Abstract
Background
The NHS Health Check Programme was introduced in 2009 to improve primary prevention of coronary heart disease, stroke, diabetes and chronic kidney disease; however, there has been debate regarding the impact. We present a retrospective evaluation of Leicester City Clinical Commissioning Group.
Methods
Data are reported on diagnosis of type 2 diabetes, hypertension, chronic kidney disease, high risk of type 2 diabetes and high risk of cardiovascular disease. Data on management following the Health Check are also reported.
Results
Over a 5-year period, 53 799 health checks were performed, 16 388 (30%) people were diagnosed with at least one condition when diagnosis of being at high risk of cardiovascular disease was defined as ≥20%. This figure increased to 43% when diagnosis of high cardiovascular risk ≥10% was included. Of the 3063 (5.7%) individuals diagnosed with type 2 diabetes, 54% were prescribed metformin and 26% were referred for structured education. Of the 5797 (10.8%) individuals diagnosed at high risk of cardiovascular disease (≥20%), 64% were prescribed statins.
Conclusions
A high proportion of new cases of people at risk of cardiovascular disease were identified by the NHS Health Check Programme. Data suggest that this has translated into appropriate preventative measures.
Keywords: cardiovascular disease, cardiovascular risk, NHS Health Check Programme
Introduction
Vascular disease is increasing globally1 and is responsible for roughly a third of all deaths in the UK.2 The NHS Health Check Programme was introduced in 2009 to improve primary prevention of stroke, heart disease, diabetes and kidney disease. The programme screens people aged 40–74 years without a history of vascular disease.3 If an individual is found to be at high risk of vascular disease, then they should be given an appropriate intervention to manage their risk and placed on a disease register for future monitoring. The Department of Health have estimated that the NHS Health Check Programme could prevent 1600 heart attacks and strokes, 4000 cases of diabetes and 650 deaths each year.3 Moreover, at least 20 000 cases of diabetes or kidney disease could be diagnosed earlier, allowing individuals to be better managed and to improve their quality of life.3
There has however been recent interest in the effectiveness of the NHS Health Check Programme4 since a Cochrane report and other studies suggested that general health checks do not result in a reduction in mortality or morbidity.5,6 The applicability of these results to the NHS Health Check Programme has been questioned, mainly because it is not a general health check but is instead focussed on vascular checks and subsequent evidence-based interventions.7,8 Indeed, modelling studies suggest that the programme would lead to the early diagnosis of a substantial number of cases, thereby potentially improving patient health.9 In addition, a recent short report showed the NHS Health Check to be effective in detection of previously undiagnosed disease.10 Nevertheless, there is a need to evaluate the NHS Health Check Programme to provide evidence regarding whether or not it is effective in terms of both increased diagnosis of previously undiagnosed vascular conditions and in subsequent delivery of appropriate interventions. Currently limited evidence is available to fully evaluate the programme, and a recent call for review of the NHS Health Check Programme has been placed by MPs.11 The aim of this study was to evaluate the implementation of the NHS Health Check Programme, including diagnosis and effective management of people identified at risk of cardiovascular disease. A further aim was to report the prevalence of people identified as high risk of cardiovascular disease using a cut point of >10% as suggested by recently updated NICE guidelines.12
Methods
Data extraction
Data from 2009 to 2014 were obtained from the Leicester City Clinical Commissioning Group UK, which includes 65 practices, where both invited and opportunistic screening has taken place. The data collection period for each year is from 1 April to 31 March the next year. Data were extracted on 13 March 2014; therefore, the data for years 2013 and 2014 are missing 13 working days. Data files for the 5 years were merged together by a unique identifier, date of birth, sex and ethnicity; this was to reduce the risk of patients being erroneously merged together. The number of patients for whom this is likely to occur, and thus, the adverse effect of this on the data is likely to be minimal. The data were entered into SystmOne by the GP or Health Care Professional as part of their usual delivery of the Health Check. The required data were then obtained centrally by the Leicester Clinical Commissioning Group using data queries written specifically for this task.
