Conceived and designed the experiments: SC RR MB. Analyzed the data: MB SS HL MOH NH RR SC. Wrote the first draft of the manuscript: MB. Contributed to the writing of the manuscript: SS HL MOH NH RR SC.
The following competing interests have been declared. (1) MB, SS, HL have support from Legal and General Assurance Society Limited for the submitted work; (2) NH, MOF, RR, SC have no relationships with Legal and General Assurance Society Limited that might have an interest in the submitted work in the previous 3 years; (3) their spouses, partners, or children have no financial relationships that may be relevant to the submitted work; and (4) MB, SS, HL, MOF, NH, RR have no nonfinancial interests that may be relevant to the submitted work; SC was a member of the NICE Programme Development Group on CVD Prevention in Populations. However, this article does not necessarily represent the views of NICE. The authors have declared that no other competing interests exist.
A modeling study conducted by Madhavi Bajekal and colleagues estimates the extent to which specific risk factors and changes in uptake of treatment contributed to the declines in coronary heart disease mortality in England between 2000 and 2007, across and within socioeconomic groups.
Coronary heart disease (CHD) mortality in England fell by approximately 6% every year between 2000 and 2007. However, rates fell differentially between social groups with inequalities actually widening. We sought to describe the extent to which this reduction in CHD mortality was attributable to changes in either levels of risk factors or treatment uptake, both across and within socioeconomic groups.
A widely used and replicated epidemiological model was used to synthesise estimates stratified by age, gender, and area deprivation quintiles for the English population aged 25 and older between 2000 and 2007. Mortality rates fell, with approximately 38,000 fewer CHD deaths in 2007. The model explained about 86% (95% uncertainty interval: 65%–107%) of this mortality fall. Decreases in major cardiovascular risk factors contributed approximately 34% (21%–47%) to the overall decline in CHD mortality: ranging from about 44% (31%–61%) in the most deprived to 29% (16%–42%) in the most affluent quintile. The biggest contribution came from a substantial fall in systolic blood pressure in the population not on hypertension medication (29%; 18%–40%); more so in deprived (37%) than in affluent (25%) areas. Other risk factor contributions were relatively modest across all social groups: total cholesterol (6%), smoking (3%), and physical activity (2%). Furthermore, these benefits were partly negated by mortality increases attributable to rises in body mass index and diabetes (−9%; −17% to −3%), particularly in more deprived quintiles. Treatments accounted for approximately 52% (40%–70%) of the mortality decline, equitably distributed across all social groups. Lipid reduction (14%), chronic angina treatment (13%), and secondary prevention (11%) made the largest medical contributions.
The model suggests that approximately half the recent CHD mortality fall in England was attributable to improved treatment uptake. This benefit occurred evenly across all social groups. However, opposing trends in major risk factors meant that their net contribution amounted to just over a third of the CHD deaths averted; these also varied substantially by socioeconomic group. Powerful and equitable evidence-based population-wide policy interventions exist; these should now be urgently implemented to effectively tackle persistent inequalities.
Coronary heart disease is a chronic medical condition in which the blood vessels supplying the heart muscle become narrowed or even blocked by fatty deposits on the inner linings of the blood vessels—a process known as arthrosclerosis; this restricts blood flow to the heart, and if the blood vessels completely occlude, it may cause a heart attack. Lifestyle behaviors, such as unhealthy diets high in saturated fat, smoking, and physical inactivity, are the main risk factors for coronary heart disease, so efforts to reduce this condition are directed towards these factors. Global rates of coronary heart disease are increasing and the World Health Organization estimates that by 2030, it will be the biggest cause of death worldwide. However, in high-income countries, such as England, deaths due to coronary heart disease have actually fallen substantially over the past few decades with an accelerated reduction in annual death rates since 2000.
Socioeconomic factors play an important role in chronic diseases such as coronary heart disease, with mortality rates almost twice as high in deprived than affluent areas. However, the potential effect of population-wide interventions on reducing inequalities in deaths from coronary heart disease remains unclear. So in this study, the researchers investigated the role of behavioral (changing lifestyle) and medical (treatments) management of coronary heart disease that contributed to the decrease in deaths in England for the period 2000–2007, within and between socioeconomic groups.
The researchers used a well-known, tried and tested epidemiological model (IMPACT) but adapted it to include socioeconomic inequalities to analyze the total population of England aged 25 and older in 2000 and in 2007. The researchers included all the major risk factors for coronary heart disease plus 45 current medical and surgical treatments in their model. They used the Index of Multiple Deprivation 2007 as a proxy indicator of socioeconomic circumstances of residents in neighborhoods. Using the postal code of residence, the researchers matched deaths from, and patients treated for, coronary heart disease to the corresponding deprivation category (quintile). Changes in risk factor levels in each quintile were also calculated using the Health Survey for England. Using their model, the researchers calculated the total number of deaths prevented or postponed for each deprivation quintile by measuring the difference between observed deaths in 2007 and expected deaths based on 2000 data, if age, sex, and deprivation quintile death rates had remained the same.
