Global CVD Deaths 1990-2013

CV Deaths in 1990 12.3 m

CV Deaths in 2013 17.3 m

Crude % change +40.8Crude % change +40.8

% change due to increased population +25.1

% change due to aging +55.0

% change in age specific death rates -39.3

Roth G. NEJM 2015

Big, modifiable causesof vascular mortality

Tobacco

Blood pressure

Blood lipids

Secondary Prevention

World tobacco deaths this century,if current smoking patterns continue

2000-19 ~100M

2020-49 ~250M

2050-99 >500M2050-99 >500M

2000-2099 ~1000M(1 billion)

1900-1999 ~100M(0.1 billion)

Number (%) of Major or All CVD for DifferentSub-Groups in PURE (n=152,609)

Baseline Condition Total no. with

Condition (%)

Follow-up Major CVD

N = 3,488 (2.23 %)CVD 7,743 (5.1) 673 (19.3)

Hypert (History or 140/90) 62,034(40.7) 2,317 (66.4)Hypert (History or 140/90) 62,034(40.7) 2,317 (66.4)

Current Smoker 31,397 (20.6) 1,021 (29.4)

CVD, Hypert or Smoker 84,078 (55) 2,822 (80.9)

Diabetes(History or FPG >7mmol) 16,071(10.5) 905 (26.0)

CVD, Hypert, Smoker or Diabetes 88,326 (57.9) 2,929 (84.0)

Mean INTERHEART Risk Score (IHRS)

Yusuf et al NEJM 2014

CVD Event Rates

Major CVD = death from CV causes, stroke, MI and HFNon major CVD = all other CVD events that led to hospitalization

Yusuf et al NEJM 2014

Potential Cumulative Impact of 4 SimpleSecondary Prevention Treatments

RRR Event rate

None 8%

ASA 25% 6%ASA 25% 6%

-Blockers 25% 4.5%

Lipid lowering 30% 3.0%

ACE-inhibitors 25% 2.3%

CUMULATIVE BENEFITS ARE LIKELY TO BE IN EXCESS OF

75% RRR, WHICH IS SUBSTANTIAL

%

Antiplatelet Agents

Proven Drugs in Secondary Prevention

Beta Blockers

ACE Inhibitors or ARBs Statins

Yusuf et al Lancet 2011

Availability of the 4 Medications byCountry Income Group

*Availability of at least one ACE-Inhibitor, beta blocker, statin, and an aspirin

Ncommunities:

Khatib PURE

Monthly Cost of 4 CVD Medications as aPercentage of Households’ Capacity-to-pay

40

60

Media

n%

incom

espenton

4C

VD

medic

ations

020

Media

n%

incom

espenton

4C

VD

medic

ations

HIC UMIC LMIC LIC ex. India India

Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural

Statins ACE-inhibitors

Beta blockers Aspirin

Effect of Lack of Availability and Affordabilityon Use Among Patients with CVD

OR and 95% CI of using the 4medications

Unadjusted Fully adjusted

Availability

4 medications available (ref) 1.00 1.00

< 4 medications available 0.10 (0.07-0.014) 0.07 (0.03-0.15)< 4 medications available 0.10 (0.07-0.014) 0.07 (0.03-0.15)

Affordability

Cost of 4 medications affordable (ref) 1.00 1.00

Cost of 4 medications not affordable 0.17 (0.12-0.24) 0.26 (0.15- 0.47)

• Affordability is defined as costs not exceeding 20% of households capacity to pay• Adjusted for: age, sex, education level, community location, years since

diagnosis, cancer diagnosis, use of other medications, smoking status,availability of 4 CVD medications, and clustered by household and community

Hypertension in PURE(17 countries; 628 communities)

Percent

HIC UMIC LMIC LIC

Prevalence 41% 50% 40% 32%Prevalence 41% 50% 40% 32%

Awareness 49% 52% 44% 41%

Treatment 47% 48% 37% 32%

Control 19% 16% 10% 13%

Chow C JAMA 2013

Association between hypertension awareness, treatment andcontrol Vs economic development GNP per capita (2012 US $)

Palafox, PURE Int J Equit Health In press.

