Factors Associated With Child Stunting, Wasting, and Underweight in 35 Low- and Middle-Income Countries | Child Development | JAMA Network Open | JAMA Network
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Figure 1.  Relative Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models
Relative Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models

Short maternal statue indicates maternal height of less than 145 cm; low maternal body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), BMI less than 18.5; child marriage, mother younger than 18 years at marriage; delayed breastfeeding, child was not breastfed within 1 hour of birth; infectious disease, child had infectious disease within 2 weeks before survey. ANC indicates antenatal care; FP, family planning; HH, household; OR, odds ratio; ORT, oral rehydration therapy; SBA, skilled birth attendant.

Figure 2.  Country-Specific Ranking of 20 Factors Associated with Stunting
Country-Specific Ranking of 20 Factors Associated with Stunting

Short maternal statue indicates maternal height of less than 145 cm; low maternal body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), BMI less than 18.5; child marriage, mother younger than 18 years at marriage; delayed breastfeeding, child was not breastfed within 1 hour of birth; infectious disease, child had infectious disease within 2 weeks before survey. ANC indicates antenatal care; FP, family planning; HH, household; ORT, oral rehydration therapy; SBA, skilled birth attendant.

Figure 3.  Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Stunting
Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Stunting

Short maternal statue indicates maternal height of less than 145 cm; low maternal body mass index (BMI, calculated as weight in kilograms divided by height in meters squared), BMI less than 18.5; child marriage, mother younger than 18 years at marriage; delayed breastfeeding, child was not breastfed within 1 hour of birth; infectious disease, child had infectious disease within 2 weeks before survey. ANC indicates antenatal care; FP, family planning; HH, household; ORT, oral rehydration therapy; SBA, skilled birth attendant.

Table 1.  Definition of 26 Direct and Indirect Factors Associated With Child Anthropometric Failures Identified From a Comprehensive Review of Conceptual Framework and Prior Studies
Definition of 26 Direct and Indirect Factors Associated With Child Anthropometric Failures Identified From a Comprehensive Review of Conceptual Framework and Prior Studies
Table 2.  Distribution of Child Anthropometric Failures by Selected Factors Among Children Aged 12 to 59 Months, Using the Most Recent Demographic Health Surveys Pooled Across 35 or 12 LMICs
Distribution of Child Anthropometric Failures by Selected Factors Among Children Aged 12 to 59 Months, Using the Most Recent Demographic Health Surveys Pooled Across 35 or 12 LMICs
Supplement.

eTable 1. Percentage of Children Aged 12 to 59 Months Classified as Having Stunting, Underweight, or Wasting in 35 Countries

eTable 2. Full Regression Results From the Pooled, Fully Adjusted Model

eTable 3. Magnitudes of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for the Pooled Sample

eFigure 1. Flow Diagram Showing Exclusions and Final Sample Sizes of the Study Population, Using the Most Recent Pooled Demographic Health Survey Data Since 2010

eFigure 2. Relative Ranking of 20 Factors Associated With Child Anthropometric Failures From Single Adjusted Models for the Pooled Sample

eFigure 3. Country-Specific Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Underweight

eFigure 4. Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Underweight

eFigure 5. Country-Specific Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Wasting

eFigure 6. Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models on Wasting

eFigure 7. Relative Ranking of 23 Factors Associated With Child Anthropometric Failures From Supplementary Analysis of Fully Adjusted Models for the Pooled Sample

eFigure 8. Country-Specific Ranking of 23 Factors Associated With Child Anthropometric Failures From Supplementary Analysis of Fully Adjusted Models on Stunting, Underweight, and Wasting in 12 Countries

eFigure 9. Country-Specific Odds Ratios for 23 Factors Associated With Child Anthropometric Failures From Supplementary Analysis of Fully Adjusted Models on Stunting, Underweight, and Wasting in 12 Countries

eFigure 10. Relative Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for Pooled Sample, Stratified by Children’s Age

eFigure 11. Country-Specific Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models, Stratified by Children’s Age

eFigure 12. Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models, Stratified by Children’s Age

eFigure 13. Relative Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for Pooled Sample, Stratified by Place of Residence

eFigure 14. Country-Specific Ranking of 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models, Stratified by Place of Residence

eFigure 15. Country-Specific Odds Ratios for 20 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models, Stratified by Place of Residence

eFigure 16. Relative Ranking of 17 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for the Pooled Sample, Excluding Source of Drinking Water, Sanitation Facility, and Household Air Quality

eFigure 17. Country-Specific Ranking of 17 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for the Pooled Sample, Excluding Source of Drinking Water, Sanitation Facility, and Household Air Quality

eFigure 18. Country-Specific Odds Ratios for 17 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for the Pooled Sample, Excluding Source of Drinking Water, Sanitation Facility, and Household Air Quality

eFigure 19. Relative Ranking of 23 Factors Associated With Child Anthropometric Failures From Fully Adjusted Models for the Pooled Sample, Adding Women’s Empowerment Factors

