tiểu luận kinh tế lượng REPORT ON FACTORS ASSOCIATED WITH BODY MASS INDEX (BMI) IN VIETNAMESE ADOLESCENCE

Obesity is a medical condition that occurs when a person carries excess weight or body fat that might affect their health.. 7.2 Variables: 7.2.1 Dependent variable: BMI BMI Body Mass

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

- GROUP ASSIGNMENT – ECONOMETRICS

REPORT ON FACTORS ASSOCIATED WITH BODY

MASS INDEX (BMI) IN VIETNAMESE

ADOLESCENCE

Class: KTEE218.1 Group 9

Lecturer: Ms Nguyen Thuy Quynh

Members: Phạm Nguyễn Xuân Lộc - 1816450047 Hoàng Ngọc Anh - 1814450010

Hoàng Hương Giang - 1814450026

Trần Thị Linh Chi - 1814450018

Nguyễn Thị Hạnh Hà - 1814450030

Hanoi 09/2019

I ABSTRACT

The BMI is a convenient rule of thumb used to broadly categorize a person as underweight, normal weight, overweight, or obese based on tissue mass muscle, fat, bone and height

The BMI is generally used as a means of correlation between groups related by general mass and can serve as a vague means of estimating adiposity BMI is easy to use as a general calculation On the whole, the index is suitable for recognizing trends within

sedentary or overweight individuals because there is a small margin of error The BMI has been used by the WHO as the standard for recording obesity statistics since the early 1980s

In this report we examine the Factors associated with Body Mass Index (BMI) in Vietnamese Adolescents

The research was conducted on 152 people whose ages were ranging from 18 to 25

The data were collected using a questionnaire form that consisted of questions concerning general characteristics of individuals: Height, weight, average time spend on excercises…

The average BMI of the individuals differs according to each person’s routine and how they consume calories

The purpose of this report is to apply econometrics to examine the effect of different factors on the BMI and thus finding ways to have a healthier life, prevent overweight and underweight

II CONTENTS

X RECOMMENDATIONS, DIFFICULTY AND LIMITATION OF THE STUDY: 19

III LIST OF ABBREVATIONS

-OLS: Ordinary Least Square regression -BMI: Body Mass Index

IV LIST OF TABLES

Table 7: Regression of bmi ( dependent) and sleep meal income exercise sex ( independent) 28

V LIST OF FIGURES

VI INTRODUCTION

BMI (Body Mass Index) is a simple, inexpensive, and noninvasive surrogate

measure of body fat In contrast to other methods, BMI relies solely on height and weight and

with access to the proper equipment, individuals can have their BMI routinely measured and calculated with reasonable accuracy Furthermore, studies have shown that BMI levels correlate with body fat and with future health risks High BMI predicts future morbidity and death Therefore, BMI is an appropriate measure for screening for obesity and its health risks

Lastly, the widespread and longstanding application of BMI contributes to its utility at the population level Its use has resulted in an increased availability of published population data that allows public health professionals to make comparisons across time, regions, and

population subgroups

Obesity is a medical condition that occurs when a person carries excess weight or

body fat that might affect their health If a person does have obesity and excess weight, this can increase their risk of developing a number of health conditions, including metabolic syndrome, arthritis, and some types of cancer Causes of Obesity varies from consuming too many calories to leading a sedentary lifestyle Recent hypotheses in the scientific community suggest the current obesity epidemic is being driven largely by environmental factors (e.g., high energy/high fat foods, fast food consumption, television watching, "super-sized"

portions, etc) Vietnamese people are bombarded with images and offers of high fat, high calorie, highly palatable, convenient, and inexpensive foods These foods are packaged in portion sizes that far exceed federal recommendations Furthermore, the physical demands of our society have changed resulting in an imbalance in energy intake and expenditure Today's stressful lifestyles compound the effects of environmental factors by impairing weight loss efforts and by promoting fat storage, increasing urbanization and changing modes of transportation, it is no wonder that obesity has rapidly increased in the last few decades,

around the world To help ease this “epidemic” we conduct this report on Factors affecting

BMI

There are lots of factors that have impact to the BMI such as: age, sex, physical activities, individual’s income, number of calories consume per day, etc However, how these factors affect BMI and the extend of affection are still very ambiguous to most people So to clear the mist, our group has done a survey on this issue Nevertheless, due to our

unexperience, we just focus on a a specific group of people Our topic is: “Factors associated

with body mass index (BMI) in Vietnamese Adolescent”

We give our everything into this report, but surely, making mistakes is inevitable We hope that after reading our report, you can give us some feedback on how we can improve the quality Thank you!

