research proposal of malnutrition

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Effective interventions to address maternal and child malnutrition: an update of the evidence

Affiliations.

1 Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada.
2 Division of Women and Child Health, The Aga Khan University, Karachi, Pakistan.
3 Robinson Research Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia.
4 Department of Pediatrics, Karjoo Family Center for Pediatric Gastroenterology, SUNY Upstate Medical University, Syracuse, NY, USA.
5 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
6 Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Centre of Excellence in Women and Child Health, The Aga Khan University, Karachi, Pakistan; Institute for Global Health and Development, The Aga Khan University, Karachi, Pakistan; Joannah and Brian Lawson Centre for Child Nutrition, Department of Nutrition, University of Toronto, Toronto, ON, Canada. Electronic address: [email protected].
PMID: 33691083
DOI: 10.1016/S2352-4642(20)30274-1

Malnutrition-consisting of undernutrition, overweight and obesity, and micronutrient deficiencies-continues to afflict millions of women and children, particularly in low-income and middle-income countries (LMICs). Since the 2013 Lancet Series on maternal and child nutrition, evidence on the ten recommended interventions has increased, along with evidence of newer interventions. Evidence on the effectiveness of antenatal multiple micronutrient supplementation in reducing the risk of stillbirths, low birthweight, and babies born small-for-gestational age has strengthened. Evidence continues to support the provision of supplementary food in food-insecure settings and community-based approaches with the use of locally produced supplementary and therapeutic food to manage children with acute malnutrition. Some emerging interventions, such as preventive small-quantity lipid-based nutrient supplements for children aged 6-23 months, have shown positive effects on child growth. For the prevention and management of childhood obesity, integrated interventions (eg, diet, exercise, and behavioural therapy) are most effective, although there is little evidence from LMICs. Lastly, indirect nutrition strategies, such as malaria prevention, preconception care, water, sanitation, and hygiene promotion, delivered inside and outside the health-care sector also provide important nutritional benefits. Looking forward, greater effort is required to improve intervention coverage, especially for the most vulnerable, and there is a crucial need to address the growing double burden of malnutrition (undernutrition, and overweight and obesity) in LMICs.

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Micronutrient powders and diarrhoea risk in infants and young children. Suchdev PS, Jefferds ME, Dewey KG, Zlotkin S, Aguayo VM, de Pee S, Kraemer K, Greig A, Arabi M, De-Regil LM; Home Fortification Technical Advisory Group. Suchdev PS, et al. Lancet Child Adolesc Health. 2021 Aug;5(8):e28-e29. doi: 10.1016/S2352-4642(21)00168-1. Lancet Child Adolesc Health. 2021. PMID: 34302747 Free PMC article. No abstract available.
Micronutrient powders and diarrhoea risk in infants and young children - Authors' reply. Keats EC, Das JK, Bhutta ZA. Keats EC, et al. Lancet Child Adolesc Health. 2021 Aug;5(8):e29-e30. doi: 10.1016/S2352-4642(21)00164-4. Lancet Child Adolesc Health. 2021. PMID: 34302748 No abstract available.

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Back to Journals » Nutrition and Dietary Supplements » Volume 13

A First Step Towards Eliminating Malnutrition: A Proposal for Universal Nutrition Screening in Pediatric Practice

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Authors Murray RD , Kerr KW , Brunton C , Williams JA , DeWitt T , Wulf KL

Received 21 October 2020

Accepted for publication 30 December 2020

Published 5 February 2021 Volume 2021:13 Pages 17—24

DOI https://doi.org/10.2147/NDS.S287981

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Gary Johanning

Robert D Murray, 1 Kirk W Kerr, 2 Cory Brunton, 2 Jennifer A Williams, 2 Tiffany DeWitt, 2 Karyn L Wulf 2, 3 1 The Ohio State University College of Medicine, Columbus, OH, USA; 2 Abbott Nutrition, Columbus, OH, USA; 3 Nationwide Children’s Hospital Division of Emergency Medicine, Columbus, OH, USA Correspondence: Karyn L Wulf Abbott Nutrition, 2900 Easton Square Place, Columbus, OH, 43219, USA Tel +1 614-370-4491 Email [email protected] Background: Childhood malnutrition remains far too common around the world today. In this paper, we discuss pediatric malnutrition in the context of protein-energy undernutrition and hidden hunger (single or multiple micronutrient deficiencies). Recent growth statistics show that nearly 150 million children under 5 years are stunted, and 50 million are wasted. At the same time, millions more children experience undernutrition of lesser severity but with negative effects on growth nonetheless. Beyond poor growth, such nutrient shortfalls can predispose children to impaired physical and mental development, which may have lifelong consequences. The World Health Organization recently set an aspirational goal “to end all forms of childhood malnutrition by 2030.” Plan of Action: Our paper proposes a stepwise strategy to raise awareness of childhood malnutrition risk and to work toward building a consensus on pediatric malnutrition screening as a pathway to ending childhood malnutrition. In a full plan for action, we propose to convene an expert Working Consensus Group on Pediatric Nutrition Screening (WCG-PNS). We propose that this group will work to (1) identify malnutrition screening tools specific for universal screening of children in hospital and community settings, (2) plan and lead healthcare professional training on how to screen for malnutrition risk, then take appropriate follow-up steps, (3) guide and advise quality improvement programs (QIPs) to facilitate incorporation of nutrition screening and interventions into everyday practice, and (4) measure and communicate potential findings in terms of health and financial outcomes. Conclusion: We recognize an urgent need for universal screening of infants and children for nutritional risk—around the world and across the continuum of care. Such screening is expected to promote early identification of children who can benefit from nutrition interventions, then ensure that these children get needed nutritional support. In this way, we aim to prevent growth impairment, which has also been associated with adverse effects on mental and physical development. Keywords: malnutrition, pediatrics, nutrition screening, nutritional interventions, undernutrition, clinical outcomes

Introduction

Childhood malnutrition is still far too common around the world today. As a triple threat, malnutrition presents in 3 forms—undernutrition, hidden hunger (micronutrient deficiencies), and overnutrition. 1 Here we discuss pediatric malnutrition in the context of protein-energy undernutrition and hidden hunger (single or multiple micronutrient deficiencies). As undernutrition and hidden hunger, pediatric malnutrition is defined as an imbalance between nutrient needs and nutrient intake, which results in a cumulative deficit of energy, protein, or micronutrients. 2 The consequences of such nutrient shortfalls can include poor growth and impaired physical or mental development. 2–4 Despite global nutrition initiatives and steady progress over the past 2 decades, the number of children worldwide with poor growth as an indicator of poor nutrition remains disturbingly high. 1 , 5 , 6 In fact, recent growth statistics show nearly 150 million children under 5 years are stunted, and 50 million are wasted. 5 At the same time, millions more children experience undernutrition of lesser severity but with negative effects on growth nonetheless. 3 For example, a recent survey study found that about 2% of US children ages 1 to 13 years were moderately undernourished based on anthropometric data, while nearly 11% had evidence of mild undernutrition. 7

Causes and Consequences of Childhood Malnutrition

Childhood Malnutrition: Causes, Mechanisms, Evidence Measures, and Treatments

Inadequate nutrition early in life impairs growth in childhood and jeopardizes a child’s likelihood of reaching full linear growth potential. 6 , 17–23 A poor nutritional start can also predispose a child to health problems from early childhood through adulthood. Malnutrition makes children more susceptible to infections, and severe malnutrition increases risk of death. 24 , 25 Disability-adjusted life years (DALYs)—a measure of overall disease burden expressed as the number of years lost due to ill-health, disability, or early death—are elevated by childhood malnutrition. 26 Developmental delays caused by early undernutrition can affect cognitive outcomes and impair productive potential as adults. 27 Taken together, impaired physical growth and cognitive ability can lead to loss of future productivity, also called “loss of human capital.” 28 As well, evidence links early childhood growth failure with increased risk factors for cardiovascular disease in later life, including dyslipidemia, hypertension, and glucose intolerance. 29

Addressing Childhood Malnutrition

identify malnutrition screening tools specific for universal screening of children in hospital and community settings,
plan and lead healthcare professional training on how to screen for malnutrition risk, then take appropriate follow-up steps,
conduct quality improvement programs (QIPs) to facilitate incorporation of nutrition screening and interventions into everyday practice, and
measure and communicate potential benefits in terms of health and financial outcomes.

Taking Action

Big-picture goals for the 21st century.

In 2012, the World Health Assembly (WHA) endorsed a comprehensive plan to improve nutrition in mothers, infants, and young children. 30 In the plan, a key goal was to reduce stunting and wasting in children; the specific global target was to reduce by 40% the number of stunted under-five children by 2025. 31 When WHO updated the plan 5 years later, the new aspirational goal was to end all forms of childhood malnutrition by 2030. 32

In the 21st Century, severe malnutrition with growth impairment has in fact trended downward. 5 , 33 For example, middle-income countries have had significant declines in the number of stunted children; countries like Bolivia and China halved rates of child undernourishment since 1990. 34 As emphasized by global nutrition leaders, further efforts are needed to sustain and extend these trends to reach the ultimate aim for “all children to be free of malnutrition in all forms.” 33

A Proposal to Fill Gaps in Pediatric Nutrition Care

Filling gaps in pediatric nutrition practice to reduce growth stunting, wasting, and other adverse outcomes of malnutrition.

Specific Gaps to Be Addressed in Updated Pediatric Nutrition Care

Hospitalized patients: Validated tools are available, but we aim to achieve consensus on selection of a single tool; such consistency will allow comparison of data across sites and settings.
Outpatient settings: Incorporate routine screening into practice; validation studies for screening tools and as for inpatients, we need consensus-based selection of a screening tool and training for its standardized use.
Ultimately, we aim to spread nutrition screening beyond hospital and clinical settings; reaching children in the community will help meet the 2030 WHO goal to end all forms of childhood malnutrition by 2030. 32

Putting Nutrition Screening into Practice: How to Do It

Nutrition screening is used for identification of risk for malnutrition or change in risk for malnutrition. A traditional approach to evaluate a child’s nutritional adequacy has been to compare a child’s growth measures with those of a standard population. The World Health Organization (WHO) has a full set of childhood growth standards by age and sex. 35 WHO characterizes growth in terms of z-scores—the number of standard deviations from the median value of the comparable WHO reference population. 35 , 36 A z-score between −1 and −2 indicates the child has evidence of mildly poor growth and may be at risk for undernutrition; a z-score between −2 and −3 indicates moderate undernutrition and < −3 signifies severe undernutrition. 2 Stunting is defined as height-for-age z-score (HAZ) < −2, wasting as weight-for-height z-score (WHZ) < −2, and underweight as weight-for-age z-score (WAZ) < −2. These cut-off values are based on the concept that the middle 95% of the statistical distribution represents the normal range, which is within two standard deviations above or below the median (z-score between −2 and +2). Similarly, growth parameters can be expressed in terms of percentiles (HAP, WHP, WAP); z-scores of −2 and −1 are approximately equivalent to the 2nd and 15th percentiles. Body Mass Index (BMI)-for-age and mid-upper arm circumference-for-age—are also advised by WHO as growth and nutrition indicators. 35 Finally, use of mid-upper arm circumference and skinfold thickness can be used as further indicators of nutritional status. 4 , 34 , 35

Selecting a Pediatric Nutrition Screening Tool

Pediatric Nutrition Screening Tools: Questions

What is the Best Screening Tool for Hospitals? For Healthcare in the Community?

Notably, all 4 of the validated pediatric tools were developed for use in inpatients. But an important gap in current pediatric nutrition screening is that use of different tools limits our ability to compare site-to-site findings on malnutrition prevalence and follow-up practice. Further, there is no pediatric screening tool yet validated for use in community clinics and pediatric practices, another major gap that needs to be filled. Screening in the outpatient setting may need to consider food insecurity and diet diversity as well as hidden hunger (micronutrient deficiencies in the setting of normal or above weight-for-height), and this may necessitate variation to current inpatient screening tools. If we do not have an agreed-upon screening tool for use in outpatient settings, we are missing many early opportunities to identify children at risk of malnutrition.

Other Gaps Related to Nutrition Screening in Pediatric Practice: Training and Policy Development

Two other major gaps exist for achieving universal pediatric nutrition screening. First of all, once screening tools are selected, it is necessary to provide training materials and practice for correct use of the screening tools. If frontline providers are not fully trained, we miss opportunities for interventions. Secondly, to underscore the importance using these tools, it will be critical for countries, regional, and local health authorities to develop policies and protocols, and gain alignment of pediatric and nutrition societies to support incorporation of screening tools into pediatric practice.

Why Addressing Childhood Nutrition Matters: Health and Economic Impact

There are many lessons that can be learned from adult healthcare practices of nutrition screening and care. Malnutrition can be viewed as an independent disease state and treated as one too; malnutrition has been shown to worsen clinical outcomes and to increase morbidity, mortality, and complication rates, thus causing additional costs. 43 On the other hand, there is ample evidence that nutrition screening identifies more patients at risk of malnutrition, allows faster implementation of interventions, and improves patient outcomes. With such improvements, there is much evidence to show that malnutrition treatment lowers healthcare resource utilization and cuts costs. 44–52 In comparison, relatively few studies have explored comparable benefits of pediatric nutrition interventions. One recent publication demonstrated pediatric patients at high nutritional risk were found to have hospital stays that were 16 days longer and $3500 more expensive than patients at moderate nutritional risk. 53 Other early findings suggest that similar results will follow for pediatric care. 53–57 These early pediatric studies build justification for screening: immediate cost-to-benefit return, not only in terms of health care costs, but also morbidity and mortality and patient quality of life.