Association between concentration indices for hypertensionawareness, treatment and control, and economic development

measured as GNP per capita (2012 US dollars)

Palafox, Int J Equit Health In press.

CV Death, MI, Stroke, Cardiac Arrest, Revasc, HF

3.5 1.25 (0.92-1.70)

HR (95% CI) P Trend

HOPE 3:Prespecified Subgroups:By Thirds of SBP

SBP

Mean

≤131.5

Diff

6.1

Cutoffs

122

Placebo

Event Rate%

0.5 1.0 2.0

Candesartan + HCTZ Better Placebo Better

3.5

4.6

7.5

1.25 (0.92-1.70)

1.02 (0.77-1.34)

0.76 (0.60-0.96)

0.009≤131.5

131.6-143.5

>143.5

6.1

5.8

5.6

122

138

154

17

HOPE 3: CV Death, MI, Stroke,Cardiac Arrest, Revasc, Heart Failure

Cu

mu

lati

ve

Haza

rdR

ate

s

0.0

60.0

80.1

0

Placebo

HR (95% CI) = 0.75 (0.64-0.88)P-value = 0.0004

Years

Cu

mu

lati

ve

Haza

rdR

ate

s

0.0

0.0

20.0

4

0 1 2 3 4 5 6 7

Rosuvastatin

6361 6241 6039 2122

6344 6192 5970 2073

Rosuva

Placebo

23

Cum

ula

tive

Hazard

Rate

s

0.0

20

.03

0.0

4

Placebo

HR (95% CI) = 0.74 (0.58-0.96)

P-value = 0.0214

0.0

10

0.0

15

0.0

20

Placebo

HR (95% CI) = 0.70 (0.52-0.95)

P-value = 0.0227

Coronary HeartDisease

Stroke

Years

Cum

ula

tive

Hazard

Rate

s

0.0

0.0

10

.02

0 1 2 3 4 5 6 7

Rosuvastatin

Years

0.0

0.0

05

0.0

10

0 1 2 3 4 5 6 7

Rosuvastatin

Coronary Heart Disease: MI, Coronary revascularization

24

CV Death, MI, Stroke,Cardiac Arrest, Revasc, Heart Failure

Cum

ula

tive

Hazard

Rate

s

0.0

60.0

80.1

0

Combination

Double Placebo

Years

Cum

ula

tive

Hazard

Rate

s

0.0

0.0

20.0

4

0 1 2 3 4 5 6 7

HR (95% CI) = 0.72 (0.57-0.89)

P-value = 0.0030

3180 4 3063 10573181 3061 10453176 3040 10193168 3035 1030

CombinationRosuvastatinCandesartan/HCTZDouble Placebo 32

38

77200

250

300

Combination - Co-Primary Outcome 2,Recurrent Events

Total -170

Total -213Total -

185

Total -263

Ev

ents

TotalEvents

HR: 0.66P Value:0.0007

136 141

175

1863444

0

50

100

150

Rosu & Cand/ HCTZ Rosu Alone Cand/ HCTZ Alone Both Placebos

1st Event Additional Events

170185

#o

fE

ven

ts

FirstEvent

HR: 0.72P Value:0.0035

20

RRR of Combination and EachIntervention vs Double Placebo

Overall

RR

R

0%

10%

20%

30%

40%

50%

28% 26%

6%

Rosuva Cand + HCTZ

Co-Primary 2

0%Combo Rosuva

OnlyCand + HCTZ

Only

34

RR

R

0%

10%

20%

30%

40%

50%

40%

20%24%

Combo RosuvaOnly

Cand+HCTZOnly

Highest Third of SBP

0%

10%

20%

30%

40%

50%

19%

31%

-8%

Combo Rosuva Only

Cand + HCTZ Only

Lower Two Thirds of SBP

Polypill in primary prevention of CVD.

•TIPS 3: 5000 people at hi risk (IHRS>10+ older age) randomized topolypill (statin+ 3 BP lowering drugs @ full dose). Results in 2019.

•HOPE 4: 50 communities (2000 with HTN) in Columbia, Malaysia &Canada randomized to intervention (NPHW for screening + LS advice+ BP lowering and statins) v usual care to assess impact on riskfactors, safety and cost effectiveness. Results in 2018.

Life style modification

• Tobacco.

