Executive summary module 1

2
GROUP ACTIVITY
CVD RISK CALCULATOR

3
Cardiovascular diseases (CVDs) is the main cause of premature mortality and morbidity
globally. According to World Health Organization (WHO), an estimated 17.9 million people
died from CVDs in 2016, accounting for 31% of all global mortality and 85% of these deaths
were caused by stroke and heart attack (WHO, 2017). CVDs is largely preventable and
therefore it is crucial to understand different CVD risk prediction tools to allow periodic
assessment that facilitates an ambulatory discussion and initiation of primary prevention
measures (Khambhati et al., 2018).
The risk factors included in most CVD risk systems are drawn from the original Framingham
Risk Score (FRS) that is based on the Framingham Heart Study (WHO, 2007). FRS was
developed in 1998 as a mean to assess the 10-year coronary heart disease (CHD) risk
(Khambhati et al., 2018). It characterizes individuals with CHD risk of ≤10% as “low risk”,
10-20% as “intermediate risk”, and ≥20% as “high risk”. Age, sex, low-density lipoprotein
(LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, blood pressure (any
antihypertensive treatment), diabetes, and smoking were included. FRS has been validated in
the USA for both genders among European Americans and African Americans (D'agostino et
al., 2001). However, it was found to have two major limitations. Firstly, FRS only predicted
CHD events and missed out important outcomes such as stroke, transient ischaemic attacks and
heart failure. Secondly, FRS could overestimate risk in populations other than the US
population, as demonstrated by (Brindle et al., 2003) when he looked at FRS performance in
British men. FRS also overestimated risk in people with low socioeconomic status in the UK
(Brindle et al., 2005) and in Chinese (Liu et al., 2004). Within the USA populations other than
European Americans and African Americans, e.g. Hispanic Americans and Native Americans
had their risk underestimated by FRS (Sacco et al., 2009).
2
FRS has been refined for several times. In 2008, the Framingham General CVD Risk Score
(FGCRS) incorporated additional CV endpoints such as stroke, heart failure, and peripheral
arterial disease (PAD) (D’agostino et al., 2008). The FGRS opined that individuals with a high
global CVD risk required more aggressive risk factor modification to address dyslipidaemia,
diabetes, and hypertension. However, this tool did not assess abdominal obesity, ECG evidence
of left ventricular hypertrophy, indications of insulin resistance, triglycerides, and family
history of premature CVD, which are important parameters in the assessment of CVD risk
(D’agostino et al., 2008).
Apart from the FRS models, the Prospective Cardiovascular Munster Study (PROCAM) score
was developed from one of the largest epidemiological studies (5389 men aged 35-65 years
with 325 acute coronary events) on CHD risk factors in Europe in 2002. This score predicts
the incidence of ischaemic events i.e. myocardial infarction and cardiac sudden death
(Assmann et al., 2007).
The risk of developing CAD was predicted using the algorithm with 8 independent risk
variables. The cardiovascular risk category is similar to the original FRS. The refined version
of the model was extended to accommodate risk estimation in women aged 20-75 years
(Cooney et al., 2009). PROCAM incorporates a broader range of frequently used diagnostic
parameters than the FRS (Siontis et al., 2012). It is an accurate mean of calculating overall
cardiovascular risk. However, the risk estimation was calibrated in one geographical location
and when applied to different geographical areas or ethnic groups it may lead to
underestimation or overestimation of risk (Cooney et al., 2009).

4
3
In 2003, the European Society of Cardiology introduced the SCORE (Systematic COronary
Risk Evaluation) model, which was developed retrospectively from 12 European cohorts
undergoing baseline examination in 1967–1991 (Conroy et al., 2003). The predictors included
in SCORE (age, sex, smoking, systolic blood pressure, and total cholesterol), the applicable
age range (40–65 years), and the predicted outcome (fatal ASCVD) were chosen by necessity.
Versions for use in high- and low-risk countries in Europe as well as national, updated
recalibrated versions are available. (Torbicki et al., 2012).
There are four categories for 10-year cardiovascular mortality risk in SCORE model: those
with risk >10% needs drug treatment, 5-10% needs lifestyle change and occasionally drug
treatment, 1-4% needs lifestyle changes and lastly, very low risk would be <1%. It is simple
and easy to use because the integer value for the risk is displayed and the risk category is color-
coded. Besides, the guidelines highlight the concept that absolute risk reduction is greater in
individuals with a higher baseline risk, while recognizing that most cardiovascular events occur
in the intermediate CVR patient group, who are more numerous and risk reduction strategies
must be complemented by public health measures (Jiménez Navarro, 2016). However, the
SCORE model has several limitations. It excludes diabetes, not applicable to those <40 and
>65 years old and it disregards all non-fatal events (first events) and solely focuses on the
prediction of fatal ASCVD in people aged 40–65 years, and lastly it has two standard versions:
one intended for countries with low cardiovascular mortality and the other intended for
countries with high cardiovascular mortality which lead to unreliable cross- sectional
recalibration approach (Mortensen and Falk, 2016).
Furthermore, QRISK2 risk score calculator (QRISK, 2018) was developed to address two
important parameters: ethnicity and deprivation. Additional factors such as presence of
4
atrial fibrillation, chronic kidney disease (stage 4 or 5) and rheumatoid arthritis were also
included. QRISK2 has been validated to give better quality of risk assessment for diabetic
patients which is prevalent in the South Asian population. It also gives better assessment in
South Asian women as compared to the modified Framingham risk score by NICE (Duerden
et al., 2015), which is known to underestimate the risk in the said population. The NICE lipid
modification guidelines (Duerden et al., 2015) recommend all GPs to use the QRISK2 to
estimate 10-year CVD risk before initiation of statin therapy in primary prevention. This saw
a decline of CVD risk in England and Wales from 20%-10% (Hippisley-Cox et al., 2008,
Finnikin et al., 2017). In UK, it has been used to estimate risk in the primary care patients
through collection of data on electronic records using the BMI, SBP and total cholesterol
values. It has, additionally, helped prioritize patients for a full formal assessment if their 10-
year risk for CVD is greater than 10%.
FINRISK is a large Finnish population survey on risk factors on chronic non- communicable
diseases carried out in Finland since 1972 that screened for risk for CVD as well as diabetes,
obesity and cancer. The FINRISK calculator calculates 10-year CVD risk with inclusion of
sex, age, smoking status, cholesterol, HDL-C, systolic BP, diabetes and 1st degree family
history of myocardial infarction. In a study by (RH Raiko et al., 2010), employing different
calculator tools among healthy Finnish adults to predict subclinical atherosclerosis, severe
CVD risk scores that include FINRISK had equal performance but SCORE was more accurate

5
at predicting low flow-mediated dilatation than FRS. It is specific to Finland and the survey
has not been reciprocated elsewhere hence it poses a question of reproducibility in other
population groups.
5
In term of diabetic-specific model, the UK Prospective Diabetes Study (UKPDS) model
incorporates glycaemia, SBP and lipid status, in addition to age, sex, ethnic group, smoking
behaviour and time since diagnosis of diabetes. The UKPDS was a randomized, intervention
trial of 5100 newly-diagnosed patients with Type 2 diabetes mellitus which aimed to determine
whether improved blood glucose control will prevent complications and reduce the associated
morbidity and mortality (Stevens et al., 2001). UKPDS has concluded that vigorous diabetic
treatment decrease the mortality and morbidity of the disease.
Secondly, the DARTS (Diabetes Audit and Research in Tayside, Scotland) model was derived
from a population cohort in Tayside, Scotland, UK. (Donnan et al., 2006). There was a total
number of 4569 diabetics without previous cardiovascular disease events and were followed
up for a maximum of 9.5 years. This study is a record linkage age of multiple data sources as
a web-based district diabetes information system for all residents in Tayside with sensitivity
97% (Morris et al., 1997). Ten risk factors such as HbA1c along with other traditional risk
factors were included. Its main outcome for its validation was the first major CHD event (fatal
or nonfatal MI). This study helps predict risk of CHD in people with type 2 diabetes and its
management.
In recent years, the American College of Cardiology/American Heart Association (ACC/AHA)
Pooled Cohort ASCVD Risk Score was introduced and derived from several patient cohorts
(Khambhati et al., 2018). In this model, endpoints are limited to hard ASCVD outcomes. (Goff
et al., 2014). Both a 10-year and lifestyle risk for adults aged 40-79 years can be calculated
(Goff et al., 2014). However, it was shown to underestimate total CVD risk as it omits
prediction of PAD, stable CAD, risk of heart failure from hypertension or ischaemic heart
disease and risk from arterial revascularization (Khambhati et al., 2018). The ASCVD
6
risk estimator also has been shown to overestimate hard ASCVD endpoints in the modern era
when attempts have been made to validate it in more contemporary cohorts; this is likely due
in part to the fact that the derivation cohort was predominantly from the 1970s and 80s
(DeFilippis et al., 2016).
Overall, the tools discussed above have been externally validated. They should be used
judiciously to identify intermediate- and high-risk population for both general CVD risk and
risk of specific events such as heart attack and stroke. Apart from the common risk factors, we
need to consider additional issues such as ethnic susceptibility and deprivation as a guide to
predict CVD risk as we often take them for granted.
References
Assmann, G., Schulte, H., Cullen, P. & Seedorf, U. 2007. Assessing risk of myocardial
infarction and stroke: new data from the Prospective Cardiovascular Münster (PROCAM)
study. European journal of clinical investigation, 37, 925-932.

6
Brindle, P., Jonathan, E., Lampe, F., Walker, M., Whincup, P., Fahey, T. & Ebrahim, S. 2003.
Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort
study. Bmj, 327, 1267.
Brindle, P. M., McConnachie, A., Upton, M. N., Hart, C. L., Smith, G. D. & Watt, G. C. 2005.
The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective
study. Br J Gen Pract, 55, 838-845.
Conroy, R., Pyörälä, K., Fitzgerald, A. e., Sans, S., Menotti, A., De Backer, G., De Bacquer,
D., Ducimetiere, P., Jousilahti, P. & Keil, U. 2003. Estimation of ten-year risk of fatal
cardiovascular disease in Europe: the SCORE project. European heart journal, 24, 987- 1003.
Cooney, M. T., Dudina, A. L. & Graham, I. M. 2009. Value and limitations of existing scores
for the assessment of cardiovascular risk: a review for clinicians. Journal of the American
College of Cardiology, 54, 1209-1227.
D'agostino, R. B., Grundy, S., Sullivan, L. M. & Wilson, P. 2001. Validation of the
Framingham coronary heart disease prediction scores: results of a multiple ethnic groups
investigation. Jama, 286, 180-187.
D’agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M. &
Kannel, W. B. 2008. General cardiovascular risk profile for use in primary care. Circulation,
117, 743-753.
DeFilippis, A. P., Young, R., McEvoy, J. W., Michos, E. D., Sandfort, V., Kronmal, R. A.,
McClelland, R. L. & Blaha, M. J. 2016. Risk score overestimation: the impact of individual
cardiovascular risk factors and preventive therapies on the performance of
7
the American Heart Association-American College of Cardiology-Atherosclerotic
Cardiovascular Disease risk score in a modern multi-ethnic cohort. European heart journal,
38, 598-608.
Donnan, P. T., Donnelly, L., New, J. P. & Morris, A. D. 2006. Derivation and validation of a
prediction score for major coronary heart disease events in a UK type 2 diabetic population.
Diabetes Care, 29, 1231-1236.
Duerden, M., O’Flynn, N. & Qureshi, N. 2015. Cardiovascular risk assessment and lipid
modification: NICE guideline. Br J Gen Pract, 65, 378-380.
Finnikin, S., Ryan, R. & Marshall, T. 2017. Statin initiations and QRISK2 scoring in UK
general practice: a THIN database study. Br J Gen Pract, 67, e881-e887.
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’agostino, R. B., Gibbons, R.,
Greenland, P., Lackland, D. T., Levy, D. & O’donnell, C. J. 2014. 2013 ACC/AHA guideline
on the assessment of cardiovascular risk: a report of the American College of
Cardiology/American Heart Association Task Force on Practice Guidelines. Journal of the
American College of Cardiology, 63, 2935-2959.

