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1.  Ethnic differences in primary care management of diabetes and cardiovascular disease in people with serious mental illness 
The British Journal of General Practice  2012;62(601):e582-e588.
Background
Patients with serious mental illness (SMI) have high rates of cardiovascular disease (CVD). In contrast to widespread perception, their access to effective chronic disease management is as high as for the general population. However, previous studies have not included analysis by ethnicity.
Aim
To identify differences in CVD and diabetes management, by ethnicity, among people with SMI.
Design and setting
Three inner east London primary care trusts with an ethnically diverse and socially deprived population. Data were obtained from 147 of 151 general practices.
Method
Coded demographic and clinical data were obtained from GP electronic health records using EMIS Web. The sample used was the GP registered population on diabetes or CVD registers (52 620); of these, 1223 also had SMI.
Results
The population prevalence of CVD and diabetes is 7.2%; this rises to 18% among those with SMI. People with SMI and CVD or diabetes were found to be as likely to achieve clinical targets as those without SMI. Blood pressure control was significantly better in people with SMI; however, they were more likely to smoke and have a body mass index above 30 kg/m2. Ethnic differences in care were identified, with south Asian individuals achieving better cholesterol control and black African or Caribbean groups achieving poorer blood pressure control.
Conclusion
Risk factor management for those with SMI shows better control of blood pressure and glycosylated haemoglobin than the general population. However, smoking and obesity rates remain high and should be the target of public health programmes. Ethnic differences in management mirror those in the general population. Ethnic monitoring for vulnerable groups provides evidence to support schemes to reduce health inequalities.
doi:10.3399/bjgp12X653642
PMCID: PMC3404337  PMID: 22867683
cardiovascular diseases; diabetes mellitus; ethnicity; health; inequalities; mental disorders; primary care
2.  Type 2 diabetes: a cohort study of treatment, ethnic and social group influences on glycated haemoglobin 
BMJ Open  2012;2(5):e001477.
Objectives
To assess whether in people with poorly controlled type 2 diabetes (HbA1c>7.5%) improvement in HbA1c varies by ethnic and social group.
Design
Prospective 2-year cohort of type 2 diabetes treated in general practice.
Setting and participants
All patients with type 2 diabetes in 100 of the 101 general practices in two London boroughs. The sample consisted of an ethnically diverse group with uncontrolled type 2 diabetes aged 37–71 years in 2007 and with HbA1c recording in 2008–2009.
Outcome measure
Change from baseline HbA1c in 2007 and achievement of HbA1c control in 2008 and 2009 were estimated for each ethnic, social and treatment group using multilevel modelling.
Results
The sample consisted of 6104 people; 18% were white, 63% south Asian, 16% black African/Caribbean and 3% other ethnic groups. HbA1c was lower after 1 and 2 years in all ethnic groups but south Asian people received significantly less benefit from each diabetes treatment. After adjustment, south Asian people were found to have 0.14% less reduction in HbA1c compared to white people (95% CI 0.04% to 0.24%) and white people were 1.6 (95% CI 1.2 to 2.0) times more likely to achieve HbA1c controlled to 7.5% or less relative to south Asian people. HbA1c reduction and control in black African/Caribbean and white people did not differ significantly. There was no evidence that social deprivation influenced HbA1c reduction or control in this cohort.
Conclusions
In all treatment groups, south Asian people with poorly controlled diabetes are less likely to achieve controlled HbA1c, with less reduction in mean HbA1c than white or black African/Caribbean people.
doi:10.1136/bmjopen-2012-001477
PMCID: PMC3488709  PMID: 23087015
diabetes & endocrinology; primary care; therapeutics; public health
3.  Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis 
The British Journal of General Practice  2012;62(603):e663-e670.
Background
Risk scores calculated from electronic patient records can be used to predict the risk of adults developing diabetes in the future.
Aim
To use a risk-prediction model on GPs’ electronic health records in three inner-city boroughs, and to map the risk of diabetes by locality for commissioners, to guide possible interventions for targeting groups at high risk.
