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Clustering of Cardiovascular Diseases Risk Factors, and Cardiovascular Risk Prediction, Primary Health Care Centers, Jeddah

Rajaa Al-Raddadi , Majed Al-Ghamdi , Farhan Al-shaalan , Zeyad Al-Harbi


Outline 1. Introduction 2. Rational 3. objectives 4. Methodology 5. Result 6. Conclusion 7. Recommendation 8. References


Introduction • Cardiovascular disease is a major cause of disability and premature death throughout the world. • Contributes substantially to the escalating costs of health care

Sipido KR, Van de Werf F. The growing burden of cardiovascular disease. Eur Heart J 2012 Jul;33(13):1540-1


Ten leading causes of burden of disease, world, 2004 and 2030


ICEBERG 

Heart attacks and strokes are only the tip of the iceberg

Risk factor burden ◦ ◦ ◦ ◦ ◦ ◦ ◦

Obesity Raised blood sugar Physical activity Raised blood lipids Unhealthy diet Smoking Raised blood pressure


Why it is important to target CVDs ?????


Ranking of 10 selected risk factors of cause of death

The Risk Factor of today is the disease of tomorrow


The global distribution of IHD mortality rates in males (age standardized, per 100 000)


The global distribution of IHD mortality rates in females ((age standardized, per 100 000


WHO Global Report on NCD, 2011


Metabolic Risk Factors,2008

WHO Global Report on NCD, 2011


Multiple risk factor interventions • Evidence ▍ A Cochrane systematic review has evaluated the

1. Multiple risk factor interventions arerisk effective prevention of effectiveness of multiple factorforinterventions cardiovascular disease in for the primary prevention of cardiovascular multifactorial high-risk groups 2. For Low Risk Population, Target disease in adults from general populations, the behaviors

occupational groups and high-risk groups

Ebrahim S, Davey Smith G. Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev. 2000;(2):CD001561


How to Address CVDs

Address social determinants of health


Objectives • To estimate the prevalence of cardiovascular risk factors • To estimate the 10 years cardiovascular risk.


Methodology  Study area  Study design  Study population & Sampling  Data collection tool  Data analysis  Limitation


Methodology Study area 

health care center

Two Centers randomly selected

During 2012.


Methodology • Study design • Analytic Cross sectional


Methodology Study Population

Sample

• PHCCs attendees

• 400 participants aged ≥30 years • Simple random sample


Data collection 1. Questionnaire First part: Second part: • demography

• • • •

smoking status Diabetes HTN family history of diabetes, cardiovascular diseases

2. Measurements        

BP Weight Height HA1c WC HC Total Cholesterol HDL


Methodology

Data Collection Tools

3.

The 10 year cardiovascular risk prediction was estimated using the WHO/ISH cardiovascular risk prediction charts.


10 Years Risk Assessment


10-year risk of cardiovascular Event > 10%

10-20%

20-30 %

< 30%


Methodology Data Analysis • SPSS version 20. • Variables were expressed as either mean (SD) for continuous variables or proportions and percentage for categorical variables • Between-group comparisons for categorical variables were performed using chi-square tests. • Multiple linear regression analyses were performed to identify factors associated with the cardiovascular risk and presented with the 95% confidence interval. • The level of significance level was set at P ≤ .05.


Results â&#x20AC;˘ The age ranged between 30 and 82 years with a mean of 46.9 and SD of 11.1 years.


Gender


Variable Age in years (n=400) Range (MeanÂąSD) Marital status (n=392) Education (n=399)

Family income (n=392)

Categories 30-40 41-50 51-60 >60 30-82 (46.9Âą11.1)

Number 141 122 90 47

Percent 35.3 30.5 22.5 11.8

Single Married Divorced/widowed No schooling <primary school Primary school Intermediate school High school Diploma University Postgraduate

14 350 28 21 22 43 50 140 29 83 11

3.6 89.3 7.1 5.3 5.5 10.8 12.5 35.1 7.3 20.8 2.8

<5000 5000-<10000 10000-<15000 15000-20000

149 153 54 23

38.0 39.0 13.8 5.9


Prevalence of smoking <0.001

%


Prevalence of Physical activity <0.001

%


Prevalence of Physical activity NS

%


Prevalence of Diabetes NS

%


Prevalence of Hypertension <0.05

%


Prevalence of Obesity <0.05

%


Prevalence of Abdominal Obesity <0.001

%


History of Heart Diseases NS

%


Family History of Diabetes NS

%


10 Years Cardiovascular Risk Prediction for the study population according to the presence of diabetes


10 Years Risk Assessment


Multiple linear regression for cardiovascular risk predictors Predictors Constant SBP DBP HA1c FBS HDL LDL cholesterol TG BMI Smoking

B -2.02 .05 -.03 -.01 .01 .01 -.01 .01 -.01 -.04 -.45

p-value .34 .001 .19 .69 .97 .99 .68 .01 .17 .35 .31

95 % Confidence Interval for B Lower Limit Upper Limit -6.29 2.24 .02 .08 -.08 .02 -.03 .02 -.01 .01 -.01 .01 -.01 .01 .01 .02 -.01 .01 -.13 .05 -1.4 .45


The Strength and limitation • The selection of random sample from the primary health care centers. However…….. • The sample was only from Jeddah city. • The small sample for the laboratory measurements.


Conclusion â&#x20AC;˘ The results of this study point out that the participants bear a substantial burden of risk factors for CVD.

â&#x20AC;˘ Among these risk factors, physical inactivity, hypertension and abdominal obesity recorded the highest prevalence.


Conclusion â&#x20AC;˘ The estimated 10 years Cardiovascular risk prediction was > 10% in (40.4%) of the diabetic patients compared to (30.7%) among non diabetics.

â&#x20AC;˘ The predictors of cardiovascular risk were systolic and blood pressure and cholesterol but not glycemic control parameter.


Recommendation • Health promotion programs to increase public health capacity towards prevention of cardiovascular risk factors are urgently needed. • Provide adequate surveillance and continuous monitoring of the cardiovascular risk factors. • Further analytic research are needed for: 1. Develop our own risk assessment tool 2. Identify the best predictors for CVDs


Competing interests The authors declare that they have no competing interests.


Acknowledgment The authors are grateful to thank the study participants.


References • Sipido KR, Van de Werf F. The growing burden of cardiovascular disease. Eur Heart J 2012 Jul;33(13):1540-1. • Deaton C, Froelicher ES, Wu LH, Ho C, Shishani K, Jaarsma T. The global burden of cardiovascular disease. Eur J Cardiovasc Nurs 2011 Jul;10 Suppl 2:S5-13. • Deaton C, Froelicher ES, Wu LH, Ho C, Shishani K, Jaarsma T. The global burden of cardiovascular disease. J Cardiovasc Nurs 2011 Jul;26(4 Suppl):S5-14.. • Fuchs FD, Fuchs SC, Moreira LB, Gus M. Proof of concept in cardiovascular risk: the paradoxical findings in blood pressure and lipid abnormalities. Vasc Health Risk Manag 2012;8:437-42. • Batsis JA, Lopez-Jimenez F. Cardiovascular risk assessment--from individual risk prediction to estimation of global risk and change in risk in the population. BMC Med 2010;8:29. • Lenz M, Muhlhauser I. [Cardiovascular risk assessment for informed decision making. Validity of prediction tools]. Med Klin (Munich) 2004 Nov 15;99(11):651-61. • Coke LA. Cardiac risk assessment of the older cardiovascular patient: the Framingham Global Risk Assessment Tools. Medsurg Nurs 2010 Jul;19(4):253-4.


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SHA24/019001  

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