Beyond the immediate short-term costs, if malnutrition in early childhood goes untreated, there can be serious tolls on human capital, evidenced by slower cognitive development, reduced schooling attainment, and decreased adult incomes by 5 to 53%. 58 On the other hand, prevention of undernutrition offers opportunities to preserve human capital, which is expected to translate as a return-on-investment by bettering economic productivity. By focusing on widespread and universal screening for malnutrition risk in hospitals and communities worldwide, we aim to detect and treat nutritional risk before it worsens.

A Plan for Action on Pediatric Nutrition Screening

Identify a validated PNS Tool for universal screening of hospital inpatients and identify a validated PNS tool for universal screening of outpatients visiting community clinics and pediatric practices. Engage key pediatric and nutrition society partners to develop and publish practice guidelines.
Design PNS Support Kits to facilitate nutrition awareness, training, and care in practice (eg, a glossary of nutrition-related terms, checklists for nutrition-related history-taking, education and training for taking anthropometric measures and interpreting them, training for effective and efficient use of nutritional screening, and guidance on follow-up nutritional intervention). The corollary to screening will be instituting nutritional intervention guidelines based on expert recommendations. This will be the key to establishing a new standard of care for at-risk children and teens.
Build multi-disciplinary groups of medical professionals for education and training on use of PNS Tools and Support Kits; such groups include physicians, nurses, and dietitians in hospitals, clinics, and community practices.
Measure outcomes of improved PNS by designing nutrition-focused quality improvement programs (QIPs), including strategies for data collection and analysis. Conduct QIP studies, analyze and share findings, and repeat steps in small “plan-do-study-act” iterative cycles for continued QIP on PNS.
Based on outcomes data from pediatric nutrition screening initiatives, conduct health economic studies that can help inform potential savings derived from healthcare resource utilization and evaluate the long-term impact of nutrition screening and intervention on increasing productivity and preserving human capital. Potential sources for saving are lowered frequency of clinical visits, shortened hospital length of stays, and fewer hospital readmissions.

Action plan to enhance Pediatric Nutrition Screening, facilitate treatment, and improve health and economic outcomes.

HCPs, healthcare professionals; QIP, quality improvement program; HEOR, health economic outcomes research.

Conclusions

We recognize an urgent need for universal screening of infants and children for nutritional risk—around the world and across the continuum of care. Such screening is expected to promote early identification of children at nutritional risk who can benefit from nutrition interventions, then ensure that these children get needed nutritional support. In this way, we aim to prevent growth impairment, which can be associated with adverse effects on mental and physical development. To this end, we propose to convene an international Working Consensus Group on Pediatric Nutrition Screening.

Data Sharing Statement

Not applicable.

Ethics Approval and Consent to Participate

Not applicable

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

There was no funding for this paper.

Dr Robert D Murray serves in the Speaker’s Bureau and provides consultation to Abbott Nutrition International. He was Professor of Pediatrics at The Ohio State University College of Medicine. Dr Kirk W Kerr, Mr Cory Brunton, Ms Jennifer A Williams, Ms Tiffany DeWitt, Dr Karyn L Wulf are employees and may be stockholders for Abbott Laboratories. The authors declare that they have no other competing interests in this work.

1. Mwangome M, Prentice AM. Tackling the triple threats of childhood malnutrition. BMC Med . 2019;17(1):210. doi:10.1186/s12916-019-1464-9

2. Mehta NM, Corkins MR, Lyman B, et al. Defining pediatric malnutrition: a paradigm shift toward etiology-related definitions. JPEN J Parenter Enteral Nutr . 2013;37(4):460–481. doi:10.1177/0148607113479972

3. Bouma S. Diagnosing pediatric malnutrition. Nutr Clin Pract . 2017;32(1):52–67. doi:10.1177/0884533616671861

4. Becker PJ, Nieman Carney L, Corkins MR, et al. Consensus statement of the academy of nutrition and dietetics/american society for parenteral and enteral nutrition: indicators recommended for the identification and documentation of pediatric malnutrition (undernutrition). J Acad Nutr Diet . 2014;114(12):1988–2000. doi:10.1016/j.jand.2014.08.026

5. United Nations Children’s Fund (UNICEF) WHO, International Bank for Reconstruction and Development/The World Bank. Levels and Trends in Child Malnutrition: Key Findings of the 2019 Edition of the Joint Child Malnutrition Estimates . Washington DC: United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank; 2019.

6. de Onis M, Branca F. Childhood stunting: a global perspective. Matern Child Nutr . 2016;12(Suppl 1):12–26.

7. Price A, Williams J, Doetsch H, Spees C, Taylor C. Utilization of current diagnostic indicators to characterize pediatric undernutrition among US children. Nutrients . 2020.

8. Tolia V. Very early onset nonorganic failure to thrive in infants. J Pediatr Gastroenterol Nutr . 1995;20(1):73–80.

9. Woolston JL. Eating disorders in infancy and early childhood. J Am Acad Child Psychiatry . 1983;22(2):114–121.

10. Joosten KF, Hulst JM. Prevalence of malnutrition in pediatric hospital patients. Curr Opin Pediatr . 2008;20(5):590–596. doi:10.1097/MOP.0b013e32830c6ede

11. Kleinert JO. Pediatric feeding disorders and severe developmental disabilities. Semin Speech Lang . 2017;38(2):116–125. doi:10.1055/s-0037-1599109

12. Romano C, Dipasquale V, Gottrand F, Sullivan PB. Gastrointestinal and nutritional issues in children with neurological disability. Dev Med Child Neurol . 2018;60(9):892–896. doi:10.1111/dmcn.13921

13. UNICEF. The State of the World’s Children 2019. Children, Food and Nutrition: Growing Well in a Changing World . New York: United Nations Children’s Fund (UNICEF); 2019.

14. Scharf RJ, Rogawski ET, Murray-Kolb LE, et al. Early childhood growth and cognitive outcomes: findings from the MAL-ED study. Matern Child Nutr . 2018;14(3):e12584. doi:10.1111/mcn.12584

15. Luby SP, Rahman M, Arnold BF, et al. Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Bangladesh: a cluster randomised controlled trial. Lancet Glob Health . 2018;6(3):e302–e315. doi:10.1016/S2214-109X(17)30490-4

16. Tickell KD, Atlas HE, Walson JL. Environmental enteric dysfunction: a review of potential mechanisms, consequences and management strategies. BMC Med . 2019;17(1):181. doi:10.1186/s12916-019-1417-3

17. UNICEF. Progress for children: a report card on nutrition. A report card on nutrition: number 4, May 2006; 2006. Available from: https://www.unicef.org/progressforchildren/2006n4/index_undernutrition.html . Accessed November 14 , 2019 .

18. Nassar MF, Shaaban SY, Nassar JF, Younis NT, Abdel-Mobdy AE. Language skills and intelligence quotient of protein energy malnutrition survivors. J Trop Pediatr . 2012;58(3):226–230. doi:10.1093/tropej/fmr081

19. Sandjaja PBK, Rojroonwasinkul N, et al. Relationship between anthropometric indicators and cognitive performance in Southeast Asian school-aged children. Br J Nutr . 2013;110(Suppl 3):S57–64. doi:10.1017/S0007114513002079

20. Perignon M, Fiorentino M, Kuong K, et al. Stunting, poor iron status and parasite infection are significant risk factors for lower cognitive performance in Cambodian school-aged children. PLoS One . 2014;9(11):e112605. doi:10.1371/journal.pone.0112605

21. Prentice AM, Ward KA, Goldberg GR, et al. Critical windows for nutritional interventions against stunting. Am J Clin Nutr . 2013;97(5):911–918. doi:10.3945/ajcn.112.052332

22. Sudfeld CR, McCoy DC, Fink G, et al. Malnutrition and its determinants are associated with suboptimal cognitive, communication, and motor development in Tanzanian children. J Nutr . 2015;145(12):2705–2714. doi:10.3945/jn.115.215996

23. Xie W, Jensen SKG, Wade M, et al. Growth faltering is associated with altered brain functional connectivity and cognitive outcomes in urban Bangladeshi children exposed to early adversity. BMC Med . 2019;17(1):199. doi:10.1186/s12916-019-1431-5

24. Walson JL, Berkley JA. The impact of malnutrition on childhood infections. Curr Opin Infect Dis . 2018;31(3):231–236. doi:10.1097/QCO.0000000000000448

25. Nalwanga D, Musiime V, Kizito S, et al. Mortality among children under five years admitted for routine care of severe acute malnutrition: a prospective cohort study from Kampala, Uganda. BMC Pediatr . 2020;20(1):182.

26. Black RE, Allen LH, Bhutta ZA, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet . 2008;371(9608):243–260. doi:10.1016/S0140-6736(07)61690-0

27. Independent Evaluation Group for Communication Strategy and Learning. What Can We Learn from Nutrition Impact Evaluations? Lessons from a Review of Interventions to Reduce Childhood Malnutrition Oin Developing Countries . Washington, DC: The International Bank for Reconstruction and Development/The World Bank; 2010.

28. Victora CG, Adair L, Fall C, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet . 2008;371(9609):340–357. doi:10.1016/S0140-6736(07)61692-4

29. DeBoer MD, Lima AA, Oria RB, et al. Early childhood growth failure and the developmental origins of adult disease: do enteric infections and malnutrition increase risk for the metabolic syndrome?. Nutr Rev . 2012;70(11):642–653.

30. de Onis M, Dewey KG, Borghi E, et al. The World Health Organization’s global target for reducing childhood stunting by 2025: rationale and proposed actions. Matern Child Nutr . 2013;9(Suppl 2):6–26. doi:10.1111/mcn.12075

31. WHO. WHO Global Targets 2025 . WHO (World Health Organization); 2012.

32. WHO. The extension of the 2025 maternal, infant and young child nutrition targets to 2030. WHO/UNICEF Discussion paper; 2017. Available from: https://www.who.int/nutrition/global-target-2025/discussion-paper-extension-targets-2030.pdf . Accessed November 21 , 2019 .

33. United Nations Children’s Fund (UNICEF) WHO, International Bank for Reconstruction and Development/The World Bank. Levels and Trends in Child Malnutrition: Key Findings of the 2018 Edition of the Joint Child Malnutrition Estimates . Geneva: United Nations Children’s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank; 2018.

34. Humger and undernourishment. OurWorldInData.org; 2020. Available from: ‘ https://ourworldindata.org/hunger-and-undernourishment . Accessed May 15 , 2020 .

35. WHO. The WHO child growth standards; 2020. Available from: https://www.who.int/childgrowth/standards/en/ . Accessed April 25 , 2020 .

36. WHO. The Z-score or standard deviation classification system. Global Database on Child Growth and Malnutrition; 2019. Available from: https://www.who.int/nutgrowthdb/about/introduction/en/index4.html . Accessed April 23 , 2020 .

37. Ferguson M, Capra S, Bauer J, Banks M. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition . 1999;15(6):458–464. doi:10.1016/S0899-9007(99)00084-2

38. Becker PJ, Gunnell Bellini S, Wong Vega M, et al. Validity and reliability of pediatric nutrition screening tools for hospital, outpatient, and community settings: a 2018 evidence analysis center systematic review. J Acad Nutr Diet . 2020;120(2):288–318 e282. doi:10.1016/j.jand.2019.06.257

39. McCarthy H, Dixon M, Crabtree I, Eaton-Evans MJ, McNulty H. The development and evaluation of the Screening Tool for the Assessment of Malnutrition in Paediatrics (STAMP©) for use by healthcare staff. J Hum Nutr Diet . 2012;25(4):311–318. doi:10.1111/j.1365-277X.2012.01234.x

40. Huysentruyt K, Alliet P, Muyshont L, et al. The STRONGkids nutritional screening tool in hospitalized children: A validation study. Nutrition . 2013;29(11–12):1356–1361. doi:10.1016/j.nut.2013.05.008

41. Gerasimidis K, Keane O, Macleod I, Flynn DM, Wright CM. A four-stage evaluation of the paediatric yorkhill malnutrition score in a tertiary paediatric hospital and a district general hospital. Br J Nutr . 2010;104(5):751–756. doi:10.1017/S0007114510001121

42. White M, Lawson K, Ramsey R, et al. Simple nutrition screening tool for pediatric inpatients. JPEN J Parenter Enteral Nutr . 2016;40(3):392–398. doi:10.1177/0148607114544321

43. Norman K, Pichard C, Lochs H, Pirlich M. Prognostic impact of disease-related malnutrition. Clin Nutr . 2008;27(1):5–15. doi:10.1016/j.clnu.2007.10.007

44. Elia M, Normand C, Laviano A, Norman K. A systematic review of the cost and cost effectiveness of using standard oral nutritional supplements in community and care home settings. Clin Nutr . 2016;35(1):125–137. doi:10.1016/j.clnu.2015.07.012

45. Elia M, Normand C, Norman K, Laviano A. A systematic review of the cost and cost effectiveness of using standard oral nutritional supplements in the hospital setting. Clin Nutr . Apr 2016;35(2):370–380.

46. Philipson TJ, Snider JT, Lakdawalla DN, Stryckman B, Goldman DP. Impact of oral nutritional supplementation on hospital outcomes. Am J Manag Care . 2013;19(2):121–128.

47. Siegel S, Fan L, Goldman A, Higgins J, Goates S, Partridge J. Impact of nutrition-focused quality improvement on length of stay. J Nutr Care Qual . 2019;34(3):203–209.

