96 research outputs found
Associations between postoperative rehabilitation of hip fracture and outcomes: national database analysis (vol 19, 211, 2018)
No full textFollowing publication of the original article [1], the author reported an error in the Title. The original article has been corrected
Development and use of methods to estimate chronic disease prevalence in small populations
No full textIntroduction
National data on the prevalence of chronic diseases on general practice registers is now available. The aim of this PhD was to develop and validate epidemiological models for the expected prevalence of chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, hypertension, overall cardiovascular disease (CVD) and high CVD risk at general practice and small area level, and to explore the extent of undiagnosed disease, factors associated with it, and its impact on population health.
Methods
Multinomial logistic regression models were fitted to pooled Health Survey for England data to derive odds ratios for disease risk factors. These were applied to general practice and small area level population data, split by age, sex, ethnicity, deprivation, rurality and smoking status, to estimate expected disease prevalence at these levels. Validation was carried out using external data, including population-based epidemiological research and case-finding initiatives. Practice-level undiagnosed disease prevalence i.e. expected minus registered disease prevalence, and hospital admission rates for these conditions, were evaluated as outcome indicators of the quality and supply of primary health care services, using ordinary least squares (OLS) regression, geographically-weighted regression (GWR), and other spatial analytic methods.
Results
Risk factors, odds of disease and expected prevalence were consistent with external data sources. Spatial analysis showed strong evidence of spatial non-stationarity of undiagnosed disease prevalence, with high levels of undiagnosed disease in London and other conurbations, and associations with low supply of primary health care services. Higher hospital admission rates were associated with population deprivation, poorer quality and supply of primary health care services and poorer access to them, and for COPD, with higher levels of undiagnosed disease.
Conclusion
The epidemiologic prevalence models have been implemented in national data sources such as NHS Comparators, the Association of Public Health Observatories website, and a number of national reports. Early experience suggests that they are useful for guiding case-finding at practice level and improving and regulating the quality of primary health care. Comparisons with external data, in particular prevalence of disease detected by general practices, suggest that model predictions are valid.
Practice-level spatial analyses of undiagnosed disease prevalence and hospital admission rates failed to demonstrate superiority of GWR over OLS methods. Disease modellers should be encouraged to collaborate more effectively, and to validate and compare modelling methods using an agreed framework. National leadership is needed to further develop and implement disease models. It is likely that prevalence models will prove to be most useful for identifying undiagnosed diseases with a slow and insidious onset, such as COPD, diabetes and hypertension
National evaluation of the NHS Health Check programme
No full textIntroduction:
I aimed to evaluate the performance of the National Health Service (NHS) Health Check in the first four years since April 2009. The programme offers all English adults aged 40-74 years, and without known vascular disease, a cardiovascular disease (CVD) risk assessment and management Health Check every five years.
Methods:
Electronic medical records of 300,000 random sample of patients who were aged 40-74 years old but not filtered by other Health Check eligibility criteria were obtained from a nationally representative UK primary care database. Multilevel logistic regression was performed to examine variations in programme coverage. Programme impact on the management of CVD risks and early detection of selected vascular conditions was examined using a difference-in-differences matching analysis. A model-based cost-utility analysis was conducted to estimate the relative long-term costs and benefits of the NHS Health Check with a lifetime time horizon.
Results:
National coverage of the programme was low in the first four years and varied between general practices and English regions although no significant differences were observed between areas of different levels of deprivation. The programme had contributed to a statistically significant but clinically modest reduction in global CVD risk and individual risk factors among attendees but smoking prevalence stayed unchanged. The prescribing of statins increased significantly but the absolute statin prescribing remained low after Health Checks. The programme appears to be cost-effective long-term, based on the benefits of medical and lifestyle interventions being realised.
