NIHRDataInsights/HRCSData huggingface dataset:
Title: Linking Human Health and Wellbeing with Weather, Climate, and the Environment: MED-MI (Medical and Environmental Data - a Mash-up Infrastructure)
Funder: Medical Research Council

Abstract: The proposed unique strategic Partnership will create a central data and analysis the MED-MI Platform (Medical and Environmental Data Mash-up Infrastructure) to link and analyse complex meteorological, environmental, and epidemiological data, building on the emerging data "mash-up" field of computational science (i.e. combining data to create new applications and resources). The Partnership will provide the cost effective unique structure and resources needed to perform the complex tasks proposed: a) Establish the database management system and MED-MI Platform; b) Perform the data "mash-up" by linking datasets with appropriate quality control/confidentiality; c) Explore feasibility and vision of the Platform using Partnership Demonstration Projects; d) Establish an initial website, and later, a portal for use by outside entities and individuals; e) Strengthen and explore collaborations (including additional pilot projects) with outside entities; f) Disseminate the MED-MI Platform; g) Develop and seek future funding for new collaborations to sustain the Strategic Partnership. Existing databases in a consistent temporal and spatial framework to enable climate, weather and environment data will be linked and analysed with human health and wellbeing data. Supported by Univ Exeter IT System, the Platform will be available to outside researchers with appropriate ethical safeguards. We will assess and illustrate feasibility and translational value of the Platform using Demonstration Projects. These Projects are selected based on: the availability of suitable data sets; predicted impact of weather and environment on human health and wellbeing; and potential clinical and public health relevance, especially in vulnerable populations. The Partnership will translate and communicate the products, presence, and value of the Platform through Demonstration Projects, workshops, and an accessible Web Portal seeking new collaborations for growth and sustainability.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Explaining inconsistency in evidence synthesis.
Funder: Medical Research Council

Abstract: Background Network meta-analysis (NMA) compares many treatments in a single analysis while using direct and indirect evidence. The consistency assumption underlies NMA. If inconsistency exists, the results of the NMA will be invalid, but causes of the inconsistency can be sought by including treatment by covariate interactions in the NMA model. If the inconsistency is eliminated when interactions are included, the results of the model with interactions could be used to draw suitable conclusions. In practice, there is uncertainty regarding what to do if inconsistency is detected, and the quality of published NMAs is poor. If a process for explaining inconsistency was developed and promoted, the quality of NMAs should improve, and the results would be valid and appropriate for making healthcare decisions. Aim The aim is to develop and promote a process for explaining inconsistency in NMA. Methods A review will be carried out to establish how interactions are explored in reviews. Existing approaches for choosing covariates will be compared with new approaches through application to reviews. Two simulation studies will be carried out which aim to compare NMA models including interactions based on individual patient data (IPD) (i.e. raw data) with models based on aggregate data from trial reports, and determine how the results of NMA are affected as the proportion of IPD increases. Simpler, new approaches to evaluate whether interactions explain inconsistency will be compared with an existing approach through application to reviews. A process for explaining inconsistency will be tailored and demonstrated through application to reviews. Opportunities Results may be reported in review updates. Collected IPD will be valuable for future methodological projects. The review will provide the chance to give guidance regarding how the exploration of interactions can be improved. The research will inform future endeavours to promote the process on a larger scale.


Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE
Title: Methodology for the identification of shared genetic aetiology between epidemiologically linked disorders
Funder: Medical Research Council

