Statistical methods for body mass index: a selective review

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported in part by the National Institute for Health Research Method Grant (NIHR RMOFS-2013-03-09) and the National Natural Science Foundation of China (Grant No. 71490725, 11261048, 11371322)

Decision-Support for Rheumatoid Arthritis Using Bayesian Networks: Diagnosis, Management, and Personalised Care.

PhD Theses.Bayesian networks (BNs) have been widely proposed for medical decision support. One advantage of a BN is reasoning under uncertainty, which is pervasive in medicine. Another advantage is that a BN can be built from both data and knowledge and so can be applied in circumstances where a complete dataset is not available. In this thesis, we examine how BNs can be used for the decision support challenges of chronic diseases. As a case study, we study Rheumatoid Arthritis (RA), which is a chronic inflammatory disease causing swollen and painful joints. The work has been done as part of a collaborative project including clinicians from Barts and the London NHS Trust involved in the treatment of RA. The work covers three stages of decision support, with progressively less available data. The first decision support stage is diagnosis. Various criteria have been proposed by clinicians for early diagnosis but these criteria are deterministic and so do not capture diagnostic uncertainty, which is a concern for patients with mild symptoms in the early stages of the disease. We address this problem by building a BN model for diagnosing RA. The diagnostic BN model is built using both a dataset of 360 patients provided by the clinicians and their knowledge as experts in this domain. The choice of factors to include in the diagnostic model is informed by knowledge, including a model of the care pathway which shows what information is available for diagnosis. Knowledge is used to classify the factors as risk factors, relevant comorbidities, evidence of pathogenesis mechanism, signs, symptoms, and serology results, so that the structure of BN model matches the clinical understanding of RA. Since most of the factors are present in the dataset, we are able to train the parameters of the diagnostic BN from the data. This diagnostic BN model obtains promising results in differentiating RA cases from other inflammatory arthritis cases. Aware that eliciting knowledge is time-consuming and could limit the uptake of these techniques, we consider two alternative approaches. First, we compare its diagnostic performance with an alternative BN model entirely learnt from data; we argue that having a clinically meaningful structure allows us to explain clinical scenarios in a way that cannot be done with the model learnt purely from data. We also examine whether useful knowledge can be retrieved from existing vi medical ontologies, such as SNOMED CT and UMLS. Preliminary results show that it could be feasible to use such sources to partially automate knowledge collection. After patients have been diagnosed with RA, they are monitored regularly by a clinical team until the activity of their disease becomes low. The typical care arrangement has two challenges: first, regular meetings with clinicians occur infrequently at fixed intervals (e.g., every six months), during which time the activity of the disease can increase (or ‘flare’) and decrease several times. Secondly, the best medications or combinations of medications must be found for each patient, but changes can only be made when the patient visits the clinic. We therefore develop this stage of decision support in two parts: the first and simplest part looks at how the frequency of clinic appointments could be varied; the second part builds on this to support decisions to adjust medication dosage. We describe this as the ‘self-management’ decision support model. Disease activity is commonly measured with Disease Activity Score 28 (DAS28). Since the joint count parts of this can be assessed by the patient, the possibility of collecting regular (e.g., weekly) DAS28 data has been proposed. It is not yet in wide use, perhaps because of the overheads to the clinical team of reviewing data regularly. The dataset available to us for this work came from a feasibility study conducted by the clinical collaborators of one system for collecting data from patients, although the frequency is only quarterly. The aim of the ‘self-management’ decision support system is therefore to sit between patient-entered data and the clinical team, saving the work of clinically assessing all the data. Specifically, in the first part we wish to predict disease activity so that an appointment should be made sooner, distinguishing this from patients whose disease is well-managed so that the interval between appointments can be increased. To achieve this, we build a dynamic BN (DBN) model to monitor disease activity and to indicate to patients and their clinicians whether a clinical review is needed. We use the data and a set of dummy patient scenarios designed by the experts to evaluate the performance of the DBN. The second part of the ‘self-management’ decision support stage extends the DBN to give advice on adjustments to the medication dosage. This is of particular clinical interest since one class of medications used (biological disease-modifying antirheumatic drugs) are very expensive and, although effective at reducing disease activity, can have severe adverse reactions. For both these reasons, decision support that allowed a patient to ‘taper’ the dosage of medications without frequent clinic visits would be very useful. This extension does not meet all the decision support needs, which ideally would also cover decision-making about the choice of medications. However, we have found that as yet there is neither sufficient data nor knowledge for this. vii The third and final stage of decision support is targeted at patients who live with RA. RA can have profound impacts on the quality of life (QoL) of those who live with it, affecting work, financial status, friendships, and relationships. Information from patient organisations such as the leaflets prepared by the National Rheumatoid Arthritis Society (NRAS) contains advice on managing QoL, but the advice is generic, leaving it up to each patient to select the advice most relevant to their specific circumstances. Our aim is therefore to build a BN-based decision support system to personalise the recommendations for enhancing the QoL of RA patients. We have built a BN to infer three components of QoL (independence, participation, and empowerment) and shown how this can be used to target advice. Since there is no data, the BN is developed from expert knowledge and literature. To evaluate the resulting system, including the BN, we use a set of patient interviews conducted and coded by our collaborators. The recommendations of the system were compared with those of experts in a set of test scenarios created from the interviews; the comparison shows promising results