The researchers found that between 2000 and 2007, death rates from coronary heart disease fell from 229 to 147 deaths per 100,000—a decrease of 36%. Both death rates and the number of deaths were lowest in the most affluent quintile and the pace of fall was also faster, decreasing by 6.7% per year compared to just 4.9% in the most deprived quintile. Furthermore, the researchers found that overall, about half of the decrease in death rates was attributable to improvements in uptake of medical and surgical treatments. The contribution of medical treatments to the deaths averted was very similar across all quintiles, ranging from 50% in the most affluent quintile to 53% in the most deprived. Risk factor changes accounted for approximately a third fewer deaths in 2007 than occurred in 2000, but were responsible for a smaller proportion of deaths prevented in the most affluent quintile compared with the most deprived (approximately 29% versus 44%, respectively). However, the benefits of improvements in blood pressure, cholesterol, smoking, and physical activity were partly negated by rises in body mass index and diabetes, particularly in more deprived quintiles.
These findings suggest that approximately half the recent substantial fall in deaths from coronary heart disease in England was attributable to improved treatment uptake across all social groups; this is consistent with equitable service delivery across the UK's National Health Service. However, opposing trends in major risk factors, which varied substantially by socioeconomic group, meant that their net contribution accounted for just a third of deaths averted. Other countries have implemented effective, evidence-based interventions to tackle lifestyle risk factors; the most powerful measures involve legislation, regulation, taxation, or subsidies, all of which tend to be equitable. Such measures should be urgently implemented in England to effectively tackle persistent inequalities in deaths due to coronary heart disease.
Please access these Web sites via the online version of this summary at
The World Health Organization has information about the
The National Heart Lung and Blood Institute provides a
The
The
More information about the
Since the 1970s, coronary heart disease (CHD) mortality in England has fallen by a remarkable 60%, with accelerated reductions in annual death rates since 2000
The most recent study in the UK modelled CHD mortality change in England and Wales between 1981 and 2000
Thus although our analysis covers a relatively short period of time, the period included a raft of measures specifically aimed to improve outcomes and reduce social inequalities. We have quantified the variation by socioeconomic circumstances (SEC) in the relative contributions of modifiable population-level risk factors and evidence-based individual treatments to the fall in CHD mortality during the period 2000 to 2007. To do this we have used the widely used and replicated IMPACT model, substantially extending the model to capture socioeconomic inequalities concealed within the overall national trends.
IMPACT is an epidemiological model used to explain the contributions of population-level risk factor changes (incidence reduction) and uptake of evidence-based treatments (case fatality reduction) to the change in CHD deaths between two points in time. This deterministic, cell-based model has been described in detail elsewhere
Data sources specific to the England population were used to construct the IMPACTSEC model. When several sources were available, we chose the most up-to-date, representative dataset that we could link to a small-area deprivation index. Population estimates and CHD death counts (2000: ICD9 410–414; 2007: ICD10 I20–I25) by sex, 5-y age bands to age 85+, and deprivation quintile were obtained from the Office for National Statistics. Emergency admissions for acute myocardial infarction were extracted from Hospital Episode Statistics and supplemented with data from the Myocardial Ischaemia National Audit Project to disaggregate ST-elevated acute myocardial infarction and non–ST-elevated acute coronary syndrome, and to apportion treatment uptake to each group. For heart failure admissions, the National Health Service (NHS) Heart Failure Survey was used to estimate in-hospital treatment uptake. The General Practice Research Database and the Health Survey for England provided data on treatment uptake in the community. Risk factor trend data came from the Health Survey for England.
Detailed information on the IMPACTSEC model, calculation methods, and data sources are provided as supporting information in
Only the Health Survey for England consistently recorded individual socioeconomic position; but all data sources recorded individual's postcode of residence. We therefore used a measure of relative area deprivation as a proxy indicator of the SECs of individuals living in small areas (
The total number of deaths prevented or postponed (DPPs) for each deprivation quintile were calculated as the difference between observed deaths in 2007 and expected deaths had age-, sex-, and quintile- specific CHD mortality rates in 2000 remained unchanged. DPPs explained by the model could be positive (i.e., deaths averted) or negative (i.e., additional deaths in 2007 relative to 2000). Any shortfalls between the DPPs explained by the model and the total DPPs for each SEC quintile were assumed to reflect either imprecision in our model parameters or omission of other, unmeasured risk factors.
The treatment component of IMPACTSEC included nine mutually exclusive CHD patient groups: ST-elevation myocardial infarction; non–ST-elevation acute coronary syndrome; secondary prevention post myocardial infarction; secondary prevention post revascularisation; chronic stable coronary artery disease; heart failure patients admitted to hospital; heart failure patients resident in the community; persons in the community taking blood pressure lowering medication; and persons in the community taking lipid lowering treatment. A total of 45 patient-treatment pairings were generated. To avoid double counting of patients treated for two or more conditions within the year (e.g., heart failure develops within 1 y after myocardial infarction in approximately 30% of survivors), we quantified overlaps between different groups and made appropriate adjustments to construct distinct, non-overlapping CHD patient subgroups (
The numbers eligible for treatment, uptake of specific treatment, 1-y case fatality rates, and relative risk reduction due to treatment, all stratified by age, sex, and CHD patient subgroups, were extracted from relevant data sources (
The estimates of medication uptake in the community recorded in our primary data sources were adjusted to reflect ordinary clinical practice. We assumed that medication adherence (i.e., the proportion of eligible patients actually taking therapeutically effective levels of medication) was 100% among hospitalised patients, 70% among symptomatic patients in the community, and 50% among asymptomatic patients in the community
Deaths prevented by each intervention were then calculated by multiplying the numbers of patients in each diagnostic group by the (adherence-adjusted) proportion of those patients who received the treatment, the baseline case fatality rate, and the relative risk reduction of that treatment. To estimate the cumulative effect of relative risk reduction for patients on a combination of drug therapies, we used the Mant and Hicks correction
Many of the treatments were already widely used in 2000. The net benefit of an intervention in 2007 was therefore calculated by subtracting the expected number of deaths prevented if 2000 uptake rates had remained unchanged from the deaths prevented using 2007 treatment uptake rates.
