 | −0.141 to 0.096 | .71 | 0.018 | −0.103 to 0.139 | .77 |
All respiratory viruses (n = 5355) | 0.004 | −0.003 to 0.010 | .28 | 0.006 | −0.0002 to 0.013 | .06 |
Four-Week Lagged Effects Model . | ||||||
---|---|---|---|---|---|---|
. | β Estimate . | 95% CI . | P Value . | β Estimate . | 95% CI . | P Value . |
Adenoviruses (n = 356) | 0.023 | −0.085 to 0.130 | .68 | 0.032 | −0.084 to 0.147 | .59 |
Bocavirus (n = 361) | −0.072 | −0.177 to 0.033 | .17 | −0.001 | −0.093 to 0.092 | .99 |
Endemic coronaviruses (n = 200) | 0.007 | −0.078 to 0.092 | .88 | 0.080 | 0.002 to 0.159 | .06 |
Influenza viruses (n = 558) | 0.022b | 0.005 to 0.039b | .02b | 0.011 | −0.010 to 0.032 | .30 |
HMPV (n = 260) | 0.088b | 0.015 to 0.161b | .02b | 0.008 | −0.084 to 0.100 | .86 |
Parainfluenza viruses (n = 477) | −0.011 | −0.065 to 0.043 | .68 | 0.018 | −0.020 to 0.057 | .37 |
Rhinoviruses/enteroviruses (n = 1786) | −0.045b | −0.077 to −0.012b | .006b | −0.014 | −0.042 to 0.014 | .33 |
RSVs (n = 1156) | 0.007 | −0.007 to 0.020 | .36 | 0.011 | −0.002 to 0.024 | .12 |
SARS-CoV-2 (n = 201) | −0.040 | −0.115 to 0.036 | .26 | 0.002 | −0.070 to 0.074 | .96 |
All respiratory viruses (n = 5355) | 0.003 | −0.001 to 0.006 | .15 | 0.003 | −0.000 to 0.006 | .07 |
Four-Week Lagged Effects Model . | ||||||
---|---|---|---|---|---|---|
. | β Estimate . | 95% CI . | P Value . | β Estimate . | 95% CI . | P Value . |
Adenoviruses (n = 356) | 0.023 | −0.085 to 0.130 | .68 | 0.032 | −0.084 to 0.147 | .59 |
Bocavirus (n = 361) | −0.072 | −0.177 to 0.033 | .17 | −0.001 | −0.093 to 0.092 | .99 |
Endemic coronaviruses (n = 200) | 0.007 | −0.078 to 0.092 | .88 | 0.080 | 0.002 to 0.159 | .06 |
Influenza viruses (n = 558) | 0.022b | 0.005 to 0.039b | .02b | 0.011 | −0.010 to 0.032 | .30 |
HMPV (n = 260) | 0.088b | 0.015 to 0.161b | .02b | 0.008 | −0.084 to 0.100 | .86 |
Parainfluenza viruses (n = 477) | −0.011 | −0.065 to 0.043 | .68 | 0.018 | −0.020 to 0.057 | .37 |
Rhinoviruses/enteroviruses (n = 1786) | −0.045b | −0.077 to −0.012b | .006b | −0.014 | −0.042 to 0.014 | .33 |
RSVs (n = 1156) | 0.007 | −0.007 to 0.020 | .36 | 0.011 | −0.002 to 0.024 | .12 |
SARS-CoV-2 (n = 201) | −0.040 | −0.115 to 0.036 | .26 | 0.002 | −0.070 to 0.074 | .96 |
All respiratory viruses (n = 5355) | 0.003 | −0.001 to 0.006 | .15 | 0.003 | −0.000 to 0.006 | .07 |
Abbreviations: HMPV, human metapneumovirus; RSV, respiratory syncytial virus.
Exponentiating the estimate would give the percent change in the incident rate for every 1-unit increase in the predictor.
P value < .05.
For IPD cases, only adenovirus detection was positively associated with IPD in the 2-week lagged effects model, whereas no other respiratory virus was temporally associated with IPD in either model. For both iGAS and IPD, no temporal associations were found in either the 2- or 4-week lagged effects models for bocavirus, endemic coronavirus, parainfluenza viruses, SARS-CoV-2, or RSV.
As iGAS demonstrated a winter/spring seasonality, whereas rhinovirus/enterovirus persisted year-round—including during the COVID-19 pandemic, during which cases of iGAS diminished (Fig 1)—we explored whether these factors confounded our negative association between iGAS and rhinovirus/enterovirus. When modeling iGAS and rhinovirus/enterovirus without season as a covariate, the β estimates remained similar to those of our original model (β = −0.061, P = .02 for the 2-week model; β = −0.046, P = .003 for the 4-week model). This suggests that seasonality is not unduly influencing the association between iGAS and rhinovirus/enterovirus counts. In a separate model in which all data from 2021 were excluded (the year in which counts of iGAS and seasonal respiratory viruses’ seasonality were most impacted), the β estimates remained negative for rhinovirus/enterovirus (β = −0.037, P = .2 for the 2-week model; β = −0.032, P = .07 for the 4-week model). Running these models without season again produced similar results (β = −0.045, P = .1 for the 2-week model; β = −0.034, P = .03 for the 4-week model).
