Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

Oumar, Bâ Hamidou; Ibrahima, Sangaré; Youssouf, Camara; Noumou, Sidibé; Souleymane, Coulibaly; Yacouba, Cissoko; Samba, Sidibé; Massama, Konaté; Kéita, Maiga Asmaou; Thiam, Doumbia Coumba; Mamadou, Touré; Mamadou, Diakité; Ichaka, Menta; Bella, Diall Ilo

doi:https://doi.org/10.1155/2021/8862762

International Journal of Hypertension

On this page

Abstract Introduction Methods Discussion Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 8862762 | https://doi.org/10.1155/2021/8862762

Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

Bâ Hamidou Oumar,¹Sangaré Ibrahima,¹Camara Youssouf,²Sidibé Noumou,¹Coulibaly Souleymane,³Cissoko Yacouba,³Sidibé Samba,³Konaté Massama,⁴Maiga Asmaou Kéita,⁵Doumbia Coumba Thiam,²Touré Mamadou,¹and Diakité Mamadou³ et al.

Academic Editor: Kai Hu

Received01 Oct 2020

Revised20 Dec 2020

Accepted06 Feb 2021

Published28 Feb 2021

Abstract

Objective. This study aimed to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department. Methodology. Data stem from a cross-sectional previous study performed from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. All patients aged 40 and more were included in the study allowing the assessment of bmi-Frm and li-WHO prediction charts. The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. for the bmi-Frm and the li-WHO chart for the Afro-D region of the WHO. The risk score for both ranged from <10 to ≥40. The data were entered in an ACCESS 2010 database, then processed by MS Excel 2010, and finally analysed using IBM SPSS Statistics 20. Continuous variables were presented as means and standard deviations, and categorical variables were presented as frequencies with percentages. was considered the statistical significance level. After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance. Results. This study involved 793 outpatients, 63.7% being female, 35.1% of them younger than 50 years, 57.9% with no formal education, and 67.7% with no medical insurance. Means for age, body mass index (BMI), and systolic blood pressure (SBP) were, respectively, 53.81 ± 16.729 years, 25.29 ± 06.151 kg/m2, and 139.49 ± 27.110 mm Hg. Using the li-WHO prediction chart gives a much higher proportion of low-risk patients compared to bmi-Frm (83.6 vs. 37.7). Sociodemographic characteristics such as education or income level were not different in risk score neither for the bmi-Frm nor for the li-WHO risk score. The percentage of agreement between both tools was 40.4%, and agreement (kappa of 0.1 and weighted kappa of 0.2) was found to be slight. Conclusion. Using the bmi-Frm and li-WHO tool gives a similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO using cholesterol. These must be confirmed in further studies and compared to data from prospective studies

1. Introduction

Cardiovascular diseases (CVD) are the number one cause of death worldwide with 17.9 million deaths in 2016 and made 31% of all causes of death mainly in low- and middle-income countries (LMIC) [1]. Furthermore, high-income countries (HIC) experience a heavy burden of CVD [2] and therefore underlying cardiovascular risk factors. CVD remain a major cause of health loss in all regions of the world [3]. Actions are therefore necessary and some of them easy to implement [4], already in childhood [5] and through implementing cost-effective population-wide strategies [6].

In Africa, particularly in sub-Saharan countries like most LMIC, there is a shift with regression of infectious diseases and increase of noncommunicable diseases [7]. Our country like most LMIC has a high burden of cardiovascular diseases due to the high prevalence of cardiovascular risk factors [8–12], low-level knowledge and awareness, and also a perception of cardiovascular diseases and their risk factors [13].

As treatment cost remains unaffordable for most people in LMIC, prevention is particularly important for chronic diseases such CVD. WHO-PEN strategy adopted by many countries and intervention trials could lead to a decrease of the burden of CVD [14–16].

To allow the prevention of CVD, tools for risk score assessment have been developed for decades, and most of them for HIC [17] and with challenges [18]. One of the most known is the Framingham risk score (FRS) which has the advantage of a published equation allowing an automatic calculation. Only one tool can be considered worldwide, namely, the prediction tool provided by the World Organization of Health and International Society of Hypertension (WHO/ISH) for all WHO 14 subregions [19].

