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Does Inclusion of Education and Marital Status Improve SCORE Performance in Central and Eastern Europe and Former Soviet Union? Findings from MONICA and HAPIEE Cohorts

  • Olga Vikhireva ,

    Affiliation Epidemiology and Public Health Department, University College London, London, United Kingdom

  • Grazyna Broda †,

    † Deceased.

    Affiliation Department of CVD Epidemiology, Prevention, and Health Promotion, the Cardinal Stefan Wyszynski Institute of Cardiology, Warsaw, Poland

  • Ruzena Kubinova,

    Affiliation Environmental Health Monitoring System, National Institute of Public Health, Prague, Czech Republic

  • Sofia Malyutina,

    Affiliations Institute of Internal and Preventive Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia, State Novosibirsk Medical University, Novosibirsk, Russia

  • Andrzej Pająk,

    Affiliation Department of Epidemiology and Population Sciences, Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Krakow, Poland

  • Abdonas Tamosiunas,

    Affiliation Laboratory of Population Research, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania

  • Zdena Skodova,

    Affiliation Preventive Cardiology Department, Institute of Clinical and Experimental Medicine, Prague, Czech Republic

  • Galina Simonova,

    Affiliation Institute of Internal and Preventive Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk, Russia

  • Martin Bobak,

    Affiliation Epidemiology and Public Health Department, University College London, London, United Kingdom

  • Hynek Pikhart

    Affiliation Epidemiology and Public Health Department, University College London, London, United Kingdom

Does Inclusion of Education and Marital Status Improve SCORE Performance in Central and Eastern Europe and Former Soviet Union? Findings from MONICA and HAPIEE Cohorts

  • Olga Vikhireva, 
  • Grazyna Broda, 
  • Ruzena Kubinova, 
  • Sofia Malyutina, 
  • Andrzej Pająk, 
  • Abdonas Tamosiunas, 
  • Zdena Skodova, 
  • Galina Simonova, 
  • Martin Bobak, 
  • Hynek Pikhart


Background and Objective

The SCORE scale predicts the 10-year risk of fatal atherosclerotic cardiovascular disease (CVD), based on conventional risk factors. The high-risk version of SCORE is recommended for Central and Eastern Europe and former Soviet Union (CEE/FSU), due to high CVD mortality rates in these countries. Given the pronounced social gradient in cardiovascular mortality in the region, it is important to consider social factors in the CVD risk prediction. We investigated whether adding education and marital status to SCORE benefits its prognostic performance in two sets of population-based CEE/FSU cohorts.


The WHO MONICA (MONItoring of trends and determinants in CArdiovascular disease) cohorts from the Czech Republic, Poland (Warsaw and Tarnobrzeg), Lithuania (Kaunas), and Russia (Novosibirsk) were followed from the mid-1980s (577 atherosclerotic CVD deaths among 14,969 participants with non-missing data). The HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study follows Czech, Polish (Krakow), and Russian (Novosibirsk) cohorts from 2002–05 (395 atherosclerotic CVD deaths in 19,900 individuals with non-missing data).


In MONICA and HAPIEE, the high-risk SCORE ≥5% at baseline strongly and significantly predicted fatal CVD both before and after adjustment for education and marital status. After controlling for SCORE, lower education and non-married status were significantly associated with CVD mortality in some samples. SCORE extension by these additional risk factors only slightly improved indices of calibration and discrimination (integrated discrimination improvement <5% in men and ≤1% in women).


Extending SCORE by education and marital status failed to substantially improve its prognostic performance in population-based CEE/FSU cohorts.


The SCORE (Systematic COronary Risk Evaluation) scale is a widely used instrument for predicting the risk of future cardiovascular disease (CVD) across European populations [1]. It estimates the 10-year risk of cardiovascular mortality based on age, gender, blood lipids, blood pressure (BP), and smoking, in 40–65-year-olds free of manifested CVD. Two versions of SCORE were created for high- and low-risk European countries. The European Society of Cardiology (ESC) recommends applying the high-risk SCORE to the populations of Central and Eastern Europe (CEE) and former Soviet Union (FSU) [2], although this version was derived without reference to local data.

As SCORE includes only conventional cardiovascular risk factors, there have been ongoing attempts to improve its performance by adding resting heart rate [3], high-density lipoprotein cholesterol [4], and other factors [5], [6] to the model. Across CEE/FSU populations, the majority of which still face high levels of fatal CVD [7], pronounced and increasing socioeconomic and sociodemographic differentials in all-cause and cardiovascular mortality have been reported [8][10]. Moreover, education and marital status have been shown to independently predict cardiovascular risk in CEE/FSU populations [11][13]. These two easily and routinely assessed parameters reflect different pathways between social circumstances and CVD. Education can act via life-style behaviours, problem-solving abilities, and acquisition of positive social, psychological and economic skills and assets [14], [15], while marital status can affect CVD risk through social connections, a sense of social and familial role, socioeconomic support, and facilitation of health-promoting behaviours [16][19]. Therefore, education and marital status are likely candidates for inclusion in cardiovascular risk models, together with conventional risk factors. However, to the best of our knowledge, the prognostic performance of CVD risk scales extended by these characteristics, or other socioeconomic and sociodemographic parameters, has not been assessed in CEE/FSU.