1.
United Nations Children’s Fund. Malnutrition. Accessed March 24, 2020. https://data.unicef.org/topic/nutrition/malnutrition/
2.
Abarca-Gómez  L, Abdeen  ZA, Hamid  ZA,  et al; NCD Risk Factor Collaboration (NCD-RisC).  Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.   Lancet. 2017;390(10113):2627-2642. doi:10.1016/S0140-6736(17)32129-3PubMedGoogle ScholarCrossref
3.
United Nations. Sustainable development goals: goal 3: ensure healthy lives and promote well-being for all at all ages. Accessed March 24, 2020. https://www.un.org/sustainabledevelopment/health/
4.
Black  R, Alderman  H, Bhutta  Z,  et al.  Executive summary of The Lancet maternal and child nutrition series.   Lancet. Published June 6, 2013. Accessed March 24, 2020. https://www.thelancet.com/series/maternal-and-child-nutritionGoogle Scholar
5.
United Nations Children’s Fund. Improving child nutrition: the achievable imperative for global progress. Accessed March 24, 2020. https://www.unicef.org/nutrition/index_68661.html
6.
Khatun  W, Alam  A, Rasheed  S, Huda  TM, Dibley  MJ.  Exploring the intergenerational effects of undernutrition: association of maternal height with neonatal, infant and under-five mortality in Bangladesh .  BMJ Glob Health. 2018;3(6):e000881.PubMedGoogle Scholar
7.
Monteiro  CA, Benicio  MHD, Conde  WL,  et al.  Narrowing socioeconomic inequality in child stunting: the Brazilian experience, 1974-2007.   Bull World Health Organ. 2010;88(4):305-311. doi:10.2471/BLT.09.069195PubMedGoogle ScholarCrossref
8.
Ozaltin  E, Hill  K, Subramanian  SV.  Association of maternal stature with offspring mortality, underweight, and stunting in low- to middle-income countries.   JAMA. 2010;303(15):1507-1516. doi:10.1001/jama.2010.450PubMedGoogle ScholarCrossref
9.
Kim  R, Rajpal  S, Joe  W,  et al.  Assessing associational strength of 23 correlates of child anthropometric failure: an econometric analysis of the 2015-2016 National Family Health Survey, India.   Soc Sci Med. 2019;238:112374. doi:10.1016/j.socscimed.2019.112374PubMedGoogle Scholar
10.
Corsi  DJ, Mejía-Guevara  I, Subramanian  SV.  Risk factors for chronic undernutrition among children in India: estimating relative importance, population attributable risk and fractions.   Soc Sci Med. 2016;157:165-185. doi:10.1016/j.socscimed.2015.11.014PubMedGoogle ScholarCrossref
11.
Nshimyiryo  A, Hedt-Gauthier  B, Mutaganzwa  C,  et al.  Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey.   BMC Public Health. 2019;19(1):175. doi:10.1186/s12889-019-6504-zPubMedGoogle ScholarCrossref
12.
Kang  Y, Aguayo  VM, Campbell  RK,  et al.  Nutritional status and risk factors for stunting in preschool children in Bhutan.   Matern Child Nutr. 2018;14(suppl 4):e12653. doi:10.1111/mcn.12653PubMedGoogle Scholar
13.
Mistry  SK, Hossain  MB, Khanam  F,  et al.  Individual-, maternal- and household-level factors associated with stunting among children aged 0-23 months in Bangladesh.   Public Health Nutr. 2019;22(1):85-94. doi:10.1017/S1368980018002926PubMedGoogle ScholarCrossref
14.
Akombi  BJ, Agho  KE, Hall  JJ, Merom  D, Astell-Burt  T, Renzaho  AMN.  Stunting and severe stunting among children under-5 years in Nigeria: a multilevel analysis.   BMC Pediatr. 2017;17(1):15. doi:10.1186/s12887-016-0770-zPubMedGoogle ScholarCrossref
15.
Headey  D, Hoddinott  J, Park  S.  Drivers of nutritional change in four South Asian countries: a dynamic observational analysis.   Matern Child Nutr. 2016;12(suppl 1):210-218. doi:10.1111/mcn.12274PubMedGoogle ScholarCrossref
16.
Kim  R, Mejía-Guevara  I, Corsi  DJ, Aguayo  VM, Subramanian  SV.  Relative importance of 13 correlates of child stunting in South Asia: insights from nationally representative data from Afghanistan, Bangladesh, India, Nepal, and Pakistan.   Soc Sci Med. 2017;187:144-154. doi:10.1016/j.socscimed.2017.06.017PubMedGoogle ScholarCrossref
17.
Danaei  G, Andrews  KG, Sudfeld  CR,  et al.  Risk factors for childhood stunting in 137 developing countries: a comparative risk assessment analysis at global, regional, and country levels.   PLoS Med. 2016;13(11):e1002164. doi:10.1371/journal.pmed.1002164PubMedGoogle Scholar
18.
USAID. The DHS program: available datasets. Accessed March 24, 2020. https://www.dhsprogram.com/data/available-datasets.cfm
19.
World Health Organization. WHO child growth standards: methods and development. Accessed March 24, 2020. https://www.who.int/childgrowth/standards/technical_report/en/
20.
Assaf  S, Kothari  MT, Pullum  T. An assessment of the quality of DHS anthropometric data. Accessed March 24, 2020. https://www.dhsprogram.com/pubs/pdf/MR16/MR16.pdf
21.
Nahar  B, Ahmed  T, Brown  KH, Hossain  MI.  Risk factors associated with severe underweight among young children reporting to a diarrhea treatment facility in Bangladesh .  J Health Popul Nutr. 2010;28(5):476-483.PubMedGoogle Scholar
22.
Fenske  N, Burns  J, Hothorn  T, Rehfuess  EA.  Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.   PLoS One. 2013;8(11):e78692. doi:10.1371/journal.pone.0078692PubMedGoogle Scholar
23.
Merchant  AT, Jones  C, Kiure  A,  et al.  Water and sanitation associated with improved child growth.   Eur J Clin Nutr. 2003;57(12):1562-1568. doi:10.1038/sj.ejcn.1601725PubMedGoogle ScholarCrossref
24.
Vollmer  S, Harttgen  K, Kupka  R, Subramanian  SV.  Levels and trends of childhood undernutrition by wealth and education according to a Composite Index of Anthropometric Failure: evidence from 146 demographic and health surveys from 39 countries.  BMJ Global Health; 2017;2(2):e000206.
25.
Head  ML, Berry  LK, Royle  NJ, Moore  AJ.  Paternal care: direct and indirect genetic effects of fathers on offspring performance.   Evolution. 2012;66(11):3570-3581. doi:10.1111/j.1558-5646.2012.01699.xPubMedGoogle ScholarCrossref
26.
Siddiqi  MNA, Haque  MN, Goni  MA.  Malnutrition of under-five children: evidence from Bangladesh.   Asian J Med Sci. 2011;2(2):113-119. doi:10.3126/ajms.v2i2.3662Google ScholarCrossref
27.
Chirande  L, Charwe  D, Mbwana  H,  et al.  Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey.   BMC Pediatr. 2015;15:165. doi:10.1186/s12887-015-0482-9PubMedGoogle ScholarCrossref
28.
Fink  G, Sudfeld  CR, Danaei  G, Ezzati  M, Fawzi  WW.  Scaling-up access to family planning may improve linear growth and child development in low and middle income countries.   PLoS One. 2014;9(7):e102391. doi:10.1371/journal.pone.0102391PubMedGoogle Scholar
29.
Lakew  Y, Bekele  A, Biadgilign  S.  Factors influencing full immunization coverage among 12-23 months of age children in Ethiopia: evidence from the national demographic and health survey in 2011.   BMC Public Health. 2015;15:728. doi:10.1186/s12889-015-2078-6PubMedGoogle ScholarCrossref
30.
United Nations Children’s Fund. Guide to DHS statistics DHS-7. Accessed March 24, 2020. https://dhsprogram.com/Data/Guide-to-DHS-Statistics
31.
Demographic and Health Surveys. Wealth index construction. Accessed March 24, 2020. https://www.dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm
32.
United Nations Children’s Fund. DHS survey indicators: characteristics of households. Accessed March 24, 2020. https://dhsprogram.com/data/DHS-Survey-Indicators-Characteristics-of-Households.cfm
33.
Subramanian  SV, Ackerson  LK, Davey Smith  G, John  NA.  Association of maternal height with child mortality, anthropometric failure, and anemia in India.   JAMA. 2009;301(16):1691-1701. doi:10.1001/jama.2009.548PubMedGoogle ScholarCrossref
34.
Subramanian  SV, Ackerson  LK, Smith  GD.  Parental BMI and childhood undernutrition in India: an assessment of intrauterine influence.   Pediatrics. 2010;126(3):e663-e671. doi:10.1542/peds.2010-0222PubMedGoogle ScholarCrossref
35.
World Health Organization. Water sanitation hygiene. Accessed March 24, 2020. https://www.who.int/water_sanitation_health/monitoring/jmp2012/key_terms/en/
36.
World Health Organization; United Nations Children’s Fund. Core questions on drinking-water and sanitation for household surveys. Accessed March 24, 2020. https://www.who.int/water_sanitation_health/monitoring/household_surveys/en/
37.
Murphy  SP, Yaktine  AL, Suitor  CW, Moats  S.  Child and Adult Care Food Program: Aligning Dietary Guidance for All. Institute of Medicine; 2011.
38.
Ladwig  KH, Marten-Mittag  B, Formanek  B, Dammann  G.  Gender differences of symptom reporting and medical health care utilization in the German population.   Eur J Epidemiol. 2000;16(6):511-518. doi:10.1023/A:1007629920752PubMedGoogle ScholarCrossref
39.
Monden  CWS, Smits  J.  Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries.   Lancet Glob Health. 2017;5(7):e673-e679. doi:10.1016/S2214-109X(17)30197-3PubMedGoogle ScholarCrossref
40.
Kruk  ME, Gage  AD, Joseph  NT, Danaei  G, García-Saisó  S, Salomon  JA.  Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries.   Lancet. 2018;392(10160):2203-2212. doi:10.1016/S0140-6736(18)31668-4PubMedGoogle ScholarCrossref
41.
Hernández-Díaz  S, Peterson  KE, Dixit  S,  et al.  Association of maternal short stature with stunting in Mexican children: common genes vs common environment.   Eur J Clin Nutr. 1999;53(12):938-945. doi:10.1038/sj.ejcn.1600876PubMedGoogle ScholarCrossref
42.
Christian  P, Lee  SE, Donahue Angel  M,  et al.  Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries.   Int J Epidemiol. 2013;42(5):1340-1355. doi:10.1093/ije/dyt109PubMedGoogle ScholarCrossref
43.
Li  Z, Li  M, Subramanian  SV, Lu  C.  Assessing levels and trends of child health inequality in 88 developing countries: from 2000 to 2014.   Glob Health Action. 2017;10(1):1408385. doi:10.1080/16549716.2017.1408385PubMedGoogle Scholar
44.
United Nations Children’s Fund. Health and child survival. Accessed March 24, 2020. https://www.unicef.org/kyrgyzstan/health-and-child-survival
45.
Hatløy  A, Hallund  J, Diarra  MM, Oshaug  A.  Food variety, socioeconomic status and nutritional status in urban and rural areas in Koutiala (Mali).   Public Health Nutr. 2000;3(1):57-65.PubMedGoogle ScholarCrossref
46.
Rah  JH, Akhter  N, Semba  RD,  et al.  Low dietary diversity is a predictor of child stunting in rural Bangladesh.   Eur J Clin Nutr. 2010;64(12):1393-1398. doi:10.1038/ejcn.2010.171PubMedGoogle ScholarCrossref
47.
Tarini  A, Bakari  S, Delisle  H.  The overall nutritional quality of the diet is reflected in the growth of Nigerian children.   Sante. 1999;9(1):23-31.PubMedGoogle Scholar
48.
Bukania  ZN, Mwangi  M, Karanja  RM,  et al.  Food insecurity and not dietary diversity is a predictor of nutrition status in children within semiarid agro-ecological zones in Eastern Kenya.   J Nutr Metab. 2014;2014:907153. doi:10.1155/2014/907153PubMedGoogle Scholar
49.
Guandalini  S, Pensabene  L, Zikri  MA,  et al.  Lactobacillus GG administered in oral rehydration solution to children with acute diarrhea: a multicenter European trial.   J Pediatr Gastroenterol Nutr. 2000;30(1):54-60. doi:10.1097/00005176-200001000-00018PubMedGoogle ScholarCrossref
50.
Pavlinac  PB, Brander  RL, Atlas  HE, John-Stewart  GC, Denno  DM, Walson  JL.  Interventions to reduce post-acute consequences of diarrheal disease in children: a systematic review.   BMC Public Health. 2018;18(1):208. doi:10.1186/s12889-018-5092-7PubMedGoogle ScholarCrossref
51.
Dangour  AD, Watson  L, Cumming  O,  et al.  Interventions to improve water quality and supply, sanitation and hygiene practices, and their effects on the nutritional status of children.   Cochrane Database Syst Rev. 2013;(8):CD009382.PubMedGoogle Scholar
52.
Freeman  MC, Garn  JV, Sclar  GD,  et al.  The impact of sanitation on infectious disease and nutritional status: a systematic review and meta-analysis.   Int J Hyg Environ Health. 2017;220(6):928-949. doi:10.1016/j.ijheh.2017.05.007PubMedGoogle ScholarCrossref
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    Original Investigation
    Global Health
    April 22, 2020