Variables collected and outcomes measured
Variables collected during the Health Check included waist circumference, HbA1c, systolic and diastolic blood pressure, total cholesterol, alcohol intake, ethnicity and physical activity. Variables were recorded using Read Codes, a system of structural encoding of information in medical records widely used by general practitioners in the UK.13 Ethnicity was categorized as White, Black, South Asian or Other ethnic group, defined as people identified themselves. Physical activity data were grouped as ‘active’ or ‘inactive’ depending on the main level indicated in the Read Code. Similarly, smoking status was grouped as ‘Never smoker’, ‘Ex-smoker’ and ‘Current smoker’ depending on the main level indicated in the Read Code, regardless of the amount smoked. Where an individual had different smoking codes in 1 year, ‘Ex-smoker’ and ‘Current smoker’ took precedence over ‘Never smoked’, while ‘Current smoker’ took precedence over ‘Ex-smoker’. Ethnicity, activity levels, alcohol intake and smoking status were all based on self-report.
Height, weight, waist circumference and body mass index values more than four standard deviations from the mean were removed as these values were likely to be erroneous. Where body mass index was missing, it was calculated as weight (kg)/height (m2) if these were available. Co-morbidity data were obtained and assigned point scores as per the Charlson Comorbidity Index and combined to calculate this index.14 Patients were assumed to have a condition if they had at least one Read Code for that condition, otherwise they were assumed not to have it.
Outcome data of the Health Checks available included diagnosis and follow-up treatment. Diagnoses were available for type 2 diabetes, hypertension, chronic kidney disease, being at high risk of type 2 diabetes (defined as a Read Code for ‘prediabetes’) and cardiovascular disease risk score. Participants were characterized as having high cardiovascular risk score of >20%, additional analysis was undertaken defining high cardiovascular disease risk of >10%, to reflect the new guidelines.15 If people had at least one Read Code for a diagnosis then they were assumed to have had that condition, otherwise they were assumed not to have had it. Follow-up treatment was defined as whether the patient was prescribed statins or metformin or referred to structured education following the Health Check. Data on follow-up treatments were obtained from Read Codes; if people had at least one Read Code for a treatment then they were assumed to have had it, otherwise they were assumed not to have had it. We used Read Codes (primary care data) as we were interested in the number of diagnoses occurring due to the Health Check Programme. This study is considered a Service Evaluation, and therefore, ethical approval was not required.
Statistical analysis
Summary measures are presented using mean (standard deviation) or median (inter-quartile range) for continuous variables and count (percentage) for categorical variables. Means were compared between groups using analysis of variance, and percentages were compared between groups using χ2 tests. Tests for trend over categorical variables were performed using linear regression analyses for continuous outcomes and logistic regression analyses for binary outcomes, with the categorical variable treated as a continuous variable.
We used our prevalence estimates to approximate the number of new cases of type 2 diabetes, being at high risk of type 2 diabetes, hypertension, chronic kidney disease and being at high risk of cardiovascular disease that would be diagnosed each year by the NHS Health Check Programme, assuming (i) that the screening uptake was 23.1% (i.e. 693 000 people screened each year; uptake as seen in the Group reported here16) and (ii) that the uptake was 6.4% (i.e. 192 000 people screened each year; the average uptake across UK authorities).16 We repeated this analysis using both categories of being diagnosed at high risk of cardiovascular disease (≥20 and ≥10%). We standardized our estimates to the ethnic, age and sex distribution of England and Wales (population estimates for the whole of the UK were not available).17 We did this by estimating the expected number of people who would be screened in each ethnic, 5-year age and sex group, and then used the disease prevalence within the respective group to estimate the number of expected cases within that group.
