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Avoidable Burden of Cardiovascular Diseases in the Eastern Mediterranean Region: Contribution of Selected Risk Factors for Cardiovascular-Related Deaths

  • Ehsan Mazloumi
  • Jalal Poorolajal
  • Nizal Sarrafzadegan
  • Hamid Reza Roohafza
  • Javad Faradmal
  • Manoochehr KaramiEmail author
Original Article
Part of the following topical collections:
  1. Epidemiology of Hypertension

Abstract

Introduction

Contribution of risk factors for cardiovascular-related deaths in the Eastern Mediterranean Region Organization (EMRO) is not estimated quantitatively.

Aim

To determine the avoidable burden of cardiovascular diseases (CVDs) due to hypertension, diabetes, smoking, overweight, and obesity in countries of EMRO of the WHO.

Methods

The comparative risk assessment methodology was used to calculate the potential impact fraction (PIF) and percentage of the avoidable burden of CVD-related death due to associated risk factors. Population exposure levels for CVDs and corresponding measures of association were extracted from published studies. The attributable burden was calculated by multiplying the Disability-Adjusted Life-Years (DALYs) for CVDs by the estimated impact fraction of risk factors. DALYs of the CVDs in all countries of the EMRO were extracted from the GBD official website in 2016.

Results

Following reduction of the current prevalence of smoking, obesity, hypertension, diabetes, and overweight to a feasible minimum risk exposure level in Lebanon, about 12.4%, 4.2%, 10.2%, 3.8%, and 5.7% of the burden of CVD-related mortality could be avoidable, respectively. The corresponding values of avoidable burden in selected EMRO countries were 5.1%, 3.5%, 9.4%, 5.9% and 5.3% in Iran and 9.5%, 4.1%, 11%, 8.2% and 5.4% in Egypt.

Conclusions

Findings suggest that health policy makers of all EMRO countries should take into account the attributable burden of CVD-related mortality due to associated risk factors to effectively develop preventive interventions.

Keywords

Global burden of diseases Noncommunicable diseases Cardiovascular diseases Risk factors 

1 Introduction

Cardiovascular diseases (CVDs) is one of the leading causes of death worldwide, especially in developed and developing countries [1]. According to the World Health Organization, 17.7 million people died from CVDs in 2015, representing 31% of all global deaths [2], and is projected to rise to more than 23.6 million by 2030 [3].

It is estimated that CVDs contribute to 45% of all non-communicable disease deaths, which is more than twice the deaths from cancer, and more than all deaths from congenital malformations, infant mortality, and nutrition disorders [4].

The Eastern Mediterranean Region (EMRO) comprises 22 countries with a population of nearly 580 million people [5]. It is estimated that 54% of deaths from non-communicable diseases in the EMRO are due to CVDs. Deaths attributed to CVDs (of total deaths) vary from 49% in Oman to 13% in Somalia. The relatively high prevalence of these non-communicable diseases can be attributed to constant lifestyles as well as risk factors such as hypertension (range from 28% in the UAE to 41% in Libya and Morocco), diabetes (range from 4% in Iran to 19% in Sudan), and hypercholesterolemia (range from 14% in Lebanon to 52% in Iran) [6].

Various risk factors, including alcohol consumption, smoking, hypertension, high body mass index, high cholesterol, high blood sugar, low fruit and vegetable consumption and physical inactivity, account for 61% of CVD deaths. In general, such risk factors contribute to the development of more than three-fourths of ischemic heart disease, which is the leading cause of death worldwide. Although these factors are major risk factors associated with CVD in high-income countries, they account for more than 84% of the global burden of disease in low-income and middle-income countries, and reduced exposure to these factors may lead to increased life expectancy [7]. Moreover, CVDs have a huge economic burden on health care and society. For instance, in 2013, CVD personal spending in the United States was estimated to be 231.1 billion USD [8].

In order to better understand the preventive role of these risk factors in CVD-related mortality, and to develop effectively, and clear strategies and justify intervention for healthcare policy-makers, the avoidable disease burden measure is applied. Policy makers are always eager to know the impact of a reduction in the level of an exposure to the desired level on the burden of the disease and incidence rate. The potential impact fraction (PIF), which has also been called generalized attributable fraction, was first introduced by Walter (1980) and was generalized by Morgeston and Bursic in 1982 to measure the population attributable fraction. The PIF measures the proportional reduction in the disease when the current distribution of the risk factor changes [9].

Due to different impacts of each exposure on CVDs, and the fact that identification of primary exposures is the basic prevention method of non-communicable diseases, simultaneous control planning for all exposures is impossible. Moreover, contribution of risk factors for cardiovascular-related deaths in the EMRO countries is not estimated quantitatively by estimates of potential impact fraction. This work aimed to determine the avoidable disease burden of CVDs mortality due to hypertension, diabetes, smoking, overweight, and obesity in countries of EMRO of the WHO.

2 Methods

In this descriptive study, the comparative risk assessment (CRA) methodology was used to assess the burden of the disease based on the existing exposures, and determine the effect of the intervention on these exposures and the potential burden of disease [10]. Cardiovascular diseases are a group of disorders of the heart and blood vessels and include: coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis and pulmonary embolism [2]. In the present study, we considered all countries of the Mediterranean region of the WHO. The criteria for entering countries into estimation were full details on required data that obtained from WHO and GBD (Global Burden of Diseases) websites. Countries with incomplete information were excluded from the study. The avoidable burden was calculated to estimate, compare and prioritize preventive measures for hypertension, diabetes, overweight, and obesity in selected countries of EMRO including Afghanistan, Egypt, Bahrain, Djibouti, Iraq, Iran, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen; and smoking in Bahrain, Egypt, Iran, Lebanon, Morocco, Oman, Pakistan, and Saudi Arabia except for Palestine, due to lack of required estimates.

