Trends in chronic childhood undernutrition in Bangladesh for small domains

Abstract

Chronic childhood undernutrition, known as stunting, is an important population health problem with short- and long-term adverse outcomes. Bangladesh has made strides to reduce chronic childhood undernutrition, yet progress is falling short of the 2030 Sustainable Development Goals targets. This study estimates trends in age-specific chronic childhood undernutrition in Bangladesh’s 64 districts during 1997–2018, using underlying direct estimates extracted from seven Demographic and Health Surveys in the development of small area time-series models. These models combine cross-sectional, temporal, and spatial data to predict in all districts in both survey and non-survey years. Nationally, there has been a steep decline in stunting from about three in five to one in three children. However, our results highlight significant inequalities in chronic undernutrition, with several districts experiencing less pronounced declines. These differences are more nuanced at the district-by-age level, with only districts in more socio-economically advantaged areas of Bangladesh consistently reporting declines in stunting across all age groups.

Keywords:

• Bangladesh
• Bayesian approach
• joinpoint analysis
• multilevel time-series model
• small area estimation
• stunting

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Please direct all correspondence to Sumonkanti Das, School of Demography, ANU College of Arts and Social Sciences, The Australian National University, RSSS Building, 146 Ellery Crescent, Acton ACT 2601, Australia; or by E-mail: [email protected].

2 Sumonkanti Das and Bernard Baffour are based in the School of Demography, Australian National University, Australia. Alice Richardson is based in the Statistical Support Network, Australian National University.

3 Funding: This work was supported by the Australian National Health and Medical Research Council (NHMRC) under Ideas Grant (APP1184720) ‘Filling in the blanks: a visualization tool to align national health data with regional health policy objectives’.

4 Acknowledgements: We would like to thank: (1) MEASURE Evaluation and the National Institute of Population Research and Training (NIPORT) for making the Bangladesh DHS data publicly available; (2) IPUMS International for providing access to data from the Bangladesh Census 1991, Census 2001, and Census 2011; (3) UNICEF for providing access to the 2019 Bangladesh MICS data; (4) The United States National Cancer Institute (NCI) for providing access to the Joinpoint Regression Software; (5) developers of the R packages used in this study; and (6) the editor and anonymous reviewers, whose valuable comments and suggestions helped us to improve the quality of the manuscript immensely.

5 Data availability statement: Microdata from the Bangladesh DHS are publicly available from the DHS website. Microdata from the Bangladesh Census 1991, 2001, and 2011 are available from the IPUMS International website. Our extracted input variables from the survey and census data have been deposited in the figshare data repository at https://doi.org/10.6084/m9.figshare.23657688.v1.

6 Ethics approval and consent to participate: No ethical approval was needed for this study because it was based on secondary analysis of data obtained from the seven Bangladesh DHS collected in 1996–97, 1999–2000, 2004, 2007, 2011, 2014, and 2017–18. The data were completely anonymous, and this study uses data with no identifiable information on the survey participants.

7 Supplementary online material: The supplementary file provides detailed information on preparation of data inputs, model development, trend estimates at various disaggregated levels, and joinpoint regression analysis.