    Factors Associated With Child Stunting, Wasting, and Underweight in 35 Low- and Middle-Income Countries

    Author Affiliations
    • 1Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
    • 2Division of Health Policy and Management, College of Health Sciences, Korea University, Seoul, South Korea
    • 3Department of Public Health Sciences, Graduate School, Korea University, Seoul, South Korea
    • 4Harvard Center for Population and Development Studies, Cambridge, Massachusetts
    • 5Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Germany
    JAMA Netw Open. 2020;3(4):e203386. doi:10.1001/jamanetworkopen.2020.3386
    Key Points español 中文 (chinese)

    Question  What are the most important factors associated with child undernutrition, and how do they vary across countries?

    Findings  In this cross-sectional study of 299 353 children aged 12 to 59 months in 35 low- and middle-income countries, household socioeconomic status and parental nutritional status were the leading factors associated with child undernutrition in pooled analyses and in most country-specific analyses. Environmental conditions, health behaviors, disease prevalence, and maternal reproductive care were less frequently associated with child undernutrition, with substantial heterogeneity among countries.

    Meaning  The findings of this study suggest that interventions to improve socioeconomic status and parental nutritional status (eg, education for women and poverty reduction) should accompany food and nutrition programs, but the potential benefits of investing in specific conditions are highly dependent on the context.

    Abstract

    Importance  Evidence on the relative importance of various factors associated with child anthropometric failures (ie, stunting, underweight, and wasting) and their heterogeneity across countries can inform global and national health agendas.

    Objective  To assess the relative significance of factors associated with child anthropometric failures in 35 low- and middle-income countries (LMICs).

    Design, Setting, and Participants  This cross-sectional study of 299 353 children who were born singleton and aged 12 to 59 months with nonpregnant mothers and valid anthropometric measures assessed the strengths of associations of 26 factors with child stunting, underweight, and wasting, using Demographic and Health Surveys (2007-2018) from 35 LMICs. Data analysis was conducted from July 2019 to February 2020.

    Exposures  A total of 9 direct factors (ie, dietary diversity score; breastfeeding initiation; vitamin A supplements; use of iodized salt; infectious disease in past 2 weeks; oral rehydration therapy for children with diarrhea; care seeking for suspected pneumonia; full vaccination; and indoor pollution) and 17 indirect factors (household wealth; maternal and paternal education; maternal and paternal height and body mass index; maternal autonomy for health care, movement, and money; water source; sanitation facility; stool disposal; antenatal care; skilled birth attendant at delivery; family planning needs; and maternal marriage age) were assessed.

    Main Outcomes and Measures  Three anthropometric failure outcomes were constructed based on the 2006 World Health Organization child growth standards: stunting (height-for-age z score less than −2 standard deviations [SDs]), underweight (weight-for-age z score less than −2 SDs), and wasting (weight-for-height z score less than −2 SDs).

    Results  Among the 299 353 children aged 12 to 59 months included in the analysis, 38.8% (95% CI, 38.6%-38.9%) had stunting, 27.5% (95% CI, 27.3%-27.6%) had underweight, and 12.9% (95% CI, 12.8%-13.0%) had wasting. In the pooled sample, short maternal height was the strongest factor associated with child stunting (odds ratio [OR], 4.7; 95% CI, 4.5-5.0; P < .001), followed by lack of maternal education (OR, 1.9; 95% CI, 1.8-2.0; P < .001), poorest household wealth (OR, 1.7; 95% CI,1.6-1.8; P < .001), and low maternal body mass index (OR, 1.6; 95% CI, 1.6-1.7; P < .001). Short paternal height was also significantly associated with higher odds of stunting (OR, 1.9; 95% CI, 1.7-2.2; P < .001). Consistent results were found for underweight (eg, short maternal height: OR, 3.5; 95% CI, 3.3-3.7; P < .001; lack of maternal education: OR, 1.8; 95% CI, 1.7-2.0; P < .001) and wasting (eg, low maternal body mass index: OR, 2.3; 95% CI, 2.1-2,4; P < .001; poorest household wealth: OR, 1.2; 95% CI, 1.1-1.3; P < .001). Parental nutritional status and household socioeconomic conditions ranked the strongest (1st to 4th) for most countries, with a few exceptions (eg, lack of maternal education ranked 18th-20th in 8 countries for child wasting). Other factors were not associated with anthropometric failures in pooled analysis and had large country-level heterogeneity; for example, unsafe water was not associated with child underweight in the pooled analysis (OR, 0.97; 95% CI, 0.95-1.00; P < .001), and it ranked from 4th to 20th across countries.

