Financial incentives and inequalities in smoking cessation interventions in primary care: before-and-after study Fiona L Hamilton, PhD, Anthony A Laverty, PhD, Kit Huckvale, MBBS, Josip Car, PhD, Azeem Majeed, MD, Christopher Millett, PhD Correspondence to Dr Laverty, Department of Primary Care, School of Public Health, Imperial College London, London W6 8RP, email [email protected]Authors’ affiliations F L Hamilton, AA Laverty, K Huckvale, J Car, A Majeed, C Millett: Department of Primary Care and Public Health, School of Public Health, Imperial College London, W6 8RP Key words or phrases Primary care Prevention Smoking cessation Inequalities Word count 3595 1
41
Embed
Effect of financial incentives on disparities in smoking ... Web viewFinancial incentives and inequalities in smoking cessation interventions in primary care: ... Bhopal RS, White
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Financial incentives and inequalities in smoking cessation interventions in primary
care: before-and-after study
Fiona L Hamilton, PhD, Anthony A Laverty, PhD, Kit Huckvale, MBBS, Josip Car,
PhD, Azeem Majeed, MD, Christopher Millett, PhD
Correspondence to Dr Laverty, Department of Primary Care, School of Public Health,
We followed QOF business rules to determine the proportion of patients with smoking status
ascertained as has previously been described,17 using smoking Read codes recorded on the
EMR of each patient. We used smoking advice and referral Read codes to determine the
proportion of smokers offered advice or referral. We used last recorded smoking status within
each time period to determine smoking point prevalence.
We tested for interactions between gender and smoking and the results were statistically
significant so we examined outcomes separately for men and women. Predictor variables for
the multiple logistic regression analysis were age, ethnicity and deprivation group. Age was
categorised as: 15-30 years; 30-49 years; 50-69 years; > 70 years on the date of introduction
of QOF+ for the baseline analysis and on the date it ended for the follow analysis, so a small
number of patients moved up an age group at follow up. We used the 2001 Census Ethnic
Categories and to maximize statistical power we collapsed these to give five ethnicity
categories: White; Black; South Asian; Mixed; Other; and a ‘Not-stated’ category for those
patients whose ethnicity was not recorded. Patients were assigned a deprivation score (Index
of Multiple Deprivation, IMD23 based on their post-code of residence, and we categorised
these into thirds. There were 490 individuals who were missing an IMD value. London
Queen Square Research Ethics Committee granted ethics approval for the use of the data for
research.
Statistical analysis
We calculated the proportion of patients with the smoking outcomes of interest before and
after the introduction of QOF+. We then examined differences in these outcomes by gender,
age group, ethnic group, deprivation, and practice size.
Univariable analyses for each potential explanatory variable (age group, ethnicity,
deprivation and practice size) and each smoking outcome variable were undertaken , and all
7
were statistically significant (not reported) except for practice size. We therefore included all
the explanatory variables except practice size in a multiple logistic regression model taking
into account clustering at the general practice level using robust confidence intervals (CI).
We did not identify any further confounders in the dataset. We conducted sensitivity analyses
with a dummy variable for a ‘missing IMD’ group and the results were not significantly
different to the analyses excluding individuals missing IMD so we did not exclude them in
the final model. We also conducted a within-group multiple logistic regression analysis with
data from pre-QOF+ and post-QOF+ and assessed pre/post differences using an interaction
term, adjusted for the other main variables, to determine whether QOF+ had influenced
outcomes overall and whether some groups had benefitted more than for others. We analysed
the data with STATA version 11.
RESULTS
1. Between group variation before and after QOF+
Smoking status ascertained
There were 41,239 patients eligible for inclusion in the study. Table 1 shows the breakdown
of these patients by demographic group and the proportion of each group having smoking
status ascertained before and after the introduction of QOF+. At baseline 62.3% had their
smoking status ascertained (55.5% of men and 67.9% of women). After the introduction of
QOF+ this percentage was 75.8% (64.2% of men and 75.8% of women), a significant
increase (AOR for men 1.37, CI 1.18 to 1.60, p<0.001; AOR for women 1.35, CI 1.17 to
1.56, p<0.001).
