Abstract

Background

The UK is embarking on a national cardiovascular risk assessment programme called NHS Health Checks; in order to be effective, high and equitable uptake is paramount.

Methods

A cross-sectional study, using data extracted from electronic medical records of persons aged 35–74 years estimated to be at a high risk of developing cardiovascular disease, to examine the uptake of the Health Checks using logistic regression and statin prescribing.

Results

A total of 44.8% of high risk patients invited for a Health Check attended. Uptake was lower among younger men but higher among patients from south Asian (AOR = 1.71 [1.29–2.27] compared with white) or mixed ethnic backgrounds (AOR = 2.42 [1.50–3.89]), and patients registered with smaller practices (AOR = 2.53 [1.09–5.84] <3000 patients compared with 3000–5999). The percentage of patients confirmed to be at high risk of CVD prescribed a statin increased from 24.7 to 44.8%.

Conclusions

Uptake of cardiovascular risk assessment and prescribing of statins in high risk patients was considerably lower than projected in the first year of NHS Health Checks programme. Targeting efforts to increase uptake and adherence to interventions in high risk populations and reinvesting resources into population wide strategies to reduce obesity, smoking and salt intake may prove more cost-effective in reducing the burden of cardiovascular disease in the UK.

Introduction

Cardiovascular disease (CVD) is the leading cause of mortality and a key driver of health inequalities in many countries.1 Health policy in the UK has often emphasized high risk rather than population approaches to prevention over the past decade despite the evidence that a combination of strategies are required to reduce the burden of CVD.2 This is reflected in the National Service Frameworks for coronary heart disease (CHD)3 and the increased investment in secondary prevention in primary care through the 2004 General Practitioner contract.

NHS Health Checks, a cardiovascular risk assessment programme for all adults aged 40–74 years in England, was introduced in 2009 at an annual cost of ∼£250 million. This population-based programme aims to both accelerate the overall reductions in CVD incidence, and reduce known socio-economic and ethnic inequalities in cardiovascular health.4 Delivered in primary care, the programme involves systematic screening, measurement of CVD risk factors, the generation of global risk estimates, risk communication and lifestyle counselling. In addition to managing individual risk factors, such as hypertension, the programme recommends that individuals with a 20% or higher risk of developing CVD in the next 10 years are offered lipid lowering medication, irrespective of lipid levels.5,6

The Department of Health estimates that, when fully established, the programme could prevent 650 deaths and 9500 non-fatal myocardial infarctions and strokes each year if universal uptake is achieved. Cost-effectiveness modelling for the programme assumes that 75% uptake will be achieved. Evidence from established preventative services in the UK, including the national cervical and breast screening programmes, suggests that uptake may be lower among socio-economically deprived and ethnic minority groups,7 particularly in the early stages of programme development.8 Evidence from the more recently introduced colorectal cancer screening programme suggests lower uptake among male patients.9

The aim of this study was to examine uptake of the Health Checks programme in the first year of implementation and explore whether participation in the programme differed with patient and practice characteristics. Secondary aims were to examine prescribing of lipid lowering medication among individuals identified to be at a high risk of a future cardiovascular event and assess CVD risk factor levels in the screened population.

Methods

NHS Health Checks in Ealing, North-West London

The NHS Health Checks programme is delivered locally by Primary Care Trusts (PCTs) in England. The Department of Health requires that all adults aged 40–74 years are invited for cardiovascular risk assessment by 2013, although PCTs have been granted considerable autonomy to determine their own timetable for implementation and which population groups to prioritize screening locally.