    Conclusions and Relevance  In this study, socioeconomic conditions and parental nutritional status were the strongest factors associated with child anthropometric failures. Poverty reduction, women’s education, and nutrition programs for households could be important strategies for reducing child undernutrition; however, country-specific contexts should be considered in national policy discussions.

    Introduction

    The global burden of child undernutrition remains high by all measures of child anthropometric failures (including stunting, underweight, and wasting).1,2 In 2018, 21.9% of children (ie, 149 million children) were estimated to have stunting.1 Immediate actions are needed to meet Sustainable Development Goal 2, ie, to end all forms of malnutrition by 2030,3 which in turn can contribute to other targets associated with child survival, educational achievements, and overall well-being. Several conceptual models have been developed to understand the causes of child undernutrition, most of which adopt multifactorial framework.4,5 The United Nations Children’s Fund (UNICEF) framework outlines socioeconomic conditions and national and global contexts as the fundamental factors affecting food security, care for children, and healthy household environment, all of which in turn further shape dietary intake, disease occurrence and, consequently, children’s nutritional status and growth.5 However, the UNICEF framework does not explicitly account for the role of parental nutritional status (eg, height and body mass index [BMI], calculated as weight in kilograms divided by height in meters squared), which may have intergenerational associations via biologic (eg, genetic disposition) and psychosocial (eg, poor living conditions) channels.6

    Randomized clinical trials on child undernutrition tend to focus on a single factor or a small subset of them, making it difficult to infer their importance relative to other known factors.7,8 Some observational studies have attempted to simultaneously assess the association of multiple factors with child anthropometric failures in India,9,10 Rwanda,11 Bhutan,12 Bangladesh,13 and Nigeria,14 but the results are not directly comparable across countries given the different sets of factors considered in each study. There are only 2 multicountry studies, both focused on South Asia,15,16 which means the cross-country heterogeneity of the relative significance of factors associated with child undernutrition has been underexplored in other regions. While a 2017 multicountry meta-analysis17 identified fetal growth restriction and unimproved sanitation as the leading risk factors for child stunting, this study did not fully account for socioeconomic factors, such as household wealth and parental education.

    Evidence regarding the relative strengths of factors associated with child anthropometric failures and their variation across countries is critical for understanding the underlying mechanisms of child undernutrition and potential context-specific interactions. Using the most recent data from the Demographic and Health Survey (DHS), we selected a comprehensive set of factors associated with child anthropometric failures and conducted a systematic analysis to assess their relative significance in 35 low- and middle-income countries (LMICs). In addition to pooled analyses, we present country-specific findings to inform the core intervention components needed to reduce child undernutrition in each country.

    Methods
    Data Source

    We drew the most recent data for LMICs from DHSs conducted between 2007 and 2018. Demographic and Household Surveys are nationally representative household surveys that collect detailed nutrition and health information on children, their parents, and households18 using a multistage, stratified sampling design. The first stage involves the division of each country in geographic areas. Within these subnational regions, populations are stratified by urban or rural area. These primary sampling units or clusters are selected with probability proportional to the contribution of that cluster’s population to the total population. In the second stage of sampling, all households within the cluster are listed, and an average of 25 houses are randomly selected for an interview by equal-probability systematic sampling.18 We excluded earlier survey rounds to avoid inconsistencies in the measurements, collection, and reporting of data required for this study. The study was reviewed by the Harvard T.H. Chan School of Public Health institutional review board and was considered exempt from full review because it was based on an anonymous, public-use data set with no identifiable information on study participants. Our study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Study Population and Sampling Size

    A total of 35 LMICs had collected data on child anthropometric measures and the factors of interest. The eligibility criteria for our analytic sample were as follows: children (1) who born singleton, (2) who were aged 12 to 59 months and alive at the time of the survey, (3) with a mother who was not pregnant at the time of survey, and (4) with valid measures on child stunting, underweight, and wasting. We identified 299 353 children from 35 LMICs in the final analytic sample for our primary analysis (eFigure 1 in the Supplement).

    Outcomes

    The following 3 anthropometric failure outcomes were constructed based on the 2006 World Health Organization child growth standards: stunting, underweight, and wasting.19 Height-for-age z score, weight-for-age z score, and weight-for-height z score were calculated by comparing the child’s measurements with the median value in the reference population of the National Center for Health Statistics International Growth Reference.20 Stunting was defined as a height-for-age z score less than −2 standard deviations (SDs) of the median, underweight as weight-for-age z score of less than −2 SDs, and wasting as weight-for-height z score less than −2 SDs.19

    Exposures

    Based on the UNICEF framework,5 its adaption in the Lancet Maternal and Child Nutrition Series,4 and previous practices,9,10,16 we selected 20 factors for our primary analysis and 6 additional factors on paternal characteristics and maternal autonomy for supplementary analyses. We classified these 26 factors associated with child anthropometric failures either directly or via intermediary causes. A total of 9 direct factors were identified, including child nutrition (dietary diversity score, breastfeeding initiation, vitamin A supplements, and use of iodized salt), disease occurrence (infectious disease in past 2 weeks), health behaviors (oral rehydration therapy for diarrhea, care seeking for suspected pneumonia, full vaccination), and living conditions (indoor pollution). The association between each of these direct factors and child anthropometric failures has been documented previously.17,21,22 The remaining 17 indirect factors included household socioeconomic status (household wealth, maternal and paternal education), parents’ nutritional status (maternal and paternal height and BMI), maternal autonomy (for health care, movement, and money), environmental conditions (water source, sanitation facility, and stool disposal), maternal reproductive care (antenatal care, skilled birth attendant at delivery, family planning needs), and maternal marriage age. Prior studies have indicated that household wealth, maternal characteristics, and household environment are strongly associated with child anthropometric failures.8,23,24 Although only a few studies have investigated the role of paternal nutritional status, we included it in the supplementary analysis owing to potential biological and psychosocial channels between fathers and their offspring.6,25 We also included maternal reproductive care variables that represent the care mothers received during pregnancy,26 the risk the child faced during birth,27 and the families’ desired birth spacing and their capacity to reach it.28 A detailed list and definitions of these factors are presented in Table 1.9,29-36

    Statistical Analysis

    We assessed the association of each factor with child anthropometric outcomes by first pooling data from all countries and then separately for each country. We included sampling weight, clustering, and stratification variables provided by DHS to ensure that the estimates were representative at the national level and in pooled analyses.16 We clustered the sample at the level of the primary sampling unit, which allows for interdependence of error terms within clusters and households.16 In pooled analyses, we reweighted observations by a country’s population size and included country fixed effects to account for the unobservable country-level factors. For both pooled and country-specific analyses, we developed 2 sets of logistic regression models for each outcome. First, we ran separate models (single-adjusted models) for each factor in which we adjusted for child’s age and sex, birth order, and maternal age at birth. Second, we performed mutually adjusted models (fully adjusted models) in which all factors, as well as child’s age and sex, birth order, maternal age at birth, and place of residence (urban vs rural), were considered simultaneously. Based on these models, we compared and ordered the factors according to their coefficient sizes (odds ratios [ORs]). For all factors, the best-off group was set as the reference category to ensure consistency in interpretation of ORs. For factors with multiple categories (ie, household wealth quintile), only the OR corresponding the worst-off group (ie, the poorest quintile) is presented in our results section.