48. Sriram K, Sulo S, VanDerBosch G, et al. Nutrition-focused quality improvement program results in significant readmission and length of stay reductions for malnourished surgical patients. JPEN J Parenter Enteral Nutr . 2018;42(6):1093–1098. doi:10.1002/jpen.1040

49. Sriram K, Sulo S, VanDerBosch G, et al. A comprehensive nutrition-focused quality improvement program reduces 30-day readmissions and length of stay in hospitalized patients. JPEN J Parenter Enteral Nutr . 2017;41(3):384–391. doi:10.1177/0148607116681468

50. Sulo S, Feldstein J, Partridge J, Schwander B, Sriram K, Summerfelt WT. Budget impact of a comprehensive nutrition-focused quality improvement program for malnourished hospitalized patients.. Am Health Drug Benefits . 2017;10(5):262–270.

51. Tyler R, Barrocas A, Guenter P, et al. Value of nutrition support therapy: impact on clinical and economic outcomes in the United States. JPEN J Parenter Enteral Nutr . 2020;44(3):395–406. doi:10.1002/jpen.1768

52. Meehan A, Loose C, Bell J, Partridge J, Nelson J, Goates S. Health system Quality Improvement: impact of prompt nutrition care on patient outcomes and health care costs. J Nurs Care Qual . 2016;31(3):217–223. doi:10.1097/NCQ.0000000000000177

53. Gambra-Arzoz M, Alonso-Cadenas JA, Jiménez-Legido M, et al. Nutrition risk in hospitalized pediatric patients: higher complication rate and higher costs related to malnutrition. Nutr Clin Pract . 2020;35(1):157–163. doi:10.1002/ncp.10316

54. Dempster R, Burdo-Hartman W, Halpin E, Williams C. Estimated cost-effectiveness of intensive interdisciplinary behavioral treatment for increasing oral intake in children with feeding difficulties. J Pediatric Psychol . 2016;41(8):857–866. doi:10.1093/jpepsy/jsv112

55. Klek S, Hermanowicz A, Dziwiszek G, et al. Home enteral nutrition reduces complications, length of stay, and health care costs: results from a multicenter study. Am J Clin Nutr . 2014;100(2):609–615. doi:10.3945/ajcn.113.082842

56. Lakdawalla DN, Mascarenhas M, Jena AB, et al. Impact of oral nutrition supplements on hospital outcomes in pediatric patients. JPEN J Parenter Enteral Nutr . 2014;38(2Suppl):42S–49S.

57. Phillips CA, Bailer J, Foster E, et al. Evaluation of an automated pediatric malnutrition screen using anthropometric measurements in the electronic health record: a quality improvement initiative. Support Care Cancer . 2020;28(4):1659–1666.

58. Shekar M, Kakietek J, D’Alimonte MR, et al. Reaching the global target to reduce stunting: an investment framework. Health Policy Plan . 2017;32(5):657–668.

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Research article
Open access
Published: 29 July 2020

Factors associated with malnutrition in children < 5 years in western Kenya: a hospital-based unmatched case control study

Edwin Gudu 1 , 2 ,
Mark Obonyo 2 ,
Victor Omballa 3 ,
Elvis Oyugi 2 ,
Cecilia Kiilu 4 ,
Jane Githuku 2 ,
Zeinab Gura 5 &
James Ransom ORCID: orcid.org/0000-0001-6528-3994 6

BMC Nutrition volume 6 , Article number: 33 ( 2020 ) Cite this article

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Globally, under-nutrition accounts for > 3 million deaths annually among children < 5 years, with Kenya having ~ 35,000 deaths. This study aimed to identify factors associated with malnutrition in children aged < 5 years in western Kenya.

We conducted a hospital-based unmatched case-control study between May and June 2017. Cases were defined as children aged 6–59 months with either z-score for weight-for-height ≤ −2SD or ≥ +2SD; weight-for-age ≤ −2SD or ≥ +2SD; or height-for-age ≤ −2SD. Controls were children aged 6–59 months with age-appropriate anthropometric measurements. Cases were consecutively recruited while systematic random sampling was used to select controls. Data from interviews and clinical records were collected and entered into Epi-Info, which was used to run unconditional logistic regression analyses.

A total of 94 cases and 281 controls were recruited. Of the cases, 84% (79/94) were under-nourished. Mother not having attended ante-natal clinic (OR = 7.9; 95% CI: 1.5–41.2), deworming (OR = 0.8; 95% CI: 0.4–1.2), and pre-lacteal feeding (OR = 1.8; 95% CI: 1.1–3.0) were associated with under-nutrition. Delayed developmental milestones (AOR = 13.9; 95% CI: 2.8–68.6); low birth weight (AOR = 3.3; 95% CI: 1.4–7.6), and paternal lack of formal education (AOR = 4.9; 95% CI: 1.3–18.9) were independently associated with under-nutrition.

Proper pre-natal care, child feeding practices and deworming programs should be enhanced to reduce pediatric malnutrition.

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Malnutrition refers to a state of either under-nutrition or over-nutrition. Under-nutrition occurs when the diet a person consumes does not meet their body’s requirement for growth and development whereas over-nutrition occurs when a person consumes too many calories [ 1 ]. Good nutrition and feeding practices are critical to a child’s growth and development especially during the first two years of life [ 2 ]. Under-nutrition impairs a child’s immunity, which can lead to recurrent infections, and impaired physical and cognitive development [ 3 ].

Under-nutrition is a major cause of morbidity and mortality especially, in low-to-middle-income (LMIC) countries. Globally, malnutrition contributes to more than 3 million deaths among children < 5 years annually [ 4 ]. UNICEF estimates that in Kenya, 239,446 children suffer from moderate acute malnutrition (MAM) and 2600 children suffer from severe acute malnutrition (SAM). Under-nutrition also contributes to about 35,000 deaths among children < 5 years each year in Kenya [ 4 ]. Stunting has also been linked to development of non-communicable diseases and lower adult productivity later in life. Children < 5 years who are prone to recurrent infectious diseases such as diarrheal illnesses, respiratory tract infections, tuberculosis and malaria often have under-nutrition as a co-morbidity [ 5 ].

The Kenya Demographic Health Survey 2014 reports that 26% of children < 5 years are stunted, 4% are wasted, and 11% are underweight. Malnutrition remains a public health concern in western Kenya. According to the survey 25.2% of children < 5 years are stunted while 8.2% are severely stunted [ 6 ]. This means that 1 in 4 children suffer from chronic under-nutrition. Therefore, identifying factors associated with malnutrition (especially under-nutrition) is vital in preventing the development of long-term deleterious effects.

This study aimed to identify clinical, demographic, and socio-economic factors associated with malnutrition in children < 5 years for public health action.

The study was carried out at Alupe Sub-County Hospital. The hospital is a level 4 hospital located in Angorom ward, Teso South Sub-County in Busia County serving a catchment population of 34,321 persons (Fig. 1 ) [ 7 ].

Map of Busia County, Western Kenya showing the constituent sub-counties including Teso South sub-county. Map source: Commission on Revenue Allocation-Kenya

Study design

We conducted a facility-based unmatched case control study carried out between May 2017 and June 2017. We chose an unmatched design due to the more limited number of cases and the inconsistency and lack of some documentation of the data available in the records at the hospital. The study population consisted of all children < 5 years attending the child welfare clinic and the outpatient clinic within the hospital during the study period.

Case definitions

Under-nourished child was defined as a child aged 6–59 months attending the hospital as an inpatient or outpatient whose anthropometric measurements were not appropriate for their age with z-scores (weight-for-height [WHZ], weight-for-age [WAZ], height-for-age [HAZ]) of <= − 2 SD. WAZ score from the WHO charts were used to define presence of under-nutrition [ 8 ].

A participant was classified as stunted if HAZ score was <−2SD and severely stunted if HAZ score was <−3SD. Wasting was defined as WHZ score < −2SD while severe wasting was WHZ score < −3SD. Any participant with WAZ score < −2SD was classified underweight.

Mid upper arm circumference (MUAC) calculations

For the MUAC cut-points to determine whether a child was under- or over-nourished, we used the cut-points of any child with MUAC < 126 mm was classified under-nourished [ 8 ].

Definition of controls

Any child aged 6–59 months attending the hospital as an inpatient or outpatient whose anthropometric measurements are appropriate for their age with z-scores between -2SD and + 2SD [ 9 ].

Sample size determination

The sample size was calculated using statistical software Epi Info® version 7.2.0. The study assumed a 95% confidence interval, 80% power, 10% wasting among controls [ 10 ], and the ratio of cases to controls of 1:3. Using these assumptions, the minimum sample size was 375 (94 cases and 282 controls).

Selection of cases and controls

The cases were sampled consecutively due to the low number seen each day for eligible children enrolled for nutritional support in welfare clinic. The sampling occurred via the data entered into the MoH Child Health Logbook, which would have each presenting child’s age, MUAC, and other information indicative of over-, under-, or at-level nutrition. The controls were selected through systematic random sampling from the data in the logbook. The average number of children < 5 years visiting the outpatient section of the child welfare clinic daily was used as a sampling frame. This was determined by obtaining the number of children visiting the out-patient clinic between April and June of three preceding years before the study. The study was conducted during weekdays within the duration of the study period hence the number of controls to be enrolled in the study on any single day was pre-determined. Using the average number of patients seen each day at the clinic and number of controls to be enrolled in the study each day, a sampling interval was determined, and the first control was picked randomly between one and the sampling interval. The sampling interval was then added to enroll the remaining controls. Any eligible participant whose legal parent/guardian did not give oral consent was replaced by the next available participant whose legal parent/guardian consented to the study.

Data collection

Triage was carried out by the hospital staffs as is the norm and all critically and severely ill patients were urgently attended to by the hospital clinicians as per procedures and guidelines of the hospital. The weight was measured using electronic digital weighing scale (Seca®). For height/length, children < 2 years were measured lying down (recumbent length) while those who were > =2 years were measured standing up. For MUAC and head circumference, a non-stretch tape was used.

A pre-tested trans-adapted interviewer-administered questionnaire was used for each study participant to obtain demographic, clinical, nutritional, social and economic information. This questionnaire was adapted from a survey sheet used in Guinea [ 11 ]. (Each patient was de-identified by a unique code to ensure their privacy and maintenance of confidentiality.)

Data management

Data entry, cleaning, validation and analysis was done using Microsoft Excel (Microsoft, Seattle, WA, USA), and Epi info version 7 (CDC, Atlanta, GA, USA). Anthropometric data was analyzed using WHO Anthro® software version 3.2.2 (WHO Anthro®) to assess nutritional indicators like weight-for-length/weight-for-height (wasting), weight-for-age (underweight or overweight), length-for-age/height-for-age (stunting), MUAC-for-age, and HC-for-age. The software then provided the z-scores based on gender, age and the anthropometric measurements. We calculated measures of central tendency and dispersion for the continuous variables and proportions for categorical variables. For univariable analysis, we calculated odds ratios (OR), 95% confidence intervals (CI), chi-square statistics and p -values. Variables with p-value ≤0.05 were statistically significant. We carried out unconditional logistic regression with variables that had p-values of < 0.2 at univariable analysis. A backward elimination stepwise method was used to identify independent factors associated with malnutrition. During model building, any variable that caused an insignificant increase in deviance on removal from the model were left out of the model while the variable that caused a significant increase in deviance on removal were retained in the model. All variables removed from the model when a backward stepwise method was performed and those known to be potential cofounders or factors associated with malnutrition from previous studies were tested for confounding, any of the mentioned variables that had a more than ten percentage change (> 10%) between the crude and adjusted odds ratio was considered as a confounder. The final model after testing for all biologically and statistically plausible interactions had only variables with p -value ≤0.05.

Description of the study participants

There were 375 participants (94 cases and 281 controls), with median age of 16 months (IQR 10, 22), and 51% (191/375) male. Males were 57% (54/94) of cases and 49% (137/281) of controls.

Nutritional status of cases

Of the cases, 84% (79/94) were under-nourished and 16% (15/94) over-nourished. Among those that were under-nourished, by assessing the WHZ score, 20% (16/79) were wasted while 9% (7/79) were severely wasted. Among the same group, using WAZ score, 39% (31/79) were underweight while 29% (23/79) were severely underweight. Using the HAZ score among the under-nourished, 46% (36/79) were stunted while 38% (30/79) were severely stunted.

Univariable and multivariable analysis of factors associated with under-nutrition

On univariable analysis, socio-demographic factors like high birth order of five or more (OR = 2.3; 95% CI: 0.9–6.0), living in urban areas (OR = 1.9; 95% CI: 0.8–4.3), children whose mothers had no formal education (OR = 2.0; 95% CI 0.9–4.4), those whose fathers had no formal education (OR = 4.6; 95% CI: 1.4–15.0) and those who came from large family sizes of more than 6 occupants (OR = 1.8; 95% CI: 1.1–3.0) had higher odds of developing under-nutrition.

Pre-natal maternal factors were also shown to increase odds of developing under-nutrition. These included: participants whose mothers’ did not attend antenatal clinic (ANC) at least once (OR = 7.9; 95% CI: 1.5–41.6), participants whose mothers who did not attend 4 ANC visits as recommended by WHO (OR = 1.6; 95% CI: 0.9–2.7) and those whose mothers had illness during pregnancy (OR = 1.7; 95% CI: 1.0–2.8). The participants who were born preterm (OR = 2.0; 95% CI: 0.6–7.4) and those with low birth weight (OR = 2.8; 95% CI: 1.2–6.2) had higher odds of under-nutrition compared to term babies and those with normal birth weights.