Ealing has a relatively socio-economically deprived population of 375 000, with a high proportion of residents from ethnic minorities. The local Health Checks programme is delivered by practice nurses and health-care assistants in general practice and remunerated through a Local Enhanced Service scheme whereby practices receive £15 per person screened. Disease-free individuals estimated to be at, or greater than, a 20% 10-year risk of a CVD event were targeted in the first year of the programme (1 September 2008 to 31 August 2009); the method of risk estimation is detailed subsequently. The PCT provided each general practice with a list of patients to be invited in year one, and the practice then contacted patients by a letter inviting them to attend a Health Check. Each practice was responsible for completing a full Health Check, including appropriate laboratory tests and reminding non-attendees. The local programme started before the national roll out of NHS Health Checks in April 2009. Ealing went beyond the Department of Health requirements by including patients with diagnosed hypertension and those prescribed statins and commenced screening at the age of 35 years, due to the high burden and earlier onset of CVD and diabetes in the area. CVD risk estimates were based on the information recorded in the GP information system in the past 5 years.

Data

We obtained data on patients estimated to be at a high risk of developing CVD from 29 of the 86 general practices in Ealing, representing some 57 240 out of a target population of ∼163 000. Dedicated software, the Oberoi primary prevention software, was used to risk stratify patients. It extracted Read-coded data on CVD risk factors (blood pressure, body mass index, total cholesterol, smoking status) and demographic characteristics (age, sex, ethnicity) from electronic medical records (EMRs) of all patients aged 35–74 years not on CVD (CHD, stroke/transient ischaemic attack) or diabetes registers. The software imputes default values for missing risk factor data based on the age and sex of the patient. The Joint British Societies 2 risk score10 was applied to the data, as recommended at the time by national clinical guidance. Registers of high-risk patients to be invited for a Health Check were produced for general practices in September 2008.

In October 2009, after the first year of Health Checks, general practices produced a list of patients who attended. We matched these records with primary prevention registers to determine programme uptake. We added further general practice level data, practice list size and number of general practitioners per practice. Additional data extractions were undertaken in 16 of the 29 practices during January 2010. We assessed the clinical measurements taken during the Health Check and the impact of the programme on prescribing of lipid lowering drugs. All additional data were considered valid if recorded on the date of Health Check attendance, or later.

Our primary outcome measure was attendance for screening and secondary outcome measure the prescription of a statin. Predictor variables included age, sex, ethnicity, deprivation, smoking and hypertensive status and practice list size.

Analysis

We generated categorical variables for all explanatory variables. We assessed levels of attendance in subgroups (Table 1) and used two-tailed z-tests for proportions and to test differences in levels of attendance.

Table 1

Patient and practice characteristics of the sample invited and attending an NHS Health Check 2008–09

  Invited
 
Attended
 
  n % n % 
Sex Female 1013 19.1 463 45.7 
 Malea 4281 80.9 1907 44.5 
Age group 35–54a 1015 19.2 416 41.0 
 55–64 2118 40.0 969 45.8 
 65–74 2161 40.8 985 45.6 
Ethnicity White Britisha 1147 21.7 472 41.2 
 South Asian 1419 26.8 752 53.0 
 Black 250 4.7 114 45.6 
 Other 329 6.2 125 38.0 
 Mixed 467 8.8 270 57.8 
 Missing 1682 31.8 637 37.9 
Local deprivation thirdb 1 deprived 1933 36.5 867 44.9 
 2 intermediatea 2096 39.6 943 45.0 
 3 affluent 1265 23.9 560 44.3 
Practice list size <3000 969 18.3 597 61.6 
 3000–5999a 2173 41.0 975 44.9 
 ≥6000 2152 40.6 798 37.1 
Hypertension Noa 3594 67.9 1498 41.7 
 Yes 1700 32.1 872 51.3 
Smoker Noa 3173 59.9 1532 48.3 
 Yes 2121 40.1 838 39.5 
Total 5294  2370 44.8  
  Invited
 