Conclusions:
The NHS Health Check needs to be improved substantially via better planning, implementation, monitoring, and management before any anticipated public health benefit are achieved. High-quality research is required to identify the most effective strategies such as a combination of CVD risk assessment programme and other population-wide programmes for the prevention of CVD.Open Acces
Development and use of methods to estimate chronic disease prevalence in small populations
No full textIntroduction National data on the prevalence of chronic diseases on general practice registers is now available. The aim of this PhD was to develop and validate epidemiological models for the expected prevalence of chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, hypertension, overall cardiovascular disease (CVD) and high CVD risk at general practice and small area level, and to explore the extent of undiagnosed disease, factors associated with it, and its impact on population health. Methods Multinomial logistic regression models were fitted to pooled Health Survey for England data to derive odds ratios for disease risk factors. These were applied to general practice and small area level population data, split by age, sex, ethnicity, deprivation, rurality and smoking status, to estimate expected disease prevalence at these levels. Validation was carried out using external data, including population-based epidemiological research and case-finding initiatives. Practice-level undiagnosed disease prevalence i.e. expected minus registered disease prevalence, and hospital admission rates for these conditions, were evaluated as outcome indicators of the quality and supply of primary health care services, using ordinary least squares (OLS) regression, geographically-weighted regression (GWR), and other spatial analytic methods. Results Risk factors, odds of disease and expected prevalence were consistent with external data sources. Spatial analysis showed strong evidence of spatial non-stationarity of undiagnosed disease prevalence, with high levels of undiagnosed disease in London and other conurbations, and associations with low supply of primary health care services. Higher hospital admission rates were associated with population deprivation, poorer quality and supply of primary health care services and poorer access to them, and for COPD, with higher levels of undiagnosed disease. Conclusion The epidemiologic prevalence models have been implemented in national data sources such as NHS Comparators, the Association of Public Health Observatories website, and a number of national reports. Early experience suggests that they are useful for guiding case-finding at practice level and improving and regulating the quality of primary health care. Comparisons with external data, in particular prevalence of disease detected by general practices, suggest that model predictions are valid. Practice-level spatial analyses of undiagnosed disease prevalence and hospital admission rates failed to demonstrate superiority of GWR over OLS methods. Disease modellers should be encouraged to collaborate more effectively, and to validate and compare modelling methods using an agreed framework. National leadership is needed to further develop and implement disease models. It is likely that prevalence models will prove to be most useful for identifying undiagnosed diseases with a slow and insidious onset, such as COPD, diabetes and hypertension.EThOS - Electronic Theses Online ServiceESRC, Dept of Health, Eastern Region Public Health Observatory, CQCGBUnited Kingdo
Associations between post-operative rehabilitation of hip fracture and outcomes: national database analysis (90 characters)
No full textRehabilitation programmes are used to improve hip fracture outcomes. There is little published trial clinical trial or population-based data on the effects of the type or provider of rehabilitation treatments on hip fracture outcomes. We evaluated the associations of rehabilitation interventions with post-operative hip fracture outcomes
The effects of the quality of primary care on diabetes outcomes
No full textBackground
Diabetes mellitus is a chronic condition primarily characterised by elevated levels of plasma glucose due to a lack of insulin. Diabetes is a risk factor for a number of acute and chronic complications and is associated with significant morbidity and mortality, the risks and impact of which may be modifiable through high-quality primary care. This thesis examines the relationship between the quality of primary care and emergency admissions, readmissions and mortality.
Methodology
Associations between primary care quality and clinical outcomes for people with diabetes were assessed at the practice and patient level. Practice-level analyses, utilising Quality and Outcomes Framework (QOF) and Hospital Episodes Statistics (HES) data, modelled the impact of practice achievement of thematically-grouped QOF indicators on standardised emergency admission rates, controlling for characteristics of the practice, the socioeconomic environment and the patient population. Patient-level analyses utilised a Clinical Practice Research Datalink (CPRD-HES) dataset linking primary care activity with hospitalisations. Modelling examined associations between QOF target-based indicators and National Diabetes Audit (NDA) care processes and rates of emergency admissions, readmissions and the odds of death.
Results
In practice-level analyses, QOF indicators pertaining to processes of care and availability of appointments were most consistently associated with reduced emergency admission rates. For patient-level analyses, a number of NDA processes were consistently associated with reduced emergency admission and readmission rates and reduced odds of death across follow-up periods ranging from one to five years. Associations with QOF targets were less consistent. Across all practice- and patient-level analyses, deprivation was strongly associated with changes in admission rates and odds of death.
Conclusions
High-quality primary care has the potential to meaningfully improve outcomes for people with diabetes; the effects of socio-economic deprivation remain sizeable even after adjustment for primary care quality.Open Acces
Is there an association between cancer and dementia in cohorts with and without T2DM? A national observational study
No full textBackground: Previous studies have suggested an inverse association and a hypothesized mutually protective effect between several forms of cancer and late-onset dementia (LOD). Type 2 diabetes (T2DM) is an established important risk factor for both diseases; however, the precise relationships between T2DM, cancer and LOD still remain poorly understood. This thesis investigates the relationship between different cancers and risk of LOD, and explores the role of prediabetes or T2DM in these associations.
Methodology: Using the Clinical Practice Research Datalink (CPRD), a massive UK primary care database, in years, 1998-2015, a sample of individuals ≥ 65 years old, with and without T2DM were identified. All individuals aged ≥ 65 years old, with and without a T2DM diagnosis were included in this analysis. Individuals with an LOD diagnosis prior to 65 years of age or prior to a T2DM diagnosis, were excluded. All study participants were followed up from the index date to the censor date. Participants were censored at point of LOD diagnosis, death, end of observation period (2015) or last data upload date (last date of follow-up), whichever came first. It was required that participants have been under observation by CPRD for > 1 year prior to cohort entry. Exploratory analyses were performed to investigate the incidence rates of LOD in both non-T2DM and T2DM cohorts. Cox proportional hazard models, with time-dependent covariates, were used to determine the risk of LOD in individuals with and without a cancer diagnosis in both non-T2DM and T2DM cohorts. The cause-specific hazard ratio (csHR) and sub-distribution hazard ratio (sdHR) for overall LOD and death in individuals with cancer were computed, to account for death as a competing risk.
