Skip to main content

Advertisement

SpringerLink
Log in

Prevalence and determinants of double burden of malnutrition in Bangladesh: evidence from a nationwide cross-sectional survey

  • Research
  • Published:
Nutrire Aims and scope Submit manuscript

A Correction to this article was published on 30 August 2022

This article has been updated

Abstract

Purpose

Globally, malnutrition in mother–child pairs is steadily diminishing. However, the coexistence of different forms of double burden of malnutrition (DBM) is rising worldwide, including Bangladesh. This study aimed to explore the coexistence of different forms of DBM and their associated factors in the same household level for Bangladesh.

Methods

The study utilized the Bangladesh Demographic and Health Survey (BDHS) 2014 dataset. Chi-square test of association was conducted to identify the significant factors for various forms of DBM. For the measurement of adjusted odds ratios with 95% confidence intervals, multivariate logistic regression (MLR) analysis was performed.

Results

In this study, the overall prevalence of DBM was 5.8% as several sociodemographic and economic factors correlate and contribute to this burden. Results of MLR analyses showed that potential factors with increasing DBM were: poor wealth stratum, use of unhygienic toilet, mother's age in years, child delivery procedure, child's birth order, and father’s education.

Conclusion

The prevalence of DBM is still high in Bangladesh, based on different associated sociodemographic factors. Public health stakeholders in Bangladesh should address these factors and implement appropriate interventions with regard to minimizing the DBM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

As a third-party user, the research data of this study is not publicly sharable but can be found upon appropriate request at https://dhsprogram.com/.

Code availability

Not applicable.

Change history

Abbreviations

DBM:

Double burden of malnutrition

AOR:

Adjusted odds ratio

BAZ:

BMI-for-age Zscores

BDHS:

Bangladesh Demographic and Health Survey

BMI:

Body mass index

CI:

Confidence interval

DALYs:

Disability-adjusted life years

EAs:

Enumeration areas

HAZ:

Height-for-age Z-scores

LMICs:

Low- and middle-income countries

MLR:

Multivariate logistic regression

NCDs:

Non-communicable diseases

NIPORT:

National Institute of Population Research and Training

NPHC:

National Population and Housing Census

OWOBM/STC:

Overweight/obese mother and stunted child

OWOBM/SWUC or DBM:

Overweight/obese mother and undernourished child

OWOBM/UWC:

Overweight/obese mother and underweight child

OWOBM/WSC:

Overweight/obese mother and wasted child

SD:

Standard deviation

SDGs:

Sustainable Development Goals

SPSS:

Statistical Package for Social Science

WAZ:

Weight-for-age Z-scores

WHO:

World Health Organization

WHZ:

Weight-for-height Z-scores

References

  1. Meenakshi JV. Trends and patterns in the triple burden of malnutrition in India. Agric Econ. 2016;47:115–34.

    Article  Google Scholar 

  2. Dutta M, Selvamani Y, Singh P, Prashad L. The double burden of malnutrition among adults in India: evidence from the National Family Health Survey-4 (2015–16). Epidemiol Health. 2019;41: e2019050. https://doi.org/10.4178/epih.e2019050.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, De OM, et al. Maternal and Child Nutrition 1 Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382:427–51. https://doi.org/10.1016/S0140-6736(13)60937-X.

    Article  PubMed  Google Scholar 

  4. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–60.

    Article  Google Scholar 

  5. Paul E, Mtumwa AH, Ntwenya JE, Vuai SAH. Disparities in risk factors associated with obesity between Zanzibar and Tanzania Mainland among women of reproductive age based on the 2010 TDHS. J Obes. 2016;2016.

  6. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789–858. https://doi.org/10.1016/S0140-6736(18)32279-7.

    Article  Google Scholar 

  7. Ferraro KF, Su Y, Gretebeck RJ, Black DR, Badylak SF. Body mass index and disability in adulthood: a 20-year panel study. Am J Public Health. 2002;92:834–40.

    Article  Google Scholar 

  8. Goffman D, Madden RC, Harrison EA, Merkatz IR, Chazotte C. Predictors of maternal mortality and near-miss maternal morbidity. J Perinatol. 2007;27:597–601.

    Article  CAS  Google Scholar 

  9. Mehta S, Manji KP, Young AM, Brown ER, Chasela C, Taha TE, et al. Nutritional indicators of adverse pregnancy outcomes and mother-to-child transmission of HIV among HIV-infected women. Am J Clin Nutr. 2008;87:1639–49.

