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15 November 2008

Diskusi Bulanan ke-2



Pada hari Sabtu tanggal 15 Nopember 2008, telah dilaksanakan Diskusi Bulanan ke-2 PDUUKM berbahasa inggris yang dilaksanakan di Mess Universitas Andalas Jalan Taman Tenaga, Hentian Kajang, Bangi. Adapun sebagai pembicara pada diskusi kali ini adalah Ibu Ferra Yanuar (FMIPA) yang mempresetasikan makalahnya yang berjudul: Statistical Analysis For Constructing Health Index. Saat ini pembicara sedang melanjutkan pendidikan doktoral di Fakulti Sains dan Teknologi Maklumat Universiti Kebangsaan Malaysia. Sebagai moderator yaitu Bapak Yulindon dari Poltek Unand. Kegiatan ini dihadiri oleh sebanyak 7 orang anggota PDUUKM.

Berikut ini ringkasan makalah tersebut:

STATISTICAL ANALYSIS FOR CONSTRUCTING HEALTH INDEX

Introduction

The estimation of health status determinants is an important input for public policy making. It helps to understand the risk of specific habits and its effects in productivity and economic growth (Savedoff and Schultz, 2000). It also brings information to the policy makers about the effect of particular public strategies in the health condition of the population (Gerdtham et al, 1999).

In this paper, we used several health-related measures commonly taken in doctors’ offices or hospitals and one of the questions asking about health problem to identify the determinants of person’s health status. We will develop an index number presenting the health status of an individual. The index will be related to a set of selected explanatory variables and examine the impacts of these explanatory variables on the index. The objective of this study was to examine whether the proposed health status index can be explained by the number of health problems the person had, food intakes, lifestyle and demographics characteristic.

Health Measure Components

The Health Risk Assessment of Hulu Langat Survey (HRA-HLS) 2006 data were used in this study. The health measure components used to calculate the health index as response variable are body height, body weight, blood pressure, cholesterol level, HDL, blood glucose, the number of common health problems the respondent had, and the respondent’s subjective judgment of their health condition.

The classifications for the body mass index (BMI) are the recognized categories established by the Centers for Disease Control and Prevention (CDC 2007a). The classifications for blood pressure are based on the American Heart Association’s (2007) recommended blood pressure level for normal, pre-hypertension, and high blood pressures. The classifications for total cholesterol and HDL are based on the National Institutes of Health’s ATP III guidelines for primary target therapy. LDL was not used due to only selected participants examined in the morning had the LDL measures. The classifications of blood glucose are based on the A1c fraction (American Diabetes Association). The general health condition is based on the answer to the question “Would you say your health in general is…” in the HRA-HLS 2006. We also compiled a list of 14 common health problems based on the HRA-HLS 2006 questionnaires. Only the participants of ages equal to or older than 14 years and had food intake information were used in the analysis. There were 7,440 participants who met these criteria. Factor analysis was used to identify indicators for each factor. A multivariate weighted regression model using full sample weight was fitted with the proposed health status determinants using the HRA-HLS 2006 data.

We used a scoring method to develop the health status index. We assigned three (3) points for each “Healthy” result, two (2) points for “Less Healthy,” and one (1) point for “Unhealthy.” The health status index is the sum of the scores of these health-measurement components; with lower scores indicating less healthy and higher scores indicating healthier of the person of interest. The minimum possible points for the health status index are seven (7) and the maximum possible points are 21. From the analysis, more than 60% of the respondents had a score of 16 and higher and the average score is 15.88.

Factor Affecting Health Status

After finding the distribution of health status index for all respondents, then by using multivariate regression we regressed the proposed health status index to various factors that have been shown to be associated with health status in many studies. These factors include socio-demographics, lifestyle and food intake. Based on factor analysis output, the indicators of socio-demographic variables include occupation, ethnic, age, gender, education, and marital status. Lifestyle include smoking habits, physical exercise, and having breakfast, meanwhile indicator variables of food intakes are consume food with fiber and consume food with high cholesterol. Using multivariate weighted regression model with full sample weight was fitted with the proposed health status determinants using the HRA-HLS 2006 data.

Results show that demographic, food intake, and lifestyle factors are related to the proposed health status. Gender has an impact on the value of the health status index. As respondent gets older, his/her health status index decreases at a decreasing rate. Employed respondents had a positive impact and being married had impact on the health status index. Respondents who had college education had higher health status index than those who had no college. The health status indices for Malay were lower than other races, an indication that Malay were less healthy, in general. Chinese had higher health status index than other ethnics. As expected, the amount of exercise is positively related to the health status index and smoking is negatively related to the health status index.

Respondents who often have breakfast had a higher health status index than those who did not eat breakfast. Results also show that as the percent of total fiber in food consumed can increase the health status index of the respondents. Meanwhile consume food with high cholesterol made respondent’s health status index decreases.

The value of beta coefficients of the coefficient estimates is derived from the parameter estimate by multiplying the standard deviation of the associated regressor and dividing by the standard deviation of the response variable. This is usually done to answer the question of which explanatory variables have a greater impact on the dependent variable in multivariate regression analysis, when the variables are measured in different units of measurement. When we rank these beta coefficients by their absolute values, we found that age had the largest impact on the health status index, which is followed by smoking habits, being Chinese, Malay, employed, college education, married, physical exercise, food intake and breakfast.

The health status index proposed in this study is an ordinal measure, the difference between the health index values of two persons can only tell who is healthier than the other, but cannot tell how much healthier. In other words, the magnitude of the difference between the values of two health index for two persons has no direct meaning. One may want to use the proposed health status index to track the average health status of a population over a period of time. For example, the HRA-HLS 2006 data can be used to derive the health status indices and these indices can be used to monitor the general health status of the Hulu Langat population. (Ferra)



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