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MULTILEVEL LINEAR REGRESSION ANALYSIS OF FACTORS INFLUENCING BODY MASS INDEX AMONG BANGLADESHI MARRIED NON-PREGNANT WOMEN

Published online by Cambridge University Press:  21 November 2016

Md Kamruzzaman ,
A. S. M. A. Mamun ,
Sheikh Muhammad Abu Bakar ,
Aik Saw ,
T. Kamarul ,
Md Nurul Islam  and
Md Golam Hossain
Md Kamruzzaman
Affiliation:
Department of Statistics, University of Rajshahi, Bangladesh
A. S. M. A. Mamun
Affiliation:
Department of Statistics, University of Rajshahi, Bangladesh
Sheikh Muhammad Abu Bakar
Affiliation:
Department of Anatomy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
Aik Saw
Affiliation:
National Orthopaedic Centre of Excellence for Research and Learning (NOCERAL), Department of Orthopaedic Surgery, University of Malaya, Malaysia
T. Kamarul
Affiliation:
National Orthopaedic Centre of Excellence for Research and Learning (NOCERAL), Department of Orthopaedic Surgery, University of Malaya, Malaysia
Md Nurul Islam
Affiliation:
Department of Statistics, University of Rajshahi, Bangladesh
Md Golam Hossain*
Affiliation:
Department of Statistics, University of Rajshahi, Bangladesh
*
1Corresponding author. Email: hossain95@yahoo.com.
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Summary

The aim of this study was to investigate the socioeconomic and demographic factors influencing the body mass index (BMI) of non-pregnant married Bangladeshi women of reproductive age. Secondary (Hierarchy) data from the 2011 Bangladesh Demographic and Health Survey, collected using two-stage stratified cluster sampling, were used. Two-level linear regression analysis was performed to remove the cluster effect of the variables. The mean BMI of married non-pregnant Bangladeshi women was 21.60±3.86 kg/m2, and the prevalence of underweight, overweight and obesity was 22.8%, 14.9% and 3.2%, respectively. After removing the cluster effect, age and age at first marriage were found to be positively (p<0.01) related with BMI. Number of children was negatively related with women’s BMI. Lower BMI was especially found among women from rural areas and poor families, with an uneducated husband, with no television at home and who were currently breast-feeding. Age, total children ever born, age at first marriage, type of residence, education level, level of husband’s education, wealth index, having a television at home and practising breast-feeding were found to be important predictors for the BMI of married Bangladeshi non-pregnant women of reproductive age. This information could be used to identify sections of the Bangladeshi population that require special attention, and to develop more effective strategies to resolve the problem of malnutrition.

Type
Research Article
Information
Journal of Biosocial Science , Volume 49 , Issue 4 , July 2017 , pp. 498 - 508
Copyright
Copyright © Cambridge University Press, 2016 

