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Overweight and obesity in people are epidemic in North America and internationally. In the United States, the number of overweight children and adolescents 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 employed 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.

A wide variety of personal behaviors affect an individual’s health. In the aggregate, 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 [

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:

1) To determine the prevalence of overweight and obesity of people in Bangladesh.

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.

For measuring the association of overweight and obesity with human disease and other factors in Bangladesh, we have mainly used the data of size 2730 individuals extracted using simple random sampling from the response of population record questionnaire of 2014 Bangladesh Demographic and Health Survey (BDHS) which contains a record of 43,772 individuals. The survey was conducted under the authority of the National Institute for Population Research and Training (NIPORT) of the Ministry of Health and Family Welfare [

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

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 = Mass kg Height m 2 = Mass lb Height in 2 × 713

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.

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 set. Again, we computed some new explanatory variable for convenience and transformed some original and computed variables that are suitable for the study. Some of the variables are coded as categorical and some are in dummy. The independent variables we used in this study are the Place of residence, Highest educational level, Religion, Sex of respondent, Wealth index, Current marital status, Ever had vaccination, Had diarrhea recently, Had fever in last two weeks, Had cough in last two weeks, Short, rapid breaths, Problem in the chest or blocked or running nose.

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.

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:

χ 2 = ∑ ( O − E ) 2 E = ∑ O 2 E − n

where, O represents the observed frequency. E is the expected frequency under the null hypothesis and computed by:

E = Row total × Column total Sample size

We will compare the value of the test statistic to the critical value of χ α 2 with degree of freedom = (r − 1) (c − 1), and reject the null hypothesis if χ 2 > χ α 2 ._{ }

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. It enables us to determine which of our independent variables have statistically significant effect on the dependent variable of interest [

logit ( p ) = b 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 + ⋯ + b k X k

where, p is the probability of presence of the characteristic of interest. The logit transformation is defined as the logged odds:

odds = p 1 − p = probability of presence of characteristics probalility of absence of characteristics

And

logit ( p ) = ln ( p 1 − p )

We can also define odds of the dependent variable equaling a case (given some linear combination x of the predictors) as follows:

odds = e β 0 + β 1 x o o o

For a continuous independent variable, the odds ratio can be defined as:

OR = odds ( x + 1 ) odds ( x ) = ( F ( x + 1 ) 1 − F ( x + 1 ) ) ( F ( x ) 1 − F ( x ) ) = e β 0 + β 1 ( x + 1 ) e β 0 + β 1 x = e β 1

This exponential relationship provides an interpretation for β_{1}: The odds multiply by e β 1 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.

Variable | χ^{2} value | df | P-Value |
---|---|---|---|

Type of place of residence | 170.901 | 2 | 0.000 |

Highest education level | 197.509 | 6 | 0.000 |

Religion | 12.171 | 6 | 0.058 |

Sex of respondent | 0.268 | 2 | 0.875 |

Wealth index | 415.054 | 8 | 0.000 |

Marital status | 67.694 | 2 | 0.000 |

Ever had vaccination | 12.297 | 2 | 0.002 |

Had diarrhea recently | 0.697 | 2 | 0.706 |

Had fever in last two weeks | 12.915 | 2 | 0.002 |

Had cough in last two weeks | 34.965 | 2 | 0.000 |

Short, rapid breaths | 702.452 | 2 | 0.000 |

Problem in the chest or blocked or running nose | 754.026 | 2 | 0.000 |

namely Religion, Sex of respondent and had diarrhea recently have insignificant effect on BMI classification [

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

Variable | β | Standard Error | Wald test | Df | P-value | Odds ratio | 95% CI for Odds ratio | |
---|---|---|---|---|---|---|---|---|