• Diet

• Physical activity• Physical activity

• Alcohol (avoid harmful use)

Cost of meeting 2 servings of fruits and 3 servings ofvegetables per day ( % of income*) (n=111,478)

* Cost and income per household memberMiller et al., Lancet Global Health 2016

Fruit intake and total CVD(excluding baseline CVD, n=130,822)

The primary model included age, sex, geographic region, energy intake, education, body mass index, current alcohol drinker,current smoker, urban/rural location, statin use, quintiles of white meat intake, quintiles of red meat intake, quintiles of bread andcereal intake, quintiles of processed food intake, quintiles of vegetable intake and adjustment of clustering in centers.

P-trend=0.002

Vegetable intake and total CVD(excluding baseline CVD, n=130,822)

The primary model included age, sex, geographic region, energy intake, education, body mass index, current alcohol drinker,current smoker, urban/rural location, statin use, quintiles of white meat intake, quintiles of red meat intake, quintiles of bread andcereal intake, quintiles of processed food intake, quintiles of fruit intake and adjustment of clustering in centers.

P-trend=0.0534

Risk of major CVD with macro-nutrients intake

%E carb

Q2 vs Q1

Q3 vs Q1

Q4 vs Q1

Q5 vs Q1

nutrients

1.02 (0.88, 1.17)

1.05 (0.91, 1.22)

1.15 (0.99, 1.34)

1.27 (1.06, 1.51)

OR (95% CI)

1.02 (0.88, 1.17)

1.05 (0.91, 1.22)

1.15 (0.99, 1.34)

1.27 (1.06, 1.51)

OR (95% CI)

Adjusted for age, sex, physical activity, smoking, education, geographic regions, whr, hypertension, history of diabetes, blood pressure medication, energy,fruit, and sugar. Community clustering is taken into account

Q5 vs Q1

%E total fat

Q2 vs Q1

Q3 vs Q1

Q4 vs Q1

Q5 vs Q1

1.27 (1.06, 1.51)

0.86 (0.74, 1.00)

0.84 (0.72, 0.99)

0.82 (0.70, 0.97)

0.78 (0.66, 0.93)

1.27 (1.06, 1.51)

0.86 (0.74, 1.00)

0.84 (0.72, 0.99)

0.82 (0.70, 0.97)

0.78 (0.66, 0.93)

1.5 1 1.5 2

Risk of major CVD with macro-nutrients intake

CHO Total fat

Adjusted for age, sex, physical activity, smoking, education, geographic regions, whr, hypertension, history of diabetes, blood pressure medication,energy, fruit, and sugar. Community clustering is taken into account

WHO WHO

Risk of major CVD with fatty acids intake

%E SFAs

Q2 vs Q1

Q3 vs Q1

Q4 vs Q1

Q5 vs Q1

%E MUFAs

nutrients

0.87 (0.75, 1.01)

0.78 (0.66, 0.93)

0.73 (0.60, 0.88)

0.81 (0.65, 1.00)

OR (95% CI)

0.87 (0.75, 1.01)

0.78 (0.66, 0.93)

0.73 (0.60, 0.88)

0.81 (0.65, 1.00)

OR (95% CI)

Adjusted for age, sex, physical activity, smoking, education, geographic regions, whr, hypertension, history of diabetes, blood pressuremedication, energy, fruit, and sugar. Community clustering is taken into account

Q2 vs Q1

Q3 vs Q1

Q4 vs Q1

Q5 vs Q1

%E PUFAs

Q2 vs Q1

Q3 vs Q1

Q4 vs Q1

Q5 vs Q1

0.88 (0.77, 1.01)

0.86 (0.73, 1.00)

0.75 (0.64, 0.90)

0.72 (0.60, 0.87)

0.99 (0.86, 1.15)

1.00 (0.85, 1.17)

0.95 (0.80, 1.13)

1.04 (0.86, 1.26)

0.88 (0.77, 1.01)

0.86 (0.73, 1.00)

0.75 (0.64, 0.90)

0.72 (0.60, 0.87)

0.99 (0.86, 1.15)

1.00 (0.85, 1.17)

0.95 (0.80, 1.13)

1.04 (0.86, 1.26)