We included seven risk factors in the model; both behavioural—smoking, physical inactivity, fruit and vegetable consumption, BMI—and physiological markers including systolic blood pressure, total serum cholesterol, and diagnosed diabetes. To quantify the mortality benefits of an absolute change in each specific risk factor between 2000 and 2007, we used two approaches: a regression-based approach for factors measured on a continuous scale (e.g., total blood cholesterol); and, a population-attributable risk fraction approach for dichotomous variables such as diagnosed diabetes. The independent regression coefficients of mortality benefit for a unit change in mean risk factor were obtained from published multivariate analyses (
CHD deaths are usually caused by multiple risk factors acting simultaneously. It is therefore recommended that enumerating mortality benefits from risk factor reductions should not use a simple additive approach. Instead, the effects of risk factor changes should be jointly estimated using the cumulative risk-reduction approach. This can be stated in the equation:
We assumed that there was no further synergy between the treatment and risk-factor components of the model. Lag times between the change in cardiovascular risk factor levels and change in CHD mortality rates were assumed to be relatively rapid
Between 2000 and 2007, the age-standardised CHD mortality rate in adults aged 25 y and over fell from 229 to 147 deaths per 100,000; a decline of 36% overall or 6.1% per year (
Adults Aged 25 and over | England | Most Affluent | IMDQ2 | IMDQ3 | IMDQ4 | Most Deprived |
Population (000s) | ||||||
2000 | 33,952 | 6,972 | 7,035 | 6,939 | 6,678 | 6,329 |
2007 | 35,281 | 7,328 | 7,363 | 7,233 | 6,906 | 6,451 |
Observed deaths | ||||||
2000 | 103,243 | 16,529 | 19,827 | 21,460 | 22,187 | 23,240 |
2007 | 74,174 | 12,312 | 14,444 | 15,347 | 15,676 | 16,395 |
Age-standardised rates (per 100,000) | ||||||
2000 | 229 | 177 | 199 | 222 | 257 | 306 |
2007 | 147 | 109 | 124 | 141 | 169 | 215 |
Annual percent fall | 6.1 | 6.7 | 6.5 | 6.3 | 5.8 | 4.9 |
Expected deaths 2007 (had 2000 rates persisted) | 112,244 | 19,665 | 22,669 | 23,696 | 23,260 | 22,953 |
DPP (Expected - observed deaths, 2007) | 38,070 | 7,353 | 8,225 | 8,349 | 7,584 | 6,558 |
Percent expected deaths averted | 33.9 | 37.4 | 36.3 | 35.2 | 32.6 | 28.6 |
Rates have been standardised to the European Union reference population aged 25 and over. Separate breakdowns for males and females are available in
IMD, index of multiple deprivation.
Nationally, there were 38,070 fewer CHD deaths in 2007 than if 2000 mortality rates had persisted, representing the “total” DPPs. Despite the slower annual rates of fall in the most deprived quintile, their higher CHD mortality rates in the base year meant that the number of DPPs by 2007 were fairly equally distributed: about 6,560 fewer deaths in the most deprived quintile versus 7,355 in the most affluent (
Overall, approximately half of the total CHD mortality fall (19,780 fewer deaths or 52%; 95% uncertainly interval ranging from 40% to 70%) was attributable to improvements in uptake of medical and surgical treatments (
England overall and by socioeconomic quintile.
Treatment by Patient Groups | England: DPP | By IMD: DPP | |||||||
|
Percent | Percent Lower limit |
Percent Upper limit |
||||||
|
|
|
|
|
|
|
|
|
|
Thrombolysis | −118 | −22 | −23 | −21 | −23 | −29 | |||
Aspirin | 24 | 5 | 4 | 5 | 3 | 7 | |||
B-blocker | 4 | 1 | 0 | 1 | 1 | 2 | |||
ACE inhibitor or ARB | 5 | 0 | 0 | 2 | 1 | 2 | |||
Clopidogrel | 65 | 12 | 14 | 14 | 13 | 12 | |||
Primary PCI | 139 | 30 | 28 | 28 | 27 | 25 | |||
Primary CABG | 1 | 0 | 0 | 0 | 0 | 0 | |||
CPR in hospital |
−252 | −33 | −51 | −57 | −53 | −57 | |||
|
|
|
|
|
|
|
|
|
|
Aspirin and heparin | 341 | 54 | 68 | 68 | 63 | 88 | |||
Aspirin alone | −114 | −15 | −24 | −21 | −21 | −31 | |||
PG IIB/IIIA inhibitors | −1 | −2 | −1 | 0 | 0 | 2 | |||