The aim of our study was to investigate whether SCORE calibration and discrimination improve after extension by such social indicators as education and marital status, using two sets of population-based CEE/FSU cohorts.


Study Population and Samples

We used the data from two international multi-centre studies – the World Health Organization (WHO) MONICA (MONItoring of trends and determinants in CArdiovascular disease) Project [20], [21] and HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study [22].

The WHO MONICA Project monitored the trends in CVD rates and risk factors in 38 populations from the mid-1980s to at least the mid-1990s [20], [21]. Risk factors were assessed in random population samples of men and women aged 35–64. In some centres, the study samples were followed for mortality. We obtained the baseline data and the data on subsequent 10-year mortality for the following MONICA samples: the Czech sample from six country districts examined in 1992; Polish Warsaw and Tarnobrzeg samples screened in 1983–84 and 1987–89; Lithuanian Kaunas samples examined in 1983–85, 1986–87, and 1992–93; and Russian Novosibirsk samples screened in 1985–86, 1988–89, and 1994–95. The numbers of subjects and response rates are shown in Table 1. At baseline, participants completed a questionnaire survey, underwent a clinical examination, and provided a blood sample. The mortality follow-up used the data from national and local mortality registers [20], [21].

Table 1. Description of participating cohorts and selection of analytical samples.

HAPIEE is a multi-centre study of CVD and other chronic conditions in CEE/FSU [22]. It follows random population samples of men and women aged 45–69 at baseline (2002–05) from the Czech Republic (Havířov/Karviná, Hradec Králové, Jihlava, Kroměříž, Liberec, and Ústí nad Labem), Poland (Krakow), Russia (Novosibirsk), and Lithuania (Kaunas). The four cohorts have been followed for cause-specific mortality and non-fatal CVD. As the Lithuanian cohort was established several years later and had fewer CVD deaths, it was not included in our analyses. The baseline data collection included a structured questionnaire survey and physical examination, with a fasting venous blood sample collection. In Czech and Polish HAPIEE, the study questionnaire was completed at home, prior to medical examination in a clinic. This explains the smaller proportion of Czech and Polish participants with non-missing data. Mortality data come from national (the Czech Republic) and local (Poland and Russia) registers [22].

As SCORE predicts cardiovascular risk in individuals over 40 and without pre-existing atherosclerotic CVD [1], we excluded subjects aged <40 at baseline and those with medical evidence or a self-reported history of doctor-diagnosed myocardial infarction, angina, or stroke. Overall, data on conventional, SCORE-comprising risk factors, education, and marital status were available for 14,969 MONICA subjects and 19,900 HAPIEE participants (Table 1).

Ethics Statement

The MONICA protocol was approved by the local ethics committees in each participating country: the Institute of Clinical and Experimental Medicine Ethics Committee (Prague, the Czech Republic), the National Institute of Cardiology Ethics Committee (Warsaw, Poland), the Jagellonian University Ethics Committee (Krakow, Poland), the Kaunas University Ethics Committee (Kaunas, Lithuania), and the Institute of Internal and Preventive Medicine Ethics Committee (Novosibirsk, Russia) [20]. The HAPIEE protocol was approved by the University College London/University College London Hospital Ethics Committee (London, UK) and by local ethics committees at each study centre: the National Institute of Public Health Ethics Committee (Prague, the Czech Republic), the Jagellonian University Ethics Committee (Krakow, Poland), and the Institute of Internal and Preventive Medicine Ethics Committee (Novosibirsk, Russia) [22]. Both studies were conducted according to the standards of the Declaration of Helsinki. All participants provided written informed consent.


The SCORE risk predictors include age, sex, smoking status, total cholesterol (TC), and systolic BP (SBP). The measurement of these parameters in MONICA and HAPIEE participants is described in detail elsewhere [20], [22], [23]. Individuals currently and regularly smoking at least one cigarette per day were regarded as current smokers; never and ex-smokers were considered non-smokers, according to the SCORE criteria [1]. SBP and TCH measurement was subjected to extensive quality control.

