Article

Effect of a UK pay-for-performance program on ethnic disparities in diabetes outcomes: interrupted time series analysis.

Department of Primary Care and Public Health, Imperial College, St. Dunstan's Road, London, United Kingdom.
The Annals of Family Medicine (Impact Factor: 4.61). 05/2012; 10(3):228-34. DOI: 10.1370/afm.1335
Source: PubMed

ABSTRACT We wanted to examine the long-term effects of the Quality and Outcomes Framework (QOF), a major pay-for-performance program in the United Kingdom, on ethnic disparities in diabetes outcomes.
We undertook an interrupted time series analysis of electronic medical record data of diabetes patients registered with 29 family practices in South West London, United Kingdom. Main outcome measures were mean hemoglobin A(1c) (HbA(1c)), total cholesterol, and blood pressure.
The introduction of QOF was associated with initial accelerated improvements in systolic blood pressure in white and black patients, but these improvements were sustained only in black patients (annual decrease: -1.68 mm Hg; 95% CI, -2.41 to -0.95 mm Hg). Initial improvements in diastolic blood pressure in white patients (-1.01 mm Hg; 95% CI, -1.79 to -0.24 mm Hg) and in cholesterol in white (-0.13 mmol/L; 95% CI, -0.21 to -0.05 mmol/L) and black (-0.10 mmol/L; 95% CI, -0.20 to -0.01 mmol/L) patients were not sustained in the post-QOF period. There was no beneficial impact of QOF on HbA(1c) in any ethnic group. Existing disparities in risk factor control remained largely intact (for example; mean HbA(1c): white 7.5%, black 7.8%, south Asian 7.8%; P <.05) at the end of the study period.
A universal pay-for-performance scheme did not appear to address important disparities in chronic disease management over time. Targeted quality improvement strategies may be required to improve health care in vulnerable populations.

0 Bookmarks
 · 
68 Views
  • Source
    The Annals of Family Medicine 01/2012; 10(3):194-5. · 4.61 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: We investigated the prevalence of chronic kidney disease and attainment of therapeutic targets for HbA1c and blood pressure in a large UK-based diabetes population. The UK National Diabetes Audit provided data from 1 January 2007 to 31 March 2008. Inclusion criteria were a documented urinary albumin:creatinine ratio and serum creatinine. Patients were stratified according to chronic kidney disease stage and albuminuria status. Chronic kidney disease was defined as an estimated glomerular filtration rate < 60 ml min(-1) 1.73 m(-2) , albuminuria or both. The proportions of patients achieving nationally defined glycaemic and systolic blood pressure targets were determined. The cohort comprised 1 423 669 patients, of whom 868 616 (61%) met inclusion criteria. Of the patients analysed, 92.2% had Type 2 diabetes. A higher proportion of people with Type 2 diabetes (42.3%) had renal dysfunction compared with those with Type 1 diabetes (32.4%). Achievement of systolic blood pressure and HbA1c targets was poor. Among people with Type 1 diabetes, 67.8% failed to achieve an HbA1c < 58 mmol/mol (7.5%). Of all people with diabetes, 37.8% failed to achieve a systolic blood pressure < 140 mmHg. Blood pressure control was poor in advanced chronic kidney disease. For example, mean (standard deviation) systolic blood pressure rose from 128.6 (15.4) mmHg among people with Type 1 diabetes and normal renal function to 141.0 (23.6) mmHg in those with chronic kidney disease stage 5 and macroalbuminuria. The high prevalence of chronic kidney disease and poor attainment of treatment targets highlights a large subset of the diabetes population at increased risk of cardiovascular mortality or progressive kidney disease.
    Diabetic Medicine 09/2013; · 3.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Electronic health data sets, including electronic health records (EHR) and other administrative databases, are rich data sources that have the potential to help answer important questions about the effects of clinical interventions as well as policy changes. However, analyses using such data are almost always non-experimental, leading to concerns that those who receive a particular intervention are likely different from those who do not, in ways that may confound the effects of interest. This paper outlines the challenges in estimating causal effects using electronic health data, and offers some solutions, with particular attention paid to propensity score methods that help ensure comparisons between similar groups. The methods are illustrated with a case study describing the design of a study using Medicare and Medicaid administrative data to estimate the effect of the Medicare Part D prescription drug program among individuals with serious mental illness.
    EGEMS (Washington, DC). 01/2013; 1(3).

Full-text

View
0 Downloads
Available from