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Cardiovascular Disease: Application of a Composite Risk Index from the Telehealth System in a District Community

Authors

  • Y. B. Yip R.N., Ph.D.,

    Corresponding authorSearch for more papers by this author
  • Thomas K. S. Wong R.N., Ph.D.,

  • Joanne W. Y. Chung R.N., Ph.D.,

  • Stanley K. K. Ko B.S.N., R.M.N.,

  • Janet W. H. Sit R.N., Ph.D.,

  • Tony M. F. Chan Ph.D.


  • Y. B. Yip, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong. Thomas K. S. Wong, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong. Joanne W. Y. Chung, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong. Stanley K. K. Ko, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong. Janet W. H. Sit, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong. Tony M. F. Chan, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong.

Dr Vera Yip, Telehealth Team, School of Nursing, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. E-mail: hsvyip@inet.polyu.edu.hk

Abstract

Abstract  Assessing a combination of cardiovascular disease (CVD)-risk factors may be a practical tool for risk assessment and for finding the high-risk group among local community members. This study examines the association between the number of CVD-risk factors, regardless of any specific combination with the CVD ambit, using data from 1,570 residents in Tsing Yi community (Hong Kong) who registered with the Telehealth System. A quantitative composite CVD Risk Index (CVDRI) with scores ranging from 0 to 6 included rankings for high systolic and diastolic blood pressure, presence of diabetes, body mass index (BMI), smoking, and age. Multivariate logistic regression was used to estimate odds ratios for the prevalence of CVD. Those with a CVDRI of 1, 2, or 3 and above were 1.7 [95% confidence interval (CI) = 1.34–3.99], 5.3 (95% CI = 3.60–7.90), and 10 times (95% CI = 6.41–15.50) more likely to have CVD, respectively, than those with a risk index of 0. Among the CVDRI components, high blood pressure had the greatest influence on CVD risk, followed by presence of diabetes and high BMI. In conclusion, a CVDRI based on existing health data from a Telehealth System was developed and used to identify local community members at risk of CVD. Nurse intervention may achieve greater reduction of CVD morbidity and mortality if multiple risk factors for the high-risk group are addressed at the same time.

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