In the original categories of education and marital status (five and four categories, respectively), the numbers of atherosclerotic CVD deaths were not sufficient for adequately powered analyses. Therefore, these factors were dichotomised and defined as “higher” (university, secondary, or vocational) vs. “lower” (primary or less) education and “married” (married/cohabiting) vs. “non-married” (single, divorced/separated, or widowed) status. In line with the SCORE end-points [1], the study outcome was atherosclerotic cardiovascular death (International Classification of Diseases (ICD) 9 codes: 401–414, 426–443 (except 426.7, 429.0, 430.0, 432.1, 437.3, 437.4, and 437.5), 798.1 and 798.2; ICD-10 codes: I10-I15, I20-I25, I44-I73 (except I45.6, I51.4, I52, I60, I62, I67.1, I67.5 and I67.7), R96.0, and R96.1).

Statistical Analyses

The high-risk version of the SCORE scale, recommended by the ESC for CEE/FSU populations [2], was used to predict the risk of fatal atherosclerotic CVD in all MONICA and HAPIEE samples. The recently introduced Czech and Polish SCORE versions were not used, as they lack a detailed description of their development and recalibration [24], [25].

The prognostic performance of risk prediction scales, such as SCORE, could be assessed via calibration and discrimination [26], [27]. Calibration reflects the closeness between predicted and observed risks. Discrimination shows how accurately the participants who will experience events (such as fatal CVD) during the follow-up are separated from those who will remain event-free. Better calibration and discrimination are denoted, respectively, by lower χ2 values and higher p-values in the Hosmer-Lemeshow test [28] and higher values of the Harrell’s C-statistic [29]. The additional prognostic information, provided by extra risk predictors, could be assessed in likelihood ratio tests (LRT). Lower LRT p-values denote more pronounced differences between the nested baseline and extended models and, hence, better predictive performance of the latter. More clinically relevant parameters of risk reclassification are net reclassification index and integrated discrimination improvement (IDI). As our additional risk factors were dichotomized and, therefore, specific to MONICA and HAPIEE samples, we used IDI, which is relatively independent of risk thresholds and categories and reflects the extended model’s ability to improve average sensitivity without compromising average specificity [30], [31].

We analysed the prognostic ability of the extended SCORE separately for men and women in each MONICA and HAPIEE sample. First, we explored the role of SCORE, education, and marital status as fatal CVD predictors, using Cox regression models. We then investigated calibration and discrimination of the SCORE extended by education and marital status, calculating Hosmer-Lemeshow χ2, Harrell’s C, LRT p, and IDI. The SCORE performance was compared for the baseline Model 1 (SCORE only) vs. Model 2 (SCORE and education), Model 3 (SCORE and marital status), and Model 4 (SCORE, education, and marital status).

The use of Cox proportional hazards regression models was justified by the high p-values in Schoenfeld’s test. The data from all MONICA waves were pooled within samples, as the SCORE-fatal CVD association demonstrated no evidence of confounding by or statistical interaction with the study wave. The competing-risk regression analyses [32], accounting for the risk of death from causes other than atherosclerotic CVD, produced very similar results (not presented) to those of the standard Cox analyses. No significant interactions between SCORE and additional risk factors were detected. Simultaneously extending SCORE by education and marital status was possible, due to low values of phi correlation coefficient (not presented). To enable comparisons between non-extended and extended models, the analyses included only subjects with known values of conventional and additional risk factors. Additional sensitivity analyses used multiply imputed data for the samples with the highest proportion of missing baseline SCORE values – Czech MONICA, Lithuanian MONICA, Czech HAPIEE, and Polish HAPIEE. The imputation model, which was based on the chained equations approach [33] and generated 10 imputations, included SCORE, education, marital status, atherosclerotic CVD death, and logarithm of survival time.

The SCORE calibration in HAPIEE was affected by the fact that the current follow-up of HAPIEE samples is less than 10 years (Table 2). However, we focused on the changes in calibration after the SCORE extension by education and marital status, rather than on SCORE calibration per se. As the Hosmer-Lemeshow test quantifies the agreement between predicted and observed events across risk deciles, it was applied to the non-dichotomised SCORE, which treats individual levels of absolute risk (percentages in the respective SCORE chart cells) as a continuous variable. All statistical analyses were performed using Stata/IC 12.0 (StataCorp LP, Texas, USA).