Prior to QOF+ men and women aged 15-30 years were less likely to have smoking status
ascertained than patients aged 30-49 years (47.5% vs 59.3% for men 15-30 years, p<0.001;
8
58.0% vs 70.4% for women 15-30 years, p<0.001). However men and women aged over-70
years were more likely to have their status recorded than those aged 30-49 years (73.0% vs
59.3% for men over 70 years, p<0.001; and 78.9% vs 70.4% for women over 70 years,
p<0.001). South Asian men and men with Mixed ethnicity were more likely to have smoking
status recorded at baseline compared with White men (64.6%, for White men vs 74.7% for
South Asian men and 76.5% for men with Mixed ethnicity, p<0.001). Women from Black,
South Asian and Other ethnic groups were more likely to have their status recorded compared
with White women. For example, 87.1% of South Asian women had their status recorded
compared with 71.6% of White women (p<0.001). There was no difference in ascertainment
of smoking status in the three deprivation categories at baseline.
After QOF+ the youngest group of men were no longer less likely to be asked about smoking,
although women 15-30 years were still less likely to be asked than women aged 30-49 years
(70.0% vs 76.2%, p<0.001). Patients aged over-70 years were still more likely to be asked
than those aged 30-49 (75.5% vs 67.5% for men, p<0.001; 85.9% vs 76.2% for women,
p<0.001).
Black men and South Asian men were more likely than White men to have their status
recorded after the introduction of QOF+ (69.8% of White men vs 72.2% of Black men,
p<0.01, and 81.2% of South Asian men, p<0.001). Women from all ethnic groups except
those with ethnicity not stated were more likely to have smoking status recorded than White
women. For example, 84.8% of Black women had their status recorded compared with 76.7%
of White women (p<0.001). After QOF+ the middle and most deprived patients were less
likely to be asked than those from more affluent areas (both genders).
Smoking cessation advice
9
Before the introduction of QOF+, 35.4% of smokers received cessation advice (32.7% of
men and 35.4% of women). After the introduction of QOF+ this proportion increased to
54.0% (54.0% of men and 54.1% of women). The AOR for men being given advice after the
introduction of QOF+ compared with the prior period was 2.29 (CI 1.45 to 3.61, p<0.001)
and 2.09 for women (CI 1.38 to 3.17, p<0.01. These results are presented in Table 2.
Before QOF+, there was little variation in the likelihood of smokers getting advice. Men aged
over-70 years were more likely to receive advice compared with men aged 30-49 years
(46.7% vs 34.6%, p<0.05). Men with Mixed ethnicity were statistically more likely to receive
smoking cessation advice than White men (58.7% vs 36.0%, p<0.001), but men whose
ethnicity was not stated were less likely (29.1% vs 36.0%, p<0.05). There were no
statistically significant differences in rates of smoking advice among the different
demographic groups for women, or for either gender for different deprivation areas.
After the introduction of QOF+ smoking cessation advice to smokers was still provided
relatively equitably. There were now no statistically significant differences in rates of advice
for different age groups for men, but women aged 15-30 years were more likely to receive
advice than those aged 30-49 years (63.7% vs 52.9%, p<0.001). There remained no
significant difference in rates of advice to patients from different ethnic groups of those living
in areas with different levels of deprivation.
Smoking prevalence
Smoking prevalence at baseline was 20.0% (25.0% of men and 16.1% of women). After the
introduction of QOF+ recorded smoking prevalence fell to 16.2% (20.8% of men and 12.5%
of women). Compared with the pre-QOF+ period, the AOR for being a current smoker was
10
0.79 for men (CI 0.75 to 0.83, p<0.001) and 0.77 for women (CI 0.72 to 0.82, p<0.001).
These results are presented in Table 3.
Pre-QOF+, smoking rates for men were lower among patients aged 15-30 years and in those
aged over-70 years compared with those aged 30-49 years (27.5% of men aged 30 to 49 vs
20.2% of men 15-30 years, p<0.001; and 11.7% of men over-70 years, p<0.001). Women
aged over-70 years were the only age group in this cohort less likely to smoke than women
aged 30-49 years (6.9% vs 16.7%, p<0.001).
