Hospital utilization rates for influenza and RSV: a novel approach and critical assessment

Abstract

Background Influenza and respiratory syncytial virus (RSV) contribute significantly to the burden of acute lower respiratory infection (ALRI) inpatient care, but heterogeneous coding practices and availability of inpatient data make it difficult to estimate global hospital utilization for either disease based on coded diagnoses alone. Methods This study estimates rates of influenza and RSV hospitalization by calculating the proportion of ALRI due to influenza and RSV and applying this proportion to inpatient admissions with ALRI coded as primary diagnosis. Proportions of ALRI attributed to influenza and RSV were extracted from a meta-analysis of 360 total sources describing inpatient hospital admissions which were input to a Bayesian mixed effects model over age with random effects over location. Results of this model were applied to inpatient admission datasets for 44 countries to produce rates of hospital utilization for influenza and RSV respectively, and rates were compared to raw coded admissions for each disease. Results For most age groups, these methods estimated a higher national admission rate than the rate of directly coded influenza or RSV admissions in the same inpatient sources. In many inpatient sources, International Classification of Disease (ICD) coding detail was insufficient to estimate RSV burden directly. The influenza inpatient burden estimates in older adults appear to be substantially underestimated using this method on primary diagnoses alone. Application of the mixed effects model reduced heterogeneity between countries in influenza and RSV which was biased by coding practices and between-country variation. Conclusions This new method presents the opportunity of estimating hospital utilization rates for influenza and RSV using a wide range of clinical databases. Estimates generally seem promising for influenza and RSV associated hospitalization, but influenza estimates from primary diagnosis seem highly underestimated among older adults. Considerable heterogeneity remains between countries in ALRI coding (i.e., primary vs non-primary cause), and in the age profile of proportion positive for influenza and RSV across studies. While this analysis is interesting because of its wide data utilization and applicability in locations without laboratory-confirmed admission data, understanding the sources of variability and data quality will be essential in future applications of these methods.

Global and national influenza-associated hospitalisation rates: Estimates for 40 countries and administrative regions

“…the Socio-Demographic Index [ 16 ]), the circulating viruses [ 16 ], hospitalisation polices (e.g. government subsidised hospitalisations for certain age groups which affects their representation in the data), the impact of ICD coding on the estimates (inclusion of primary, secondary, and any ICD codes [ 2 ]), and even societal and cultural factors which affect health care usage. These different factors could significantly impact hospitalisation rates and, therefore, should be considered when pooling data from diverse settings.…”

“…Influenza is a common respiratory disease with high incidence rates reported in countries worldwide [ 1 , 2 ]. Despite its substantial health and economic impact, information regarding influenza and its associated severity, including hospitalisations, is still limited [ 3 , 4 ].…”

“…WHO estimates that annual influenza epidemics result in roughly three to five million cases of severe illness (hospitalisations [ 5 ]) annually [ 1 ]. There are many different approaches for assessing the burden of severe influenza cases, ranging from the use of International Statistical Classification of Diseases and Related Health Problems (ICD) codes to identify hospitalised cases [ 2 ], population-based sentinel surveillance networks based on laboratory-confirmed influenza hospitalisations [ 6 , 7 ], literature reviews and modeling procedures to summarize the findings [ 8 ] and the use of time series regression models reporting excess hospitalisations [ 3 ]. Efforts to estimate the burden of influenza disease at a national level have increased in recent years, especially following the publication of the WHO manual on estimating the disease burden associated with seasonal influenza [ 7 ].…”

Estimating the burden of adult hospital admissions due to RSV and other respiratory pathogens in England

“…However, the burden of RSV disease is not well described among the older population. 10 RSV infection causes a similar clinical syndrome to many other viruses, including influenza; in this age group, 11 there is little routine diagnostic testing for respiratory viruses in many countries including England, and finally patients may present late with complications of infection when the virus is no longer detectable. There are many candidate RSV vaccines and therapeutics in advanced development.…”

“…Understanding the impact of RSV on morbidity, mortality, and healthcare usage is important to inform public health policy. However, the burden of RSV disease is not well described among the older population 10 . RSV infection causes a similar clinical syndrome to many other viruses, including influenza; in this age group, 11 there is little routine diagnostic testing for respiratory viruses in many countries including England, and finally patients may present late with complications of infection when the virus is no longer detectable.…”