VII CHAPTER I: RATIONALE OF THE STUDY

7.1 Basis for variables and model choosing

● Based on the characteristics of BMI, any factor alone can not show whether a person's weight is sensible or not, but using it in combination with other indicators can provide a more complete picture Therefore, we decided to choose 8 variables that are both oriented and indefinitely affecting BMI for Vietnamese adolescence in general as:

1 Height

2 Weight

3 Gender

4 Personal income

5 Numbers of minutes spending on exercising

6 Number of sleeping hours per day

7 Packets of milk drinks per day

8 Total number of meals per day

● Multiple Regression Model is the model that we will use mainly in this report In this

case, we want to examine the factors that affect to BMI of a Vietnamese so that we have both dependent and independent variables Based on the results of the independent variables, we can predict the dependent variables (BMI)

7.2 Variables:

7.2.1 Dependent variable: BMI

BMI (Body Mass Index) is the body index used by doctors and health professionals to

determine whether a person's body is obese, overweight or too thin Usually, people use to calculate the level of obesity The only downside of the BMI is that it cannot calculate the amount of fat in the body - the potential risk factor for future health.1

Your BMI is calculated as follows: BMI = (body weight) / (height x height)

- body weight: in kg;

- height x height: in m;

The BMI evaluation board follows World Health Organization (WHO) standards and is

specifically for Asians (IDI & WPRO) You can assess your own BMI through the statistics table below:

1 Wikipedia

Table 1 : The BMI evaluation

● Weight: the amount that something or someone weighs.

● Gender: the physical and/or social condition of being male or female.

● Personal income: money earned by a person over a particular period of time.

● Numbers of minutes spending on exercising: is any bodily activity that enhances or

maintains physical fitness and overall health and wellness

● Number of sleeping hours per day: is a naturally recurring state of mind and body,

characterized by altered consciousness, relatively inhibited sensory activity, inhibition

of nearly all voluntary muscles, and reduced interactions with surroundings

● Packets of milk drunk per day: a packet of milk is 180ml.

● Total number of meals per day: when food is eaten.

7.2.3 Model

Multiple regression model: Multiple regression model is a statistical method used to predict

the value a dependent variable based on the values of two or more independent variables.The variable called the dependent variable sometimes can be the outcome, target or criterion variable The variables we are using to predict the value of the dependent variable, called the independent variables sometimes can be the predictor, explanatory or regressor variables.2

7.3 Assess about BMI metric

● BMI is a quick and simple way to get an overall view of your health: It is easy to

use formula and makes it useful for measuring across populations With its simplistic design, it can easily be applied to research that compares data on obesity rates

between different age ranges in geographical locations The simplicity of calculating a

2statistics.laerd.com

BMI also makes it easy for anyone to quickly assess basic information about their physical health at home without having to go to a medical professional or buy expensive equipment

● BMI works extremely well when used for what it’s designed for — to calculate in measure obesity and weight across large populations Because weight is not a

direct correlation to fat, and amount of fat on one’s body is not always directly correlated to health issues, BMI measurements are more accurate when used to study the rates of obesity and malnutrition among populations When used in this way, BMI can lead to productive conversations about health while still encouraging body

positivity and self-love

● BMI is a widely used metric: Many people, including physicians, use BMI as a

measure of health and fitness According to the National Heart, Lung, and Blood

Institute, it is a measure of body fat based on weight that applies to both men and

women

VIII CHAPTER II: EMPIRICAL RESEARCH

8.1 Literature review

In 1972, Keys et al severely criticized the validity of Metropolitan Life Insurance published data Instead, Keys et al, using better documented weight for height data, popularized the Quetelet Index in population-based studies They referred to it as the body mass index (BMI)

The distribution of BMIs in adult American men and women was determined in 1923

in 1026 individuals The median BMI was 24, but the mean BMI was 25 The distribution curve clearly indicated a skewing toward an increase in BMI, and this trend has continued

The reason for choosing this topic: Our topic focuses on people in Vietnam with some new variables: “average hours of exercise per day”, “income per day”, “meals per day”

Similarities are we both using quantitative analysis methods and about factors affecting BMI