    We performed 6 sets of supplementary analyses. First, we included 3 additional paternal characteristics for a subset of 188 290 children from 12 countries that had collected data on fathers. Second, we stratified children by age (<2 years and ≥2 years) given their different dietary demands.37 Third, we performed stratified analyses by urban and rural areas. For the second and third analyses, we followed previous practice38 and used Bonferroni correction to deal with the type I error from multiple testing. Fourth, we reestimated the fully adjusted models after removing source of drinking water, sanitation facility, and household air quality because these indicators had been considered in the construction of household wealth index in DHS. Fifth, we reran the models, adding covariates on children’s birth weight and birth interval. As more than half of the children (170 451 of 299 353 [56.9%]) had missing or invalid birth weight or birth interval, only on a subset of 128 902 children was used for this supplementary analysis. Sixth, we added 3 indicators of maternal autonomy for a subset of 142 638 children (47.6%) with available data.

    We used Stata version 14.2 (StataCorp) for all analyses. We adopted the MI command for multiple imputations for observations with missing value on 1 or more factors of interest.39,40 All statistical tests were 2-tailed, and P < .05 was considered statistically significant.

    Results

    Of 319 566 children who met the inclusion criteria, 20 213 (9.3%) were excluded because of missing (absent, refused, and missing for other reasons) or implausible anthropometric measures. A total of 299 353 children aged 12 to 59 months from 35 LMICs were included in the primary analysis (eFigure 1 in the Supplement). A total of 154 412 (51.6%) were boys, and 218 006 (72.8%) lived in rural areas. Overall, 38.8% (95% CI, 38.6%-38.9%) of children had stunting, 27.5% (95% CI, 27.3%-27.6%) had underweight, and 12.9% (95% CI, 12.8%-13.0%) had wasting (Table 2). The prevalence of anthropometric failures varied among countries, from 18.8% (95% CI, 17.9%-19.8%) in Peru to 61.1% (95% CI, 59.6%-62.6%) in Burundi for stunting, 2.9% (95% CI, 2.3%-3.5%) in Kyrgyzstan to 37.5% (95% CI, 35.8%-39.2%) in Niger for underweight, and 0.6% (95% CI, 0.4%-0.8%) in Peru to 19.0% (95% CI, 18.8%-19.2%) in India for wasting (eTable 1 in the Supplement). Overall, the burden of child anthropometric failures was higher in poorer households (eg, stunting among children with lowest vs highest wealth quintile, 51.2% [95% CI, 50.8%-51.5%] vs 22.3% [95% CI, 21.9%-22.7%]), and those with mothers who were less educated (eg, underweight among children whose mothers had no schooling vs ≥college education, 35.9% [95% CI, 35.6%-36.2%] vs 14.4% [95% CI, 13.9%-14.8%]), had shorter height (eg, wasting among children whose mothers were <145 cm vs ≥160 cm, 18.3% [95% CI, 17.8%-18.8%] vs 8.8% [95% CI, 8.5%-9.0%]), and had lower BMI (eg, stunting among children with mothers with BMI <18.5 vs ≥25.0, 49.7% [95% CI, 49.3%-50.1%] vs 26.4% [95% CI, 26.1%-26.8%]) (Table 2).

    Pooled Analyses
    Stunting

    The full regression results from pooled analyses are presented in eTable 2 in the Supplement. In single-adjusted models, all factors except for lack of vitamin A supplement, history of infectious disease, and no iodized salt use were significantly associated with higher odds of stunting (eFigure 2A in the Supplement). Short maternal height showed the strongest association with child stunting (OR, 4.4; 95% CI, 4.2-4.6; P < .001), followed by lack of maternal education (OR, 3.5; 95% CI, 3.3-3.7; P < .001) and poorest household wealth (OR, 3.4; 95% CI, 3.2-3.5; P < .001). The magnitude of associations substantially attenuated for most factors in the fully adjusted model; however, 15 remained statistically significant (Figure 1A). Conditional on all other factors, short maternal height had the strongest association with child stunting, with an OR of 4.7 (95% CI, 4.5-5.0; P < .001), followed by lack of maternal education (OR, 1.9; 95% CI,1.8-2.0; P < .001), poorest household wealth (OR, 1.7; 95% CI,1.6-1.8; P < .001), and low maternal BMI (OR, 1.6; 95% CI,1.6-1.7; P < .001).

    Underweight

    In single-adjusted models, all factors were significantly associated with higher odds of underweight, except for no care seeking for suspected pneumonia, no vitamin A supplement, infectious disease during past 2 weeks, and unsafe water (eFigure 2B in the Supplement). Short maternal height had the strongest association with child underweight (OR, 5.3; 95% CI, 5.0-5.6; P < .001), followed by low maternal BMI (OR, 4.8; 95% CI, 4.6-5.0; P < .001) and poorest household wealth (OR, 3.4; 95% CI, 3.2-3.6; P < .001). In the fully adjusted model, we found 11 factors to be significantly associated with higher odds of underweight (Figure 1B), including short maternal height (OR, 3.5; 95% CI, 3.3-3.7; P < .001), low maternal BMI (OR, 2.7; 95% CI, 2.6-2.9; P < .001), lack of maternal education (OR, 1.8; 95% CI, 1.7-2.0; P < .001), and poorest household wealth (OR, 1.6; 95% CI,1.5-1.8; P < .001).

    Wasting

    In single-adjusted models, there were 10 factors significantly associated with higher odds of wasting, with short maternal height (OR, 4.4; 95% CI, 4.2-4.6; P < .001), lack of maternal education (OR, 3.5; 95% CI, 3.3-3.7; P < .001), and poorest household wealth (OR, 3.4 95% CI, 3.2-3.5; P < .001) having the largest magnitudes (eFigure 2C in the Supplement). The fully adjusted model showed consistent results in terms of the factors with the largest magnitudes, such as low maternal BMI (OR, 2.3; 95% CI, 2.1-2.4; P < .001), no maternal education (OR, 1.2; 95% CI, 1.1-1.4 ; P < .001), poor dietary diversity (OR, 1.2; 95% CI, 1.1-1.3), and poorest household wealth (OR, 1.2; 95% CI, 1.1-1.3; P < .001) (Figure 1C).

    Country-Specific Analyses
    Stunting

    Short maternal height had the strongest association with stunting for all 35 countries, with ORs being ranked first in 22 countries and between second and fifth in 11 countries (Figure 2). Lack of maternal education, low maternal BMI, and poorest household wealth were also strongly associated with stunting for most countries. However, there were several exceptions. For example, lack of maternal education ranked 19th in Gambia. The ranking of other factors, such as unsafe sanitation, no skilled birth attendant at birth, and poor household air quality, varied largely across countries. The magnitudes of ORs for all factors were also heterogeneous (Figure 3). For example, the magnitudes of ORs for short maternal height ranged from 0.8 (95% CI, 0.3-2.4) in Guinea to 15.5 (95% CI, 3.5-97.1) in Togo; the magnitudes of ORs for poor household air quality ranged from 0.4 (95% CI, 0.0-3.5) in Sierra Leone to 3.8 (95% CI, 1.3-11.5) in Democratic Republic of the Congo.

    Underweight

    Short maternal height was most strongly associated with higher odds of underweight (ranked 1st-4th) in 29 countries; however, it ranked 20th in Namibia. Low maternal BMI was also strongly associated with underweight across all 35 countries, ranking between 1st and 6th. The relative rankings for lack of maternal education and poorest household wealth varied largely across countries. For example, poorest household wealth ranked 1st to 4th in 13 of 35 countries, while it ranked 10th or lower for 10 countries (eFigure 3 in the Supplement). All other factors showed great heterogeneity in their relative rankings across countries. We also observed substantial variations in the factors’ magnitudes across countries (eFigure 4 in the Supplement). For example, the magnitudes of ORs for lack of maternal education ranged from 0.7 (95% CI, 0.4-1.3) in Myanmar to 33.3 (95% CI, 2.3-483.9) in Lesotho; the magnitudes of ORs for no care seeking for suspected pneumonia ranged from 0.7 (95% CI, 0.3-1.2) in Comoros to 5.1 (95% CI, 2.0-13.2) in Namibia.

