Surveillance for Healthcare-Associated Influenza-Like Illness in Pediatric Clinics: Validity of Diagnosis Codes for Case Identification

Traditional definition of healthcare-associated influenza underestimates cases associated with other healthcare exposures in a population-based surveillance system

OBJECTIVE

To provide comprehensive population-level estimates of the burden of healthcare-associated influenza.

DESIGN

Retrospective cross-sectional study.

SETTING

US Influenza Hospitalization Surveillance Network (FluSurv-NET) during 2012-2013 through 2018-2019 influenza seasons.

PATIENTS

Laboratory-confirmed influenza-related hospitalizations in an 8-county catchment area in Tennessee.

METHODS

The incidence of healthcare-associated influenza was determined using the traditional definition (ie, positive influenza test after hospital day 3) in addition to often underrecognized cases associated with recent post-acute care facility admission or a recent acute care hospitalization for a noninfluenza illness in the preceding 7 days.

RESULTS

Among the 5,904 laboratory-confirmed influenza-related hospitalizations, 147 (2.5%) had traditionally defined healthcare-associated influenza. When we included patients with a positive influenza test obtained in the first 3 days of hospitalization and who were either transferred to the hospital directly from a post-acute care facility or who were recently discharged from an acute care facility for a noninfluenza illness in the preceding 7 days, we identified an additional 1,031 cases (17.5% of all influenza-related hospitalizations).

CONCLUSIONS

Including influenza cases associated with preadmission healthcare exposures with traditionally defined cases resulted in an 8-fold higher incidence of healthcare-associated influenza. These results emphasize the importance of capturing other healthcare exposures that may serve as the initial site of viral transmission to provide more comprehensive estimates of the burden of healthcare-associated influenza and to inform improved infection prevention strategies.

Validation of algorithms for identifying outpatient infections in MS patients using electronic medical records

Background Our multiple sclerosis (MS) stakeholder groups expressed concerns about whether MS disease-modifying therapies (DMTs) increase the risk of specific outpatient infections. Validated methods for identifying the risk of these selected outpatient infections in the general population either do not exist, exclude the clinically important possibility of recurrent infections, or are inaccurate, largely because existing studies relied primarily on International Classification of Diseases (ICD) codes to identify infectious outcomes. Additionally, no studies have validated methods among the MS population, where some MS symptoms can be mistaken for infections (e.g., urinary tract infections (UTIs)). Objective To utilize multiple data elements in the electronic health record (EHR) to improve accurate identification of selected outpatient infections in an MS cohort and general population controls. Methods We searched Kaiser Permanente Southern California's EHR based on ICD-9/10 codes for specified outpatient infections from 1/1/2008-12/31/2018 among our MS cohort (n=6000) and 5:1 general population controls matched on age, sex, and race/ethnicity (n=30,010). Random sample chart abstractions from each group were used to identify common coding errors for outpatient pneumonia, upper and lower respiratory tract infection, UTIs, herpetic infections (herpes zoster (HZ), herpes simplex virus (HSV)), fungal infections, otitis media, cellulitis, and influenza. This information was used to define discrete infectious episodes and to identify the algorithm with the highest positive predictive value (PPV) after supplementing the ICD-coded episodes with radiology, laboratory and/or pharmacy data. Results PPVs relying on ICD codes alone were inaccurate, particularly for identifying recurrent herpetic infections (HZ (42%) and HSV (60%)), UTIs (42%) and outpatient pneumonia (20%) in MS patients. Defining and validating episodes improved the PPVs for all the selected infections. The final algorithms' PPVs were 80-100% in MS and 75-100% in the general population, after including dispensed treatments (UTI, herpetic infections and yeast vaginitis), timing of dispensed treatments (UTI, herpetic infections and yeast vaginitis), removal of prophylactic antiviral use (herpetic infections), and inclusion of selected laboratory (UTIs) and imaging results (pneumonia). The only exception was outpatient pneumonia, where PPVs improved but remained ≤70%. There were no significant differences in the PPVs for the final algorithms between the MS and general population. Conclusions Provided herein are accurate and validated algorithms that can be used to improve our understanding of how the risk of recurrent outpatient infections are influenced by MS treatments, MS-related disability, and co-morbidities. Findings from such studies will be important in helping patients and clinicians engage in shared decision-making and in developing strategies to mitigate risks of recurrent infections.