In order to estimate the avoidable disease burden, the population exposure level was extracted from current WHO reports [2, 11, 12, 13, 14], the crude effect of smoking was obtained from Yusuf S (the INTERHEART study) as Odds ratio, crude effect of overweight and obesity was obtained from Borrell as hazard ratio, and crude effect of diabetes and hypertension was obtained from a cohort study by Isfahan cardiovascular research center, the Isfahan University of Medical Sciences, Iran [15, 16, 17]. It should be noted that the reason for application of similar magnitude of relative risk as the crude effect of various exposures on CVD mortality in this study was the similarity of causal model for the effects of risk factors among countries. However, the population level of exposures (risk factors) was specific for any of EMRO countries

The PIF was used to measure the population attributable fraction. PIF measures the proportional reduction in the disease when the existing distribution of the risk factor changes [9]. The PIF is calculated using the following equation:
$${\text{PIF}} = \frac{{\mathop \sum \nolimits_{i = 1}^{n} PiRRi - \mathop \sum \nolimits_{i = 1}^{n} P^{\prime}iRRi}}{{\mathop \sum \nolimits_{i = 1}^{n} PiRRi}}$$
where RR is the relative risk at a given exposure level I (defined as the crude effect of various exposures on CVD mortality in this study), Pi is the population level of exposure, and P’i is the population level of exposure at a given counterfactual exposure level. Murray and Lopez identified four types of distribution of counterfactual exposure: theoretical, plausible, feasible, and cost-effective minimum risk. This study used feasible and theoretical minimum risk exposure level [18].

If the current exposure level in these countries reaches the theoretical minimum risk exposure (scenario 1, that was set zero at this projected level of exposure), or feasible minimum risk (scenario 2, in this case, the prevalence of cigarette smoking is reduced by 30% compared to the primary exposure level, and a 25% reduction relative to the primary exposure level was reported for obesity, overweight, diabetes and hypertension), indicating the potential impact fraction of CVD-related mortality.

In this study, RR is defined as the crude effect of various exposures on CVD mortality. The population level of exposure is similar to the level of risk factors such as smoking, hypertension, diabetes, overweight and obesity in different countries, and the different counterfactual level of exposure is the level of exposure in hypothetical scenarios, which is reported as the avoidable fraction of each exposure for CVD-related mortality. Moreover, estimates value of DALYs of the CVDs in all countries of the EMRO were obtained from the GBD official website in 2016 [19].

By using the above equation, PIF was calculated and multiplied by DALYs estimates, and the attributable fractions associated with each exposure in both scenarios for CVD-related mortality were calculated using counterfactual analysis. The estimated magnitude of the potential impact fraction for each risk factor is reported as the avoidable number of DALYs by countries. Data analysis was performed using Excel 2013 and Stata version 14. The study was provided ethical approval by the Hamadan University of Medical Sciences IR.UMSHA.REC.1396.824.

3 Results

Following reduction of the current prevalence of diabetes to the theoretical minimum risk exposure levels in Egypt, i.e. zero prevalence of diabetes, 32.7% (1633,555 DALYs) of the burden of CVD-related mortality, could be avoidable. The corresponding values of avoidable burden in EMRO countries were 30.6% (25,873 DALYs) in Kuwait, 30.2% (233,183 DALYs) in Saudi Arabia, 15.7% (4407 DALYs) in Djibouti, 15.3% (27,984 DALYs) Lebanon and 12.6% (50,288 DALYs) Somali. Also, following reduction of the current prevalence of diabetes to a feasible minimum risk exposure level, i.e. 25% reduction rather than primary of diabetes prevalence in Egypt, 8.2% (408,389 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden in EMRO countries were 7.7% (6468 DALYs) in Kuwait, 7.5% (58,296 DALYs) in Saudi Arabia, 3.9% (1102 DALYs) in Djibouti, 3.8% (6996 DALYs) Lebanon and 3.1% (12,572 DALYs) Somali. Details on PIF estimates and avoidable burden of CVDs due to diabetes mellitus by different scenario is shown in Table 1 and Fig. 1.
Table 1

Potential Impact Fraction and Avoidable Burden of diabetes mellitus for CVD related mortality by different scenario

Country

Prevalence

HR

Prevalence theoretical minimum risk (scenario 1)

Prevalence feasible minimum risk (scenario 2)

PIF-scenario 1

%

PIF-scenario 2

%

DALY (2016)

Avoidable burden (scenario 1)

Avoidable burden (scenario 2)

Afghanistan

8.4 (5.5–12.1)

4 (2.9–5.5)

0

6.3 (4.1–9.1)

20.1 (9.5–35.3)

5.0 (2.4–8.8)

2,257,810

454,447 (213,618–795,971)

113,612 (53,405–198,993)

Bahrain

8.5 (5.5–12.3)

4 (2.9–5.5)

0

6.4 (4.1–9.2)

20.3 (9.5–35.6)

5.1 (2.4–8.9)

26,892

5464 (2544–9581)

1366 (636–2395)

Djibouti

6.2 (3.6–9.6)

4 (2.9–5.5)

0

4.7 (2.7–7.2)

15.7 (6.4–30.2)

3.9 (1.6–7.5)

28,103

4407 (1799–8478)

1102 (450–2119)

Egypt

16.2 (11.8–21.2)

4 (2.9–5.5)

0

12.2 (8.9–15.9)

32.7 (18.3–48.8)

8.2 (4.6-12.2)

4,994,780

1,633,555 (914,744–2,438,598)

408,389 (228,686–609,649)

Iran

10.3 (7.6–13.9)

4 (2.9–5.5)

0

7.7 (5.7–10.4)

23.6 (12.6–38.5)

5.9 (3.2–9.6)

3,194,164

754,008 (403,038–1,229,129)

188,502 (100,760–307,282)

Iraq

13.2 (9.3–18)

4 (2.9–5.5)

0

9.9 (7–13.5)

28.4 (15–44.8)

7.1 (3.8–11.2)

1,991,629

564,961 (299,074–891,281)

141,240 (74,769–228,820)

Jordan

13.1 (9.5–17.5)

4 (2.9–5.5)