Opposed to the WHO/ISH prediction chart [20], FRS has been used for various populations [21–24].

Since laboratory tests are expensive, laborless tools such as these 2 cited above are the main tools which can be easily used for the prevention and management of patients with cardiovascular diseases and risk factors. Despite the fact that these tools are essential, data on their use and results are scarce.

To fill this lack of publication and guide their use, this study was performed, aiming to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department.

2. Methods

Data for this study stem from a cross-sectional one carried out from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. We selected patients aged 40 and more with available data allowing FRS estimating.

2.1. Sampling and Data Collection

We included all patients who came to visit during the study time since the required data are already part of the usual clinical exam.

A minimal size of 384 patients was needed for the study using the following formula: n = z² x p (1–p)/m². This was obtained with a value set at 0.5, a confidence interval of 95%, z of 1.96, and a margin error of 5%.

Briefly, all patients underwent history taking and complete clinical examination with measures of weight, height, blood pressure, waist, and hip circumference.

Data were first collected using a questionnaire and then inserted in a MS Access database built for this purpose.

2.2. Definition of Terms

(i)Height and weight were measured using a rigid stadiometer and digital balance, respectively. Readings were recorded to the nearest centimetre and the nearest kg, with participants being weighted and measured without shoes and only light clothed.(ii)Body mass index (BMI) was derived from weight and height as weight (kg)/height² (m²)(iii)Hip circumference (HC) was measured around the widest portion of the buttock whereas waist circumference (WC) was measured with a nonstretchable tape over the light-clothed abdomen at the narrowest point between the lowest rib and the iliac crest. The waist-to-hip ratio (WHr) was WC divided by HC.(iv)Income level was based on a multiple of the minimum obligatory salary (MOS) (actually 40 000 FCFA or 71, 5 USD) from 1 to 4 MOS.(v)Education level was assessed in terms of years attending in school with four levels defined as follows:(vi)None: no school attending(vii)Primary school: 1 to 6 years(viii)High school: 7 to 12 years(ix)University: 12 years or more(x)Blood pressure has been measured using a CONTEC 08A electronic sphygmomanometer allowing the assessment of mean arterial pressure from systolic blood pressure (SBP) and diastolic blood pressure (DBP) by adding to the DBP a third of the differential pressure.

2.3. Risk Assessment

The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. [15] and the cholesterol-free WHO prediction chart for the Afro-D region. For both, risk assessment was from <10 to ≥40.

2.4. Data Analysis

The data were first exported from the ACCESS database to MS Excel and finally analysed using IBM SPSS Statistics 20. Continuous variables are presented as means and standard deviations, and categorical variables are presented as frequencies with percentages. was considered the statistical significance level.

After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance.

2.5. Ethical Approval

The study as part of a thesis for the medical student has been approved by the institutional board of the UH-GT.

2.6. Results

The study involved 793 outpatients, 63.7% (505) being female and 35.1% (278) younger than 50 years. Most patients had no formal education (57.9%) and no medical insurance (67.7%). More than half of the patients did n t give an income level, and the remaining 12.9% and 13.9% had income levels of 1 and 4, respectively (Table 1).

Using both tools, patients with a risk <10 were highly represented in different proportions with 83.5% for li-WHO and 37.7% for bmi-Frm prediction (Figure 1).

3. Sociodemographic Characteristics and Risk Scores (Table 3)

3.1. With bmi-Frm Tool

Patients without medical insurance had 43.2% versus 26.2% for those with medical insurance in the lowest risk group and 15.5% versus 21.9% in the highest risk group. The proportion of patients decreased from <10 to 30−<40 and increased in the highest risk score (≥40) for both patients with and without medical insurance ().

Female patients had 49.1% in the lowest group and 7, 9% in the highest versus, respectively, 17.7% and 34.4% for male patients ().

There was a clear trend in the lowest and highest age groups with a decrease of the proportion of patients from 86.0% (<10) to 0.0% (≥40) for the age group 40–49 and opposite behaviour for patients ≥70 with 1.4% (<10) to 17.5% (≥40) ().

Education level and income level showed no statistical difference according to the risk score with respective values of 0.944 and 0.175.

