Faculty Bibliography

Computerised Medical Record (CMR) data are widely used for secondary purposes such as service evaluation and epidemiological research. Data are increasingly aggregated from different medical facilities with various CMR vendors over time. It is increasingly difficult to manage the large quantity of data. Experiential learning in diabetes and chronic kidney disease (CKD) suggests simplistic processing can lead to errors. To maximise analytical ability for the Quality Improvement in CKD (QICKD) trial, we developed an agile data management process. By removing the need to import and process data in a relational database, we reduced processing and analysis time. We demonstrated usage of our new agile method to rapidly develop complex queries to identify how blood pressure varied between patients included or excluded from Quality and Outcomes Frameworks (QOF) pay-for-performance (P4P) targets in UK primary care. We describe a novel specification language that allows clinicians to focus on identifying variables to extract useful information from CMRs. Data for research questions were available in <1hour instead of longer times previously required through use of an SQL database.

Strict control of systolic blood pressure is known to slow progression of chronic kidney disease (CKD). Here we compared audit-based education (ABE) to guidelines and prompts or usual practice in lowering systolic blood pressure in people with CKD. This 2-year cluster randomized trial included 93 volunteer general practices randomized into three arms with 30 ABE practices, 32 with guidelines and prompts, and 31 usual practices. An intervention effect on the primary outcome, systolic blood pressure, was calculated using a multilevel model to predict changes after the intervention. The prevalence of CKD was 7.29% (41,183 of 565,016 patients) with all cardiovascular comorbidities more common in those with CKD. Our models showed that the systolic blood pressure was significantly lowered by 2.41 mm Hg (CI 0.59-4.29 mm Hg), in the ABE practices with an odds ratio of achieving at least a 5 mm Hg reduction in systolic blood pressure of 1.24 (CI 1.05-1.45). Practices exposed to guidelines and prompts produced no significant change compared to usual practice. Male gender, ABE, ischemic heart disease, and congestive heart failure were independently associated with a greater lowering of systolic blood pressure but the converse applied to hypertension and age over 75 years. There were no reports of harm. Thus, individuals receiving ABE are more likely to achieve a lower blood pressure than those receiving only usual practice. The findings should be interpreted with caution due to the wide confidence intervals.

BACKGROUND: Most chronic diseases are managed in primary and ambulatory care. The chronic care model (CCM) suggests a wide range of community, technological, team and patient factors contribute to effective chronic disease management. Ontologies have the capability to enable formalised linkage of heterogeneous data sources as might be found across the elements of the CCM. OBJECTIVE: To describe the evidence base for using ontologies and other semantic integration methods to support chronic disease management. METHOD: We reviewed the evidence-base for the use of ontologies and other semantic integration methods within and across the elements of the CCM. We report them using a realist review describing the context in which the mechanism was applied, and any outcome measures. RESULTS: Most evidence was descriptive with an almost complete absence of empirical research and important gaps in the evidence-base. We found some use of ontologies and semantic integration methods for community support of the medical home and for care in the community. Ubiquitous information technology (IT) and other IT tools were deployed to support self-management support, use of shared registries, health behavioural models and knowledge discovery tools to improve delivery system design. Data quality issues restricted the use of clinical data; however there was an increased use of interoperable data and health system integration. CONCLUSIONS: Ontologies and semantic integration methods are emergent with limited evidence-base for their implementation. However, they have the potential to integrate the disparate community wide data sources to provide the information necessary for effective chronic disease management.

Background: Understanding how cancer incidence evolves during economic growth is useful for forecasting the economic impact of cancerous diseases, and for governing the process of resources allocation in planning health services. We analyse the relationship between economic growth and cancer incidence in order to describe and measure the influence of an increasing real per capita income on the overall rate of cancer incidence. Method:We test the relationship between real per capita income and the overall rate of cancer incidence with a cross-sectional analysis, using data from the World Bank and the World Health Organization databases, for 165 countries in 2008. We measure the elasticity of cancer incidence with respect to per capita income, and we decompose the elasticities coefficients into two components: age-effect and lifestyle-effect. Results: An Engel's model, in a double-log quadratic specification, explains about half of the variations in the age-standardised rates and nearly two thirds of the variations in the incidence crude rates. All the elasticities of the crude rates are positive, but less than one. The income elasticity of the age-standardised rates are negative in lower income countries, and positive (around 0.25 and 0.32) in upper middle and high income countries, respectively. Conclusions:These results are used to develop a basic framework in order to explain how demand-side economic structural changes may affect the long run evolution of cancer incidence. At theoretical level, a J-Curve is a possible general model to represents, other things being equal, how economic growth influence cancer incidence

BACKGROUND: Chronic kidney disease (CKD) is a known risk factor for cardiovascular events and all-cause mortality. We investigate the relationship between CKD stage, proteinuria, hypertension and these adverse outcomes in the people with diabetes. We also study the outcomes of people who did not have monitoring of renal function. METHODS: A cohort of people with type 1 and 2 diabetes (N = 35,502) from the Quality Improvement in Chronic Kidney Disease (QICKD) cluster randomised trial was followed up over 2.5 years. A composite of all-cause mortality, cardiovascular events, and end stage renal failure comprised the outcome measure. A multilevel logistic regression model was used to determine correlates with this outcome. Known cardiovascular and renal risk factors were adjusted for. RESULTS: Proteinuria and reduced estimated glomerular filtration rate (eGFR) were independently associated with adverse outcomes in people with diabetes. People with an eGFR < 60 ml/min, proteinuria, and hypertension have the greatest odds ratio (OR) of adverse outcome; 1.58 (95% CI 1.36-1.83). Renal function was not monitored in 4460 (12.6%) people. Unmonitored renal function was associated with adverse events; OR 1.35 (95% CI 1.13-1.63) in people with hypertension and OR 1.32 (95% CI 1.07-1.64) in those without. CONCLUSIONS: Proteinuria, eGFR < 60 ml/min, and failure to monitor renal function are associated with cardiovascular and renal events and mortality in people with diabetes.