Design and setting
Cross-sectional analysis of electronic general practice records from three deprived and ethnically diverse inner-city boroughs in London.
Method
A cross-sectional analysis of 519 288 electronic primary care records was performed for all people without diabetes aged 25–79 years. A validated risk score, the QDScore, was used to predict 10-year risk of developing type 2 diabetes. Descriptive statistics were generated, including subanalysis by deprivation and ethnicity. The proportion of people at high risk (≥20% risk) per general practice was geospatially mapped.
Results
Data were obtained from 135 out of 145 general practices (91.3%); 1 in 10 people in this population were at high risk (≥20%) of developing type 2 diabetes within 10 years. Of those with known cardiovascular disease or hypertension, approximately 50% were at high risk. Male sex, increasing age, South Asian ethnicity, deprivation, obesity, and other comorbidities increased the risk. Geospatial mapping revealed hotspots of high risk.
Conclusion
Individual risk scores calculated from electronic records can be aggregated to produce population risk profiles to inform commissioning and public health planning. Specific localities were identified (the ‘East London diabetes belt’), where preventive efforts should be targeted. This method could be used for other diseases and risk states, to inform targeted commissioning and preventive research.
doi:10.3399/bjgp12X656793
PMCID: PMC3459773  PMID: 23265225
diabetes mellitus, type 2; risk; QDScore, QDiabetes, electronic medical record; general practice; public health
4.  Ethnic and social disparity in glycaemic control in type 2 diabetes; cohort study in general practice 2004–9 
Objective
To determine whether ethnic group differences in glycated haemoglobin (HbA1c) changed over a 5-year period in people on medication for type 2 diabetes.
Design
Open cohort in 2004–9.
Setting
Electronic records of 100 of the 101 general practices in two inner London boroughs.
Participants
People aged 35 to 74 years on medication for type 2 diabetes.
Main outcome measures
Mean HbA1c and proportion with HbA1c controlled to ≤7.5%.
Results
In this cohort of 24,111 people, 22% were White, 58% South Asian and 17% Black African/Caribbean. From 2004 to 2009 mean HbA1c improved from 8.2% to 7.8% for White, from 8.5% to 8.0% for Black African/Caribbean and from 8.5% to 8.0% for South Asian people. The proportion with HbA1c controlled to 7.5% or less, increased from 44% to 56% in White, 38% to 53% in Black African/Caribbean and 34% to 48% in South Asian people. Ethnic group and social deprivation were independently associated with HbA1c. South Asian and Black African/Caribbean people were treated more intensively than White people.
Conclusion
HbA1c control improved for all ethnic groups between 2004–9. However, South Asian and Black African/Caribbean people had persistently worse control despite more intensive treatment and significantly more improvement than White people. Higher social deprivation was independently associated with worse control.
doi:10.1258/jrsm.2012.110289
PMCID: PMC3407404  PMID: 22396467
5.  Cardiovascular multimorbidity: the effect of ethnicity on prevalence and risk factor management 
The British Journal of General Practice  2011;61(586):e262-e270.
Background
Multimorbidity is common in primary care populations. Within cardiovascular disease, important differences in disease prevalence and risk factor management by ethnicity are recognised.
Aim
To examine the population burden of cardiovascular multimorbidity and the management of modifiable risk factors by ethnicity.
Design and setting
Cross-sectional study of general practices (148/151) in the east London primary care trusts of Tower Hamlets, City and Hackney, and Newham, with a total population size of 843 720.
Method
Using MIQUEST, patient data were extracted from five cardiovascular registers. Logistic regression analysis was used to examine the risk of being multimorbid by ethnic group, and the control of risk factors by ethnicity and burden of cardiovascular multimorbidity.