7
Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., Minhas, R., Sheikh, A. &
Brindle, P. 2008. Predicting cardiovascular risk in England and Wales: prospective derivation
and validation of QRISK2. Bmj, 336, 1475-1482.
Jiménez Navarro, M. F. 2016. Comments on the 2016 ESC Guidelines on Cardiovascular
Disease Prevention in Clinical Practice. Revista Española de Cardiología (English Edition),
69, 894-899.
Khambhati, J., Allard‐Ratick, M., Dhindsa, D., Lee, S., Chen, J., Sandesara, P. B., O'Neal, W.,
Quyyumi, A. A., Wong, N. D. & Blumenthal, R. S. 2018. The art of cardiovascular risk
assessment. Clinical cardiology, 41, 677-684.
Liu, J., Hong, Y., D'Agostino Sr, R. B., Wu, Z., Wang, W., Sun, J., Wilson, P. W., Kannel, W.
B. & Zhao, D. 2004. Predictive value for the Chinese population of the Framingham CHD risk
assessment tool compared with the Chinese Multi-Provincial Cohort Study. Jama, 291, 2591-
2599.
Morris, A. D., Boyle, D. I., MacAlpine, R., Emslie-Smith, A., Jung, R. T., Newton, R. W. &
MacDonald, T. M. 1997. The diabetes audit and research in Tayside Scotland (DARTS) study:
electronic record linkage to create a diabetes register. Bmj, 315, 524-528.
Mortensen, M. B. & Falk, E. 2016. Limitations of the SCORE-guided European guidelines on
cardiovascular disease prevention. European heart journal, 38, 2259-2263.
National Heart, L. & Institute, B. 2002. Third report of the National Cholesterol Education
Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol
in adults (Adult Treatment Panel III) final report. Circulation, 106, 3143.
QRISK. 2018. The QRISK®2-2018 risk calculator [Online]. Available: https://qrisk.org/
[Accessed 15 March 2019].
RH Raiko, J., Magnussen, C. G., Kivimäki, M., Taittonen, L., Laitinen, T., Kähönen, M., Hutri-
Kähönen, N., Jula, A., Loo, B.-M. & Thomson, R. J. 2010. Cardiovascular risk scores in the
prediction of subclinical atherosclerosis in young adults: evidence from the cardiovascular risk
in a young Finns study. European Journal of Cardiovascular Prevention & Rehabilitation, 17,
549-555.
Sacco, R. L., Khatri, M., Rundek, T., Xu, Q., Gardener, H., Boden-Albala, B., Di Tullio, M.
R., Homma, S., Elkind, M. S. & Paik, M. C. 2009. Improving global vascular risk prediction
with behavioral and anthropometric factors: the multiethnic NOMAS (Northern Manhattan
Cohort Study). Journal of the American College of Cardiology, 54, 2303-2311.
Siontis, G. C., Tzoulaki, I., Siontis, K. C. & Ioannidis, J. P. 2012. Comparisons of established
risk prediction models for cardiovascular disease: systematic review. Bmj, 344, e3318.
Stevens, R. J., Kothari, V., Adler, A. I., Stratton, I. M. & Holman, R. R. 2001. The UKPDS
risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56).
Clinical science, 101, 671-679.

8
Torbicki, A., Vahanian, A., Parkhomenko, A., Pająk, A., Ceriello, A., Hoes, A., Manolis, A.
J., Popescu, B. A., Brotons, C., Deaton, C., Funck-Brentano, C., Funck-Brentano, C., Wolpert,
C., Ceconi, C., Baigent, C., Moulin, C., Hasdai, D., Vanuzzo, D., Fitzsimons, D., Ezquerra, E.
A., van der Velde, E., Rocha, E., Rigo, F., Mancia, G., Burell, G., Diener, H.-C., Baumgartner,
H., Deckers, J., Bax, J., De Sutter, J., McMurray, J., Knuuti, J., Rallidis, L., Ruilope, L. M.,
Viigimaa, M., Cooney, M. T., Volpe, M., Tendera, M., Kirby, M., Larsen, M. L., Wiklund, O.,
Kirchhof, P., Sirnes, P. A., Sirnes, P. A., Kolh, P., Jankowski, P., Hambrecht, R., Fagard, R.,
Del Prato, S., Windecker, S., McDonagh, T., Sechtem, U., Keil, U., Dean, V., Aboyans, V.,
Reiner, Ž., Fras, Z., Perk, J., Members:, A. T. F., Reviewers:, D., :, E. C. f. P. G., guidelines:,
O. e. w. c. t. p. o. t., Mezzani, A., Deaton, C., Vrints, C., Wood, D., Prescott, E., Zannad, F.,
Germano, G., De Backer, G., Gohlke, H., Graham, I., Zamorano, J. L., Ryden, L., Verschuren,
M., Benlian, P., Ebrahim, S., Scholte Op Reimer, W. J. M., Hobbs, R., Reiner, Ž., Albus, C.,
Boysen, G., Cifkova, R., Fisher, M., Hoes, A., Scherer, M., Karadeniz, S. & Syvänne, M. 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)†. European Heart
Journal, 33, 1635-1701.
Turner, R., Holman, R., Matthews, D., Oakes, S., Bassett, P., Stratton, I., Cull, C., Manley, S.
& Frighi, V. 1991. UK Prospective Diabetes Study (UKPDS). VIII. Study design, progress and
performance. Diabetologia, 34.
WHO, W. H. O. (ed.) 2007. Prevention of cardiovascular disease: guidelines for assessment
and management of cardiovascular risk: World Health Organization.

10
Week 1.
Epidemiology of CVD (11)
CVD models (21)
Ethnicity and CVD (31)
CVD Risk Assessment (43)
hsCRP (51)

11
Epidemiology of CVD
03/04/19Dev Datta (Course Director)
Hi all
Please consider the following. If you could post your responses over the next 24 hours and
we will continue our discussion on this topic though the week.
Don't forget to ensure you review the student resources. This will help you ensure that
your posts are excellent. Don't 'copy and paste' from other resources, reference
appropriately and answer the question posed. Good luck!
The IMPACT mortality model has been used to evaluate the contribution made by
medical or surgical treatments and changes in cardiovascular risk factors to the decline
in mortality rates from coronary heart disease in England and Wales. Between 1981 and
2000 mortality rates fell by 62% and 45% respectively in men and women aged 25-84;
with 42% of the reduction attributed to treatment in individuals and 58% to reductions
in population risk factors.
Which risk factors do you think saw the largest decline between 1981 and 2000?
Which risk factors saw an adverse trend?
A similar model was applied to male and female mortality rates from CHD in England
between 2000 and 2007. The model was able to explain 86% of the reduction, with 52%
due to treatment but only 34% due to reductions in major cardiovascular risk factors.
Public health measures to reduce risk factors across the entire population were also
introduced during this period. What measures did these include?
Thanks
Dev
REPLY
Re: Week 1. Epidemiology of CVD
03/04/19Hoe Leong Sii
Coronary heart diseases (CHD) is a public health concern as it remains as a significant cause
of morbidity and mortality worldwide. Therefore, many models have been developed and
validated to determine the trend in CHD mortality, including the IMPACT mortality model.
The IMPACT mortality model is a cell-based epidemiological model used to measure the
number of CHD deaths prevented or postponed by each particular risk factor and cardiac
intervention in England and Wales over a duration of approximately 19 years (1981-2000) Unal
B et al. (2004, pp.1102).
Among the risk factors that were reported in the study by Unal B et al.(2004, pp.1104), smoking
showed the largest decline at 34% and this has accounted for almost 50% reduction in the

12
overall mortality of the study population. The reduction of risk factor was also seen for total
cholesterol level, blood pressure and deprivation as reported in the same study.
On the contrary, there was increment or adverse trend observed for risk factors and this
included physical activity, obesity and diabetes Unal B et al.(2004, pp.1104). Among the three
risk factors, obesity recorded the largest adverse trend at 186%. Overall, this has resulted to
more than 7000 deaths in England and Wales between 1981 and 2000.
The same model was then applied to examine the CHD mortality in England between 2000 and
2007. Throughout the period, several public health measures were undertaken to try to improve
CHD outcomes. According to Bajekal M et al.(2012), these measures comprised of the
prohibition of smoking advertisement and a thorough legislation on smoke-free. Agreement on
the reduction of salts and artificial trans-fat in food processing was also carried out voluntarily
to improve the health of the study population and subsequently, reducing the CHD deaths.
References
Unal, B., Critchley, J.A. and Capewell, S., 2004. Explaining the decline in coronary heart
disease mortality in England and Wales between 1981 and 2000. Circulation, 109(9), pp.1101-
1107. AHA Journals [Online] Available at: https://www.ahajournals.org (Accessed: 4 March
2019)
Bajekal, M., Scholes, S., Love, H., Hawkins, N., O'flaherty, M., Raine, R. and Capewell, S.,
2012. Analysing recent socioeconomic trends in coronary heart disease mortality in England,
2000–2007: a population modelling study. PLoS medicine, 9(6), p.e1001237. PLoS [Online]
Available at: https://journals.plos.org (Acceseed: 4 March 2019)
REPLY
03/04/19Win Ko Ko
Dear Dr Dev Data,
I am Win Ko Ko from Myanmar.
My discussion for this forum is attached with pdf.
This is my first time in online course and I am not familiar to it.
So if there is any mistake, I am humbled to learn.
Best regards,
Winkoko
IMPACT in epidemiology of CVD.pdf
152 KB
Monday, March 4, 2019, 11:01 PM
REPLY

