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We have an established, long-term cohort study of cardiovascular disease and other common chronic diseases in older men, currently aged 78-98 years (the British Regional Heart Study). Morbidity rates in this study population are exceptionally high, with many study participants developing cardiovascular disease and other physical illnesses; fractures and dementia are also major health problems.
Our research in this cohort study of older men focuses on an important area of research which is to identify preventable determinants of chronic diseases in older people. We carry out research on a range of social, biological, behavioural and environmental determinants of health and disease in the study. The Study aims to establish the contributions of potentially important factors (obesity, diabetes, health behaviours, environmental and social factors) to prevent cardiovascular disease, diabetes, dementia, other chronic diseases and disability in later life. The purpose of this research is to provide evidence on prevention of these chronic diseases and conditions in order to inform strategies that will reduce the health and social care burden in older populations. Evidence from population-based studies of older people such as our cohort, is needed for robust evidence-based strategies to reduce chronic diseases and in turn the health and social care burden in older populations.
In order to obtain an accurate assessment of chronic disease outcomes, we are seeking to supplement information from regular reviews of General Practice records with information on hospital consultations and admissions (HES Data). This data would help to consolidate and enrich our cohort data collected through GP Record review and Participant questionnaires. The cohort has been followed-up for morbidity and mortality outcomes since 1978-80, therefore, we are requesting HES data to be linked back for as far back as possible for this cohort (i.e. all the years of HES data).
The Mental Health Minimum Dataset (MHMDS) will be restricted to a dementia focussed dataset with medications and will be kept for no longer than necessary. We are really primarily concerned with dementia - Alzheimer's disease and vascular dementia and their main subtypes and symptoms. Dementia is the primary focus of the investigation, we will need to take account of other mental health diagnoses which could be early manifestations of a dementia outcome or mask the diagnosis (e.g. depression)- i.e. limited dementia-focussed dataset with medications.
The Diagnostic Imaging Dataset (DID) provides detailed information about diagnostic imaging tests. This will allow us to discriminate between the different major types of dementia in particular Alzheimer's disease (AD) and vascular dementia. As research into begins to focus more on early detection and intervention, imaging methods like magnetic resonance imaging (MRI) for diagnosis is increasingly important to understand the stage of the disease. This will help inform pathways to preventing the progress of the disease. Several potential biomarkers are being studied for their ability to indicate early stages of Alzheimer's disease. With stored bloods the British Regional Heart Study and links to incident AD the BRHS has the potential to test new hypotheses relating blood markers to the development of early stages of Alzheimer's disease.
The information on diagnostic imaging tests from the Diagnostic Imaging Dataset (DID) will also be highly relevant to the diagnosis of two major cardiovascular outcomes, cardiac failure and stroke. Results of cardiac imaging studies will inform the diagnosis of heart failure, and will allow us to discriminate between systolic and diastolic heart failure, which are importantly different clinical entities. The results of brain imaging studies, which are now extensively used in the investigation of cerebrovascular disease, will allow us to discriminate between different stroke subtypes, and will allow us to add new knowledge on the occurrence and determinants of these conditions.
We are planning on linking to these national datasets to the data you have provided to us.
If you do not want us to do this linkage, you can opt out by telephoning 02078302335 or email the study research manager Lucy Lennon l.lennon@ucl.ac.uk