Lower | Upper | |||||||

Type of Place of residence | ||||||||

Urban | 0.845 | 0.167 | 25.562 | 1 | 0.000 | 2.329 | 1.678 | 3.232 |

Rural (ref) | ||||||||

Highest educational level | ||||||||

No education | −0.886 | 0.325 | 7.411 | 1 | 0.006 | 0.412 | 0.218 | 0.780 |

Primary | −0.638 | 0.284 | 5.049 | 1 | 0.025 | 0.529 | 0.303 | 0.922 |

Secondary | −0.413 | 0.236 | 3.058 | 1 | 0.048 | 0.661 | 0.416 | 1.051 |

Higher (ref) | ||||||||

Religion | ||||||||

Islam | −2.604 | 1.549 | 2.827 | 1 | 0.093 | 0.074 | 0.004 | 1.539 |

Hinduism | −2.541 | 1.572 | 2.612 | 1 | 0.106 | 0.079 | 0.004 | 1.717 |

Buddhism | −1.622 | 1.880 | 0.744 | 1 | 0.388 | 0.198 | 0.005 | 7.865 |

Christianity (ref) | ||||||||

Sex of respondent | ||||||||

Male | 0.218 | 0.160 | 1.874 | 1 | 0.171 | 1.244 | 0.910 | 1.701 |

Female (ref) | ||||||||

Wealth index | ||||||||

Poorest | −1.714 | 0.324 | 28.082 | 1 | 0.000 | 0.180 | 0.096 | 0.340 |

Poorer | −1.424 | 0.291 | 3.921 | 1 | 0.000 | 0.241 | 0.136 | 0.426 |

Middle | −0.405 | 0.241 | 2.839 | 1 | 0.029 | 0.667 | 0.416 | 1.068 |

Richer | −0.659 | 0.223 | 8.697 | 1 | 0.003 | 0.518 | 0.334 | 0.802 |

Richest (ref) | ||||||||

Current marital status | ||||||||

Unmarried | −0.177 | 0.270 | 0.426 | 1 | 0.014 | 0.838 | 0.493 | 1.424 |

Married (ref) | ||||||||

Ever had vaccination | ||||||||

No | −0.036 | 0.257 | 0.019 | 1 | 0.042 | 1.128 | 0.835 | 1.697 |

Yes (ref) | ||||||||

Had diarrhea recently | ||||||||

No | −0.511 | 0.367 | 1.944 | 1 | 0.163 | 0.600 | 0.292 | 1.230 |

Yes (ref) | ||||||||

Had fever in last two weeks | ||||||||

no | 0.473 | 0.202 | 5.514 | 1 | 0.019 | 1.605 | 1.081 | 2.383 |

Yes (ref) |

Had cough in last two weeks | ||||||||
---|---|---|---|---|---|---|---|---|

Yes | 2.337 | 0.273 | 73.512 | 1 | 0.000 | 10.348 | 6.066 | 17.655 |

no (ref) | ||||||||

Short, rapid breaths | ||||||||

Yes | −4.805 | 0.269 | 319.736 | 1 | 0.000 | 2.008 | 1.805 | 2.412 |

no (ref) | ||||||||

Problem in the chest or blocked or running nose | ||||||||

Yes | 5.085 | 0.264 | 37.542 | 1 | 0.000 | 16.529 | 9.252 | 27.077 |

No (ref) |

ref = reference category, *reference category of dependent variable (BMI classification) is normal weight.

Richest as the reference category. Current marital status is also a significant factor which odds ratio is 0.838 with 95% confidence interval (0.493, 1.424). It indicates that there is 0.838 times less chance of being overweight or obese for Unmarried group comparing with the married group. The odds ratio of the variable ever had vaccination indicates that there is 1.128 times greater chance of being overweight or obese for those who did not take vaccine considering those who took vaccine as the reference category. The variable had fever in last two weeks is come out as a significant factor of BMI classification with odds ratio 1.605 which means that there is 1.605 times greater chance of being overweight or obese for the people who had no fever comparing those who had fever in last two weeks. The value of odds ratio of the variable had cough in last two weeks is 10.348 and its 95% confidence interval is (6.066, 17.655). It indicates that there is 10.348 times greater chance of being overweight or obese for those who had cough considering those who had no cough in last two weeks as the reference category. The variable Short, rapid breaths are come out as a significant factor of BMI classification whose odds ratio is 2.008 and 95% confidence interval for odds ratio is (1.805, 2.412). This means that there is 2.008 times greater chance of being overweight or obese for the people who had short, rapid breaths considering the group of people who had no problem of short, rapid breath as the reference category. Finally, the variable Problem in the chest or blocked or running nose is also a significant factor of BMI classification. Its odds ratio is 16.529 and the 95% confidence interval for odds ratio is (9.252, 27.077) which indicates that there is 16.539 times greater chance of being overweight or obese for the group of people who had problem in the chest or blocked or running nose comparing with the group of people who had no problem in the chest or blocked or running nose.

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 among the overweight or obese people. From the research, it is also evident that among the overweight or obese people, there are 1.17% poorest, 1.98% are poorer, 3.81% are middle class, 4.98% are richer and the remaining 9.16% are richest. Based on the findings of our study, we can elucidate that obese people constitute a heterogeneous group in which the susceptibility for different disease differs substantially according to subsets of other biologic and socio-demographic circumstances. The results indicate a possibility to identify overweight and obese individuals with an increased risk of different diseases with global risk assessment. From this study, it is clear that overweight and obesity have significant impact on physical and psychological health status of people and leads to an increased risk of disease and mortality later on their life. Nowadays, it is becoming a serious public health concern all over the world. Based on the exploratory data, it is clearly shown that overweight and obese subjects have more unhealthy life years than normal weight subjects. This study provides evidence-based on large cohort studies, that there is a motivation for the development, implementation, and evaluation of new weight gain prevention programs. Recently, the World 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.

The authors declare no conflicts of interest regarding the publication of this paper.

Islam, M.R. and Haque, M.A. (2020) Measuring the Association of Overweight and Obesity with Human Disease and Other Factors in Bangladesh. Open Journal of Statistics, 10, 402-411. https://doi.org/10.4236/ojs.2020.103026