3.2. With li-WHO Tool

Female and male patients showed the highest proportion of risk group <10 with, respectively, 86.7% and 78.7%.

The low-risk group <10 was the most important but in decreasing proportions with increasing age.

As for bmi-Frm, education and income level showed no statistical difference according to the risk score ( values of 0.624 and 0.569, respectively).

4. Agreement

The percentage of agreement was 40.4% (321/793) and the kappa value was 0, 1 meaning slight agreement. The weighted kappa of 0, 2 revealed light improvement of agreement (Table 4).

4.1. Agreement Status and Sociodemographic Characteristics (Table 5)

There was significantly more agreement among female patients (52.5%) versus 19.4% for male patients (). The agreement rate decreased significantly with increasing age from 87.4% for patients under 50 years to 04.9% for those older than 70 years ().

Patients without medical insurance showed more agreement with 46% versus 28.9% for those with medical insurance ().

We noticed no difference in agreement related to neither education level nor income level.

5. Discussion

To the best of our knowledge, this study is the first to use FRS in the Malian population in a relatively large sample of 793 outpatients seen consecutively. As lipid test is costly, we presented data for the bmi-version of the FRS [25] and the li-WHO risk score.

As can be read from Figure 1, the proportion of low-risk patients is very low compared to that of the laborless version of the WHO/ISH (Afro-D region). Clearly, the WHO/ISH prediction chart detected a few high-risk patients whereas FRS found about 10 times more high-risk subjects.

Similar African studies using WHO risk score found more low-risk patients in 87.7% for only women [26], 94.5% of slum residents [27], 76.9% of civil servants [28], and 87.6% among adults [29].

In our study, there was no significant difference regarding education level or income level as opposed to data from other LMIC countries like Iran [22] or Tunisia [30] in selected population studies with men and socially disadvantaged subjects showing a higher risk.

African data are contrasting with no difference using Framingham models for Ahmed et al. [31] and limited interchangeability of Framingham laboratory and nonlaboratory tools for Boateng et al. [32]. Both studies involved the same populations in different locations, Somalis for the first and Ghanaians for the latter.

Increasing age is known to be associated with cardiovascular risk factors (CVrf) and therefore cardiovascular events [33]. Thus, Borhanuddin et al. found a higher risk score for male subjects [34]. Female sex was also found to have a lower risk of cardiovascular events [35]. This is certainly explainable by the presence of more risk factors among males.

As per name, CVrf are associated with an increase of the global risk. This fact is known for decades in quantitative estimating by some authors [36]. It is also not surprising that the pressure values and level of risk are directly related as shown in Table 4. Advancing age has been found with increased prevalence of hypertension in Africa [37].

It is also established that anthropometric characteristics are related to cardiovascular risk. This is the case for WC [37, 38] and WHR [39] and explains our findings shown in Table 3.

Blood pressure is strongly related to cardiovascular risk. Several studies brought insights into the role of hypertension in the development of CVD [33, 40]. Blood pressure gradation was used in treatment [41] with our data showing a direct increase of the mean pressure values with increasing risk level.

As our study is the first of its kind, some questions remain unanswered and should be addressed in further studies.

First, this finding needs to be confirmed using a larger sample size. Some authors [42] pointed out a temporal trend in the performance of the FRS, with others recognizing the need for a model of 10-year absolute CVD risk based on a long-term cohort study for a country-based or ethnicity-based CVD.

Country-based or ethnicity-based CVD risk-prediction models are needed because of substantial variations in the four main contributors to CVD risk assessment in predictive models (mean CVD risk, mean levels of major risk factors, relative risk of a risk factor, and epidemiology of CHD and stroke) among different countries or ethnic groups [21]. Besides classic models with traditional CVrf, the prediction model with lifestyle factors showed an improvement of the predictive ability for cardiovascular death [43]. All these fields are to be explored based on local data.

The main problems will be certainly financial issues as large sample size studies need substantial budgets.

Female patients, those younger than 50, had significantly more agreement; this could mean that there is more agreement in patients with low risk. It is known that female patients especially before menopause had a lower risk than male patients. It is also known that the risk of cardiovascular diseases increases with age.

