Trends and determinants of obesity among women of reproductive age - PDF document

Trends and determinants of obesity among women of reproductive age in Bangladesh 2004-2014: A multivariate decomposition analysis M Sheikh Giash Uddin, Department of Statistics, Jagannath University, Dhaka Mohammed Ahsanul Alam, NIPORT, Dhaka, Bangladesh Abstract: To assess the current levels, trends and gaps between socioeconomic groups of obesity in Bangladeshi women. The study also identified correlates that effect on obesity. Anthropometric data associated with socio-demographic characteristics among ever-married women, were extracted from the Bangladesh Demographic and Health Surveys conducted in 2004 to 2014. Households’ socioeconomic status was measured using principal component analysis. Logistic regression analyses were used to identify the determinants of obesity and logit-based decomposition analysis conducted for factor contributing to recent changes. It is found that prevalence of overweight increased (2004: 8.8%; 2014: 23.7%). The risk of being overweight was higher among women who were older and of higher socioeconomic status. The rich-poor gap in obesity is also significant. Wealth index, place of residence, use of contraception and the women ’s education are identified as important correlates of women’s obesity . Women with upper quintile were 6.6 times more likely to be overweight compared with lowest quintile (OR 6.6; 95% CI: 5.6-7.8). Introduction Bangladesh has made commendable achievements on several Millennium Development Goals (MDG) set by the United Nations in the year 2000. These include reducing under five mortality two- third by 2015, taking primary education enrolment up to nearly 98 percent (NIPORT et al 2016). Maternal mortality rate and reducing prevalence of underweight children below 5 years are on track. For any populous country like Bangladesh, nutrition is of prime concern ( NIPORT et al., 2012) . Though the problem of underweight children aged below five has been tackled, there are still the more than a third of our children who suffer from stunting. The prevalence of overweight women is also a growing concern in developing countries. Obesity has significant health and economic consequences. In adults, they are associated with an increased risk of developing various non-communicable diseases (NCDs), including hypertension, coronary heart disease, diabetes, stroke and some forms of cancer (Brown et al 2000; Sturmand and Hattori 2013;

(Dixon, 2010; Vucenik and Stains, 2012; Ojeda et al., 2014)) . In developing countries, maternal underweight is a leading risk factor for preventable death and diseases (Friedrich, 2002; Popkin et al., 2012). Overweight individuals are predisposed to a wide range of health problems such as diabetes and heart disease as well as poor birth outcomes for women ( Dixon, 2010) . In many countries, though, chronic energy deficiency, characterized by a BMI of less than 18.5 among adults remains the predominant problem, leading to low work productivity and reduced resistance to illness. In contrast, it has also been found that fats and oils constitute a large proportion of the daily diet of people with higher SES and that most of the population does not consume adequate fruits and vegetables. Another cause is urbanization which has been reported to be associated with a shift of the BMI distribution of a population towards higher values, which is related to changes of the diet as well as lifestyle, in particular a reduction of physical activity. The projection suggests that by 2035 about half of the Bangladesh population will be urban, which will have impacts on the requirements for provision of basic needs, including the health care services. Rapid urbanization accompanied by rural urban migration is one challenge that must be addressed in improving maternal health and reducing risk of non-communicable diseases. In the light of above, the main aim is to assess the current levels, trends and gaps between socioeconomic groups of obesity in Bangladeshi women. The study also identified correlates that affect on obesity. Materials and Methods The study is based on four data sets of the Bangladesh Demographic and Health Surveys (DHS) carried out in 2004, 2007, 2011 and 2014 [NIPORT et al., 2005; NIPORT et al., 2009; NIPORT et al., 2013; NIPORT et al, 2016]. These surveys were conducted by the National Institute of Population Research and Training (NIPORT). All DHS are nationally representative and apply a common methodology across countries. Data for anthropometric measurements associated with socio- demographic characteristics among ever-married women, were extracted from these surveys. Body mass index (BMI) is calculated as weight in kilograms divided by height in meters squared. Obese is defined whose BMI is greater than or equal to 25. The present analysis included 10716, 10164, 16022 and 16534 women from last four surveys. The analysis excluded pregnant women and women with a birth in the preceding 2 months. Statistical Analysis