1.5 1 1.5 2

Risk of major CVD with fatty acids from majorfood sources

Dairy SFAs

T2 vs. T1

T3 vs. T1

Red meat SFAs

nutrients

0.89 (0.80, 0.98)

0.79 (0.70, 0.90)

OR (95% CI)

0.89 (0.80, 0.98)

0.79 (0.70, 0.90)

OR (95% CI)

Adjusted for age, sex, physical activity, smoking, education, geographic regions, whr, hypertension, history of diabetes, blood pressuremedication, energy, and fruit. Community clustering is taken into account

Red meat SFAs

T2 vs. T1

T3 vs. T1

White meat SFAs

T2 vs. T1

T3 vs. T1

0.92 (0.82, 1.03)

0.91 (0.80, 1.03)

0.88 (0.80, 0.97)

0.91 (0.81, 1.03)

0.92 (0.82, 1.03)

0.91 (0.80, 1.03)

0.88 (0.80, 0.97)

0.91 (0.81, 1.03)

1.5 1 1.5 2

Saturated fat vs CVD

HR

(95

%C

I) 2.2

1.8

Saturated fat vs LDL-C

33

.1

P for trend <0.001

mm

ol/

l

Impact of SFA on Risk factors vs Events

HR

(95

%C

I)

1.8

1.4

1

0.6

2.7

2.8

2.9

<2 4-6 8-10 >12

% energy from SFA

% energy from SFA

mm

ol/

l

WHO

WHO

Adjusted mean (CI) of ApoB/ApoA ratio by % energy providedby carbohydrate and saturated fatty acids

Carbohydrate Saturated fatty acids

P for trend <0.001.8 .8P for trend <0.001

Models are adjusted for age, geographic region, sex, smoking, physical activity, urban/rural location, BMI, cholesterol lowering medications, fruit andvegetable, energy, sodium and fiber. Community clustering is taken into account

WHO WHO

.7.7

5A

po

B/A

po

A

<45 50-55 60-65 >70

%E by cho

.7.7

5A

po

B/A

po

A

<2 4-6 8-10 >12

%E by SFA

Beta for each 10%E =0.05

Adjusted mean (CI) of ApoB/ApoA ratio by % energyprovided by various types of fatty acids

Monounsaturated fatty acids Polyunsaturated fatty acids

P for trend <0.001.8A

poB

/ApoA

.8A

poB

/ApoA

P for trend <0.001

Beta for each 5%E =-0.005Models are adjusted for age, geographic region, sex, smoking, physical activity, urban/rural location, BMI, cholesterol lowering medications, fruit andvegetable, energy, sodium and fiber. Community clustering is taken into account

.7.7

5A

poB

/ApoA

<2 4-6 8-10 >12

%E by MUFA

.7.7

5A

poB

/ApoA

<2 3-4 5-6 >7

%E by PUFA

Systolic BP Diastolic BP

Mean BP by Na excretion and hypertension status(N=133,118) *

* Adjusted for age, sex, education, BMI, alcohol, smoking, and geographic region

Overall (N=133,118)

Hypertension(N=63,559)

No Hypertension(N=69,559)

Na vs CVD by hypertension status: (133,000, 4 studies)

Mortality & major CVD versus level of physical activity

Lear et al PURE Unpublished data

Total , recreational and non-recreational PA (occup,transport and housework ) with mortality and major CVD.

Lear et al PURE, Unpublished data

2014-2016 WHF Emerging Leaders Cohorts74 Emerging Leaders representing 32 countries

2014 theme: secondary prevention

Host: Salim Yusuf

McMaster University, Canada

2015 theme: raised blood pressure

Host: Jaime Miranda

38

Host: Jaime Miranda

U. Peruana Cayetano Heredia, Peru

2016 theme: tobacco

Host: Denis Xavier

St. John’s Research Institute, India

PROGRAM TIMELINE (whfel.org)

Nov-Dec: advertise and open application cycle

Feb: selection of participants

Feb-May: online training

June: think tank seminar

June-July: funding decisions, cohort follow-up

WHO Health Systems Framework

39

WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.

WHO Health Systems Framework

40

WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.