Attended
 
  n % n % 
Sex Female 1013 19.1 463 45.7 
 Malea 4281 80.9 1907 44.5 
Age group 35–54a 1015 19.2 416 41.0 
 55–64 2118 40.0 969 45.8 
 65–74 2161 40.8 985 45.6 
Ethnicity White Britisha 1147 21.7 472 41.2 
 South Asian 1419 26.8 752 53.0 
 Black 250 4.7 114 45.6 
 Other 329 6.2 125 38.0 
 Mixed 467 8.8 270 57.8 
 Missing 1682 31.8 637 37.9 
Local deprivation thirdb 1 deprived 1933 36.5 867 44.9 
 2 intermediatea 2096 39.6 943 45.0 
 3 affluent 1265 23.9 560 44.3 
Practice list size <3000 969 18.3 597 61.6 
 3000–5999a 2173 41.0 975 44.9 
 ≥6000 2152 40.6 798 37.1 
Hypertension Noa 3594 67.9 1498 41.7 
 Yes 1700 32.1 872 51.3 
Smoker Noa 3173 59.9 1532 48.3 
 Yes 2121 40.1 838 39.5 
Total 5294  2370 44.8  

aReference group for z-test.

bPatients were assigned to a local deprivation third using the Index of Multiple Deprivation 2007 (1 = most deprived, 3 = least deprived).

P < 0.01, < 0.05.

We used multi-level logistic regression to analyse health check attendance, building mixed models with patient variables at level 1 and practice at level 2. We included variables listed earlier, plus number of general practitioners per practice. First, we tested each variable for whether a naïve (no level 2 structure) random effect or random slope model fitted best; ethnicity was modelled with a random slope, with the remaining variables random effects. We built the final model using backward stepwise selection, using Akiake Information Criterion.11 We included interaction terms in the model building: between age and sex; ethnicity and sex (both found more widely in healthcare12,13) testing their inclusion using log-likelihood tests. Goodness of fit was assessed using a Hosmer–Lemeshow chi2 test, with residual analysis for poor fitting models. We calculated the median odds ratio (MOR)14 and present the variance partitioning coefficient (VPC), to demonstrate the amount of variance in uptake at the practice level.

In the subset of 16 practices with data obtained after year one, we calculated mean values for cardiovascular risk factors using a cross section of data from prior Health Check and assessed the sensitivity of using incomplete baseline data to risk stratify patients. We examined changes in statin prescribing in patients at low and high risk of developing CVD based on complete information on risk factors and assessed the sensitivity of the risk score and statin prescribing in different demographic groups. All analyses were conducted using STATA version 11.0 SE. Ethical approval for the study was granted from the London Research Ethics Committee.

Results

In the 29 practices, 5294 patients aged 35–74 years with an estimated cardiovascular risk ≥20% were invited for a Health Check, out of a total population of 57 240. Table 1 displays the summary characteristics of the population invited and attending a Health Check.

A total of 44.8% of patients invited for a Health Check during 2008–09 attended with considerable variation by patient and practice characteristics (Table 1). Attendance was significantly lower among younger patients (19.2% in those aged 35–54 years) and smokers (40.1%), while significantly higher among patients from south Asian (53.0%) or mixed (57.8%) ethnic backgrounds, those with diagnosed hypertension and patients registered with smaller practices (61.6, 44.9, 37.1% for <3000, 3000–5999 and ≥6000 respectively).

Variation in uptake

The variables selected in the regression analysis are shown in Table 2. The final model showed satisfactory goodness of fit. South Asian (AOR 1.71 [1.29–2.27] and patients of mixed ethnicity (AOR = 2.42 [1.50–3.89]) were significantly more likely to attend than white, as were older patients, those diagnosed with hypertension; and patients from smaller practices. There was a significant interaction between age and sex, with women in the youngest age group being more likely to attend the health check than men (AOR = 1.71 [1.29–2.27]). Deprivation was not selected in the final model; we re-ran analyses with its inclusion but found no change in model coefficients. There was considerable variance between practices’ attendance, demonstrated by the high median odds ratio, with the VPC showing 28% of the total variance to be due to practice level factors.

