Measuring the Association of Overweight and Obesity with Human Disease and Other Factors in Bangladesh

Overweight and obesity in people are epidemic in North America and inter-nationally. In the United States, the number of overweight children and adoles-cents has doubled in the last two to three decades, and similar increasing rates are being observed worldwide, including developing countries as Bangladesh where an increase in Westernization of behavioral and dietary lifestyles is evident. Human diseases associated with overweight and obesity are similar in children as in the adult population. The main purpose of this study was to examine the association between overweight and obesity with human disease and other factors in Bangladesh. We have attempted to estimate the relationship between associated variables by using the Pearson Chi-Square test. It also showed how important an individual variable is by itself. The study also em-ployed a statistical technique namely, logistic regression analysis which has been used to find out the association of overweight and obesity with human disease and other factors. We have seen that 16.96% people are overweight and 4.14% people are obese, i.e. 21.1% people are overweight or obese in Bangladesh. From the logistic regression analysis technique among the independent variables type of place of residence, Highest educational level, Wealth index, Current marital status, Ever had vaccination, Had fever in last two weeks, Had cough in last two weeks, Short, rapid breaths, Problem in the chest or blocked or running nose have significant effect on dependent variable BMI classification.

gate, these behaviors affect the health of populations. The leading behaviors that have been singled out as especially damaging to the health of the Bangladeshi population are overweight and obesity. Awareness of the association of obesity with health problems is longstanding. During the past few decades, the prevalence of obesity has grown to epidemic proportions, and this condition is now known to be a major contributor to the global burden of disease. Obesity prevalence is still increasing rapidly, not only in industrialized countries but also in non-industrialized countries, particularly in those undergoing economic transition. Worldwide, around 250 million people are obese, and the World Health Organization (WHO) has estimated that in 2025, 300 million people will be obese [1]. Attitudes toward obesity differ across populations and, with economic changes, may change within populations over time. In industrialized countries, obesity is most common among those with low socio-economic status. The opposite is true in non-industrialized countries, where obesity is most often seen among individuals with high income and may be considered a status symbol.
This effect may change as non-industrialized countries become more affluent and obesity is seen increasingly in those with low socio-economic status. Overweight and obesity are important determinants of human disease. The increasing prevalence of obesity contributes to a reduction in quality of life. Overweight and obesity in adulthood are associated with multiple co-morbidities; most notably type-2 diabetes, cardiovascular disease, and a number of cancers. Dramatic rises in childhood obesity prevalence over the last 30 years have brought increasing attention to the potential long-term health consequences of childhood obesity. However, our understanding of the associations of childhood obesity with long-term health is incomplete. In particular, due to the tracking of adiposity from childhood into later life, it remains unclear whether childhood obesity has an effect on adult health that is independent of adult weight status. Previous reviews have examined the relationship between childhood obesity and morbidity in adulthood, but have not considered whether the effects of childhood adiposity are independent of adult overweight. The objective of this review was to systematically evaluate the current evidence on the contribution of childhood body mass index (BMI) to adult disease risk, independent of adult BMI. We paid particular attention to the methods used to assess these independent effects, and considered the strengths and limitations of these approaches.

Objectives of the Study
The main purposes of this study are to describe secular changes in overweight, obesity and the health status as well as disease of the Bangladeshi people. A logical statement of the objectives is most helpful to any scientific undertaking.
Without this, it becomes complex in the context of reaching a decision and making inference. So in this research, our first step is to give clear statement of objectives of the study. The specific objectives of this study are as follows: 2) To explore the present situation of different human diseases in Bangladesh.
3) To measure the association of overweight and obesity with human disease and other factors in Bangladesh.

Variables
Though there are numerous factors, we considered only a few selected variables in our study.

Response Variable
To measure the association of overweight and obesity with human disease and other factors in Bangladesh, we used Body Mass Index (BMI) as the response variable in this study. The dependent variable used in this analysis was found directly from the BDHS data set which is supported as a dichotomous variable. Body mass index is one measure of obesity (or Non-obesity). The BMI is universally expressed in (kg/m 2 )/(lb/in 2 ), resulting from mass in kilograms and height in meters. If pounds and inches are used, a conversion factor of 703 (kg/m 2 )/(lb/in 2 ) must be applied. When the term BMI is used informally, the units are usually omitted. The BMI is calculated by the following formula: BMI is classified as follows: a BMI from 18.5 up to 24.9 may indicate normal weight, a BMI lower than 18.5 suggests the person is underweight, a number from 25 up to 29.9 may indicate the person is overweight, and a number from 30 upwards suggests the person is obese.

Independent Variables
A great deal of explanatory variables is considered in our study. All the explanatory variables used in the analysis were not found directly from the BDHS data

Methodology
In this research, both bivariate and multivariate techniques have used to perform the analysis of data. The Pearson Chi-Square test has used for bivariate analysis and the logistic regression has used for the multivariate analysis. Both techniques are described briefly below. In this study, statistical analysis has been carried out using IBM SPSS 21.

Chi-Square Test
A chi-square test, also written as χ 2 test, is any statistical hypothesis test wherein the sampling distribution of the test statistic is chi-square distribution when the null hypothesis is true. A chi-squared test can be used to attempt rejection of the null hypothesis that the data are independent. It is also used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. Recall that we can summarize two categorical variables within a two-way table, also called an r × c contingency table, where r = number of rows, c = number of columns. Our question of interest is "Are the two variables independent?" This question is set up using the following hypothesis statements: Null Hypothesis: The two categorical variables are independent.
Alternative Hypothesis: The two categorical variables are dependent.
The chi-square test statistic is calculated by using the formula: where, O represents the observed frequency. E is the expected frequency under the null hypothesis and computed by: Row total Column total Sample size E × =

Logistic Regression
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the  [3]. The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable) and a set of independent (predictor or explanatory) variables.
Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest: where, p is the probability of presence of the characteristic of interest. The logit transformation is defined as the logged odds: probability of presence of characteristics odds 1 probalility of absence of characteristics We can also define odds of the dependent variable equaling a case (given some linear combination x of the predictors) as follows: This exponential relationship provides an interpretation for β 1 : The odds multiply by 1 e β for every 1-unit increase in x. The coefficients in the logistic regression model tell us how much the logit changes based on the values of the independent variables.

Results and Discussion of Logistic Regression Analysis
In this research, BMI classification is used as the explained (dependent) variable which has three categories: underweight, normal weight and overweight or obese. Therefore we have used multinomial logistic regression instead of binary logistic regression. The results of logistic regression are shown in Table 2. From

Summary and Conclusion
The study shows that 10.77% male and 10.33% female people are overweight or obese and 11.87% live in urban area and the remaining 9.23% live in rural area Health Report showed that healthy life expectancy increased forcefully during the last century in 193 countries. If the prevalence of obesity will further increase, it is reasonable to expect that healthy life expectancy may be adversely affected in recent future in societies with a high prevalence of obesity.