In this study, patients without medical insurance had a greater agreement. An explanation could be found in the proportion (not showed) of patients with medical insurance in the age groups of 50–59 and 60–69 with 35.5% and 47.3%, respectively.

Education level and income level showed no relationship in this study.

6. Limits

Income level could not be fully studied as data for most patients were not available.

7. Conclusion

Using bmi-Frm and li-WHO tool gives similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO cholesterol. These data must be confirmed in further studies and compared to data from prospective studies.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

Cardiovascular diseases (CVDs), 2019. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
L. J. Laslett, P. Alagona, B. A. Clark et al., “The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues,” Journal of the American College of Cardiology, vol. 60, no. 25, pp. S1–S49, 2012.
View at: Publisher Site | Google Scholar
G. A. Roth, C. Johnson, A. Abajobir, F. Abd-Allah, S. F. Abera et al., “Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015,” Journal of the American College of Cardiology, vol. 70, no. 1, pp. 1–25, 2017.
View at: Publisher Site | Google Scholar
World Health Organization, Prevention of Cardiovascular Disease: Guidelines for Assessment and Management of Cardiovascular Risk, World Health Organization, Geneva, Switzerland, 2007.
H. C. McGill, C. A. McMahan, and S. S. Gidding, “Preventing heart disease in the 21st century,” Circulation, vol. 117, no. 9, pp. 1216–1227, 2008.
View at: Publisher Site | Google Scholar
R. L. Sacco, G. A. Roth, K. S Reddy, D. K. Arnett, R. Bonita, and T. A. Gaziano, “The heart of 25 by 25: achieving the goal of reducing global and regional premature deaths from cardiovascular diseases and stroke,” Circulation, vol. 133, no. 23, pp. e674–e690, 2020.
View at: Publisher Site | Google Scholar
T. A. Gaziano, “Cardiovascular disease in the developing world and its cost-effective management,” Circulation, vol. 112, no. 23, pp. 3547–3553, 2005.
View at: Publisher Site | Google Scholar
H. O. Bâ, I. Menta, Y. Camara et al., “Cardiovascular risk factors (CVrf) in rural and urban areas Mali: data from the step 2013 survey,” World Journal of Cardiovascular Diseases, vol. 8, no. 1, pp. 35–47, 2018.
View at: Publisher Site | Google Scholar
A. A. Alsheikh-Ali, M. I. Omar, F. J. Raal et al., “Cardiovascular risk factor burden in Africa and the Middle East: the Africa Middle East cardiovascular epidemiological (ACE) study,” PLoS One, vol. 9, no. 8, Article ID e102830, 2014.
View at: Publisher Site | Google Scholar
F. J. Raal, A. A. Alsheikh-Ali, M. I. Omar et al., “Cardiovascular risk factor burden in Africa and the Middle East across country income categories: a post hoc analysis of the cross-sectional Africa Middle East Cardiovascular Epidemiological (ACE) study,” Arch Public Health, vol. 76, p. 15, 2018.
View at: Publisher Site | Google Scholar
F. P. Cappuccio and M. A. Miller, “Cardiovascular disease and hypertension in sub-Saharan Africa: burden, risk and interventions,” Internal and Emergency Medicine, vol. 11, no. 3, pp. 299–305, 2016.
View at: Publisher Site | Google Scholar
S. Hamid, W. Groot, and M. Pavlova, “Trends in cardiovascular diseases and associated risks in sub-Saharan Africa: a review of the evidence for Ghana, Nigeria, South Africa, Sudan and Tanzania,” The Aging Male, vol. 22, no. 3, pp. 169–176, 2019.
View at: Publisher Site | Google Scholar
D. Boateng, F. Wekesah, J. L. Browne et al., “Knowledge and awareness of and perception towards cardiovascular disease risk in sub-Saharan Africa: a systematic review,” PLoS One, vol. 12, no. 12, Article ID e0189264, 2017.
View at: Publisher Site | Google Scholar