Table 2

Associations between patient and practice characteristics and Health Check attendance; multivariate analysis

Fixed effects   AOR P 95% CI
 
Sex (with age interaction) 35–54 Female 1.71 0.037 1.03 2.85 
  Male 1.00    
 55–64 Female 1.22 0.212 0.89 1.67 
  Male 1.00    
 65–74 Female 0.96 0.756 0.76 1.22 
  Male 1.00    
Age group  35–54 1.00    
  55–64 1.74 <0.001 1.34 2.25 
  65–74 2.27 <0.001 1.47 3.50 
Ethnicity  White British 1.00    
  South Asian 1.71 <0.001 1.29 2.27 
  Black 1.34 0.136 0.91 1.98 
  Other 1.15 0.519 0.76 1.74 
  Mixed 2.42 <0.001 1.50 3.89 
  Missing 0.51 0.015 0.30 0.88 
Practice list size  <3000 2.53 0.030 1.09 5.84 
  3000–5999 1.00    
  ≥6000 0.79 0.599 0.33 1.88 
Hypertension  No 1.00    
  Yes 1.31 <0.001 1.15 1.51 
Smoker  No 1.00    
  Yes 0.88 0.097 0.75 1.02 
Random effects   Var (SE)   
  Practice intercept 1.30 (0.44)   
  MOR 2.97    
  Ethnicity (slope) 0.046 (0.016)   
  VPC (ρ0.28    
Fixed effects   AOR P 95% CI
 
Sex (with age interaction) 35–54 Female 1.71 0.037 1.03 2.85 
  Male 1.00    
 55–64 Female 1.22 0.212 0.89 1.67 
  Male 1.00    
 65–74 Female 0.96 0.756 0.76 1.22 
  Male 1.00    
Age group  35–54 1.00    
  55–64 1.74 <0.001 1.34 2.25 
  65–74 2.27 <0.001 1.47 3.50 
Ethnicity  White British 1.00    
  South Asian 1.71 <0.001 1.29 2.27 
  Black 1.34 0.136 0.91 1.98 
  Other 1.15 0.519 0.76 1.74 
  Mixed 2.42 <0.001 1.50 3.89 
  Missing 0.51 0.015 0.30 0.88 
Practice list size  <3000 2.53 0.030 1.09 5.84 
  3000–5999 1.00    
  ≥6000 0.79 0.599 0.33 1.88 
Hypertension  No 1.00    
  Yes 1.31 <0.001 1.15 1.51 
Smoker  No 1.00    
  Yes 0.88 0.097 0.75 1.02 
Random effects   Var (SE)   
  Practice intercept 1.30 (0.44)   
  MOR 2.97    
  Ethnicity (slope) 0.046 (0.016)   
  VPC (ρ0.28    

Note: odds ratios are adjusted for all variables in the table.

Risk factor levels after Health Check attendance

Table 3 presents a summary of three major cardiovascular risk factors in the population attending. An estimated 50.6% of patients had a blood pressure of 140/90 mmHg or greater, with 31.6% prescribed anti-hypertensive therapy, 66.5% had a total cholesterol of 5 mmol/l or greater, and 26.0% had a BMI of 30 kg/m2 or greater. Diagnosed hypertension rose from 32.0 to 39.2% over the year. Of the patients designated at high risk from pre-existing EMR data, 74.5% were confirmed to have a ≥20% risk score after their risk factor data were completed during the check. The predictive accuracy varied by sub-group with south Asian (82.4% [P = 0.003]); the oldest (65–74: 82.4% [P = 0.009]); and most deprived (88.0% [P < 0.001]) having most accurate, while white patients (67.2% [P = 0.022]) and women (61.7% [P < 0.001]) had poorer prediction.