Abstract: There is an abundance of analysis methods for detecting genetic associations with a single complex trait, but sparseness in powerful and robust methods to jointly search for shared genetic associations between two disorders that are epidemiologically linked. This motivates the main aim of developing such joint analysis methods. Data for a given trait may either be raw sequencing or genotyping data or in the form of summary statistics from a genetic association study. Therefore, in analysing the genetic data from two traits together, the three possible combinations of data that may be available need to be considered, and each combination requires a different analysis approach to be developed. Besides the immediate data at hand, there is relevant external information, such as previously detected associations with either trait, as well as functional information. This external information is only occasionally accounted for in single trait analyses, and so far, has not been considered for joint trait analyses. Incorporation of prior genetic and biological information adds another degree of complexity to the methodology development, but should increase the power to detect shared associations with disease. Approaches to tackle the problem include linear models, use of Bayes factors, Bayesian decision theory, and chance-corrected measures of agreement in a regression framework. Extensive simulation studies will be carried out to determine the most powerful analysis approach, which will then be applied to real datasets that include osteoarthritis, metabolic and psychiatric disorders.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: The effect of intervention on individual health: application of methods to analyse heterogeneity of treatment and heterogeneity of treated.
Funder: Medical Research Council

Abstract: The aim of this Fellowship is to determine the causal and heterogeneous effects of the Quality and Outcomes Framework (QOF), on individual health behaviours and mental health conditions. The QOF was introduced in 2004 and offered financial rewards to practices for achievements of clinical indicators in specific incentivised conditions. The objectives are to answer the following research questions: - Are variations in health behaviours determined by heterogeneity in the treatment quality provided by primary care practices and/or by individual heterogeneity in cognitive abilities? - Is the effect of debt problems on mental health influenced by the quality of mental health care offered by primary care practices? The methodology is a novel application of the Continuous Treatment Approach to a longitudinal setting (Hirano and Imbens, 2004). I will exploit the introduction of the policy and differential levels of achievement rates to examine their effect on health behaviours and mental health conditions. I will analyse clusters of health behaviours to determine policy spillovers onto unincentivised behaviours. Variations in practice-level achievements and individual-level characteristics (proxied by their cognitive abilities and debt problems) will allow me to determine how such effects can be attributed to heterogeneity of the quality provided by practices and/or to heterogeneity across the treated individuals. This analysis will be possible with the usage of newly created linkages between the English Longitudinal Study of Ageing, primary care practices and measures of house price shocks. The scientific scope of the study includes: a) the novel application of the Continuous Treatment Approach to longitudinal data for determining the heterogeneous effect of policies and b) the exploitation of new data linkages. The medical opportunity of the study involves the study of how clusters of health behaviours are related to different health care interventions.


Research activity codes: 8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: GENERIC HEALTH RELEVANCE
Title: Exploiting the protein-protein interaction network to identify common genetic variants associated with complex diseases
Funder: Medical Research Council

Abstract: The project aims to: 1) perform in-depth analysis of nsSNPs at protein interfaces; 2) develop an algorithm to prioritise and characterise nsSNPs, by combining data from the interactome, with those from currently available structure and sequence-based tools; 3) implement this algorithm to identify new common nsSNPs associated with complex diseases. The following hypotheses will be addressed: 1) a structural system biology method can enhance the number of identified deleterious nsSNPs, compared to standard sequence and structure methods; 2) the location of nsSNPs on different interfaces of the same can explain protein pleiotropism; 3) a predictive algorithm based on the functional effects of nsSNP can be developed, maximising the ability of GWAS to identify disease SNPs; 4) the algorithm can help identify new common nsSNPs associated with cardiovascular and metabolic disorders in Indian-Asians and in cancer. The study comprises of four work packages: 1) Analysis of nsSNPs located at interfaces: this will involve characterisation of wild type residues and nsSNPs by means of structure, sequence, biophysical and structural system biology methods. A dedicated database will be created to disseminate results; 2) Manual analysis of nsSNPs strongly associated with disease, from the LOLIPOP study; 3) Development of a prediction algorithm and dedicated web server to predict deleterious nsSNPs. The performance of this method will be evaluated using nsSNPs annotated in Uniprot, for both training and testing, with a support vector machine and a 5-fold cross-validation; 4) Application of the prediction algorithm for the analysis of common nsSNPs identified in the LOLIPOP study. The project will generate invaluable information on the genetics of cardiovascular and metabolic disorders. The prediction algorithm will be freely available for implementation in any disease, thus, aiding the medical community to develop clinical algorithms for risk stratification.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: POPULATION HEALTH MEETS EVOLUTIONARY ANTHROPOLOGY: FAMILY STRUCTURE, RURAL LIVELIHOODS AND CHILD HEALTH IN TANZANIA
Funder: Medical Research Council