S. O. Oti, S. J. van de Vijver, C. Kyobutungi et al., “A community-based intervention for primary prevention of cardiovascular diseases in the slums of Nairobi: the SCALE UP study protocol for a prospective quasi-experimental community-based trial,” Trials, vol. 14, no. 1, p. 409, 2013.
View at: Publisher Site | Google Scholar
S. Van De Vijver, S. Oti, J. Addo, A. De Graft-Aikins, and C. Agyemang, “Review of community-based interventions for prevention of cardiovascular diseases in low- and middle-income countries,” Ethnicity & Health, vol. 17, no. 6, pp. 651–676, 2012.
View at: Publisher Site | Google Scholar
World Health Organization, Package of Essential Noncommunicable (PEN) Disease Interventions for Primary Health Care in Low-Resource Settings, World Health Organization, Geneva, Switzerland, 2020, https://www.who.int/nmh/publications/essential_ncd_interventions_lr_settings.pdf.
M. T. Cooney, A. Dudina, R. D'Agostino, and I. M. Graham, “Cardiovascular risk-estimation systems in primary prevention,” Circulation, vol. 122, no. 3, pp. 300–310, 2010.
View at: Publisher Site | Google Scholar
F. Farzadfar, “Cardiovascular disease risk prediction models: challenges and perspectives,” Lancet Glob Health, vol. 7, no. 10, pp. e1288–e1289, 2019.
View at: Publisher Site | Google Scholar
WHO, WHO/ISH Cardiovascular Risk Prediction Charts, World Health Organization, Geneva, Switzerland, 2019, https://www.who.int/cardiovascular_diseases/guidelines/Chart_predictions/en/.
H. Bâ, I. Sangare, S. Camara et al., “Cardiovascular risk (CV) assessment according to the WHO cholesterol free chart in the cardiology department of university hospital Gabriel Touré,” Cardiology & Vascular Research, vol. 2, no. 2, pp. 1–5, 2018.
View at: Publisher Site | Google Scholar
D. Zhao, J. Liu, W. Xie, and Y. Qi, “Cardiovascular risk assessment: a global perspective,” Nature Reviews Cardiology, vol. 12, no. 5, pp. 301–311, 2015.
View at: Publisher Site | Google Scholar
M. Nakhaie, B. Koor, S. Salehi, and F. Karimpour, “Prediction of cardiovascular disease risk using framingham risk score among office workers, Iran, 2017,” Saudi Journal of Kidney Diseases and Transplantation, vol. 29, no. 3, p. 608, 2018.
View at: Publisher Site | Google Scholar
R. B. D’Agostino, R. S. Vasan, M. J Pencina et al., “General cardiovascular risk profile for use in primary care: the framingham heart study,” Circulation, vol. 117, no. 6, pp. 743–753, 2008.
View at: Publisher Site | Google Scholar
J. Bosomworth, “Practical use of the Framingham risk score in primary prevention Canadian perspective,” Canadian Family Physician, vol. 57, pp. 417–423, 2011.
View at: Google Scholar
Framingham Heart Study 2019. https://www.framinghamheartstudy.org/fhs-risk-functions/cardiovascular-disease-10-year-risk/.
E. Umuerri and I. Obiebi, “Total cardiovascular risk assessment of women in delta state, Nigeria, using the World Health Organization/International Society of Hypertension risk prediction chart,” Journal of Clinical and Preventive Cardiology, vol. 9, no. 1, pp. 2–7, 2020.
View at: Publisher Site | Google Scholar
A. Vusirikala, F. Wekesah, C. Kyobutungi, and O. Oyebode, “Assessment of cardiovascular risk in a slum population in Kenya: use of World Health Organisation/International Society of Hypertension (WHO/ISH) risk prediction charts - secondary analyses of a household survey,” BMJ Open, vol. 9, no. 9, p. e029304, 2019.
View at: Publisher Site | Google Scholar
O. A. Babatunde, S. O. Olarewaju, A. A. Adeomi et al., “10-year risk for cardiovascular diseases using WHO prediction chart: findings from the civil servants in South-western Nigeria,” BMC Cardiovasc Disord, vol. 20, p. 154, 2020.
View at: Publisher Site | Google Scholar
J. M. Pedro, M. Brito, and H. Barros, “Cardiovascular risk assessment in Angolan adults: a descriptive analysis from CardioBengo, a community-based survey,” International Journal of Hypertension, vol. 2018, Article ID 2532345, 6 pages, 2018.
