Time filter

Source Type

Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2010.2.3.3-3 | Award Amount: 3.85M | Year: 2010

Over the last years, large outbreaks of Crimean Congo Hemorrhagic fever virus (CCHFV) in several European countries and neighbouring areas are on the rise. This disease poses a great threat to public health due to its high mortality rate, modes of transmission and geographical distribution. Climate changes and observation of the CCHFV vector in central Europe alarm the European community as we cannot exclude that future outbreaks will take place in non-endemic area of Europe. To date, there is no vaccine available and no selective antiviral drug for the management of the disease. The general knowledge of migration, epidemiology, re-assortment and recombination of the virus is very limited. To fill these gaps, the CCH Fever project proposes to create a multidisciplinary collaborative research environment by bringing together selected competitive advantages such as: operative capacity with appropriate high security research facilities, reference centers and clinical samples from endemic areas and an international network of experienced researchers. This multidisciplinary research consortium will facilitate the progress in several key research areas of the field. This program will mainly focus on (i) developing sensitive and biosafe state-of-art diagnostic tools for CCHFV, (ii) gathering the forces and resources in Europe to build a Biobank of clinical samples, (iii) building a comprehensive database consisting in clinical, laboratory and surveillance data, (iv) taking advantage of unique and state-of art tools to progress towards vaccine candidates and specific antivirals against this bio-threat and (v) disseminating the appropriate knowledge to the health care workers in endemic regions and contributing to capacity building. These achievements will provide tools for local and European public health authorities to prevent or counter future outbreaks and monitor the spread of the disease thanks to the established novel and unique tools and resources.

Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH.2010.2.3.3-1 | Award Amount: 16.37M | Year: 2011

To address the call for proposals Biology and control of vector-borne infections in Europe launched by the European Commission, we want to investigate the biological, ecological and epidemiological components of vector-borne diseases (VBD) introduction, emergence and spread, and to propose innovative tools for controlling them, building on the basis of acquired knowledge. We have selected the main groups of arthropod vectors involved in the transmission of vector-borne diseases in Europe: ticks, mosquitoes, sandflies, and biting midges (Culicoides). We have also selected the main diseases of actual or possible importance in human and veterinary public health. Rodents, insectivores and rodent-borne diseases have also been considered, both for their direct importance in public health, and for the major role of rodents and insectivores as reservoir hosts of many pathogens. We have put a strong focus on vector- and disease-quantitative modelling. The resulting predictive models will be used to assess climate or environmental change scenarios, as well as vector or disease control strategies. Human behaviour and risk perception are an important component of VBD introduction, emergence and spread. The consequences triggered by VBD for human and veterinary public health in Europe are just starting to emerge in public awareness. We will also account for this aspect of human and veterinary public health in our proposal. Finally, the set of innovative research methods, tools and results obtained during the project will be a step forward a generic approach of VBD in terms of disease monitoring and early warning systems, and will reinforce the general framework for an integrated pest and disease management system. For all these aspects, we will benefit from, and amplify the strong scientific results, capacity building, and research networks established by EDEN project on emerging, vector-borne diseases in a changing European environment.

Agency: Cordis | Branch: FP7 | Program: CP-FP-SICA | Phase: HEALTH.2010.3.4-7 | Award Amount: 3.45M | Year: 2011