13
03/04/19Win Ko Ko
Coronary heart disease (CHD) is still the major cause of death in worldwide. National and international
health organizations are trying to reduce the mortality rate of CHD for which they need to figure out what
factors are causing CHD and what factors are mostly related to CHD.
The international preventive policy model, IMPACT, has developed since 2001 and used to explain CHD
mortality trends in over twenty diverse populations, including England and Wales.
(https://www.liverpool.ac.a/psychology-health-and-society/impact/case-studies/impact/, nodate)
The cell-based IMPACT mortality model identified and incorporated data for men and women 25 to 84 years
old in England and Wales, detailing (1) CHD patient numbers, (2) uptake of specific medical and surgical
treatments, (3) population trends in major cardiovascular risk factors (smoking, total cholesterol,
hypertension, obesity, diabetes, physical activity, and socioeconomic deprivation), (4) effectiveness of
specific cardiological treatments, and (5) effectiveness of specific risk factor reductions (Unal et al., 2004,
pp.1101).
It showed that CHD mortality rates halved between 1981 and 2000. Biggest contribution came from the
reduction in smoking (48.1%), along with decreases in serum total cholesterol levels (9.6%), blood pressure
(9.5%), and deprivation (3.4%) (Unal et al., 2004, pp.1104).
Adverse trends were seen for obesity, physical activity, and diabetes. They together caused 7650 additional
CHD deaths. The prevalence of obesity increased by 186%, resulting in an estimated additional 2095 CHD
deaths. Diabetes prevalence increased by 66% with 2890 additional CHD deaths, and indirect evidence
suggested a 30% decrease in physical activity, with some 2660 additional deaths (Unal et al., 2004,
pp.1104).
The extended IMPACTSEC model was applied to male and female mortality rates from CHD in England
between 2000 and 2007. This model included all the major risk factors for CHD: smoking, systolic blood
pressure, total cholesterol, body mass index (BMI), diabetes, physical inactivity, along with fruit and
vegetable consumption; plus all 45 medical and surgical treatments currently in use in nine patient
groups(Bajekal et al., 2012, pp.2). The model suggests that approximately half the recent CHD mortality fall
in England was attributable to improved treatment uptake (Bajekal et al., 2012, pp.1).
Public health measures to reduce risk factors across the entire population were also introduced during this
period. These measures included the ban on tobacco advertising (2003); comprehensive smoke-free
legislation (2007), and voluntary agreements to reduce salt and artificial trans-fats in processed food (Bajekal
et al., 2012, pp.2).
References
IMPACT Coronary Heart Disease Policy and Prevention Model, https://www.liverpool.ac.a/psychology-
health-and-society/impact/case-studies/impact/(nodate), (Accessed: 4th March 2019)
Unal, B., Critchley, J.A., Capewell, S., 2004. Explaining the Decline in Coronary Heart Disease Mortality
in England and Wales Between 1981 and 2000. Circulation 109, 1101–1107.
https://doi.org/10.1161/01.CIR.0000118498.35499.B2 (Accessed: 4th March 2019)
Bajekal, M., Scholes, S., Love, H., Hawkins, N., O’Flaherty, M., Raine, R., Capewell, S., 2012. Analysing
Recent Socioeconomic Trends in Coronary Heart Disease Mortality in England, 2000–2007: A Population
Modelling Study. PLoS Med. 9, e1001237. https://doi.org/10.1371/journal.pmed.1001237 (Accessed: 4th
March 2019)
REPLY

14
Excellent answer Hoe Leong. Clearly written with appropriate references.
Dev
REPLY
Hi Winkoko
Your approach to answering the post is good. You have answered the question and provided
appropriate references.
You can attach additional information if you wish, but if you post directly on the forum, as you
have now done, this aids interaction and discussion.
Thanks
Dev
REPLY
03/05/19Win Ko Ko
Thanks for your advice, Dr Dav.
REPLY

15
Thank you Dr Dev for your comments!
REPLY
03/06/19Dr Preeti Jabbal
Coronary artery disease (CHD) is one of the leading causes of mortality and morbidity in the
UK and the USA and a common cause of premature deaths. The Impact mortality model looks
into preventative measures both primary and secondary.
The mortality from CHD reduced by 50% between 1981 and 2000 in England and Wales; 40%
decrease was attributed to advanced cardiological treatments while 60% was to reduction in
major risk factors with smoking cessation showing the largest decline in prevalence by
48%. Total cholesterol reduction showed a prevalence decline by 9.5%. Better control of
hypertension too showed a prevalence decline by 9.5%.
Adverse trends were seen for physical activity, obesity and diabetes as they imposed a relative
risk but were each a cause of additional CHD death. Prevalence of obesity increased by 186%
resulting in additional deaths. Diabetes prevalence increased by 66% while 30% reduction in
physical activity resulted in additional deaths.
The impact model helped in policy making advocating various public health measures to
reduce the risk factors through programmes to increase public awareness such as smoking
cessation, healthy eating, weight control, regular exercises and importance of regular health
check ups.
References:
1. Exploring the decline in coronary heart disease mortality in England and Wales between
1981 and 2000 by Belgin Unal, Julia Alison Critchley and Simon Capewell - March 2004
2. Extending the IMPACT coronary heeart model to different policy contexts -Lead research
organization - University
of Liverpool
3. Modeling the decline in coronary heart disease deaths in England and Wales 1981-2000-
p614 -Unal, Critchley and Capewell
REPLY

16
03/06/19Jacob Shabani
Coronary heart disease (CHD) is a major contributor to morbidity and mortality globally. CHD
mortality models have been developed to show the impact of medical and public health
interventions in reduction of CVD associated deaths. 1 The risks factors that showed the largest
decline between 1981 and 2000 are smoking (48.1%), serum total cholesterol (9.6%), blood
pressure (9.5%) and deprivation (3.4%) 2. Adverse trends were shown with obesity, diabetes
and physical activity 2. Additionally in England, CHD mortality dropped annually by
approximately 6% between 2000 and 2007. 3. The biggest public health measures introduced
to mitigate the effect of risk factors included the ban on tobacco advertising done in 2003 and
comprehensive smoke-free legislation in 2007. There was also voluntary agreements to reduce
salt and artificial trans-fats in processed food. 3,4. Bibliography 1. Capewell S, Ford E, Croft
J, Critchley J, Greenlund K, Labarthe D. Cardiovascular risk factor trends and potential for
reducing coronary heart disease mortality in the United States of America. Available
https://www.who.int/bulletin/volumes/88/2/08-057885/en/. Accessed 5 March 2019. 2. Unal
B, Critchley J, Capewell S. Explaining the Decline in Coronary Heart Disease Mortality in
England and Wales Between 1981 and 2000. Circulation. 2004;109:1101-1107. 3. Bajekal M,
Scholes S, Love H, Hawkins N, O'Flaherty M, Raine R, et al. (2012) Analysing Recent
Socioeconomic Trends in Coronary Heart Disease Mortality in England, 2000–2007: A
Population Modelling Study. PLoS Med 9(6): e1001237.
https://doi.org/10.1371/journal.pmed.1001237 4. UK Food Standards Agency (2006) New salt
reduction targets published as part of FSA campaign to reduce salt in our diets. Available:
http://webarchive.nationalarchives.gov.uk/20120206100416/http://food.gov.uk/news/pressrel
eases/2006/mar/targets. Archived on 6th Dec 2011. Accessed 5 March 2019.
REPLY
Thanks Preeti
Good answer
thanks
Dev
REPLY

17
Thanks Jacob
Take care when posting your references, that they are in line with the University requirements.
The information is within the student resources. Your answer is fine.
Dev
REPLY
03/08/19Sofia Jarombwereni Natshikare Nepembe
Coronary heart disease remains one of the leading causes of morbidity and mortality
worldwide. Despite lack of evidence/research in Namibia, it is among the top 5 causes of
morbidity and mortality. Efforts are continuously being made to try and lower the burden of
the disease. The primary preventative measure is to address reduction in risk factors. Over the
years, various studies have been conducted to explore the significance in the relationship of
certain risk factors in the development of coronary heart disease. According to Capewell et al
(2009), ‘approximately 44% of the substantial CHD mortality decline in the United States
between 1980 and 2000 was attributable to changes in major risk factors and 47% to specific
cardiological treatments.’
The IMPACT mortality model incorporates smoking, cholesterol, blood pressure, obesity,
diabetes and physical activity and deprivation. Unal et al (2006) found that the IMPACT model
can “be used to estimate the proportion of a mortality decline (or increase) over a certain time
span that might be attributed to specific treatments or risk factor changes. It can also examine
the consequences of increasing treatments provided, or reducing risk factor levels.
In a study done in Scotland by Capewell et al (1994).
Modest gains from individual treatments produced a large cumulative survival benefit.
Reductions in major risk factors explained about half the fall in coronary mortality.
This goes to say that the focus in curbing the burden of coronary heart disease should really be
placed on primary preventative measures; which is; addressing the risk factors.
To answer the question posed, the IMPACT mortality model demonstrated that smoking
cessation showed the largest decline in cardiovascular disease followed by cholesterol and
blood pressure control. Diabetes was found to be the risk factor contributing most to the
development of coronary heart disease; followed by physical inactivity and obesity; Capewell
S (2008).

18
A similar model was undertaken in England between the years 2000 – 2007. Bajekal et al
(2012) concluded that about half the fall in mortality from coronary heart disease was
secondary to the improved treatment uptake but of course one should also take into account
opposing trends in significant risk factors.
The IMPACT model certainly creates a guide when one wants to explore avenues for policy
interventions in curbing the burden of coronary heart disease.
References:
1.) Bajekal, M. Scholes, S. Love, H. Hawkins, N. O’Flaherty, M. Raine, R. Capewell, S
(2012)‘Analysing recent socioeconomic trends in coronary heart disease mortality in England,
2000 – 2007: A population Modelling Study’PLoS Medicine 9(6) [Online] Available
at: www.plosmedicine.org Accessed: 07 March 2019.
2.) Capewell S (2008) Studying mortality trends: The IMPACT CHD Policy
Model [University of Liverpool] 14th
January.
3.) Capewell, S. Ford, ES. Croft, JB. Critchley, JA. Greenlund, KJ. And Labarthe, DR. (2009)
‘Cardiovascular risk factor trends and potential for reducing coronary heart disease mortality
in the United States of America’ Bulletin of the World Health Organization 2010;88:120-130.
doi:10.2471/BLT.08.057885.
4.) Capewell, S. Morrison, CE. McMurray, JJ. (1999) ‘Contribution of modern
cardiovascular treatment and risk factor changes to the decline in coronary heart disease
mortality in Scotland between 1975 and 1994. Heart 1999; 81:380-386.
5.) Unal, B. Capewell, S. and Critchley JA. (2006) ‘Coronary heart disease policy model: a
systemic review’ BMC Public Health 2006, 6:213 doi:10.1186/1471-2458-6-213 [Online]
Available at: http://www.biomedcentral.com/1471-2458/6/213. Accessed: 07 March 2019.
REPLY
03/09/19Sam Ang Eik
At least there are three possible contributing factors, which is supposed to increase in
cardiovascular diseases (CVD) in developing countries include firstly, decreased mortality
form acute infectious diseases and increases life expectancy and will lead people reaching
middle and old age; secondarily, lifestyle and socioeconomic changes with many urbanization
may result in many risks factors for CVD; Thirdly, the susceptibility of certain population (e.g.
genetics) may also cause higher impact on clinical events compared to Western population.
Lifestyle changes such as diet, physical activity and tobacco are also risk factors leading into
CVD. (Salim et al, 2002, p.5).
Simon et al. (2015, p.7) has noted that Atherosclerotic Cardiovascular Disease is the leading
cause of mortality worldwide and is a major public health epidemic that put many burden on
the population. So, there should be prevention and control such as behavior modification to
improve diet, physical activity and other healthy lifestyles which are needed to achieve
healthier environments and lifestyles.
References