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References

Ali, S. M. & Lindström, M. (2006) Socioeconomic, psychosocial, behavioural, and psychological determinants of BMI among young women: differing patterns for underweight and overweight/obesity. European Journal of Public Health 16(3), 325–331.Google Scholar
Bharati, S., Pal, M., Bhattacharya, B. N. & Bharati, P. (2007) Prevalence and causes of chronic energy deficiency and obesity in Indian women. Human Biology 79(4), 395–412.Google Scholar
Brener, N. D., Eaton, D. K., Lowry, R. & McManus, T. (2004) The association between weight perception and BMI among high school students. Obesity Research 12(11), 1866–1874.CrossRefGoogle ScholarPubMed
Chatterjee, S. & Hadi, A. S. (2006) Regression Analysis by Example . John Wiley & Sons, New Jersey.Google Scholar
Cilliers, J., Senekal, M. & Kunneke, E. (2006) The association between the body mass index of first-year female university students and their weight-related perceptions and practices, psychological health, physical activity and other physical health indicators. Public Health Nutrition 9(2), 234–243.Google Scholar
Dunn, O. J. & Clark, V. A. (1974) Applied Statistics: Analysis of Variance and Regression. John Wiley & Sons, Toronto.Google Scholar
Gnudi, S., Sitta, E. & Lisi, L. (2009) Relationship of body mass index with main limb fragility fractures in postmenopausal women. Journal of Bone and Mineral Research 27(4), 479–484.Google ScholarPubMed
Herrera, H., Rebato, E., Arechabaleta, G., Lagrange, H., Salces, I. & Susanne, C. (2003) Body mass index and energy intake in Venezuelan University students. Nutrition Research 23(3), 389–400.Google Scholar
Hosegood, V. & Campbell, O. M. (2003) Body mass index, height, weight, arm circumference, and mortality in rural Bangladeshi women: a 19-y longitudinal study. American Journal of Clinical Nutrition 77(2), 341–347.CrossRefGoogle ScholarPubMed
Hossain, M. G., Bharati, P., Aik, S., Lestrel, P. E., Abeer, A. & Kamarul, T. (2012) Body mass index of married Bangladeshi women: trends and association with socio-demographic factors. Journal of Biosocial Science 44(4), 385–399.Google Scholar
Hossain, M. G., Islam, S., Aik, S., Zaman, T. K. & Lestrel, P. E. (2010) Age at menarche of university students in Bangladesh: secular trends and association with adult anthropometric measures and socio-demographic factors. Journal of Biosocial Science 42(5), 677–687.Google Scholar
Hossain, M. G., Kamruzzaman, M., Islam, S., Islam, M. A. & Alam, R. (2012) Association between body mass index and socio-demographic factors of university female students in Bangladesh: observed trends in BMI. South Asian Anthropologist 12(2), 175–181.Google Scholar
Hou, Z., Quan, S., Qian, W., Yang, W., Xu, L. & Cao, Y. (2014) Relationship between body mass index and outcome of in vitro fertilization and embryo transfer cycle in patients with endometriosis. Nan Fang Yi Ke Da Xue Xue Bao 34(3), 379–382.Google ScholarPubMed
Khan, M. H. R. & Shaw, J. E. H. (2011) Multilevel logistic regression analysis applied to binary contraceptive prevalence data. Journal of Data Science 9, 93–110.Google Scholar
Khan, M. M. & Kraemer, A. (2009) Factors associated with being underweight, overweight and obese among ever-married non-pregnant urban women in Bangladesh. Singapore Medical Journal 50(8), 804–813.Google Scholar
Lamon-Fava, S., Wilson, P. W. & Schaefer, E. J. (1996) Impact of body mass index on coronary heart disease risk factors in men and women. The Framingham Offspring Study. Arteriosclerosis Thrombosis and Vascular Biology 16(12), 1509–1515.Google Scholar
Mohsena, M., Goto, R. & Mascie-Taylor, C. G. N. (2015) Maternal nutritional status (as measured by height, weight and BMI) in Bangladesh: trends and socio-economic association over the period 1996 to 2007. Public Health Nutrition 19(8), 1438–1445.Google Scholar
Morin, S., Tsang, J. F. & Leslie, W. D. (2009) Weight and body mass index predict bone mineral density and fractures in women aged 40 to 59 years. Osteoporosis International 20(3), 363–370.Google Scholar
Najat, Y., Alice, A., Abbass, A. & Rizk, S. (2008) Eating habits and obesity among Lebanese university students. Nutrition Journal 7, 32.Google Scholar
NIPORT (2009) Bangladesh Demographic and Health Survey, 2007. National Institute of Population Research and Training, Mitra and Associates, ICF International, Dhaka, Bangladesh, and Calverton, MD, USA.Google Scholar
NIPORT (2013) Bangladesh Demographic and Health Survey, 2011. National Institute of Population Research and Training, Mitra and Associates, ICF International. Dhaka, Bangladesh, and Calverton, MD, USA.Google Scholar
Okoh, M. (2013) Socio-demographic correlates of overweight and obesity among women of reproductive age in Nigeria. African Journal Reproductive Health 17(4), 66–76.Google Scholar
Ortega, R. M., Requejo, A. M., Quintas, E., Redondo, M. R., Lopez-Sobaler, M. & Andres, P. (1997) Concern regarding bodyweight and energy balance in a group of female university students from Madrid: differences with respect to body mass index. Journal of the American College of Nutrition 16(3), 244–251.Google Scholar
Park, S. & Lake, E. T. (2005) Multilevel modeling of a clustered continuous outcome: nurses’ work hours and burnout. Nursing Research 54(6), 406–413.Google Scholar
Pierce, B. L., Kalra, T., Argos, M., Parvez, F., Chen, Y., Islam, T. et al. (2010) A prospective study of body mass index and mortality in Bangladesh. International Journal of Epidemiology 39(4), 1037–1045.Google Scholar
Pryer, J. A., Rogers, S. & Rahman, A. (2003) Factors affecting nutritional status in female adults in Dhaka slums, Bangladesh. Social Biology 50(3-4), 259–269.Google ScholarPubMed
Shafique, S., Akhter, N., Stallkamp, G., de Pee, S., Panagides, D. & Bloem, M. W. (2007) Trends of under- and overweight among rural and urban poor women indicate the double burden of malnutrition in Bangladesh. International Journal of Epidemiology 36(2), 449–457.Google Scholar
Sundararajan, K., Campbell, M. K., Choi, Y. H. & Sarma, S. (2014) The relationship between diet quality and adult obesity: evidence from Canada. Journal of American College of Nutrition 33(1), 1–17.Google Scholar
Tavani, A., Negri, E. & La Vecchia, C. (1994) Determinants of body mass index: a study from northern Italy. International Journal of Obesity and Related Metabolic Disorders 18(7), 497–502.Google Scholar
Wilson, P. W., D’Agostino, R. B., Sullivan, L., Parise, H. & Kannel, W. B. (2002) Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Archives of Internal Medicine 162(16), 1867–1872.Google Scholar