Results
Invalid Health Check criteria
Of the 57 182 original records, data from 1000 (1.7%) were for people younger than 40 years and 1945 (3.4%) were for people older than 74 years; these individuals were therefore ineligible for the Health Check. People who had a history of vascular disease or who had previously been diagnosed with diabetes are also ineligible for the Health Check. A total of 407 (0.7%) people were ineligible due to previous history of myocardial infarction, 658 (1.1%) due to cerebrovascular disease, 218 (0.4%) due to peripheral vascular disease and 135 (0.2%) due to type 1 diabetes mellitus. Those with ineligible Health Check data were removed from the dataset and were not included in any of the analyses detailed.
Number of Health Checks performed
The total number of Health Checks performed over the 5-year period was 53 799. This number increased substantially over time from the initial pilot year (2009 and 2010, n = 395) to 19 578 in 2013 and 2014, and appears to be now at a stable level with a similar number of Checks performed in 2012 and 2013 and 2013 and 2014. Of those attending, 24 652 (45.8%) were of White ethnicity and 20 033 (37.2%) were South Asian.
Health Check measures
Table 1 shows the mean values of the biomedical measures for the overall population (n = 53 799) and by key demographics. Data on lifestyle measures showed that overall 24.1% (n = 12 728) individuals were classed as current smokers and 54.2% (n = 28 581) as ex-smokers. The median (inter-quartile range) number of alcohol units consumed per week was 6.0 units (IQR: 2,14). The percentage of individuals who consumed above the recommended limit (>21 units for men; >14 units for women) on a weekly basis was 16.2% (n = 3492). Men (P < 0.001, men 19.1%, women 11.9%) and people of White ethnicity (P < 0.001, white = 18.4%, South Asian = 10.6%, Black = 8.0%, Other = 8.7%) were most likely to consume over the recommended weekly alcohol limit.
Table 1.
Mean (standard deviation) of biomedical measures by key demographics
| Variable | Number of people | BMI, kg/m2 | Waist circumference, cm | HbA1c, % | Systolic BP, mmHg | Diastolic BP, mmHg | Total cholesterol, mmol/l | Number currently smoker (%) |
|---|---|---|---|---|---|---|---|---|
| Gender | ||||||||
| Men | 25 562 | 27.0 (4.5) | 95.1 (15.5) | 5.9 (0.7) | 130.0 (14.4) | 79.2 (9.2) | 5.3 (5.7) | 7716 (30.9) |
| Women | 28 224 | 27.7 (5.5) | 89.4 (15.8) | 5.8 (0.7) | 126.3 (15.4) | 76.9 (9.2) | 5.4 (2.1) | 5023 (18.1) |
| P-valuea | <0.001 | <0.001 | 0.002 | <0.001 | <0.001 | 0.517 | <0.001 | |
| Ethnicity | ||||||||
| White | 24 652 | 27.8 (5.3) | 92.9 (16.7) | 5.7 (0.6) | 129.3 (14.9) | 78.3 (9.4) | 5.3 (1.0) | |
| South Asian | 20 033 | 26.7 (4.6) | 91.2 (14.8) | 5.9 (0.7) | 126.7 (14.9) | 77.4 (8.9) | 5.4 (7.3) | 8052 (33.2) |
| Black | 2785 | 28.6 (5.2) | 91.6 (17.8) | 5.9 (0.7) | 127.5 (15.4) | 78.6 (9.8) | 5.0 (1.0) | 2646 (13.5) |
| Other ethnic group | 2763 | 27.2 (4.9) | 91.2 (15.3) | 5.9 (0.8) | 126.0 (15.3) | 78.0 (9.5) | 5.3 (1.0) | 487 (17.8) |
| P-valuea | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.893 | <0.001 | |
| Age, years | ||||||||
| 40–49 | 19 442 | 27.3 (5.1) | 90.2 (15.9) | 5.7 (0.6) | 123.8 (14.5) | 77.7 (9.6) | 5.3 (1.0) | 4475 (24.3) |
| 50–59 | 17 940 | 27.4 (5.0) | 92.3 (15.9) | 5.9 (0.7) | 128.0 (14.8) | 78.6 (9.2) | 5.6 (8.3) | 4397 (24.5) |
| 60–74 | 16 417 | 27.3 (5.0) | 93.9 (15.6) | 5.9 (0.7) | 133.0 (14.4) | 77.6 (8.8) | 5.3 (1.8) | 3867 (23.6) |
| P-valueb | 0.998 | <0.001 | <0.001 | <0.001 | 0.698 | 0.430 | <0.001 | |
BMI, body mass index; BP, blood pressure.
a P-values test for a difference between groups and were estimated using one-way analysis of variance.
b P-values test for a linear trend across groups and were estimated using linear regression.
