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Body mass index of women in Bangladesh: comparing Multiple Linear Regression and Quantile Regression

Published online by Cambridge University Press:  07 April 2020

Sorif Hossain Open the ORCID record for Sorif Hossain [Opens in a new window] ,
Raaj Kishore Biswas Open the ORCID record for Raaj Kishore Biswas [Opens in a new window]  and
Md Amir Hossain
Sorif Hossain*
Affiliation:
Institute of Statistical Research and Training, University of Dhaka, Bangladesh
Raaj Kishore Biswas
Affiliation:
Transport and Road Safety (TARS) Research Centre, School of Aviation, University of New South Wales, Australia
Md Amir Hossain
Affiliation:
Institute of Statistical Research and Training, University of Dhaka, Bangladesh
*
*Corresponding author. Email: [email protected]
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Abstract

This study explored the association between socio-demographic factors and the body mass index (BMI) of women of reproductive age (15–49 years) in Bangladesh. Data from the 2014 Bangladesh Demographic and Health Survey (BDHS-14) were analysed using Multiple Linear Regression (MLR) and Quantile Regression (QR) analyses. The study sample comprised 15,636 non-pregnant women aged 15–49. The mean BMI of the women was 22.35±4.12 kg/m2. Over half (56.75%) had a BMI in the normal range (18<BMI<25 kg/m2), and 18.50%, 20.00% and 4.75% were underweight (BMI≤18 kg/m2), overweight (25≤BMI<30 kg/m2) and obese (BMI≥30 kg/m2), respectively. The results of the MLR found that age, wealth index, urban/rural place of residence, geographical division, womenʼs educational status, husbandʼs educational status, womenʼs working status and total number of children ever born were significantly (p<0.001) associated with respondents’ mean BMI. The QR results showed different associations between socio-demographic factors and mean BMI, as well as a different conditional distribution of mean BMI. Overall, the results indicated that women with uneducated husbands, with little or no education and from less-affluent households from rural areas tended to be more underweight compared with women in other groups. The inter-relationship between the study womenʼs mean BMI and associated socio-demographic factors was assessed using QR analysis to identify the most vulnerable cohorts of women in Bangladesh.

Keywords

Body mass indexQuantile regressionDouble burden
Type
Research Article
Information
Journal of Biosocial Science , Volume 53 , Issue 2 , March 2021 , pp. 247 - 265
Copyright
© The Author(s) 2020. Published by Cambridge University Press