Smoking rates were lower in Black, South Asian and Other men compared with White men
(25.5% of White men smoked vs 21.8% of Black men, p<0.05; 15.27% of South Asian men,
p<0.001; and 20.9% of Other men, p<0.001). Women from Black, South Asian and Other
groups were also less likely to smoke than White women, with the South Asian women being
least likely to smoke of all groups (3.3% vs 18% of White women, p<0.001).
Smoking rates were higher in more deprived groups compared with those from the most
affluent areas. For example, 28.6% of men in the most deprived areas smoked compared with
20.3% of men in the least deprived areas (p<0.001).
After QOF+, smoking rates reduced in all age groups, and remained significantly lower in
men aged 15-30 years and in men and women aged over-70 years compared with those aged
30-49 years.
Smoking prevalence fell among White men such that they were now not significantly greater
than those for Black men and men with Mixed ethnicity now were more likely to smoke than
White men (28.1% vs 21.3%, p<0.05). South Asian men and men with Other ethnicity
continued to have lower rates of smoking than White men (21.3% for White men vs 13.2%
for South Asian men, p<0.001; 18.1% for Other men, p<0.001). Smoking rates among Black,
11
South Asian and Other women remained lower than those of White women. For example,
only 2.1% of South Asian women smoked compared with 14.6% of White women (p<0.001).
Smoking rates remained higher among those from the middle and most deprived areas
compared with those from the most affluent areas following the introduction of QOF+.
2. Within group changes in outcome measures after QOF+
Table 4 shows the stratified comparisons for the association between the introduction QOF+
and smoking outcomes for men and women of different age groups, ethnic groups and
deprivation levels, in each case adjusted for the other variables. There were statistically
significant improvements for almost all age groups and ethnic groups, and for all levels of
deprivation, so we have described below those with the largest effects.
Smoking status ascertained
The youngest age groups saw the biggest improvements in recording rates (for men aged 15-
30 years, AOR 2.54, CI 2.33 to 2.77, p<0.001; for women 15-30 years, AOR 2.48, CI 2.28 to
2.71, p<0.001). South Asian patients saw a greater benefit (AOR for women 2.61, CI 1.87 to
3.64, p<0.001). Men aged over-70 years were not significantly more likely to be asked about
smoking after QOF+ than previously. Men with Mixed ethnicity were as likely to be asked
about smoking after QOF+ as before.
Smoking cessation advice
Women aged 15-30 years received the biggest improvement in smoking cessation advice
(AOR 2.03, CI 2.38 to 3.88, p<0.001). Patients aged over-70 years were not significantly
more likely to be given smoking cessation advice after the introduction of QOF+. South
Asian patients and those with Mixed ethnicity also received similar levels of advice after
QOF+ as before. Improvements for the different deprivation groups were similar.
12
Smoking prevalence
Women aged 15-30 years and men aged 30-49 years saw the largest improvements following
the introduction of QOF+ (AOR for women 15-30 0.64, CI 0.56 to 0.72, p<0.001; AOR for
men aged 30 to 49 0.73, CI 0.58 to 0.79, p<0.001). Patients aged over-70 years were the only
group not to have reduced their smoking rates significantly after the introduction of QOF+.
Black and South Asian patients and men with Mixed or Other ethnicity had similar rates of
smoking before and after the introduction of QOF+.
DISCUSSION
The introduction of a local financial incentive programme targeting primary prevention was
associated with an increased proportion of patients having their smoking status recorded, an
increased proportion of smokers receiving smoking cessation advice, and a reduction in
smoking prevalence. However, despite equitable levels of ascertainment of smoking status
and delivery of cessation advice, important inequalities in smoking prevalence persisted.
Young women were less likely to have their smoking status recorded over the study period.
South Asian men and women from ethnic minority groups were more likely to have their
smoking status recorded before and after the introduction of QOF+, although South Asian
patients were less likely to smoke than White patients. People from deprived areas were less
likely to be asked about smoking than those from the least deprived areas after the
introduction of QOF+, received similar rates of advice as the least deprived, but had higher
smoking prevalence.