    Wasting

    Low maternal BMI ranked within the top 5 factors associated with wasting in most countries, except Comoros, Namibia, São Tomé and Príncipe, and Zambia. Short maternal height, poorest household wealth, and lack of maternal education were strongly associated with higher odds of child wasting for some countries but were found to have weaker associations in many other countries. For example, lack of maternal education ranked between 1st and 4th in 12 countries but ranked between 18th and 20th in 8 countries (eFigure 5 in the Supplement). The strength of association for each factor and child wasting also showed large variations across countries (eFigure 6 in the Supplement). For example, the magnitudes of ORs for low maternal BMI ranged from 1.2 (95% CI, 0.7-2.0) in Zambia to 40.0 (95% CI, 5.7-279.2) in Swaziland; the magnitudes of ORs for unimproved sanitation ranged from 0.6 (95% CI, 0.2-2.0) in Namibia to 16.8 (95% CI, 3.8-74.0) in Lesotho.

    Supplementary Analyses

    In the first supplementary analysis with paternal height, BMI, and education, we found that paternal factors had weaker associations with child anthropometric failures compared with maternal indicators (eFigure 7 in the Supplement). Short paternal height was associated with stunting with an OR of 1.9 (95% CI, 1.7-2.2; P < .001) compared with an OR of 4.5 (95% CI, 4.2-4.8; P < .001) for short maternal height. Paternal anthropometry had statistically significant associations with all types of child anthropometric failure, although their rankings and magnitudes varied across countries. For example, low paternal BMI was significantly associated with stunting (OR, 1.2; 95%CI, 1.1-1.4; P < .001), underweight (OR, 1.5; 95%CI, 1.3-1.7; P < .001), and wasting (OR, 1.2; 95%CI, 1.1-1.5; P < .001) in pooled analysis; however, the magnitudes of low paternal BMI ranged widely, from 4th in Namibia (OR, 2.5; 95%CI, 0.8-7.8) to 23th in Swaziland (OR, 0.5; 95%CI, 0.1-2.1) for stunting, from 3rd in Zimbabwe (OR, 1.8; 95%CI, 0.8-4.0) to 20th in Sierra Leone (OR, 0.9; 95% CI, 0.5-1.8) for underweight, and from 1st in Nepal (OR, 2.9; 95% CI, 0.9-9.7) to 23rd in Lesotho (OR, 0.1; 95% CI, 0.0-4.9) for wasting. Lack of paternal education ranked low and had small effect sizes in most countries (eFigure 8 and eFigure 9 in the Supplement).

    As second and third supplementary analyses, we stratified children by age and by urban and rural residence. We found consistent results for short maternal stature, lack of maternal education, poorest household wealth, and low maternal BMI, but moderate differences were observed for other factors across the stratified groups. For example, no oral rehydration therapy for diarrhea was not associated with child stunting among children younger than 2 years (OR, 1.04; 95% CI, 0.95-1.13), but it was associated with stunting among children aged 2 years and older (OR, 1.2; 95% CI, 1.1-1.3) (eFigure 10 in the Supplement). The results from country-specific stratified analyses are summarized in eFigure 11 to eFigure 15 in the Supplement.

    In the fourth supplementary analysis, we excluded source of drinking water, sanitation facility, and household air quality from the fully adjusted models to avoid potential multicollinearity, and the rankings and magnitudes of all factors remained largely the same (eFigure 16, eFigure 17, and eFigure 18 in the Supplement). Moreover, we adopted variance inflation factor (VIF) to check for multicollinearity. For example, for the outcome of child stunting, the regression model including all factors had a VIF of less than 4 for all factors, except for the poorest quintile of household wealth index (VIF, 6.19) and no maternal education (VIF, 5.46). After removing source of drinking water, sanitation facility, and household air quality from the regression model, all VIFs reduced to less than 4, indicating relatively low multicollinearity.

    The magnitude of the selected factors remained largely the same after additionally controlling for birth characteristics (ie, birth weight and preceding birth interval) in the fifth supplementary analysis (eTable 3 in the Supplement). Finally, indicators on women’s empowerment ranked low and had nonsignificant ORs for all 3 anthropometric failures (eFigure 19 in the Supplement).

    Discussion

    Maternal nutritional status (height and BMI) and poor household socioeconomic conditions (household wealth and maternal education) were the leading factors associated with child anthropometric failures in our pooled analyses. Fathers’ nutritional status also appeared to be associated with child anthropometric status, but paternal education was not. Despite some exceptions, parental nutritional status and poor household socioeconomic conditions were the strongest factors in most countries. The relative significance and absolute magnitude of other factors, such as care-seeking behaviors, reproductive care, and air quality, showed considerable heterogeneity among countries.

    A rich volume of observational studies supports our findings regarding maternal height and BMI,8,16 but paternal anthropometry remains largely unexplored. The associations between short parental height and child anthropometric status may be attributed to both shared genetic background and common environmental determinants (eg, diet, culture, social class) that first affect parents during their early childhood and subsequently affect the growth of their offspring.41 The consistent association between maternal BMI and child anthropometric failures may be attributed to intrauterine intergenerational transmission of low maternal BMI during pregnancy, giving infants a high risk of low birth weight and being small for gestational age, which forms the fetal origins of subsequent childhood undernutrition.34,42 While we did not have data on maternal BMI during pregnancy, BMI at the time of the survey is likely to be associated with previous weight. The influence of maternal BMI on child anthropometric status attenuated only moderately after adding paternal BMI.34

    Our pooled estimates on household wealth and maternal education were comparable with previous multicountry studies.7,43 Across countries, household wealth had moderate heterogeneity in associations with child stunting and underweight. The relative importance of maternal education ranged from very high (eg, Côte d’Ivoire, Mali, Ghana) to low (eg, Gambia, Kyrgyzstan, Myanmar). Such heterogeneity may be partially explained by differences in macroeconomic status, health system, and the existence of national and local programs. For example, the relatively weaker association between socioeconomic conditions and child anthropometric failures in Kyrgyzstan may be explained by investments in primary care facilities and hospitals in disadvantaged areas.44

    Children’s dietary diversity, oral rehydration therapy for diarrhea, and sanitation facilities were associated with all outcomes in the pooled analyses, but the results varied among countries. Country-level heterogeneity in the association between dietary diversity and child anthropometric failures has been documented in previous observational studies and randomized clinical trials, with a protective effect found in Mali45 and Bangladesh46 but not in Niger47 or Kenya.48 Different levels of food security and the existence of nutritional supplement programs (eg, Foodlets, Sprinkles, and lipid-based nutrient supplements) in some countries may explain the observed heterogeneity. Inconsistent findings on the association of oral rehydration therapy for diarrhea with outcomes may be because of the differential prevalence of children very close to the anthropometric failure cutoffs given that only they would be substantially affected by the occurrence of diarrhea and oral rehydration treatment.49,50 The heterogeneous association between sanitation facility and child undernutrition may be attributed to differences in complementarity of toilet maintenance, including other water and hygiene practices.51,52

    Limitations

    There are several limitations to this study. First, factors in the fully adjusted models may be associated with each other and serve as confounders or mediators. Multicollinearity can increase the standard errors of the coefficients and weaken the significance levels, but it does not result in biased estimates. Moreover, the low VIF for all factors presented in the supplementary analysis section indicated low multicollinearity. Second, the use of observational data and cross-sectional analysis limit our capacity to make any causal inferences. Third, some factors analyzed in this study, such as breastfeeding history, care-seeking behavior, and disease history, were self-reported and, therefore, are prone to potential measurement errors.