Recent health financing reforms in low and middle income countries aim to introduce affordable prepayment and subsidies for low socio-economic groups. However, while such reforms have led to increased utilization of care, often the poor and informal sector continue to be excluded from coverage. Health Inc. puts forward the hypothesis that social exclusion is an important cause of the limited success of recent health financing reforms. First, social exclusion can explain barriers to accessing health care due to disrespectful, discriminatory or culturally inappropriate practices of medical professionals and their organisations, within the context of poor accessibility and quality of care. As a consequence, removing financial barriers does not necessarily guarantee equitable access to care. Second, social exclusion can explain barriers to accessing the health financing mechanism itself. Differential access to information, bureaucratic processes, complex eligibility rules and/or crude and stigmatizing criteria for means testing prevent socially excluded groups from enrolling in financing schemes, even if they are fully subsidised. Social inclusion, by contrast, may explain why more powerful, wealthy and vocal groups disproportionately capture the benefits of publicly funded health care. In four countries/states (Ghana, Karnataka, Maharashtra and Senegal), Health Inc. employs mixed methods to analyse whether different types of financing arrangements overcome social exclusion and also increase social inclusion by empowering socially marginalised groups. A multi-sectoral stakeholder analysis will also explore whether vulnerable populations participate in policy making and whether their needs are understood. Health Inc. will compare policies across contexts in order to elicit lessons. Local policy makers and population groups will then be consulted in a feasibility analysis to identify and test policy recommendations, which will be widely disseminated.

White I.R.,Institute of Public Health | Royston P.,University College London | Wood A.M.,University of Cambridge
Statistics in Medicine | Year: 2011

Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. Copyright © 2010 John Wiley & Sons, Ltd.

Wei Y.,Institute of Public Health | Higgins J.P.,Institute of Public Health
Statistics in Medicine | Year: 2013

Multivariate meta-analysis allows the joint synthesis of effect estimates based on multiple outcomes from multiple studies, accounting for the potential correlations among them. However, standard methods for multivariate meta-analysis for multiple outcomes are restricted to problems where the within-study correlation is known or where individual participant data are available. This paper proposes an approach to approximating the within-study covariances based on information about likely correlations between underlying outcomes. We developed methods for both continuous and dichotomous data and for combinations of the two types. An application to a meta-analysis of treatments for stroke illustrates the use of the approximated covariance in multivariate meta-analysis with correlated outcomes. © 2012 John Wiley & Sons, Ltd.

The burden of chronic conditions is high in low- and middle-income countries and poses a significant challenge to already weak healthcare delivery systems in these countries. Studies investigating chronic conditions among the urban poor remain few and focused on specific chronic conditions rather than providing overall profile of chronic conditions in a given community, which is critical for planning and managing services within local health systems. We aimed to assess the prevalence and health- seeking behaviour for self-reported chronic conditions in a poor neighbourhood of a metropolitan city in India. We conducted a house-to-house survey covering 9299 households (44514 individuals) using a structured questionnaire. We relied on self-report by respondents to assess presence of any chronic conditions, including diabetes and hypertension. Multivariable logistic regression was used to analyse the prevalence and health-seeking behaviour for self-reported chronic conditions in general as well as for diabetes and hypertension in particular. The predictor variables included age, sex, income, religion, household poverty status, presence of comorbid chronic conditions, and tiers in the local health care system. Overall, the prevalence of self-reported chronic conditions was 13.8% (95% CI = 13.4, 14.2) among adults, with hypertension (10%) and diabetes (6.4%) being the most commonly reported conditions. Older people and women were more likely to report chronic conditions. We found reversal of socioeconomic gradient with people living below the poverty line at significantly greater odds of reporting chronic conditions than people living above the poverty line (OR = 3, 95% CI = 1.5, 5.8). Private healthcare providers managed over 80% of patients. A majority of patients were managed at the clinic/health centre level (42.9%), followed by the referral hospital (38.9%) and the super-specialty hospital (18.2%) level. An increase in income was positively associated with the use of private facilities. However, elderly people, people below the poverty line, and those seeking care from hospitals were more likely to use government services. Our findings provide further evidence of the urgent need to improve care for chronic conditions for urban poor, with a preferential focus on improving service delivery in government health facilities.