Previous research
The improvement in the ascertainment in smoking status (13.5%) was low compared with
findings from a systematic review examining the effect of financial incentive on smoking
cessation17 which found increases in recording smoking status ranging from 19%24 to 52%.25
13
However, in our study we focused on primary prevention, and the eligible patients might not
routinely attend their GP surgery, whereas other studies often included patients with
smoking-related conditions who have regular reviews during which smoking could be
discussed. Financial incentives may have a bigger impact in this group, as clinicians may
be more motivated to provide advice to patients with smoking related conditions.
Other researchers have found similar differences in smoking outcomes between groups after
the introduction of national QOF as we did. For example, with respect to age and gender,
McGovern et al in their QOF study of patients with CHD26 found that older patients were less
likely to be asked about smoking pre-QOF but after the introduction of QOF this difference
was attenuated; people from more deprived areas benefitted more than those from the least
deprived; female smokers were more likely to be offered advice compared with males;
however, smokers over the age of 75 years were less likely to be offered advice compared
with younger smokers.
With regard to ethnicity, our findings have confirmed those of Lyratzopoulos et al27 who
studied UK primary care patients attending for cardiovascular risk screening and found that
South Asian patients were significantly less likely to smoke than people of other ethnicities.
These findings may mask differences in risk factors within the South Asian group. Other
researchers have found higher smoking rates among Bangladeshi and Pakistani men
compared with Indian men.28 29 For smoking cessation advice, we found provision of smoking
cessation advice to be fairly equitable by ethnic group and agreed with other research
conducted in the US30-32 and UK.24
Strengths and limitations
The study used data from a large number of patients without smoking-related diseases
registered at general practices in an ethnically diverse area of North West London. Findings
14
from the studies may be generalisable to other health systems with universal coverage that
provide financial incentives for primary prevention activities.
Using primary care data to estimate smoking prevalence is subject to a number of biases
which can lead to both over- and under-estimates.33 For this study we used the last record of
smoking status to determine smoking prevalence. This method can overestimate smoking
prevalence as smokers who do not regularly visit their GP may have stopped smoking since
the last visit. However, determining prevalence by limiting the sample to those asked within
the previous 27 months tends to underestimate prevalence as current smokers may avoid
visiting their GP, and this is perhaps more likely in this group of patients who did not have
smoking-related chronic diseases. We feel that our approach is the most appropriate given the
limitations of the data, and the smoking prevalence rates among our sample of patients were
similar to those found in national surveys.1
Although we found an apparent increase in the delivery of smoking cessation advice, we
were not able to assess the quality of the advice as this is not specifically coded. We are also
not able to determine whether the advice was acted upon by the patients and translated to
behaviour change, i.e. quitting. Recorded advice may not be perceived as advice or even
remembered by patients.34 Similarly, we were not able to examine quit rates as they are not
recorded in primary care, but the decrease in prevalence may be suggestive of an effect in the
absence of a change in tobacco control policy nationally or any specific local initiatives
during the study period. Better data linkage with NHS smoking cessation services could
improve monitoring of quit rates. Smoking status recorded in general practice is self-reported
and not verified by carbon monoxide reading or urinary cotinine, so subject to reporting bias.
It is not possible to attribute changes in smoking outcomes to the financial incentive as this
was a pre-post study as we did not have data to examine underlying trends. Previous research
15
suggests marked improvement in smoking and other outcome measures were associated with
the introduction of QOF.35 Smoke-free legislation was introduced in July 2007 and NICE
guidance to local authorities for providing smoking cessation services was published in
February 2008,36 which may have contributed to for some of the improvements in smoking
outcomes measured. In addition, smoking cessation was a focus for targeted promotion by
Hammersmith & Fulham, for example using banner adverts on lampposts across the borough.
KickIt also undertook targeted interventions for Black and Minority Ethnic groups over the
study period, which could have influenced smoking prevalence in these groups in addition to
the effect of QOF+. Financial incentive schemes can have unintended outcomes, including
gaming,37 adverse patient selection,38 and relative neglect of unrewarded activities.39 We did
not examine these outcomes in this study.