    Conclusions

    This systematic investigation of the comparative importance of direct and indirect factors associated with child anthropometric failures suggests the universal importance of improving maternal nutritional status and household socioeconomic circumstances. The relative importance of other factors was weaker and more heterogeneous among countries, suggesting the need for context-specific understanding to inform national policies and programs.

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    Article Information

    Accepted for Publication: February 25, 2020.

    Published: April 22, 2020. doi:10.1001/jamanetworkopen.2020.3386

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Li Z et al. JAMA Network Open.

    Corresponding Author: Rockli Kim, ScD, Division of Health Policy and Management, College of Health Sciences, Korea University, 145 Anam-ro, Seongbuk-gu, Hana Science Building, Room 355, Seoul 02841, South Korea (rocklikim@korea.ac.kr).

    Author Contributions: Drs Li and Kim had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Li, Kim, Subramanian.

    Acquisition, analysis, or interpretation of data: Li, Kim, Vollmer.

    Drafting of the manuscript: Li.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Li.

    Administrative, technical, or material support: Li.

    Supervision: Kim, Vollmer, Subramanian.

    Conflict of Interest Disclosures: None reported.

    Additional Information: The authors acknowledge the support of the Demographic and Health Survey Program (https://www.dhsprogram.com) for providing access to the data.