This study employed descriptive and trend analysis of prevalence of obesity, examination of the determinants of use, and decomposition of changes in percent of obesity. The trend in obesity was analyzed using descriptive analyses, stratified by region, urban-rural residence, and selected socio-demographic characteristics. The trend was examined separately for the periods 2004, 2007, 2011 and 2014 . Logistic regression analysis was also done to identify the determinants of obesity among young married women, using data from the 2014 BDHS. Complex sample survey methodology was considered during analysis. Hence, the study adjusted for the effects of clustering due to sampling procedures and non-response. We used Blinder-Oaxaca (Oaxaca Blinder)- decomposition (Blinder 1973; Oaxaca 1973), or as multivariate decomposition, decomposition techniques, component analysis, shift-share analysis or regression decomposition as detailed by Powers and Yun (2009),this approach provides a way to analyze the outcome of two different groups. Multivariate decomposition analysis of change in obesity was employed to answer the major research question of this study. The analysis was a regression decomposition of the difference in percent in obesity between two surveys. The purpose of the decomposition analysis was to identify the sources of changes in the obesity in the last decade. Both changes in population composition and population behavior related to obese (effect) are important. This method is used for several purposes in demography, economics, and other fields. The present analysis focused on how obesity responds to changes in women’s characteristics and how these factors shape differences across surveys conducted at different times. The technique utilizes the output from a logistic regression model to parcel out the observed difference in prevalence of obesity into components. This difference can be attributed to compositional changes between surveys (i.e. differences in characteristics) and to changes in effects of the selected explanatory variables (i.e. differences in the coefficients due to changes in population behavior). Hence, the observed difference in body mass index between different surveys is additively decomposed into a characteristics (or endowments) component and a coefficient (or effects of characteristics) component.

The model can be presented as follows: ∆ Y 2014-2004 =(X 2014 – X 2004 ) β 2014 + X 2004 (β 2014 - β 2004 ) + [(X 2014 - X 2004 )( β 2014 - β 2004 )] Where ∆ Y : Difference in mean prediction between 2014 and 2004, i ….X k : Different characteristics and β i …. β k : estimated regression coefficients; ( X 2014 – X 2004 ) β 2014 : represent the difference due to endowment; X 2004 ( β 2014 - β 2004 ): represent the difference due to coefficients, [( X 2014 - X 2004 )( β 2014 - β 2004 )]: represent the difference in interaction between endowment and coefficients. The Blinder-Oaxaca decomposition outputs provide details on endowments, coefficients, and interaction between the two time periods. As in the results, the interaction part is not statistically significant at the level of 5%, only the two major parts of the results will be presented: Endowments- part of the changes in occurrence of obesity due to differences in characteristics and Coefficients- part of the changes in occurrence of obesity due to effects of explanatory variables. In this analysis, socio-economic status was assessed by constructing a household wealth index (WI) based on principal components analysis. The DHS WI is an asset-based index that reflects the relative socioeconomic status of the household and is widely used in low- and middle-income countries to quantify inequalities and to control the confounding effect of socioeconomic variables. Descriptive statistics were use such as mean, standard deviations (SD) or percentages where appropriate. To measure the significance association among variables bivariate analysis were used with chi-square test. Logistic regression model was used to identify the proximate correlates on obesity among women. Results The mean BMI for ever-married women age 15-49 years were 20.0 to 22.3, which falls in the normal BMI classification. During 2004 – 2014, a decreasing trend in the prevalence of thinness and an increasing trend in overweight were detected. Over the period, the prevalence of obesity steadily increased from 8.8 to 23.8% among women. Among urban women, prevalence of obesity increased considerably from 19.7% in 2004 to 36.4% in 2014. The prevalence of obesity increased at 1.7 percentage point/year in urban areas and 1.3 percentage-point/years in rural areas. The bi-vatiate analysis shows that the proportion of overweight women increases with age (Table 1). Urban