0

9.8 (7.1–13.1)

28.2 (15.3–44.1)

7.1 (3.8–11)

193,483

54,587 (29,584–85,241)

13,647 (7396–21,310)

Kuwait

14.7 (10.4–20.2)

4 (2.9–5.5)

0

11 (7.8–15.2)

30.6 (16.5–47.6)

7.7 (4.1–11.9)

84,543

25,873 (13,949–40,257)

6468 (3487–10,064)

Lebanon

6 (3.7–9.1)

4 (2.9–5.5)

0

4.5 (2.8–6.8)

15.3 (6.6–29.1)

3.8 (1.6–7.3)

183,450

27,984 (12,049–53,297)

6996 (3012–13,324)

Libya

13.7 (9.9–18.2)

4 (2.9–5.5)

0

10.3 (7.4–13.7)

29.1 (15.8–45)

7.3 (4.11.3)

228,768

66,636 (36,219–103,002)

16,659 (9055–25,751)

Morocco

12.4 (8.7–17.2)

4 (2.9–5.5)

0

9.3 (6.5–12.9)

27.1 (14.2–43.6)

6.8 (3.5–10.9)

1,479,258

401,081 (209,835–645,403)

100,270 (52,459–161,351)

Oman

7.5 (5.1–10.9)

4 (2.9–5.5)

0

5.6 (3.8–8.2)

18.4 (8.8–32.9)

4.6 (2.2–8.2)

122,999

22,592 (10,866–40,477)

5648 (2716–10,119)

Pakistan

9.8 (6.5–13.8)

4 (2.9–5.5)

0

7.4 (4.9–10.4)

22.7 (11–38.3)

5.7 (2.7–9.6)

9,829,314

2,233,244 (1,080,481–3,765,579)

558,311 (270,120–941,395)

Qatar

12.8 (8.4–18.5)

4 (2.9–5.5)

0

9.6 (6.3–13.9)

27.7 (13.8–45.4)

6.9 (3.4–11.4)

28,216

7829 (3883–12,819)

1957 (971–3205)

Saudi Arabia

14.4 (10.4–19.5)

4 (2.9–5.5)

0

10.8 (7.8–14.6)

30.2 (16.5–46.7)

7.5 (4.1–11.7)

772,957

233,183 (127,535–361,262)

58,296 (31,884–90,316)

Somalia

4.8 (2.9–7.6)

4 (2.9–5.5)

0

3.6 (2.2–5.7)

12.6 (5.2–25.5)

3.1 (1.3–6.4)

399,506

50,288 (20,863–101,812)

12,572 (5216–25,453)

Sudan

6.6 (4.1–9.9)

4 (2.9–5.5)

0

5 (3.1–7.4)

16.5 (7.2–30.8)

4.1 (1.8–7.7)

1,728,685

285,709 (124,932–532,777)

71,427 (31,233–133,194)

Syrian Arab Republic

11.9 (8.5–16.1)

4 (2.9–5.5)

0

8.9 (6.4–12.1)

26.3 (13.9–42)

6.6 (3.5–10.5)

800,071

210,483 (111,245–336,127)

52,621 (27,811–84,032)

Tunisia

12.2 (8.7–16.4)

4 (2.9–5.5)

0

9.2 (6.5–12.3)

26.8 (14.2–42.5)

6.7 (3.5–10.6)

470,385

126,033 (66,725–199,738)

31,508 (16,681–49,934)

United Arab Emirates

8 (5.2–11.8)

4 (2.9–5.5)

0

6 (3.9–8.9)

19.4 (9–34.7)

4.8 (2.2–8.7)

325,300

62,961 (29,250–112,824)

15,740 (7312–28,206)

Yemen

7.7 (4.6–11.4)

4 (2.9–5.5)

0

5.8 (3.7–8.6)

18.8 (8.5–33.9)

4.7 (2.1–8.5)

1,231,987

231,185 (104,929–417,719)

57,796 (26,232–104,430)

Fig. 1

Potential Impact Fraction of diabetes mellitus, obesity, overweight, hypertension, smoking to CVD related mortality by different scenarios. a Diabetes mellitus, b hypertension, c obesity, d overweight, e smoking

Following reduction of the current prevalence of hypertension to the theoretical minimum risk exposure levels in Somali, i.e. zero prevalence of hypertension, 47.5% (189,571 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden among EMRO countries were 47.1% (4625,866 DALYs) in Pakistan, 45.7% (798,182 DALYs) in Soudan, 36.2% (44,506 DALYs) Oman, 35% (9882 DALYs) Qatar and 31.3% (101,726 DALYs) in UAE. As well as, following reduction of the current prevalence of hypertension to a feasible minimum risk exposure level in Somali, i.e. 25% reduction rather than primary of hypertension prevalence, 11.9% (47,393 DALYs) of the burden of CVD-related mortality, could be avoidable. The corresponding values of avoidable burden in EMRO countries were 11.8% (1156,466 DALYs) in Pakistan, 11.4% (197,296 DALYs) in Soudan, 9% (11,126 DALYs) Oman, 8.8% (2471 DALYs) Qatar and 7.8% (25,431 DALYs) in UAE (Table 2, Fig. 1).
Table 2

Potential Impact Fraction and Avoidable Burden of hypertension for CVD related mortality by different scenario

Country

Prevalence

HR

Prevalence theoretical minimum risk (scenario 1)

Prevalence feasible minimum risk (scenario 2)

PIF-scenario 1

%

PIF-scenario 2

%

DALY (2016)

Avoidable burden (scenario 1)

Avoidable burden (scenario 2)

Afghanistan

23 (17–29.8)

4.5 (3.3–6.1)

0

17.3 (12.8–22.4)

44.6 (28.4–60.4)

11.1 (7.1–15.1)

2,257,810

1,006,946 (640,583–1,363,897)

251,736 (160,146–340,974)

Bahrain

16.3 (11.3–22.3)

4.5 (3.3–6.1)

0

12.2 (8.5–16.7)