ACE inhibitor or ARB | 44 | 6 | 8 | 9 | 10 | 10 | |||
B-blocker | 27 | 5 | 5 | 6 | 5 | 6 | |||
Clopidogrel | 203 | 36 | 40 | 46 | 41 | 40 | |||
CABG surgery | 1 | 2 | 1 | 0 | 0 | −1 | |||
PCI | 54 | 11 | 10 | 12 | 12 | 10 | |||
CPR in hospital |
−259 | −40 | −48 | −58 | −55 | −58 | |||
|
|
|
|
|
|
|
|
|
|
Aspirin | 351 | 65 | 69 | 76 | 87 | 55 | |||
B-blocker | 862 | 158 | 197 | 194 | 167 | 146 | |||
ACE inhibitor or ARB | 903 | 166 | 200 | 190 | 175 | 172 | |||
Statin | 1,303 | 241 | 280 | 269 | 268 | 245 | |||
Warfarin | 92 | 8 | 30 | 17 | 15 | 21 | |||
Rehabilitation |
0 | 0 | 0 | 0 | 0 | 0 | |||
|
|
|
|
|
|
|
|
|
|
Aspirin | 45 | 10 | 10 | 11 | 8 | 7 | |||
B-blocker | 154 | 29 | 31 | 34 | 31 | 30 | |||
ACE inhibitor or ARB | 179 | 35 | 37 | 37 | 34 | 36 | |||
Statin | 174 | 31 | 36 | 38 | 37 | 32 | |||
Warfarin | 0 | 0 | 0 | 1 | 0 | −1 | |||
Rehabilitation (post CABG) |
0 | 0 | 0 | 0 | 0 | 0 | |||
Rehabilitation (post PTCA) | 36 | 7 | 7 | 7 | 7 | 7 | |||
|
|
|
|
|
|
|
|
|
|
Aspirin in community | 818 | 139 | 159 | 176 | 179 | 165 | |||
Statin in community | 2,488 | 443 | 523 | 526 | 510 | 485 | |||
ACE inhibitor or ARB | 1,292 | 241 | 281 | 268 | 261 | 241 | |||
CABG surgery | 236 | 27 | 44 | 45 | 57 | 63 | |||
|
|
|
|
|
|
|
|
|
|
ACE inhibitor | 49 | 8 | 9 | 10 | 10 | 11 | |||
B-blocker | 39 | 6 | 8 | 8 | 9 | 9 | |||
Spironolactone | 37 | 6 | 7 | 8 | 8 | 8 | |||
Aspirin | 126 | 21 | 22 | 27 | 32 | 23 | |||
|
|
|
|
|
|
|
|
|
|
ACE inhibitor or ARB | 737 | 125 | 158 | 171 | 146 | 137 | |||
B-blocker | 1,592 | 284 | 325 | 353 | 338 | 292 | |||
Spironolactone | 617 | 105 | 134 | 120 | 129 | 129 | |||
Aspirin | 389 | 50 | 72 | 88 | 97 | 83 | |||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Subtotals for England (column 1) have been rounded to nearest 5.
The 95% uncertainty interval corresponds to the lower (2.5th percentile) and upper (97.5th percentile) limits of the uncertainty analysis. These are shown to indicate range around the central estimate of percent of DPPs explained (column 3).
Sub-totals (in rows) for CHD patient groups.
We have assumed no change in cardiopulmonary resuscitation (CPR) uptake in the community between 2000 and 2007. Their contribution to DPPs have therefore been set to zero.
No change in uptake between 2000 and 2007.
Abbreviations: B-blocker, beta-blocker; CABG, coronary artery bypass graft; CAD, coronary artery disease; IMD, index of multiple deprivation; MI, myocardial infarction; NSTEACS, non-ST elevation acute coronary syndrome; PCI, percutaneous coronary intervention; PG, platelet glycoprotein; PTCA, percutaneous transluminal coronary angioplasty; STEMI, ST elevation myocardial infarction.
Risk Factors | England: DPP | By IMD: DPP | |||||||
|
Percent | Percent Lower Limit |
Percent Upper Limit |
||||||
|
1,040 | 2.7 | 0.1 | 5.6 | 93 | 148 | 189 | 242 | 368 |
|
600 | 1.6 | 0.8 | 2.3 | 91 | 109 | 119 | 130 | 155 |
|
11,160 | 29.3 | 18.4 | 40.0 | 1,861 | 2,168 | 2,321 | 2,391 | 2,421 |
|
2,090 | 5.5 | 3.0 | 8.3 | 232 | 498 | 287 | 351 | 722 |
|
1,555 | 4.1 | 1.1 | 8.3 | 305 | 334 | 342 | 301 | 273 |
|
−640 | −1.7 | −2.9 | −0.4 | −111 | −129 | −135 | −133 | −135 |
|
−2,820 | −7.4 | −12.6 | −2.5 | −365 | −438 | −533 | −577 | −908 |
|
12,990 | 34.1 | 21.1 | 47.3 | 2,107 | 2,690 | 2,591 | 2,704 | 2,896 |
|
19,780 | 52.0 | 40.2 | 69.7 | 3,670 | 4,055 | 4,420 | 4,166 | 3,471 |
|
32,770 | 86.1 | 64.8 | 107.3 | 5,777 | 6,746 | 7,012 | 6,870 | 6,367 |
|
5,300 | 13.9 | 1,576 | 1,479 | 1,337 | 714 | 191 | ||
|
38,070 | 100 | 7,353 | 8,225 | 8,349 | 7,584 | 6,558 |
Subtotals for England (column 1) have been rounded to nearest 5.
The 95% uncertainty interval corresponds to the lower (2.5th percentile) and upper (97.5th percentile) limits of the uncertainty analysis.
After subtracting DPPs due to hypertension treatment in primary prevention.
After subtracting DPPs due to statin treatment in primary prevention.
See
Abbreviations: IMD, index of multiple deprivation.
