AREA SUB URBANA
SUELO RURAL DE PROTECCIÓN
6.3.1 Introduction
In the previous chapters, I have examined the dynamism of CHD mortality trends in several settings. Trends can fluctuate over time, sometimes quite rapidly in the matter of a few years. I also discussed some compelling arguments suggesting that changes in risk factors rather than evidence- based treatments might be the major drivers of these rapid changes.
However, our current understanding of coronary heart disease causation suggests that coronary heart disease development is a long process, probably taking the entire life course of individuals. Correspondingly, a change in risk factors will be followed after several decades by a change in mortality. Consequently, the rapid trend changes I described so far seem to not fit adequately within this framework.
Most of the research on the drivers of these trends in coronary heart disease rates has been conducted in Western countries, with longstanding declining trends. Generally, studies looked at trends well after the peak of the epidemic has been reached. For example, for the US studies this question was addressed over the period 1980-2000144,200, while the peak of incidence was observed in mid 1960s. Similarly, in most European countries, the onset of the decline was in the 1970s, while most studies have tended to focus on the last two decades of the 20th century. 148,149,265-267
Do countries experiencing more recently the onset of the decline phase of the CHD epidemics have the same trend drivers as countries with longstanding declines? In countries with long established decline phases, risk factors explain about two thirds of the observed decline, while evidence based treatments explain about one third. However, the adoption of effective treatments happened towards the end of the century, and even then, treatment uptake levels in countries with strong health care systems were disappointingly low.265,268 Thus, bigger contributions from evidence based treatments are plausible in countries that started the decline phase of the CHD epidemic more recently, since the revolution in evidence base cardiology was firmly underway in the 1990s.
As I described earlier in this chapter, Poland is a key Central European country in which to study the abrupt changes in rates. Cardiovascular disease deaths increased steadily through the seventies and eighties, continuing to the very end of the communist era. The dramatic socioeconomic changes then occurred during the transition to a market economy were accompanied
by sharp falls in mortality from 1991 onwards -one of the fastest declines in the world- resulting in substantial improvements in life expectancy, mainly attributable to decreases in cardiovascular mortality.269 Several countries in central Europe experienced equally dramatic political and socioeconomic changes in the nineties, including the Czech Republic, East Germany, Hungary and Romania.270
I have already discussed the Zatonsky/Willett hypothesis that this might be related to dietary changes, as a consequence of the elimination of subsidies for animal fats during the socialist era, resulting in a fall in the consumption of saturated fats, whilst the intakes of polyunsaturated fats, fruits and vegetables all increased following the introduction of a market economy.187,252
However, the potential contribution of treatments and other risk factors remains unclear. For instance, smoking prevalence in men also fell significantly during that period.187,253 Indeed, changes in other cardiovascular risk factors might also have played a significant role on the observed mortality decline. Furthermore, improvements in evidence-based treatments for established coronary disease also became widely used in recent decades in Poland. This included therapies for acute myocardial infarction, CHD and heart failure, hypertension and hypercholesterolemia, as well as coronary bypass surgery, coronary angioplasty and stenting.
My aim was therefore to explain the contribution of risk factor changes and evidence based treatments in the recent fall in coronary mortality observed in Poland since 1990. Such an analysis is potentially important, both for understanding past trends and for planning future strategies.
6.3.2 Methods
To explain the changes in cardiovascular mortality in Poland between 1991 and 2005 we used the IMPACT CHD mortality model. This has been previously validated in the U.K., Italy, Sweden, Canada and the U.S.144,149,267,271 A detailed description of the model methods and data source is available in appendix A2.
The model goal is to quantify what proportion of the coronary heart disease deaths prevented or postponed in the Polish population between 1991 and 2005 can be explained by risk factors and treatments. The model is comprehensive, incorporating all usual treatments for coronary heart disease and heart failure plus all major cardiovascular risk factors, including smoking, blood pressure, cholesterol, diabetes, obesity and physical activity.