Abel G.A.,Institute of Public Health
British Journal of Cancer | Year: 2015

Background:Although overall sociodemographic and cancer site variation in the risk of cancer diagnosis through emergency presentation has been previously described, relatively little is known about how this risk may vary differentially by sex, age and deprivation group between patients with a given cancer.Methods:Data from the Routes to Diagnosis project on 749 645 patients (2006–2010) with any of 27 cancers that can occur in either sex were analysed. Crude proportions and crude and adjusted odds ratios were calculated for emergency presentation, and interactions between sex, age and deprivation with cancer were examined.Results:The overall proportion of patients diagnosed through emergency presentation varied greatly by cancer. Compared with men, women were at greater risk for emergency presentation for bladder, brain, rectal, liver, stomach, colon and lung cancer (e.g., bladder cancer-specific odds ratio for women vs men, 1.50; 95% CI 1.39–1.60), whereas the opposite was true for oral/oropharyngeal cancer, lymphomas and melanoma (e.g., oropharyngeal cancer-specific odds ratio for women vs men, 0.49; 95% CI 0.32–0.73). Similarly, younger patients were at higher risk for emergency presentation for acute leukaemia, colon, stomach and oesophageal cancer (e.g., colon cancer-specific odds ratio in 35–44- vs 65–74-year-olds, 2.01; 95% CI 1.76–2.30) and older patients for laryngeal, melanoma, thyroid, oral and Hodgkin’s lymphoma (e.g., melanoma specific odds ratio in 35–44- vs 65–74-year-olds, 0.20; 95% CI 0.12–0.33). Inequalities in the risk of emergency presentation by deprivation group were greatest for oral/oropharyngeal, anal, laryngeal and small intestine cancers.Conclusions:Among patients with the same cancer, the risk for emergency presentation varies notably by sex, age and deprivation group. The findings suggest that, beyond tumour biology, diagnosis through an emergency route may be associated both with psychosocial processes, which can delay seeking of medical help, and with difficulties in suspecting the diagnosis of cancer after presentation.British Journal of Cancer advance online publication, 3 March 2015; doi:10.1038/bjc.2015.52 www.bjcancer.com. © 2015 Cancer Research UK

Seaman S.R.,Institute of Public Health | White I.R.,Institute of Public Health
Statistical Methods in Medical Research | Year: 2013

The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys. This article is a review of the use of IPW in epidemiological research. We describe how the bias in the complete-case analysis arises and how IPW can remove it. IPW is compared with multiple imputation (MI) and we explain why, despite MI generally being more efficient, IPW may sometimes be preferred. We discuss the choice of missingness model and methods such as weight truncation, weight stabilisation and augmented IPW. The use of IPW is illustrated on data from the 1958 British Birth Cohort. © The Author(s) 2011 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Seaman S.R.,Institute of Public Health
BMC medical research methodology | Year: 2012

Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X2. In 'passive imputation' a value X* is imputed for X and then X2 is imputed as (X*)2. A recent proposal is to treat X2 as 'just another variable' (JAV) and impute X and X2 under multivariate normality. We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV's performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression.

Reid J.E.,Institute of Public Health | Wernisch L.,Institute of Public Health
Nucleic Acids Research | Year: 2011

MEME and many other popular motif finders use the expectation-maximization (EM) algorithm to optimize their parameters. Unfortunately, the running time of EM is linear in the length of the input sequences. This can prohibit its application to data sets of the size commonly generated by high-throughput biological techniques. A suffix tree is a data structure that can efficiently index a set of sequences. We describe an algorithm, Suffix Tree EM for Motif Elicitation (STEME), that approximates EM using suffix trees. To the best of our knowledge, this is the first application of suffix trees to EM. We provide an analysis of the expected running time of the algorithm and demonstrate that STEME runs an order of magnitude more quickly than the implementation of EM used by MEME. We give theoretical bounds for the quality of the approximation and show that, in practice, the approximation has a negligible effect on the outcome. We provide an open source implementation of the algorithm that we hope will be used to speed up existing and future motif search algorithms. © 2011 The Author(s).

Loading Institute of Public Health collaborators
Loading Institute of Public Health collaborators