    References
    1.
    United Nations Children’s Fund. Malnutrition. Accessed March 24, 2020. https://data.unicef.org/topic/nutrition/malnutrition/
    2.
    Abarca-Gómez  L, Abdeen  ZA, Hamid  ZA,  et al; NCD Risk Factor Collaboration (NCD-RisC).  Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.   Lancet. 2017;390(10113):2627-2642. doi:10.1016/S0140-6736(17)32129-3PubMedGoogle ScholarCrossref
    3.
    United Nations. Sustainable development goals: goal 3: ensure healthy lives and promote well-being for all at all ages. Accessed March 24, 2020. https://www.un.org/sustainabledevelopment/health/
    4.
    Black  R, Alderman  H, Bhutta  Z,  et al.  Executive summary of The Lancet maternal and child nutrition series.   Lancet. Published June 6, 2013. Accessed March 24, 2020. https://www.thelancet.com/series/maternal-and-child-nutritionGoogle Scholar
    5.
    United Nations Children’s Fund. Improving child nutrition: the achievable imperative for global progress. Accessed March 24, 2020. https://www.unicef.org/nutrition/index_68661.html
    6.
    Khatun  W, Alam  A, Rasheed  S, Huda  TM, Dibley  MJ.  Exploring the intergenerational effects of undernutrition: association of maternal height with neonatal, infant and under-five mortality in Bangladesh .  BMJ Glob Health. 2018;3(6):e000881.PubMedGoogle Scholar
    7.
    Monteiro  CA, Benicio  MHD, Conde  WL,  et al.  Narrowing socioeconomic inequality in child stunting: the Brazilian experience, 1974-2007.   Bull World Health Organ. 2010;88(4):305-311. doi:10.2471/BLT.09.069195PubMedGoogle ScholarCrossref
    8.
    Ozaltin  E, Hill  K, Subramanian  SV.  Association of maternal stature with offspring mortality, underweight, and stunting in low- to middle-income countries.   JAMA. 2010;303(15):1507-1516. doi:10.1001/jama.2010.450PubMedGoogle ScholarCrossref
    9.
    Kim  R, Rajpal  S, Joe  W,  et al.  Assessing associational strength of 23 correlates of child anthropometric failure: an econometric analysis of the 2015-2016 National Family Health Survey, India.   Soc Sci Med. 2019;238:112374. doi:10.1016/j.socscimed.2019.112374PubMedGoogle Scholar
    10.
    Corsi  DJ, Mejía-Guevara  I, Subramanian  SV.  Risk factors for chronic undernutrition among children in India: estimating relative importance, population attributable risk and fractions.   Soc Sci Med. 2016;157:165-185. doi:10.1016/j.socscimed.2015.11.014PubMedGoogle ScholarCrossref
    11.
    Nshimyiryo  A, Hedt-Gauthier  B, Mutaganzwa  C,  et al.  Risk factors for stunting among children under five years: a cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey.   BMC Public Health. 2019;19(1):175. doi:10.1186/s12889-019-6504-zPubMedGoogle ScholarCrossref
    12.
    Kang  Y, Aguayo  VM, Campbell  RK,  et al.  Nutritional status and risk factors for stunting in preschool children in Bhutan.   Matern Child Nutr. 2018;14(suppl 4):e12653. doi:10.1111/mcn.12653PubMedGoogle Scholar
    13.
    Mistry  SK, Hossain  MB, Khanam  F,  et al.  Individual-, maternal- and household-level factors associated with stunting among children aged 0-23 months in Bangladesh.   Public Health Nutr. 2019;22(1):85-94. doi:10.1017/S1368980018002926PubMedGoogle ScholarCrossref
    14.
    Akombi  BJ, Agho  KE, Hall  JJ, Merom  D, Astell-Burt  T, Renzaho  AMN.  Stunting and severe stunting among children under-5 years in Nigeria: a multilevel analysis.   BMC Pediatr. 2017;17(1):15. doi:10.1186/s12887-016-0770-zPubMedGoogle ScholarCrossref
    15.
    Headey  D, Hoddinott  J, Park  S.  Drivers of nutritional change in four South Asian countries: a dynamic observational analysis.   Matern Child Nutr. 2016;12(suppl 1):210-218. doi:10.1111/mcn.12274PubMedGoogle ScholarCrossref
    16.
    Kim  R, Mejía-Guevara  I, Corsi  DJ, Aguayo  VM, Subramanian  SV.  Relative importance of 13 correlates of child stunting in South Asia: insights from nationally representative data from Afghanistan, Bangladesh, India, Nepal, and Pakistan.   Soc Sci Med. 2017;187:144-154. doi:10.1016/j.socscimed.2017.06.017PubMedGoogle ScholarCrossref
    17.
    Danaei  G, Andrews  KG, Sudfeld  CR,  et al.  Risk factors for childhood stunting in 137 developing countries: a comparative risk assessment analysis at global, regional, and country levels.   PLoS Med. 2016;13(11):e1002164. doi:10.1371/journal.pmed.1002164PubMedGoogle Scholar
    18.
    USAID. The DHS program: available datasets. Accessed March 24, 2020. https://www.dhsprogram.com/data/available-datasets.cfm
    19.
    World Health Organization. WHO child growth standards: methods and development. Accessed March 24, 2020. https://www.who.int/childgrowth/standards/technical_report/en/
    20.
    Assaf  S, Kothari  MT, Pullum  T. An assessment of the quality of DHS anthropometric data. Accessed March 24, 2020. https://www.dhsprogram.com/pubs/pdf/MR16/MR16.pdf
    21.
    Nahar  B, Ahmed  T, Brown  KH, Hossain  MI.  Risk factors associated with severe underweight among young children reporting to a diarrhea treatment facility in Bangladesh .  J Health Popul Nutr. 2010;28(5):476-483.PubMedGoogle Scholar
    22.
    Fenske  N, Burns  J, Hothorn  T, Rehfuess  EA.  Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.   PLoS One. 2013;8(11):e78692. doi:10.1371/journal.pone.0078692PubMedGoogle Scholar
    23.
    Merchant  AT, Jones  C, Kiure  A,  et al.  Water and sanitation associated with improved child growth.   Eur J Clin Nutr. 2003;57(12):1562-1568. doi:10.1038/sj.ejcn.1601725PubMedGoogle ScholarCrossref
    24.
    Vollmer  S, Harttgen  K, Kupka  R, Subramanian  SV.  Levels and trends of childhood undernutrition by wealth and education according to a Composite Index of Anthropometric Failure: evidence from 146 demographic and health surveys from 39 countries.  BMJ Global Health; 2017;2(2):e000206.
    25.
    Head  ML, Berry  LK, Royle  NJ, Moore  AJ.  Paternal care: direct and indirect genetic effects of fathers on offspring performance.   Evolution. 2012;66(11):3570-3581. doi:10.1111/j.1558-5646.2012.01699.xPubMedGoogle ScholarCrossref
    26.
    Siddiqi  MNA, Haque  MN, Goni  MA.  Malnutrition of under-five children: evidence from Bangladesh.   Asian J Med Sci. 2011;2(2):113-119. doi:10.3126/ajms.v2i2.3662Google ScholarCrossref
    27.
    Chirande  L, Charwe  D, Mbwana  H,  et al.  Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey.   BMC Pediatr. 2015;15:165. doi:10.1186/s12887-015-0482-9PubMedGoogle ScholarCrossref
    28.
    Fink  G, Sudfeld  CR, Danaei  G, Ezzati  M, Fawzi  WW.  Scaling-up access to family planning may improve linear growth and child development in low and middle income countries.   PLoS One. 2014;9(7):e102391. doi:10.1371/journal.pone.0102391PubMedGoogle Scholar
    29.
    Lakew  Y, Bekele  A, Biadgilign  S.  Factors influencing full immunization coverage among 12-23 months of age children in Ethiopia: evidence from the national demographic and health survey in 2011.   BMC Public Health. 2015;15:728. doi:10.1186/s12889-015-2078-6PubMedGoogle ScholarCrossref
    30.
    United Nations Children’s Fund. Guide to DHS statistics DHS-7. Accessed March 24, 2020. https://dhsprogram.com/Data/Guide-to-DHS-Statistics
    31.
    Demographic and Health Surveys. Wealth index construction. Accessed March 24, 2020. https://www.dhsprogram.com/topics/wealth-index/Wealth-Index-Construction.cfm
    32.
    United Nations Children’s Fund. DHS survey indicators: characteristics of households. Accessed March 24, 2020. https://dhsprogram.com/data/DHS-Survey-Indicators-Characteristics-of-Households.cfm
    33.
    Subramanian  SV, Ackerson  LK, Davey Smith  G, John  NA.  Association of maternal height with child mortality, anthropometric failure, and anemia in India.   JAMA. 2009;301(16):1691-1701. doi:10.1001/jama.2009.548PubMedGoogle ScholarCrossref
    34.
    Subramanian  SV, Ackerson  LK, Smith  GD.  Parental BMI and childhood undernutrition in India: an assessment of intrauterine influence.   Pediatrics. 2010;126(3):e663-e671. doi:10.1542/peds.2010-0222PubMedGoogle ScholarCrossref
    35.
    World Health Organization. Water sanitation hygiene. Accessed March 24, 2020. https://www.who.int/water_sanitation_health/monitoring/jmp2012/key_terms/en/
    36.
    World Health Organization; United Nations Children’s Fund. Core questions on drinking-water and sanitation for household surveys. Accessed March 24, 2020. https://www.who.int/water_sanitation_health/monitoring/household_surveys/en/
    37.
    Murphy  SP, Yaktine  AL, Suitor  CW, Moats  S.  Child and Adult Care Food Program: Aligning Dietary Guidance for All. Institute of Medicine; 2011.
    38.
    Ladwig  KH, Marten-Mittag  B, Formanek  B, Dammann  G.  Gender differences of symptom reporting and medical health care utilization in the German population.   Eur J Epidemiol. 2000;16(6):511-518. doi:10.1023/A:1007629920752PubMedGoogle ScholarCrossref
    39.
    Monden  CWS, Smits  J.  Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries.   Lancet Glob Health. 2017;5(7):e673-e679. doi:10.1016/S2214-109X(17)30197-3PubMedGoogle ScholarCrossref
    40.
    Kruk  ME, Gage  AD, Joseph  NT, Danaei  G, García-Saisó  S, Salomon  JA.  Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries.   Lancet. 2018;392(10160):2203-2212. doi:10.1016/S0140-6736(18)31668-4PubMedGoogle ScholarCrossref
    41.
    Hernández-Díaz  S, Peterson  KE, Dixit  S,  et al.  Association of maternal short stature with stunting in Mexican children: common genes vs common environment.   Eur J Clin Nutr. 1999;53(12):938-945. doi:10.1038/sj.ejcn.1600876PubMedGoogle ScholarCrossref
    42.
    Christian  P, Lee  SE, Donahue Angel  M,  et al.  Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries.   Int J Epidemiol. 2013;42(5):1340-1355. doi:10.1093/ije/dyt109PubMedGoogle ScholarCrossref
    43.
    Li  Z, Li  M, Subramanian  SV, Lu  C.  Assessing levels and trends of child health inequality in 88 developing countries: from 2000 to 2014.   Glob Health Action. 2017;10(1):1408385. doi:10.1080/16549716.2017.1408385PubMedGoogle Scholar
    44.
    United Nations Children’s Fund. Health and child survival. Accessed March 24, 2020. https://www.unicef.org/kyrgyzstan/health-and-child-survival
    45.
    Hatløy  A, Hallund  J, Diarra  MM, Oshaug  A.  Food variety, socioeconomic status and nutritional status in urban and rural areas in Koutiala (Mali).   Public Health Nutr. 2000;3(1):57-65.PubMedGoogle ScholarCrossref
    46.
    Rah  JH, Akhter  N, Semba  RD,  et al.  Low dietary diversity is a predictor of child stunting in rural Bangladesh.   Eur J Clin Nutr. 2010;64(12):1393-1398. doi:10.1038/ejcn.2010.171PubMedGoogle ScholarCrossref
    47.
    Tarini  A, Bakari  S, Delisle  H.  The overall nutritional quality of the diet is reflected in the growth of Nigerian children.   Sante. 1999;9(1):23-31.PubMedGoogle Scholar
    48.
    Bukania  ZN, Mwangi  M, Karanja  RM,  et al.  Food insecurity and not dietary diversity is a predictor of nutrition status in children within semiarid agro-ecological zones in Eastern Kenya.   J Nutr Metab. 2014;2014:907153. doi:10.1155/2014/907153PubMedGoogle Scholar
    49.
    Guandalini  S, Pensabene  L, Zikri  MA,  et al.  Lactobacillus GG administered in oral rehydration solution to children with acute diarrhea: a multicenter European trial.   J Pediatr Gastroenterol Nutr. 2000;30(1):54-60. doi:10.1097/00005176-200001000-00018PubMedGoogle ScholarCrossref
    50.
    Pavlinac  PB, Brander  RL, Atlas  HE, John-Stewart  GC, Denno  DM, Walson  JL.  Interventions to reduce post-acute consequences of diarrheal disease in children: a systematic review.   BMC Public Health. 2018;18(1):208. doi:10.1186/s12889-018-5092-7PubMedGoogle ScholarCrossref
    51.
    Dangour  AD, Watson  L, Cumming  O,  et al.  Interventions to improve water quality and supply, sanitation and hygiene practices, and their effects on the nutritional status of children.   Cochrane Database Syst Rev. 2013;(8):CD009382.PubMedGoogle Scholar
    52.
    Freeman  MC, Garn  JV, Sclar  GD,  et al.  The impact of sanitation on infectious disease and nutritional status: a systematic review and meta-analysis.   Int J Hyg Environ Health. 2017;220(6):928-949. doi:10.1016/j.ijheh.2017.05.007PubMedGoogle ScholarCrossref
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