A philosophical analysis of research in the medical sciences: the qualitative-quantitative divide is cultural rather than epistemic

Much critical attention has been paid to the use of qualitative research in the medical sciences, with proponents advancing discussions of what it is and how it may be appraised, and critics arguing that it is of exploratory use only. Using philosophical analysis, I argue that such discussions are flawed insofar as they endorse the idea that qualitative and quantitative research are epistemically distinct categories involving different types of knowledge. Rather, I claim that such approaches are actually culturally distinct involving different intellectual histories. Thus highlighting that qualitative research may not necessarily be exploratory, and that the qualitative-quantitative divide could be closed through the development of innovative social strategies. This makes possible not only shared standard setting practices, but also novel techniques which could optimise medical research to improve health care and save lives

“Giving something back”: a systematic review and ethical enquiry into public views on the use of patient data for research in the United Kingdom and the Republic of Ireland

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of dat

Bayesian computational methods for stochastic epidemics

Mathematical modelling has become a useful and commonly-used tool in the analysis of infectious disease dynamics. Understanding disease spread is of considerable importance for public health planning and the prevention of future outbreaks, and mathematical analysis of disease outbreaks offers insight which may not be so easily obtained through direct biological study. One key aspect, in mathematical analysis of infectious diseases specifically, is that generally the epidemic process is only partially observed. We might be able to identify the time at which infective individuals become symptomatic or recover, but rarely are we able to observe when infection began, or from whom it was transmitted. This leads to a number of complications with analysis, which will be a focus of this work. The first part of this thesis describes a full Bayesian analysis for such an outbreak with only partial observation of the disease process. We will perform the first Bayesian analysis of the Abakaliki smallpox data, which have been widely cited within the infectious disease modelling literature, to include the full data. In order to do this, we use data augmented Markov Chain Monte Carlo (DA-MCMC) techniques to perform parameter estimation. Analysis involves interpretation of these parameter estimates as well as model assessment with simulation-based methods. We also compare our results to a previous analysis which used an approximate likelihood expression. The second part of this thesis describes novel approximate likelihood methods, motivated in part by the results of the Abakaliki study. Although DA-MCMC is generally considered the standard tool for analysis of partial epidemic data, it often struggles for large population sizes and large amounts of missing data, both through issues of highly correlated missing data and of potentially limiting computation times. We suggest that likelihood approximation methods are a useful tool for dealing with these issues. We develop a series of such methods, which essentially assume some independence in the outbreak population in order to obtain likelihood expressions which do not depend on any missing data. These methods will be motivated and developed, and then illustrated both by simulation study and by application to real data

Bayesian computational methods for stochastic epidemics

Mathematical modelling has become a useful and commonly-used tool in the analysis of infectious disease dynamics. Understanding disease spread is of considerable importance for public health planning and the prevention of future outbreaks, and mathematical analysis of disease outbreaks offers insight which may not be so easily obtained through direct biological study. One key aspect, in mathematical analysis of infectious diseases specifically, is that generally the epidemic process is only partially observed. We might be able to identify the time at which infective individuals become symptomatic or recover, but rarely are we able to observe when infection began, or from whom it was transmitted. This leads to a number of complications with analysis, which will be a focus of this work. The first part of this thesis describes a full Bayesian analysis for such an outbreak with only partial observation of the disease process. We will perform the first Bayesian analysis of the Abakaliki smallpox data, which have been widely cited within the infectious disease modelling literature, to include the full data. In order to do this, we use data augmented Markov Chain Monte Carlo (DA-MCMC) techniques to perform parameter estimation. Analysis involves interpretation of these parameter estimates as well as model assessment with simulation-based methods. We also compare our results to a previous analysis which used an approximate likelihood expression. The second part of this thesis describes novel approximate likelihood methods, motivated in part by the results of the Abakaliki study. Although DA-MCMC is generally considered the standard tool for analysis of partial epidemic data, it often struggles for large population sizes and large amounts of missing data, both through issues of highly correlated missing data and of potentially limiting computation times. We suggest that likelihood approximation methods are a useful tool for dealing with these issues. We develop a series of such methods, which essentially assume some independence in the outbreak population in order to obtain likelihood expressions which do not depend on any missing data. These methods will be motivated and developed, and then illustrated both by simulation study and by application to real data

Muscle strength and functional ability in recreational female golfers and less active non-golfers over the age of 80 Years