36.3 (20.8–53.3)

9.1 (5.2–13.3)

26,892

9769 (5605–14,346)

2442 (1401–3584)

Djibouti

21.8 (15.3–29.2)

4.5 (3.3–6.1)

0

16.4 (11.5–21.9)

43.3 (26.3–59.9)

10.8 (6.6–15)

28,103

12,162 (7385–16,839)

3041 (1846–4210)

Egypt

22.6 (17.8–27.9)

4.5 (3.3–6.1)

0

17.0 (13.4–20.9)

44.2 (29.3–58.8)

11.0 (7.3–14.7)

4,994,780

2,205,958 (1,464,251–2,938,029)

551,490 (366,063–734,507)

Iran

17.1 (13–21.6)

4.5 (3.3–6.1)

0

12.8 (9.8–16.2)

37.4 (23.2–52.5)

9.4 (5.8–13.1)

3,194,164

1,195,938 (742,584–1,677,409)

298,985 (185,646–419,352)

Iraq

19.4 (14.2–24.9)

4.5 (3.3–6.1)

0

14.6 (10.7–18.7)

40.4 (24.9–56)

10.1 (6.2–14)

1,991,629

805,429 (495,131–1,116,141)

201,357 (123,783–279,035)

Jordan

16.4 (11.8–21.6)

4.5 (3.3–6.1)

0

12.3 (8.9–16.2)

36.5 (21.6–52.5)

9.1 (5.4–13.1)

193,483

70,559 (411,725–101,607)

17,640 (10,431–25,402)

Kuwait

17.7 (12.7-23.4)

4.5 (3.3–6.1)

0

13.3 (9.5–17.6)

38.3 (22.8–54.5)

9.6 (5.7–13.6)

84,543

32,340 (19,305–46,081)

8085 (4826–11,520)

Lebanon

19.7 (14.7–25.7)

4.5 (3.3–6.1)

0

14.8 (11–19.3)

40.8 (25.5–56.8)

10.2 (6.4–14.2)

183,450

74,868 (46,803–104,235)

18,717 (11,701–26,059)

Libya

20.5 (15.3–26.3)

4.5 (3.3–6.1)

0

15.4 (11.5–19.7)

41.8 (26.3–57.4)

10.4 (6.6–14.3)

228,768

95,570 (60,121–131,277)

23,892 (15,030–32,819)

Morocco

23.8 (18–30.3)

4.5 (3.3–6.1)

0

17.9 (13.5–22.7)

45.4 (29.5–60.8)

11.4 (7.4–15.2)

1,479,258

672,243 (437,087–899,466)

168,061 (109,272–224,866)

Oman

16.2 (11.6–21.6)

4.5 (3.3–6.1)

0

12.2 (8.7–16.2)

36.2 (21.3–52.5)

9 (5.3–13.1)

122,999

44,506 (26,171–64,593)

11,126 (6543–16,148)

Pakistan

25.4 (19.9–31.8)

4.5 (3.3–6.1)

0

19.1 (14.9–23.9)

47.1 (31.7–62)

11.8 (7.9–15.5)

9,829,314

4,625,866 (3,113,788–6,089,316)

1,156,466 (778,447–1,522,329)

Qatar

15.4 (9.9–22.2)

4.5 (3.3–6.1)

0

11.6 (7.4–16.7)

35.0 (18.7–53.2)

8.8 (4.7–13.3)

28,216

9882 (5289–15,010)

2471 (1322–3753)

Saudi Arabia

19.1 (14.2–24.7)

4.5 (3.3–6.1)

0

14.3 (10.7–18.5)

40.1 (24.9–55.8)

10 (6.2–14)

772,957

309,693 (192,162–431,642)

77,423 (48,040–107,910)

Somalia

25.8 (19–33.5)

4.5 (3.3–6.1)

0

19.4 (14.3–25.1)

47.5 (30.7–63.2)

11.9 (7.7–15.8)

399,506

189,571 (122,591–252,369)

47,393 (30,648–63,092)

Sudan

24 (17.8–30.8)

4.5 (3.3-6.1)

0

18.0 (13.4–23.1)

45.7 (29.3–61.2)

11.4 (7.3–15.3)

1,728,685

789,182 (506,775–1,057,862)

197,296 (126,694–264,465)

Syrian Arab Republic

20.3 (15.2–26.1)

4.5 (3.3–6.1)

0

15.2 (11.4–19.6)

41.5 (26.2–57.2)

10.4 (6.5–14.3)

800,071

332,330 (209,246–457,622)

83,083 (52,312–114,405)

Tunisia

22.8 (17.4–28.6)

4.5 (3.3–6.1)

0

17.1 (13.1–21.5)

44.4 (28.8–59.4)

11.1 (7.2–14.9)

470,385

208,769 (135,691–279,507)

52,192 (33,923–69,877)

United Arab Emirates

13 (8.4–18.8)

4.5 (3.3–6.1)

0

9.8 (6.3–14.1)

31.3 (16.4–49)

7.8 (4.1–12.3)

325,300

101,726 (53,246-159,547)

25,431 (13,312–39,887)

Yemen

22.5 (16.4–29.5)

4.5 (3.3–6.1)

0

16.9 (12.3–22.1)

44.1 (27.6–60.2)

11 (6.9–15)

1,231,987

542,763 (340,616–741,234)

135,691 (85,153–185,308)

By reduction of the current prevalence of obesity to the theoretical minimum risk exposure levels in Kuwait, i.e. zero prevalence of obesity, 19.1% (16,187 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden among EMRO countries were 18.3% (141,456 DALYs) in Saudi Arabia, 17.8% (5030 DALYs) in Qatar, 4.5% (78,168 DALYs) Sudan, 4.2% (16,896 DALYs) Somali, 2.8% (63,205 DALYs) in Afghanistan. In addition, following reduction of the current prevalence of obesity in Kuwait to a feasible minimum risk exposure level, i.e. 25% reduction rather than primary of obesity avoidable, 4.8% (4047 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden in EMRO countries were 4.6% (35,364 DALYs) in Saudi Arabia, 4.5% (1258 DALYs) in Qatar, 1.1% (19,542 DALYs) Sudan, 1.1% (4224 DALYs) Somali and 0.7% (15,801 DALYs) in Afghanistan (Table 3).
Table 3