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References

Allen, R (1939) The assumptions of linear regression. Economica 6(22), 191–201.CrossRefGoogle Scholar
Amugsi, DA, Dimbuene, ZT, Bakibinga, P, Kimani-Murage, EW, Haregu, TN and Mberu, B (2016) Dietary diversity, socioeconomic status and maternal body mass index (BMI): quantile regression analysis of nationally representative data from Ghana, Namibia and Sao Tome and Principe. BMJ Open 6(9), e012615.CrossRefGoogle ScholarPubMed
Ashraf, MA, Akhter, S and Noor, SI (2014) Consumer behavior in fast food marketing in Bangladesh: a case study. Developing Country Studies 4(9), 34–44.Google Scholar
Baker, P and Friel, S (2014) Processed foods and the nutrition transition: evidence from Asia. Obesity Reviews 15(7), 564–577.CrossRefGoogle ScholarPubMed
Balarajan, Y and Villamor, E (2009) Nationally representative surveys show recent increases in the prevalence of overweight and obesity among women of reproductive age in Bangladesh, Nepal, and India. Journal of Nutrition 139(11), 2139–2144.CrossRefGoogle ScholarPubMed
Baqui, AH, Lee, AC, Koffi, AK, Khanam, R, Mitra, DK, Dasgupta, SK and Quaiyum, A (2019) Prevalence of and risk factors for abnormal vaginal flora and its association with adverse pregnancy outcomes in a rural district in north-east Bangladesh. Acta Obstetricia et Gynecologica Scandinavica 98(3), 309–319.CrossRefGoogle Scholar
Bhuiya, A and Mostafa, G (1993) Levels and differentials in weight, height and body mass index among mothers in a rural area of Bangladesh. Journal of Biosocial Science 25(1), 31–38.CrossRefGoogle Scholar
Bhuyan, AR (2006) Millennium Development Goals (MDGs): a review of Bangladeshʼs achievements. Journal of Islamic Economics, Banking and Finance 1, 51–66.Google Scholar
Biswas, RK, Kabir, E and Khan, HT (2019a) Socioeconomic transition and its influence on body mass index (BMI) pattern in Bangladesh. Journal of Evaluation in Clinical Practice 25(1), 130–141.CrossRefGoogle ScholarPubMed
Biswas, RK, Rahman, N, Khanam, R, Baqui, AH and Ahmed, S (2019b) Double burden of underweight and overweight among women of reproductive age in Bangladesh. Public Health Nutrition 22(17), 3163–3174.CrossRefGoogle ScholarPubMed
Cardozo, ER, Dune, TJ, Neff, LM, Brocks, ME, Ekpo, GE, Barnes, RB and Marsh, EE (2013) Knowledge of obesity and its impact on reproductive health outcomes among urban women. Journal of Community Health 38(2), 261–267.CrossRefGoogle ScholarPubMed
Chen, CM, Chang, CK and Yeh, CY (2012) A quantile regression approach to re-investigate the relationship between sleep duration and body mass index in Taiwan. International Journal of Public Health 57(3), 485–493.CrossRefGoogle ScholarPubMed
Das, S, Fahim, SM, Islam, MS, Biswas, T, Mahfuz, M and Ahmed, T (2019) Prevalence and sociodemographic determinants of household-level double burden of malnutrition in Bangladesh. Public Health Nutrition 22(8), 1425–1432.CrossRefGoogle ScholarPubMed
Davino, C, Furno, M and Vistocco, D (2013) Quantile Regression: Theory and Applications. John Wiley & Sons.Google Scholar
De Jersey, SJ, Nicholson, JM, Callaway, LK and Daniels, LA (2013) An observational study of nutrition and physical activity behaviours, knowledge, and advice in pregnancy. BMC Pregnancy and Childbirth 13(1), 115.CrossRefGoogle ScholarPubMed
Faraway, JJ (2016) Linear Models with R. Chapman and Hall/CRC.CrossRefGoogle Scholar
Franke, GR (2010) Multicollinearity. In (ed)Wiley International Encyclopedia of Marketing.Google Scholar
Gnudi, S, Sitta, E and 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.CrossRefGoogle ScholarPubMed
Goryakin, Y and Suhrcke, M (2014) Economic development, urbanization, technological change and overweight: what do we learn from 244 Demographic and Health Surveys? Economics and Human Biology 14, 109–127.CrossRefGoogle ScholarPubMed
Ha, DT, Feskens, EJ, Deurenberg, P, Mai, LB, Khan, NC and Kok, FJ (2011) Nationwide shifts in the double burden of overweight and underweight in Vietnamese adults in 2000 and 2005: two national nutrition surveys. BMC Public Health 11(1), 62.CrossRefGoogle Scholar
Hao, L, Naiman, DQ and Naiman, DQ (2007) Quantile Regression. Sage Publishers, Thousand Oaks.CrossRefGoogle Scholar