    Article  CAS  Google Scholar 

  10. Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, et al. Maternal and child undernutrition: consequences for adult health and human capital. The Lancet. Lancet Publishing Group; 2008. pp. 340–357. https://doi.org/10.1016/S0140-6736(07)61692-4

  11. Pednekar MS, Hakama M, Hebert JR, Gupta PC. Association of body mass index with all-cause and cause-specific mortality: findings from a prospective cohort study in Mumbai (Bombay). India Int J Epidemiol. 2008;37:524–35.

    Article  Google Scholar 

  12. Selvamani Y, Singh P. Socioeconomic patterns of underweight and its association with self-rated health, cognition and quality of life among older adults in India. Fürnsinn C, editor. PLoS One. 2018;13: e0193979. https://doi.org/10.1371/journal.pone.0193979

  13. Mendez MA, Monteiro CA, Popkin BM. Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr. 2005;81:714–21.

    Article  CAS  Google Scholar 

  14. Balarajan Y, Villamor E. Nationally representative surveys show recent increases in the prevalence of overweight and obesity among women of reproductive age in Bangladesh, Nepal, and India. J Nutr. 2009;139:2139–44. https://doi.org/10.3945/jn.109.112029.

    Article  CAS  PubMed  Google Scholar 

  15. Popkin BM. Nutrition in transition: the changing global nutrition challenge. Asia Pac J Clin Nutr. 2001;10:S13–8.

    Article  Google Scholar 

  16. Shafique S, Akhter N, Stallkamp G, de Pee S, Panagides D, Bloem MW. Trends of under-and overweight among rural and urban poor women indicate the double burden of malnutrition in Bangladesh. Int J Epidemiol. 2007;36:449–57.

    Article  Google Scholar 

  17. Shukla HC, Gupta PC, Mehta HC, Hébert JR. Descriptive epidemiology of body mass index of an urban adult population in western India. J Epidemiol Community Heal. 2002;56:876–80.

    Article  CAS  Google Scholar 

  18. Subramanian SV, Perkins JM, Khan KT. Do burdens of underweight and overweight coexist among lower socioeconomic groups in India? Am J Clin Nutr. 2009;90:369–76.

    Article  CAS  Google Scholar 

  19. Kamal SMM, Hassan CH, Alam GM. Dual burden of underweight and overweight among women in Bangladesh: patterns, prevalence, and sociodemographic correlates. J Health Popul Nutr. 2015;33:92.

    PubMed  PubMed Central  Google Scholar 

  20. Biswas RK, Kabir E, Khan HTA. Socioeconomic transition and its influence on body mass index (BMI) pattern in Bangladesh. J Eval Clin Pract. 2019;25:130–41.

    Article  Google Scholar 

  21. National Institutes of Health, National Heart, Lung and BI. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. WMJ : official publication of the State Medical Society of Wisconsin. 1998.

  22. Kosaka S, Umezaki M. A systematic review of the prevalence and predictors of the double burden of malnutrition within households. British Journal of Nutrition. 2017. pp. 1118–1127. https://doi.org/10.1017/S0007114517000812

  23. Saibul N, Shariff ZM, Lin KG, Kandiah M, Ghani NA, Rahman HA. Food variety score is associated with dual burden of malnutrition in orang Asli (Malaysian indigenous peoples) households: Implications for health promotion. Asia Pac J Clin Nutr. 2009;18:412–22. https://doi.org/10.6133/apjcn.2009.18.3.14.

    Article  PubMed  Google Scholar 

  24. Ihab AN, Rohana AJ, Wan Manan WM, Wan Suriati WN, Zalilah MS, Rusli AM. The coexistence of dual form of malnutrition in a sample of rural Malaysia. Int J Prev Med. 2013;4:690–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Conde WL, Monteiro CA. Nutrition transition and double burden of undernutrition and excess of weight in Brazil. American Journal of Clinical Nutrition. 2014. pp. 1617S-1622S.https://doi.org/10.3945/ajcn.114.084764

  26. Kimani-Murage EW, Muthuri SK, Oti SO, Mutua MK, Van De Vijver S, Kyobutungi C. Evidence of a double burden of malnutrition in urban poor settings in Nairobi, Kenya. PLoS One. 2015;10. https://doi.org/10.1371/journal.pone.0129943

  27. Dieffenbach S, Stein AD. Stunted child/overweight mother pairs represent a statistical artifact, not a distinct entity. J Nutr. 2012;142:771–3. https://doi.org/10.3945/jn.111.153387.