Discussion
In this population-based study of invasive streptococcal disease and trends in respiratory viral detection causing medically attended ARIs, we found significant temporal associations between influenza virus and rhinovirus/enterovirus detection in the prior 2 or 4 weeks with iGAS. Despite the similar number of IPD cases, we found a temporal association between only adenovirus detection in the prior 2 weeks and IPD. Our findings differ from those of earlier population-based studies that have implicated influenza virus and RSV with increased rates of IPD.12,14 This could be due to the declining incidence of IPD nationally at the time of our study, broadening serotype coverage of newer pneumococcal vaccines, and dissimilar study periods during which circulation of respiratory viruses varied. In addition, this difference could be influenced by a high proportion of children with an underlying chronic medical condition (57%) in our study compared with other population-based IPD studies.21 Alternatively, although our study included a large, defined population, our cases of IPD may have been too sparse or heterogenous to identify associations with specific respiratory viruses.
Similar to other time-series studies, a positive temporal association was found between iGAS with influenza virus cases.9,10 This potential viral-bacterial interaction has been hypothesized to occur through different mechanisms, including the promotion of bacterial adherence and invasion into the respiratory tract, facilitating intravascular dissemination of bacteria, disruption of mucosal immunity, and upregulation of proinflammatory cytokines.22–24 On the other hand, the temporal association seen with HMPV and rhinoviruses/enteroviruses has not been frequently reported with iGAS, with only a few studies having examined respiratory viruses beyond influenza viruses.10 HMPV has also been associated with an increased risk of IPD and nonspecific bacteremia, and these bacterial-viral interactions may have a similar underlying pathogenesis as those seen with influenza viruses.11,13 Because we only found a temporal association between HMPV in 1 of the 2 lagged effects models, further studies are needed to assess the interaction between HMPV and iGAS and whether interventions or vaccines targeted at reducing the incidence of HMPV could potentially have indirect effects on invasive streptococcal disease, similar to those seen with influenza vaccination.25,26
To our knowledge, the negative association between rhinoviruses/enteroviruses and iGAS has not been reported in other studies. The directionality of this association persisted in additional models that factored the differences in seasonality between iGAS and rhinovirus/enterovirus as well as the persistence of rhinovirus/enterovirus during the COVID-19 pandemic. Although codetection of viruses and bacteria is typically associated with increased severity of disease, a preceding rhinovirus/enterovirus has been found in some studies to decrease the severity of a subsequent viral illness, including influenza.27–29 This protective effect has been postulated to derive from the upregulation of local mucosal immunity and suppression of viral replication, although this effect may be specific to certain rhinovirus subtypes.30,31 The mechanism in which rhinoviruses/enteroviruses interfere with iGAS, whether directly or through its interaction with influenza viruses, is unknown and requires further investigation from larger population-based studies.
There are several limitations that are important to consider when interpreting our results. Our study methodology may incompletely capture less severe manifestations of invasive streptococcal disease such as pneumonia that were evaluated and treated in a non-CCHMC ambulatory setting. In addition, cultures positive for Streptococcus that were found only at referral hospitals or infections that did not produce a culture positive for Streptococcus, including necrotizing fasciitis and toxic shock syndrome, for which we cannot directly attribute the infection to S pyogenes or S pneumoniae, were not captured or included. Because of our modest cohort size, we were unable to examine associations with specific subtypes of respiratory viruses (for example, influenza virus A vs B) or specific subsets of iGAS or IPD such as IPD from different culture sources. Our sample size also precluded any subanalysis looking at the temporal association between respiratory viruses and invasive streptococcal disease before and during the COVID-19 pandemic. Although our study is strengthened by its population-based approach, population-level findings may not be applicable on an individual level. Additionally, the objective of our respiratory virus surveillance program is to identify the pathogens associated with medically attended ARIs, not to capture asymptomatic or nonmedically attended ARIs. Our study is strengthened by the prospective enrollment of children with ARI and invasive streptococcal disease from a single county in a children’s hospital that cares for nearly all children in that county.
In conclusion, we found a positive temporal association between influenza virus infections and iGAS that has been similarly described in other ecological or population-based time-series studies. A positive temporal association was also found with HMPV and iGAS as well as adenovirus and IPD, although this was not consistent in our models. Additionally, we found a negative temporal association between rhinoviruses/enteroviruses and iGAS that was consistent across our time-series models. Viral-bacterial interactions should be further explored because they can inform our strategies to further mitigate the risk of invasive streptococcal disease.
Mr Teoh conceptualized and designed the study, coordinated the data collection, collected and analyzed the data, drafted the initial manuscript, and critically reviewed and revised the manuscript. Mr Fenchel analyzed the data, conceptualized and conducted the statistical analysis, and critically reviewed and revised the manuscript. Mr Griffin coordinated the data collection, collected and analyzed the data, and critically reviewed and revised the manuscript. Ms Ankrum conceptualized and designed the study and critically reviewed and revised the manuscript. Dr Prasanphanich conceptualized and designed the study, collected the data, and critically reviewed and revised the manuscript. Ms Toepfer provided access to New Vaccine Surveillance Network (NVSN) respiratory virus data and critically reviewed and revised the manuscript. Dr Moline provided access to NVSN respiratory virus data and critically reviewed and revised the manuscript. Dr Staat conceptualized and designed the study, provided access to NVSN respiratory virus data, provided supervision of the project, and critically reviewed and revised the manuscript. Dr Huang conceptualized and designed the study, provided financial support, provided supervision of the project, and critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
CONFLICT OF INTEREST DISCLOSURES: All authors have no relevant conflicts of interest to disclose.
FUNDING: REDCap electronic data capture tools were hosted at the Center for Clinical & Translational Science & Training (CCTST). The CCTST at the University of Cincinnati is funded by the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program, grant UL1TR001425. The CTSA program is led by the NIH’s National Center for Advancing Translational Sciences (NCATS). The New Vaccine Surveillance Network was funded under a cooperative agreement between Cincinnati Children’s Medical Center and the Centers for Disease Control and Prevention (award number: U01IP001155-01-00). The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.