19
Yusuf, S., Ounpuu, S., Anand, S.2002. The Global Epidemic of Atherosclerotic Cardiovascular
Disese. Medical Principles and Practices. 11(Suppl 2),pp 3-8.
Barquera, S., Pedroza-Tobias, A., Medina, C., Hernandez-Barrera, L., Bibbins-Domingo, K.,
Lozano, R. and E.Moran, A. 2015.Global Overview of the Epidemiology of Atherosclerotic
Cardiovascular Disease.
REPLY
Good post Sofia
You have covered the relevant material and your post is clear and well-referenced. You are
correct to use quotation marks for material directly taken from another resource, particularly
where you are quoting numerical data. Try and avoid using this too much though as putting
material into your own words will help you remember and understand it.
Dev
REPLY
Thanks Sam
Some reasonable points. Make sure you read the post carefully and answer the question posed
however. This question looks at what changes have happened and have impacted on CVD
mortality and morbidity.
Dev
REPLY

20
03/15/19Rio Alexsandro
Decline risk factor in 1981-2000? For the CHD mortality decline in the US between 1980 and 2000,
reductions in major risk factors contributed 42% when DPP is used as the metric whereas a contribution of
79% is noted when LYG is the metric chosen. Approximately 44% was attributed to changes in risk factors,
including reductions in total cholesterol (24%), systolic blood pressure (20%), smoking prevalence (12%),
and physical inactivity (5%). Greater decline in CHD mortality would have been seen had the rise in BMI
and diabetes prevalence been controlled.
Adverse Trend? Recent trends in the increased prevalence of obesity both in adults and children in the US
and other developed countries, but also in developing countries, are associated with corresponding trends in
diabetes, again highlighting the need for primordial and primary prevention with emphasis on policy and
environmental changes that support and facilitate healthy lifestyle and behavioral choices.
The decline in CVD mortality rate in recent years has neither been uniform for all population subgroups nor
for all causes of CVD death. In addition, despite the decades of the decline, substantial disparities in mortality
rates continue by race, ethnicity, and sex. Unequal access to preventive interventions is one of the
contributing factors. Differences in CVD mortality also exist by geographic location and socioeconomic
status and are often attributed to related differences in risk factor status; social and environmental
differences; and inequities in access to care and the quality of care received. Because of their close
association with CVD events and mortality, it is of interest to examine their influence on the decline of CVD
mortality in the US and internationally.
Public Health Measures to reduce risk factor accros the entire population? Promote CV health and its
education among the population in variety of setting like health care services, occupational sites, educational
institutes, and communities.Therefore, this could lead to identify the CVD and risk factors burden and helps
in planning to reduce the burden of disease and risk factors. Consequently, implementation of above
recommendations will contribute to reducing CVD. CVD can be prevented by addressing risk factors, and
focusing on the determinants using population-wide strategies. Worldwide, efforts to reduce CVD is still
unsuccessful, the country should initiate education, policies, system, and environment changes. Primary and
preventive is the way, because the curative management is very expensive. In our country experience, we
have a high deficit on insurance because of CVD and in the end the tobacco company will help to cover. It
means that the tobacco cannot be controlled.
1. George A. Mensah. 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268076/. Published in final
edited form as: Circ Res. 2017 Jan 20; 120(2): 366–380. doi: 10.1161/CIRCRESAHA.116.309115. Jan 20
2.Santosh Kumar. 2017 Cardiovascular Disease and Its Determinants: Public Health Issue
http://www.imedpub.com/articles/cardiovascular-disease-and-its-determinants-public-health-
issue.php?aid=18223. 2:1.Published Date: January 05, 2017
3.Liverpool. 2008. https://gtr.ukri.org/projects?ref=G0500920. Extending the IMPACT coronary heart
disease model to different policy contexts. Lead Research Organisation: University of Liverpool . Jul 06 -
Dec 08

21
CVD models
Through this course, we will consider many aspects of prevention of CVD from risk factor
calculators, obesity, smoking prevention to management of complex lipid disorders and
hypertension.
For now our focus is on epidemiology. It is important we try and understand the data that we
often encounter in practice.
We have just considered the IMPACT model. Why should we have such models? How are they
able to provide us with valuable data? Are there any limitations that we should consider?
thanks
Dev
REPLY
Re: Week 1: CVD models
Models such as the IMPACT model comprise of simplified descriptive tools and mathematics’
equations with clearly stated model design, assumptions and intended uses. (J. A. Critchley
and S. Capewell, 2002, pp.110). The main reason of using such model is to answer
sophisticated questions that could not be addressed by traditional research methodologies as
they are known to have many limitations with conflicting results (J. A. Critchley and S.
Capewell, 2002, pp.110). For instance, traditional epidemiological studies are done in a quick
and cross-sectional manner with many potential bias and those studies usually did not include
minority group and older population. Apart from that, models are being used because they can
explain the disease trend in the past, predict future events, evaluate policy options and even
help in decision making (J. A. Critchley and S. Capewell, 2002, pp.110).
Models are able to provide us with valuable data based on the published evidence of a specific
disease. A good example would be the IMPACT model that utilised the published evidence of
the effectiveness of various risk factors and treatments to calculate the risk reduction and the
mortality rate of coronary heart disease (CHD) in England and Wales between 1981 and 2000
after combining the local epidemiological data Unal B et al. (2004, pp. 1101) Furthermore,
certain interventions that are known to reduce disease prevalence can be introduced to the
models and the results can then be compared to the group without the intervention.This can
help to understand the benefits of a specific intervention which can be useful to evaluate the
policy options as mentioned in the above paragraph.
However, there are few limitations of the models that we should address. Firstly, current
models are focusing mainly on the mortality with limited risk factors and treatments and others
factors such as the quality of life and life expectancy are being missed (J. A. Critchley and S.
Capewell, 2002, pp.114). Not to forget, the models are too complicated and might be difficult

22
for policy makers to understand. This could be a big disadvantage as policy makers are the
people involved in changing the health policy.
References
Critchley, J.A. and Capewell, S., 2002. Why model coronary heart disease?. European heart
journal, 23(2), pp.110-116.
Unal, B., Critchley, J.A. and Capewell, S., 2004. Explaining the decline in coronary heart
disease mortality in England and Wales between 1981 and 2000. Circulation, 109(9), pp.1101-
1107. AHA Journals [Online] Available at: https://www.ahajournals.org (Accessed: 5 March
2019)
REPLY
03/05/19Win Ko Ko
The differences in distribution of cardiovascular diseases and its associated risk factors in the population are
needed to be established. Blackburn (1997, p.8) provided strong evidence of diverse sociocultural
contribution to cardiovascular disease and highlighted the importance of a population-wide approach in
making policies for preventive strategies. The exposition of trends of cardiovascular disease mortality and
risk factors will inform the stakeholders to make an appropriate decision in consideration of necessary and
cost-effective interventions.
Lewsey et al., (2015, p.201) commented that ‘a challenge in generating evidence of the effectiveness of
preventive interventions for coronary heart disease (CHD) is that randomized trials are the short term in
nature and so often modeling is necessary to predict longer-term cost-effectiveness.’ Therefore, policy
models became the tools for policymakers who needed the issue of generalization due to the restraints of
resources and time. Most of the CHD policy models also have the potential to predict future trends.
Weinstein et al., (2003, p.9) defined a model as, "a logical mathematical framework that permits the
integration of facts and values to produce outcomes of interest to clinicians and decision makers" or,
alternatively as: "an analytical methodology that accounts for events over time and across populations based
on data drawn from primary or secondary sources".
Various models have been developed to estimate the relative contributions and hence the population impact
of medical and public health interventions (Capewell et al., 2010). Models synthesize evidence on health
consequences and costs from many sources, including data from clinical trials, observational studies,
insurance claim databases, case registries, public health statistics, and preference surveys. For decisions
about resource allocation, the end result of a model is often an estimate of cost per quality-adjusted life year
(QALY) gained or other measure of value for-money (Weinstein et al., 2003, p.9).

23
Unal et al., (2006, p.1) reviewed systematically 72 articles describing 42 CHD policy models which used
different modeling methods mainly micro-simulation, cell-based and life table analyses. Among six principle
CHD policy models, IMPACT model used initially smoking, cholesterol, blood pressure – then also obesity,
diabetes and physical activity and deprivation as the risk factors in men and women aged 25-84. But in
IMPACT-SEC model, it added fruit and vegetable consumption plus 45 medical and surgical treatments.
The major limitation of policy model is its own quality which is evaluated on the basis of choice of sensitivity
analysis, validity, data quality, transparency, assumptions, confounding, lag times, competing causes and
comprehensive inclusion of multiple coronary heart disease categories, a range of treatments and major risk
factors (Unal et al., 2006, p.3).
Choice of policy model can be different between developed and developing countries depending on their
available data inputs and resources. Limitations of a previous policy model will become the lessons for the
future policy model.
References
Blackburn, H., 1997. Epidemiological basis of a community strategy for the prevention of cardiopulmonary
diseases. Ann. Epidemiol. 7, S8–S13. https://doi.org/10.1016/S1047-2797(97)80004-X
Capewell, S., Ford, E.S., Croft, J.B., Critchley, J.A., Greenlund, K.J., Labarthe, D.R., 2010. Cardiovascular
risk factor trends and options for reducing future coronary heart disease mortality in the United States of
America. Bull. World Health Organ. 88, 120–130. https://doi.org/10.2471/BLT.08.057885
Lewsey, J.D., Lawson, K.D., Ford, I., Fox, K.A.A., Ritchie, L.D., Tunstall-Pedoe, H., Watt, G.C.M.,
Woodward, M., Kent, S., Neilson, M., Briggs, A.H., 2015. A cardiovascular disease policy model that
predicts life expectancy taking into account socioeconomic deprivation. Heart 101, 201–208.
https://doi.org/10.1136/heartjnl-2014-305637
Unal, B., Capewell, S., Critchley, J.A., 2006. Coronary heart disease policy models: a systematic review.
BMC Public Health 6. https://doi.org/10.1186/1471-2458-6-213
Weinstein, M.C., O’Brien, B., Hornberger, J., Jackson, J., Johannesson, M., McCabe, C., Luce, B.R., ISPOR
Task Force on Good Research Practices--Modeling Studies, 2003. Principles of good practice for decision
analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices--
Modeling Studies. Value Health J. Int. Soc. Pharmacoeconomics Outcomes Res. 6, 9–17.
REPLY
An impact model captures the process of creating 'impact' which begins with deploying inputs
to conduct research activities and produce outputs which are then translated to short to medium
into long term impact. It is a computer based statistical model which looks into preventative
measures to reduce mortality from CHD as highlighted by the study carried out in England and
Wales from 1981 -2000. Such models help in identifying risk factors and in stratifying the risk
factors with the largest decline to least and the adverse trends as well.