Table 3

Cardiovascular risk factors in patients who attended a Health Check (n = 1033)

Blood pressure Mean systolic BP 138.0 [137.1–139.0] 
 Mean diastolic BP 80.9 [80.3–81.5] 
 BP > 140/90 50.6% 
 Diagnosed Hypertensiona 39.2% 
 Prescribed anti-hypertensiveb 31.6% 
Lipids Mean 5.27 [5.20–5.34] 
 Cholesterol ≥6 mmol/l 36.9% 
 Cholesterol ≥5 mmol/l 66.5% 
 Prescribed statins 42.4% 
BMI Mean 27.6 [27.3–27.9] 
 BMI ≥30 kg/m2 26.0% 
 BMI ≥25 kg/m3 69.2% 
Smoking Prevalence 37.1% 
Blood pressure Mean systolic BP 138.0 [137.1–139.0] 
 Mean diastolic BP 80.9 [80.3–81.5] 
 BP > 140/90 50.6% 
 Diagnosed Hypertensiona 39.2% 
 Prescribed anti-hypertensiveb 31.6% 
Lipids Mean 5.27 [5.20–5.34] 
 Cholesterol ≥6 mmol/l 36.9% 
 Cholesterol ≥5 mmol/l 66.5% 
 Prescribed statins 42.4% 
BMI Mean 27.6 [27.3–27.9] 
 BMI ≥30 kg/m2 26.0% 
 BMI ≥25 kg/m3 69.2% 
Smoking Prevalence 37.1% 

aClinical diagnosis in medical records, not based on latest blood pressure reading.

bWithin the last 2 years.

Figure 1 shows statin prescribing in patients who received a Health Check and were confirmed to have a risk score ≥20% (n = 770). Before the health check, 24.9% were prescribed statins (a further 1.8% had a valid exclusion in their EMR—these were either drug contraindications or refusal of therapy). After the intervention, 43.4% were prescribed statins, with 6.1% excluded—a relative increase of 74%. Statin prescribing increased from 27.0 to 39.6% in patients assessed to be at a low risk of CVD as a result of the Health Check. In those low risk patients who were prescribed statins after the Health Check, the mean total cholesterol was 4.78 (4.52–5.03); 75 (72.1%) had a total cholesterol >4 mmol/l, 39 (37.5%) >5 mmol/l and 59 (56.7%) were diagnosed with hypertension. There was variation between subgroups prescribing (Table 4). South Asian patients, women and those at the highest risk saw markedly larger increases in statin prescribing, while white patients the lowest increase.

Table 4

Differences in statin prescribing before and after the Health Check in patient subgroups

  Statin prescription (%)
 
Relative increase 
  Pre-Health Check Post-Health Check  
Sex Male 21.1 42.5 2.01 
 Female 25.4 57.9 2.28 
Ethnicity White 23.0 33.4 1.45 
 South Asian 24.9 54.0 2.17 
 Black 29.4 47.1 1.60 
 Mixed 25.5 46.0 1.80 
Deprivation thirda 1 deprived 22.0 40.1 1.82 
 2 intermediate 26.3 46.8 1.78 
 3 affluent 27.3 48.9 1.79 
Risk <20% 27.0 39.5 1.46 
 20–29.9% 23.1 41.9 1.81 
 30–39.9% 29.9 47.3 1.58 
 ≥40% 22.4 53.3 2.38 
  Statin prescription (%)
 
Relative increase 
  Pre-Health Check Post-Health Check  
Sex Male 21.1 42.5 2.01 
 Female 25.4 57.9 2.28 
Ethnicity White 23.0 33.4 1.45 
 South Asian 24.9 54.0 2.17 
 Black 29.4 47.1 1.60 
 Mixed 25.5 46.0 1.80 
Deprivation thirda 1 deprived 22.0 40.1 1.82 
 2 intermediate 26.3 46.8 1.78 
 3 affluent 27.3 48.9 1.79 
Risk <20% 27.0 39.5 1.46 
 20–29.9% 23.1 41.9 1.81 
 30–39.9% 29.9 47.3 1.58 
 ≥40% 22.4 53.3 2.38 

aLocal third, where 1 = most deprived.

Fig. 1

Statin prescribing before and after the Health Checks.

Fig. 1

Statin prescribing before and after the Health Checks.