Potential Impact Fraction and Avoidable Burden of obesity for CVD related mortality by different scenario

Country

Prevalence

HR

Prevalence theoretical minimum risk (scenario 1)

Prevalence feasible minimum risk (scenario 2)

PIF-scenario 1

%

PIF-scenario 2

%

DALY (2016)

Avoidable burden (scenario 1)

Avoidable burden (scenario 2)

Afghanistan

4.5 (2.8–6.7)

1.6 (1.3–2)

0

3.4 (2.1–0.5)

2.8 (0.9–6.3)

0.7 (0.2–1.6)

2,257,810

63,205 (21,292–141,774)

15,801 (5323–35,444)

Bahrain

28.7 (23.5–34.2)

1.6 (1.3–2)

0

21.5 (17.6–25.7)

15.5 (7.4–25.2)

3.9 (1.8–6.4)

26,892

4173 (1990–6853)

1043 (497–1713)

Djibouti

12.2 (8.6–16.7)

1.6 (1.3–2)

0

9.2 (6.5–12.5)

7.2 (2.8–14.3)

1.8 (0.7–3.6)

28,103

2035 (798–4022)

509 (200–1005)

Egypt

31.1 (26.7–35.5)

1.6 (1.3–2)

0

23.3 (20–26.6)

16.6 (8.3–26.2)

4.1 (2.1–6.5)

4,994,780

829,131 (415,690–1,308,596)

207,283 (103,922–327,149)

Iran

25.5 (22.2–28.9)

1.6 (1.3–2)

0

19.1 (16.7–21.7)

14.0 (7–22.4)

3.5 (1.8–5.6)

3,194,164

448,150 (224,175–716,147)

112,037 (56,044–179,037)

Iraq

27.4 (22.7–32.4)

1.6 (1.3–2)

0

20.6 (17–24.3)

14.9 (7.2–24.5)

3.7 (1.8–6.1)

1,991,629

297,145 (142,700–487,377)

74,286 (35,675–121,844)

Jordan

33.4 (29–37.9)

1.6 (1.3–2)

0

25.1 (21.8–28.4)

17.6 (9–27.5)

4.4 (2.2–6.9)

193,483

34,075 (17,365–53,176)

8519 (4341–13,294)

Kuwait

37 (32.2–42)

1.6 (1.3–2)

0

27.8 (24.2–31.5)

19.1 (9.9–29.6)

4.8 (2.5–7.4)

84,543

16,187 (8342–25,006)

4047 (2086–6251)

Lebanon

31.3 (26.2–36.6)

1.6 (1.3–2)

0

23.5 (19.7–27.5)

16.7 (8.2–26.8)

4.2 (2–6.7)

183,450

30,616 (15,005–49,153)

7654 (3751–12,288)

Libya

31.8 (26.5–37.4)

1.6 (1.3–2)

0

23.9 (19.9–28.1)

16.9 (8.3–27.2)

4.2 (2.1–6.8)

228,768

38,686 (18,908–62,270)

9671 (4727–15,568)

Morocco

25.6 (20.7–30.9)

1.6 (1.3–2)

0

19.2 (15.5–23.2)

14.1 (6.6–23.6)

3.5 (1.6–5.9)

1,479,258

208,243 (97,265–349,191)

52,061 (24,316–87,298)

Oman

22.9 (18.3–27.9)

1.6 (1.3–2)

0

17.2 (13.7–20.9)

12.8 (5.9–21.8)

3.2 (1.5–5.5)

122,999

15,722 (7205–2806)

3931 (1801–6708)

Pakistan

7.8 (5.7–10.3)

1.6 (1.3–2)

0

5.9 (4.3–7.7)

4.8 (1.9–9.3)

1.2 (0.5–2.3)

9,829,314

467,349 (186,871–917,878)

116,837 (46,718–229,469)

Qatar

33.9 (27.7–40.5)

1.6 (1.3–2)

0

25.4 (20.8–30.4)

17.8 (8.6–28.8)

4.5 (2.2–7.2)

28,216

5030 (2429–8133)

1258 (607–2033)

Saudi Arabia

35 (30.5–39.5)

1.6 (1.3–2)

0

26.3 (22.9–29.6)

18.3 (9.4–28.3)

4.6 (2.3–7.1)

772,957

141,456 (72,625–218,866)

35,364 (18,156–54,717)

Somalia

6.9 (4.5–9.8)

1.6 (1.3–2)

0

5.2 (3.4–7.4)

4.2 (1.5–8.9)

1.1 (0.4–2.2)

399,506

16,896 (6020–35,657)

4224 (1505–8914)

Sudan

7.4 (4.9–10.5)

1.6 (1.3–2)

0

5.6 (3.7–7.9)

4.5 (1.6–9.5)

1.1 (0.4–2.4)

1,728,685

78,168 (28,328–164,264)

19,542 (7082–41,066)

Syrian Arab Republic

25.8 (20.7–31.3)

1.6 (1.3–2)

0

19.4 (15.5–23.5)

14.2 (6.6–23.8)

3.5 (1.6–6)

800,071

113,386 (52,607–190,725)

28,346 (13,152–47,681)

Tunisia

27.3 (22.4–32.1)

1.6 (1.3–2)

0

20.5 (16.8–24.1)

14.9 (7.1–24.3)

3.7 (1.8–6.1)

470,385

69,962 (33,289–114,302)

17,490 (8322–28,576)

United Arab Emirates

29.9 (24–36)

1.6 (1.3–2)

0

22.4 (18–27)

16.1 (7.5–26.5)

4.0 (1.9–6.6)

325,300

52,251 (24,542–86,109)