Post-natal factors such as failure to complete or not being up-to-date on immunizations as per the national immunization schedule (OR = 2.2; 95% CI: 0.7–7.2) and human immunodeficiency virus (HIV) sero-exposure (OR = 1.4; 95% CI: 0.6–3.5) and delayed developmental milestones (OR = 18.9; 95% CI: 4.1–87.5) also increased the odds of developing under-nutrition. The participants who were eligible for deworming and had been dewormed at least once were protected from under-nutrition (OR = 0.8; 95% CI: 0.4–1.2). Infant and young child feeding practices also affected nutritional status of the participants. During the study period, 24% (89/375) of the participants were receiving pre-lacteal feeds increased their odds of under-nutrition (OR 1.8; 95% CI: 1.0–3.1). Exclusive breastfeeding for 6 months as recommended by the WHO was also widely practiced with 72% (271/375) of the participant’s parents adhering to this guideline. Forty percent (149/375) of the participant’s parents still used bottle with nipple for feeding, while 31% (116/375) ceased breastfeeding before the recommended 2 years of age. However, during the study period, there was no statistically significant association between duration of exclusive breastfeeding for the first six months of life (OR = 1.1; 95% CI 0.6–2.0), cessation of breastfeeding at less than 2 years (OR = 0.6; 95% CI 0.2–1.7) or bottle with nipple feeding (OR = 0.9; 95% CI 0.5–1.5) and developing under-nutrition.

Economic factors of the families also affected the nutritional status of the participants. Those whose mothers were unemployed had higher chances of under-nutrition (OR = 1.8; 95% CI 1.0–3.1) whereas those families with an average monthly income of above 5000 Kenya shillings (KES) were protective of under-nutrition (OR 0.7; 95% CI 0.4–1.2) (Table 1 ).

On multivariable analysis, delayed developmental milestones (AOR = 13.9; 95% CI: 2.8–68.6); low birth weight (AOR = 3.3; 95% CI: 1.4–7.6) and paternal lack of formal education (AOR = 4.9; 95% CI: 1.3–18.9) were found to be independently associated with under-nutrition.

The study identified various factors affecting nutritional status among children < 5 years which need to be adequately addressed. This included both pre-natal and post-natal factors as well as infant and young child feeding practices. Therefore, consistent follow-up of pregnant mothers from the antenatal period and post-natal care of the children < 5 years needs to be enhanced.

Among the undernourished, we found that stunting was the most common form of malnutrition, followed by children who were underweight and wasting being the least common among the study population. Stunting was common among cases of under-nutrition and over-nutrition alike. Stunting is a chronic form of malnutrition that results from prolonged non-adherence to proper dietary requirements to meet the body’s physiological needs. These findings were similar to those of a demographic and health survey carried out in the Western Kenya in 2014 [ 6 ]. Other studies carried out in Burundi and Uganda also had similar findings [ 12 , 13 ].

Deworming of children > 1 year of age was also found to be protective of under-nutrition. This finding was in line with another study done in India among pre-school children which showed substantial weight gain among children who were dewormed [ 14 ]. This is because intestinal nematodes affect absorption of both micro and macronutrients which are vital for a child’s growth. However, current systematic reviews show little benefit is derived from mass deworming. They show that children found to be worm infested are the ones that gain weight more significantly compared to non-worm infested children [ 15 , 16 ].

Proper breastfeeding practices for children are advocated for by WHO [ 17 ]. Children that are breastfed up to 2 years of age show quicker linear growth than those breastfed for shorter durations [ 2 ]. Feeding practices such as bottle with nipple feeding, breastfeeding within thirty minutes of delivery, exclusive breast feeding for 6 months and cessation of breastfeeding at 2 years were also assessed during the study. However, they were not statistically significantly associated with under-nutrition. In contrast, the giving of pre-lacteal feeds adversely affected nutritional status and predisposed the children to under-nutrition. This has also been shown by other studies [ 18 , 19 , 20 ]. This could be because pre-lacteal feeding affects the quality and quantity of breastfeeding which in turn affects the nutritional intake by the child. As such, proper education on feeding practices during post-natal period should be enhanced.

Children with under-nutrition were also shown to be more likely to have delayed developmental milestones. This finding was consistent with other studies [ 21 , 22 , 23 ]. This could be because they lack the macro and micronutrients necessary for normal growth and development. Children with prematurity and low birth weight also had higher odds of under-nutrition. These findings were similar from a review done in several countries [ 24 ]. This could be because they require more nutrients for catch-up growth which if not provided in adequate quantities leave them vulnerable to develop under-nutrition. These children should therefore be followed up more closely.

We also found that lack of parental formal education was linked to development of under-nutrition with paternal illiteracy being shown to have a greater influence. This finding concurred with other studies [ 20 , 25 ]. This could be because the community being a patriarchal society, the fathers control the family’s resources. As such, lack of formal education could mean no formal employment and by extension no regular source of income to provide for their families.

Our study also showed that the cases of over-nutrition were also high, compared to findings of other studies in Kenya, despite the hospital serving a population of predominantly low socio-economic status [ 26 ]. This clearly points to the double burden of malnutrition that is supported by other literature based on a critical review done in other lower middle income countries [ 27 ]. This is a new development over the last couple of decades that needs to be further explored to halt and decrease the burden of cases of over-nutrition.

During the study period, children aged less than 12 months were more likely to be over-nourished. This finding was similar to another study carried out in Kenya in 2009 [ 28 ]. This could be because younger children are more likely to receive more attention and feeding effort from their parents as compared to older children. Male gender was also positively associated with over-nutrition. A study carried out in Kenya in 2016 had similar findings [ 29 ]. This could probably be due to the value and cultural preferences placed on the male child. As such, they are likely to be better fed as compared to the girl child. This has also been shown in other Sub-Saharan African countries [ 30 ].

Children who came from households in urban areas and those who came from families with higher average monthly income had higher odds of over-nutrition. This finding was similar to other studies [ 28 ]. This could be due to the higher levels of income which increase their ability to provide more than enough nutrition for their growing children.

Maternal lack of formal education also increased the chances of developing over-nutrition. This finding was contrary with other studies carried out in Sub-Saharan Africa [ 30 ]. High birth weight was also linked to increased chances of over-nutrition. This has also been shown by other studies [ 30 , 31 ]. However, the exact mechanism of this link has not yet been clearly described.

We conducted a hospital-based case-control study and as such, its findings cannot be generalized to the entire population of under-five children in Western Kenya. The data collected on some of the variables could be susceptible to recall bias more so if the child was brought in by a guardian. Another limitation of the study was that the study relied on participants’ self-reported data, which was prone to recall bias and social desirability bias and interviewer bias due to the retrospective tracking of information beyond the advantages of case control study. The other limitation was that since it was a case–control study, which means it cannot establish the relationship between exposure and disease. Anthropometric measures and their technical errors are another limitation because it can result in misclassification of children’s nutritional status. However, we gave strict attention to the study procedures, including the process of training the research team and workers at the hospital, standardization of anthropometric measurements, and close and supportive supervision throughout the field activities to minimize biases.

Proper pre-natal care, child feeding practices and deworming programs should be enhanced. As such, we recommend that close monitoring especially of children more likely to be malnourished should be enhanced. This can be done by providing job aids to providers to help them talk to parents about adherence to key recommended practices such as appropriate feeding, continuous auditing of patient outcomes, and better use of data for improved decision-making should be implemented at these facilities.

Proper infant and young child feeding practices and deworming should be emphasized. Provider advocacy and better health education to parents should be intensified in the region for better outcomes. The hospital in conjunction with Busia County Government should organize for regular outreach to the community targeting pregnant and lactating mothers, strengthen deworming programs for children > 1 year and all children with delayed developmental milestones. It should also organize for health advocacy camps targeting the parents with children < 5 years to educate them on the proper infant and young child feeding practices.

Availability of data and materials

All data generated or analyzed during this study are available upon request to the corresponding author.

Abbreviations

Moderate Acute Malnutrition

Mid Upper Arm Circumference

Weight for Height Z-score

Weight for Age Z-score

Height for Age Z-score

Standard Deviation

Adjusted Odds Ratio

Confidence Interval

UNICEF, Progress for children, in UNICEF. 2016.

Google Scholar

Onyango AW, Esrey SA, Krammer MS. Continued breastfeeding and child growth in the second year of life: a prospective cohort study in western Kenya. Lancet. 1999;354(9195):2041–5.

Article CAS Google Scholar

Services, J.C.o.I.C.s, Orphan Nutrition. 2017.

Fanzo J, Hawkes C, Udomkesmalee E, Afshin A, Allemandi L, Assery O, Baker P, Battersby J, Bhutta Z, Chen K, Corvalan C. Global Nutrition Report. 2019.

Intitute, I.F.P.R. Global nutrition report. Washington DC: International Food Policy Research Intitute; 2015.

Statistics, K.N.B.o. 2014 Kenya Demographic and Health Survey. Nairobi: Kenya National Beaureau of statistics; 2015.

System KHI. DHIS2, in DHIS2; 2016.

Van den Broeck J, Willie D, Younger N. The World Health Organization child growth standards: expected implications for clinical andepidemiological research. 2009;247–51.

Organization, W.H. World Health Organization (WHO) - global database on child growth and malnutrition. 2017.

Shikur B. The relationship between malaria and malnutrition among under five children in Adami Tulu District, south-Central Ethiopia: a case-control study. Addis Ababa: Addis Ababa University College of health sciences; 2014.

Sory DI, Sory D, N’fanly C, Ibrahima D, Mamoudou BE, Moussa C, et al. Neonatal mortality associated with the referral of low birth weight newborns to the Institute of Child Nutrition and Health (INSE). Open J Pediatrics. 2019;9(4):287–95.

Article Google Scholar

Yiga, P. E. T. E. R. (2016). Child Growth and Determinant Factors: A Case Study of Burundi. Unpublished dissertation. Universiteit Gent.

Gilbert H. An investigation on factors associated with malnutrition among underfive children in Nakaseke and Nakasongola districts, Uganda. BMC Pediatr. 2015;15:(134):134.

Shally A, Peto R, Pande V. Effects of deworming on malnourished preschool children in India. PLoS Negl Trop Dis. 2008;2(4):223.

Taylor R. Deworming school children in developing countries. Cochrane: Cochrane Library; 2015.

Tim A. Deworming delusions? Mass drug administration in east African schools. J Biosoc Sci. 2016;48:116–47.

Organization, W.H. Indicators for assessing breastfeeding practices. Geneva: World Health Organization; 1991.

Nayak R, Mallapur D. Determinants of nutritional status in under five children- a cross sectional study. Ann Commun Health. 2014;2(2):26–9.

Wagaye F, Tariku A. Undernutrition and associated factors among children aged 6-59 months in east Belesa District, Northwest Ethiopia: a community based cross-sectional study. BMC Public Health. 2016;16(506):1–10.

Asfaw M, Lamessa D. Prevalence of undernutrition and associated factors among children aged between six to fifty nine months in Bule Hora district, South Ethiopia. BMC Public Health. 2015;15(41):41.

AA. Children at risk for developmental delay can be recognized by stunting, being underweight, ill health, little maternal schooling or high gravidity. NIH Public Access. 2010;51(6):652–9.

Hyun P. The impact of nutritional status and longitudinal recovery of motor and cognitive milestones in internationally adopted children. Int J Environ Res Public Health. 2010;8:105–16.

Deepti D. A community-based study on growth and development of under-five children in an Urbanized Village of South Delhi. J Trop Pediatr. 2016;62:446–56.

Christian P, Lee S. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int J Epidemiol. 2013;42:1340–55.

Zaida H, Luis S. Cross-Sectional Study of Malnutrition and Associated Factors among School Aged Children in Rural and Urban Settings of Fogera and Libo Kemkem Districts, Ethiopia; 2014.

Hoffman D, Cacciola T, Barrios P, Simon J. Temporal changes and determinants of childhood nutritional status in Kenya and Zambia. J Health Popul Nutr. 2017;36(1):27.

Tzioumis E, Adair LS. Childhood dual burden of under- and overnutrition in low- and middle-income countries: A critical review. Food Nutr Bull. 2014;35(2):230–43.

Constance G. Childhood overweight and obesity among Kenyan pre-school children: association with maternal and early child nutritional factors. Public Health Nutr. 2009;13(4):496–503.

Ole Tankoi EO, Asito SA, Adoka S. Determinants of Malnutrition among Children Aged 6–59 Months in Trans-Mara East Sub-County, Narok County, Kenya. Int J Public Health Saf. 2016;1(116):2.

Keino S. Determinants of stunting and overweight among young children and adolescents in sub-Saharan Africa. Food Nutr Bull. 2014;35(2):167–78.

Qiao Y. Birth weight and childhood obesity: a 12-country study. Int J Obes Suppl. 2015;5:74–9.

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Acknowledgements

We would like to appreciate the participants whose data were used in this study. We also acknowledge Alupe Sub-County Hospital and Busia County Government.

This study was fully funded by Kenya Field Epidemiology and Laboratory Training Program. The funding body of this study did not participate in the design or conclusion of the study.

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Edwin Gudu, Mark Obonyo, Elvis Oyugi & Jane Githuku

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West Pokot County Health Department, Kapenguria, West Pokot, Kenya

Cecilia Kiilu

Ministry of Health, Division for Human Resource for Health Development, Nairobi, Kenya

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Contributions

EG, MO, JG, EO, and JR conceived the study, EG collected data, EG, VO, EO, CK, JG, ZG, and MO analyzed the data and drafted the manuscript. All authors helped with the interpretation of the results, read, critically reviewed and approved the final manuscript.

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Correspondence to James Ransom .

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Ethics approval and consent to participate.

