Using a large data set (n = 811), the relationship between acute respiratory infection illness severity and inflammatory biomarkers was investigated to determine whether certain symptoms are correlated more closely than others with the inflammatory biomarkers, interleukin-8 (IL-8) and nasal neutrophils. Participants with community acquired acute respiratory infection underwent nasal lavage for IL-8 and neutrophil testing, in addition to multiplex polymerase chain reaction (PCR) methods for the detection and identification of respiratory viruses. Information about symptoms was obtained throughout the duration of the illness episode using the well-validated Wisconsin Upper Respiratory Symptom Survey (WURSS-21). Global symptom severity was calculated by the area under the curve (AUC) plotting duration versus WURSS total. Of the specimens tested, 56% were positively identified for one or more of nine different respiratory viruses. During acute respiratory infection illness, both IL-8 and neutrophils positively correlate with AUC (rs = 0.082, P = 0.022; rs = 0.080, P = 0.030). IL-8 and neutrophils correlate with nasal symptom severity: runny nose (r = 0.13, P = <0.00001; r = 0.18, P = <0.003), plugged nose (r = 0.045, P = 0.003; r = 0.14, P = 0.058), and sneezing (r = −0.02, P = <0.0001; r = −0.0055, P = 0.31). Neutrophils correlate with some quality of life measures such as sleeping well (r = 0.15, P = 0.026). Thus, the study demonstrates that IL-8 and neutrophils are correlated with severity of nasal symptoms during acute respiratory infection. Further research is necessary to determine if the concentration of these or other biomarkers can predict the overall duration and severity of acute respiratory infection illness.
Semi-continuous data, also known as zero-inflated continuous data, have a substantial portion of responses equal to a single value (typically 0) and a continuous, right-skewed distribution among the remaining positive values. For jointly modeling multivariate clustered semi-continuous responses, the covariate effects in the positive parts can be proportionally constrained to the covariate effects in the logistic part, yielding a multivariate two-part fixed effects model. It is shown that, both theoretically and experimentally, the proportionally constrained model is more efficient than the unconstrained model in terms of parameter estimation, and thus provides a deeper understanding of the data structure when the proportionality structure holds. A robust variance estimation method is also introduced and tested under various model mis-specified cases. The proposed model is applied to data from a randomized controlled trial evaluating potential preventive effects of meditation or exercise on duration and severity of acute respiratory infection illness. The new analysis infers that meditation not only has highly significant effects on reduction of acute respiratory infection severity and duration, but also has significant effects on preventing acute respiratory infection, which was not previously reported in the literature.
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