Harun, MA, Ahmed, F and Maniruzzaman (2013) Customer hospitality: the case of fast food industry in Bangladesh. World Journal of Social Sciences 3(6), 88–104.Google Scholar
Hocking, RR (1976) A biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 32(1), 1–49.CrossRefGoogle Scholar
Hosegood, V and Campbell, OM (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, Bharati, P, Aik, S, Lestrel, PE, Abeer, A and 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.CrossRefGoogle ScholarPubMed
Hutcheson, GD (2011) Ordinary least-squares regression. In Moutinho, L and Hutcheson, GD (eds) The SAGE Dictionary of Quantitative Management Research, Sage Publications, Thousand Oaks, pp. 224–228.Google Scholar
Islam, A, Islam, N, Bharati, P, Aik, S and Hossain, G (2016) Socio-economic and demographic factors influencing nutritional status among early childbearing young mothers in Bangladesh. BMC Womenʼs Health 16(1), 58.CrossRefGoogle ScholarPubMed
Islam, N, Angeles, G, Mahbub, A et al. (2006) Slums of Urban Bangladesh: Mapping and Census 2005. Centre for Urban Studies, National Institute of Population Research and Training and MEASURE Evaluation, Dhaka, and Chapel Hill, NC, USA.Google Scholar
Kabeer, N, Mahmud, S and Tasneem, S (2011) Does paid work provide a pathway to womenʼs empowerment? Empirical findings from Bangladesh. IDS Working Paper Volume 2011 No. 375 Google Scholar
Kabir, I, Khanam, M, Agho, KE, Mihrshahi, S, Dibley, MJ and Roy, SK (2012) Determinants of inappropriate complementary feeding practices in infant and young children in Bangladesh: secondary data analysis of Demographic Health Survey 2007. Maternal & Child Nutrition 8, 11–27.CrossRefGoogle Scholar
Kamal, SM, Hassan, CH and Alam, GM (2015) Dual burden of underweight and overweight among women in Bangladesh: patterns, prevalence, and sociodemographic correlates. Journal of Health, Population, and Nutrition 33(1), 92.Google ScholarPubMed
Kamruzzaman, M, Mamun, A, Bakar, SMA, Saw, A, Kamarul, T, Islam, MN and Hossain, MG (2017) Multilevel linear regression analysis of factors influencing body mass index among Bangladeshi married non-pregnant women. Journal of Biosocial Science 49(4), 498–508.CrossRefGoogle ScholarPubMed
Kandala, NB and Emina, JB (2016) The dual burden of nutrition transition among women in sub-Saharan Africa: a case study of underweight in Nigeria. Journal of Biosocial Science 48(4), 486–501.CrossRefGoogle ScholarPubMed
Khan, MM and 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.Google ScholarPubMed
Koenker, R and Hallock, KF (2001) Quantile regression. Journal of Economic Perspectives 15(4), 143–156.CrossRefGoogle Scholar
Koenker, R and Machado, JA (1999) Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association 94(448), 1296–1310.CrossRefGoogle Scholar
Koenker, R, Portnoy, S, Ng, PT, Zeileis, A, Grosjean, P and Ripley, BD (2018) Package ‘quantreg’. URL: Cran R-project. org.Google Scholar
Kumar, D, Hossain, A and Gope, MC (2015) Role of micro credit program in empowering rural women in Bangladesh: a study on Grameen Bank Bangladesh Limited. Asian Business Review 3(4), 114–120.CrossRefGoogle Scholar
Lamon-Fava, S, Wilson, PW and Schaefer, EJ (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.CrossRefGoogle ScholarPubMed
McLaren, L, Auld, MC, Godley, J, Still, D and Gauvin, L (2010) Examining the association between socioeconomic position and body mass index in 1978 and 2005 among Canadian working-age women and men. International Journal of Public Health 55(3), 193–200.CrossRefGoogle ScholarPubMed
Melby, CL, Orozco, F, Ochoa, D, Muquinche, M, Padro, M and Munoz, FN (2017) Nutrition and physical activity transitions in the Ecuadorian Andes: differences among urban and rural-dwelling women. American Journal of Human Biology 29(4), e22986.CrossRefGoogle Scholar
Mohsena, M, Goto, R and Mascie-Taylor, CGN (2015) Regional variation in maternal and childhood undernutrition in Bangladesh: evidence from demographic and health surveys. WHO South-East Asia Journal of Public Health 4(2), 139.CrossRefGoogle ScholarPubMed
Montgomery, DC, Peck, EA and Vining, GG (2012) Introduction to Linear Regression Analysis. John Wiley & Sons.Google Scholar
Morin, S, Tsang, JF and Leslie, WD (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.CrossRefGoogle ScholarPubMed