Diagnosis, co-morbidities and treatments
Table 2 shows the number and percentage of people identified with cardiovascular disease risk by key demographics. Overall 16 388 (30%) of people were diagnosed with at least one condition when using ≥20% cardiovascular risk; however, this number increased to 23 071 (43%) when using the ≥10% cardiovascular risk data. Figure 1 shows the inter-relationship between the conditions of interest, detected vascular risk and co-morbidities (for both high risk of cardiovascular disease as ≥10 and ≥20%); those with chronic kidney disease were most likely to have at least one co-morbidity. Table 3 shows the number of people receiving follow-up treatment. For those diagnosed with type 2 diabetes, 53.9% were prescribed metformin and 25.8% for structured diabetes education. For those identified at high risk of cardiovascular disease (≥20%), 64.2% were prescribed statins.
Table 2.
Diagnoses by key demographicsa
| Variables | T2DM | High T2DM risk | Hypertension | CKD | High CVD risk (≥10%) | High CVD risk (≥20%) | Total number of Health Checks |
|---|---|---|---|---|---|---|---|
| Gender | |||||||
| Men | 1673 (6.5) | 1179 (4.6) | 5144 (20.1) | 315 (1.2) | 10 076 (53.4) | 4301 (22.8) | 28 224 (100.0) |
| Women | 1390 (4.9) | 1495 (5.3) | 4843 (17.2) | 529 (1.9) | 6056 (28.3) | 1496 (7.0) | 25 564 (100.0) |
| P-valueb | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Ethnicity | |||||||
| White | 859 (3.5) | 1023 (4.2) | 4859 (19.7) | 475 (1.9) | 7846 (45.0) | 2820 (16.2) | 24 652 (100.0) |
| South Asian | 1753 (8.8) | 1360 (6.8) | 3633 (18.1) | 236 (1.2) | 6402 (40.1) | 2424 (15.2) | 20 033 (100.0) |
| Black | 132 (4.7) | 90 (3.2) | 520 (18.7) | 44 (1.6) | 364 (17.6) | 82 (4.0) | 2785 (100.0) |
| Other | 149 (5.4) | 111 (4.0) | 388 (14.0) | 28 (1.0) | 468 (22.6) | 142 (6.8) | 2763 (100.0) |
| P-valueb | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Age, years | |||||||
| 40–49 | 701 (3.6) | 752 (3.9) | 1523 (7.8) | 67 (0.3) | 1132 (7.7) | 213 (1.4) | 19 442 (100.0) |
| 50–59 | 1123 (6.3) | 987 (5.5) | 3009 (16.8) | 189 (1.1) | 4919 (35.5) | 1039 (7.5) | 17 940 (100.0) |
| 60–74 | 1239 (7.6) | 936 (5.7) | 5465 (33.3) | 588 (3.6) | 10 084 (86.6) | 4545 (39.0) | 16 417 (100.0) |
| P-valuec | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| CVD risk | |||||||
| Low (<10%) | 751 (3.1) | 1249 (5.2) | 2166 (9.0) | 90 (0.4) | — | — | 24 169 (100.00) |
| High (≥10%) | 1431 (8.9) | 1130 (7.0) | 5501 (34.1) | 556 (3.5) | — | — | 16 135 (100.00) |
| P-valueb | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Low (<20%) | 1431 (4.2) | 1957 (5.7) | 4894 (14.2) | 246 (0.7) | — | — | 34 507 (100.00) |
| High (≥20%) | 751 (13.0) | 422 (7.3) | 2773 (47.8) | 400 (6.9) | — | — | 5797 (100.0) |
| P-valueb | <0.001 | <0.001 | <0.001 | <0.001 | |||
| Smoking | |||||||
| Current smoker | 676 (5.3) | 503 (4.0) | 2433 (19.1) | 194 (1.5) | 4629 (52.6) | 1964 (22.3) | 12 739 (100.00) |
| Ex-smoker | 645 (5.6) | 562 (4.9) | 2767 (24.2) | 264 (2.3) | 4290 (52.1) | 1726 (21.0) | 11 449 (100.00) |
| Never smoked | 1580 (5.5) | 1511 (5.3) | 4665 (16.3) | 360 (1.3) | 7071 (31.5) | 2083 (9.3) | 28 593 (100.00) |
| P-valueb | 0.51 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Total | 3063 (5.7) | 2675 (5.0) | 9997 (18.6) | 844 (1.6) | 16 135 (40.0) | 5797 (10.8) | 53 799 (100.0) |
Data are count (percentage).