13,063 (6135–21,527)

Yemen

14.1 (10.8–17.8)

1.6 (1.3–2)

0

10.6 (8.1–13.4)

8.3 (3.5–15.1)

2.1 (0.9–3.8)

1,231,987

101,973 (43,636–186,158)

25,493 (10,909–46,539)

Following reduction of the current prevalence of overweight to the theoretical minimum risk exposure levels in Kuwait, i.e. zero prevalence of overweight, 24.1% (20,361 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden in EMRO countries were 23.6% (6666 DALYs) in Qatar, 23.3% (180,017 DALYs) in Saudi Arabia, 10.2% (175,621 DALYs) Sudan, 9.8% (39,162 DALYs) Somali and 8% (180,096 DALYs) in Afghanistan . By reduction of the current prevalence of overweight to a feasible minimum risk exposure level, i.e. 25% reduction rather than primary of overweight prevalence, 6% (5090 DALYs) of the burden of CVD-related mortality, in Kuwait could be avoidable. The corresponding values of avoidable burden in EMRO countries were 5.9% (1666 DALYs) in Qatar, 5.8% (45,004 DALYs) in Saudi Arabia, 2.5% (43,905 DALYs) Sudan, 2.5% (9791 DALYs) Somali and 2% (45,024 DALYs) in Afghanistan. Details on countries by different scenarios and risk factors are shown as supplemental file in Table4.

Following reduction of the current prevalence of smoking to the theoretical minimum risk exposure levels in Lebanon, i.e. zero prevalence of smoking, 41.5% (76,090 DALYs) of the burden of CVD-related mortality could be avoidable. The corresponding values of avoidable burden in EMRO countries were 39.1% (10,527 DALYs) in Bahrain, 22% (27,084 DALYs) in Oman and 16.9% (540,829 DALYs) in Iran. Moreover, by reduction of the current prevalence of smoking to a feasible minimum risk exposure level, i.e. 30% reduction rather than primary of hypertension prevalence, 12.4% (22,828 DALYs) of the burden of CVD-related mortality, in Lebanon could be avoidable. The corresponding values of avoidable burden in EMRO countries were 11.7% (3158 DALYs) in Bahrain, 6.6% (8125 DALYs) in Oman and 5.1% (162,249 DALYs) in Iran (Details are shown as supplemental file in Table5)

4 Discussion

Knowledge of the magnitude of the attributable burden of CVDs due to various risk factors is useful for health policy to effectively develop prevention intervention. Magnitude of contribution by each risk factor to relevant diseases’ burden among communities provides priorities health policymakers and justify intervention. The results of this study suggest that studied risk factors negatively influenced community overall health and effective intervention should be established to reduce and control these risk factors. Hypertension and smoking were shown to have the highest avoidable and malignant disease burden of CVD-related death, which was consistent with the results of previous studies on all-causes deaths [20, 21, 22].

Our findings indicated that the highest avoidable disease burden of CVD-related mortality due to diabetes was reported in Egypt, Kuwait, and Saudi Arabia at both feasible and theoretical minimum risk exposure levels, whereas the lowest avoidable disease burden of CVD-related mortality was reported in Somali, Lebanon, and Djibouti. Karami and colleagues showed that the PIF of diabetes for CVD-related mortality was 11.2% in women at a zero exposure level, and 7.7 at an exposure level of 4%. They also found that the PIF of diabetes for CVD-related mortality was 5.6 at a zero exposure level, and 7.7 at an exposure level of 3% in men [23].

A study in Spain revealed that 2800 deaths from CVDs (about 6% of all cases of CVDs) were attributed to diabetes among Spanish adults [24]. A cohort conducted in the countries of Western Asia region showed that the prevalence of diabetes varied from 2.6% to 15.1%, and the attributable risk associated with diabetes was more than 12% in CVD-related deaths [25]. Another study ha shown that a blood glucose higher than the optimal level is a contributor to CVD-related deaths, accounting for 13–21% of all deaths throughout the world [26].

Also, the highest avoidable disease burden of CVD-related mortality due to obesity and overweight was reported in Kuwait, Saudi Arabia, and Qatar, and the lowest was reported in Afghanistan, Somalia and Sudan at both feasible and theoretical minimum risk exposure levels. Azimi et al. reported the PAF of obesity for CVD-related death as 5.9% in males and 9.9% in females [27]. A study conducted in Markazi province in Iran showed that the avoidable burden of CVDs due to obesity and overweight were 0.72% and 2.53%, respectively in males, and 6.2% and 7.85% in females. It was also argued that the three main factors for the last years due to premature death were obes (BMI > 30) due to stroke, obesity-attributable to coronary artery disease and overweight (BMI > 25) due to coronary artery disease in women [28]. Also, Mokdad and colleagues showed that hypertension, high cholesterol, and high body mass index were the major risk factors associated with CVDs, and the DALYs for these factors were 17,159,331, 9852,820, and 8427,021 years, respectively [5].

The results of this study show the highest avoidable disease burden of CVD-related mortality for hypertension was reported in Somali, Pakistan, and Soudan, and the lowest avoidable disease burden of CVD-related mortality was reported in UAE, Qatar, and Oman at both feasible and theoretical minimum risk exposure levels. Biderafsh and colleagues showed that the avoidable burden of CVDs due to systolic hypertension in the first, second and third scenarios were 3.5%, 7%, and 22.05% respectively, while the avoidable burden of CVDs due to diastolic hypertension were 4%, 9.38% and 35.68%, respectively [29]. Norman and colleagues showed that 51% of myocardial infarctions in men and 48% in women were attributed to systolic hypertension equal or above 115 mm Hg [30]. Lopez et al. also showed that the avoidable burden of CVDs due to hypertension for myocardial infarction was 49% and 59% in men and women, respectively [31]. In Japan, the findings also found that 43% of CVDs and 48% of deaths due to stroke were attributed to smoking [32]. Lawes et al. also indicated that every 10 mmHg reduction in systolic hypertension reduced 36% of deaths due to stroke in people with an average age of 63 years [33]. According to the results of Azimi et al., in a population with hypertension within the normal range (less than 140/190 mm), 11.7% of the male and 19.2% of the female CVD cases will be reduced [34].