Results: Separate analyses amongst 217,335 individuals with T2DM and 739,061 without T2DM were performed. The mean age (SD) of individuals with T2DM at cohort entry was 71.62 (7.09) years (47.3% females) vs.70.80 (7.66) years (56.9 % females) in the non-T2DM cohort. During follow-up, a total of 165,272 (22 %) and 32,022 (15 %) cancer cases and 51,733 (7 %) and 11,450 (5%) LOD cases were identified in the non-T2DM and T2DM cohorts, respectively. In the non-T2DM cohort, 10,602 (6 %) had both LOD and cancer diagnosis vs. 1,172 (4%) in the T2DM cohort. The incidence rate of LOD was higher in females in both non-T2DM and T2DM cohorts (non-T2DM: 7.15 per 1,000 person years in males and 10.04 per 1,000 person years in females; T2DM: 6.96 per 1,000 person years in males and 10.57 per 1,000 person years in females). There was a higher risk for LOD in cancer individuals in the
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non-T2DM cohort [HR 1.16, 95 % CI (1.13-1.20)]. Conversely, in the T2DM cohort, there was a significantly lower risk for developing LOD in lung cancer participants vs. no cancer group [HR 0.52, 95 % CI (0.29-0.94)]. In the presence of death as a competing risk for LOD, lung cancer showed an even more intensified “protective relationship” (sdHR 0.11 (95% CI 0.06, 0.21), when compared to the cause specific hazard ratios (csHR of 1.16 (95% CI 1.13, 1.20). The cumulative incidence function curves showed that in the presence of death, there is a protective effect of cancer on LOD incidence in both cohorts (not observed for csHRs).
Conclusion: Examining the cause-specific and sub distribution hazard models led to the conclusion that the inverse association observed between cancer, lung cancer and LOD, especially in the T2DM cohort, is most likely due to mortality selection. Careful consideration of statistical model specifications is imperative, particularly in older adult population research, where mortality is inevitable.Open Acces
Managing and exploiting routinely collected NHS data for research
No full textIntroduction Health research using routinely collected National Health Service (NHS) data derived from electronic health records (EHRs) and health service information systems has been growing in both importance and quantity. Wide population coverage and detailed patient-level information allow this data to be applied to a variety of research questions. However, the sensitivity, complexity and scale of such data also hamper researchers from fully exploiting this potential.Objective Here, we establish the current challenges preventing researchers from making optimal use of the data sets at their disposal, on both the legislative and practical levels, and give recommendations as to how these challenges can be overcome.Method A number of projects has recently been launched in the UK to address poor research data management practices. Rapid Organisation of Healthcare Research Data (ROHRD) at Imperial College, London produced a useful prototype that provides local researchers with a one-stop index of available data sets together with relevant metadata.Findings Increased transparency of data sets’ availability and their provenance leads to better utilisation and facilitates compliance with regulatory requirements.Discussion Research data resulting from NHS data is often not utilised fully, or is handled in a haphazard manner that prevents full auditability of the research. Furthermore, lack of informatics and data management skills in research teams act as a barrier to implementing more advanced practices, such as provenance capture and detailed, regularly updated, data management strategies. Only by a concerted effort at the levels of research organisations, funding bodies and publishers, can we achieve full transparency and reproducibility of the research.</p
Model for estimating the population prevalence of chronic obstructive pulmonary disease: cross sectional data from the Health Survey for England.
No full textBACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major but neglected public health problem. Currently 1.4% of the England population has a clinical diagnosis of COPD, but the true burden of the disease has not been known with certainty, as many cases remain undiagnosed. METHODS: A mathematical model based on cross sectional data from a representative sample of the population in England (the Heath Survey for England 2001, n = 10,750) was developed allowing estimates on the prevalence of COPD (defined based on the presence of airflow obstruction) to be obtained. Logistic regression analysis was used to investigate and choose risk factors for inclusion in the model and to derive the prevalence estimates based on the strength of association between selected risk factors and the outcome COPD. The model allows the prevalence to be estimated in populations at national level and also at regional and large local areas, based on their compositions according to age, sex, smoking and ethnicity, and on area degrees of urbanisation and deprivation. We applied the model to measure the prevalence of COPD in England and in some sub-groups of the population within the country. RESULTS: The prevalence of COPD in England is estimated as 3.1% (3.9% in men and 2.4% in women) in the population over 15 years of age, and 5.3% (6.8% in men and 3.9% in women) in 45 year-olds and over. There was a 7-fold variation in the prevalence across subgroups of the population, with lowest values in Asian women from wealthy rural areas (1.7%), and highest in black men from deprived urban areas (12.5%). CONCLUSION: The model can be used to estimate population prevalence of COPD from large general practices to national level, and as a tool to identify areas of high levels of unmet needs for COPD priority health actions. The results from the model highlight the importance of including variables other than age, sex and smoking, i.e. levels of deprivation, urbanisation and ethnicity, when estimating population prevalence of COPD. The model should be validated at local level and incorporated into case-finding strategies
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