All available Polish data sources were therefore systematically identified and critically reviewed as inputs in the Polish IMPACT model. The analysis was confined to adults aged between 25 and 74 years. Mortality and demographic data were taken from routine national statistics. Coronary heart disease patient numbers and treatments were obtained from cross-sectional national and local studies; country representative surveys (WOBASZ258, NATPOL260,261, Pol- MONICA259), national registries (CABG registry257, acute coronary syndromes registry255,256) and hospital discharge databases. Expert opinions were also elicited where objective data were deficient. Data quantifying changes in the prevalence of cardiovascular risk factors were taken from national representatives surveys (NATPOL, WOBASZ,) as well as from the best regional and local epidemiological studies (Pol-MONICA and CINDI WHO272). More details on the data sources of the Polish IMPACT model are available in appendix A2.
Calculating the number of deaths prevented or postponed to be explained
Age and sex specific mortality rates for coronary heart disease were obtained from the Polish Central Statistical Office. Substantial changes in the coding of causes of death in Poland were introduced in 1997. I have discussed in section 6.2 the coding quality issues concerning polish cardiovascular mortality data for the period 1991-1996, and the data used was corrected for these issues with the approach developed by Jasisnki et al.254
The number of CHD deaths expected in 2005 if the 1991 mortality rates had persisted was then subtracted from the number of deaths actually observed in 2005 to produce the fall in mortality that the model needed to explain.
We then estimated the proportions of the total number of deaths prevented or postponed (DPPs) which could be attributed to the use of treatments and to changes in cardiovascular risk factors.
Estimating Treatment benefits
The treatment arm of the Model includes the following populations of patients:
-those hospitalized with an acute myocardial infarction (AMI),
-Patients admitted to the hospital with unstable angina,
-Patients who have undergone revascularisation procedure (coronary artery bypass grafting (CABG), or a percutaneous coronary intervention (PCI), with or without stent.
-Community-dwelling patients with angina pectoris (no revascularisation)
-Patients admitted to hospital with heart failure,
-Community-dwelling patients with heart failure (no hospital admission).
-Hypertensive individuals eligible for hypertensive therapy
-Hypercholesterolemia subjects eligible for cholesterol lowering therapy
For each of the groups, we estimated the number of DPPs that were attributable to various treatments. The size of each individual group was determined using data from hospital episodes statistics, disease registers and surveys (See appendix A2.)
Data on the clinical effectiveness of each intervention and therapy were based on the most recent meta-analyses and large randomized clinical trials. Details on the magnitude of the risk reduction for each treatment and its uptake are available in appendix A2
The number of deaths prevented or postponed as a result of each individual intervention in each group of CHD patients in the year 2005 was then calculated by multiplying the number of patients in each diagnostic group by their baseline case-fatality rate over 1 year, by the proportion of these patients receiving a specific treatment, and by the relative reduction in one–year case-fatality by the administered treatment.
For example, in Poland, in 2005, approximately 12 230 men aged 55-64 were hospitalized with acute myocardial infarction. Their expected age-specific 1-year case-fatality rate without treatment was approximately 5.4%. From registry data256 96% of them were given aspirin or other antiplatelet drugs, interventions with an expected mortality reduction of 15%. The number of deaths prevented or postponed for at least a year by the use of aspirin among men aged 55 to 64 were then calculated as:
[Eq 1] 12 230 x 0.054 x 0.96 x 0.15 = 95 fewer deaths
This process was then repeated for men and women in each age group, each patient group and each therapy. Some adjustments were made to these basic analyses. Many evidence-based
therapies were not used Poland in 1991 (e. g. statins, or primary angioplasty in acute myocardial infarction). However, in some cases the use of some drugs or procedures in 1991 was not negligible (for instance antihypertensive treatment or aspirin in acute myocardial infarction). In such cases, in order to obtain the net benefit, the number of deaths prevented or postponed as a result of the therapy as used in 1991 was calculated and subtracted from the number calculated for 2005.