24
The data captured using such models has helped to explore the CHD trends in a variety of
population across the world -Ireland, Finland and China. It is also considered a policy model
whereby standard treatments can be compared and all the risk factors stratified.
It can be used in policy making and decision making to increase public awareness through
various programmes such as smoking cessation, healthy eating, weight loss, regular exercise
and regular health check ups.
The impact model can be complicated to use, therefore, policy and decision makers would
prefer a 'user friendly' version and where one could look into cost implication of various
treatments offered. It could also be improved to offer a range of different populations to
examine at national, regional and local levels. In addition to this, it would be helpful in
capturing additional epidemiological outputs such as hospital admissions and need for
revacularization.
Reference:
Extending the impact coronary heart disease model to different policy contexts -Lead research
organization-University of Liverpool
REPLY
Why have cardiovascular models
Cardiovascular Mortality Models such as IMPACT allow us to combine and analyze a large
repository of data on uptake and effectiveness of cardiovascular interventions and risk factor
trends. (1) These models have been used in various countries and different time spans,
employing the same methodology. (1, 2, 3, 4) This enables us to have temporal and regional
comparisons on effectiveness of interventions to reduce cardiovascular mortality.
Additionally by using these models, data can be transparently be integrated and sensitivity
analyses done to elucidate any assumptions by carrying out sensitivity studies. (1, 5)
These models provide valuable data based on routine health statistics, country demographic
information, registries and periodic community surveys (1)
However these models have some limitations. Most of the models are reliant on the extent and
quality of data available on CHD trends and treatment uptakes (1). For instance when
considering mortality data, the model only takes into account CVD deaths, yet a reduction in s
risk factor like smoking would also decrease death from cancer. Models that analyze all cause
mortality will be better predictors of the efficacy of an intervention. The models also focus on
mortality without due regard to morbidity trends and quality of life. These models also make
assumptions that estimates of efficacy from randomized controlled trials are generalized to real
life world and clinical practice.(1)
References

25
1. Unal B, Critchley J, Capewell S. Explaining the Decline in Coronary Heart Disease
Mortality in England and Wales Between 1981 and 2000. Circulation. 2004;109:1101-1107.
2. Capewell S, Ford E, Croft J, Critchley J, Greenlund K, Labarthe D. Cardiovascular
risk factor trends and potential for reducing coronary heart disease mortality in the
United States of America. Available https://www.who.int/bulletin/volumes/88/2/08-
057885/en/. Accessed 5 March 2019.
3. Bajekal M, Scholes S, Love H, Hawkins N, O'Flaherty M, Raine R, et al.
(2012) Analysing Recent Socioeconomic Trends in Coronary Heart Disease Mortality in
England, 2000–2007: A Population Modelling Study. PLoS Med 9(6):
e1001237. https://doi.org/10.1371/journal.pmed.1001237
4. Malhan, S et al. Modelling The Burden Of Cardiovascular Disease In Turkey And
The Impact Of Reducing Modifiable Risk Factors .
5. Critchley J, Capewell S. Why model coronary heart disease? Eur Heart J. 2002;23:110–
116
REPLY
Excellent post. Well done.
Dev
REPLY
Excellent post. Well done Win Ko Ko
Dev
REPLY

26
Thanks Preeti,
Note that 'IMPACT' was the name of one model for England and Wales and others do
exist.Take care with your reference(s) please- the one you have provided is not presented
correctly.
thanks
Dev
REPLY
Thanks Jacob
Good post
Dev
REPLY
03/07/19Win Ko Ko
Thanks Dr Dav.
Winkoko
REPLY

27
Thank you Dr Dev!
REPLY
CVD Models
In order to tackle the burden of cardiovascular disease (CVD) and to aid in pin-pointing areas
of concern, policy models have to be created. To date, there has been a variety of models which
have been developed to try and ‘explain past trends and predict future possibilities.’ Unal et al
(2006). These models help to identify key areas which can be tackled when making national
guidelines and interventional measures.
In a comparison study contrasting different policy models, Unal, Capewell and Critchley
(2006) deemed the IMPACT model to have been ‘comprehensive and considers all principal
CHD categories and over 20 specific CHD treatments.’
The IMPACT model can be used to ‘estimate the proportion of a mortality decline (or increase)
over a certain time span that might be attributed to specific treatments or risk factor changes.
It can also examine the consequences of increasing treatments provided, or reducing risk factor
levels. Other outputs include life years gained and cost-effectiveness of specific interventions.’
Unal, Capewell and Critchley (2006)
Models can allow a large amount of
evidence to be considered simultaneously, by combining and integrating into a
coherent whole different types of data from controlled trials, routine surveillance
and expert consensus. Models have been extensively used in policy making and
resource allocation, since they permit policy makers to examine future options,
or to simulate the effects of different scenarios within a population.
These models maybe be somewhat difficult to understand/comprehend. Specific softwares are
required to be able to use the models and specific data collection/reading tools are required.
They need a computer literate person. As much as this might be difficult to comprehend, in
many countries, clinical data is still not digitalized and computers are reserved for offices. A

28
concern of user friendliness comes into question. Can the models be duplicated and applied to
all population groups and across all continents?
References:
1.) Unal, B. Capewell, S. Critchley, JA. (2006) Coronary heart disease policy models: a
systemic review BMC Public Health 2006, 6:213 doi:10.1186/1471-2458-6-213 [Online]
Available at: http://www.biomedcentral.com/1471-2458/6/213. Accessed: 08 March 2019.
SJN Nepembe
REPLY
03/11/19Sam Ang Eik
How we can prevent cardiovascular disease is by doing regular exercise, continue healthy diet,
avoid tobacco usage, keep an optimal blood pressure and normal blood glucose and normal
blood lipid profiles. ( De Backer, 2017). That’s why they have to model coronary heart disease
to understand about its epidemiology and contribute to policy making in preventing
cardiovascular disease. ( Chritchley J.A. et al, 2002).
With reference to EuroHeart II Work Package 6, 2014. CHD Mortality Projections to
2020, Comparing Different Policy Scenarios, The IMPACT model was developed in the
1990s by Capewell and colleagues and has been widely used in many countries to quantify
how much of the recent decline in coronary heart disease (CHD) mortality can be attributed to:
1/ medical treatment and 2/ population risk factor changes. The study is retrospective of data
from some countries in Europe. It found that the model can help population decrease in
cardiovascular risk factors such as cigarette smoking, dietary salt, saturated fat and physical
inactivity, which is able to decrease future coronary heart deaths in Europe. However, the
analyses met some inconvenienced risk factor trends in recent years in many countries
including blood pressure, cholesterol, obesity and diabetes. These adverse trends should be
considered having more prevention and challenges.
References
De Backer, G. (2017). Prevention of cardiovascular disease: recent achievements and
remaining challenges. European Society of Cardiology, 15(13).
Chritley, J.A. and Capewell, S. (2002). Why model coronary heart disease?. 23(2), pp.114.
EuroHeart II Work Package 6, 2014. CHD Mortality Projections to 2020, Comparing Different
Policy Scenarios
REPLY

29
Good post Sofia
I know I am responding to all of your posts in one go, but note my previous point about
minimising use of direct quotes. It is academically correct to do this, but it shouldn't detract
from opportunities to put statements in your own words.
Dev
REPLY
Thanks Sam
A little bit more detail to demonstrate that you really understand the utility and limitations of
models would be helpful.
Dev
REPLY
Thank you for the feedback. Will be very helpful indeed.
REPLY

30
Why should we have? CVD nowadays have a shifting paradigm to the younger age. In our experience we
found many times ACS in 30 years old. CVD can be prevented by addressing risk factors, and focusing on
the determinants using population-wide strategies. Worldwide, efforts to reduce CVD is still unsuccessful,
the country should initiate education, policies, system, and environment changes. Primary and preventive is
the way, because the curative management is very expensive. In our country experience, we have a high
deficit on insurance because of CVD and in the end the tobacco company will help to cover. It means that
the tobacco cannot be controlled. Furthermore, following recommendation and planning steps will help to
decline mortality and morbidity rate of CVD. IMPACT is the only comprehensive CHD policy model. It is
truly comprehensive, including all patient groups, all standard treatments and all major risk factors.
Conceptually simple, but methodologically sophisticated, all assumptions are explicit, transparent, and
subjected to rigorous sensitivity analyses. Published model outputs include deaths postponed, life-years-
gained, and cost effectiveness of different interventions.
How it provide valuable data? The model has been validated, and has been used specifically in efforts to
explain CHD mortality trends in more than 15 countries worldwide. Using this model, Ford et al estimated
that reductions in major risk factors contributed about 44%. Working with policy makers and decision
makers to i)Create a user-friendly interface for the model,
ii) Offer a range of different populations to examine at national, regional and local levels, and
iii) Be able to address current and future policy issues.
Limitation? However, the current IMPACT Model is complicated to use.
3.Liverpool. 2008. https://gtr.ukri.org/projects?ref=G0500920. Extending the IMPACT coronary heart
disease model to different policy contexts. Lead Research Organisation: University of Liverpool . Jul 06 -
Dec 08
REPLY
03/20/19Sam Ang Eik
I would thank you so much, Dr Dev, for advice, I will research more on this.
Best regards,
Sam

31
Week 1- Ethnicity and CVD
Here's a question for this weekend...
A 59-year-old caucasian man, who recently emigrated to the UK from Eastern Europe,
visits his doctor in the UK. He is seeking help for a painful toe. He is otherwise fit and
well. He admits to drinking more than 30 units of alcohol a week. He is known to also
suffer from gout and is an asthmatic. On examination his blood pressure is found to be
160/95 mmHg and his BMI is 31 kg/m2. His blood results demonstrate a total cholesterol
of 7.9 mmol/L and triglycerides 4.5 mmol/L, fasting glucose 6.9 mmol/L.
Which independent major risk factors for CVD are present in this man?
How would originating from Eastern Europe have had an effect on his CVD risk?
What other links are there between ethnicity and CVD risk?
Thanks!
Dev
REPLY
Re: Week 1- Ethnicity and CVD
The major independent risk factors for cardiovascular disease (CVD) in this 59-year-old
Caucasian man are obesity (Kannel WB et al, 1991, pp.183), high blood pressure (Stamler J et
al, 1993, pp. 598), dyslipidaemia (Musunuru K, 2007, pp.907) and heavy alcohol consumption
(Ronksley, 2011). According to the Framingham Heart Study by Culleton BF et al. (1999, pp.
7), serum uric acid level (induced by gout) is not associated with cardiovascular risk. Besides,
impaired fasting glucose as shown in this man is not a risk factor of CVD as the Funagata
Diabetes Study by Tominaga M et al (1999, pp. 920) highlighted that only impaired glucose
tolerance (and not the impaired fasting glucose) is a risk factor for CVD.
Many studies including the MONICA (Monitoring Trends and Determinants in Cardiovascular
Disease) Project by the World Health Organization (WHO) has shown that the incidence of
coronary events was higher and was rising in central and eastern Europe, while the incidence
of coronary events was falling rapidly in northern and western Europe (Tunstall-Pedoe et al,
2003). Apart from that, the MONICA Project also shows that the case fatality from coronary
heart disease (CHD) was higher in many populations in central and eastern Europe (Tunstall-
Pedoe et al, 2003). Death rates from stroke were also higher in central and eastern Europe than
in northern, southern and western Europe (Rayner M et al, 2009) Therefore, being a 50-year-
old man originated from eastern European may render him a greater incidence of coronary
events as well as a higher mortality rates from coronary events and stroke. It is still
controversial on the CVD incidence and mortality rate, but it’s believed to be due to lifestyles

