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Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

Honeyford K, Baker R, Bankart MJ, Jones DR - BMJ Open (2014)

Bottom Line: One practice was excluded as it served a restricted practice list.An important positive association between premature CHD mortality and smoking prevalence was shown when smoking prevalence was added to other population and service characteristics.It may also provide useful estimates of smoking prevalence in local areas by aggregating practice based data.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Sciences, University of Leicester, Leicester, UK.

No MeSH data available.


Related in: MedlinePlus

Relationship between QOF estimates for the general population and those with chronic conditions (2012/2013), (A) Association between estimates (dashed line: estimates are equal; solid line: fitted line). (B) Bland-Altman plot showing relationship between difference in estimates and mean difference (solid line: mean difference; dashed lines: 95% limits of agreement). SM07 and SM08 (2012/2013) used for QOF estimates for the general population; SM05 and SM06 (2012/2013) used for QOF estimates for those with chronic conditions. QOF, QOF, Quality and Outcomes Framework
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BMJOPEN2014005217F2: Relationship between QOF estimates for the general population and those with chronic conditions (2012/2013), (A) Association between estimates (dashed line: estimates are equal; solid line: fitted line). (B) Bland-Altman plot showing relationship between difference in estimates and mean difference (solid line: mean difference; dashed lines: 95% limits of agreement). SM07 and SM08 (2012/2013) used for QOF estimates for the general population; SM05 and SM06 (2012/2013) used for QOF estimates for those with chronic conditions. QOF, QOF, Quality and Outcomes Framework

Mentions: Smoking prevalence in those with chronic conditions was lower than in the general practice population. The mean difference between the two estimates was −3.05% (95% limits of agreement: (−8.65, 1.56)).The Bland-Altman plot does not suggest a strong pattern, despite some evidence that the difference increases as the average increases (figure 2). There was a strong positive correlation (Rp=0.92, p<0.0001) between the overall estimate of smoking prevalence within a practice population and in those with chronic conditions. A regression model was developed to predict smoking prevalence in the general population based on the prevalence in those with chronic conditions; removal of outliers improved model fit.


Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

Honeyford K, Baker R, Bankart MJ, Jones DR - BMJ Open (2014)

Relationship between QOF estimates for the general population and those with chronic conditions (2012/2013), (A) Association between estimates (dashed line: estimates are equal; solid line: fitted line). (B) Bland-Altman plot showing relationship between difference in estimates and mean difference (solid line: mean difference; dashed lines: 95% limits of agreement). SM07 and SM08 (2012/2013) used for QOF estimates for the general population; SM05 and SM06 (2012/2013) used for QOF estimates for those with chronic conditions. QOF, QOF, Quality and Outcomes Framework
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4120299&req=5

BMJOPEN2014005217F2: Relationship between QOF estimates for the general population and those with chronic conditions (2012/2013), (A) Association between estimates (dashed line: estimates are equal; solid line: fitted line). (B) Bland-Altman plot showing relationship between difference in estimates and mean difference (solid line: mean difference; dashed lines: 95% limits of agreement). SM07 and SM08 (2012/2013) used for QOF estimates for the general population; SM05 and SM06 (2012/2013) used for QOF estimates for those with chronic conditions. QOF, QOF, Quality and Outcomes Framework
Mentions: Smoking prevalence in those with chronic conditions was lower than in the general practice population. The mean difference between the two estimates was −3.05% (95% limits of agreement: (−8.65, 1.56)).The Bland-Altman plot does not suggest a strong pattern, despite some evidence that the difference increases as the average increases (figure 2). There was a strong positive correlation (Rp=0.92, p<0.0001) between the overall estimate of smoking prevalence within a practice population and in those with chronic conditions. A regression model was developed to predict smoking prevalence in the general population based on the prevalence in those with chronic conditions; removal of outliers improved model fit.

Bottom Line: One practice was excluded as it served a restricted practice list.An important positive association between premature CHD mortality and smoking prevalence was shown when smoking prevalence was added to other population and service characteristics.It may also provide useful estimates of smoking prevalence in local areas by aggregating practice based data.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Sciences, University of Leicester, Leicester, UK.

No MeSH data available.


Related in: MedlinePlus