Abstract: OBJECTIVES: The central objective of the research is to improve current understandings of the impact of family structure on child wellbeing in the developing world. The project will be grounded in the theoretical perspective of Evolutionary Anthropology, emphasizing i) the role of contextual factors in determining relationships between family structure and child wellbeing; and ii) the potential for contextual variation in such relationships to feedback into cultural norms determining family formation. DATA: Data will come from the NGO-run Whole Village Project, which collects demographic, economic and development data from 56 villages in northern Tanzania (3500+ households). Comparative analyses will also be carried out using the Tanzanian 2010 DHS. Child wellbeing will be assessed by health (heights & weights of children under 5yrs) and educational attainment (attendance & school level-for-age for children under 15yrs). Family structure is recorded in terms of i) family size; ii) age at reproduction; and iii) polygynous vs. monogamous marriage. METHODS: Multilevel regression analyses will be used to determine the individual, household and community-level determinants of child wellbeing. Within this framework, particular focus will be given to quantifying interactions between family structure and i) household-level factors, such as wealth, livelihood type and food insecurity; and ii) community-level factors, focusing on service provision (proximity to health clinics, water source, schools). SCIENTIFIC & MEDICAL OPPORTUNITIES: The project will generate policy relevant knowledge about specifically when and where patterns of family structure place children at risk. It will also consider how variation in rural livelihoods and service provision shifts incentives for family formation. By considering crosscutting issues at the intersection of population health and evolutionary anthropology, the project will forge links between these divergent fields of research.


Research activity codes: 2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health categories: GENERIC HEALTH RELEVANCE
Title: Developing appropriate methods for handling missing data in health economic evaluation.
Funder: Medical Research Council

Abstract: Motivation: Missing data raise outstanding methodological concerns in health economic evaluation. The main issue is that individuals with missing costs or outcomes tend to be systematically different from those with complete data. Multiple imputation (MI) have been proposed for handling missing data in cost-effectiveness analyses (CEA), but this MI approach may be insufficient in settings such as multicentre trials and non-randomised studies (NRS). If missing data are not addressed appropriately this can lead to biased cost-effectiveness results and incorrect estimates of uncertainty. Aim: To develop appropriate methods for handling missing data in health economic evaluation. Methodology: Firstly, multilevel MI and full-Bayesian hierarchical approaches will be developed and tested in a simulation study for handling missing data in CEA that use multicentre trials. Case studies in intensive care will be used to investigate whether cost-effectiveness results change according to method. Secondly, the performance of MI and full-Bayesian approaches will be compared with that of dr methods, which may be attractive in NRS where the imputation model may be more difficult to specify. Implications of method choice will be assessed in the evaluation of medical devices, alternative forms of elective surgery and providers' performance. Thirdly, the project will develop a framework for exploring the robustness the cost-effectiveness results to departures from the missing at random assumption. The research will develop approaches such as full-Bayesian selection models and pattern mixture models that explicitly address possible missing not at random mechanisms Research output: This project will provide insights about the relative merits of alternative methods for handling missing data across different settings in health economic evaluation. Methods will be widely disseminated so that future studies can provide a sound basis for policy making.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Modelling population health and behaviour change in the transition to a low carbon society
Funder: Medical Research Council