Treatments by Patient Groups; Risk Factors | England | Most Affluent | IMDQ2 | IMDQ3 | IMDQ4 | Most Deprived |
|
||||||
STEMI |
|
−0.1 | −0.3 | −0.3 | −0.4 | −0.6 |
NSTEACS |
|
0.8 | 0.7 | 0.7 | 0.7 | 1.0 |
Secondary prevention post MI |
|
8.7 | 9.4 | 8.9 | 9.4 | 9.7 |
Secondary prevention post revasc |
|
1.5 | 1.5 | 1.5 | 1.5 | 1.7 |
Chronic stable CAD |
|
11.6 | 12.2 | 12.2 | 13.3 | 14.6 |
Heart failure in the hospital |
|
0.6 | 0.6 | 0.6 | 0.8 | 0.8 |
Heart failure in the community |
|
7.7 | 8.4 | 8.8 | 9.4 | 9.8 |
Hypertension treatment |
|
4.9 | 5.0 | 4.9 | 4.5 | 4.2 |
Hyperlipidemia treatment (statins) |
|
14.3 | 11.9 | 15.6 | 15.7 | 11.8 |
|
|
|
|
|
|
|
|
||||||
Smoking | 2.7 | 1.3 | 1.8 | 2.3 | 3.2 | 5.6 |
Diabetes | −7.4 | −5.0 | −5.3 | −6.4 | −7.6 | −13.8 |
Physical inactivity | 1.6 | 1.2 | 1.3 | 1.4 | 1.7 | 2.4 |
Systolic blood pressure, mmHg | 29.3 | 25.3 | 26.4 | 27.8 | 31.5 | 36.9 |
Total cholesterol, mmol/l | 5.5 | 3.2 | 6.1 | 3.4 | 4.6 | 11.0 |
BMI | −1.7 | −1.5 | −1.6 | −1.6 | −1.7 | −2.1 |
Fruit and vegetable consumption | 4.1 | 4.2 | 4.1 | 4.1 | 4.0 | 4.2 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||||||
|
|
|
|
|
|
|
- Due to treatment uptake |
|
|
|
|
|
|
- Due to risk factor change |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Sub-totals (in rows).
DPP counts for England (column 1) have been rounded to nearest 5. All counts are in italics.
Abbreviations: CAD, coronary artery disease; IMD, index of multiple deprivation; NSTEACS, non-ST elevation acute coronary syndrome; MI, myocardial infarction; revasc, revascularisation; STEMI, ST elevation myocardial infarction.
The most substantial contribution to deaths prevented by treatments came from statin treatment for hyperlipidemia (14% of the total mortality reduction, 5%–32%), management of chronic stable coronary artery disease (13%; 10%–17%), and secondary prevention following myocardial infarction or revascularisation (11%; 8%–13%) (
Treatment by Patient Groups | Eligible Patients |
England | Most Affluent | IMDQ2 | IMDQ3 | IMDQ4 | Most Deprived | ||||||
2000 |
2007 |
2000 | 2007 | 2000 | 2007 | 2000 | 2007 | 2000 | 2007 | 2000 | 2007 | ||
|
|
||||||||||||
Thrombolysis |
|
|
79.4 | 58.6 | 77.9 | 59.5 | 75.4 | 57.1 | 76.2 | 56.0 | 77.4 | 52.5 | |
Aspirin |
|
|
93.6 | 96.6 | 94.7 | 96.3 | 93.1 | 95.4 | 93.2 | 95.6 | 93.4 | 96.4 | |
B-blocker |
|
|
74.8 | 70.9 | 72.3 | 69.1 | 71.0 | 69.8 | 69.6 | 69.5 | 69.9 | 72.4 | |
ACE inhibitor or ARB |
|
|
79.8 | 76.6 | 78.9 | 75.6 | 75.4 | 75.5 | 75.4 | 74.8 | 77.3 | 79.2 | |
Clopidogrel |
|
|
26.9 | 88.7 | 25.7 | 87.7 | 28.0 | 88.4 | 28.5 | 88.4 | 28.9 | 89.2 | |
Primary PCI |
|
|
2.9 | 24.2 | 3.4 | 21.8 | 3.8 | 23.3 | 4.3 | 24.5 | 4.8 | 24.8 | |
Primary CABG |
|
|
0.1 | 0.1 | 0.0 | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 | 0.0 | 0.1 | |
CPR in hospital |
|
|
9.9 | 6.5 | 11.6 | 7.1 | 11.7 | 6.3 | 11.8 | 6.7 | 11.6 | 6.3 | |
|
|
||||||||||||
Aspirin and heparin |
|
|
67.1 | 79.7 | 65.0 | 80.3 | 66.7 | 79.9 | 65.7 | 80.2 | 57.9 | 78.8 | |
Aspirin alone |
|
|
21.5 | 13.5 | 23.9 | 12.3 | 21.5 | 12.7 | 23.5 | 12.6 | 28.9 | 13.1 | |