Description of the Study Samples

The baseline characteristics of our analytical samples are presented in Table 2. The mean age of participants was close to 52 years in MONICA and 57 years in HAPIEE, with relatively similar sizes of the five-year age groups. Smoking prevalence was high in both studies, with the exception of MONICA women from Tarnobrzeg, Kaunas, and Novosibirsk. Czech and Polish men participating in HAPIEE smoked less than their MONICA counterparts. By contrast, among Russian women, smoking prevalence was higher in HAPIEE than in MONICA. In both studies, mean levels of TC were close to 6 mmol/l and tended to be slightly higher in women. The highest mean SBP levels, exceeding 140 mm Hg, were observed for MONICA participants from Warsaw and Czech HAPIEE men. The proportion of lower-educated people was markedly higher in MONICA than in HAPIEE. In both studies, the majority of participants were married. The median follow-up duration was 10 years in MONICA and 6.2–8.2 years in HAPIEE (Table 2). The highest percentage of atherosclerotic CVD deaths was observed for Russian MONICA and HAPIEE men. In women, the observed risk of fatal CVD was lower, but reflected the same ranking.

SCORE, Education, and Marital Status as Predictors of Fatal Atherosclerotic CVD

As shown in Tables 34, in most MONICA and HAPIEE samples, the high-risk SCORE ≥5% at baseline was a significant predictor of atherosclerotic CVD mortality, both before and after controlling for education, marital status, or both (adjusted hazard ratios (HR) 1.5–5.8 for MONICA and 2.7–8.5 for HAPIEE). After adjustment for SCORE, a significant association between fatal CVD and education was demonstrated in four out of eight male MONICA and HAPIEE samples; for the association between fatal CVD and marital status, this figure was five out of eight (Table 3). For female MONICA and HAPIEE samples, the respective figures were three out of eight and one out of eight (Table 4).

Table 3. Prognostic performance of the high-risk SCORE (≥5% vs. <5%) in MONICA and HAPIEE men before and after inclusion of education (lower vs. higher) and marital status (non-married vs. married).

Table 4. Prognostic performance of the high-risk SCORE (≥5% vs. <5%) in MONICA and HAPIEE women before and after inclusion of education (lower vs. higher) and marital status (non-married vs. married).

Calibration and Discrimination of SCORE Extended by Education and Marital Status

In most MONICA samples, the extension of SCORE by education slightly reduced Hosmer-Lemeshow’s χ2 values, which indicated a modest improvement in the SCORE calibration (Tables 34). The addition of marital status to SCORE improved calibration only in Czech men, Polish women from Tarnobrzeg, and Lithuanian women. Across most HAPIEE samples, adding education and marital status to SCORE somewhat improved its calibration.

A slight increase in Harrell’s C-statistic was observed for MONICA and HAPIEE men and women, once education and marital status were added to SCORE. According to the LRT results, the extended SCORE demonstrated improved discrimination in some MONICA and HAPIEE samples. However, in both studies, the values of IDI were <5% in men and ≤1% in women, which suggests a modest improvement in SCORE performance.

Overall, calibration and discrimination of the original, non-extended SCORE appeared to be similar, or only marginally worse, compared to the SCORE modifications extended by education and/or marital status.


To the best of our knowledge, this is the first attempt to assess the prognostic performance of SCORE extended by education and marital status in CEE/FSU. The high-risk SCORE significantly predicted atherosclerotic cardiovascular mortality in two sets of population-based cohorts, both before and after adjustment for additional risk factors. Education and marital status were significantly and independently from SCORE associated with fatal CVD in some samples. However, adding these factors to SCORE did not substantially improve its calibration and discrimination, which justifies the use of the original, non-extended scale.

Strengths and Limitations

Several methodological issues should be considered when interpreting our results. First, we used the data from two separate studies, which covered different historical periods, characterized by major albeit heterogeneous changes in several domains: conventional cardiovascular risk factors (for example, smoking prevalence declined among Czech men, while it increased among Russian women [34]); socioeconomic and sociodemographic characteristics (such as improved educational attainment [7], but also rising income inequalities and decreasing proportion of married/cohabiting people [35]); and diverging trends in cardiovascular mortality after 1990 [7], which cannot be explained by the change from the ICD-9 to ICD-10 classification in the late 1990s [36][41]. We acknowledge that, since our data come from two sets of studies in population samples, our estimates of a longer-term mortality risk may not fully represent the trends in national rates.

Second, although MONICA and HAPIEE samples are not representative for the whole countries (for example, they are predominantly urban), they are the best available CEE/FSU sources of individual-level cohort data on the levels of CVD risk factors and mortality in the 1980–2000s. The comparability of MONICA and HAPIEE data was high, due to the similarity of the study protocols.

Third, as in most epidemiological studies, both MONICA and HAPIEE participants tended to be healthier and more affluent than non-responders. This potential discrepancy could be enhanced by the complete case analyses and dilute the association of interest. The available multiple imputation methods use the assumption of data missing (completely) at random [42], [43]. However, this assumption was unlikely to be met in our samples, as suggested by the typically higher levels of total and CVD mortality across study- and country-specific subgroups with missing vs. available SCORE. While excluding the observations with non-randomly missing values is not entirely bias-free, in our sensitivity analyses the Cox regression results across the samples with the highest SCORE missingness were similar for complete and multiply imputed data (not presented). Therefore, the possible selection bias due to non-response and SCORE missingness and the resulting potential underestimation of the strength of the association between CVD risk factors and mortality were unlikely to be substantial.