Results
The crude prevalence of cardiovascular multimorbidity among patients with at least one cardiovascular condition was 34%. People of non-white ethnicity are more likely to be multimorbid than groups of white ethnicity, with adjusted odds ratios of 2.04 (95% confidence interval [CI] = 1.94 to 2.15) for South Asians and 1.23 (95% CI = 1.18 to 1.29) for groups of black ethnicity. Achievement of targets for blood pressure, cholesterol, and glycated haemoglobin (HbA1c) was higher for patients who were multimorbid than unimorbid. For cholesterol and blood pressure, South Asian patients achieved better control than those of white and black ethnicity. For HbA1c levels, patients of white ethnicity had an advantage over other groups as the morbidity burden increased.
Conclusion
The burden of multiple disease varies by ethnicity. Risk factor management improves with increasing levels of cardiovascular multimorbidity, but clinically important differences by ethnicity remain and contribute to health inequalities.
doi:10.3399/bjgp11X572454
PMCID: PMC3080231  PMID: 21619750
cardiovascular diseases; comorbidity; ethnicity; primary care
7.  Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning 
BMJ Open  2012;2(1):e000711.
Objective
To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.
Study design
Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data.
Sample and setting
Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases.
Methods
The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25–79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps.
Results
Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at ‘high risk’ of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated ‘hot spots’ where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement.
Conclusions
Producing small-area geospatial maps of diabetes risk calculated from general practice electronic record data across a district-wide population was feasible but not straightforward. Geovisualisation of epidemiological and environmental data, made possible by interdisciplinary links between public health clinicians and human geographers, allows presentation of findings in a way that is both accessible and engaging, hence potentially of value to commissioners and policymakers. Impact studies are needed of how maps of chronic disease risk might be used in public health and urban planning.
Article summary
Article focus
To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams.
Key messages
Creating small-area geospatial maps of risk of type 2 diabetes is feasible using routinely collected data from electronic general practice records.
Maps complement a traditional statistical approach to public health data, requiring different ways of processing and presenting information.
Such maps may be of use to commissioners and public health planners who seek to make sense of vast amounts of routine health information.
Strengths and limitations of this study
The study uses routinely collected local individual patient data to generate high-quality small-area maps of disease risk across an entire district.
Quality and completeness of the data set from which the geospatial maps were derived was high.
A potential limitation of our study is the uniqueness of the local IT context. In order for the method used here to be successfully reproduced by others, a number of conditions need to be met.
doi:10.1136/bmjopen-2011-000711
PMCID: PMC3282296  PMID: 22337817
8.  Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database 
Objective To develop, validate, and evaluate a new QRISK model to estimate lifetime risk of cardiovascular disease.
Design Prospective cohort study with routinely collected data from general practice. Cox proportional hazards models in the derivation cohort to derive risk equations accounting for competing risks. Measures of calibration and discrimination in the validation cohort.
Setting 563 general practices in England and Wales contributing to the QResearch database.
Subjects Patients aged 30–84 years who were free of cardiovascular disease and not taking statins between 1 January 1994 and 30 April 2010: 2 343 759 in the derivation dataset, and 1 267 159 in the validation dataset.
Main outcomes measures Individualised estimate of lifetime risk of cardiovascular disease accounting for smoking status, ethnic group, systolic blood pressure, ratio of total cholesterol:high density lipoprotein cholesterol, body mass index, family history of coronary heart disease in first degree relative aged <60 years, Townsend deprivation score, treated hypertension, rheumatoid arthritis, chronic renal disease, type 2 diabetes, and atrial fibrillation. Age-sex centile values for lifetime cardiovascular risk compared with 10 year risk estimated using QRISK2 (2010).
Results Across all the 1 267 159 patients in the validation dataset, the 50th, 75th, 90th, and 95th centile values for lifetime risk were 31%, 39%, 50%, and 57% respectively. Of the 10% of patients in the validation cohort classified at highest risk with either the lifetime risk model or the 10 year risk model, only 18 385(14.5%) were at high risk on both measures. Patients identified as high risk with the lifetime risk approach were more likely to be younger, male, from ethnic minority groups, and have a positive family history of premature coronary heart disease than those identified with the 10 year QRISK2 score. The lifetime risk calculator is available at www.qrisk.org/lifetime/.