PG IIB/IIIA |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
68.6 | 73.1 | 64.5 | 72.2 | 65.9 | 72.5 | 64.3 | 72.7 | 67.0 | 75.0 | |
B-blocker |
|
|
66.1 | 68.2 | 62.7 | 67.7 | 63.5 | 66.7 | 61.7 | 66.3 | 62.8 | 69.2 | |
Clopidogrel |
|
|
43.5 | 87.1 | 44.2 | 86.9 | 42.3 | 86.8 | 45.6 | 85.9 | 45.4 | 86.3 | |
CABG surgery |
|
|
3.5 | 3.4 | 3.2 | 2.7 | 3.2 | 2.6 | 2.9 | 2.3 | 2.5 | 2.1 | |
PCI |
|
|
3.6 | 7.7 | 3.4 | 6.9 | 3.2 | 7.0 | 2.9 | 6.4 | 2.5 | 5.7 | |
CPR in hospital |
|
|
4.6 | 2.3 | 4.9 | 2.2 | 5.3 | 2.1 | 5.6 | 2.5 | 5.8 | 2.5 | |
|
|
||||||||||||
Aspirin |
|
|
58.8 | 73.5 | 63.5 | 76.2 | 62.5 | 76.5 | 65.7 | 76.1 | 71.2 | 80.0 | |
B-blocker |
|
|
29.2 | 52.7 | 30.5 | 55.6 | 29.8 | 56.4 | 31.7 | 56.9 | 32.3 | 56.9 | |
ACE inhibitor or ARB |
|
|
30.8 | 63.9 | 29.2 | 63.0 | 29.5 | 63.9 | 30.9 | 63.4 | 30.5 | 67.1 | |
Statin |
|
|
61.7 | 85.1 | 58.2 | 84.6 | 56.3 | 83.9 | 56.3 | 84.8 | 58.7 | 84.2 | |
Warfarin |
|
|
7.9 | 7.2 | 7.3 | 6.6 | 6.7 | 6.5 | 7.1 | 7.1 | 7.9 | 6.1 | |
Rehabilitation (post CABG) |
|
|
73.0 | 73.0 | 73.0 | 73.0 | 73.0 | 73.0 | 73.0 | 73.0 | 73.0 | 73.0 | |
Rehabilitation (post PTCA) |
|
|
10.0 | 20.0 | 10.0 | 20.0 | 10.0 | 20.0 | 10.0 | 20.0 | 10.0 | 20.0 | |
|
|
||||||||||||
Aspirin |
|
|
56.4 | 72.4 | 60.0 | 74.3 | 59.2 | 74.5 | 58.8 | 74.8 | 63.3 | 75.8 | |
B-blocker |
|
|
34.0 | 54.0 | 34.0 | 54.6 | 31.6 | 53.3 | 32.2 | 52.9 | 31.7 | 52.4 | |
ACE inhibitor or ARB |
|
|
32.3 | 62.6 | 32.5 | 62.3 | 31.0 | 61.6 | 30.6 | 61.2 | 30.5 | 62.5 | |
Statin |
|
|
39.8 | 77.9 | 39.5 | 77.8 | 35.9 | 76.6 | 34.7 | 76.6 | 36.2 | 78.1 | |
Warfarin |
|
|
7.7 | 8.3 | 6.7 | 8.9 | 6.5 | 7.9 | 6.2 | 7.7 | 6.2 | 7.6 | |
Rehabilitation |
|
|
45.0 | 45.0 | 45.0 | 45.0 | 45.0 | 45.0 | 45.0 | 45.0 | 45.0 | 45.0 | |
|
|
||||||||||||
Aspirin in community |
|
|
38.7 | 57.2 | 42.6 | 61.4 | 44.7 | 64.3 | 42.9 | 63.4 | 45.0 | 65.3 | |
Statin in community |
|
|
25.4 | 63.4 | 24.2 | 65.4 | 23.7 | 66.5 | 23.0 | 66.3 | 23.3 | 69.2 | |
ACE inhibitor or ARB |
|
|
19.9 | 45.1 | 19.0 | 45.5 | 20.5 | 45.8 | 20.1 | 45.5 | 19.7 | 46.5 | |
CABG surgery |
|
|
8.8 | 9.8 | 8.7 | 9.7 | 9.6 | 10.3 | 8.7 | 9.7 | 7.7 | 8.8 | |
|
|
||||||||||||
ACE inhibitor |
|
|
51.8 | 57.6 | 52.1 | 57.9 | 52.7 | 58.6 | 53.4 | 59.4 | 55.2 | 61.4 | |
B-blocker |
|
|
24.3 | 27.0 | 24.5 | 27.2 | 25.0 | 27.8 | 25.6 | 28.5 | 27.1 | 30.1 | |
Spironolactone |
|
|
19.8 | 22.0 | 20.0 | 22.3 | 20.4 | 22.7 | 20.8 | 23.1 | 21.8 | 24.3 | |
Aspirin |
|
|
56.6 | 71.9 | 59.8 | 73.3 | 58.6 | 74.1 | 58.1 | 75.3 | 62.2 | 74.4 | |
|
|
||||||||||||
ACE inhibitor or ARB |
|
|
48.2 | 70.2 | 44.5 | 69.3 | 43.4 | 67.8 | 45.9 | 69.2 | 46.6 | 68.4 | |
B-blocker |
|
|
10.7 | 35.1 | 11.2 | 34.6 | 10.8 | 34.9 | 9.4 | 34.2 | 10.1 | 32.4 | |
Spironolactone |
|
|
4.3 | 14.7 | 3.9 | 14.9 | 3.6 | 13.0 | 3.9 | 15.1 | 4.0 | 14.9 | |
Aspirin |
|
|
37.9 | 46.3 | 38.3 | 49.9 | 37.3 | 50.3 | 37.0 | 51.8 | 40.0 | 52.7 | |
|
|
|
|
8.3 | 14.0 | 8.2 | 13.8 | 8.6 | 13.9 | 8.2 | 13.0 | 8.3 | 12.7 |
|
|
|
|
1.0 | 7.9 | 1.1 | 8.5 | 1.1 | 9.1 | 1.4 | 10.3 | 1.3 | 9.1 |
The overall treatment uptake rate is a weighted average over all age groups 25+ and both sexes.