CKD, chronic kidney disease; CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus.
aDiagnoses were based on Read Codes entered by the GP.
b P-values test for differences between categories and were estimated using χ2 tests.
c P-values test for a trend across the year groups and were estimated using logistic regression.
Fig. 1.
Percentage of individuals diagnosed with multiple conditions of interest as identified in the NHS Health Check Programme. Figures represent the percentage of individuals with both the row condition and the column condition. The size of the circle represents the percentage diagnosed with both conditions. CKD, chronic kidney disease; CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus. The top figure represents data when being at high risk of CVD defined as ≥20%, the lower figure represents data when being at risk of CVD defined as ≥10%.
Table 3.
Number (percentage) of people receiving follow-up treatment by demographic characteristics
| Characteristics |
T2DM
|
High T2DM risk | High CVD risk (≥20%) | |
|---|---|---|---|---|
| Metformin | Diabetes education | Metformin | Statins | |
| Gender | ||||
| Men | 904 (54.0) | 400 (23.9) | 51 (4.3) | 2710 (63.0) |
| Women | 747 (53.7) | 390 (28.1) | 55 (3.7) | 1014 (67.8) |
| P-valuea | 0.871 | 0.009 | 0.395 | 0.001 |
| Ethnicity | ||||
| White | 498 (58.0) | 188 (21.9) | 27 (2.6) | 1937 (68.7) |
| South Asian | 887 (50.6) | 498 (28.4) | 67 (4.9) | 1435 (59.2) |
| Black | 75 (56.8) | 34 (25.8) | 3 (3.3) | 45 (54.9) |
| Other | 87 (58.4) | 33 (22.2) | 5 (4.5) | 94 (66.2) |
| P-valuea | 0.002 | 0.003 | 0.041 | <0.001 |
| Age, years | ||||
| 40–49 | 409 (58.4) | 203 (29.0) | 36 (4.8) | 106 (49.8) |
| 50–59 | 599 (53.3) | 325 (28.9) | 39 (4.0) | 623 (60.0) |
| 60–74 | 643 (51.9) | 262 (21.2) | 31 (3.3) | 2995 (65.9) |
| P-valueb | 0.009 | <0.001 | 0.125 | <0.001 |
| Total | 1651 (53.9) | 790 (25.8) | 106 (4.0) | 3724 (64.2) |
CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus.
a P-values test for differences between categories and were estimated using χ2 tests.
b P-values test for a trend across the year groups and were estimated using logistic regression.
Discussion
Main finding
This study provides a summary of the NHS Health Check Programme conducted between 2009 and 2014 in a local authority where uptake is well above the national average (23.1% compared with 6.4%).16 Overall, 30% (n = 16 388) people were diagnosed with at least one condition. This number increases to 43% (n = 23 071) when classifying individuals at high risk of cardiovascular disease using new NICE recommendations of ≥10%.12,15 Overall, 5.7% of people were diagnosed with type 2 diabetes, of whom 54% were prescribed metformin and 26% were referred for structured education. Of those diagnosed at high risk of cardiovascular disease (≥20%), 64% were prescribed statin.
