Assessing the impact of reducing risk factors for cardio-vascular disease in Thailand.

Cardiovascular disease (CVD) is a global health problem and there has been an epidemiological transition of CVD from high income countries to low-middle income countries. In the case of Thailand, the prevalence of heart disease and stroke is increasing. In order to reduce the risk of CVD, the Ministry of Public Health in Thailand has implemented a number of primary CVD prevention strategies within the last decade. These strategies are being specifically implemented to address the future potential economic burden of increasing CVD. However, the economic impact of reducing multiple risk factors, at a population level in Thailand, in terms of health care costs is unclear. In order to plan for investment in public health interventions within finite resources, it is imperative that decision makers have sufficient information to identify the target populations and risk reduction strategies, and to assess the impact of these strategies on the population.This study aims to estimate the future prevalence of CVD in Thailand over the next 5-10 years and the potential economic and health benefits of strategies to reduce the population risk factors during this period.The mathematic CVD cost-offset model has been developed in this study in 7 stages. 1) Descriptive analysis of the CVD risk profile data from the 4th National Health Examination Survey (NHESIV) 2008-2009 data set in order to explore the association of CVD risk factors in Thailand. 2) Calculate the probability of future CVD event which applies the CVD risk prediction equation. 3) Estimate of the number of future CVD events. 4) Validation of the estimated number of annual CVD event with the actual CVD hospitalisation event in Thailand. 5) Calculate the cost of hospital admission due to CVD from the Universal Coverage Health Care Scheme (UC) data in 2009. 6) Estimate the burden of CVD in terms of the DALYs. 7) Estimate the impact of reducingCVD risk factors in different scenarios. The study outcomes being the number of hospitalisation cost savings, number of premature death savings, DALY savings and health care cost savings. The outcomes will also account for the uncertainty analysis.As indicated above, no studies currently exist that focus specifically on the mathematic model for estimating the future situation of CVD in Thailand. Therefore, this study represents an original contribution to that knowledge. The findings of this study will contribute to health policy by providing specific new knowledge and information regarding Thai CVD risk factors and the impact of the risk reduction which will assist health policy makers in the planning and future investment in prevention programs for CVD in Thailand. Moreover, it is expected that the finding from this research will establish a CVD prediction model for Thailand, and one which may be applicable and compatible to the Asia and Pacific regions

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

Objective: The objectives of this study are: 1.) To calculate the probability of Cardiovascular Disease (CVD) events by applying three different equations, which are: the Asia-Pacific Cohort Study (APCS) equation, the Framingham-Asia equation and the original Framingham equation, to the individual risk factors data from the NHESIV, Thailand. 2) To estimate the number of 8-10 years CVD events. 3) To validate and identify the most suitable CVD risk equations for the Thai population. The individual risk factors from the NHESIV dataset was entered into a Microsoft Excel spreadsheet as the baseline population. Methods: Asia-Pacific Collaborative Cohort Study (APCCS) equations, the Framingham-Asia equation and the original Framingham equation, are applied to calculate the probability of 8 to 10 years CVD events by age groups and gender. The CVD events in this analysis refer to all fatal and non-fatal CVD events (ICD10, I00-I99), which include Ischemic heart disease (IHD) (ICD10, I20-I25) and stroke (ICD10, I60-I69). Results: The 4th National Health Examination Survey IV 2009 (NHESIV) dataset has been entered into a Microsoft Excel spreadsheet as the baseline population. APCCS, the Framingham-Asia and the original Framingham equations, were applied to the NHESIV dataset. The APCCS equation calculated the average 8-years probability of getting CVD as 8.3% in men and 7.8% in women. The 8-year likelihood of CVD in the Framingham-Asia equation was 7.2% in men and 8.1% in women. The original Framingham equation showed the highest probability of 10-years CVD which were 18.8% in men and 11.1% in women. Conclusions: The original Framingham equation overestimated the risk of CVD in the Thai population in all age groups. The Asia-Pacific Cohort Study (APCCS) and the Framingham-Asia equations, both performed better estimation than the original Framingham equation in both men and women

Risk factors associated with Cardiovascular Disease (CVD) in Thailand from the 4th National Health Examination Survey 2008-2009