Fourth, education and marital status are only two parameters out of the wide range of social indicators which potentially influence CVD risk. For example, material deprivation may have strong effects on cardiovascular mortality [44], [45]. However, we focused on education and marital status, as they are collected in most epidemiological and clinical studies and are easily measurable, even in the primary care settings. Moreover, education and marital status reflect different pathways between social circumstances and CVD, and they have both been shown to independently predict cardiovascular risk across CEE/FSU populations [11][13]. The dichotomisation of education and marital status, while increasing the risk of residual confounding, was necessary in order to obtain sufficient numbers of atherosclerotic CVD deaths across exposure categories (see Methods). The marked decrease in the prevalence of lower education between MONICA and HAPIEE cohorts (for example, from 64% in Czech MONICA men to 5% in Czech HAPIEE men) reflects improved access to further education in more recent birth cohorts [7], as well as the urban nature of the HAPIEE cohort (Czech MONICA sample was not exclusively urban). Some differences in the contextual meaning of lower education or non-married status across countries and over time, specifically in terms of their influence on cardiovascular risk, are possible. Nonetheless, these potential differences were unlikely to substantially affect the magnitude of the associations between socioeconomic parameters, SCORE, and fatal CVD, as our analyses were study- and country-specific.

Finally, the baseline levels of conventional and additional risk factors were likely to change during the follow-up period and, therefore, result in potential regression dilution bias, or underestimation of the association of interest [46]. However, the estimation of the future outcome risk based on the current exposure levels agrees with the general concept of risk prediction.

Consistency with other Studies

The validity of our findings, despite the potential limitations discussed above, is supported by the fact that the levels of major risk factors and CVD mortality in MONICA and HAPIEE samples reflect the respective national cross-sectional estimates and trends presented in the WHO Global InfoBase [34] and WHO systematic reviews and reports [47][49].

SCORE significantly predicted cardiovascular risk, independently of social characteristics, not only across MONICA and HAPIEE samples, but also in adults from Austria [50] and Greece [51]. In most MONICA and HAPIEE samples, lower education and non-married status were linked to an increase in CVD risk even after controlling for SCORE. This agrees with the results for male participants of the Russian Lipid Research Clinics (LRC) Study [11] and MONICA-Novosibirsk [12], as well as with the findings from USA [52], [53], Finland [54], the Netherlands [55], and UK [18], [56], [57].

The lack of statistically significant, SCORE-independent associations between fatal CVD and education or marital status in most MONICA and HAPIEE samples could be partly explained by the limited numbers of atherosclerotic cardiovascular deaths. Moreover, the mechanisms of adverse effects of lower education and non-married status on cardiovascular health involve conventional risk factors, such as smoking [56]. Controlling for these factors, captured by SCORE, might over-adjust the association between education, marital status, and CVD mortality.

There is an extensive evidence of a marked and increasing social gradient in all-cause and cardiovascular mortality in CEE/FSU populations [8][10], [12], [58]. Therefore, we hypothesized that education and marital status, once added to conventional risk factors, would improve the cardiovascular risk prediction in CEE/FSU. However, in both MONICA and HAPIEE, the SCORE calibration and discrimination were very similar for the original (non-extended) scale and for the scale extended by education and marital status.

While we did not find relevant external evidence from CEE/FSU, several American and British studies reported no or minimal improvement in the performance of the Framingham coronary risk scale extended by various socioeconomic parameters [59][61]. Other studies have shown a better prognostic performance of the cardiovascular risk scales incorporating socioeconomic characteristics (such as ASSIGN and QRISK/QRISK2), compared to traditional instruments (such as the Framingham scale), but they assessed non-nested models [62][67] and, therefore, could not directly address the issue of incremental prognostic value of socioeconomic parameters. In this respect, our results are consistent with the negative findings from the studies that used nested models [59][61].


Our main finding – little improvement in the SCORE prognostic performance after inclusion of education and marital status – has several implications. First, our study supported the use of the original, non-extended SCORE in CEE/FSU populations and, therefore, confirmed the important role of conventional risk factors. Controlling them at both the population and individual levels should reduce the CVD burden in CEE/FSU and prevent the reversal of declining CVD rates elsewhere [2], [68], [69]. Second, other sociodemographic and socioeconomic parameters, such as area-level deprivation, when added to SCORE separately or in combination, could independently predict fatal CVD and have incremental prognostic value in specific populations. However, if these parameters are to be routinely used, they need to be easily and objectively measured. Finally, there is a growing interest in the use of extended risk models among individuals at intermediate risk [26], [70][72]. The latest ESC guidelines [2], published after our findings were obtained, recommend the novel biomarker measurement and cardiovascular imaging among asymptomatic adults at “moderate risk” (SCORE ≥1% and <5%). Therefore, it is important to investigate whether additional risk factors provide clinically and statistically significant improvement in the SCORE performance among people with intermediate risk levels.


