title: Total cardiovascular risk approach to improve efficiency of cardiovascular prevention in resource constrain settings.
abstract: OBJECTIVE
  To determine the population distribution of cardiovascular risk in eight low- and middle-income countries and compare the cost of drug treatment based on cardiovascular risk (cardiovascular risk thresholds ≥ 30%/≥ 40%) with single risk factor cutoff levels.
  
  
  METHODS
  Using World Health Organization (WHO) and the International Society of Hypertension risk prediction charts, cardiovascular risk was categorized in a cross-sectional study of 8,625 randomly selected people aged 40-80 years (mean age, 54.6 years) from defined geographic regions of Nigeria, Iran, China, Pakistan, Georgia, Nepal, Cuba, and Sri Lanka. Cost estimates for drug therapy were calculated for three countries.
  
  
  RESULTS
  A large fraction (90.0-98.9%) of the study population has a 10-year cardiovascular risk <20%. Only 0.2-4.8% are in the high-risk categories (≥ 30%). Adopting a total risk approach and WHO guidelines recommendations would restrict unnecessary drug treatment and reduce the drug costs significantly.
  
  
  CONCLUSIONS
  Adopting a total cardiovascular risk approach instead of a single risk factor approach reduces health care expenditure by reducing drug costs. Therefore, limited resources can be more efficiently used to target high-risk people who will benefit the most. This strategy needs to be complemented with population-wide measures to shift the cardiovascular risk distribution of the whole population.
authors.affiliation: Noncommunicable Diseases and Mental Health Cluster, World Health Organization, Geneva, Switzerland. mendis@who.int
authors.forename: Shanthi
authors.initials: S
authors.lastname: Mendis
authors.forename: Lars H
authors.initials: LH
authors.lastname: Lindholm
authors.forename: Simon G
authors.initials: SG
authors.lastname: Anderson
authors.forename: Ala
authors.initials: A
authors.lastname: Alwan
authors.forename: Rajendra
authors.initials: R
authors.lastname: Koju
authors.forename: Basden J C
authors.initials: BJ
authors.lastname: Onwubere
authors.forename: Azhar Mahmood
authors.initials: AM
authors.lastname: Kayani
authors.forename: Nihal
authors.initials: N
authors.lastname: Abeysinghe
authors.forename: Alfredo
authors.initials: A
authors.lastname: Duneas
authors.forename: Sergo
authors.initials: S
authors.lastname: Tabagari
authors.forename: Wu
authors.initials: W
authors.lastname: Fan
authors.forename: Nizal
authors.initials: N
authors.lastname: Sarraf-Zadegan
authors.forename: Porfirio
authors.initials: P
authors.lastname: Nordet
authors.forename: Judith
authors.initials: J
authors.lastname: Whitworth
authors.forename: Anthony
authors.initials: A
authors.lastname: Heagerty
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000191:economics / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control*; D002681:China / Q000453:epidemiology; D003362:Cost-Benefit Analysis; D003430:Cross-Sectional Studies; D003462:Cuba / Q000453:epidemiology; D003906:Developing Countries / Q000191:economics*; D005260:Female; D005845:Georgia / Q000453:epidemiology; D006295:Health Resources / Q000191:economics / Q000600:supply & distribution*; D006801:Humans; D006937:Hypercholesterolemia / Q000453:epidemiology / Q000517:prevention & control; D006973:Hypertension / Q000453:epidemiology / Q000517:prevention & control; D007492:Iran / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D009390:Nepal / Q000453:epidemiology; D009549:Nigeria / Q000453:epidemiology; D010154:Pakistan / Q000453:epidemiology; D018579:Patient Selection; D018570:Risk Assessment; D012307:Risk Factors; D013188:Sri Lanka / Q000453:epidemiology; D014944:World Health Organization