Compliance, (adherence, the proportion of treated patients actually taking effective levels of medication), was assumed to be 100% among hospital patients, 70% among symptomatic community patients and 50% among asymptomatic community patients.273 To avoid double counting of patients, it was necessary to identify potential overlaps between different groups of patients. For example, approximately half the patients having CABG surgery have a previous AMI220, approximately 25% of AMI survivors develop heart failure within 12 months274, and over 50% of CHD patients have a history of hypertension.275
To quantify the relative reduction in case-fatality rate for individual patients receiving multiple treatments, we used the conventional Mant and Hicks cumulative relative benefit approach276:
[Eq 2] Relative Benefit = 1 - [(1-relative reduction in case-fatality rate for treatment A) X (1- relative reduction in case-fatality rate for treatment B) X ... X (1- relative reduction in case-fatality rate for treatment N).
For example, considering appropriate treatments for AMI survivors, applying relative risk reductions (RRR) for aspirin, beta-blockers ACE inhibitors statins and rehabilitation then gives:
[Eq 3] Relative Benefit = 1 - [(1 –aspirin RRR) X (1 - beta-blockers RRR) X (1 - ACE inhibitors RRR) X (1- statins RRR) X (1- rehabilitation RRR)]
= 1 - [(1- 0.15) X (1-0.23) X (1-0.20) X (1- 0.22) X (1- 0.26)]
= 1 - [(0.85) X (0.77) X (0.80) X (0.78) X (0.74)]
= 0.70 i.e. a 70% lower case fatality
Risk factor changes and mortality benefits
We estimated the contribution of risk factors to the mortality decline by using two approached: the regression approach for continuous risk factors and the population attributable risk fraction approach for discrete risk factors.
Regression approach
Regression coefficients published in the literature were used to calculate the number of deaths prevented or postponed as result of change in systolic blood pressure, mean cholesterol concentration and body mass index (BMI). The number of deaths prevented and postponed due to change in that risk factor was then calculated as the product of the number of CHD deaths observed in the baseline year (1991), the change in risk factor level and the coefficient quantifying the change in CHD mortality per unit of absolute change in that risk factor.
For example, there were 2534 CHD deaths among women aged 55-64 years in 1991, the base year. Mean systolic blood pressure in this group decreased by 5.4 mmHg between 1991 and 2005. The largest meta-analysis demonstrates an age- and sex-specific reduction in mortality of 50 percent for every 20 mmHg reduction in systolic blood pressure, generating a logarithmic coefficient of – 0.035277. The number of deaths prevented or postponed was then estimated as:
[Eq 4] [deaths in 1991] * (1-EXP(coefficient*change)
= 2534 (1-EXP(-0.035*5.4)) = 436 fewer deaths
Population attributable risk approach
A population attributable risk fraction approach was used to assess the effect of changes in the prevalence of smoking, diabetes and physical inactivity, using the standard formula:
[Eq 5] ((P x (RR-1)) / (1+P x (RR-1)) ,
Where P = prevalence of the risk factor and RR = the relative risk for CHD mortality associated with that risk factor.
To assess the decline in CHD mortality, the number of coronary heart disease deaths in 1991 (the base year) was multiplied by the difference between the population-attributable risk fraction in 1991 and that in 2005.
We assumed that there was no further synergy between the treatment and risk factor sections of the model, or between the major risk factors because the regression coefficients and relative risks for each risk factor were each independent, being obtained from multivariate analyses. Deaths prevented or postponed as a result of risk factor changes were then systematically quantified for each patient group. Lag times between risk factor rate change and event rate change were not modelled. We assumed, as in other countries, that any time lag would be relatively unimportant over a period of fifteen years (1991-2005).278
Model validation: comparison of estimated with observed mortality changes
The model produces estimates of the total number of CHD deaths prevented or postponed attributable to each treatment and to change in each specific risk factor. These estimates were then summed and compared with the observed changes in mortality for men and women in each specific age group. Any shortfall in the overall model estimate was then presumed to be attributable either to inaccuracies in our methodology or to other, unmeasured risk factors.
Sensitivity analyses
All the above assumptions were tested in a multi-way sensitivity analysis using the analysis of extremes method.279 This method consist in choosing for each model parameter, a lower and upper value using 95% confidence intervals where available, or otherwise using + 20% values (for patient numbers, treatment uptake, and compliance), and then recalculating the model outputs using these “extremes” values.