Ethical clearance was sought from Institutional Review Ethics Committee Moi University/Moi teaching and Referral Hospital under reference FAN: IREC 1870. Permission for the study was also sought from the County health department and the hospital administration. During the interviews informed oral consent was obtained from parents/legal guardians of all study participants after explaining the objectives of the study. The authors used oral consent to accommodate the low literacy rates in the populations served by this hospital in Alupe. Measures were taken to assure confidentiality of the information provided during these interviews and codes were used to de-classify personal identifying information of study participants. Measures were taken to assure collected data were properly stored and secured and only accessible to the investigators.

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This study was based on patient records, thus consent for publication was not applicable.

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Gudu, E., Obonyo, M., Omballa, V. et al. Factors associated with malnutrition in children < 5 years in western Kenya: a hospital-based unmatched case control study. BMC Nutr 6 , 33 (2020). https://doi.org/10.1186/s40795-020-00357-4

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Research note
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Published: 11 July 2019

Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey

Abay Kassa Tekile ORCID: orcid.org/0000-0001-9505-2804 1 ,
Ashenafi Abate Woya 1 &
Garoma Wakjira Basha 1

BMC Research Notes volume 12 , Article number: 391 ( 2019 ) Cite this article

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The aim of this study was to assess the risk factors for malnutrition among children aged 0–59 months in Ethiopia. The analyzed data were obtained from the 2016 EDHS and 9495 under-5 years’ children were considered in this analysis. The data was extracted, edited and analyzed by using SPSS Version 23.0. Both bivariate and multivariable binary logistic regression model was used to identify the determinants of children malnutrition.

The prevalence of stunting, wasting, and underweight were 38.3%, 10.1%, and 23.3%, respectively. About 19.47% of children were both stunted and underweighted, and only 3.87% of children had all the three conditions. Among the factors that considered in this study, age of a child, residence region, mothers’ education level, mothers’ BMI, household wealth index, sex of a child, family size, water and toilet facility were significantly associated with malnutrition in Ethiopia. The authors concluded that malnutrition among under-five children was one of the public health problems in Ethiopia. Therefore, the influence of these factors should be considered to develop strategies for reducing malnutrition in Ethiopia.

Introduction

Malnutrition among under-5 year children is a common public health problem and it is one of the main reasons for the death of children in developing countries [ 1 ]. As of the World Health Organization report, about 35% of under-five children’s death is associated with malnutrition in the world [ 2 ]. There are 165 million stunted, 99 million under-weighted, and 51 million wasted children globally [ 3 ].

The prevalence of stunting has decreased from 58% in 2000 to 44% in 2011 in Ethiopia. The prevalence of wasting is changed from 12% in 2000 to 10% in 2011. The prevalence of underweight has consistently decreased from 41% in 2000 to 29% in 2011 [ 4 ]. In Tanzania, a high prevalence of underweight (46.0%), stunting (41.9%) and wasting (24.7%) are observed in 2017. In addition, 33% of children are both stunted and underweight, 21% of children are underweight and wasted, and 12% of children are stunted and wasted [ 5 ]. In Ethiopia, more than one-third of a child deaths are associated with malnutrition [ 6 ]. Moreover, the proportion of malnutrition is higher among anemic children compared to those of non-anemic [ 7 ].

Different researchers conducted a study on malnutrition among under-five children in different parts of the country. These studies were mainly focused on the prevalence and determinants of malnutrition among under-five children, but they gave little attention for exploring the relationship between under-five malnutrition and child anemia. So, the main aim of this study was to explore the major factors of malnutrition and its association with anemia by using updated data from the 2016 EDHS.

Study design and sampling

The 2016 Ethiopia Demographic and Health Survey data was used for this study. The 2016 EDHS used a two-stage stratified sampling design to select households. In the first stage, there were 645 enumeration areas (202 in urban and 443 in rural areas) based on the 2007 Ethiopia Population and Housing Census (PHC). A total of 18,008 households were considered, of which 16,650 (98% of response rate) households were eligible. The women were interviewed by distributing questio-ners and information on their birth history and 9495 under-five children were considered for this study [ 8 ].

Measurements

The dependent variable for this study was the malnutrition status of under-5 year children (stunting, underweight and wasting). Children whose height-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered as stunted. If the weight-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population then the child is underweight. Children whose weight for height Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered as wasted [ 8 ]. Socio-demographic, socio-economic and health-related variables were considered as independent variables in this study.

Statistical data analysis

The data were extracted, edited, and analyzed by using SPSS version 23 for Windows. Then a weighted analysis was conducted using the same sampling weight given for each region in Ethiopia DHS to compensate for the unequal probability of selection between the strata [ 8 , 12 ]. Bivariate logistic regression was performed and variable with P-value less than 0.25 were transported into multivariable binary logistic regression analysis to identify the determinant of malnutrition of under-five children. Finally, variables with P-values < 0.05 in the multivariable logistic regression model were taken as statistically significant.

Samples of 9495 under-five children were considered in this research. The weighted prevalence of stunting, underweight, and wasting were 38.3%, 23.3%, and 10.1%, respectively. About 66% of interviewed mothers had no education and only 2% of them attended higher education. About 44% of children were found between 0 to 24 months and more than half (51.1%) were males. Only 11% of the respondents were from urban areas and 32% were in the rich wealth index. Around 20% of children’s mother were underweighted (having body mass index less than 18.5) (Additional file 1 ).

Determinants of stunting

Among the factors that considered in this study, child’s age, residence region, mothers’ education level, wealth index, child sex, toilet facility, size of a child, mothers’ BMI and number of children per household were associated with stunting. Compared to children of 0–24 months, the odds of stunting among children in the age group of 25–47 months were 2.645 times higher. The child in the age group of 48–59 months was 1.763 times higher. Compared with children in Tigray region, the risk of being stunted was decreased by 32%, 33%, and 60%, among children living in Afar, Oromia, and Somali regions, respectively. The risk of being stunted among children whose mothers attended primary education was 0.87 times less compared to children whose mother did not attend education. The risk of being stunted among children whose mothers attended secondary and higher education were 0.606 and 0.453 times less compared to children whose mother did not attend education respectively. Compared to male children, the probability of being stunted among female children was decreased by 16%. Compared to children living in households with poor economic status, the odds of being stunted among children living in households with medium and rich economic status were decreased by 20% and 31%, respectively.

Children born with small size were 1.509 times more likely to be stunted than children born larger (AOR = 1.509; 95% CL 1.332–1.709) and children who had born with medium size were 1.189 times more likely to be stunted than children born larger (AOR = 1.062; 95% CL 1.062–1.331). Children born to underweight mothers (BMI < 18.5) were 2.163 (AOR: 2.163, 95% CI 1.750, 2.673) times more likely to be stunted compared to those born to overweight mothers (Table 1 ).

Determinants of under-weight

Age of child, sex of a child, mothers’ education level, mothers’ BMI, region, household wealth index, water facility, toilet facility, size of child and a number children were associated with under-weight (P < 0.05). The risk of being underweighted was 1.748, 1.837 times more likely among children that were aged 24–47, and 48–59 months than those aged 0–24 months. Compared to Tigray region, the odds of being under-weighted was 0.741, 0.664 and 0.393 times lower among children from Oromia, Gambella and Addis Ababa respectively.

The risk of being underweight for children whose mother attend primary, secondary and higher education were 0.771, 0.645, and 0.551 times lower than children whose mothers who did not attend formal education. Children from a household with middle and rich economic status were 0.794 and 0.565 times less likely to be under-weighted compared to children living in a household with poor household economic status.

Female children were 0.856 times less likely to be under-weighted as compared to male children. Children who were born with small size were 1.898 times more likely to be under-weighted than children born larger (AOR = 1.898; 95% CL 1.653–2.180) and children who had born with medium size were 1.324 times more likely to be under-weighted than children born larger (AOR = 1.324; 95% CL 1.164–1.507). Children born to underweight mothers (BMI < 18.5) were 3.162 (AOR: 3.162, 95% CI: 2.410, 4.148) times more likely to be underweight compared to Children born to overweight mother (Table 2 ).

Determinants of wasting

Results of multivariable binary logistic regression model showed that the age of a child, sex of a child, mothers’ education level, household wealth index, a region of residence, water facility, and family size were significantly associated with wasting. Children of the rich household were less likely to be wasted compared to children living in a household with poor household economic status. The risk of being wasted was 0.52 and 0.607 times lower among children of 25–47 and 48–59 months than those 0–24 months. Compared to children from the Tigray region, the odds of being wasted of children from Somali region was 1.671 times higher. The odds of being wasted in SNNPR and Addis Ababa were 0.365 and 0.338 times lower compared to Tigray region respectively. The odds of being wasted was 0.778 times lower among female children than male children (AOR = 0.778, 95% CI 0.681, 0.889). The odds of being wasted was 1.223 times higher among children who lived in household members of 6–10 than children who had lived in household members of 1–5 (AOR = 1.223, 95% CI 1.066, 1.403) (Additional file 2 ).

Associations between children’s anemia and malnutrition

This study showed that among stunted, underweighted, and wasted children, 61%, 64.3%, and 68.2% were anemic respectively. Moreover, the percentages of stunting, wasting, and underweighting were higher among anemic children as compared to no-anemic children. Stunted children were 1.222 times more likely to be anemic compared to those of not stunted (AOR: 1.222, 95% CI 1.101, 1.356). Underweighted children were 1.222 times more likely to be anemic compared to those of not underweight (AOR: 1.222, 95% CI 1.077, 1.386). Wasted children were 1.557 times more likely to be anemic compared to those of not wasted (AOR: 1.557, 95% CI 1.315, 1.844) (Table 3 ).

In this study, the prevalence of malnutrition and associated factors in Ethiopia was assessed. The prevalence of stunting, underweighting, and wasting were 38.3%, 23.3%, and 10.1% respectively. These prevalence were relatively lower than the previous study conducted in Ethiopia [ 9 , 10 ] and in Tanzania [ 5 ], but it was higher than the study conducted in Nairobi, Kenya [ 11 ].

In this study, as the age of a child increase, the probability of a child to be stunted and underweighted will be increased. This finding was in line with the studies that conducted in Ethiopia, in which poor nutritional status of children was associated with the old age of children [ 12 , 13 , 14 ]. In all the three forms of malnutrition (stunting, underweight and wasting), the risk of malnutrition was less prevalent among females than males. This finding was consistent with previous findings [ 15 , 16 , 17 ]. This study revealed that the levels of malnutrition had a significant regional variation ranging from 14.6% in Addis Ababa to over 46.7% in Amhara regions of the country. This finding is similar with [ 10 , 18 ].

Children whose mothers had primary and above educational level were significantly less likely to be stunted and underweighted as compared to children whose mothers had never attended formal education. This finding was consistent with the study conducted in Ethiopia [ 10 ] and Bangladesh [ 19 ] which showed that as mothers’ educational level increase, the likelihood of the children to be stunted and underweighted will be decreased. Mothers with BMI less than 18.5 (underweight) were more likely to have stunted, underweighted and wasted children as compared to overweighted mothers. This finding is similar with other previously conducted studies [ 10 , 19 , 20 ].

As of this study, children who were smaller at birth were more likely to be stunted and underweighted. This finding was supported by study conducted previously in SNNPR, Ethiopia. [ 13 ]. A similar study that conducted in Dale Woreda, Southern Ethiopia showed that the larger the family size, the poorer nutritional status of children would be resulted [ 17 ]. In the current study, anemia and malnutrition of children were highly associated with that anemic children were more likely to be malnutrition as compared to non-anemic [ 7 ].

Conclusions

The prevalence of stunting was still high in Ethiopia. The key determinants of malnutrition in Ethiopia were the child age, maternal education, region, household wealth status, religion, sex of child, number of children, a child size, water and toilet facility. The influence of these factors should be considered to develop strategies for reducing malnutrition in Ethiopia. Finally, improving living standards of children is important to get a better health care, reduces child malnutrition, and child mortality.

Limitations of the study

This study was based on cross-sectional study design. Thus, the authors did not see the seasonal variation of malnutrition status and establish causal relationship. There were some missing values for some variables in the dataset. Therefore, the authors fail to consider some important factors which could affect the interpretation of the results.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request (in SPSS code).

Abbreviations

Statistical Package for Social Science

crude odds ratio

adjusted odds ratio

Ethiopian Demographic and Health Survey

Central Statistical Agency

body mass index

World Health Organization

Southern Nations, Nationalities and People Region

WHO. Malnutrition—the global picture 2000. Geneva: WHO; 2000.

Google Scholar

WHO. Communicable disease and severe food shortage technical note. Geneva: WHO; 2010.

Black RE, Victora CG, Walker SP, Bhutta ZA. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890):427–51.

Article Google Scholar

EDHS. Central Statistical Agency [Ethiopia] and ORC Macro. Ethiopia Demographic and Health Survey (EDHS); 2011.

Mgongo M, Chotta NAS, Hasham TH, Uriyo JG, Darmian DJ. Underweight, stunting and wasting among children in Kilimanjaro Region, Tanzania; a population-based cross-sectional study. Int J Environ Res Public Health. 2017;14(509):1–12.

Debela BL, Shively G, Holden S. Does Ethiopia’s productive safety net program improve child nutrition? Food Secur. 2015;7(6):1273–89.

Teji K, O’Connor TP, Belachew T, O’Brien NM. Anemia and undernutrition among children aged 6–23 months in two agroecological zones of rural Ethiopia. Pediatric Health Med Ther. 2016;7:131–40.

EDHS. Central Statistical Agency [Ethiopia] and ORC Macro. Ethiopia Demographic and Health Survey (EDHS); 2016.

Asfaw M, Wondaferash M, Taha M, Dube L. Prevalence of undernutrition and associated factors among children aged between six to fifty nine months in Bule Hora district, South Ethiopia. BMC Public Health. 2015;15(41):1–9.