View at: Publisher Site | Google Scholar
O. Saidi, D. Malouche, M. O’Flaherty et al., “Assessment of cardiovascular risk in Tunisia: applying the Framingham risk score to national survey data,” BMJ Open, vol. 6, no. 11, Article ID e009195, 2016.
View at: Publisher Site | Google Scholar
S. H. Ahmed, N. Marjerrison, M. K. R. Kjøllesdal et al., “Comparison of cardiovascular risk factors among Somalis living in Norway and somaliland,” International Journal of Environmental Research and Public Health, vol. 16, no. 13, p. 2353, 2019.
View at: Publisher Site | Google Scholar
D. Boateng, C. Agyemang, E. Beune et al., “Cardiovascular disease risk prediction in sub-Saharan African populations-comparative analysis of risk algorithms in the RODAM study,” International Journal of Cardiology, vol. 254, pp. 310–315, 2018.
View at: Publisher Site | Google Scholar
S. Lewington, R. Clarke, N. Qizilbash, R. Peto, and R. Collins, “Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies,” The Lancet, vol. 360, pp. 1903–1913, 2002.
View at: Publisher Site | Google Scholar
B. Borhanuddin, A. Mohd Nawi, S. A. Shah et al., “10-Year cardiovascular disease risk estimation based on lipid profile-based and BMI-based framingham risk scores across multiple sociodemographic characteristics: the Malaysian cohort project,” The Scientific World Journal, vol. 2018, Article ID 2979206, 8 pages, 2018.
View at: Publisher Site | Google Scholar
K. Velu, M. Böhm, R. Schmieder et al., “Impact of sex on cardiovascular outcome in patients at high cardiovascular risk,” Circulation, vol. 126, no. 8, pp. 934–941, 2012.
View at: Publisher Site | Google Scholar
C. Ogedegbe, M. Joffres, M. Joffres, D. MacLean, and G. Flowerdew, “A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. The Canadian Heart Health Surveys,” International Journal of Obesity, vol. 25, no. 5, pp. 652–661, 2001.
View at: Publisher Site | Google Scholar
R. BeLue, T. A. Okoror, J. Iwelunmor et al., “An overview of cardiovascular risk factor burden in sub-Saharan African countries: a socio-cultural perspective,” Globalization and Health, vol. 5, no. 1, p. 10, 2009.
View at: Publisher Site | Google Scholar
S. B. Van Dijk, T. Takken, E. C. Prinsen, and H. Wittink, “Different anthropometric adiposity measures and their association with cardiovascular disease risk factors: a meta-analysis,” Netherlands Heart Journal, vol. 20, no. 5, pp. 208–218, 2012.
View at: Publisher Site | Google Scholar
M. Dalton, A. J. Cameron, P. Z. Zimmet et al., “Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults,” Journal of Internal Medicine, vol. 254, no. 6, pp. 555–563, 2003.
View at: Publisher Site | Google Scholar
S. S. Welborn and N. D. Wong, “Hypertension and cardiovascular disease: contributions of the framingham heart study,” Global Heart, vol. 8, no. 1, pp. 49–57, 2013.
View at: Publisher Site | Google Scholar
P. Muntner and P. K. Whelton, “Using predicted cardiovascular disease risk in conjunction with blood pressure to guide antihypertensive medication treatment,” Journal of the American College of Cardiology, vol. 69, no. 19, pp. 2446–2456, 2017.
View at: Publisher Site | Google Scholar
S. Rospleszcz, B. Thorand, T. De Las Heras Gala et al., “Temporal trends in cardiovascular risk factors and performance of the framingham risk score and the pooled cohort equations,” Journal of Epidemiology and Community Health, vol. 73, no. 1, pp. 19–25, 2019.
View at: Publisher Site | Google Scholar
M. Nishimoto, M. Tagawa, M Matsui et al., “A prediction model with lifestyle in addition to previously known risk factors improves its predictive ability for cardiovascular death,” Scientific Reports, vol. 9, no. 1, Article ID 12953, 2019.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2021 Bâ Hamidou Oumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

873

Downloads

766

Citations