Implications for policy and practice
Previous research has shown that financial incentives in the national QOF appear to be
associated with reductions in smoking prevalence in people with smoking-related conditions18
and our study suggests that a local version of QOF incentivising primary prevention may be
effective in reducing smoking prevalence, so there may be merit in providing flexibility in the
development of such local incentive programmes.19 As around 80% of the UK population
visits their GP each year, greater use of financial incentives to promote smoking cessation
activities in primary care for primary prevention could translate to a large public health
impact, as smoking is known to be the biggest risk factor for premature mortality. As
smoking is the greatest risk factor for premature mortality it is important that smoking
indicators remain in the national QOF even if levels of recording status and providing advice
are high, especially as performance tends to fall off if incentives are removed, as seen with
other QOF indicators.40
16
However, our study found QOF+ had little effect on existing inequalities between groups for
smoking cessation activities. Other authors have found that successful quit rates are lower
among smokers from lower socio-economic groups,41 42 younger age, female gender and
Black ethnicity.43 44 Extending incentives for targeted smoking cessation work for groups with
high smoking prevalence could help to reduce inequalities, but combining this approach with
population-level policies may have more impact: a recent systematic review examining the
effects of six different tobacco control interventions on socio-economic disparities in
smoking found that pricing had the most potential to reduce inequalities.
Future research
Research using longer time trend analysis for follow up of smokers could be used to examine
the impact of financial incentives on smoking outcomes overall and among different socio-
demographic groups. Linking primary care data with Hospital Episode Statistics (HES) data
would be helpful to examine the impacts of financial incentives for smoking on longer-term
outcomes such as myocardial infarction, stroke and TIA. There is a lack of cost-effectiveness
studies examining the use of financial incentives for smoking cessation. We did not undertake
a cost-effectiveness analysis in this study as we did not collect cost data for the smoking
component of QOF+. However, modelling the cost-effectiveness would be a useful next step
to evaluating the utility of financial incentives for smoking cessation in primary care..
CONCLUSIONS
The introduction of financial incentives as a primary prevention intervention was associated
with improvements in the ascertainment of smoking status, and in smoking prevalence, but
inequalities with respect to age and ethnicity persisted after the introduction of financial
incentives for smoking cessation. More encouragingly, all groups benefited from increased
rates of advice to smokers. Financial incentive schemes may therefore have a part to play in
17
reducing smoking prevalence for patients who have yet to develop smoking-related
conditions when combined with individual-level smoking cessation services and strong
tobacco control policies.
FUNDING
This article presents independent research commissioned by the National Institute for Health
Research (NIHR) under the Collaborations for Leadership in Applied Health Research and
Care (CLAHRC) programme for north-west London. The views expressed in this publication
are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department
of Health.
DECLARATION OF INTERESTS
None of authors declare any competing interests.
18
Table 1: Smoking indicators in QOF+
QOF+ Indicator QOF+ points Payment stages
+ SMOKING 1The percentage of patients aged 15 years or older whose notes record smoking status since July 01 2008 inclusive.
This indicator is equivalent to RECORDS 23 IN QOF.
6 points 90% 95%
+ SMOKING 2
The percentage of patients aged 15 years or older who smoke whose notes contain a record that smoking cessation advice or referral to a local smoking cessation service has been offered since December 01 2009 inclusive.
11 points 60% 95%
Table 2: Ascertainment of smoking status before and after QOF+*
*Patients in Hammersmith & Fulham without diseases specifically incentivised for smoking cessation activities under national QOF; N = denominator (number of registered patients); % = percentage of patients with smoking status ascertained; AOR = Adjusted Odds Ratio; CI = 95% Confidence Interval; $ = Reference group; = missing IMD (490 pre/post QOF+); *p<0.05; **p<0.01; ***p<0.001
Table 3: Provision of smoking advice before and after QOF+*
N = denominator (number of patients with a smoking code); % = percentage recorded as current smokers; AOR = Adjusted Odds Ratio; CI = 95% Confidence Interval; $ = Reference group; = missing IMD (416 pre-QOF+; 436 post-QOF+); *p<0.05; **p<0.01; *p<0.001
25
REFERENCES
1. HSCIC. Statistics on Smoking: England, 2013: Health and Social Care Information Centre (HSCIC) http://www.hscic.gov.uk/, 2013.