32
such as smoking and alcohol consumption, socio-economic inequalities as well as the treatment
of the disease (Rayner M et al, 2009).
There are other links found between ethnicity and CVD risk. According to Cooper RS (2001),
the mortality rates in heart diseases were 2-3 times higher among African Americans as
compared to Asians in the United States (US) because of the social inequality. Studies also
show that people of African origin (both Caribbeans and West Africans) had higher incidence
of stroke in comparison with white Europeans and have lower incidence of CHD as compared
to general population (Lip et al, 2007) Furthermore, South Asians populations were found to
have higher mortality from CHD across the world (Lip et al, 2007). This is proven when the
South Asians living in the United Kingdom (UK) have a 50% greater risk of premature deaths
than the general population (Lip et al, 2007).
References
Kannel, W.B., Cupples, L.A., Ramaswami, R., Stokes III, J., Kreger, B.E. and Higgins, M.,
1991. Regional obesity and risk of cardiovascular disease; the Framingham Study. Journal of
clinical epidemiology, 44(2), pp.183-190.
Stamler, J., Stamler, R. and Neaton, J.D., 1993. Blood pressure, systolic and diastolic, and
cardiovascular risks: US population data. Archives of internal medicine, 153(5), pp.598-615.
Musunuru, K., 2010. Atherogenic dyslipidemia: cardiovascular risk and dietary intervention.
Lipids, 45(10), pp.907-914.
Ronksley, P.E., Brien, S.E., Turner, B.J., Mukamal, K.J. and Ghali, W.A., 2011. Association
of alcohol consumption with selected cardiovascular disease outcomes: a systematic review
and meta-analysis. BMJ: British Medical Journal (Online), 342. (Accessed: 10 March 2019)
Culleton, B.F., Larson, M.G., Kannel, W.B. and Levy, D., 1999. Serum uric acid and risk for
cardiovascular disease and death: the Framingham Heart Study. Annals of internal medicine,
131, pp.7-13.
Tominaga, M., Eguchi, H.I.D.E.Y.U.K.I., Manaka, H.I.D.E.O., Igarashi, K., Kato, T.A.K.E.O.
and Sekikawa, A.K.I.R.A., 1999. Impaired glucose tolerance is a risk factor for cardiovascular
disease, but not impaired fasting glucose. The Funagata Diabetes Study. Diabetes care, 22(6),
pp.920-924.
Tunstall-Pedoe, H., Kuulasmaa, K., Tolonen, H., Davidson, M., Mendis, S. and WHO
MONICA Project, 2003. MONICA monograph and multimedia sourcebook: world's largest
study of heart disease, stroke, risk factors, and population trends 1979-2002.
Rayner, M., Allender, S., Scarborough, P. and British Heart Foundation Health Promotion
Research Group, 2009. Cardiovascular disease in Europe. European Journal of Cardiovascular
Prevention & Rehabilitation, 16(2_suppl), pp.S43-S47.
Cooper, R.S., 2001. Social inequality, ethnicity and cardiovascular disease. International
journal of epidemiology, 30(suppl_1), p.S48.
Lip, G.Y.H., Barnett, A.H., Bradbury, A., Cappuccio, F.P., Gill, P.S., Hughes, E., Imray, C.,
Jolly, K. and Patel, K., 2007. Ethnicity and cardiovascular disease prevention in the United
Kingdom: a practical approach to management. Journal of human hypertension, 21(3), p.183.
REPLY

33
The major independent risk factors for CVD present in this man are age (59 years) and stress
most probably related to his low socioeconomic status as he hails from Eastern Europe
which has high poverty levels and he is a recent immigrant most probably with no regular
income and poor living standards.
As mentioned above, originating from Eastern Europe, he would have faced poverty and
hardships as he was growing up due to the political events there. Over the years due to
prevailing conditions, he would have and would be undergoing chronic stress which is known
to affect behaviour and factors that increase the risk of CVD. He smokes, is obese, drinks
excessively, is prediabetic, has deranged lipid profile. mildly hypertensive and sedentary due
to his existing asthma, gout and stress. He could be trying to "manage" his stress through
smoking, excessive alcohol consumption and "comfort" eating of unhealthy food. In addition
to this, his chronic stress would initiate inflammatory, haemostatic and autonomic processes
which would increase his CVD risk. At the cellular level,stress leads to oxidation in myocytes
by increased formation of Reactive Oxygen Species and reduced antioxidant reserve which at
optimum levels serves as a defense mechanism in cardiac and vascular myocytes. There is
increased intracellular calcium overload and depletion of Nitric oxide: hence endothelial
damage with ongoing atherosclerosis which would increase his risk of CVD.
Regarding Ethnicity and Cardiovascular risk, 7.9% of the UK population comprises of black
and minority groups - south asians and chinese.This group is associated with increase
cardiovascular morbidity and mortality as compared to the white population. The africans and
black carribeans, as compared to south asians and europeans, have a lower risk of CVD but
higher risk of stroke, hypertension and end stage renal disease. The south asians have a greater
risk of CVD than the african carribean blacks and europeans. In the UK, 50% of the south
asians have a greater risk of dying prematurely from CVD than general population. The
pakistanis and bangladeshis have a higher mortality rate amongst the south asians due to poor
socioeconomic status. The south asians also have a higher prevalence of developing Diabetes
as compared to other ethnic groups; hence icreased risk to CVD. The prevalence of CVD in
the chinese population in the UK is low compared to the blacks and south asians. Overall, the
2 main ethnic groups(blacks and south asians), show an increased risk of CVD as compared to
the whites.
References:
Samuel.S,G,Erica,S.Preventing cardiovascular disease;going beyond conventional risk
assessment assessment.Available on
https://doi.org/10.1161/CIRCULATION119.013886,circulation2015;1.1:230-231
Andrew,S and Mika,K(2012). Stress and cardiovascular disease.Cardiology9,360-370
Dhalla.N.S,Tersah.R.M,Nelticaden.T. Role of oxidative stress in cardiovascular disease-
Journal of Hypertension:June 2000-volume18-issue-p655-673

34
Lip.G.Y,Barnett.A.H,Bradbury.A,Cappuccio.F.P,Gill.P.S,Hughes.E,Imray.C,Jolly.K,Patel.K(
2007)Ethnicity and cardiovascular disease prevention in the UK:Availabe on
https://www.nature.com/articles/1002126.Journal of hypertension21-183-211
Therese.T,Alun.H,Peter.W,Jamil.M,Naveed.S,Paul.M.Ethnicity and prediction of
cardiovascular disease performance of QRISK2 and Framingham scores in a UK tr-ethnic
prospective cohort study.
.
REPLY
03/10/19Win Ko Ko
Pioneering work conducted by the Framingham Heart Study project in the United States and the Seven
Countries study in the 1960s and many other studies since then, including the WHO MONICA Project and
the INTERHEART study, have provided further insights into the risk factors and determinants of
cardiovascular diseases (CVDs) (Mendis et al., 2011, p.19). Cardiovascular risk factors can be classified in
different ways (O’Donnell and Elosua, 2008).
It is widely accepted that age, sex, high blood pressure, smoking, dyslipidemia, and diabetes are the major
risk factors for developing CVDs (Kannel et al., 1987). By Brian Boudi, 2016, conventional risk factors are
age more than 45 years in men, family history of early heart disease and African-American or Asian race.
Modifiable risk factors are high blood cholesterol, high blood pressure, diabetes mellitus (DM), obesity, lack
of physical activity, mental stress and depression.
Therefore, this 59-year old Caucasian gentleman has both modifiable and non-modifiable independent risk
factors.
The independent modifiable major risk factors of this gentleman are his total cholesterol level of 7.9 mmol/l,
triglycerides 4.5 mmol/l, his blood pressure of 160/95 mmHg and BMI of 31 kg/m2 according to O’Donnell
and Elosua, 2008. According to Yusuf et al., 2004, drinking more than 30 units of alcohol a week accounts
for major risk factor. Gout is a modifiable, but not an independent risk factor for CVD (Kuwabara, 2016).
Mentioning that he is asthmatic, there is still need to explore his symptom control status and use of steroid
medication because long term steroid use can increase the risk of hypertension, dyslipidemia, impaired blood
glucose and weight gain (Fardet and Fève, 2014). Although DM is a strong risk factor for CVDs, his impaired
fasting glucose (IFG) 6.9 mmol/l is a controversial risk factor for CVDs but according to Bertoni et al., 2016,
IFG is associated with heightened risk for silent myocardial infarction.
The non-modifiable risk factor of this gentleman is the age of 59 years (O’Donnell and Elosua, 2008).
For further management of this gentleman, thorough history taking, physical examination and some
investigations are necessary, such as his family history of early CVDs, his lifestyles such as smoking habit,
physical activity, diet including salt, meat, oil and vegetable intake, his heart rate, any evidence of left
ventricular hypertrophy, evidence of albuminuria, etc., to see the whole picture of CVD risk in this gentleman
for proper interventions.
Although CVD mortality and morbidity is still increased in Central and Eastern Europe according to (Roth
et al., 2017), the international WHO project MONICA, Finnish/Russian/Estonian, Swedish/Lithuanian, and
US/Russian surveys have shown that there were no substantial differences between Eastern Europe and
democratic countries regarding the prevalence of traditional risk factors with the significant exception of
male smokers (Ginter, 1998).