Eligible patient numbers rounded to nearest 5.
Sub-totals (in rows) for CHD patient groups.
Abbreviations: B-blocker: beta-blocker; CABG: coronary artery bypass graft; CAD, coronary artery disease; CPR, cardiopulmonary resuscitation; IMD, index of multiple deprivation; NSTEACS, non-ST elevation acute coronary syndrome; MI, myocardial infarction; PCI, percutaneous coronary intervention; PG, platelet glycoprotein; PTCA, percutaneous transluminal coronary angioplasty; STEMI, ST elevation myocardial infarction.
Of the deaths prevented due to population-level risk factor changes, the largest contribution came from the fall in systolic blood pressure amongst those not on hypertensive medications (11,160 fewer deaths, or 29%; 18%–40%) (
Risk Factors | Overall Levels | Absolute Change in Percentage Points, 2000–2007 | ||||||
2000 | 2007 | Englandc | Most Affluent | IMDQ2 | IMDQ3 | IMDQ4 | Most Deprived | |
|
||||||||
Male |
|
|
|
−2.6 | −3.1 | −3.6 | −4.1 | −4.8 |
Female |
|
|
|
−2.5 | −3.0 | −3.4 | −4.0 | −4.6 |
|
||||||||
Male |
|
|
|
2.4 | 2.7 | 2.8 | 2.6 | 3.6 |
Female |
|
|
|
1.6 | 1.4 | 1.6 | 2.1 | 2.8 |
|
||||||||
Male |
|
|
|
−6.9 | −6.7 | −6.8 | −6.9 | −7.2 |
Female |
|
|
|
−4.3 | −4.2 | −4.2 | −4.2 | −4.4 |
|
||||||||
Male |
|
|
|
−2.6 | −2.6 | −2.5 | −2.5 | −2.4 |
Female |
|
|
|
−5.3 | −5.5 | −5.5 | −5.5 | −5.5 |
|
||||||||
Male |
|
|
|
−0.2 | −0.2 | −0.2 | −0.1 | −0.1 |
Female |
|
|
|
−0.2 | −0.2 | −0.2 | −0.2 | −0.2 |
|
||||||||
Male |
|
|
|
0.4 | 0.4 | 0.4 | 0.3 | 0.3 |
Female |
|
|
|
0.2 | 0.2 | 0.2 | 0.2 | 0.3 |
|
||||||||
Male |
|
|
|
0.4 | 0.4 | 0.4 | 0.4 | 0.3 |
Female |
|
|
|
0.4 | 0.4 | 0.4 | 0.4 | 0.4 |
See
England average weighted by 2007 population distribution in 10-y age bands.
IMD, index of multiple deprivation.
In contrast, the benefits attributable to statin lowering of total cholesterol levels were double those attributable to the fall in cholesterol levels in the population not on treatment (approximately 5,300 versus 2,090 fewer deaths, respectively). Between 2000 and 2007, hyperlipidemia treatment increased nine-fold across all social groups from 1% to 9% (
Favourable trends in smoking, fruit and vegetable consumption, and physical activity were modest; together only contributing about 3,195 fewer deaths, or 8% (2%–18%) of the overall mortality fall (
Mortality gains due to positive trends in smoking, fruit and vegetable consumption, and physical activity risk factors were negated by increases in BMI and diabetes (together contributing 3,460 additional deaths, equivalent to an 9% increase in mortality (−17% to −3%) (
The percentage unexplained by the model varied by age, sex, and SECs. The model fit was generally good overall and in women and men living in the most deprived areas (
Between 2000 and 2007, English CHD mortality rates fell by an impressive 36%, resulting in approximately 38,000 fewer CHD deaths in 2007. However the relative mortality inequalities between rich and poor persisted and even increased slightly over this period. This study is the first, to our knowledge, to analyse the socioeconomic components concealed within the overall mortality reductions attributable specifically to risk factor trends and to evidence-based treatments. By using deprivation scores for area of residence as a unified marker of SECs across all relevant large databases of population health and health service use in England, the study had adequate statistical size to quantify the impact of changes in risk factors and treatments within socioeconomic groups, even over a relatively short period of 7 y. Understanding these recent trends, and their socially divergent trajectories, will be crucial to planning the most effective and equitable future strategies to prevent cardiovascular disease and reduce inequalities.
Approximately half the fall in CHD mortality was attributable to increased medical therapies. These benefits largely reflected a doubling of drug use for community patients with chronic disease (who represent the largest CHD burden). In contrast, the contribution of medical interventions in hospital was relatively modest. Firstly, because the numbers of patients admitted to hospital with acute disease were much smaller. Secondly, because few new treatments were introduced after 2000 other than clopidogrel and primary angioplasty. And thirdly, the uptake rates for existing hospital-based treatments were already close to maximum levels in 2000. The age-specific prevalence of CHD is socially graded. With similar levels of uptake of treatments across socioeconomic quintiles in both base and final years, this meant that the benefits of increased treatment were distributed remarkably evenly across social groups, which suggests a fairly equitable distribution of therapies across the NHS.