We thank study participants and local collaborators in the Czech Republic, Poland, Lithuania, and Russia. We appreciate the important contribution of our deceased coauthor, Professor Grazyna Broda, who sadly passed away in July 2013.

Author Contributions

Conceived and designed the experiments: OV GB RK SM AP AT ZS GS MB HP. Performed the experiments: OV GB RK SM AP AT ZS GS MB HP. Analyzed the data: OV. Wrote the paper: OV MB HP. Revised the manuscript critically for important intellectual content and approved the final draft: OV GB RK SM AP AT ZS GS MB HP.


  1. 1. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, et al. (2003) Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24: 987–1003.
  2. 2. Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, et al. (2012) European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 33: 1635–1701.
  3. 3. Cooney MT, Dudina A, De Bacquer D, Fitzgerald A, Conroy R, et al. (2009) How much does HDL cholesterol add to risk estimation? A report from the SCORE Investigators. Eur J Cardiovasc Prev Rehabil 16: 304–314.
  4. 4. Cooney MT, Vartiainen E, Laatikainen T, Joulevi A, Dudina A, et al. (2010) Simplifying cardiovascular risk estimation using resting heart rate. Eur Heart J 31: 2141–2147.
  5. 5. Sehestedt T, Jeppesen J, Hansen TW, Wachtell K, Ibsen H, et al. (2010) Risk prediction is improved by adding markers of subclinical organ damage to SCORE. Eur Heart J 31: 883–891.
  6. 6. Schottker B, Muller H, Rothenbacher D, Brenner H (2013) Fasting plasma glucose and HbA(1c) in cardiovascular risk prediction: a sex-specific comparison in individuals without diabetes mellitus. Diabetologia 56: 92–100.
  7. 7. World Health Organisation (2013) European Health for All database, World Health Organization Regional Office for Europe, Copenhagen, Denmark. Accessed 7 February 2014.
  8. 8. Shkolnikov VM, Leon DA, Adamets S, Andreev E, Deev A (1998) Educational level and adult mortality in Russia: an analysis of routine data 1979 to 1994. Soc Sci Med 47: 357–369.
  9. 9. Murphy M, Bobak M, Nicholson A, Rose R, Marmot M (2006) The widening gap in mortality by educational level in the Russian Federation, 1980–2001. Am J Public Health 96: 1293–1299.
  10. 10. Leinsalu M, Stirbu I, Vagero D, Kalediene R, Kovacs K, et al. (2009) Educational inequalities in mortality in four Eastern European countries: divergence in trends during the post-communist transition from 1990 to 2000. Int J Epidemiol 38: 512–525.
  11. 11. Dennis BH, Zhukovsky GS, Shestov DB, Davis CE, Deev AD, et al. (1993) The association of education with coronary heart disease mortality in the USSR Lipid Research Clinics Study. Int J Epidemiol 22: 420–427.
  12. 12. Malyutina S, Bobak M, Simonova G, Gafarov V, Nikitin Y, et al. (2004) Education, marital status, and total and cardiovascular mortality in Novosibirsk, Russia: a prospective cohort study. Ann Epidemiol 14: 244–249.
  13. 13. Sidorenkov O, Nilssen O, Grjibovski AM (2012) Determinants of cardiovascular and all-cause mortality in northwest Russia: a 10-year follow-up study. Ann Epidemiol 22: 57–65.
  14. 14. Winkleby MA, Jatulis DE, Frank E, Fortmann SP (1992) Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health 82: 816–820.
  15. 15. Galobardes B, Lynch J, Smith GD (2007) Measuring socioeconomic position in health research. Br Med Bull 81–82: 21–37.
  16. 16. Hajdu P, McKee M, Bojan F (1995) Changes in premature mortality differentials by marital status in Hungary and in England and Wales. Eur J Public Health 5: 259–264.
  17. 17. Robles TF, Kiecolt-Glaser JK (2003) The physiology of marriage: pathways to health. Physiol Behav 79: 409–416.
  18. 18. Molloy GJ, Stamatakis E, Randall G, Hamer M (2009) Marital status, gender and cardiovascular mortality: behavioural, psychological distress and metabolic explanations. Soc Sci Med 69: 223–228.
  19. 19. Whisman MA, Uebelacker LA, Settles TD (2010) Marital distress and the metabolic syndrome: linking social functioning with physical health. J Fam Psychol 24: 367–370.
  20. 20. Tunstall-Pedoe H (2003) World Health Organisation MONICA Project. MONICA Monograph and Multimedia Sourcebook: World’s largest study of heart disease, stroke, risk factors and population trends 1979–2002. Geneva: World Health Organisation.
  21. 21. Kuulasmaa K, Tunstall-Pedoe H, Dobson A, Fortmann S, Sans S, et al. (2000) Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet 355: 675–687.