CSA. Key Findings on the 2014 urban employment unemployment survey: Addis Ababa, Addis Ababa, Ethiopia and Calverton, Maryland, USA; 2014.

Chesire EJ, Oago ASS, Oteba LP, Echoka E. Determinants Of under nutrition among school age children in a Nairobi peri-urban slum. East Afr Med J. 2008;85(10):471–9.

CAS PubMed Google Scholar

Endris N, Asefa H, Dube L. Prevalence of malnutrition and associated factors among children in rural Ethiopia. Biomed Res Int. 2017;2017:6.

Ayalew E. The prevalence of stunting and associated factors among children age 6–59 months at Mizan-Aman Town, Bench Maji Zone, SNNPR Region, Ethiopia; 2015.

Terefe D, Amare T, Gashaw AB, Molla MW. Stunting, wasting and associated factors among children aged 6–24 months in Dabat health and demographic surveillance system site: a community based cross sectional study in Ethiopia; 2017.

Demissie S, Worku A. Magnitude and factors associated with malnutrition in children 6–59 months of age in pastoral community of Dollo Ado district, Somali region, Ethiopia. Sci J Public Health. 2013;1(4):175–83.

Debeko DD, Goshu TA. Nutritional status of under-five children in Hawassa Zuria District, Southern Ethiopia. Am J Health Res. 2015;3(5):286–92.

Wolde M, Berhan Y, Chala A. Determinants of underweight, stunting and wasting among schoolchildren. BMC Public Health. 2015;15(8):1–9.

Megabiaw B. Prevalence and determinants of chronic malnutrition among under-5 Children in Ethiopia. Int J Child Health Nutr. 2013;2:230–6.

Saddiqi MNA, Haque MN, Goni MA. Malnutrition of under-five children: evidence from Bangladesh. Asian J Med Sci. 2011;2011(2):113–9.

Yisak H, Gobena T, Mesfin F. Prevalence and risk factors for under nutrition among children under five at Haramaya district, Eastern Ethiopia. BMC Pediatr. 2015;15(212):1–7.

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Acknowledgements

We would like to thank Statistics department staff of Bahir Dar University for their valuable suggestion and encourage. We also acknowledge Ethiopia Central Statistical Agency for giving us permission to use the data for our study.

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Abay Kassa Tekile, Ashenafi Abate Woya & Garoma Wakjira Basha

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AKT designed the current study, edited, analyzed the data and interpreted the results, and wrote the manuscript. AAW and GWB participated in the data analysis, manuscript writing, and acted as second reviewer. All authors read and approved the final manuscript.

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Since the data was obtained from Ethiopian Central Statistical Agency, the ethical clearance for the survey was approved by Ethical Review Board of Ethiopia Central Statistical Agency (CSA) and all participants who agreed to take part in the survey signed a consent form.

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Additional files

Additional file 1..

Characteristics of the Study Participants (EDHS, 2016). Descriptive statistics of study variables.

Additional file 2.

Results of multivariable logistic regression to identify the significant determinants related to wasting.

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Tekile, A.K., Woya, A.A. & Basha, G.W. Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey. BMC Res Notes 12 , 391 (2019). https://doi.org/10.1186/s13104-019-4444-4

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A THESIS PROPOSAL ON NUTRITIONAL STATUS OF 5 TO 10 YEARS CHILDREN OF NAMJE, VEDETAR VDC OF DHANKUTA DISTRICT

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Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter?

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Although there are inequalities in child health and survival in the Democratic Republic of Congo (DRC), the influence of distal determinants such as geographic location on children's nutritional status is still unclear. We investigate the impact of geographic location on child nutritional status by mapping the residual net effect of malnutrition while accounting for important risk factors.

Our novel empirical approach is able to flexibly determine to what extent the substantial spatial pattern of malnutrition is driven by detectable factors such as socioeconomic factors and can be attributable to unmeasured factors such as conflicts, political, environmental and cultural factors.

Although childhood malnutrition was more pronounced in all provinces of the DRC, after accounting for the location's effects, geographic differences were significant: malnutrition was significantly higher in rural areas compared to urban centres and this difference persisted after multiple adjustments. The findings suggest that models of nutritional intervention must be carefully specified with regard to residential location.

Childhood malnutrition is spatially structured and rates remain very high in the provinces that rely on the mining industry and comparable to the level seen in Eastern provinces under conflicts. Even in provinces such as Bas-Congo that produce foods, childhood malnutrition is higher probably because of the economic decision to sell more than the population consumes. Improving maternal and child nutritional status is a prerequisite for achieving MDG 4, to reduce child mortality rate in the DRC.

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Malnutrition prevents children from reaching their full physical and mental potential. Health and physical consequences of prolonged states of malnourishment among children are: delay in their physical growth and motor development; lower intellectual quotient (IQ), greater behavioural problems and deficient social skills; susceptibility to contracting diseases [ 1 , 2 ]. Furthermore, child malnutrition is associated with approximately 60 percent of under-five mortality in Sub-Saharan Africa (SSA) countries [ 3 ].

The majority of studies on child nutritional status have described prevalence of malnutrition among under-five children and analyzed socioeconomic, demographic and cultural factors associated with child malnutrition in SSA [ 4 – 7 ]. However, little is known about the links between child's nutritional status and distal determinants including geographic location and the environment due to restricted methodologies.

Our study aims to investigate the impact of geographic location as a proxy for distal factors and their influences on nutritional status of children. The province of residence is taken as a modifiable variable which can help explain the variation of malnutrition among children between different provinces.

Four reasons justify the interest of this study: first, geographic location is an important modifier of known predictors of malnutrition and is associated with food security and accessibility, especially in the context of conflict affected country such as the DRC.

Second, through the use of our empirical methods we can investigate inequalities in childhood malnutrition by mapping the residual net effect of spatial pattern of malnutrition more flexibly than most previous work.

Third, the methods also allow us to investigate non-linear effects of some risk factors prior to and after controlling for the socioeconomic determinants. This enables us to determine to what extent the substantial spatial pattern of malnutrition is driven by socioeconomic factors or point to the influence of omitted variables with strong spatial structure or possibly conflicts, political or environmental and cultural factors or even epidemiological processes that account for this spatial structure.

Fourth, the worsening socioeconomic, cultural and political context of the DRC needs to be investigated. The DRC is one of the SSA countries characterised by extreme poverty, high incidence of childhood diseases, high mortality and poor infrastructure: 75 percent of people are malnourished [ 1 ]; hundreds of thousands of children have died due to malnutrition over the past 12 years [ 3 ]. Furthermore, the country continues to experience armed conflicts and political instability since 1990. However, regardless of the worsening socioeconomic, political and health situations little is known about inequalities in childhood malnutrition across socio-economic strata or provinces although preliminary reports from the existing national surveys highlight the problem of malnutrition among children.

Background on study area

The DRC is the third largest country (by area: 2,344,858 km 2 ) in Africa and with immense natural resources distributed across its 11 provinces. It is, with the population of more than 68 million, the eighteenth most populous nation in the world, and the fourth most populous nation in Africa, 62 percent of which are under the age of fifteen.

Poverty and vulnerability are the main characteristics of the Congolese population. First, the World Bank estimated that the DRC's per capita gross domestic product (GDP) in 1999 was 78 US Dollar. The GDP has since declined. External debt at the end of 2000 was 12.9 billion US$ which, according to the Word Bank, equals roughly 280 percent of the GDP and to 900 percent of the exports. The accumulated debt and severe economic decline are due to both recent war and decades of corruption and economic mismanagement [ 8 , 9 ].

Further, since 1996, the DRC has been hit by conflict, which has devastated and destabilized the country and claimed the lives of an estimated six million civilians [ 10 ]. People continue to live in crisis conditions in many parts of the country. The eastern provinces (Orientale, Katanga, North and South Kivu), and more recently the province of Equateur, are afflicted by violence.

The ongoing Congolese crisis has claimed more lives than any conflict since World War II [ 10 ], and it continues to be of concern to the international community. Despite many political agreements signed since the start of the conflict, there is little expectation and prospect for peace as lives of vulnerable groups such as women and children continue to be shattered as conflict re-emerged in the eastern part of the country and a new front of violence opened in the province of Equateur. These conflicts have continued to hinder the DRC's ability to drive development efforts forward, so the population continues to suffer the consequences. Compounding this situation is the lack of leadership, mismanagement, corruption, rapid deterioration of the socio-economic conditions and the fall of prices of mineral resources which the national economy rely on because of the global financial crisis, which resulted in a sharp drop in revenues and massive loss of employment. Little progress is made in the implementation of the Government's Priority Action Plan on agriculture as most resources are concentrated on the army. Programmes are urgently needed to improve food security and auto-dependence, which would thereby reduce the country's over-reliance on humanitarian interventions to address the long-lasting acute and chronic malnutrition the country, continues to face [ 11 ].

Thus, humanitarian needs in the country remain colossal. According to the Central Emergency Respond Fund report in 2008, conflict has generated up to 1.35 million internally displaced persons (IDPs) in only three provinces, corroding the coping mechanisms of millions of people. With the continuation of conflict and the actions of abusive armed groups have increased food prices, matched with the limited ability of productive areas to feed population centres due to logistic constraints have generated malnutrition rates of up to 20 percent in certain health zones [ 11 ].

Consequently, chronic malnutrition is a serious problem, affecting some 48 percent of children in the DRC [ 12 ].

Preliminary reports from three nutritional national surveys (the 1995 and 2001 Multiple Indicator Cluster Surveys (1995 and 2001 MICS) and the 2007 Demographic and Health Survey (2007 DRCDHS) show that nutritional situation in the DRC remains critical [ 12 , 13 ]. Specifically, nutritional status of children under the age of 5 indicated deterioration in terms of acute malnutrition (stunting, wasting and underweight). Stunting rate was respectively 34 percent in 1995, 31 percent in 2001, and 46 percent in 2007. The nutritional status of mothers is also critical: about 19 percent of them were suffering from low Body Mass Index (BMI) in 2007.

The ever worsening political climate in Eastern provinces, resulted in war since 1996, has created an unprecedented hardship on the population, especially on children as they are more prone than adults to suffer from nutritional deficiencies because of their physiologically less stable situation [ 8 ]. Very high malnutrition rates have been recorded in the war provinces because of insecurity. But even in peace areas untouched by the present conflict nearly half of the children are malnourished [ 14 ]. Malnutrition remains one of the main factors associated with the high childhood morbidity and mortality [ 15 , 16 ].

National estimates of malnutrition may conceal important intra-provincial differences due to diverse cultural norms that might affect nutritional practices and the impact of the ongoing conflict on food security. It is therefore, important to examine patterns of malnutrition at a more disaggregated province level.

We recognise that a province in a country such as the DRC is a large unit of observation, but the provinces' estimates are more informative compared to the use of national estimates of malnutrition.

There is no specific empirical study undertaken to investigate determinants of malnutrition among children in the DRC. We therefore investigate the impact of geographic location on childhood malnutrition while taking into account the effect of the important risk factors of malnutrition present in the DHS database that might confound or mediate the inequalities of the spatial patterns observed at the province-level in order to gain a good understanding of the extent of malnutrition in a post-conflict country. The results will enrich the current literature with recent data on malnutrition, making it more understandable and helping to establish more effective intervention policies to monitor and evaluate achievement of the Millennium Development Goals (MDGs) in countries devastated by conflict. The policy interventions that would not account for unobservable distal factors (such as conflicts, political, environment etc...) will not deliver the required outcomes and will prolong the vulnerability of children in the DRC.

Geographic Location in the DRC DHS

By applying the spatial analysis to the disaggregated province-level, we are able to establish whether the spatial effects cross the boundaries between the provinces or are distinct, which would also give us a sense on the relative importance of policies versus geographic factors in causing malnutrition.

While the eastern provinces used to be the major food producers of the country, repeated looting of crops by armed groups and general insecurity over many years has undermined production.

In other parts of the country with better security conditions, crumbled infrastructure has significantly decreased the country's food production capacity. Households and major food importers maintain food reserves at a bare minimum because of the volatile political and economic environment, as well as the frequent threats of looting.

High prices have also hit the DRC hard. Food prices have increased by 52 percent in June 2009 compared to figures from May 2008 [ 1 ]. This is probably due to the lack of national policy for food production and the reliance of the DRC on food aid (the DRC relies 100% on aid). The financial and economic crisis has also affected mining activities. Acute malnutrition is at dangerously high levels in some parts of the DRC. Acute malnutrition is above the emergency threshold in the Kasaï provinces (centre). Even the worst affected parts of North Kivu do not have such high rates perhaps due to humanitarian interventions. Malaria, malnutrition, acute respiratory infections, tuberculosis, and diarrhea are the main causes of child mortality, according to the Ministry of Health. Deteriorating health conditions have allowed the resurgence of epidemics such as measles and typhoid fever.

As conflict continues to prevail in Province Orientale, South, North Kivu and Equateur, children are subject to starvation, and there is an increase in child mortality and morbidity. An almost total lack of basic health and social infrastructure has had a negative impact on child health.