    Article  CAS  PubMed  Google Scholar 

  28. Garrett JL, Ruel MT. Stunted child-overweight mother pairs: Prevalence and association with economic development and urbanization. Food Nutr Bull. 2005;26:209–21. https://doi.org/10.1177/156482650502600205.

    Article  PubMed  Google Scholar 

  29. Roemling C, Qaim M. Dual burden households and intra-household nutritional inequality in Indonesia. Econ Hum Biol. 2013;11:563–73. https://doi.org/10.1016/j.ehb.2013.07.001.

    Article  PubMed  Google Scholar 

  30. Doak CM, Adair LS, Bentley M, Monteiro C, Popkin BM. The dual burden household and the nutrition transition paradox. International Journal of Obesity. 2005. pp. 129–136.https://doi.org/10.1038/sj.ijo.0802824

  31. Khan MS, Halder HR, Rashid M, Afroja S, Islam M. Impact of socioeconomic and demographic factors for underweight and overweight child in Bangladesh: a polytomous logistic regression model. Clin Epidemiol Glob Heal. 2020.

  32. Jehn M, Brewis A. Paradoxical malnutrition in mother-child pairs: untangling the phenomenon of over- and under-nutrition in underdeveloped economies. Econ Hum Biol. 2009;7:28–35. https://doi.org/10.1016/j.ehb.2009.01.007.

    Article  PubMed  Google Scholar 

  33. Wibowo Y, Sutrisna B, Hardinsyah H, Djuwita R, Korib MM, Syafiq A, et al. Relationship between intra-household food distribution and coexistence of dual forms of malnutrition. Nutr Res Pract. 2015;9:174–9. https://doi.org/10.4162/nrp.2015.9.2.174.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rahman MS, Rahman MA, Maniruzzaman M, Howlader MH. Prevalence of undernutrition in Bangladeshi children. J Biosoc Sci. 2020;52:596–609.

    Article  Google Scholar 

  35. Rahman A, Rahman S, Shakur SM, Howlader H. Risk factors of chronic childhood malnutrition : an analysis of the Bangladesh demographic and health survey 2014 data. J Public Heal From Theory to Pract. 2020. https://doi.org/10.1007/s10389-020-01281-4

  36. Hong R. Effect of economic inequality on chronic childhood undernutrition in Ghana. Public Health Nutr. 2007;10:371–8.

    Article  Google Scholar 

  37. NIPORT, Mitra and Associates. Bangladesh Demographic and Health Survey. 2014; 328.

  38. World Health Organisation. WHO | WHO Child Growth Standards: methods and development. Geneva World Heal Organ: World Health Organization; 2014.

    Google Scholar 

  39. WHO. Global database on child growth and malnutrition: World Health Organization. WHO. World Health Organization; 2018.

  40. Fongar A, Gödecke T, Qaim M. Various forms of double burden of malnutrition problems exist in rural Kenya. BMC Public Health. 2019;19:1543.

    Article  Google Scholar 

  41. Hossain Z, Akter R, Sultana N, Kabir E. Modelling zero-truncated overdispersed antenatal health care count data of women in Bangladesh. PLoS One. 2020;15. https://doi.org/10.1371/journal.pone.0227824

  42. Siddiquee T, Halder HR, Islam MA. Exploring the influencing factors for non-utilisation of healthcare facilities during childbirth: a special mixed-method study of Bangladesh and 13 other low- and middle-income countries based on Demographic and Health Survey data. Fam Med Community Heal. 2019;7: e000008. https://doi.org/10.1136/fmch-2018-000008.

    Article  Google Scholar 

  43. Tanwi TS, Chakrabarty S, Hasanuzzaman S. Double burden of malnutrition among ever-married women in Bangladesh: a pooled analysis. BMC Womens Health. 2019;19:24.

    Article  Google Scholar 

  44. Hauqe SE, Sakisaka K, Rahman M. Examining the relationship between socioeconomic status and the double burden of maternal over and child under-nutrition in Bangladesh. Eur J Clin Nutr. 2019;73:531–40.

    Article  Google Scholar 

  45. Das S, Shah MF, Islam MS, Biswas T, Mahfuz M, Ahmed T. Prevalence and sociodemographic determinants of household-level double burden of malnutrition in Bangladesh. Public Health Nutr. 2019;22:1425–32.

    Article  Google Scholar 

  46. Sunuwar DR, Singh DR, Pradhan PMS. Prevalence and factors associated with double and triple burden of malnutrition among mothers and children in Nepal: evidence from 2016 Nepal demographic and health survey. BMC Public Health. 2020;20:1–11.