  22. 22. Peasey A, Bobak M, Kubinova R, Malyutina S, Pajak A, et al. (2006) Determinants of cardiovascular disease and other non-communicable diseases in Central and Eastern Europe: rationale and design of the HAPIEE study. BMC Public Health 6: 255.
  23. 23. Vikhireva O, Pajak A, Broda G, Malyutina S, Tamosiunas A, et al. (2013) SCORE performance in Central and Eastern Europe and former Soviet Union: MONICA and HAPIEE results. Eur Heart J 35: 571–577.
  24. 24. Cifkova R, Byma S, Ceska R, Horky K, Karen I, et al. (2005) Prevence kardiovaskularnich onemocneni v dospelem veku. Spolecne doporuceni ceskych odbornych spolecnosti. Supplementum Cor Vasa 47: 3–14.
  25. 25. Podolec P, Kopec G, Pajak A, Undas A, Kozek E, et al. (2007) Polish forum for prevention guidelines on cardiovascular risk assessment. Kardiol Pol 65: 100–104.
  26. 26. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, et al. (2009) Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119: 2408–2416.
  27. 27. Royston P, Moons KG, Altman DG, Vergouwe Y (2009) Prognosis and prognostic research: Developing a prognostic model. BMJ 338: b604.
  28. 28. Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S (1997) A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 16: 965–980.
  29. 29. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15: 361–387.
  30. 30. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27: 157–172.
  31. 31. Pencina MJ, D’Agostino RB, Vasan RS (2010) Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med 48: 1703–1711.
  32. 32. Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94: 496–509.
  33. 33. Kenward MG, Carpenter J (2007) Multiple imputation: current perspectives. Stat Methods Med Research 16: 199–218.
  34. 34. World Health Organisation (2011) Global InfoBase, World Health Organization, Geneva, Switzerland. Accessed 7 February 2014.
  35. 35. United Nations (2008) Demographic Yearbook 2006. New York: United Nations Statistics Division.
  36. 36. Andreev EM, Nolte E, Shkolnikov VM, Varavikova E, McKee M (2003) The evolving pattern of avoidable mortality in Russia. Int J Epidemiol 32: 437–446.
  37. 37. Zaridze D, Maximovitch D, Lazarev A, Igitov V, Boroda A, et al. (2009) Alcohol poisoning is a main determinant of recent mortality trends in Russia: evidence from a detailed analysis of mortality statistics and autopsies. Int J Epidemiol 38: 143–153.
  38. 38. Cifkova R, Skodova Z, Bruthans J, Holub J, Adamkova V, et al. (2010) Longitudinal trends in cardiovascular mortality and blood pressure levels, prevalence, awareness, treatment, and control of hypertension in the Czech population from 1985 to 2007/2008. J Hypertens 28: 2196–2203.
  39. 39. Grigoriev P, Shkolnikov V, Andreev E, Jasilionis D, Jdanov D, et al. (2011) Mortality in Belarus, Lithuania, and Russia: divergence in recent trends and possible explanations. Eur J Population 26: 245–274.
  40. 40. Bandosz P, O’Flaherty M, Drygas W, Rutkowski M, Koziarek J, et al. (2012) Decline in mortality from coronary heart disease in Poland after socioeconomic transformation: modelling study. BMJ 344: d8136.
  41. 41. Bruthans J, Cifkova R, Lanska V, O’Flaherty M, Critchley JA, et al. (2012) Explaining the decline in coronary heart disease mortality in the Czech Republic between 1985 and 2007. Eur J Prev Cardiol [Epub ahead of print].
  42. 42. Donders AR, van der Heijden GJ, Stijnen T, Moons KG (2006) Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 59: 1087–1091.
  43. 43. Rubin DB (1996) Multiple imputation after 18+ years. J Am Stat Assoc 91: 473–489.
  44. 44. Kaplan GA, Keil JE (1993) Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 88: 1973–1998.
  45. 45. Harper S, Lynch J, Smith GD (2011) Social determinants and the decline of cardiovascular diseases: understanding the links. Annu Rev Public Health 32: 39–69.
  46. 46. Clarke R, Shipley M, Lewington S, Youngman L, Collins R, et al. (1999) Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol 150: 341–353.
  47. 47. Farzadfar F, Finucane MM, Danaei G, Pelizzari PM, Cowan MJ, et al. (2011) National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3.0 million participants. Lancet 377: 578–586.
  48. 48. Danaei G, Finucane MM, Lin JK, Singh GM, Paciorek CJ, et al. (2011) National, regional, and global trends in systolic blood pressure since 1980: systematic analysis of health examination surveys and epidemiological studies with 786 country-years and 5.4 million participants. Lancet 377: 568–577.
