Application of Cardiovascular disease (CVD) risk assessment equations to the Thai population

Inthawong, Rungkarn; Khatab, Khaled; Whitfield, Michael F.; Collins, Karen; Raheem, Maruf A.; Ishmael, Mubarak

doi:10.30881/beij.00013

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…However, this equation is suitable for use within Asian populations. It has been validated in the performance of the equations within the Thai cohort [4] and also in this study [38]. The model could perform a more precise estimation if the country-specific CVD risk assessment equations were available.…”

Section: Limitations Of the Studymentioning

confidence: 94%

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

Inthavong¹,

Khatab²,

Whitfield³

et al. 2020

OALib

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Cardiovascular disease (CVD) is considered to be one of the leading health issues in Thailand. CVD not only contributes to an increase in the number of hospital admissions year by year but also impacts on the rising health care expenditure for the treatment and long-term care of CVD patients. Therefore, this study is aimed at examining the impacts of risk reduction strategies on the number of CVD hospital admissions, Disability-Adjusted Life Years (DALYs) and the costs of hospitalisation. To estimate such impacts a CVD cost-offset model was applied using a Microsoft Excel spreadsheet. The number of the midyear population was classified by age, gender and the CVD risk factor profiles from the recent Thai National Health Examination Survey (NHES) IV. This survey was chosen as the baseline population. The CVD risk factor profiles included age, gender, systolic blood pressure, total cholesterol, and smoking status. The Asia-Pacific Collaborative Cohort Study (APCCS) equation was applied to predict the probability of developing CVD over the next eight-year period. Estimates on the following were obtained from the model: 1) the CVD events both fatal and non-fatal; 2) the difference between the projected number of deaths and the actual number of deaths in that population; 3) the number of patients who are expected to live with CVD; 4) the DALYs from the estimated number of fatal and non-fatal events; 5) the cost of hospital admissions. Four CVD risk strategy scenarios were investigated as follows: 1) the do-nothing scenario; 2) the optimistic scenario; 3) achieve the UN millennium development goal; and 4

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Section: Limitations Of the Studymentioning

confidence: 94%

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

Inthavong¹,

Khatab²,

Whitfield³

et al. 2020

OALib

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, this equation is suitable for use within Asian populations. It has been validated in the performance of the equations within the Thai cohort [4] and also in this study [39]. The model could perform a more precise estimation if the country-specific CVD risk assessment equations were available.…”

Section: Limitations Of the Studymentioning

confidence: 94%

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

Khatab¹,

Inthavong²,

Whitfield³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cardiovascular disease (CVD) is considered to be one of the leading health issues in Thailand. CVD not only contributes to an increase in the number of hospital admissions year on year but also impacts on the rising health care expenditure for the treatment and long-term care of CVD patients. Therefore, this study is aimed at examining the impacts of risk reduction strategies on the number of CVD hospital admissions, Disability-Adjusted Life Years (DALYs) and the costs of hospitalisation. To estimate such impacts a CVD cost-offset model wasapplied using a Microsoft Excel spreadsheet. The number of the mid-year population was classified by age, gender and the CVD risk factor profiles from the recent Thai National Health Examination Survey (NHES) IV. This survey was chosen as the baseline population. The CVD risk factor profiles included age, gender, systolic blood pressure, total cholesterol, and smoking status. The Asia-Pacific Collaborative Cohort Study (APCCS) equation was applied to predict the probability of developing CVD over the next eight-year period. Estimates on the following were obtained from the model: (1.) the CVD events both fatal and non-fatal; (2.) the difference between the projected number of deaths and the actual number of deaths in that population; (3.) the number of patients who are expected to live with CVD; (4.) the DALYs from the estimated number of fatal and non-fatal events; (5.) the cost of hospital admissions. Four CVD risk strategy scenarios were investigated as follows: (1.) the do nothing scenario; (2.) the optimistic scenario; (3.) achieve the UN millennium development goal; and (4.) the worst-case scenario. The findings showed that over the next eight years there are likely to be 3,297,428 recorded cases of CVD; 5,870,049 cases of DALYs; and, approximately ฿57,000 million, ($1.9 billion), is projected as the total cost of hospital admissions. However, if the current health policy can reduce the levels of risk factors as defined in the optimistic scenario or such policy meets the specifications of the UN millennium development goal,there would be a significant reduction in the number of hospital admissions. These are estimated to be a reduction of 522,179 male and 515,416 female cases. With these results it is expected that health care costs would save approximately ฿9,000 million, ($298.3 million), for CVD and 900,000 million of DALYs over the next eight years. However, if there is an upward trend in the risk factors as predicted in the worst-case scenario, then there will be an increase of 428,220 CVD cases; consequently, DALYs cases may rise by 766,029 while the hospitalisation costs may increase by approximately ฿7,000 million, ($232.1 million). Based on our findings, reducing the levels of CVD risk factors in the population will drastically reduce: (1.) the number of CVD cases; (2.) DALYs cases; and (3.) health care costs. Therefore it is recommended that the health policy should enhance the primary prevention programs which would be targeted at reducing the CVD risk factors in the population.

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Application of Cardiovascular disease (CVD) risk assessment equations to the Thai population

Cited by 2 publications

References 13 publications

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

The Impact of Risk Factors Reduction Scenarios on Hospital Admissions, Disability-Adjusted Life Years and the Hospitalisation Cost of Cardiovascular Disease in Thailand

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