Objectives: This study aims to describe the current situation of Cardiovascular Disease (CVD) and to explore the association of the modifiable risk factors with Coronary heart disease (CHD) and stroke in Thailand. Methods: The 4th National Health Examination Survey (NHESIV) dataset has been used in this study. 19,342 participants aged ≥15 years have completed the data gathering process on CVD risk factors have been included in the analysis, which comprises 9,246 men and 10,096 women. The descriptive statistic, the bivariate analysis and the multiple logistic regression have been performed to describe and explore the association among CVD risk factors, CHD and stroke. The modifiable risk factors included in the analysis are age, BMI, total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diabetes and regular smoking. Results: The mean age of the participants is 52.7 years. The prevalence of CHD was 2.5% in men, and 2.3% in women and the prevalence of stroke was 2.5% in men and 1.6% in women. The overall prevalence of diabetes was 10.1%, and regular smoking was 17.9%. When exploring the association of the modifiable risk factors with CHD and stroke, using the bivariate and multivariate analysis, the results show that factors associated with both CHD and stroke are being aged 55 and over, high blood pressure and diabetes. Obesity, high triglyceride level and low HDL-C, only showed association with CHD but does not show any significant association to stroke. Having a high triglyceride level was related to stroke just in women but does not show any association in men. The factors that do not show significant association in both CHD and stroke are high total cholesterol and being a regular smoker. Conclusions: Although the analysis of the cross-sectional data was not able to identify the cause and effect of the factors relating to CHD or stroke, it showed some association with these modifiable risk factors for CVD. The modifiable risk factors, such as high blood pressure, obesity and diabetes need to be of concern in considering the CVD prevention strategies in Thailan

Development of a traffic accident simulation system for main roads in Loei Province, Thailand: Application of a geographic information system and multiple logistic regression with clustering

Traffic accidents are a major and crucial problem worldwide. The development of a traffic accident simulation system applied by using a geographic information system and multiple logistic regression with clustering can provide drivers with safe routes as well as guidelines for assessing the risk points of accidents in each subdistrict. This research is based on case-control study design. The data were collected by using two types of questionnaires: a questionnaire for 35 community leaders and a questionnaire for 580 community residents based on the distance at which main routes pass through the subdistrict area. The data were analysed through multiple logistic regression with clustering, and the standardized coefficient of the selected variables was then added to the equation as a weight in the traffic accident simulation system. The results of the study indicated that 11 variables affected traffic accidents. These factors were evaluated in order to predict traffic accidents (Pseudo R square=0.5906). Standardized coefficient of variables was applied in a geographic information system to simulate traffic accidents on roads. This study was distinctive for its analysis, which examined the clusters of variables that were the subdistrict-level data, including surroundings and road conditions at the riskiest location in each subdistrict. The data were analysed based on their quality as subdistrict data clusters. The analysis results were then applied as the weight of variables used in the GIS to obtain the values appropriate to the data clusters’ quality for the GIS to properly simulate traffic accidents in each area

Urban and rural variation in clustering of metabolic syndrome components in the Thai population: results from the fourth National Health Examination Survey 2009

<p>Abstract</p> <p>Background</p> <p>Information on the distribution of Metabolic syndrome (MetS) and its combinations by urban/rural areas in lower-middle income countries has been limited. It is not clear how the various combinations of MetS components varied by urban/rural population and if particular combinations of MetS are more common. This study aimed to estimate the prevalence of MetS and combinations of MetS components according to sex and urban/rural areas from a nationally representative sample of Thai adults.</p> <p>Methods</p> <p>Data from the fourth National Health Examination Survey of 19,256 Thai adults aged 20 years and over were analyzed. MetS was defined using the harmonized criteria of six international expert groups with Asian-specific cut-point for waist circumference.</p> <p>Results</p> <p>The prevalence of MetS was 23.2% among adults aged ≥ 20 years (19.5% in men and 26.8% in women). Among men, the prevalence of MetS in urban was higher than those in rural areas (23.1% vs 17.9%, <it>P </it>< 0.05), but among women, the prevalence was higher in rural areas (27.9% vs 24.5%, <it>P </it>< 0.05). Overall, an individual component of low high density lipoprotein (HDL) and hypertriglyceridemia were more common in rural areas, while obesity, high blood pressure and hyperglycemia were more common in urban areas. The most common combination of MetS components in men was the clustering of low HDL, hypertriglyceridemia, and high blood pressure (urban: 3.4% vs. rural: 3.9%, adjusted OR 0.9, 95%CI 0.7, 1.1). Among women, the most common combination was the clustering of obesity, low HDL, and hypertriglyceridemia (urban: 3.9% vs rural: 5.9%, adjusted OR 0.8, 95%CI 0.6, 0.9), followed by the clustering of these three components with high blood pressure (urban: 3.1% vs. rural 4.5%, adjusted OR 0.8, 95%CI 0.7, 0.9).</p> <p>Conclusion</p> <p>Metabolic syndrome affects both urban and rural population with different pattern of MetS combinations. Dyslipidemia and obesity were the most common components among women in rural areas, hence, interventions to prevent and control these factors should be strengthened.</p