In England in 2016, 34% of men and 46% of women had a very high waist circumference These proportions rose from 20% and 26% respectively in 1993 to 31% and 38% in 2001 As with obesity, there were 617,000 admissions to NHS hospitals in 2016/17 where obesity was recorded as either a primary or secondary diagnosis, an increase of 18 per cent on 2015/16 (525,000) Around two thirds of the admissions where obesity was recorded

as either a primary or secondary diagnosis in 2016/17 were for women (66 per cent)

Websites or mobile phone apps were used by 8% and activity trackers or fitness monitors by 6% Overall 47% of adults said they were trying to lose weight 66 per cent of men and 58 percent of women aged 19 and over met the government's aerobic guidelines in

2016 21 percent of men and 25 percent of women were classed as inactive in 2016 24 per cent of men and 28 per cent of women consumed the recommended five portions of fruit and vegetables a day in 2016 Half of the people who reported they were trying to lose weight were not using any of the aids or support asked about.3

3 Health Survey for England, 2016: Summary of key findings

8.4 Quanlitative analysis

8.4.1 Empirical model Multiple regression model

According to the basis of the BMI was devised by Adolphe Quetelet, a Belgian astronomer, mathematician, statistician and sociologist, from 1830 to 1850 during which time

he developed what he called "social physics", The modern term "Body Mass Index" (BMI) for the ratio of human body weight to squared height was coined in a paper published in the July 1972 edition of the Journal of Chronic Diseases by Ancel Keys and others We classified the independent variables in two categories:

(1) individual factors: sex, height, weight, the number of meals per day, physical activities, sleeping hours

(2) family and social factors: income

The dependent variable is BMI (Body Mass Index)

We entered all of the predictors (individual, family) into one model using a multiple

exercise per day, income per day As a result, we will set up to represent those disturbances

The model is:

X2 Independent

variable (Quantitative variable)

meals

X3 Independent

variable (Quantitative variable)

https://files.digital.nhs.uk/pdf/s/q/hse2016-summary.pdf

(Quantitative variable)

D1 Independent

variable (Qualitative variable)

-Equations for the Ordinary Least Squares regression

Ordinary Least Squares regression (OLS) is more commonly named linear regression (simple

or multiple depending on the number of explanatory variables).

In the case of a model with p explanatory variables, the OLS regression model writes:

Y = β 0 + Σ j=1 p β j X j + ε

where Y is the dependent variable, β0 , is the intercept of the model, Xj corresponds to the jth

explanatory variable of the model (j=1 to p), and ε is the random error with expectation 0 and

β = (X’Dx) -1 X’ Dy σ² = 1/(W –p*) Σ i=1 n w i (y i - y i )

where β is the vector of the estimators of the βi parameters, X is the matrix of the explanatory variables preceded by a vector of 1s, y is the vector of the n observed values of the dependent variable, p* is the number of explanatory variables to which we add 1 if the intercept is not fixed, wi is the weight of the ith observation, and W is the sum of the wiweights, and D is a matrix with the wi weights on its diagonal

The vector of the predicted values can be written as follows:

y = X (X’ Dx) -1 X’Dy

- Variable selection in the OLS regression

An automatic selection of the variables is performed if the user selects a too high number of variables compared to the number of observations The theoretical limit is n-1, as with greater values the X’X matrix becomes non-invertible

The deleting of some of the variables may however not be optimal: in some cases we might not add a variable to the model because it is almost collinear to some other variables or to a block of variables, but it might be that it would be more relevant to remove a variable that is already in the model and to the new variable

For that reason, and also in order to handle the cases where there a lot of explanatory variables, other methods have been developed

8.4.3 Data sources

In oder to estimate the model, we obtain data from a survey whose form consists of some questions about height (metres), weight (kg), minutes per day for doing exercises or working out, income (VND) including individuals’ salary and allownance from families, sleeping hours, the number of meals per day, sex (female/male)

-The expectation of β2 is negative value because if one person eat less meals per day the metabolic system will malfunctioned so BMI will be greater than usual because of gaining weight

-The expectation of β3 is positive value as we follow the assumption that higher income, higher standard of living which helps people afford the fee for gym or another sports

-The expectation of β4 is positive value because if one spends more time on doing exercise, it can help his/her high and lower weight which increase his/her BMI -The expectation of β5 is positive value because we define the value of female label as 0; the value of male label is equal to 1 and we expected that BMI of female would be higher than male