Reductions in major cardiovascular risk factors explained over two-fifths of the fall in CHD mortality (43%). However, the net benefit was much smaller (approximately 34%) because adverse trends in BMI and diabetes potentially increased mortality by some 9%.
The single largest contribution to the overall CHD mortality decrease came from population falls in blood pressure
Small increases in fruit and vegetable consumption and physical activity were seen across all social groups. Furthermore, moderate declines in smoking levels were actually greater in deprived areas. This may reflect the benefit of cumulative tobacco control policies since 2000, reinforced by the targeting of cessation services in deprived areas
However, after excluding the effect of statin therapy, the decline in cholesterol levels in the wider population was modest. This finding may well reflect a failure to implement more effective dietary policies
The absolute gap in CHD mortality between the most affluent and most deprived groups narrowed over the period of our study, however relative inequalities widened. This was unlikely to be due to differential treatment of diagnosed patients because levels of uptake of evidence-based therapies were similar for all groups. The pace of fall in mortality in the most affluent groups was faster; but changes in risk factor levels could not explain about 20% of this fall. By contrast, in the most deprived quintile, changes in risk factor levels explained almost all of the remaining CHD mortality fall after accounting for deaths averted due to increase in treatment uptake rates. Perhaps the most likely explanation for this difference is a social gradient in effect modification. Thus, the current model assumed that the mortality decrease per unit change in risk factor was similar across deprivation quintiles. However, the benefits of a specific decrease in blood pressure or cholesterol may be disproportionately higher in more affluent groups, perhaps reflecting synergy with other positive trends
Alternative explanations for the fraction of the mortality fall unexplained by the model include the possible omission of more “upstream” risks such as psychosocial stress, which might differentially benefit affluent groups
Compared with previous IMPACT analyses from a baseline of the 1980s
The IMPACT model has been replicated and validated in diverse national populations. This is the first IMPACT study to quantify the socioeconomic components of the contributions of changes in treatment and risk factors to falls in coronary mortality. The main datasets used are reasonably representative of the socioeconomic distribution of the English population and large enough for reasonably accurate estimates of socioeconomic change.
However, a number of limitations should also be acknowledged. These include the use of area-level categorisation of SECs. However, area deprivation correlates well with individual socioeconomic position and may also help to capture the contextual effects of living conditions
Approximately 14% of the CHD mortality fall was not explained by the model and the uncertainty analysis also produced wide limits in the percentage explained (86%; 65%–107%). The model fit was also less good in men in affluent areas, as discussed earlier. However, the model fit was generally good overall and in women and in men living in the most deprived areas. As with all models attempting to capture complex and interacting changes, it remains possible that there were additional (unquantifiable) sources of error not captured by the uncertainty analysis.
Approximately half of the recent substantial CHD mortality fall in England was attributable to medical therapies. Benefits were relatively even across social groups. These findings are consistent with equitable service delivery across the NHS. Treatment uptake in hospitals was close to maximum levels over the entire period, while follow-up treatment of cardiovascular patients in the community substantially improved and was equitable. This suggests the Qualities and Outcome Framework that was being implemented in general practice during the study period was an effective incentive for improving uptake overall
However, the net gains from risk factor improvements were small, reflecting modest recent decreases in powerful cardiovascular risk factors such as smoking and cholesterol, and further eroded by continuing rises in BMI and diabetes. This throws a spotlight on recent UK policies for salt reduction and tobacco control (relatively effective) and healthier diets (relatively neglected). Elsewhere, the successful introduction of effective, powerful, rapid, and cost-saving policy interventions have achieved substantial reductions in the saturated fat, trans-fats, sugars, and calories hidden in processed food, takeaways, and sweetened drinks
Technical appendix for the IMPACTSEC model. Contents are as follows. Section 1: Overview of the IMPACTSEC model. Table A: Population and patient data sources. Table B: Data sources for treatment uptake levels: medical and surgical treatments included in the model. Table C: Risk factors: variable definitions and source. Table D: Cumulative benefit: adjustment factors by age, sex, and IMD quintile. Table E: CHD mortality rates in 2000 and 2007 by sex and deprivation quintiles. Table F: Clinical efficacy of interventions: relative risk reductions obtained from meta-analyses, and randomised clinical trials. Table G: Case fatality rates for each patient group. Table H: Treatment uptake in 2000 and 2007. Table I: Beta coefficients for major risk factors. Table J: Relative risk values for CHD mortality: smoking, diabetes, and physical inactivity. Table K: Risk factor levels in 2000 and 2007 by sex and deprivation quintiles. Table L: Model fit by age, sex, and deprivation quintiles. Table M: Uncertainty analysis: parameter distributions, functions, and sources. Table N: Assumptions and overlap adjustments used in the IMPACTSEC Model. Table O: “Fixed gradients” for measuring risk factor change between two time points for deprivation quintiles.
(DOC)
We thank Iain Buchan, David Blane, Peter Goldblatt, Adrian Gallop, Klim McPherson, Martin McKee, Richard Morris, and Colin Sanderson of the IMPACTSEC advisory group for comments on study design and emerging findings. Particular thanks go to Kate Walters for help with clinical coding lists to extract prescribing data from the General Practice Research Database (GPRD) and to Gianluca Baio for statistical advice.
angiotensin receptor blocker
angiotensin-converting enzyme inhibitors
body mass index
coronary heart disease
deaths prevented or postponed
National Health Service
socioeconomic circumstance