Discussion

Main finding of this study

Uptake of the NHS Health Checks programme was lower in year one (45%) than estimated in Department of Health modelling (75%) and was significantly lower among younger men and smokers but higher among patients from south Asian or mixed ethnic backgrounds, those with diagnosed hypertension and patients registered with smaller practices. The percentage of patients confirmed to be at a high risk of CVD who were prescribed a statin increased 25–45% compared with the Department of Health estimate of 85%.

What is already known on this topic

Few countries have introduced large scale cardiovascular risk assessment programmes and the literature evaluating the performance of such schemes in routine care settings is sparse. Evaluations of earlier local CVD risk assessment programmes in the UK have found uptake from 29 to 66% with lower participation among the materially deprived, men and smokers.15,16 In the OXCHECK trial, there was poor uptake in deprived patients, and men.17 However, an evaluation of a pilot CVD risk screening programme in Stockport identified only modest differences in uptake (solely in men) by deprivation group. Horgan et al.18 assessed the risk status of patients given a health check analogous to, but predating, the national programme carried out in pharmacies. The population screened had relatively low levels of risk, although screening did not target those at a high risk.

What this study adds

Higher attendance in south Asian patients contrasts previous research that has shown lower uptake of preventative services when compared with white British.7 This may be because the Health Checks programme is more firmly rooted in general practice, where patients of south Asian origin can have a higher attendance.19 Another possible explanation is that Ealing has many general practitioners of south Asian origin, and cultural concordance between patients and physicians, which has shown to improve patient satisfaction,20 may improve attendance. Patients in practices with the smallest list sizes saw high rates of attendance. Practices with smaller list sizes have greater perceived physician availability,21 and longer consultation time,22 which can improve patient satisfaction and compliance. In both sexes, older patients had the highest attendance; older patients have high attendance at general practice12 and a greater risk of vascular disease.

A large proportion of patients were confirmed to be at high risk after risk factor assessment. Estimation of vascular risk using existing medical record data is efficient.23 The accuracy of risk prediction improved in south Asian, older patients and the most deprived; groups shown to have the most complete data,24 with more complete data improving prediction.23 However, risk prediction was incorrect in 33% of the white population and 38% of women.

Although prescribing of statins appeared to increase as a result of the Health Checks programme, only 45% of patients confirmed to be at a high risk of CVD were prescribed a statin at the end of the study period. Given the population included patients already taking statins and diagnosed with hypertension before the Health Check, prescribing may be lower when targeting patients with no previous vascular management. The low rate of prescribing may be due to a number of factors, including patient and practitioner beliefs about the risks, and benefits of prescribing medications in persons without established disease.25 Furthermore, there could be considerable delay after the Health Check before starting prescribing a statin. The prescription rate was higher in south Asian patients, possibly due to their greater perceived risk of CVD by GPs and patients; however, prescribing was lower in the deprived third. The increase in statin prescribing seen in patients identified at low CVD risk based on complete risk factor information is of concern and suggests that programmes should be monitored for inappropriate prescribing.

The workload implications of NHS Health Checks for primary care are considerable24 and the cost-effectiveness of the programme has been questioned.26 Modelling for the programme undertaken by the Department of Health was based on a number of key assumptions; 75% attendance, 85% of high-risk patients will take up statins and 70% adherence in those patients prescribed statins. Our findings of 45% attendance and 43% take up of statins in high-risk individuals suggest that these assumptions may have been over-optimistic,27 but these may improve in subsequent years.

Although the new coalition government currently supports Health Checks, the funding for this programme has been withdrawn in some areas in England.28 In the NHS’ current financial situation, the most cost-effective policy options must be considered. The options include maintaining current mass screening; our findings suggest that the current programme's goals of attendance and adherence to interventions are probably unachievable without additional resources to support primary care. This is borne out by uptake of vascular screening achieved in well-resourced OXCHECK and British Family Heart Study trials, which was 80 and 73%, respectively. A second option is to adopt a targeted screening approach; several studies have demonstrated that targeted screening approaches may be more cost-effective than mass screening.29,30 We provide further evidence that the use of existing medical record data could be efficient in targeting high-risk groups for intervention. A final option is to reinvest resources into population-wide strategies to increase physical activity, reduce smoking, salt,31 saturated and transfat32 intake; such approaches appear consistently effective and cost-saving.33