This study uses data from the 2007 DRC Demographic Health Survey (DRC-DHS), a national representative investigation on children's and women's health. The DRC-DHS data has comparable information on community and household characteristics as well as on nutrition and health of women aged 15-49 years and their children under-five years old at the time of the survey. The samples covered all regions, urban and rural areas. In total 9,000 households (3,690 in urban areas and 5,310 in rural areas) were sampled. All women between the ages of 15 to 49 living in these households were interviewed. Mother and under-five nutritional module covers a sub-sample of one household out of two from the 9,000 selected households. The data contains information on 9,995 women and 8,992 children under the age of 5. The DHS data is of good quality. However, the information provided by this survey is cross-sectional. The samples collected under the DHS survey is drawn together using stratified multistage sampling designs, often with over-sampling of smaller domains such as urban areas or certain regions of a country. In many instances, these data are subsequently analyzed using statistical software designed for simple randomly sampled data. Such analyses fail to take into account the impact of the underlying complex sampling design on regression parameter estimates. Consequently, conclusions drawn from these analyses may give misleading estimates on important health indicators on which public policies are based. Techniques that account for the survey design such as weighting, stratification, and hierarchical regression can be used. Furthermore, DHS data use cluster-sampling to draw upon women respondents via multistage sampling, where: at the first stage, a stratified sample of enumeration areas (villages/communities) is taken; at the second stage, a sample of households within the selected communities is taken; and finally, at the third stage, all women respondents (aged 15-49 years) in the sample households are included. Cluster sampling is a cost-saving measure, without the need to list all the households. However, statistically, it creates analytical problems in that observational units are not independent. Thus, statistical analyses that rely upon the assumption of independence are no longer valid. We focus on the hierarchical regression technique using Bayesian Geo-additive models to take into account the above mentioned issues.

Nutritional status

According to the World Health Organization (WHO) [ 17 ], malnutrition has three commonly used comprehensive types named stunting, wasting and underweight measures by height for age, weight for height and weight for age indexes respectively.

Stunting or growth retardation or chronic protein-energy malnutrition (PEM) is deficiency for calories and protein available to the body tissues and it is inadequate intake of food over a long period of time, or persistent and recurrent ill-health. This height-for-age index (stunting) is less sensitive to temporary food shortages and thus seems to be considered as the most reliable indicator. Because studies have shown that wasting is volatile over seasons and periods of sickness and underweight shows seasonal weight recovery and being overweight for some children can also affect weight-for-age index [ 8 ].

Wasting or acute protein-energy malnutrition captures the failure to receive adequate nutrition during the period immediately before the survey, resulting from recent episodes of illness and diarrhoea in particular or from acute food shortage. Underweight status is a composite of the two preceding ones, and can be due to either chronic, acute malnutrition or PEM.

In the three surveys, nutritional status was assessed according to weight-for-age, weight-for-height and height-for-age using the US National Center for Health Statistics/WHO international reference tables and charts [ 17 , 18 ]. Wasting, stunting and underweight were defined as weight-for-height, height-for-age and weight-for- age of 2SD or more below the corresponding median of the reference population, respectively; while severe wasting, severe stunting was defined as 3SD or more below the same median, respectively.

We focused on stunted children (2 SD of height-for-age below the median value) as our covariates were better able to explain chronic than acute malnutrition. We used the Z-Score (in a standardized form) as a continuous variable to maximize the amount of information available in the data set.

It is worth mentioning that, because of the drawback of the international reference population in correctly capturing nutritional status of children around the world; recently a new reference standard has been generated from which Z-scores can be calculated. For the purpose of this paper, the use of the new reference standard would not change the qualitative results. A detailed discussion on the new reference standard can be found in [ 19 ].

Figure 1 shows a histogram and kernel density estimates of the distribution of the Z-scores, together with a normal density, with mean and variance estimated from the sample. This gave us clear evidence that a Gaussian regression model is a reasonable choice for our inference for the dependent variable stunting.

Histogram, kernel density of stunting (left) and mean standardized Z-score for stunting by child's age (right)

Correlates of Malnutrition

Child nutritional status is actually caused by multiple factors including, but not exclusively, those with illness, disease, and biological causes. A fuller understanding of illness and disease must include considerations of cultural, psychological, social and political factors present in the physical environment where the child lives. This premise has been expanded in many different areas such as medical, child psychology and sociology and now forms a fundamental part of a great deal of social science research and practice.

Mosley and Chen [ 20 ] in their study of the causes of death in children in developing and low income countries, placed risk factors within an analytical framework or including the interactions among socio-economic, cultural, environmental and biomedical factors. The framework focuses on the factors or determinants according to how direct the impact of the determinant was on the risk of death, i.e. the proximity of the risk posed to the children.

The Proximate factors include biological agents of disease e.g., microbes and vectors, and other elements which directly influence child's exposure to the agents of disease and ill health.

Distal factors include features of the wider socio-cultural, environmental and political context affecting both the child; his/her care givers e.g. public health policies and safety as well as cultural norms, environmental degradation which dictate how a family may respond to an illness.

These associations illustrate the vulnerability of children in any population who live in the environment where many of these determinants become unavailable or unstable.

Since we are interested in multiple causes of malnutrition, when modelling the determinants of malnutrition, we can distinguish between immediate, intermediate, and underlying determinants [ 3 ]. While malnutrition is always immediately related to either insufficient nutrient intake or the inability of the body to absorb nutrients (primarily due to illness), these are themselves caused by food security, care practises, and the health environment at the household level, which themselves are influenced by the socioeconomic and demographic situation of households, communities and public health policies [ 3 , 21 , 22 ]. Factors such as food security, care practises and health environment are a matter of public health policies. We refer to them as distal determinants of malnutrition.

In order to capture this complex chain of causation, various approaches have been taken each focusing on a particular level of causality. Studies [ 21 , 23 ] have estimated structural equations that address the interactions; Caputo, et al. [ 24 ] have used graphical chain models to assess the causal pathways, and other studies [ 5 ] have used multi-level modelling techniques. However, with the available data, it is not always clear how to separate intermediate from underlying determinants. For example, mother's education might be influencing care practises, an intermediate determinant, and the resources available to the household, an underlying determinant. On the other hand, child province of residence, a distal determinant, might influence food prices and security, intermediate determinants, and food availability, an underlying determinant.

Given these difficulties, our approach is to estimate models that mainly focus on factors that are mostly underlying determinants of malnutrition, although some might also be considered intermediate determinants and distal determinants. The most important covariate included in this analysis is the geographic location where the child lives that includes features of the wider socio-cultural and political context affecting both the child and his/her care givers. Other selected socio-demographics variables available in the data are grouped as individual child's characteristics, mother's characteristics, household economic level and community's characteristics. Regarding the covariates, we were guided by the previous literature on the subject and the conceptual framework outlined in [ 3 ].

Unfortunately, the surveys do not generate an income variable and we therefore rely on a household asset index as a proxy for the socio-economic status of the households which has been found to be quite reliable. Ownership of consumer items, such as a radio or car, as well as characteristics of the dwelling such as floor or roof type, toilet facilities and water source are items that measure poverty in these setting and the World Bank and others have used these items to generate an asset index, using Principal Components Analysis (PCA). We use the first principal component derived from the data to obtain the index for each household. We sort children by the asset index and establish cut-off values for percentiles of the population. We then refer to the bottom third as 'low socioeconomic status, the next third as 'medium socioeconomic status, the top third as 'high socioeconomic status' (see Table 1 ).

Among the underlying determinants of chronic malnutrition, we considered as a proxy measure of current or recent socioeconomic status (SES), the asset index, household size, the nutritional status of the mother (measured by her BMI), health knowledge and care practices measured by mother's education, mother's marital status, birth interval and place of delivery of children.

We also control for the sex of the child, urban rural location, and the age of child. Based on prior own work as well as other literature [ 17 , 22 , 23 ], we investigated a potentially non-linear pattern of effects of the mother's BMI as well as the age pattern on malnutrition. For illustration, the empirical distribution of the stunting Z-score by child's age is shown in Figures 1 (right). It is obvious that the effect of child's age on the mean Z-score of stunting is nonlinear. It will be difficult to model the possibly nonlinear effect of such covariates through a parametric functional form, which well justifies our use of a flexible semi-parametric model. Empirical distributions of all factors used in the analysis, are given in Table 1 .

Statistical analysis

Historically, variations in malnutrition prevalence has been related to household socio-economic factors because it determines the amount of resources (such as food, good sanitation, and health care) that are available to infants and neglected temporal and geographic gradients and other variations in risk, in order to generate hypotheses towards the cause of malnutrition.

We examine spatial variation in under-five malnutrition with flexible geo-additive semi-parametric mixed model while simultaneously controlling for spatial dependence and possibly nonlinear effects of covariates within a simultaneous, coherent regression framework. Individual data records were constructed for children. Each record represents a child and consists of nutritional status and a list of covariates. For the 8,992 children born within the last five years before the survey, 3,663 children have information on anthropometric measures. Because the predictor contains usual linear terms, nonlinear effects of metrical covariates and geographic effects in additive form, such models are also called geo-additive models. Kammann [ 25 ] proposed this type of models within an empirical Bayesian approach. Here, we apply a fully Bayesian approach as suggested in [ 26 ] which is based on Markov priors and uses Markov Chain Monte Carlo (MCMC) techniques for inference and model checking. For model choice, we routinely used the Deviance Information Criterion (DIC) developed in Spiegelhalter et al. [ 27 ], as a measure of fit and model complexity.

Geo-additive and geo-referenced disaggregated province level or site-specific analysis is a means of managing spatial and temporal variability of determinant of different types: distal, proximate and intermediate factors which are deemed to affect child nutritional status.

The aim of site-specific province analysis is to accelerate policy interventions, optimise inputs (unobserved factors such as distal ones: food security and prices policies, environmental etc...), improve child nutrition by taking into account the environmental impact and reduce the timescale to attain the Millennium Development Goals (MDGs). It is an approach that deals with multiple groups of factors input to improve child nutritional status in order to satisfy the actual needs of parts of the provinces rather than average needs of the whole country.

The analysis was carried out using version 0.9 of the BayesX software package [ 28 ], which permits Bayesian inference based on MCMC simulation techniques. The statistical significance of apparent associations between potential risk factors and stunting was explored in chi-square and Mann-Whitney U -tests, as appropriate. Multivariate analysis was used to evaluate the significance of the posterior mean determined for the fixed, non-linear effects and spatial effects. A P -value of < 0.05 was considered indicative of a statistically significant difference. We also run a sensitivity analysis for the choice of priors. Standard choices for the hyper-parameters are a = 1 and b = 0:005 or a = b = 0:001: Je?rey's Non-informative prior is closer to the later choice, and since practical experience shows that regression parameters depend on the choice of hyper-parameters, we have investigated in our application the sensitivity to this choice.

It would be beyond the scope of this paper to go into the details of estimation procedures. Please refer to Appendix 1 for a detailed explanation of the statistical methods. The method has also been discussed in more detail in [ 22 ].

Table 1 shows individual characteristics of the sample population prior to multiple adjustments of all factors that might confound or mediate the observed spatial variation within provinces on stunting.

Of the overall sample of 8,992 children, 41 percent (3,663) of the sample children had measurement on their height and weight to ascertain their nutritional status. Of those 50.8 percent was female and the overall prevalence of malnutrition (stunting) was 43.9 percent.

The prevalence of stunting was higher among boys compared to girls (46.1 versus 41.7 percent), has an inverse linear association with the age of the child (higher in the age groups ranging from 4 years, followed by 3 years, 2 years, 1 years but lower in the younger age (0 year): 55.1, 49.4, 48.5, 46.5 versus 23.1 percent), higher in rural areas compared with urban areas (48.4 versus 37.2 percent), higher among children born outside the hospital compared with their counterpart born in hospitals (49.1 versus 41.8 percent), linearly associated with maternal education (higher among children from non educated mother, followed by children from mothers with primary education but lower among children from mothers with secondary or higher education: 49.8, 47.0 versus 35.2 percent ), linearly associated with socio-economic status of the household (higher among children from the poorest household, followed by children from poor, middle or rich households but lower among children from richest households: 49.8, 48.0, 45.5, 43.9 versus 28.7 percent ), very high in Sud Kivu (46.1 percent) and Kasai Occidental (46.1 percent) provinces, followed by Nord Kivu (45.0 percent), Katanga (44.4 percent), Bandundu (42.4 percent), Kasai Oriental (42.0 percent), Bas Congo (40.3 percent), Maniema (39.1 percent), Equateur (36.7 percent), Orientale (35.3 percent) provinces, but lower in Kinshasa, the capital city (16.4 percent).

On the other hand, there were no statistically significant association observed between the prevalence of stunting and gender of the household's head, mother's marital status, preceding birth interval of the child, and household's size.

The geographical distribution of the crude prevalence of the standardized Z-scores for the response variable stunting by province display in Table 1 shows distinct spatial patterns. While in Kinshasa, Orientale and Equateur provinces, it appears that stunting was lower, there seem to be more areas of high stunting in North-Eastern of the DRC that is affected by conflict and the three provinces that relied heavily on local mineral mining (Katanga and the two Kasai). In addition to local small-area variability, there might also be an underlying smooth spatial component, which crosses provincial borders due to displacement of population during the conflicts, something we investigated below. The provincial prevalence shown in Table 1 also suggested that we should examine the spatial pattern of stunting at a more disaggregated province level as the national prevalence of 43.9 percent glossed over important intra-province differentials.

In the multivariate analysis the results for the fixed effects in Table 2 suggest that female children are slightly less stunted, as found in other studies [ 22 , 29 ]. In fact, the corresponding posterior mean, -0.12 for male, is negative and the 10% and 90% quintiles are both negative - indicating that the effect is statistically significant. Children living in rural areas are more stunted than their counterpart in urban areas. Maternal education rather than paternal education has a positive impact on children's nutritional status as well as household's socio-economic status. Children from low socioeconomic households were, as expected, more stunted than children from high income backgrounds.