35
People of certain ethnic groups experience a disproportionately greater burden of CVD including coronary
heart disease (CHD) and stroke. South Asians have a higher prevalence of coronary heart disease (CHD)
and cardiovascular mortality compared with Europeans. African-Americans demonstrate higher rates of
CHD and stroke while African/Caribbeans in the UK have lower CHD rates and higher stroke rates than
British Europeans. Other non-European groups such as the Chinese and Japanese exhibit consistently high
rates of stroke but not CHD, while Mexican Americans have a higher prevalence of both stroke and CHD,
and North American native Indians also have high rates of CHD. While conventional cardiovascular risk
factors such as smoking, blood pressure and total cholesterol predict risk within these ethnic groups, they do
not fully account for the differences in risk between ethnic groups, suggesting that alternative explanations
might exist (Forouhi and Sattar, 2006).
Reference
Bertoni, A.G., Kramer, H., Watson, K., Post, W.S., 2016. Diabetes and Clinical and Subclinical CVD. Glob.
Heart 11, 337–342. https://doi.org/10.1016/j.gheart.2016.07.005
Brian Boudi, F. (2016). Risk Factors for Coronary Artery Disease: Practice Essentials, Risk Factor
Biomarkers, Conventional Risk Factors. [online] Emedicine.medscape.com. Available at:
https://emedicine.medscape.com/article/164163-overview [Accessed 10 Mar. 2019]
Fardet, L., Fève, B., 2014. Systemic Glucocorticoid Therapy: a Review of its Metabolic and Cardiovascular
Adverse Events. Drugs 74, 1731–1745. https://doi.org/10.1007/s40265-014-0282-9
Forouhi, N.G., Sattar, N., 2006. CVD risk factors and ethnicity—A homogeneous relationship? Atheroscler.
Suppl. 7, 11–19. https://doi.org/10.1016/j.atherosclerosissup.2006.01.003
Ginter, E., 1998. Cardiovascular disease prevention in eastern Europe. Nutr. Burbank Los Angel. Cty. Calif
14, 452–457.
Kannel, W., Wolf, P., Garrison, R., Cupples, L. and D'Agostino, R. (1987). The Framingham study.
[Bethesda, Md.]: National Heart, Lung and Blood Institute.
Kuwabara, M., 2016. Hyperuricemia, Cardiovascular Disease, and Hypertension. Pulse 3, 242–252.
https://doi.org/10.1159/000443769
Mendis, S., Puska, P., Norrving, B., World Health Organization, World Heart Federation, World Stroke
Organization (Eds.), 2011. Global atlas on cardiovascular disease prevention and control. World Health
Organization in collaboration with the World Heart Federation and the World Stroke Organization, Geneva.
O’Donnell, C.J., Elosua, R., 2008. Cardiovascular Risk Factors. Insights From Framingham Heart Study.
Rev. Esp. Cardiol. Engl. Ed. 61, 299–310. https://doi.org/10.1016/S1885-5857(08)60118-8
Roth, G.A., Johnson, C., et al., 2017. Global, Regional, and National Burden of Cardiovascular Diseases for
10 Causes, 1990 to 2015. J. Am. Coll. Cardiol. 70, 1–25.
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P.,
Varigos, J., Lisheng, L., 2004. Effect of potentially modifiable risk factors associated with myocardial
infarction in 52 countries (the INTERHEART study): case-control study. The Lancet 364, 937–952.
https://doi.org/10.1016/S0140-6736(04)17018-9
REPLY
Excellent post, well done
Dev
REPLY

36
Excellent post Preeti,
You have answered the post well and have also thought about relevant and related factors.
Good to see your scientific considerations too which are of course important. We will cover
more of this in later weeks of this module.
Dev
REPLY
Excellent post Win Ko Ko
Dev
REPLY
03/11/19Win Ko Ko
Thanks Dr Dev
REPLY

37
Thanks Dr Dev!
REPLY
The major independent risk factors for cardiovascular disease in this gentleman are gout, age,
excessive alcohol intake, hypertriglyceridaemia, obesity, elevated blood pressure, high total
cholesterol and impaired fasting glucose.
Berenson et al demonstrated that atherosclerotic changes of fatty streaks and fibrous plaques
worsened with age(1). This study also shown that the extent of atherosclerotic lesions were
positively correlated with increased blood pressure, increased body mass index, elevated
systolic blood pressure, increased diastolic blood pressure, elevated total cholesterol and
hypertriglyceridaemia. The multicentre, multiracial case-controlled studies of INTERHEART
and INTERSTROKE provide evidence on the link between modifiable risk factors and
cardiovascular disease. Yusuf et al(2) in the INTERHEART study demonstrated that history
of hypertension was associated with increased risk of myocardial infarction (Odds Ratio [OR]
1.91, Population attributable risk [PAR] 17.9%). The INTERHEART study showed that BMI
had a modest and graded effect of myocardial infarction BMI showed a modest and graded
association with myocardial infarction (OR 1·44, 95% CI 1·32–1·57).(3) It is worthwhile
noting that when BMI was adjusted for waist to hip ratio, the odds ratio dropped to 1·12 (·03–
1·22), and the odds ratio dropped to non-signiﬁcance (0·98, 0·88–1·09) after adjustment for
other risk factors. (3).
The INTERSTROKE study demonstrated that significant modifiable risk factors of stroke
present in this patient were history of hypertension (OR 2·64, 99% CI 2·26–3·08; PAR 34·6%,
99% CI 30·4–39·1) and alcohol intake (OR 1·51, 1·18–1·92 for more than 30 drinks per
month) (4)
The role of hyperuricaemia as an independent risk factor is unclear. Wu et al shows that elderly
> 65 years, after adjusting for confounding factors hyperuricaemia independently the risk of
incident CAD (HR=1.71, 95% CI 1.26–2.34) (5). In China, Wu et la showed that amongst
adults hyperuricaemic subjects had higher cardiovascular risk factor clustering compared to
normouricaemic patients in both prevalence and dose response association (6). The Busselton
Health Survey done in Western Australia, showed that hyperuricaemia was not independently
predictive of death or incident cardiovascular events. The case presentation shows a gentleman
with gout. A systematic review confirms that Gout is an independent risk factor for
cardiovascular and all-cause mortality (7). In a study in Israel, Impaired fasting glucose was
found to have a strong and independent association for increased cardiovascular disease (8).
Park et al also demonstrated that impaired fasting glucose was a predictor for stroke and
coronary heart disease (9) . A metanalysis of 17 population based studies reveals that
hypertriglyceridaemia predict subsequent coronary artery disease in Caucasian patients (10).

38
Eastern Europeans immigrating to the UK have been shown to have increased cardiovascular
risk on account of ethnicity and increased psychosocial stress. An analysis of cardiovascular
mortality trends in England and wales showed a slow decline amongst people born in Eastern
Europe, with slow declines in the first decade amongst those born in Hungary (11).
Additionally peoples of Eastern European extraction had high risk of Ischaemic heart disease
and stroke compared to Canadian counterparts and immigrants from Western Europe (12). The
INTERHEART study revealed that psychosocial risk factors (i.e. social deprivation, stress at
work or in family life and depression) is associated with increased risk for myocardial
infarction (MI) (RR 2.3 for men). (2).
Ethnicity plays a role increased rate of cardiovascular risk, not only on account of the race but
also the area of origin. An Australian study showed lower adjusted acute myocardial admission
and CHD mortality when Australian-born were compared to for migrants from the Western
Europe with natives of middle East having higher rates. (13). This study revealed that
adjustment for SES and region of residence had little impact on migrant differentials. Both the
INTERHEART and INTERSTROKE studies revealed the same modifiable risk factors were
responsible for majority of adverse cardiovascular outcomes leading credence to the fact that
these factors are more important than race (2)(4). Migrant populations may also not have the
same educational experiences as locals. Low education is associated with increased
cardiovascular events especially in high-income countries (14). An Australian study showed
that Immigrant populations have lower education status and increased work place stress than
people born in high income countries(15)
References
1. Berenson GS, Srinivasan SR, Bao W, Newman WP, Tracy RE, Wattigney WA.
Association between multiple cardiovascular risk factors and atherosclerosis in children and
young adults. N Engl J Med. 1998;338(23):1650–1656.
2. Yusuf S, Hawken S, Ôunpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially
modifiable risk factors associated with myocardial infarction in 52 countries (the
INTERHEART study): case-control study. The lancet. 2004;364(9438):937–952.
3. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, et al. Obesity
and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control
study. The Lancet. 2005;366(9497):1640–1649.
4. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors
for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE
study): a case-control study. The Lancet. 2010;376(9735):112–123.
5. Wu J, Lei G, Wang X, Tang Y, Cheng H, Jian G, et al. Asymptomatic hyperuricemia
and coronary artery disease in elderly patients without comorbidities. Oncotarget [Internet].
2017 Oct 6 [cited 2019 Mar 11];8(46). Available from:
http://www.oncotarget.com/fulltext/21079
6. Wu J, Qiu L, Cheng X, Xu T, Wu W, Zeng X, et al. Hyperuricemia and clustering of
cardiovascular risk factors in the Chinese adult population. Sci Rep [Internet]. 2017 Dec [cited
2019 Mar 11];7(1). Available from: http://www.nature.com/articles/s41598-017-05751-w
7. Lottmann K, Chen X, Schädlich PK. Association Between Gout and All-Cause as well
as Cardiovascular Mortality: A Systematic Review. Curr Rheumatol Rep. 2012
Apr;14(2):195–203.
8. Shaye K, Amir T, Shlomo S, Yechezkel S. Fasting glucose levels within the high normal
range predict cardiovascular outcome. Am Heart J. 2012 Jul;164(1):111–6.

39
9. Park C, Guallar E, Linton JA, Lee D-C, Jang Y, Son DK, et al. Fasting Glucose Level
and the Risk of Incident Atherosclerotic Cardiovascular Diseases. Diabetes Care. 2013 Jul
1;36(7):1988–93.
10. Hokanson JE. Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol.
1998;81(4):7B–12B.
11. Harding S, Rosato M, Teyhan A. Trends for coronary heart disease and stroke mortality
among migrants in England and Wales, 1979–2003: slow declines notable for some groups.
Heart. 2008;94(4):463–470.
12. Sohail QZ, Chu A, Rezai MR, Donovan LR, Ko DT, Tu JV. The Risk of Ischemic Heart
Disease and Stroke Among Immigrant Populations: A Systematic Review. Can J Cardiol. 2015
Sep;31(9):1160–8.
13. Taylor R, Chey T, Bauman A, Webster I. Socio-economic, migrant and geographic
differentials in coronary heart disease occurrence in New South Wales. Aust N Z J Public
Health. 1999;23(1):20–26.
14. Rosengren A, Subramanian SV, Islam S, Chow CK, Avezum A, Kazmi K, et al.
Education and risk for acute myocardial infarction in 52 high, middle and low-income
countries: INTERHEART case-control study. Heart. 2009;95(24):2014–2022.
15. Daly A, Carey RN, Darcey E, Chih H, LaMontagne AD, Milner A, et al. Workplace
psychosocial stressors experienced by migrant workers in Australia: A cross-sectional study.
PloS One. 2018;13(9):e0203998.
REPLY
03/14/19Sam Ang Eik
According National Health Service, UK, people should not drink more than 14 units per week
(both men and women). This man is considered a heavy consumption of alcohol, which may
lead many cardiovascular diseases such as coronary heart disease, heart failure, etc. (Bell, S. et
al, 2017). There is increased risks of cardiovascular disease in Asian and Caucasian with a high
blood pressure, smoking, high lipid levels, elevated BMI and diabetes. (Peters, SAE. et al,
2017).
Ethnic minorities in Europe appears differently affected by many CV risk factors such as
hypertension, type 2 diabetes and dyslipidemia. There is correlation between genetic and
environmental factors in causing CV diseases. Different diet and socioeconomic change may
also be a contributing effects leading to many cardiovascular diseases. (Canto et al. 2018).
Reference
https://www.nhs.uk/live-well/alcohol-support/calculating-alcohol-units/
Bell, S. et al. (2017). Association between clinically recorded alcohol consumption and initial
presentation of 12 cardiovascular diseases: population based cohort study using linked health
records. the bmj | BMJ 2017;356:j909 | doi: 10.1136/bmj.j909.