What is already known on this topic
The demographic cover of the Health Checks in this Clinical Commissioning Group is generally excellent and is representative of the local population.16 As is generally observed in clinical practice, women were more likely to attend than men. The ethnic distribution of those attending reflects that of this Clinical Commissioning Group reasonably well.18 Since the monitoring of ethnicity is vital to ensure equitable coverage of the Health Check Programme, ethnicity recording should potentially be a national quality criterion.
Generally, the average biomedical measurements observed in the Leicester City Health Check population were similar to national averages based on the 2011 Health Survey for England data; the average BMI reported nationally was 27 kg/m2 in both men and women19 compared with 27 kg/m2 for men and 28 kg/m2 for women in the Leicester City Health Check population. Total cholesterol averages were also similar in Leicester City to national averages (men: 5.1 mmol/l nationally, 5.1 mmol/l locally; women: 5.2 mmol/l nationally, 5.3 mmol/l locally).20 Leicester City averages were very slightly higher than national ones for HbA1c (men: 5.7% nationally, 5.9% locally; women: 5.7% nationally; 5.8% locally)21 and blood pressure (men: 129/73 mmHg nationally, 130/79 mmHg locally; women: 122/72 mmHg nationally, 126/77 mmHg locally).22
Approximately one in three (30%) of those who had a Health Check were diagnosed with at least one of type 2 diabetes, being at high risk of type 2 diabetes, chronic kidney disease, hypertension or being at high risk of cardiovascular disease. This figure increases to approximately two in five (43%) if identifying individuals as being at high cardiovascular risk using ≥10%. Of those undergoing a Health Check, 5.7% were diagnosed with type 2 diabetes; this is somewhat higher than in other Health Check evaluation studies23 and may be representative of the local population. Although disease prevalence was higher among people at high cardiovascular disease risk than among those at low risk, many conditions were diagnosed in those who are considered at low cardiovascular disease risk, suggesting that screening should occur regardless of this risk.
The percentage of people receiving adequate treatment following diagnosis could be improved upon; however, these figures tended to be higher than those observed elsewhere in the country. For example, the percentage referred to structured education for type 2 diabetes was 26%, but this is much higher than national referral rates, which indicated that only 6% of people with established type 2 diabetes were offered education and only 1.6% of those were recorded as attending.24 Likewise, 64% of those found to be at high cardiovascular disease risk (≥20%) were prescribed statins, which is higher than analogous figures reported in other localities (53%).25 Indeed prescription of 64% is far higher than that reported recently in a population Clinical Practice Research Datalink study where only 18 and 16% of men and women with cardiovascular risk >20% were prescribed statins.26 Furthermore, data on statins maybe an underestimate, the number of people who declined treatment was not recorded when the Health Check Programme began. The importance of prescribing statins to those at high cardiovascular disease risk to prevent cardiovascular disease is emphasized in recent guidelines published by the American College of Cardiology–American Heart Association Task Force on Practice Guidelines, although these guidelines also highlight the importance of discussions between the clinician and patient prior to initiating cardioprotective statin treatment.27
What this study adds
Data presented in this report reflect a high-performing authority that works closely with general practices, stakeholders and patient representatives within it. Reflecting a successful commitment to training for its partners, and as such shows how The NHS Health Check Programme can be successfully implemented.