8.4.5 Estimation results

In order to conduct a multiple linear regression with the independent and dependent variables given, the data should meet the Gauss-Markov Assumptions The multiple regression model is linear in parameters, is met since the ensuing multiple regression

is of the form:

y = β0 +β1X1 + β2X2 + β3X3 + … + u

We use sum command to determine Obs (Observations), Mean, Std.dev (Standard

Deviation), Max and Min of the variables

sex 152 4671053 5005661 0 1

height 152 1.630526 118354 1.45 1.85 weight 152 62.14474 12.39601 40 82

From the table above it can be seen that:

-There are 152 observations

-The height of the sample we had collected ranges from 1.45 to 1.85 with average value at 1.630526

-The weight of 152 observations ranges from 40 to 82 -The BMI ranges from 12.541143 to 39.00119 on average of 23.8024

We use tabulation command to find out the portion of male and female took part in the

survey:

sex Freq Percent Cum

As we can draw from the table, of the 152 people take part in the survey, there are:

-81 females, which account for 53.29 percent of the sample -71 males, which take the percentage of 46.71

We use regress command to estimate the coeffecients as below:

reg bmi sleep meal income exercise sex

Residual 48.7724273 146 334057721 Total 5500.66254 151 36.4282287

Number of obs = 152 F(5, 146) = 3264.04 Prob > F = 0.0000 R-squared = 0.9911 Adj R-squared = 0.9908 Root MSE = 0.57798

bmi Coef Std Err t P>|t| [95% Conf Interval]

sleep 3.960645 0313438 126.36 0.0000 3.898699 4.02259 meal -.0575852 0425167 -1.35 0.178 -0.141613 026442 income 2.17e-08 3.05e-08 0.71 0.478 -3.86e-08 8.2e-08 exercise 0173715 0581383 0.3 0.766 -.09753 132273 sex 0965581 0965238 1.00 0.319 -.0942064 028732 _cons 2295313 2371914 0.97 0.335 -.2392409 698303

This means that:

- When the sleeping hours, meal per day, income per month, and average minutes for doing exercise equal to 0, then the BMI reach to the minimum point, at 12.86602

- for every 1% increase in sleeping hours of a person, the model predicts that their BMI increases by 3.96 percentage points

- for every 1% increase in meal per day of a person, the model predicts that their BMI increases by -0.57 percentage points

- for every 1% increase in income per month of a person, the model predicts that their BMI increases a trivial number

- for every 1% increase in average minutes for doing exercise per day the model predicts that their BMI increases 0.017371 percentage points

Superficially analyzing figures:

-Number of observations: n = 152 -Total Sum of Squares: SST = 5500.66254 -Explained Sum of Squares: SSE = 5451.89011 -Residual Sum of Squares: SSR = 48.7724273 -Determination Coeffiecient (R-squared): R2 = 0.9911 -Adjusted R-squared: R2 = 0.9908

From the results above, we can see that R2 = 0.9911 which is a huge number This means 99.11% of the sample variation in IBM (dependent variable) is explained by the changes

in sleeping amount, meals per day, income per month, average minutes for doing exercise per day and sex (independent variable) Even though R2 is very large, we will still take deeper investigation in the following part

Interval confidence

From the regression result table, we see

-The confidence interval of the intercept is [-0.2392409 ; 0.69803]

-The confidence interval for β1 is

3.898699 ≤ β1 ≤ 4.02259 That is, the confidence interval [3.898699 ;4.02259] includes the true β1 coefficient with 95% percent of confidence coefficient Thus, if 100 samples of size 152 are collected and 100 confidence interval are constructed, we expect 95 of them to contain the true population parameter β1 since the interval does not include the null – hypothesized value of zero, we can reject the null hypothesis that true β1 is zero with

95 percent confidence In other words, with the influence of other variables held constant, the sleeping hours impacts on the BMI

-The confidence interval for β2 is

-0.141613 ≤ β2 ≤ 0.026442 That is, the confidence interval [-0.141613 ; 0.026442] includes the true β2 coefficient with 95% percent of confidence coefficient Thus, if 100 samples of size 152 are collected and 100 confidence interval are constructed, we expect 95 of them to contain the true population parameter β2 Since the interval includes the null – hypothesized value of zero, we cannot reject the null hypothesis that true β2 is zero with 95 percent confidence Therefore, β2 is not significant at the level of 5 percent

-The confidence interval for β3 is

-3.86e-08 ≤ β3 ≤ 8.2e-08 That is, the confidence interval [-3.86e-08 ;8.2e-08] includes the true β3 coefficient with 95% percent of confidence coefficient Thus, if 100 samples of size 152 are collected