    Article  Google Scholar 

  47. Tanwi TS, Chakrabarty S, Hasanuzzaman S, Saltmarsh S, Winn S. Socioeconomic correlates of overweight and obesity among ever-married urban women in Bangladesh. BMC Public Health. 2019;19:842.

    Article  Google Scholar 

  48. Abuya BA, Ciera J, Kimani-Murage E. Effect of mother’s education on child’s nutritional status in the slums of Nairobi. BMC Pediatr. 2012;12:80.

    Article  Google Scholar 

  49. Meshram II, Arlappa N, Balakrishna N, Mallikharjuna Rao K, Laxmaiah A, Brahmam GN. Trends in the prevalence of undernutrition, nutrient and food intake and predictors of undernutrition among under. Asia Pac J Clin Nutr. 2012;21:568–76.

    PubMed  Google Scholar 

  50. Anik AI, Mosfequr Rahman M, Mostafizur Rahman M, Ismail Tareque M, Nuruzzaman Khan M, Mahmudul Alam M. Double burden of malnutrition at household level: a comparative study among Bangladesh, Nepal, Pakistan, and Myanmar. Ghose B, editor. PLoS One. 2019;14: e0221274. https://doi.org/10.1371/journal.pone.0221274

  51. Oddo VM, Rah JH, Semba RD, Sun K, Akhter N, Sari M, et al. Predictors of maternal and child double burden of malnutrition in rural Indonesia and Bangladesh. Am J Clin Nutr. 2012;95:951–8.

    Article  CAS  Google Scholar 

  52. Rahman MS, Rahman MA, Maniruzzaman M, Howlader MH. Prevalence of undernutrition in Bangladeshi children. J Biosoc Sci. 2019; 1–14. https://doi.org/10.1017/s0021932019000683

  53. Rahman A, Chowdhury S, Rahman A. Determinants of chronic malnutrition among preschool children in Bangladesh Malnutrition Among Preschool. 2015; 161–173. https://doi.org/10.1017/S0021932006001295

  54. Khor GL, Sharif ZM. Dual forms of malnutrition in the same households in Malaysia - a case study among Malay rural households. Asia Pac J Clin Nutr. 2003;12:427–37.

    PubMed  Google Scholar 

  55. Hasan MT, Soares Magalhaes RJ, Williams GM, Mamun AA. The role of maternal education in the 15-year trajectory of malnutrition in children under 5 years of age in B angladesh. Matern Child Nutr. 2016;12:929–39.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the authority of the Demographic and Health Survey (DHS) for providing the data to use in this research.

Author information

Author notes

  1. Md. Ashfikur Rahman and Henry Ratul Halder contributed equally to this work.

Authors and Affiliations

  1. Development Studies Discipline, Social Science School, Khulna University, Khulna, Bangladesh

    Md. Ashfikur Rahman, Bayezid Khan, Nuzhat Fatema & Md. Hasan Howlader

  2. Rady Faculty of Health Sciences, Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada

    Henry Ratul Halder

  3. Faculty of Mathematics and Science, Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada

    Tanjim Siddiquee

  4. Statistics Discipline, Science, Engineering and Technology School, Khulna University, Khulna, Bangladesh

    Henry Ratul Halder, Tanjim Siddiquee, Syeda Afia Farjana & Harun Or Roshid

  5. Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh

    Shaharior Rahman Razu

Authors
  1. Md. Ashfikur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henry Ratul Halder
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanjim Siddiquee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Syeda Afia Farjana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Harun Or Roshid
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bayezid Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nuzhat Fatema
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Md. Hasan Howlader
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shaharior Rahman Razu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MAR and HRH: conceived the study, performed the statistical analyses, and drafted the original manuscript. TS, SAF, and HOR: drafted and conducted critical review of the manuscript. NF, BK, MHH, and SRR: writing, reviewing,editing, validation, and investigation. All authors read and approved the final version of this manuscript.

Corresponding author

Correspondence to Md. Ashfikur Rahman.

Ethics declarations

Ethical approval

The study used secondary data of DHS program; therefore, we did not needed any further ethical approval.

Consent for participation

The DHS program obtained participant’s consent before the survey.

Consent for publication

This study utilized secondary data from the DHS program, and we were permitted to use the data for independent research and publication.

Competing interests

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, M.A., Halder, H.R., Siddiquee, T. et al. Prevalence and determinants of double burden of malnutrition in Bangladesh: evidence from a nationwide cross-sectional survey. Nutrire 46, 11 (2021). https://doi.org/10.1186/s41110-021-00140-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41110-021-00140-w

Keywords

Search

Navigation