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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
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Big, modifiable causesof vascular mortality
Tobacco
Blood pressure
Blood lipids
Secondary Prevention
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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)
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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)
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Mean INTERHEART Risk Score (IHRS)
Yusuf et al NEJM 2014
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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
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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
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%
Antiplatelet Agents
Proven Drugs in Secondary Prevention
Beta Blockers
ACE Inhibitors or ARBs Statins
Yusuf et al Lancet 2011
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Availability of the 4 Medications byCountry Income Group
*Availability of at least one ACE-Inhibitor, beta blocker, statin, and an aspirin
Ncommunities:
Khatib PURE
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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
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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
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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
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Association between hypertension awareness, treatment andcontrol Vs economic development GNP per capita (2012 US $)
Palafox, PURE Int J Equit Health In press.
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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.
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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
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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
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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
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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
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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
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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
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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.
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Life style modification
• Tobacco.
• Diet
• Physical activity• Physical activity
• Alcohol (avoid harmful use)
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Overall (N=133,118)
Hypertension(N=63,559)
No Hypertension(N=69,559)
Na vs CVD by hypertension status: (133,000, 4 studies)
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Mortality & major CVD versus level of physical activity
Lear et al PURE Unpublished data
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Total , recreational and non-recreational PA (occup,transport and housework ) with mortality and major CVD.
Lear et al PURE, Unpublished data
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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
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WHO Health Systems Framework
39
WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.
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WHO Health Systems Framework
40
WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.
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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.
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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
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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.
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WHO Health Systems Framework
44
WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.
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• 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%
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• 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.
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WHO Health Systems Framework
47
WHO. (2007). Everybody’s business: strengtheninghealth systems to improve health outcomes: WHO’sframework for action.
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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 $1per pack taxation.
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FCTC Costs and Revenue
FCTCimplementation costs
(2014 INT$)
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.
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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)
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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)