The results of this study show the highest avoidable disease burden of CVD-related mortality for smoking was observed in Lebanon and Bahrain, whereas the lowest avoidable disease burden of CVD-related mortality was reported in Iran and Oman at both feasible and theoretical minimum risk exposure levels. A study conducted in Korea reported the highest avoidable disease burden of CVD-related mortality at a feasible minimum risk exposure level as .52 among men and .33 among women [35]. Also, Cui et al. reported that smoking accounted for 19.7% of all-cause deaths and 11.58% of DALYs among men, and 2.43% and 1.35% among women [20].

There are some strengths regarding our study. First, we have used appropriate avoidable burden and reported relevant DALYs instead of reporting crude percentage. Second, we have estimated the contribution of risk factors for CVDs mortality among EMRO countries in this study.

As a limitation, we did not include Palestine because of lack of appropriate data on the population level of exposures to estimate the contribution of risk factors for CVDs mortality.

5 Conclusions

The results of the present study are in comply with similar estimates that calculated population attributable fraction of risk factors. Moreover, our findings indicated considerable burden of the studied risk factors includes hypertension and smoking in EMRO countries. Accordingly, appropriate interventions are required to reduce and control these risk factors.

Notes

Acknowledgements

This study has been adapted from an MSc thesis at Hamadan University of Medical Sciences.

Author contributions

All authors have approved the manuscript. MK has established first idea data analysis and drafted manuscript, EM helped to design and conduct the study. All authors have had a substantial contribution in data gathering, manuscript drafting, and critical revision of manuscript and data analysis.

Compliance with ethical standards

Funding

The study was partially funded by Vice-chancellor for Research and Technology, Hamadan University of Medical Sciences (No. 9611247770).

Conflict of interest

Authors have no conflicts of interests to declare.

Ethical approval

Protocol of this study has been approved by Ethics Committee of Hamadan University of Medical Sciences (No. 9611247770).

Informed consent

No individual data were collected from participants. We did not obtained informed consent form because of using aggregate data.

Supplementary material

40292_2019_319_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 19 kb)