An example of calculating lower and upper bound estimates for DPPs for treatment with aspirin among men aged 55-64 years who were hospitalized with an AMI is presented in Table 6-3.
We used 95% confidence intervals from the Antithrombotic Trialists’ Collaboration meta- analysis280 for relative mortality reduction; lower and upper bound estimates for the other parameters were calculated as minus or plus 20% [except for treatment uptake that was capped at 99%]. Multiplying all the lower-bound estimates yielded the minimum [lower bound] estimate and multiplying the upper-bound estimates yielded the maximum [upper bound] estimate.
a) Maximum and minimum values for each variable were deliberately forced to provide a wider range rather than a narrower one, e.g. relative mortality reduction +20% rather than say, +10%.
b) The resulting product, for instance the minimum estimate, was generated by assuming that the lowest feasible values all occurred at the same time, a most unlikely situation.
Table 6-3. Example of sensitivity analysis
Patient numbers Treatment Uptake Relative Mortality Reduction*
One year case fatality Deaths prevented or postponed A B C D (A x B x C x D) Best Estimate 12 226 0.96 0.15 0.054 95 Minimum estimate 9 781 0.77 11%* 0.043 36 Maximum estimate 14671 0.99 19%* 0.065 179
* 95% CI from the Antithrombotic Trialists’ Collaboration meta-analysis280
6.3.3 Results
The large decline in CHD mortality rates since 1991 resulted in 26 200 fewer CHD deaths in 2005. The model explained approximately 23 715 (91%) of this mortality decrease.
Approximately 37% of the mortality fall was attributable to treatments and approximately 54% was attributable to changes in risk factors. A good agreement between estimated and observed number of deaths was generally observed across all gender and age groups. However, in middle aged men, the number of predicted deaths prevented or postponed was underestimated.
Medical and surgical treatments
Estimated numbers of CHD deaths prevented or postponed by medical and surgical treatment in 2005 are presented in Table 6-4. All treatments accounted for approximately 9 640 fewer deaths, representing approximately 37% of the mortality decrease.
The largest reductions came from heart failure treatments in hospital and in the community, which resulted in approximately 3100 fewer deaths in 2005 (12% of the observed mortality
reduction). Initial treatments for acute myocardial infarction or unstable angina (generated approximately 2450 fewer deaths, 9% of the observed fall). Secondary prevention therapies after myocardial infarction or revascularization explained approximately 1930 (7%) fewer deaths, followed by chronic angina treatments some 710 (3%), hypertension treatments approximately 580 (2%) and statins for hypercholesterolemia in primary prevention some 880 (3%) fewer deaths.
Risk factor changes
Estimated numbers of CHD deaths prevented or postponed by changes in the exposure to risk factors are presented in Table 6-5. Approximately 14,070 of the CHD deaths prevented or postponed (54%) were attributable to changes in risk factors, of which the majority, (41% of the fall in men and 33% in women) was attributable to large decreases in mean cholesterol concentration (declining by 0.4 mmol/L).
The effects of changes in smoking and mean blood pressure in men and women were more complex. Smoking prevalence decreased in men by 15.7% explaining approximately 15% of their mortality fall. In women very little change in smoking prevalence was observed, which thus had virtually no effect on CHD mortality. Mean systolic blood pressure fell by 2.7 mmHg in men and by 5.2 mmHg in women. After subtracting the effects of hypertension treatments, these blood pressure falls explained approximately 29% of the mortality decrease in women and an 8% increase in deaths in men. Increased leisure time physical activity explained approximately 10% of the decrease in deaths in the Polish population.
However, these gains were partially offset by approximately 1810 additional deaths attributable to increases in body mass index (-4% and -5% for men and women respectively) and increasing diabetes prevalence (-1% and -8% respectively).
Sensitivity Analysis
Under the assumptions of the sensitivity analysis, the relative contributions of specific risk factor changes and treatment effects remained similar. The extreme minimum and maximum numbers of CHD deaths prevented or postponed were 14 050 (54%) and 36 840 (141%) of the observed mortality fall.