Morgan, K and Sonnino, R (2010) The urban foodscape: world cities and the new food equation. Cambridge Journal of Regions, Economy and Society 3(2), 209–224.CrossRefGoogle Scholar
Mummery, WK, Schofield, GM, Steele, R, Eakin, EG and Brown, WJ (2005) Occupational sitting time and overweight and obesity in Australian workers. American Journal of Preventive Medicine 29(2), 91–97.CrossRefGoogle ScholarPubMed
Murakami, K, Ohkubo, T and Hashimoto, H (2017) Distinct association between educational attainment and overweight/obesity in unmarried and married women: evidence from a population-based study in Japan. BMC Public Health 17(1), 903.CrossRefGoogle ScholarPubMed
Neiderud, CJ (2015) How urbanization affects the epidemiology of emerging infectious diseases. Infection Ecology & Epidemiology 5(1), 27060.CrossRefGoogle ScholarPubMed
Neupane, S, Prakash, KC and Doku, DT (2015) Overweight and obesity among women: analysis of demographic and health survey data from 32 Sub-Saharan African Countries. BMC Public Health 16(1), 30.CrossRefGoogle Scholar
NIPORT (2014) Bangladesh Demographic and Health Survey BDHS 2014: Key Indicators. National Institute of Population Research and Training (NIPORT), Mitra and Associates, and ICF International, Dhaka, Bangladesh, and Calverton, MD, USA. Google Scholar
Nonyane, BA, Norton, M, Begum, N, Shah, RM, Mitra, DK, Darmstadt, GL and Baqui, AH (2019) Pregnancy intervals after stillbirth, neonatal death and spontaneous abortion and the risk of an adverse outcome in the next pregnancy in rural Bangladesh. BMC Pregnancy and Childbirth 19(1), 62.CrossRefGoogle ScholarPubMed
Nuttall, FQ (2015) Body mass index: obesity, BMI, and health: a critical review. Nutrition Today 50(3), 117.CrossRefGoogle ScholarPubMed
Popkin, BM (2014) Nutrition, agriculture and the global food system in low and middle income countries. Food Policy 47, 91–96.CrossRefGoogle ScholarPubMed
Pryer, JA, Rogers, S and Rahman, A (2003) Factors affecting nutritional status in female adults in Dhaka slums, Bangladesh. Social Biology 50(3–4), 259–269.Google ScholarPubMed
Rahman, S (2013) Obesity in junk food generation in Asia: a health time bomb that needs early defusing! South East Asia Journal of Public Health 3(1), 1–2.CrossRefGoogle Scholar
Rahman, MM and Nasrin, SO (2009) Mothers nutritional status in an impoverished nation: evidence from rural Bangladesh. The Internet Journal of Nutrition and Wellness 7(1).Google Scholar
Rahman, MS, Mondal, MNI, Islam, MR, Ahmed, KM, Karim, MR and Alam, MS (2015) Under weightiness among ever-married non-pregnant women in Bangladesh: a population based study. Universal Journal of Food and Nutrition Science 3(2), 29–36.CrossRefGoogle Scholar
Reardon, T, Boughton, D, Tschirley, D et al. (2015) Urbanization, diet change, and transformation of the downstream and midstream of the agrifood system: effects on the poor in Africa and Asia. Faith & Economics 66, 43–63.Google Scholar
Ruel, MT, Alderman, H and Maternal and Child Nutrition Study Group (2013) Nutrition-sensitive interventions and programmes: how can they help to accelerate progress in improving maternal and child nutrition? The Lancet 382(9891), 536–551.CrossRefGoogle ScholarPubMed
Sakia, R (1992) The box-cox transformation technique: a review. Journal of the Royal Statistical Society: Series D (The Statistician) 41(2), 169–178.Google Scholar
Sarma, H, Saquib, N, Hasan, MM, Saquib, J, Rahman, AS, Khan, JR and Ahmed, T (2016) Determinants of overweight or obesity among ever-married adult women in Bangladesh. BMC Obesity 3(1), 13.CrossRefGoogle ScholarPubMed
Schmidhuber, J and Shetty, P (2005) The nutrition transition to 2030. Why developing countries are likely to bear the major burden. Acta Agriculturae Scandinavica, Section C 2(3–4), 150–166.Google Scholar
Sharaf, MF, Mansour, EI and Rashad, AS (2019) Child nutritional status in Egypt: a comprehensive analysis of socioeconomic determinants using a quantile regression approach. Journal of Biosocial Science 51(1), 1–17.CrossRefGoogle ScholarPubMed
Shafique, S, Akhter, N, Stallkamp, G, de Pee, S, Panagides, D and Bloem, MW (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.CrossRefGoogle Scholar
Tareque, MI, Begum, S and Saito, Y (2014) Inequality in disability in Bangladesh. PLoS One, 9(7).CrossRefGoogle ScholarPubMed