References

  1. 1.
    Xu S, Jiayong Z, Li B, Zhu H, Chang H, Shi W, et al. Prevalence and clustering of cardiovascular disease risk factors among Tibetan adults in China: a population-based study. PloS One. 2015;10(6):e0129966.  https://doi.org/10.1371/journal.pone.0129966.Google Scholar
  2. 2.
    World Health Organization. Cardiovascular diseases (CVDs) fact sheet. WHO. 2017. http://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 14 Aug 2017.
  3. 3.
    World Health Organization. Prevention of cardiovascular disease: guidelines for assessment and management of total cardiovascular risk. WHO. 2007. http://www.who.int/cardiovascular_diseases/publications/Prevention_of_Cardiovascular_Disease/en/. Accessed 17 May 2018.
  4. 4.
    Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016;37(42):3232–45.  https://doi.org/10.1093/eurheartj/ehw334.Google Scholar
  5. 5.
    Tehrani-Banihashemi A, Moradi-Lakeh M, El Bcheraoui C, Charara R, Khalil I, Afshin A. Burden of cardiovascular diseases in the Eastern Mediterranean Region, 1990–2015: findings from the Global Burden of Disease 2015 study. Int J Public Health. 2018;63(Suppl 1):137–49.  https://doi.org/10.1007/s00038-017-1012-3.Google Scholar
  6. 6.
    World Health Organization. Cardiovascular diseases. WHO. 2018. http://www.emro.who.int/health-topics/cardiovascular-diseases/index.html. Accessed 14 Aug 2017.
  7. 7.
    World Health Organization. Global health risks: mortality and burden of disease attributable to selected major risks. WHO. 2009. http://www.who.int/iris/handle/10665/44203. Accessed 17 May 2018.
  8. 8.
    Dieleman JL, Baral R, Birger M, et al. Us spending on personal health care and public health, 1996–2013. JAMA. 2016;316(24):2627–46.  https://doi.org/10.1001/jama.2016.16885.Google Scholar
  9. 9.
    Drescher K, Becher H. Estimating the generalized impact fraction from case-control data. Biometrics. 1997;53(3):1170–6.  https://doi.org/10.2307/2533576.Google Scholar
  10. 10.
    Murray CJ, Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S. Comparative quantification of health risks conceptual framework and methodological issues. Popul Health Metrics. 2003;1(1):1.Google Scholar
  11. 11.
    World Health Organization. WHO global report on trends in prevalence of tobacco smoking. WHO. 2015. http://www.who.int/iris/handle/10665/156262. Accessed 17 May 2018.
  12. 12.
    World Health Organization. Prevalence of obesity among adults, BMI ≥ 30, crude Estimates by country. WHO. 2017. http://apps.who.int/gho/data/node.main.BMI30C?lang=en. Accessed 17 May 2018.
  13. 13.
    World Health Organization. Raised blood pressure (SBP ≥ 140 OR DBP ≥ 90), crude (%) Estimates by country. WHO Web Site. 2017. http://apps.who.int/gho/data/node.main.A875?lang=en. Accessed 17 May 2018.
  14. 14.
    World Health Organization. Raised fasting blood glucose (≥ 7.0 mmol/L or on medication) (crude estimate) Estimates by country. WHO. 2017. http://apps.who.int/gho/data/node.main.A869?lang=en. Accessed 17 May 2018.
  15. 15.
    Borrell LN, Samuel L. Body mass index categories and mortality risk in US adults: the effect of overweight and obesity on advancing death. Am J Public Health. 2014;104(3):512–9.  https://doi.org/10.2105/ajph.2013.301597.Google Scholar
  16. 16.
    Sarrafzadegan N, Hassannejad R, Marateb HR, Talaei M, Sadeghi M, Roohafza HR, et al. Correction: PARS risk charts: a 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region. PLoS One. 2018;13(1):e0191379.  https://doi.org/10.1371/journal.pone.0191379.Google Scholar
  17. 17.
    Yusuf S, Hawken S, Ounpuu 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. Lancet (Lond Engl). 2004;364(9438):937–52.  https://doi.org/10.1016/s0140-6736(04)17018-9.Google Scholar
  18. 18.
    Gakidou E, Afshin A, Abajobir AA, Abate KH, Abbafati C, Km A. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England). 2017;390(10100):1345–422.  https://doi.org/10.1016/s0140-6736(17)32366-8.Google Scholar
  19. 19.
    GBD. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016. GBD. 2017. http://ghdx.healthdata.org/record/global-burden-disease-study-2016-gbd-2016-population-estimates-1950-2016. Accessed 17 May 2018.
  20. 20.
    Cui F, Zhang L, Yu C, Hu S, Zhang Y. Estimation of the Disease Burden Attributable to 11 Risk Factors in Hubei Province, China: a comparative risk assessment. Int J Environ Res Public Health. 2016;13(10):944.Google Scholar
  21. 21.
    Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, et al. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med. 2009;6(4):e1000058.  https://doi.org/10.1371/journal.pmed.1000058.Google Scholar
  22. 22.
    World Health Organization. The World Health Report 2002: reducing risks, promoting healthy life. World Health Organization; 2002.Google Scholar
  23. 23.
    Karami M, Khosravi Shadmani F, Najafi F. Estimating the contribution of diabetes on the attributable burden of cardiovascular diseases in Kermanshah, West of Iran. Iran J Epidemiol. 2012;8(3):33–8.Google Scholar
  24. 24.
    Banegas JR, Rodríguez-Artalejo F, Graciani A, Villar F, Herruzo R. Mortality attributable to cardiovascular risk factors in Spain. Eur J Clin Nutr. 2003;57:S18.  https://doi.org/10.1038/sj.ejcn.1601804.Google Scholar
  25. 25.
    Collaboration APCS. Prevalence of diabetes mellitus and population attributable fractions for coronary heart disease and stroke mortality in the WHO South-East Asia and Western Pacific Regions. Asia Pacific J Clin Nutr. 2007;16(1):187–92.  https://doi.org/10.6133/apjcn.2007.16.1.24.Google Scholar
  26. 26.
    Danaei G, Lawes CMM, Vander Hoorn S, Murray CJL, Ezzati M. Global and regional mortality from ischaemic heart disease and stroke attributable to higher-than-optimum blood glucose concentration: comparative risk assessment. Lancet. 2006;368(9548):1651–9.  https://doi.org/10.1016/S0140-6736(06)69700-6.Google Scholar
  27. 27.
    Azimi SS, Khalili D, Hadaegh F, Yavari P, Mehrabi Y, Azizi F. Calculating population attributable fraction for cardiovascular risk factors using different methods in a population based cohort study. J Res Health Sci. 2015;15(1):22–7.Google Scholar
  28. 28.
    Eshrati B, Hasanzadeh J, Mohammad Beigi A. Calculation of population atributable burden of excess weight and obesity to non-contagious diseases in Markazi provience of Iran. Koomesh J. 2010;11(2):83–90.Google Scholar
  29. 29.
    Biderafsh A, Karami M, Faradmal J, Poorolajal J. Estimating the potential impact fraction of hypertension as the main risk factor of stroke: Application of the distribution shift method. J Epidemiol Glob Health. 2015;5(3):231–7.  https://doi.org/10.1016/j.jegh.2014.11.002.Google Scholar
  30. 30.
    Norman R, Gaziano T, Laubscher R, Steyn K, Bradshaw D. Estimating the burden of disease attributable to high blood pressure in South Africa in 2000. S Afric Med J. 2007;97(8 Pt 2):692–8.Google Scholar
  31. 31.
    Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367(9524):1747–57.  https://doi.org/10.1016/S0140-6736(06)68770-9.Google Scholar
  32. 32.
    Takashima N, Ohkubo T, Miura K, Okamura T, Murakami Y, Fujiyoshi A, et al. Long-term risk of BP values above normal for cardiovascular mortality: a 24-year observation of Japanese aged 30 to 92 years. J Hypertens. 2012;30(12):2299–306.  https://doi.org/10.1097/HJH.0b013e328359a9f7.Google Scholar
  33. 33.
    Lawes CM, Bennett DA, Feigin VL, Rodgers A. Blood pressure and stroke: an overview of published reviews. Stroke. 2004;35(4):1024.Google Scholar
  34. 34.
    Azimi S, Khalili D, Hadaegh F, Mehrabi Y, Yavari P, Azizi F. Direct estimate of population attributable fraction of risk factors for cardiovascular diseases: Tehran glucose and lipid study. Iran J Epidemiol. 2012;7(4):9–18.Google Scholar
  35. 35.
    Lee H, Yoon SJ, Ahn HS, Moon OR. Estimation of potential health gains from reducing multiple risk factors of stroke in Korea. Public Health. 2007;121(10):774–80.  https://doi.org/10.1016/j.puhe.2007.03.002.Google Scholar

Copyright information

© Italian Society of Hypertension 2019

Authors and Affiliations

  1. 1.Department of Epidemiology, School of Public HealthHamadan University of Medical SciencesHamadanIran
  2. 2.Research Center for Health SciencesHamadan University of Medical SciencesHamadanIran
  3. 3.Isfahan Cardiovascular Research Center, Cardiovascular Research InstituteIsfahan University of Medical SciencesIsfahanIran
  4. 4.School of Population and Public Health, Faculty of MedicineUniversity of British ColumbiaVancouverCanada
  5. 5.Modeling of Noncommunicable Diseases Research CenterHamadan University of Medical SciencesHamadanIran
  6. 6.Social Determinants of Health Research CenterHamadan University of Medical SciencesHamadanIran

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