Table 6-4 Estimated coronary heart disease deaths prevented or postponed by medical and surgical treatments in Poland in 2005
Patient groups & specific treatments§
Patients Eligible† Deaths Prevented or Postponed
Number † % of total mortality fall§
Best estimate Minimum Maximum Best
estimate Minimum Maximum
Acute myocardial infarction 52 180 1 340 370 2 550 5.1 1.4 9.7
Unstable angina 105 920 1 110 550 1 850 4.2 2.1 7.1
Secondary prevention post-
myocardial infarction 213 970 1 300 520 2 650 4.9 2 10.1
Secondary prevention post-
CABG/PCI 100 890 630 260 1 310 2.4 1 5
Chronic angina 706 670 710 300 1 510 2.7 1.1 5.8
Heart failure with hospital
admission 18 330 1 470 700 3 550 5.6 2.7 13.6
Heart failure in the community 122 680 1 630 730 3 760 6.2 2.8 14.4
Hypertension treatments 8 488 520 580 -440 1 260 2.2 -1.7 4.8
Statins for primary prevention
lipid reduction 14 046 930 880 360 1 830 3.4 1.4 7
Total Treatments 9 640 3 350 20 270 36.8 12.8 77.5
†reported numbers are rounded to nearest 10. §may not sum to total due to rounding.
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Table 6-5 Estimated coronary deaths prevented or postponed as a result of risk factor changes in men and women in Poland 1991 - 2005
Population risk factor
Absolute level of
risk factor Change in risk factor
Beta regression coefficient
RR
Deaths Prevented or Postponed
Number of deaths† Percent of total reduction‡
1991 2005 Absolute
change
Relative change (%)
Best
estimate§ Min§ Max§
Best % Estimate Min % Max % Smoking prevalence (%) men 55.8 40.1 -15.7 -28 3.1 2980 2390 3580 15% 12% 18% women 28.1 25.1 -3 -4 4.2 -10 -10 -10 0% 0% 0% Systolic blood pressure (mmHg) * men 140.1 137.4 -2.7 -1.8 -0.034 -1720 -1250 -2380 -8% -1% -12% women 136.6 131.5 -5.2 -3.4 -0.042 1690 1100 2360 29% 19% 40% Total cholesterol (mmol/l)§§ men 5.6 5.2 -0.4 -8.6 -0.95 8390 6010 10340 41% 29% 51% women 5.6 5.2 -0.4 -7.6 -0.91 1920 1440 2200 33% 25% 38% Physical inactivity (%) men 64.6 38.7 -25.9 -40.1 1.29 2000 1600 2400 10% 8% 12% women 68.8 44.5 -24.3 -35.3 1.35 630 510 760 11% 9% 13% Body mass index (kg/m2) men 26 26.9 0.9 3.2 0.03 -870 -480 -1340 -4% -2% -7% women 25.7 26.6 0.9 3.2 0.027 -290 -160 -450 -5% -3% -8% Diabetes prevalence (%) men 2.9 3.3 0.4 12.7 2.47 -190 -130 -250 -1% -1% -1% women 3.3 4.2 0.9 28.5 3.4 -460 -310 -630 -8% -5% -11% Total risk factors Men 10 600 8130 12340 52% 40% 61% Women 3 480 2570 4230 60% 44% 73%
RR: relative risk §antihypertensive treatment effects subtracted
†reported numbers are rounded to nearest 10. ‡ may not sum to total due to rounding. §§ statin effects subtracted.
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6.3.4 Interpretation
There were 26,200 fewer coronary deaths in Poland in 2005 compared with 1991, approximately 55% being attributable to beneficial changes in risk factors and 37% to the increased use of evidence-based treatments.
The major contributors to the mortality fall were large falls in total cholesterol, plus beneficial reductions in systolic blood pressure in women and decreased smoking in men. Physical activity also contributed to the decline in deaths. Worryingly, adverse trends negated some of these benefits, specifically increases in the prevalence of obesity, diabetes and blood pressure levels in men and smoking prevalence in women.
The most important treatment contributions came from therapies for heart failure, angina