2. Marmot M. Fair society, healthy lives. The Marmot Review: strategic review of health inequalities in England post-2010: Institute of Health Equity, 2010.
3. HSCIC. Health Survey for England 2004: Volume 1, The health of minority ethnic groups: Health and Social Care Information Centre http://www.hscic.gov.uk/, 2006.
4. Lee JT, Glantz SA, Millett C. Effect of smoke-free legislation on adult smoking behaviour in England in the 18 months following implementation. PloS one 2011;6(6):e20933.
5. Lightwood JM, Glantz SA. Declines in Acute Myocardial Infarction After Smoke-Free Laws and Individual Risk Attributable to Secondhand Smoke. Circulation 2009;120(14):1373-79.
6. Millett C, Lee JT, Laverty AA, Glantz SA, Majeed A. Hospital Admissions for Childhood Asthma After Smoke-Free Legislation in England. Pediatrics 2013;131(2):e495-e501.
7. Chesterman J, Judge K, Bauld L, Ferguson J. How effective are the English smoking treatment services in reaching disadvantaged smokers? Addiction 2005;100 Suppl 2:36-45.
8. Bauld L, Judge K, Platt S. Assessing the impact of smoking cessation services on reducing health inequalities in England: observational study. Tobacco Control 2007;16(6):400-04.
9. Murray RL, Bauld L, Hackshaw LE, McNeill A. Improving access to smoking cessation services for disadvantaged groups: a systematic review. Journal of Public Health 2009;31(2):258-77.
10. van Doorslaer E, Masseria C, Koolman X, for the OECD Health Equity Research Group. Inequalities in access to medical care by income in developed countries. Canadian Medical Association Journal 2006;174(2):177-83.
11. Papadakis S, McDonald P, Mullen KA, Reid R, Skulsky K, Pipe A. Strategies to increase the delivery of smoking cessation treatments in primary care settings: a systematic review and meta-analysis. Preventive medicine 2010;51(3-4):199-213.
12. Vogt F, Hall S, Marteau TM. General practitioners' and family physicians' negative beliefs and attitudes towards discussing smoking cessation with patients: a systematic review. Addiction 2005;100(10):1423-31.
13. Doran T, Fullwood C, Gravelle H, Reeves D, Kontopantelis E, Hiroeh U, et al. Pay-for-performance programs in family practices in the United Kingdom. New England Journal of Medicine 2006;355(4):375-84.
14. Epstein AM, Lee TH, Hamel MB. Paying physicians for high-quality care. New England Journal of Medicine 2004;350(4):406-10.
15. Roland M. Linking physicians' pay to the quality of care--a major experiment in the United kingdom. New England Journal of Medicine 2004;351(14):1448-54.
16. Rosenthal MB, Landon BE, Normand SL, Frank RG, Epstein AM. Pay for performance in commercial HMOs. New England Journal of Medicine 2006;355(18):1895-902.
17. Hamilton FL, Laverty AA, Vamos EP, Majeed A, Millett C. Effect of financial incentives on ethnic disparities in smoking cessation interventions in primary care: cross-sectional study. Journal of Public Health (Oxf) 2012.
18. Hamilton, Greaves F, Majeed A, Millett C. Effectiveness of providing financial incentives to healthcare professionals for smoking cessation activities: systematic review. Tobacco Control 2013;22(1):3-8.
19. Millett C, Majeed A, Huckvale C, Car J. Going local: devolving national pay for performance programmes. BMJ 2011;342:c7085.
20. Majeed A. Sources, uses, strengths and limitations of data collected in primary care in England. Health Statistics Quarterly 2004;Q(21):5-14.
21. ONS. Office for National Statistics: Integrated Household Survey April 2010 to March 2011: Experimental Statistics, 2011.
22. HSCIC. Read Codes. www.hscic.gov.uk, 2014.23. Department for communities and local government. English Indices of
Deprivation 2010. www.gov.uk, 2011.24. Millett C, Gray J, Saxena S, Netuveli G, Majeed A. Impact of a pay-for-
performance incentive on support for smoking cessation and on smoking prevalence among people with diabetes. Canadian Medical Association Journal 2007;176(12):1705-10.