  49. 49. World Health Organisation (2011) Global status report on alcohol and health. Geneva: World Health Organisation.
  50. 50. Ulmer H, Kollerits B, Kelleher C, Diem G, Concin H (2005) Predictive accuracy of the SCORE risk function for cardiovascular disease in clinical practice: a prospective evaluation of 44 649 Austrian men and women. Eur J Cardiovasc Prev Rehabil 12: 433–441.
  51. 51. Vlismas K, Panagiotakos DB, Pitsavos C, Chrysohoou C, Skoumas Y, et al. (2011) The role of dietary and socioeconomic status assessment on the predictive ability of the HellenicSCORE. Hellenic J Cardiol 52: 391–398.
  52. 52. Feldman JJ, Makuc DM, Kleinman JC, Cornoni-Huntley J (1989) National trends in educational differentials in mortality. Am J Epidemiol 129: 919–933.
  53. 53. Fiscella K, Franks P (2004) Should years of schooling be used to guide treatment of coronary risk factors? Ann Fam Med 2: 469–473.
  54. 54. Laaksonen M, Talala K, Martelin T, Rahkonen O, Roos E, et al. (2008) Health behaviours as explanations for educational level differences in cardiovascular and all-cause mortality: a follow-up of 60 000 men and women over 23 years. Eur J Public Health 18: 38–43.
  55. 55. Mendes de Leon CF, Appels AW, Otten FW, Schouten EG (1992) Risk of mortality and coronary heart disease by marital status in middle-aged men in the Netherlands. Int J Epidemiol 21: 460–466.
  56. 56. Ben-Shlomo Y, Smith GD, Shipley M, Marmot MG (1993) Magnitude and causes of mortality differences between married and unmarried men. J Epidemiol Community Health 47: 200–205.
  57. 57. Ebrahim S, Wannamethee G, McCallum A, Walker M, Shaper AG (1995) Marital status, change in marital status, and mortality in middle-aged British men. Am J Epidemiol 142: 834–842.
  58. 58. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, et al. (2008) Socioeconomic inequalities in health in 22 European countries. N Engl J Med 358: 2468–2481.
  59. 59. Ramsay SE, Morris RW, Whincup PH, Papacosta AO, Thomas MC, et al. (2011) Prediction of coronary heart disease risk by Framingham and SCORE risk assessments varies by socioeconomic position: results from a study in British men. Eur J Cardiovasc Prev Rehabil 18: 186–193.
  60. 60. Macleod J, Metcalfe C, Smith GD, Hart C (2007) Does consideration of either psychological or material disadvantage improve coronary risk prediction? Prospective observational study of Scottish men. J Epidemiol Community Health 61: 833–837.
  61. 61. Fiscella K, Tancredi D, Franks P (2009) Adding socioeconomic status to Framingham scoring to reduce disparities in coronary risk assessment. Am Heart J 157: 988–994.
  62. 62. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, et al. (2007) Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 335: 136.
  63. 63. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, et al. (2008) Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 336: 1475–1482.
  64. 64. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P (2008) Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 94: 34–39.
  65. 65. Collins GS, Altman DG (2009) An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. BMJ 339: b2584.
  66. 66. Woodward M, Brindle P, Tunstall-Pedoe H (2007) Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart 93: 172–176.
  67. 67. De la Iglesia B, Potter JF, Poulter NR, Robins MM, Skinner J (2011) Performance of the ASSIGN cardiovascular disease risk score on a UK cohort of patients from general practice. Heart 97: 491–499.
  68. 68. Rose G (2001) Sick individuals and sick populations. Int J Epidemiol 30: 427–432.
  69. 69. Mendis S, Lindholm LH, Anderson SG, Alwan A, Koju R, et al. (2011) Total cardiovascular risk approach to improve efficiency of cardiovascular prevention in resource constrain settings. J Clin Epidemiol 64: 1451–1462.
  70. 70. Lloyd-Jones DM, Liu K, Tian L, Greenland P (2006) Narrative review: Assessment of C-reactive protein in risk prediction for cardiovascular disease. Ann Intern Med 145: 35–42.
  71. 71. Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G, et al. (2009) Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. JAMA 302: 49–57.
  72. 72. Lloyd-Jones DM (2010) Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 121: 1768–1777.