Model correction of diagnostic coding-based RSV incidence for children 0–4 years in the US

TL;DR

This study improves estimates of RSV-related lower respiratory infections in young children by adjusting for incomplete diagnostic coding in insurance claims data.

Contribution

A novel modeling approach to correct RSV incidence estimates using claims data and surveillance positivity rates.

Findings

Only 42% of predicted RSV cases were correctly coded in claims data.

RSV was estimated to cause 10–43% of LRTI cases in children 0–4 years.

Modeling adjustments using NREVSS data can improve claims-based RSV incidence estimates.

Abstract

Although administrative claims data have a high degree of completeness, not all medically attended Respiratory Syncytial Virus-associated lower respiratory tract infections (RSV-LRTIs) are tested or coded for their causative agent. We sought to determine the attribution of RSV to LRTI in claims data via modeling of temporal changes in LRTI rates against surveillance data.

We estimated the weekly incidence of LRTI (inpatient, outpatient, and total) for children 0–4 years using 2011–2019 commercial insurance claims, stratified by HHS region, matched to the corresponding weekly NREVSS RSV and influenza positivity data for each region, and modelled against RSV, influenza positivity rates, and harmonic functions of time assuming negative binomial distribution. LRTI events attributable to RSV were estimated as predicted events from the full model minus predicted events with RSV positivity rate set to 0.

Approximately 42% of predicted RSV cases were coded in claims data. Across all regions, the percentage of LRTI attributable to RSV were 15–43%, 10–31%, and 10–31% of inpatient, outpatient, and combined settings, respectively. However, when compared to coded inpatient RSV-LRTI, 9 of 10 regions had improbable corrected inpatient LRTI estimates (predicted RSV/coded RSV ratio < 1). Sensitivity analysis based on separate models for PCR and antigen-based positivity showed similar results.

Underestimation based on coding in claims data may be addressed by NREVSS-based adjustment of claims-based RSV incidence. However, where setting-specific positivity rates is unavailable, we recommend modeling across settings to mirror NREVSS’s positivity rates which are similarly aggregated, to avoid inaccurate adjustments.

The online version contains supplementary material available at 10.1186/s12879-024-09474-y.

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