Wide variability in processesacross hospitals

Acute Coronary Syndrome Care in Kerala, India

41

across hospitals

Huffman MD, et al. Circ Cardiovasc Qual Outcomes 2013; 6:436-43.

Quality Improvement Toolkit Components

1. Audit/feedback reporting mechanism to inform monthly qualityimprovement meetings for Plan-Do-Study-Act cycle

2. Standardized admission and discharge order sets; clinical pathways

3. Patient education materials: diet, activity, and tobacco cessation3. Patient education materials: diet, activity, and tobacco cessationadapted to Keralan context

4. Code and rapid response team training assistance

Communication within cohorts in intervention phase

42

ACS QUIK: Stepped Wedge Design

Acute Coronary Syndrome Quality Improvement in Kerala (ACS QUIK)Cluster randomized, stepped wedge clinical trial (63 hospitals)Sponsored by NHLBI, CSI-K, and NU GHILaunched November 10, 2014; 21,849 participants enrolled

Evaluating the effect of a quality improvement toolkit adapted to Kerala

1o outcome: 30-day MACE rates1o outcome: 30-day MACE rates2o outcomes: Health-related quality of life; microeconomic costs

Step 5

Step 4

Step 3

Step 2

Step 1

0-4months

4-8months

8-12 months12-16

months16-20

months20-24

months

Huffman MD, et al. Am Heart J 2016: accepted.

WHO Health Systems Framework

44

WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.

• Antignac et al.demonstrated 16% of CVDdrugs were substandard ina survey of 7 sub-SaharanAfrican countries usingHPLC (2012 – 2014)

Medical products – SEVEN

45Antignac M, et al. JAMA Cardiol 2016; Epub aheadof print.

Substandard rates by drug:

• Amlodipine: 29%

• Captopril 26%

• Simvastatin 18%

• Acenocoumarol: 0%

• High performance liquid chromatography maybe the gold standard but is not readily portablenor scalable for widespread use.

• Global Pharma Health Fund (sponsor: MerckGermany) has created a mobile mini-laboratoryfor rapid drug quality verification and counterfeit

Global Pharma Health Fund MinilabTM

46www.gphf.org

for rapid drug quality verification and counterfeitmedicines detection.

• Inspection, disintegration testing, and thinlayer chromatography

• WHO’s Global Surveillance and Monitoring System for substandard,spurious, falsely labeled, falsified, and counterfeit medical products waslaunched in W. Africa in 2013 and will report to WHA in 2017.

WHO Health Systems Framework

47

WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.

Financing – FACTc

Context: WHO Framework Convention on Tobacco Control (FCTC) isa powerful tool to reduce tobacco consumption but is not fullyimplemented.

Problem: Lack of funding is the major obstacle to implementing FCTCmeasures; country-level data on costs of implementation and costs ofinaction are needed.

48

inaction are needed.

Proposed solution: Create country-level cost estimates for FCTCimplementation and inaction, as well as revenue estimates through $1per pack taxation.

FCTC Costs and Revenue

FCTCimplementation costs

(2014 INT$)

Quarterly taxrevenue*

(2014 INT$)

India 1.6 B 32 B

Russia 0.9 B 101 B

49

Russia 0.9 B 101 B

China 3.9 B 783 B

WHF EL FACTc team. Presented at COP7(Delhi, Nov 2016) and shown with permission.

*Revenue generatedfrom INT$1 per packprice increase.

Road Map to Reducing CVD Globallyby 25% by 2025

1. Build partnerships across health disciplines, non-medical, medicalorganizations and govts for control of NCDs.

2. Develop reliable health information systems to monitor mortality,morbidity and health behavioursmorbidity and health behaviours

3. Vigorously enforce tobacco control, implement hypertensiondetection & control and secondary prevention utilizing NPHW

(Yusuf ,Wood, Ralston, Reddy. Lancet 2015)

Road Map: Reducing CVD Globally by25% by 2025

4. Improve access and affordability of proven drugs(low costcombination pills) and healthy foods.

5. Develop expertise in knowledge translation and implementation(Emerging Leaders Program, train Family doctors, NPHW).(Emerging Leaders Program, train Family doctors, NPHW).

6. Engage civil society and community organizations in CVD control.

(Yusuf, Wood, Ralston & Reddy. Lancet 2015)