Conclusions Compared with using a 10 year QRISK2 score, a lifetime risk score will tend to identify patients for intervention at a younger age. Although lifestyle interventions at an earlier age could be advantageous, there would be small gains under the age of 65, and medical interventions carry risks as soon as they are initiated. Research is needed to examine closely the cost effectiveness and acceptability of such an approach.
doi:10.1136/bmj.c6624
PMCID: PMC2999889  PMID: 21148212
9.  Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore 
Objective To develop and validate a new diabetes risk algorithm (the QDScore) for estimating 10 year risk of acquiring diagnosed type 2 diabetes over a 10 year time period in an ethnically and socioeconomically diverse population.
Design Prospective open cohort study using routinely collected data from 355 general practices in England and Wales to develop the score and from 176 separate practices to validate the score.
Participants 2 540 753 patients aged 25-79 in the derivation cohort, who contributed 16 436 135 person years of observation and of whom 78 081 had an incident diagnosis of type 2 diabetes; 1 232 832 patients (7 643 037 person years) in the validation cohort, with 37 535 incident cases of type 2 diabetes.
Outcome measures A Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort and to derive a risk equation in men and women. The predictive variables examined and included in the final model were self assigned ethnicity, age, sex, body mass index, smoking status, family history of diabetes, Townsend deprivation score, treated hypertension, cardiovascular disease, and current use of corticosteroids; the outcome of interest was incident diabetes recorded in general practice records. Measures of calibration and discrimination were calculated in the validation cohort.
Results A fourfold to fivefold variation in risk of type 2 diabetes existed between different ethnic groups. Compared with the white reference group, the adjusted hazard ratio was 4.07 (95% confidence interval 3.24 to 5.11) for Bangladeshi women, 4.53 (3.67 to 5.59) for Bangladeshi men, 2.15 (1.84 to 2.52) for Pakistani women, and 2.54 (2.20 to 2.93) for Pakistani men. Pakistani and Bangladeshi men had significantly higher hazard ratios than Indian men. Black African men and Chinese women had an increased risk compared with the corresponding white reference group. In the validation dataset, the model explained 51.53% (95% confidence interval 50.90 to 52.16) of the variation in women and 48.16% (47.52 to 48.80) of that in men. The risk score showed good discrimination, with a D statistic of 2.11 (95% confidence interval 2.08 to 2.14) in women and 1.97 (1.95 to 2.00) in men. The model was well calibrated.
Conclusions The QDScore is the first risk prediction algorithm to estimate the 10 year risk of diabetes on the basis of a prospective cohort study and including both social deprivation and ethnicity. The algorithm does not need laboratory tests and can be used in clinical settings and also by the public through a simple web calculator (www.qdscore.org).
doi:10.1136/bmj.b880
PMCID: PMC2659857  PMID: 19297312
10.  Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 
BMJ : British Medical Journal  2008;336(7659):1475-1482.
Objective To develop and validate version two of the QRISK cardiovascular disease risk algorithm (QRISK2) to provide accurate estimates of cardiovascular risk in patients from different ethnic groups in England and Wales and to compare its performance with the modified version of Framingham score recommended by the National Institute for Health and Clinical Excellence (NICE).
Design Prospective open cohort study with routinely collected data from general practice, 1 January 1993 to 31 March 2008.
Setting 531 practices in England and Wales contributing to the national QRESEARCH database.
Participants 2.3 million patients aged 35-74 (over 16 million person years) with 140 000 cardiovascular events. Overall population (derivation and validation cohorts) comprised 2.22 million people who were white or whose ethnic group was not recorded, 22 013 south Asian, 11 595 black African, 10 402 black Caribbean, and 19 792 from Chinese or other Asian or other ethnic groups.
Main outcome measures First (incident) diagnosis of cardiovascular disease (coronary heart disease, stroke, and transient ischaemic attack) recorded in general practice records or linked Office for National Statistics death certificates. Risk factors included self assigned ethnicity, age, sex, smoking status, systolic blood pressure, ratio of total serum cholesterol:high density lipoprotein cholesterol, body mass index, family history of coronary heart disease in first degree relative under 60 years, Townsend deprivation score, treated hypertension, type 2 diabetes, renal disease, atrial fibrillation, and rheumatoid arthritis.
Results The validation statistics indicated that QRISK2 had improved discrimination and calibration compared with the modified Framingham score. The QRISK2 algorithm explained 43% of the variation in women and 38% in men compared with 39% and 35%, respectively, by the modified Framingham score. Of the 112 156 patients classified as high risk (that is, ≥20% risk over 10 years) by the modified Framingham score, 46 094 (41.1%) would be reclassified at low risk with QRISK2. The 10 year observed risk among these reclassified patients was 16.6% (95% confidence interval 16.1% to 17.0%)—that is, below the 20% treatment threshold. Of the 78 024 patients classified at high risk on QRISK2, 11 962 (15.3%) would be reclassified at low risk by the modified Framingham score. The 10 year observed risk among these patients was 23.3% (22.2% to 24.4%)—that is, above the 20% threshold. In the validation cohort, the annual incidence rate of cardiovascular events among those with a QRISK2 score of ≥20% was 30.6 per 1000 person years (29.8 to 31.5) for women and 32.5 per 1000 person years (31.9 to 33.1) for men. The corresponding figures for the modified Framingham equation were 25.7 per 1000 person years (25.0 to 26.3) for women and 26.4 (26.0 to 26.8) for men). At the 20% threshold, the population identified by QRISK2 was at higher risk of a CV event than the population identified by the Framingham score.
Conclusions Incorporating ethnicity, deprivation, and other clinical conditions into the QRISK2 algorithm for risk of cardiovascular disease improves the accuracy of identification of those at high risk in a nationally representative population. At the 20% threshold, QRISK2 is likely to be a more efficient and equitable tool for treatment decisions for the primary prevention of cardiovascular disease. As the validation was performed in a similar population to the population from which the algorithm was derived, it potentially has a “home advantage.” Further validation in other populations is therefore advised.
doi:10.1136/bmj.39609.449676.25
PMCID: PMC2440904  PMID: 18573856
11.  Effects of Spectral Characteristics of Ganzfeld Stimuli on the Photopic Negative Response (PhNR) of the ERG 
Purpose
To determine flash and background colors that best isolate the photopic negative response (PhNR) and maximize its amplitude in the primate ERG.
Methods
Photopic full-field flash ERGs were recorded from anesthetized macaque monkeys before and after pharmacologic blockade of Na+-dependent spiking activity with tetrodotoxin (TTX, 1 to 2 μM, n = 3), blockade of ionotropic glutamatergic transmission with cis-2,3 piperidine dicarboxylic acid (PDA, 3.3–3.8 mM, n = 3) or laser-induced monocular experimental glaucoma (n = 6), and from six normal human subjects. Photopically matched colored flashes of increasing stimulus strengths were presented on scotopically matched blue, white, or yellow backgrounds of 100 scot cd/m2 using an LED-based stimulator.
Results
PhNRs that could be eliminated by TTX or severe experimental glaucoma were present in responses to brief (<5 ms) and long-duration (200 ms) stimuli of all color combinations. In normal monkey and human eyes for brief low-energy flashes, PhNR amplitudes were highest for red flashes on blue backgrounds and blue flashes on yellow backgrounds. For high-energy flashes, amplitudes were more similar for all color combinations. For long-duration stimuli, the PhNRon at light onset in monkeys was larger for red and blue stimuli, regardless of background color, than for spectrally broader flashes, except for stimuli >17.7 cd/m2 when PhNRons were all of similar amplitude. For red flashes, eliminating the PhNRon pharmacologically or by glaucoma removed the slowly recovering negative wave that normally followed the transient b-wave and elevated the whole ON response close to the level of the b-wave peak. However, for white, blue, and green flashes, a lower-amplitude plateau that could be removed by PDA remained.