40
Peters SAE, et al. (2017). Clustering of risk factors and the risk of incident cardiovascular
disease in Asian and Caucasian populations: results from the Asia Pacific Cohort Studies
Collaboration. BMJ Open 2018;8:e019335. doi:10.1136/bmjopen-2017-019335
Canto et al. 2018. Why are there ethnic differences in Cardio-metabolic risk factors and
cardiovascular diseases?. JRSM. Volume 15, pp.1-5.
REPLY
Major Independent Risk Factor? The IMPACT model incorporates major CHD risk factors such as cigarette
smoking, high blood pressure, elevated total cholesterol, obesity, diabetes, and physical inactivity and all
established medical and surgical interventions for CHD. In this case Cholesterol level, obesity, high blood
pressure, diabetes is the major problem approximately 44% was attributed to changes in risk factors.
Eastern Europe Risk? Hartley found substantial variation in the declines by country. Declines in ischemic
heart disease (IHD) were more than 60% over this time period for Western Europe whereas Eastern European
states had much less decline. There were periods in the decade of the 1990s where countries like Croatia,
Latvia, and Slovenia showed substantial increases in IHD mortality. The authors suggested that these
increases were influenced by the social and political changes following the fall of Communism. Social
constructs also play a role in trends in mortality in England. Prediction in 2030, Allen et al. noted that all
economic groups demonstrated declining IHD mortality rates.
Ethnicity and Risk? The decline in CVD mortality rate in recent years has neither been uniform for all
population subgroups nor for all causes of CVD death. In addition, despite the decades of the decline,
substantial disparities in mortality rates continue by race, ethnicity, and sex
Hi everyone
We now move to week 2 where we will consider CVD risk assessment. Let's start of with
this vignette to get our discussion going:
A 40-year-old female psychiatric nurse attends her GP practice as part of a health check
programme. She jokes with her friends that life begins at 40 but in reality she is becoming
increasingly concerned about her future cardiovascular health. Her father developed
angina in his late 50s and died from heart failure aged 70. Her mother was a lifelong
smoker, had multiple small strokes and died in a nursing home with multi-infarct
dementia aged 73. She smokes 20 cigarettes per day, weighs 73 kg, BMI 25 kg/m². The
practice nurse carries out some blood tests and measures her blood pressure at 150/80
mmHg. The blood tests show that her total cholesterol is 5.9 mmol/L, HDL cholesterol 0.9

41
mmol/L. The results are then discussed by the patient with both the practice nurse and
her general practitioner.
What is the most significant single cardiovascular risk factor for this individual?
In calculating her cardiovascular risk, which risk calculator is specifically highlighted in
NICE lipid modification guidelines?
If her cardiovascular risk was to be estimated by the Reynolds risk score, which is
sometimes considered to be more appropriate for women, which additional laboratory
testing would be needed?
thanks
Dev
REPLY
Re: CVD Risk Assessment
03/11/19Win Ko Ko
NICE recommended that people older than 40 should have their estimate of CVD risk so that life has
definitely begun for this lady (National Institute for Health and Care Excellence, 2014, p.10).
The most significant single cardiovascular risk factor for this lady is smoking 20 cigarettes per day.
According to INTERACT study, smoking in women ≤65 years of age who is from western Europe has more
risk of CVDs and quitting smoking is very important for this lady because of her age and country if she is
from western Europe (Yusuf et al., 2004, p.948).
The other CVD risk factors of this lady are her blood pressure of 150/90 mmHg. For lipid profile, NICE
suggested using clinical findings, family history and lipid profile to judge for further management rather
than the use of strict lipid cut-off value (National Institute for Health and Care Excellence, 2014, p.17). Other
non-HDL cholesterol measurement including triglyceride concentration is still need to be explored.
According to Framingham study, the occurrence of angina in her father at late 50s is not accounted as the
family history of early CVDs. Her mother history of multiple small strokes should be considered as a CVD
risk if the age of occurrence is less than 65 years (Kolber and Scrimshaw, 2014).
NICE specifically highlighted to use QRISK risk assessment tool to assess CVD risk (National Institute for
Health and Care Excellence, 2014, p.11).
Cardiovascular disease (CVD) risk calculators assist clinicians in estimating a patient’s risk of a
cardiovascular event. These calculated risk estimates (RE) are often used to place patients into specific risk
categories which is then used to guide intervention recommendations or determine the benefits of treatment.
There are at least 25 calculators for CVD risk assessment and Reynolds risk score is one of them (Allan et
al., 2013).
Traditional risk assessment tools, such as the Framingham Risk Score, significantly underestimate risk in
women by classifying most women as having low risk for CVD. Such under-appreciation of risk has led to
the development of alternative tools such as the Reynold's risk score incorporating a marker of inflammation:
high sensitivity C-reactive protein (hsCRP) (Park and Pepine, 2015). Therefore, hsCRP level should be
measured in this lady.
Reference

42
Allan, G.M., Nouri, F., Korownyk, C., Kolber, M.R., Vandermeer, B., McCormack, J., 2013. Agreement
Among Cardiovascular Disease Risk Calculators. Circulation 127, 1948–1956.
https://doi.org/10.1161/CIRCULATIONAHA.112.000412
Kolber, M.R., Scrimshaw, C., 2014. Family history of cardiovascular disease. Can. Fam. Physician Med.
Fam. Can. 60, 1016.
Park, K.E., Pepine, C.J., 2015. Assessing cardiovascular risk in women: looking beyond traditional risk
factors. Trends Cardiovasc. Med. 25, 152–153. https://doi.org/10.1016/j.tcm.2014.10.024
National Institute for Health and Care Excellence (2014). Cardiovascular disease: risk assessment and
reduction, including lipid modification (NICE Clinical Guideline: CG181). Available at:
https://www.nice.org.uk/guidance/cg181 (Accessed: 11th March 2019)
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P.,
Varigos, J., Lisheng, L., 2004. Effect of potentially modifiable risk factors associated with myocardial
infarction in 52 countries (the INTERHEART study): case-control study. The Lancet 364, 937–952.
https://doi.org/10.1016/S0140-6736(04)17018-9

43
Week 2:
CVD Risk Assessment (44)
hsCRP (52)
Case 2 (63)

44
CVD Risk Assessment
In my opinion, the most significant single cardiovascular risk factor for this 40-year-old female
psychiatric nurse is being a smoker who smokes 20 cigarettes per day. Although age has a
greater homogeneity of variance (x2
) than smoking as identified by Ridker PM et al. (2007,
pp.614) in Reynolds Risk Model, the female nurse is relatively young and is at her pre-
menopausal stage in which she’s being protected by oestrogen (by up-regulation of high-
density lipoprotein (HDL), de-regulation of low-density lipoprotein (LDL) and the overall
reduction of total cholesterol) and thus the risk of developing heart diseases is decreased
significantly. The risk would increase significantly when she reaches the age of 60 to 65, in
which the level of oestrogen drops and the risk of heart disease becomes the same as men.
Otherwise, parental history of angina <60 years old, hypertension, hypercholesterolaemia are
considered as relatively weakerdeterminants of cardiovascular disease (CVD) when compared
with age and smoking.
There are several risk calculators that have been validated and included in the NICE Lipid
Modification guideline (NICE, 2018). Framingham and QRISK2 are two online assessment
tools that are available for the estimation of the 10-year risk of having a cardiovascular event
in people who do not have a history of heart disease. Among these two, NICE recommends
that QRISK2 risk calculator tool should be done to assess CVD risk for the primary prevention
of CVD in people up to and including age 84 years (NICE, 2018). Therefore, QRISK2 should
be highlighted and carried out to this nurse when she is present at a primary care clinic or
attending to a general practitioner. This is because proper preventive therapy (lipid
modification therapy) can be offered to people who have a 10% or greater 10-year risk of
developing CVD.
Apart from Framingham, QRISK2 and UKPDS risk model (for diabetic patients), Reynolds
Risk Score has been identified as a more appropriate risk assessment tool for women (Ridker
PM et al,2007, pp.614). In addition to total serum cholesterol and HDL-C that are included in
Framingham and QRISK2, Ridker PM et al (2007, pp. 611-619) included high-sensitivity C-
reactive protein (hsCRP) in the Reynolds Risk Score and it is considered as an additional
laboratory testing for this patient if the risk is calculated according to Reynolds Risk Score. It
is proven that Reynolds model has improved accuracy for global cardiovascular risk
prediction.
References
Ridker, P.M., Buring, J.E., Rifai, N. and Cook, N.R., 2007. Development and validation of
improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds
Risk Score. Jama, 297(6), pp.611-619.
National Institute for Health and Care Excellence (NICE) Clinical Guideline. Lipid
modification. Available at: https://www.nice.org.uk/guidance/cg181/evidence/lipid-
modification-update-full-guideline-243786637 (Accessed: 11 March 2019)
REPLY

45
The cardiovascular risk assessment using risk calculators helps in estimating a patient's risk of
an event. The calculated risk estimates(RE) are then used to place patients into specific risk
categories which is then used to guide intervention recommendations or determine benefits of
treatment.
There are many calculators - Framingham, QRISK2, JSB3, Reynolds to mention a few and
they all have their limitations.They look at the 10year risk and this can help define primary
prevention strategies in primary healthcare settings.
Regarding the case scenario, the most significant single cardiovascular risk factor is smoking.
She is a heavy smoker. The NICE lipide modification guidelines specifically highlights the
QRISK2 calculator, which is used for primary prevention of CVD in primary care setting. It
helps in identifying patients who are likely to be at high risk. It is better than Framingham
which tends to overestimate risk in low risk patients and underestimate risk in high risk
patients. QRISK usually gets updated every April. In this case scenario, using the QRISK2
calculator, the nurse is at 7.8% risk with a QRISK health heart age of 64.
The Reynolds risk score is considered better than the Framingham risk calculator in assessing
cardiovascular risk in women as it is better calibrated and validated for use in this category. It
looks at age, smoking, systolic blood pressure, total cholesterol and any parent with heart attack
before 60 years. In addition to this, it requires laboratory testing of high sensitive CRP level.
References:
QRISK2 calculator. Available on https://qrisk.org/2017/index.php
Reynolds risk score for cardiovascular risk in women.Available on
https://www.medcalc.com/reynolds-risk-score-cardiovascular-risk-women
Cardiovascular risk assessment and lipide modification:NICE guidelines(2015)Available on
https://www.ncbi.nim.nih.gov/pmc/articles/PMC4484941/2015
Allan.G,Faeza.N,Christine.K,Michael.R,Ben.V,James.M.Agreement among cardiovascular
disease risk calculators.Available on
https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATION AHA.112.000412
REPLY

46
Excellent answer Win Ko Ko
I'm going to create a new post on hsCRP....
Thanks
Dev
REPLY
Excellent answer, well done
Dev
REPLY
Very good answer Preeti
Dev
REPLY
CVS risk assessment
In the battle against cardiovascular diseases, it is important to target certain risk factors through
primary prevention. Risk factors for cardiovascular disease can be broadly classified into 2
types: non-modifiable and modifiable risk factors. Non-modifiable risk factors are self-
explanatory, and this includes age (depending on the risk assessment tool used); male sex,
family history of cardiovascular disease and ethnic background. Emphasis on primary

Executive summary module 1

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to Executive summary module 1

Similar to Executive summary module 1 (20)

Recently uploaded

Recently uploaded (20)

Executive summary module 1