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Articles by C. Millett
Total Records ( 4 ) for C. Millett
  C. Millett , K. Khunti , J. Gray , S. Saxena , G. Netuveli and A. Majeed
 

AimTo examine associations between obesity, ethnicity and intermediate clinical outcomes in diabetes.


MethodsPopulation-based, cross-sectional study using electronic primary care medical records of 7300 people with diabetes from White, Black and south Asian ethnic groups.


ResultsThe pattern of obesity differed within ethnic groups, with rates significantly higher in younger when compared to older Black (women, 63% vs. 44%, P=0.002; men, 37% vs. 20%, P=0.005) and south Asian (women, 47% vs. 27%,P=0.01; men, 21% vs. 13%, P=0.05) people. Obese people with diabetes were significantly less likely to achieve an established target for blood pressure control (adjusted odds ratio 0.50, 95% confidence interval 0.42, 0.59). Differences in mean systolic blood pressure in obese and normal weight persons were significant in the White group but not in the Black groups or south Asian groups (6.9 mmHg, 1.9 mmHg and 2.7 mmHg, respectively). Differences in mean diastolic blood pressure between obese and normal weight persons were 4.8 mmHg, 3.6 mmHg and 3.4 mmHg in the White, Black and south Asian groups. Mean HbA1c and achievement of an established treatment target did not differ significantly with obesity in any ethnic group.


ConclusionsObesity is more prevalent amongst younger people than older people with diabetes in ethnic minority groups. The relationship between obesity and blood pressure control in diabetes differs markedly across ethnic groups. Major efforts must be implemented, especially in young people, to reduce levels of obesity in diabetes and improve long-term outcomes.

  S. De Lusignan , K. Khunti , J. Belsey , A. Hattersley , J. Van Vlymen , H. Gallagher , C. Millett , N. J. Hague , C. Tomson , K. Harris and A. Majeed
  Aims: Incorrect classification, diagnosis and coding of the type of diabetes may have implications for patient management and limit our ability to measure quality. The aim of the study was to measure the accuracy of diabetes diagnostic data and explore the scope for identifying errors.Method: We used two sets of anonymized routinely collected computer data: the pilot used Cutting out Needless Deaths Using Information Technology (CONDUIT) study data (n=221958), which we then validated using 100 practices from the Quality Improvement in Chronic Kidney Disease (QICKD) study (n=760588). We searched for contradictory diagnostic codes and also compatibility with prescription, demographic and laboratory test data. We classified errors as: misclassified-incorrect type of diabetes; misdiagnosed-where there was no evidence of diabetes; or miscoded-cases where it was difficult to infer the type of diabetes.Results: The standardized prevalence of diabetes was 5.0 and 4.0% in the CONDUIT and the QICKD data, respectively: 13.1% (n=930) of CONDUIT and 14.8% (n=4363) QICKD are incorrectly coded; 10.3% (n=96) in CONDUIT and 26.2% (n=1143) in QICKD are misclassified; nearly all of these cases are people classified with Type 1 diabetes who should be classified as Type 2. Approximately 5% of T2DM in both samples have no objective evidence to support a diagnosis of diabetes. Miscoding was present in approximately 7.8% of the CONDUIT and 6.1% of QICKD diabetes records.Conclusions: The prevalence of miscoding, misclassification and misdiagnosis of diabetes is high and there is substantial scope for further improvement in diagnosis and data quality. Algorithms which identify likely misdiagnosis, misclassification and miscoding could be used to flag cases for review.
  A. R. H. Dalton , R. Alshamsan , A. Majeed and C. Millett
  Background  We examined associations between patient and practice characteristics and exclusions from quality indicators for diabetes during the first 3 years of the Quality and Outcomes Framework, a major pay-for-performance scheme in the UK.

Methods  Three cross-sectional analyses, conducted using data from the electronic medical records of all patients with diabetes registered in 23 general practices in Brent, North West London between 2004/2005 and 2006/2007. Patterns of exclusions were examined for three intermediate outcome indicators.

Results  Excluded patients were less likely to achieve treatment targets for HbA1c (2004/2005, 2006/2007), blood pressure (2005/2006, 2006/2007) and cholesterol (2005/2006). Black and South Asian patients were more likely to be excluded from the HbA1c indicator than White patients [adjusted odds ratio = 1.64 (1.17-2.29) in 2005/2006]. Patients diagnosed with diabetes duration of > 10 years [adjusted odds ratio = 2.01 (1.65-2.45) for HbA1c in 2006-2007] and those with co-morbidities (adjusted odds ratio, ≥ 3 co-morbidities compared with no co-morbidity for HbA1c adjusted odds ratio = 1.90 (1.24-2.90) in 2004/2005] were more likely to be excluded. Larger practices excluded more patients from the HbA1c indicator [adjusted odds ratio, practice ≥ 7000 compared with < 3000, 3.52 (2.35-5.27) in 2005-2006]. More deprived practices consistently excluded more patients from all indicators, whilst in 2007 older patients were excluded to a larger degree [adjusted odds ratio = 2.52 (1.21-5.28) ≥ 75 compared with 18-44 for blood pressure control].

Conclusions  Patients excluded from pay-for-performance programmes may be less likely to achieve treatment goals and disproportionately come from disadvantaged groups. Permitting physicians to exclude patients from pay-for-performance programmes may worsen health disparities.

  D. C. Gibbons , M. A. Soljak , C. Millett , J. Valabhji and A. Majeed
 

Aims

Accurate measurement of emergency diabetes admissions is essential for healthcare delivery and research. This study examines whether current approaches to identifying diabetes-related admissions may underestimate the true burden on hospital care.

Methods

Data spanning the period 1 January 2006 to 31 December 2010 inclusive were extracted from Hospital Episode Statistics data for England. Emergency admissions citing diabetes (E10, E11, E13 or E14) in any diagnosis position in adults (≥ 17 years) were included. E10 and E11 were considered analogous to Type 1 and Type 2 diabetes mellitus respectively; E13 and E14 were grouped as ‘other or unspecified’ diabetes mellitus. For admissions citing diabetes multiple times, those with concordant citations were classified as appropriate; discordant citations were assigned to the ‘other or unspecified’ group. Frequencies of diabetes classifications and complications for each diagnosis position and frequencies of all International Classification of Diseases 10th revision codes for the primary diagnosis field were calculated.

Results

In total, 2 443 046 admissions were identified. Diabetes was cited as the primary diagnosis in 6.2% and most commonly cited as the third diagnosis (23.1%). Type 2 diabetes mellitus was the most common (85.0%). The majority of diabetes citations were ‘without complication’ (2 188 965, 89.6%). The most common primary diagnosis was ‘chest pain, unspecified’ (R07.4, 99 678, 4.1%).

Conclusions

Reliance on the primary diagnosis field to identify emergency admissions in people with diabetes grossly underestimates the true burden placed on hospital care and leads to underestimates of effect sizes in studies utilizing admission rates as outcome measures. An alternative strategy to identify such admissions is required.

 
 
 
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