Conclusions
For weak to moderate flash strengths, the best stimulus for maximizing PhNR amplitude is one that primarily stimulates one cone type, on a background with minimal adaptive effect on cones. For stronger stimuli, differences in amplitude are smaller. For long-duration stimuli, red best isolates the PhNRon because it minimizes the overlapping lower-level plateau that originates from the activity of second-order hyperpolarizing retinal neurons.
doi:10.1167/iovs.07-0218
PMCID: PMC2100398  PMID: 17898309
12.  Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study 
BMJ : British Medical Journal  2007;335(7611):136.
Objective To derive a new cardiovascular disease risk score (QRISK) for the United Kingdom and to validate its performance against the established Framingham cardiovascular disease algorithm and a newly developed Scottish score (ASSIGN).
Design Prospective open cohort study using routinely collected data from general practice.
Setting UK practices contributing to the QRESEARCH database.
Participants The derivation cohort consisted of 1.28 million patients, aged 35-74 years, registered at 318 practices between 1 January 1995 and 1 April 2007 and who were free of diabetes and existing cardiovascular disease. The validation cohort consisted of 0.61 million patients from 160 practices.
Main outcome measures First recorded diagnosis of cardiovascular disease (incident diagnosis between 1 January 1995 and 1 April 2007): myocardial infarction, coronary heart disease, stroke, and transient ischaemic attacks. Risk factors were age, sex, smoking status, systolic blood pressure, ratio of total serum cholesterol to high density lipoprotein, body mass index, family history of coronary heart disease in first degree relative aged less than 60, area measure of deprivation, and existing treatment with antihypertensive agent.
Results A cardiovascular disease risk algorithm (QRISK) was developed in the derivation cohort. In the validation cohort the observed 10 year risk of a cardiovascular event was 6.60% (95% confidence interval 6.48% to 6.72%) in women and 9.28% (9.14% to 9.43%) in men. Overall the Framingham algorithm over-predicted cardiovascular disease risk at 10 years by 35%, ASSIGN by 36%, and QRISK by 0.4%. Measures of discrimination tended to be higher for QRISK than for the Framingham algorithm and it was better calibrated to the UK population than either the Framingham or ASSIGN models. Using QRISK 8.5% of patients aged 35-74 are at high risk (20% risk or higher over 10 years) compared with 13% when using the Framingham algorithm and 14% when using ASSIGN. Using QRISK 34% of women and 73% of men aged 64-75 would be at high risk compared with 24% and 86% according to the Framingham algorithm. UK estimates for 2005 based on QRISK give 3.2 million patients aged 35-74 at high risk, with the Framingham algorithm predicting 4.7 million and ASSIGN 5.1 million. Overall, 53 668 patients in the validation dataset (9% of the total) would be reclassified from high to low risk or vice versa using QRISK compared with the Framingham algorithm.
Conclusion QRISK performed at least as well as the Framingham model for discrimination and was better calibrated to the UK population than either the Framingham model or ASSIGN. QRISK is likely to provide more appropriate risk estimates to help identify high risk patients on the basis of age, sex, and social deprivation. It is therefore likely to be a more equitable tool to inform management decisions and help ensure treatments are directed towards those most likely to benefit. It includes additional variables which improve risk estimates for patients with a positive family history or those on antihypertensive treatment. However, since the validation was performed in a similar population to the population from which the algorithm was derived, it potentially has a “home advantage.” Further validation in other populations is therefore required.
doi:10.1136/bmj.39261.471806.55
PMCID: PMC1925200  PMID: 17615182

Results 1-25 (29)