Muscle strength and functional ability decline with age. Physical activity can slow the decline but whether recreational golf is associated with slower decline is unknown. This cross-sectional, observational study aimed to examine the feasibility of testing muscle strength and functional ability in older female golfers and non-golfers in community settings. Thirty-one females over aged 80, living independently (golfers n = 21, mean age 83, standard deviation (±) 2.1 years); non-golfers, n = 10 (80.8 ± 1.03 years) were studied. Maximal isometric contractions of handgrip and quadriceps were tested on the dominant side. Functional ability was assessed using the Timed Up and Go (TUG) and health-related quality of life using the Short Form-36 questionnaire. Grip strength, normalised to body mass, was greater in golfers (0.33 ± 0.06 kgF/kg) than non-golfers (0.29 ± 0.06), however, the difference was not statistically significant (p = 0.051). Quadriceps strength did not differ (golfers 2.78 ± 0.74 N/kg; non-golfers 2.69 ± 0.83; p = 0.774). TUG times were significantly faster (p = 0.027) in golfers (10.4 ± 1.9 s) than non-golfers (12.6 ± 3.21 s; within sarcopenic category). Quality of life was significantly higher in golfers for the physical categories (Physical Function p < 0.001; Physical p = 0.033; Bodily pain p = 0.028; Vitality p = 0.047) but psychosocial categories did not differ. These findings indicated that the assessment techniques were feasible in both groups and sensitive enough to detect some differences between groups. The indication that golf was associated with better physical function than non-golfers in females over 80 needs to be examined by prospective randomised controlled trials to determine whether golf can help to achieve the recommended guidelines for strengthening exercise to protect against sarcopenia

Pair-based likelihood approximations for stochastic epidemic models

Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense that it is very computationally expensive to obtain. Although data-augmentated Markov chain Monte Carlo (MCMC) methods provide a solution to this problem, employing a tractable augmented likelihood, such methods typically deteriorate in large populations due to poor mixing and increased computation time. Here we describe a new approach that seeks to approximate the likelihood by exploiting the underlying structure of the epidemic model. Simulation study results show that this approach can be a serious competitor to data-augmented MCMC methods. Our approach can be applied to a wide variety of disease transmission models, and we provide examples with applications to the common cold, Ebola and foot-and-mouth disease

For the greater good? Patient and public attitudes to use of medical free text data in research

Objectives: Electronic health records (EHRs) contain rich information for understanding health conditions and their treatment. A large proportion of clinical information in EHRs is stored in narrative free text. This text is currently under-utilised due to privacy concerns, as it is harder to remove patient identifiers from text than from structured data. Automated de-identification of clinical text is now possible using heuristic or machine-learning-based systems. We conducted a review of the literature on patient and public understanding and attitudes towards the use of patients’ medical data for research, particularly seeking views on free text. The aim was to inform and develop a governance framework for the de-identification and use of medical free text for research, and to instigate a wider discussion on the topic. Approach: We undertook a systematic search in Web of Science and ScienceDirect with terms such as “public attitudes” and “electronic health records”. 3480 results were sifted by title, abstract and full text. Forty-two articles were retained for review, these reported on studies of patient and public perceptions, understanding and attitudes towards the use of patients’ medical data in research. Results: Research participants were positively inclined towards information in records being used in research “for the greater good”. However, no clear patterns by age, ethnicity, education level or SES emerged as to who was more favourable to data use. Participants generally trusted health care professionals and public sector researchers with de-identified medical data, whereas government health agencies and commercial entities were not trusted. No explicitly feared harms associated with data use were articulated. However the general objections appeared to be a dislike of personal data being exploited for commercial gain, and a dislike of personal data being moved around and used without personal knowledge or consent. Notably the use of EHR medical text for research did not emerge as a specific patient/public concern. De-identification was important to participants but text was not identified as a distinct privacy risk. Conclusion: This review demonstrates that transparency about data usage, and working “for the greater good” rather than financial gain, appear to be the most important public concerns to be addressed when using patients’ medical data. Governance frameworks for using EHRs must now be enhanced to provide for the use of medical text. This will involve informing both regulators and the public about the current capabilities of automated de-identification, and developing other assurances to safeguard patients’ privacy

Transmission analysis of a large tuberculosis outbreak in London:a mathematical modelling study using genomic data

Outbreaks of tuberculosis (TB) - such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 - provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters