Role of viral and bacterial pathogens in causing pneumonia among Western Australian children: a case–control study protocol

Bhuiyan, Mejbah Uddin; Snelling, Thomas L; West, Rachel; Lang, Jurissa; Rahman, Tasmina; Borland, Meredith L; Thornton, Ruth; Kirkham, Lea-Ann; Sikazwe, Chisha; Martin, Andrew C; Richmond, Peter C; Smith, David W; Jaffe, Adam; Blyth, Christopher C

We will report the median log-transformed pathogen load of each pathogen for cases and controls and compare them using student t-test or Mann-Whitney U-test method, whichever is applicable, to determine if the pathogen load is different between cases and controls. A multivariable regression analysis (model 3) will be performed to determine the odds of being a case for 1 log 10 increase in pathogen load/mL for each viral and bacterial pathogen after adjusting for demographic factors; where numbers permit, the same approach will be used to determine increase in the odds of severe pneumonia (versus control status) (model 4). We will explore receiver-operating curve (ROC) and corresponding area under the curve (AUC) to find the optimal load cut-points (sensitivity and specificity) to discriminate CAP cases from controls, and severe CAP from non-severe CAP, for each pathogen [14] . We will also analyse data using classification and regression tree (CART) and Bayesian network methods as alternative approaches, to select thresholds levels for each pathogen load to discriminate between cases and controls. All analyses will be performed using STATA software version 13.0 (StataCorp, Texas, USA).