Elevated Atopic Comorbidity in Patients with Food Protein-Induced Enterocolitis

BACKGROUND

Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE-mediated food allergy. Its relationship to the major atopic manifestations (atopic dermatitis [AD], IgE-mediated food allergy [IgE-FA], allergic rhinitis [AR], asthma) is not understood.

OBJECTIVE

To determine the clinical characteristics, epidemiologic features, and natural history of FPIES in relation to the major atopic manifestations.

METHODS

We examined our primary care birth cohort of 158,510 pediatric patients, of whom 214 patients met 2017 FPIES diagnostic criteria. We measured the influence of FPIES on developing subsequent atopic disease.

RESULTS

Pediatric FPIES incidence was between 0.17% and 0.42% depending on birth year. As in prior reports, most patients had an acute presentation (78%), and milk, soy, oat, rice, potato, and egg were common triggers. The mean age of diagnosis was 6.8 months. Atopic comorbidity was higher in patients with FPIES compared with healthy children (AD, 20.6% vs 11.7%; IgE-FA, 23.8% vs 4.0%; asthma, 26.6% vs 18.4%; AR, 28.0% vs 16.7%; P < .001 χ²). However, longitudinal analyses indicated that prior FPIES did not influence the rate of atopy development.

CONCLUSIONS

The incidence of FPIES in our cohort was initially low, but is increasing. Food allergen distribution, presentation, and age of onset are similar to prior reports. Patients with FPIES have high rates of atopic comorbidity. However, longitudinal analysis does not support direct causation as the etiology of these associations. Rather it suggests a shared predisposition to both types of allergy, or associative bias effects. This work refines our understanding of the natural history of FPIES by elucidating associations between FPIES and atopy.

Incidence of Healthcare-Associated Influenza-Like Illness After a Primary Care Encounter Among Young Children

BACKGROUND

Despite potential respiratory virus transmission in pediatric clinics, little is known about the risk of healthcare-associated viral infections attributable to outpatient encounters. We evaluated whether exposure to a pediatric clinic visit was associated with subsequent influenza-like illness (ILI).

METHODS

Using electronic health record data, we conducted a retrospective cohort study of all children aged <6 years who presented to a provider in a 29-clinic pediatric primary care network for a non-ILI-related encounter over 2 respiratory virus seasons (September 1, 2012, to April 30, 2014). We defined a risk period for potential healthcare-associated (HA) ILI of 1 to 8 days after a non-ILI clinic visit and identified all cases of ILI to compare the incidences of ILI visits 1 to 8 days after a non-ILI encounter and those of visits >8 days after a non-ILI encounter.

RESULTS

Among 149987 children <6 years of age (mean age, 2.5 years) with ≥1 non-ILI visit during the study period, 531928 total encounters and 13951 (2.9%) ILI encounters were identified; 1941 (13.9%) occurred within the HA-ILI risk window. The incidence rate ratios (IRRs) for ILI 1 to 8 days after compared with ILI >8 days after a non-ILI visit during season 1 were 1.36 (95% confidence interval, 1.22-1.52) among children ≥2 years of age and 1.01 (95% confidence interval, 0.93-1.09) among children <2 years of age. Estimates remained consistent during season 2 and with a risk window of 3, 4, or 9 days.

CONCLUSIONS

Pediatric clinic visits during a respiratory virus season were significantly associated with an increased incidence of subsequent ILI among children aged 2 to 6 years but not among those aged <2 years. These findings support the hypothesis that respiratory virus transmission in a pediatric clinic can result in HA ILI in young children.

Implementation of a rapid influenza A/B and RSV direct molecular assay improves emergency department oseltamivir use in paediatric patients

PURPOSE

Influenza A virus (FluA), influenza B virus (FluB) and respiratory syncytial virus (RSV) illnesses increase hospitalizations during seasonal epidemics.

METHODOLOGY

To determine the utility of the Simplexa FluA/B & RSV Direct Assay (Direct Flu/RSV) and its impact on oseltamivir use, we offered this assay to emergency department (ED) patients with influenza-like illness.

RESULTS

Utilization of the Direct Flu/RSV provided a turnaround time (TAT) of 2 hours. Compared to the flu season prior to implementation of the Direct Flu/RSV, clinicians were more likely to prescribe 5 days of oseltamivir therapy for Direct Flu/RSV-positive patients in comparison to those with a negative test.

CONCLUSIONS

Use of Direct Flu/RSV provides results rapidly, which leads to more appropriate use of oseltamivir. The ease of use of this assay and quick TAT allows for prompt decision-making, which is essential for patient care and effective disease control during the influenza season.