Addressing the Analytic Challenges of Cross-Sectional Pediatric Pneumonia Etiology Data

Abstract

Despite tremendous advances in diagnostic laboratory technology, identifying the pathogen(s) causing pneumonia remains challenging because the infected lung tissue cannot usually be sampled for testing. Consequently, to obtain information about pneumonia etiology, clinicians and researchers test specimens distant to the site of infection. These tests may lack sensitivity (eg, blood culture, which is only positive in a small proportion of children with pneumonia) and/or specificity (eg, detection of pathogens in upper respiratory tract specimens, which may indicate asymptomatic carriage or a less severe syndrome, such as upper respiratory infection). While highly sensitive nucleic acid detection methods and testing of multiple specimens improve sensitivity, multiple pathogens are often detected and this adds complexity to the interpretation as the etiologic significance of results may be unclear (ie, the pneumonia may be caused by none, one, some, or all of the pathogens detected). Some of these challenges can be addressed by adjusting positivity rates to account for poor sensitivity or incorporating test results from controls without pneumonia to account for poor specificity. However, no classical analytic methods can account for measurement error (ie, sensitivity and specificity) for multiple specimen types and integrate the results of measurements for multiple pathogens to produce an accurate understanding of etiology. We describe the major analytic challenges in determining pneumonia etiology and review how the common analytical approaches (eg, descriptive, case-control, attributable fraction, latent class analysis) address some but not all challenges. We demonstrate how these limitations necessitate a new, integrated analytical approach to pneumonia etiology data.

Keywords: attributable fraction analysis; case-control analysis; etiology; latent class analysis.; pneumonia.

Figure 1.

Differences in pneumonia etiology by specimens, assays, and analytical approaches. Pneumonia etiology using results of blood culture testing (A); results of blood culture adjusted for sensitivity (B); results of nasopharyngeal (NP) polymerase chain reaction (PCR) for 7 viruses (C); results of NP PCR, limited to pathogens for which the case-control odds ratio was significantly greater than 1 (type 1 error = 0.05) (D); results of method D after applying attributable fraction (AF) adjustment (E); results of blood culture and NP PCR from cases only (F); results of blood culture and NP PCR, limited to pathogens for which the NP PCR case-control odds ratio was significantly greater than 1 (type 1 error = 0.05) (G); results of method G after applying AF adjustment (H). Each “method” is a combination of the study design (case-only or case-control), analytical approach (raw results, adjustment for measurement error, odds ratio, AF), and assumed measurement error (ie, sensitivity and specificity) of the assay. The analyses were performed on a hypothetical study of 1000 pneumonia cases and 1000 controls (for case-control comparisons). The hatched slice represents a bacterial etiology (ie, cases positive by blood culture only); the black slice represents those with a mixed bacterial and viral etiology (ie, cases positive by blood culture and viral PCR); the solid gray slice represents viral etiology (ie, cases positive by viral PCR only). Abbreviations: ADENO, adenovirus; AF, attributable fraction; ECOL, Escherichia coli; ENFA, Enterococcus faecium; FLUA, influenza A; FLUB, influenza B; HBOV, human bocavirus; HINF, Haemophilus influenzae; HMPV, human metapneumovirus A/B; KPNE, Klebsiella pneumoniae; MCAT, Moraxella catarrhalis; N/A, not applicable; NMEN, Neisseria meningitidis; NP, nasopharyngeal; OR, odds ratio; PAER, Pseudomonas aeruginosa; PCR, polymerase chain reaction; PNEU, Streptococcus pneumoniae; RHINO, human rhinovirus; RSV, respiratory syncytial virus A/B; SASP, Salmonella species; SAUR, Staphylococcus aureus; Unk, unknown.

Figure 2.

Probability of 1 or more false-positive test results with increasing number of tests performed. Probability calculation based on binomial theorem [eg, probability ≥1 false positive = 1 – probability of zero false positives = 1– (specificity^{number of tests})]. In this example, specificity is set at 95%.