Despite low uptake overall, we found no evidence of poorer uptake among deprived and ethnic minority groups. However, we found considerable variation in attendance by practice, and attendance was significantly lower among younger men and smokers. Moreover, statin prescribing was consistently lower in deprived groups. Ongoing local and national monitoring and evaluation is therefore essential to ensure that the programme is delivered equitably. Future research should examine uptake of the programme in different settings and populations, adherence to lifestyle and therapeutic interventions, and programme impacts on risk factor control and cardiovascular end points.

Limitations of this study

This study is, to our knowledge, the first to examine the uptake of the Health Checks programme in England. We used patient level data, derived from patients’ EMRs in general practice, and methods appropriate to the nested data. Although the population studied is not representative of the whole country, our evaluation is important, given that the overall effectiveness of the programme and its impact on inequalities in cardiovascular health will be largely determined by attendance in deprived areas. Further, little national data are available to evaluate this programme, especially at a patient level, making local evaluations vital. We were only able to obtain data from 29 of the 86 practices in Ealing due to incompatible software and low data returns. However, the demographic profile of participating patients was similar to those in Ealing as a whole. Ealing used a wider age range than the national minimum (35–74), but only 16 patients lay outside the national Health check age range.

We present data on statin prescribing but could not assess whether prescriptions were collected by patients or examine drug adherence. Medication use carries a perception of adverse outcomes34 and patients may also be unclear about the benefits of medication in the absence of a diagnosed condition. A previous study suggests that the 1-year adherence rate to statins for primary prevention of CVD was 46%.27 We were unable to determine whether patients received and adhered to other interventions in the NHS Health Checks, including weight management, and exercise promotion and anti-hypertensive therapy.

Our data did not cover all patients eligible for a Health Check, rather focusing on those estimated to be at a high risk of CVD. The uptake in the remaining population may differ, although it is unclear how. Being labelled as high risk might motivate patients to attend,35 conversely membership of the high-risk group might be associated with poor health behaviours and low attendance. We only present data from the first year of the programme, and uptake may increase as the programme becomes embedded. Finally, our measure of social deprivation, found not to influence attendance, was an area level measure based on the patient's postcode. As the study setting contained few affluent areas, our finding that attendance did not vary with socio-economic status should be interpreted with some caution.

Conclusions

Uptake of the NHS Health Checks programme in Ealing was considerably lower than Department of Health projections, despite financial incentives for general practices. Targeting limited resources to increase uptake, improve risk communication and adherence to interventions in high-risk populations may be more cost-effective and increase the population benefits of this programme. However, a wider question remains about whether the £250 million annual cost of this programme would be better spent on population-wide strategies to reduce obesity, smoking and improve diet.

Contributors and sources

All authors contributed to the study. A.D. conducted and A.B. supervised the statistical analysis. A.D. and C.M. wrote the first draft of the paper. A.B., C.O. and A.M. helped interpret the data analysis and reviewed the manuscript critically for important intellectual content. A.D. will act as guarantor.

Funding

This study was supported by NHS Ealing (A.D.), Higher Education Funding Council for England and the National Institute for Health Research Collaboration for Leadership in Applied Health Research & Care scheme (C.M.). Dr Foster Intelligence (AB and the Dr Foster Unit), a joint venture with the Information Centre. The authors are grateful for the support from the National Institute for Health Research Biomedical Research Centre scheme, the National Institute for Health Research Collaboration for Leadership in Applied Health Research & Care scheme and the Imperial Centre for Patient Safety and Service Quality. The authors are also grateful for past support from the Medical Research Council and the Engineering and Physical Sciences Research Council.

Acknowledgements

We thank the 29 general practices that participated in the study.

Ethical approval: London Research Ethics Committee.

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