25. Tahrani AA, McCarthy M, Godson J, Taylor S, Slater H, Capps N, et al. Diabetes care and the new GMS contract: the evidence for a whole county. The British journal of general practice : the journal of the Royal College of General Practitioners 2007;57(539):483-5.
26. McGovern MP, Boroujerdi MA, Taylor MW, Williams DJ, Hannaford PC, Lefevre KE, et al. The effect of the UK incentive-based contract on the management of patients with coronary heart disease in primary care. Family Practice 2008;25(1):33-9.
27. Lyratzopoulos G, McElduff P, Heller RF, Hanily M, Lewis PS. Comparative levels and time trends in blood pressure, total cholesterol, body mass index and smoking among Caucasian and South-Asian participants of a UK primary-care based cardiovascular risk factor screening programme. BMC Public Health 2005;5:125.
28. Begh RA, Aveyard P, Upton P, Bhopal RS, White M, Amos A, et al. Promoting smoking cessation in Pakistani and Bangladeshi men in the UK: pilot cluster randomised controlled trial of trained community outreach workers. Trials 2011;12:197.
29. Bhopal RS. Heterogeneity among Indians, Pakistanis, and Bangladeshis is key to racial inequities. BMJ 2002;325(7369):903.
30. Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health care: how are programs being designed? Medical Care Research and Review 2007;64(5 Suppl):283S-304S.
31. Chin MH, Walters AE, Cook SC, Huang ES. Interventions to Reduce Racial and Ethnic Disparities in Health Care. Medical Care Research and Review 2007;64(5 suppl):7S-28S.
32. Trinidad DR, Pérez-Stable EJ, White MM, Emery SL, Messer K. A Nationwide Analysis of US Racial/Ethnic Disparities in Smoking Behaviors, Smoking Cessation, and Cessation-Related Factors. American Journal of Public Health 2011;101(4):699-706.
33. High-dose acetylsalicylic acid after cerebral infarction. A Swedish Cooperative Study. Stroke (00392499) 1987;18(2):325-34.
34. Szatkowski L, McNeill A, Lewis S, Coleman T. A comparison of patient recall of smoking cessation advice with advice recorded in electronic medical records. BMC public health 2011;11:291.
35. Koshy E, Millett C. The ‘Quality and Outcomes Framework’: improving care, but are all patients benefiting? Journal of the Royal Society of Medicine 2008;101(9):432-33.
36. NICE. Public health guidance 10: Smoking cessation services in primary care, pharmacies, local authorities and workplaces, particularly for manual working groups, pregnant women and hard to reach communities: NICE, 2008.
37. Christopher H, Hood C. Gaming in Targetworld: The Targets Approach to Managing British Public Services. Public Administration Review 2006;66(4):515-21.
38. Shen Y. Selection incentives in a performance-based contracting system. Health services research 2003;38(2):535-52.
39. McDonald R, Roland M. Pay for Performance in Primary Care in England and California: Comparison of Unintended Consequences. Annals of family medicine 2009;7(2):121-27.
40. Lester H, Schmittdiel J, Selby J, Fireman B, Campbell S, Lee J, et al. The impact of removing financial incentives from clinical quality indicators: longitudinal analysis of four Kaiser Permanente indicators. BMJ 2010;340:c1898.
41. Kotz D, West R. Explaining the social gradient in smoking cessation: it’s not in the trying, but in the succeeding. Tobacco Control 2009;18(1):43-46.
42. Hiscock R, Judge K, Bauld L. Social inequalities in quitting smoking: what factors mediate the relationship between socioeconomic position and smoking cessation? Journal of Public Health 2011;33(1):39-47.
43. Wenig JR, Erfurt L, Kröger CB, Nowak D. Smoking cessation in groups—who benefits in the long term? Health Education Research 2013;28(5):869-78.
44. Piper ME, Cook JW, Schlam TR, Jorenby DE, Smith SS, Bolt DM, et al. Gender, race, and education differences in abstinence rates among participants in two randomized smoking cessation trials. Nicotine & Tobacco Research 2010;12(6):647-57.