We additionally conducted modelling on extrapolated data to estimate potential numbers which could be identified across England and Wales. Assuming 3 million people per year are eligible for the health checks; if 693 000 people are screened each year (23.1% uptake, as seen in this local authority16), then it is estimated that each year 25 745 people would be diagnosed with type 2 diabetes, 29 077 at high risk of type 2 diabetes, 133 016 with hypertension, 12 742 with chronic kidney disease and 211 283 and 76 326 with high cardiovascular disease risk of ≥10 and ≥20%, respectively. An estimated 200 956 people each year would be diagnosed with at least one of these conditions, and this would increase to 289 926 in line with updated NICE guidelines defining high risk of cardiovascular disease as ≥10%. If 192 000 people are screened each year (6.4% uptake, as is the national average across all local authorities16) then it is estimated that each year 7133 people would be diagnosed with type 2 diabetes, 8056 at high risk of type 2 diabetes, 36 853 with hypertension, 3530 with chronic kidney disease and 58 537 and 21 147 with high risk of cardiovascular disease of ≥10 and ≥20%, respectively. An estimated 55 679 people each year would be diagnosed with at least one of these conditions increasing to 80 326 when lowering the threshold for high risk of cardiovascular disease from ≥20 to ≥10%. Although placing an initial burden on the healthcare system, over time this is likely to result in a substantial reduction in cardiovascular events, which is expected to outweigh the increase in cost associated with the initial increase in preventative care. Importantly, future research must be conducted to evaluate whether implementation of interventions leads to improved outcomes for patients.
Limitations of this study
This study reports data from a large multi-ethnic population and demonstrates good update across different ethnic groups. We believe this is the first detailed evaluation of a large Clinical Commissioning Group which also provides estimated case data. It is the first study to report data using new NICE recommendations of using ≥10% as being at high risk of cardiovascular disease. Leicester City Clinical Commissioning Group was listed as the best performing local authority nationally in 2013;16 this analysis supports this finding with body mass index and blood pressure being recoded for 96.8 and 98%, respectively, for those whom NHS Health Check data were recorded. Completeness of data for HbA1c, waist circumference, alcohol intake and reported physical activity varied across localities, which we acknowledge as a limitation of the evaluation; however, this is a limitation shared by most studies that use data primarily collected for other purposes. We report prevalence of people identified with type 2 diabetes, at high risk of type 2 diabetes, hypertension, at risk of cardiovascular disease and chronic kidney disease. Furthermore, data on the interventions implemented are also reported and discussed. We acknowledge that a limitation of this study is that Leicester may not be representative of all other Clinical Commissioning Groups. We reported data on people having a cardiovascular disease risk of >10%; however, this is a recent recommendation from NICE12 and therefore statin prescribing does not reflect current recommendations.
A small proportion of attendees (<2% in 2013 and 2014) were given a Health Check despite not meeting the eligibility criteria; this figure is relatively similar to that seen in other evaluation studies.28 It is notable that the proportion of ineligible Health Checks performed decreased substantially over time. A template for entering the Health Check data was introduced half way through the implementation process which is likely to explain this reduction, at least in part. Therefore, it may be helpful to have the use of a data entry template as a quality criterion nationally.
Conclusion
There has been growing debate regarding the NHS Health Check Programme; this study shows that a high number of people at risk of cardiovascular disease have been identified in Leicester City due to the NHS Health Check Programme. To some extent, this has translated into appropriate preventative measures for vascular disease being put into place, but there are areas where further improvements could be made for the Clinical Commissioning Group, and by focusing on these it is more likely that the NHS Health Check Programme will impact positively on patients' health outcomes.
Conflict of interest statement
D.H.B., N.B.A., D.R., N.J., A.F. and I.B. have no competing interests to declare. M.J.D. has received funds for research, honoraria for speaking at meetings and has served on Advisory boards for Lily, Sanofi Aventis, MSD and Novo Nordisk, Janssen, Astra Zeneca and Boehringer Ingelheim. K.K. has received funds for research, honoraria for speaking at meetings and has served on Advisory boards for Astra Zeneca, GSK, Lily, Novartis, Pfizer, Servier, Sanofi Aventis, MSD and Novo Nordisk. P.C., M.J.D. and K.K. are co-authors on, Updated: The Handbook for Vascular Risk Assessment, Risk Reduction and Risk Management 2012; http://www.screening.nhs.uk/publications.
Funding
This work was supported by the Leicester City Council, with additional support from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care—East Midlands (NIHR CLAHRC—EM) , the Leicester Clinical Trials Unit and the NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit which is a partnership between University Hospitals of Leicester NHS Trust, Loughborough University and the University of Leicester.
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