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      "title_narrative":["A mathematical modeling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions"],
      "description_narrative":["Tuberculosis (TB) is a major cause of disease and death globally. In 2015, WHO estimated there were 9.6 million TB cases and 1.5 TB deaths. Nearly 500,000 of these cases were resistant to two or more of the main drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.  Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Some countries have good systems for recording causes of deaths, which can be used to estimate the number of deaths caused by TB. Increasingly, large and expensive prevalence surveys are being used to estimate the number of people with active disease in a population. These estimates are less subject to bias, but measure a different quantity. Little work has explored the best way of combining these three data sources.  A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models yield the number of new cases, deaths, and also the prevalence of disease. They explicitly represent disease transmission and so introduce a dependence between the number of new cases in different years. These models involve parameters evidenced from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models needing calibration, but will be done in this project using statistical techniques that also allow comparison of different models' performance.   TB burden estimation and calibration of transmission models are almost always carried out on a country-by-country basis. Many parameters describing disease progression are likely to be similar in different countries, even if their exact values differ for unknown reasons. Hierarchical modelling techniques allow such parameters to be correlated between countries. This can improve precision, particularly for countries with little data, as estimates can be informed by data from neighbouring countries. I will explore these techniques for the transmission model, and also in statistical modelling aiming to account for the observed patterns of drug-resistance. The transmission model will ultimately be extended to include different types of drug resistance.  As new treatments and vaccines emerge, those with responsibility for public health will want to understand the potential impact these new technologies can have in terms of gains in health, and changes in spending. Producing cost-effectiveness and budget impact evidence requires a model that includes transmission, in order to account for indirect benefits accrued by avoiding secondary cases. We will use our model to provide guidance to decision-makers seeking to maximise health gain with limited resources. We will also analyse sources of uncertainty in the model to identify future research that would have most value in increasing the precision of burden estimates and in reducing decision uncertainty around the introduction of new interventions.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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      "title_narrative":["Development and evaluation of system dynamics methods to engage with policy makers on the prevention and control of diabetes in a middle income region"],
      "description_narrative":["Diabetes is a serious and growing problem globally. Evidence suggests diabetes disproportionately affects people in low- and middle-income countries, where 80% of people with diabetes live, both in terms of numbers of people affected as well as outcomes and deaths. Diabetes affects between 10 and 20% of the adult population in the Caribbean region with deaths due to diabetes estimated to be 35% higher than in the neighbouring United States. Not only are prevalence and mortality a large burden but also rates of complications such as lower-limb amputation are also high. Much of the high burden of diabetes can be attributed to major risk factors such as physical inactivity and obesity.  Health systems with limited resources in these developing countries are struggling to meet the growing epidemic. There is a strong political will in the region to tackle diabetes and other non-communicable diseases (NCDs). In 2007, the Heads of Government of the Caribbean Community put forth the Port of Spain Declaration (POSD) on NCDs, definitively challenging the high burden of these diseases in the region and pledging action through policies to strengthen prevention and treatment.  This laid the groundwork for a global political movement to recognise NCDs on the public health agenda culminating in the United Nations High Level (UNHLM) meeting on NCDs in September 2011.  Both the POSD and UNHLM strongly emphasise the importance of policy measures for reducing NCD risk factors and put forth policies and targets.  However, evidence on how to achieve a reduction in overweight/obesity and physical inactivity and subsequently reducing NCDs at the population level is scarce, particularly in developing countries. While the risk factors and determinants of NCDs like diabetes are well studied and established, research has not been able to conclusively demonstrate real-world interventions that can reduce their popburden or change the course of the epidemic.   Systems science, which combines multiple factors and complex interrelationships, may offer a solution to evaluating and testing policies for diabetes reduction. It does this by explicitly taking into account system behaviour that is non-linear and complex, with feedback loops and time delays. Within systems science, system dynamics modelling is a methodology incorporating input from experts and stakeholders and combining that with quantitative research evidence to produce a map of a system with the ability to simulate outcomes by changing parameters. The approach has been used effectively in a wide variety of fields including engineering, agriculture, energy planning, business dynamics, and health including diabetes. However, few models have been developed for use in middle-income countries.  This study will be the first to explicitly develop a model for diabetes in developing countries, drawing from work successfully conducted by the Centers for Disease Control in the United States and their model for diabetes. The study will apply the rigorous qualitative methods required by interviewing stakeholders, experts and policy makers in the region as well as gathering evidence from research published on risk factors, outcomes, and health system performance for diabetes in the Caribbean. The study will use the developed model to engage stakeholders and policy makers in time for the on going evaluation of the POSD as a tool for effective policy planning. It will also evaluate the utility of this method in the region in engaging policy makers to think in terms of systems and with long time horizons. The results of this development study will be used to build a larger model incorporating economics and costs, which can then be adapted and used in other low- and middle-income countries.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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      "title_narrative":["OODA GCRF and Newton Consolidation Accounts University of Exeter"],
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