Abstract: Modelling is needed in public health because we want to estimate that which we cannot directly observe. Models should use the best evidence, represent uncertainty, and be as transparent as possible. The research will have two foci, modelling health impacts and modelling behaviour change. 1) Modelling health impacts: My macro simulation modelling has highlighted the untapped potential of cycling in the UK both to benefit public health and reduce greenhouse gas emissions. In this fellowship I will collaborate with other researchers and policy makers to use the model to answer practical questions, to improve the model, and make it more user friendly. I will also develop a new physical activity and diet model to answer questions about how interventions, including price changes, could affect population health and health inequalities. 2) Modelling behaviour change: I will use two types of computer simulation models that can aid understanding of complex systems; agent based modelling (ABM) and system dynamic (SD). The ABMs will focus on how local cycling cultures spread through social influence, and how this affects policy. Behavioural rules will be developed through re-analysis of qualitative data. SD models, created through a group model building process, will be developed to answer questions on the transition to healthier and more sustainable food and transport systems Insights from each model will inform the other. Across all the simulation models I will use exploratory modelling and analysis to test robustness of policy options across plausible scenarios. This fellowship will continue my transition into a fully independent public health researcher, with core expertise in modelling, and produce innovative models with wider application to problems of behaviour change. Formal training will include courses on ABM, SD modelling, and qualitative data analysis. Informal training will occur through collaboration with researchers with a range of expertise.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: GENERIC HEALTH RELEVANCE
Title: Methods for handling missing data and covariate measurement error in individual participant data meta-analysis
Funder: Medical Research Council

Abstract: The overall aim of the proposed research is to develop and apply methods based on multiple imputation (MI) to tackle the issues of missing data and covariate measurement error in IPD meta-analysis. This will be achieved through pursuing the following objectives: 1) I will critically evaluate existing multiple imputation (MI) approaches which can be used for imputing systematically missing data. Parameter identifiability problems will be tackled through use of ridge priors. I will explore the extension of a full conditional specification approach to imputation of systematically missing data to accommodate missingness in categorical variables. 2) I will develop an MI approach for (sporadic and systematically) missing data which accommodates non-linear covariate effects and interactions in the substantive model, by extending the approach I have recently proposed for the setting of single studies. 3) I will develop an MI approach for covariate measurement error which accommodates non-linear covariate effects and interactions in the substantive model, by extending the approach I have recently proposed for imputing partially observed covariates in the context of single studies. 4) I will develop an MI approach for covariate measurement error which handles studies without repeat measurements. This approach will explicitly model between-study heterogeneity in the measurement error distribution and distribution of underlying covariates. 5) Existing theory on congeniality between imputation and substantive models will be used to establish the order in which meta-analysis and pooling across imputations should be performed when a multi-level imputation model is used. 6) I will investigate the feasibility of doubly-robust estimators for missing data in IPD meta-analysis, and their use for investigating sensitivity to mis-specification of the imputation model. 7) I will actively disseminate the methods developed through their implementation in statistical packages.


Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE
Title: Computational Approaches to In Vivo Cell Signalling: Inference, Network Structure and Dynamic Decision-Making
Funder: Medical Research Council

Abstract: A major challenge in biomedical science is to analyse the dynamic process by which healthy and diseased cells transfer the information conveyed by extracellular signals, and to understand how this process alters their behaviour. Technological progress in microfluidics and imaging makes it possible to stimulate single cells with carefully controlled input signals and measure their 'noisy' (stohastic) responses over time. Hence, exciting opportunities arise for studying and understanding information transfer through network of cell signalling and gene regulation. Reliable and tailored computational methods are currently needed to quantify information transfer trough these networks; to probe how information transfer relates to their architecture; to describe which features of fluctuating signals are encoded in their dynamics; and to understand how signal processing affects cellular behaviour. This research proposal aims to provide novel statistical approaches for quantitative analysis of signal processing by single cells. These methods will provide point estimates (along with appropriate confidence intervals) of information-theoretic quantities (entropy and mutual information) that can be used to quantify information transfer in cell signalling pathways and gene regulatory networks. Using these methods I will study signal transduction by mitogen-activated protein kinase (MAPK) cascades. The aim will be two-fold: to probe the relationship between network structure and information transfer, and to analyse the dynamics of cellular decision-making. In collaboration with the McArdle Lab (University of Bristol) I will analyse the effect of feedback control on ERK signalling in mammalian cells. In collaboration with the Swain Lab (University of Edinburgh), I will investigate how both network structure and the features (statistics) of input fluctuations determine the dynamics of cellular decision-making, using yeast's mating response system (driven by MAPKs Fus3 and Kss1).


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE