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68 Pay for Quality KCE Reports 118 The change was statistically significant for all indicators except for achieving cholesterol target. Women have not benefitted equally from the introduction of the new contract. Pre-contract women were as likely as men to have recording of HbA1c, blood pressure, serum creatinine and cholesterol. Post contract women were less likely to have HbA1c, serum creatinine and cholesterol recorded. 128 In one historical comparison study, before-after time point and one historical comparison study with multiple time points, the authors looked at ethnic differences in the quality of certain indicators of diabetes care, including the achievement of HbA1c targets. In a first study was found that Hba1c levels reduced over time for all ethnic groups but the magnitude of the improvement appeared to differ between ethnic groups even after adjusting for the effects of age, gender, years since the diagnosis, practice size and deprivation of the area where the patient lives and the area where the practice is located. A significant reduction between the pre- and post-contract measurement of HbA1c was found for the Whites but not for the Blacks and South Asians, resulting in a widening of the existing ethnic disparities in care for Blacks and South Asians. The introduction of the pay-for-performance was associated with a significantly greater improvement in women than in men. The impact on HbA1c was not found to vary significantly with the neighbourhood deprivation. 157 In a second study with a similar methodology but on a smaller database (adjusting for the effects of age, gender, deprivation level and the clustering of patients in practices), Millet et al. (2007) found that the proportion of patients reaching HbA1c targets increased significantly after introduction of the contract. These increases were broadly uniform across all seven ethnic groups (no significant differences between the groups), except for the Black Caribbean group, which had HbA1c improvements that were significantly lower than in the White British group. 155 In the same studies described in the above paragraph, Millet et al also investigated ethnic differences in BP targets in diabetic patients. Blood pressure reduced over time for all ethnic groups. However the magnitude of the improvement appeared to differ between ethnic groups even after adjusting for the effects of age, gender, years since the diagnosis, practice size and deprivation of the area where the patient lives and the area where the practice is located. The average reductions in blood pressure where lower in the black patients than in the white patients, resulting in a widening of the existing ethnic disparities for Blacks. No significant difference between South Asian patients and White patients was found. 157 In their second study, Millet et al (2007), found that the proportion of patients reaching BP targets increased significantly after introduction of the contract. These increases were broadly uniform across all seven ethnic groups (no significant differences between the groups), except for the Black Caribbean group, which had BP improvements that were significantly lower than in the White British group and this in spite of the fact that this patient group is most in need because of their hereditary predisposition for cardiovascular diseases. 155 One historical comparison study, with multiple time points looked at QOF indicators for smokers with diabetes. This study showed that significantly more patients had their smoking status ever recorded in 2005 than in 2003. They found a larger increase in recording for women and the non-white ethnic groups (except Bangladeshi) even after adjustment for age, ethnic background, deprivation status and practice clustering. Also the proportion of patients with documented smoking cessation advice increased significantly. No difference was found according to age, sex or ethnic group. Concerning the prevalence of smoking an overall reduction in smoking was observed (20% to 16.2%). Both women and men benefited from this reduction but women benefited less. The reduction was not significantly different in the most and least affluent groups and the existing gap remained. Black African and Bangladeshi patients benefited less from this reduction compared to Whites. 156
KCE Reports 118 Pay for Quality 69 4.5.1.3 Blood Pressure Ashworth et al (2008) conducted a historical comparison study with multiple time points, after the introduction of the QOF, adjusting for practice size and other not specified possible confounding factors. They reported on BP recording and management of BP control in the general patient population (i.e. not in a patient group with a specific condition). Blood pressure recording systematically rises over the years. Initially, there was a 1.7% gap between mean blood pressure recording levels in practices located in the least deprived fifth of communities compared with the most deprived fifth, but, three years later, this gap had narrowed to 0.2%. Improvements in achievement have resulted in the near disappearance of the achievement gap between least and most deprived areas. 169 4.5.1.4 Generic findings Exception reporting has been introduced within QOF to allow practices to pursue the quality improvement agenda and not be penalized, where, for example, patients do not attend for review, or where a medication cannot be prescribed due to a contraindication or side-effects. However, there is some concern that exception reporting will worsen health care disparities because financial incentives encourage providers to ‘cherry pick’ healthier patients or exclude those not achieving targets from public reporting mechanisms, mostly socially deprived patients or patients with a different ethnic background. Additionally, such incentives may widen health care disparities if they increase the resource gap between high and low performing health care providers. Despite this potential for harm, information on the impact of P4P incentives on health care disparities remains limited. 158 We identified five studies looking at the phenomenon of exception reporting and the 84 , 134 , 147 , 160, 230 possible impact of it on inequity in health care. In one cross-sectional study immediately after the introduction of the new GMS, Doran et al (2008) analyzed determining factors in the rate of exception reporting by English physicians. They found that characteristics of patients and practices explained only 2.7% in the variance of exception reporting. Living in income deprived households and being a member of racial or ethnic minority were small but statistically significant determinants. 84 Doran et al (2006) assessed the effect of exception reporting on reported achievement in a cross sectional study in 2005. They found that an increase of 1% in the estimated proportion of patients excluded was associated with an increase of 0.31% in achieved quality for every 1000 patients on the practice list. 147 Sigfrid et al (2006) explored whether exception reporting is linked to socioeconomic deprivation. They found that patients with diabetes living in deprived areas are more likely to be ‘exception reported’ from QOF clinical indicators. 160 Simpson et al (2006) did an analysis on a cross sectional database to study exception reporting for TIA and stroke. They found no significant association between the practice’s exception reporting and the practice having proportionately more female, older or deprived stroke/TIA patients. Stroke/TIA patients with the ‘top level’ exclusion code ‘patient unsuitable for inclusion’ were more likely to be female, older, and have a diagnosis with dementia when compared to those patients without such a code. The youngest and patients from more deprived parts of Scotland were more likely to have the exception codes: ‘informed dissent’ or ‘no response to letters’. Females were more likely to be excluded from the specific quality indicators of achieving blood pressure or cholesterol control. More deprived patients were not likely to be excluded from these quality indicators. Younger and more deprived patients were more likely to be recorded as having refused to attend for review or not replying to letters asking for attendance at primary care clinics. 134
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