Persistent Bacterial Bronchitis: time to venture beyond the Umbrella

Chronic cough in children is common and frequently mismanaged. In the past, cough was diagnosed as asthma and inappropriate asthma therapies prescribed and esca- lated. It has been realized that persistent bacterial bronchitis (PBB) is a common cause of wet cough and responds to oral antibiotics. The initial definition comprised a history of chronic wet cough, positive bronchoalveolar (BAL) cultures for a respiratory pathogen and response to a 2-week course of oral amoxicillin–clavulanic acid. This is now termed PBB-micro; PBB-clinical eliminates the need for BAL. PBB-extended is PBB-micro or PBB-clinical but resolution necessitating 4 weeks of antibiotics; and recurrent PBB is > 3 attacks of PBB-micro or-clinical/year. However, the airway has only a limited range of responses to chronic inflammation and infection, and neutrophilic airway disease is seen in many other conditions, such as cystic fibrosis and primary ciliary dyskinesia, both chronic suppurative lung disease endotypes, whose recognition has led to huge scientific and clinical advances. There is an urgent need to extend endotyping into PBB, especially PBB-recurrent. We need to move from associative studies and, in particular, deploy sophisticated modern –omics technologies and systems biology, rather as has been done in the context of asthma in U-BIOPRED. In summary, the use of the term PBB has done signal service in pointing us away from prescribing asthma therapies to children with infected airways, but we now need to move beyond a simple description to teasing out underlying endotypes

Modelling the prevalence of wildlife diseases using simulated diagnostic test data

Bayesian Latent Class Models (BLCMs) are algorithms that are used to infer disease prevalence when true disease statuses and gold-standard diagnostic tests are not available. However, limited attention has been given to the specification and validation of BLCMs, which are necessary if credible estimates of diagnostic test performance and disease prevalence are to result. Across six technical chapters, this thesis investigates the fundamental principles of specification and validation via a series of experiments that apply BLCMs to ante-mortem diagnostic test data. To achieve this, simulated arrays of diagnostic test data are generated to reflect the reality of the imperfect trapping and testing efforts that take place in nature. Moreover, the classic Hui-Walter algorithm is generalised within a Bayesian framework to unlock the capability of BLCMs to handle both varying prior information and varying hypotheses simultaneously. Methods to validate BLCMs are developed and then scaled up across a wide range of possible diagnostic testing scenarios via the creation of procedures to explore high-dimensional parameter spaces. For the first time, it is demonstrated that the credibility of BLCM inferences is in fact predictable. Among the key findings discovered are dependence structures that are critical to the identifiability of BLCMs; these structures are uncovered at the limits of parameter spaces, and between the means and variances of the inferred statistics. Accordingly, methods are explored to mitigate for these structures as a further prerequisite to obtaining credible estimates. Attention then turns to testing the core assumptions used to specify the generalised Hui-Walter algorithm. The assumptions about where the true values of diagnostic test performance and disease prevalence exist are removed, and the resulting sensitivity analyses provide confirmation that the findings reported throughout the thesis are indeed generalisable, even to unusual testing scenarios. With a rigorous validation protocol in place, a novel class of time-dependent BLCMs is specified, and then provided with data from one of the world’s longest running wildlife studies. New and rigorously validated inferences of disease prevalence are revealed, and anecdotal trends are corroborated, highlighting the real-world applications of this thesis

Lung development and aging

The onset of chronic obstructive pulmonary disease (COPD) can arise either from failure to attain the normal spirometric plateau or from an accelerated decline in lung function. Despite reports from numerous big cohorts, no single adult life factor, including smoking, accounts for this accelerated decline. By contrast, five childhood risk factors (maternal and paternal asthma, maternal smoking, childhood asthma and respiratory infections) are strongly associated with an accelerated rate of lung function decline and COPD. Among adverse effects on lung development are transgenerational (grandmaternal smoking), antenatal (exposure to tobacco and pollution), and early childhood (exposure to tobacco and pollution including pesticides) factors. Antenatal adverse events can operate by causing structural changes in the developing lung, causing low birth weight and prematurity and altered immunological responses. Also important are mode of delivery, early microbiological exposures, and multiple early atopic sensitizations. Early bronchial hyperresponsiveness, before any evidence of airway inflammation, is associated with adverse respiratory outcomes. Overlapping cohort studies established that spirometry tracks from the preschool years to late middle age, and those with COPD in the sixth decade already had the worst spirometry at age 10 years. Alveolar development is now believed to continue throughout somatic growth and is adversely impacted by early tobacco smoke exposure. Genetic factors are also important, with genes important in lung development and early wheezing also being implicated in COPD. The inescapable conclusion is that the roots of COPD are in early life, and COPD is a disease of childhood adverse factors interacting with genetic factors