Prediction of cardiovascular disease risk among low-income urban dwellers in metropolitan Kuala Lumpur, Malaysia

Biomed Res Int. 2015:2015:516984. doi: 10.1155/2015/516984. Epub 2015 Mar 2.

Abstract

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cardiovascular Diseases / diagnosis
  • Cardiovascular Diseases / mortality*
  • Comorbidity
  • Cross-Sectional Studies
  • Employment
  • Female
  • Humans
  • Malaysia / epidemiology
  • Male
  • Marital Status
  • Middle Aged
  • Obesity / diagnosis
  • Obesity / mortality*
  • Poverty / statistics & numerical data*
  • Prevalence
  • Prognosis
  • Risk Assessment / methods
  • Risk Factors
  • Sex Distribution
  • Social Class
  • Survival Analysis*
  • Survival Rate
  • Urban Population / statistics & numerical data*