Machine learning in the social and health sciences

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research

EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Advancing tools for human early lifecourse exposome research and translation (ATHLETE)

Copyright © 2021 The Authors. Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE’s results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.European Union’s Horizon 2020 research and innovation programme under grant agreement number 874583—the Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project; Ramón y Cajal fellowship (RYC-2012-10995) awarded by the Spanish Ministry of Economy and Finance; Ramón y Cajal fellowship (RYC-2012-10995) awarded by the Spanish Ministry of Economy and Finance; National Institute of Environmental Health Sciences (R21ES029681, R01ES029944, R01ES030364, R01ES030691, and P30ES007048); National Institutes of Health supported Dr. Conti (P01CA196569, R01CA140561) and Dr. Stratakis (P30DK048522); National Institute for Health Research under its Applied Research Collaboration Yorkshire and Humber; Consolidator Grant from the European Research Council (ERC-2014-CoG-648916); European Union’s Horizon 2020 co-funded programme European Research Area Net on Biomarkers for Nutrition and Health (European Research Area Healthy Diet for a Healthy Life) (Early life programming of childhood health project [number 696295; 2017], ZonMW, The Netherlands [number 529051014; 2017]; Agence Nationale de Securite Sanitaire de l’Alimentation de l’Environnement et du Travail (EST-18 RF-25)

TOWARDS SCALABLE MENTAL HEALTH: LEVERAGING DIGITAL TOOLS IN COMBINATION WITH COMPUTATIONAL MODELING TO AID IN TREATMENT AND ASSESSMENT OF MAJOR DEPRESSIVE DISORDER

Major depressive disorder (MDD) is a debilitating disorder that impacts the lives of nearly 280 million individuals worldwide, representing 5% of the overall adult population. Unfortunately, these statistics have been both trending upward and are also likely an underestimate. This can be primarily attributed to lack of screening paired with a lack of providers. Worldwide, there are roughly 450 individuals living with MDD per mental health care provider. Adding to this burden, approximately half of affected individuals that do receive care of any kind will fail to remain in remission. The goal of this thesis work is to leverage statistical and machine learning models to help close these gaps in both MDD assessment and treatment. The data used in this thesis comes from a variety of sources including cross-sectional data from a physician wellness visit, randomized controlled trial (RCT) data from various digital interventions for MDD, and longitudinal data assessing individual’s depressive symptoms over time from the Tracking Depression Study. Supervised machine learning methods were applied to the wellness visit data to predict MDD presence and the RCT data to predict treatment response. The implication of these approaches is that in practice, they could enable passive assessments of MDD followed by personalized treatment planning using scalable interventions. As an addition to these machine learning approaches, statistical models were used to analyze longitudinal MDD symptom data to further understand individual changes in symptom dynamics. This work lays the foundation for dynamic treatment allocation that adapts as an individual’s experience changes