We also estimated the posterior mean of stunting and plotted it against child's age and mother's BMI. As hypothesised, Figure 2 shows that there is a bell shaped, non-linear relationship between the effects of child's age (left), mother's BMI (right) and stunting. Shown are the posterior means together with the 80% and 95% pointwise credible intervals. As found in other countries of SSA [ 22 ], these data show that the effect of mother's BMI on child's nutritional status to be in the form of an inverse U shape. While the inverse U looks nearly symmetric, the descending portion exhibits a much larger range in the credible region. This appears quite reasonable as obesity of the mother (possibly due to a poor quality diet) is likely to pose less of a risk for the nutritional status of the child as very low BMIs, which suggest acute undernutrition of the mother [ 22 ]. The Z-score is highest (and thus stunting lowest) at a BMI of around 30-35. The figure also shows that there are few women with high BMI (40 or higher) in the survey, but this is likely to represent an artefact of the small numbers sampled at this BMI range.

Non-linear effects of and child's age (left) and mother's body mass index (right) on stunting . Shown are posterior mean of stunting within the 80% and 95% credible interval

Figure 2 left shows the effect of the child's age on its nutritional status. As hypothesised and commonly suggested by the nutritional literature [ 22 ], we are able to discern the continuous worsening of the nutritional status up until about 20 months of age. This deterioration sets in right after birth and continues, more or less linearly, until 20 months. Such an immediate deterioration in nutritional status is not as expected as the literature typically suggests that the worsening is associated with weaning at around 4-6 months. One reason for this finding could be that, according to the surveys, most parents give their children liquids other than breast milk shortly after birth, which might contribute to infections at these early ages.

After 20 months, stunting stabilizes at a low level. Through reduced growth and the waning impact of infections, children are apparently able to reach a low-level equilibrium that allows their nutritional status to stabilize.

We also see a sudden improvement of the Z-score around 24 months of age. This is picking up the effect of a change in the data set that makes up the reference standard. Until 24 months, the currently used international reference standard is based on white children in the US of high socioeconomic status, while after 24 months; it is based on a representative sample of all US children [ 17 ]. Since the latter sample exhibits worse nutritional status, comparing the Congolese children to that sample leads to a sudden improvement of their nutritional status at 24 months [ 17 , 22 ].

This anomaly of the reference standard is one reason for the replacement of this reference population by a new reference standard from the WHO [ 19 , 29 ].

Figure 3 explores province specific net spatial effects of undernutrition. We report results of the model that includes the total residual spatial effects of the province (i.e. the sum of both the structured and unstructured spatial effects). The left panel of Figure 3 shows the total residual spatial effects of the province and the right panel of Figure 3 indicates the significance of the observed spatial effects in the form of a posterior probability map. The levels correspond to significantly negative (black colour), significantly positive (white colour) and insignificant (grey colour). Three important observations emerge. First, there is a strong north-south gradient in these provincial effects with a fairly sharp dividing line running through the centre of the country. Over and above the impact of the fixed effects, there appear to be negative influences of malnutrition in the south-east that are quite general and affect most of the provinces there. Given that the south-eastern provinces are all affected by the ongoing conflict than the rest of the country, it is likely that food security and price policies, environmental factors and associated conflict e.g. relying on food aids and, lack of public infrastructure, lack of farming due to conflicts are responsible for this pronounced regional pattern. Therefore, humanitarian assistance that the population mostly relies on in these conflict-affected provinces might have short-term impact on child nutritional status. Second, living in the capital Kinshasa and Sud-Kivu is associated with significantly better nutrition despite Sud Kivu being affected by the conflict and surrounded by provinces with negative effects (Nord Kivu and Maniema). Note that both rates of prevalence of stunting in Kinshasa and Sud-Kivu are above the emergency threshold of 15 percent. As in most developing countries, living in the capital provides access to nutrition and health care that is superior in ways that have not been captured adequately in the fixed effects. The advantage in nutritional status of children living in Sud- Kivu may be due to the fact that the province receives more food aid than any other province in the DRC. Many aids organizations are based in this province and there has been an influx of food aids in this province. Therefore, in the province of Sud-Kivu, children have probably more benefited from international food assistance. In other provinces that are affected by conflicts such as Nord-Kivu where many aid organizations are also based particularly in Goma and there has been an influx of food aid in this province, it is surprising that many children still suffer from severe malnutrition even though food is abundant where they live. One possible explanation is that the lack of food is due to the fear of cultivation in unsecured environment. Another possible explanation is that most children in these provinces live in displacement camps and the higher intensity of the conflict due to the predation of the abundant mineral resources in this area by armed groups [ 15 ].

Total residual spatial effect of stunting (left) and posterior probabilities (right) of stunting for the full model

In the two Kasai , one explanation for the high rate of stunting may be due to the fact that the first livelihood activities are wage labour and mining activities and few people are involved in agriculture. This explanation might also be true for for higher malnutrition observed in Katanga, which also relies on mining in addition to the impact of war.

To compare our province-specific nonlinear spatial effects with our simple fixed effects for provinces (Table 1 ), Figure 3 presents a map that shows those provincial effects. One can only distinguish three main provinces effects. Better nutritional status is found in the Orientale province and Equateur province as well as Kinshasa, worse nutritional status in the eastern provinces under conflicts and non significant effect for provinces in the south of the DRC. In contrast, the crude provincial fixed effects shown in Table 1 miss most of the findings we discussed above. In particular, the sharp North-South gradient present in the province analysis is clearly now visible as the three eastern provinces include provinces on both sides of that divide. Moreover, the positive effect of Kinshasa is simply averaged in with the Bas Congo and Bandundu provinces. Clearly, a lot is lost when relying on these crude estimates of modelling spatial effects.

The DHS data provides a consistent, large and national database that can be used to analyze patterns of malnutrition in the DRC. This study has shown the relationships between malnutrition and the geographic location as well as a number of other risk factors that could explain the site-specific variation at the province level.

Our results show that children's chronic malnutrition is highly prevalent in the entire country with rates largely above 40 percent. The DRC has a deficit of food and limited food productivity despite the country's enormous potential for agricultural production. Only the western part of the country is a net producer, in particular the province of Bas Congo.

Over the last ten years, there has been a significant decline of the production of almost all agricultural products. According to the World Food Programme (WFP), the production of cassava has decreased by 23 percent between 1992 and 2006; the production of plantain has decreased by 75 percent between 1990 and 2006. There has been an increase of the maize production (by 33 percent between 1990 and 2006) however in Maniema and North Kivu the production has decreased by 22, in Katanga by 12 percent.

The deterioration in food productivity is the result of many factors but can be attributed mainly to distal factors such as lack of implementation of national policy for food production, security and conflicts. The agricultural system is mainly subsistence-oriented. According to the WFP, more than 93 percent of households have access to land, however the majority cultivates less than 1 hectare, which does not allow for adequate production for sale or own consumption. Cultivation techniques are still very traditional and households lack farming tools. Few households have a plough or a tractor. Agricultural inputs, such as fertilizers are not available. Eight years after the launch of the government PMURR programme (Programme Multi sectoriel des Urgences pour la Reconstruction et la rehabilitation) to make fertilizers available to farmers, the programme has yet to make an impact on the agricultural sector. Also, the year 2010 was declared by the government as the agricultural year to push many reforms in the sector but the impact of such programmes is yet to be seen.

Seeds are often of low quality, and productivity is low. These are clearly areas where if there were a national policy, this could make a difference for the DRC. Also, in the Eastern provinces people do not cultivate due to the violence, in the provinces such as Katanga, the two Kasai and Orientale, the young generation has left the agricultural sector to work in the mining industries (gold, diamond and coltan). In the eastern provinces, only 18 percent of households own livestock. When they do, it is usually in small quantity. Goat is the main livestock owned [ 30 ].

The results of these rates are similar to the one of the other countries [ 30 ]. Likewise, the risk of stunting is higher in rural areas, among children from less educated mothers and living in poorer household after controlling for other variables in the model [ 7 ]. As in most countries of SSA, there is substantial spatial province difference in child nutritional status in the DRC. Kinshasa's population is essentially urban, the proportion of the most educated women is higher compared to other provinces and accessibility to health facilities and safe drinking water is better whereas rural children, or less educated mothers have difficult access to health facilities, and consume about half the calories daily than their urban counterparts [ 1 ].

The major finding of this study is that malnutrition rates remain very high in the provinces that rely on the mining industry (two Kasai and Katanga) comparable to the level seen in Eastern provinces under war. One possible explanation may be found in the nutritional behaviour of the population that do not give certain types of food to children on cultural grounds even though the food is nutritious and in the reliance of the population living in these provinces on artisanal mining industry and the neglect of agriculture. A survey on food security showed that Kasai occidental has the worst indicator on population availability for food. There is a real hunger problem in this province, because the population that lives in this province does not want to work in agriculture and it prefers to work in the traditional extraction of diamonds. Even in provinces such as Bas-Congo that produces foods, the population sells more than it consumes. The higher rate of malnutrition observed in the eastern provinces under war is not surprising; the lack of food is due to insecurity rather than their inability to produce food because these provinces are known as traditionally pastoral and agricultural provinces.

Another observation drawn from this paper (Table 1 ) is the gap in malnutrition rates between the province of Kinshasa and all other provinces. In fact, Kinshasa's stunting prevalence is very low compared with the national rate. But it is above the emergency threshold by humanitarian standard. In spite of the generalized state of poverty in the country, incomes are higher in Kinshasa; as a result, economically, the population of Kinshasa enjoys better access to food products. The presence of more educated mothers and their partners in Kinshasa, and the lowest rate of poorest people living there may permit better nutritional practices.

The strong evidence of statistically significant difference of malnutrition between socio economic groups mainly between poorest, poorer, middle and richer groups compared to the richest group confirms the reality that in the DRC affording food for the majority of the population is still a challenge [ 1 ]. According to the WFP, about 55 percent of households' expenditure is spent on food (only 40 percent in Bandundu). The main source of food is people's own production. The second source of food is the market, except for the two provinces of Kivu, where households rely first on the markets to access food.

Hence, in richer households, often children are well fed and cared for and provided with a safe and stimulating environment, through which they are more likely to survive, to have fewer diseases and illnesses, and to fully develop thinking, language, emotional and social skills [ 12 ]. But in poorer households, most children are affected by the resurgence of kwashiorkor - lack of proteins in the diet - although this remains controversial. This is certainly due to the increasing poverty among parents who cannot afford to buy proteins (groundnuts, beans, meat, fish, and milk) for their children. Findings are largely consistent with findings of others studies on malnutrition by socio economic status (SES) in SSA [ 7 ] and highlight that poorer children have a higher risk of becoming stunted than richer ones.

The gap observed on stunting prevalence between children from uneducated mothers or those whose mothers have a primary school level of education compared with those from mothers with secondary or high level of education remains high. In fact, education could make a difference by empowering mothers (decision on type of nutrition and/or use of preventive medicine). Similar results have been found in Cameroon [ 7 ] and in most developing countries [ 21 ]. Education could also help the mothers make informed nutritional decisions about cultural norms on certain types of food for children.

With reference to other variables, male children seem to be more exposed to the risk of malnutrition than female children. There is no obvious explanation for this gender difference but in Asia, for instance, gender's difference has been attributed to boys' preference over girls [ 29 ]. Also, older children are more prone to be exposed to anthropometric failure than their counterparts aged less than one. Mainly, older children are mixed breastfed, even not breastfed at times, while younger children may be protected by the mother's immune system at birth [ 22 ]. The risk could be also due to lack of foods in the households due to poverty or the lack of hygiene by mothers, when cooking children foods.

The direct causes of malnutrition are the lack of access to drinking water (in the DRC, it is estimated that more than two thirds of the population has no access to drinking water), morbidity (malaria, respiratory infections and diarrhoea) and poor food consumption [ 22 , 30 ]. Also, breast feeding practices are inadequate and according to the WFP, about 12 percent of the under 18 children are orphans. The prevalence changes significantly across the country, and it is higher in the East (more than 16 percent in province Orientale) [ 30 ].

This study has been able to determine that in the DRC, childhood malnutrition is spatially structured and rates remain very high in the provinces that rely on the mining industry and comparable to the level seen in Eastern provinces under war. In war-affected provinces, we are able to determine that childhood malnutrition is higher probably because of the environmental impact caused by war because these provinces are known as traditionally pastoral and agricultural provinces. Furthermore, the massive influx of population especially from Rwanda, Uganda and Sudan fleeing conflicts has further exacerbated the food crisis. Food aids has helped but it is unsustainable. Even in provinces such as Bas-Congo that produce foods, childhood malnutrition is higher because of the economic decision to sell more than the population consumes.

In summary, in the DRC the improvement of the nutritional status of children would help avert child deaths from diarrhoea, pneumonia, malaria, HIV and measles. Consequently it would reduce neonatal mortality, helping achieve MDG 1, which main aim is to reduce poverty and hunger. There is an urgent need for national policies to improve the security of people and implement agricultural policies for auto-dependent agriculture (the DRC has the potential with plenty of land for agriculture). In other words, improving maternal and child nutrition is a prerequisite for achieving MDG 4, to reduce the child mortality rate. Also, nutritional programmes and policies that will try to reduce female illiteracy and provide basic infrastructures in rural areas in order to reduce gaps in health care between socio-economic groups are likely to succeed. The majority of the poorest household lives in rural areas and poorest children are more exposed to the risk of being malnourished. Hence, there is an urgent need to build programmes which aim to reduce poverty in both rural and urban areas, and which will take into account inequalities observed between provinces in the DRC.

Classical linear regression models of the form

for observations ( y i , w i ), i = 1,...., n , on a response variable y and a vector w of covariates assume that the mean E ( y i | w i ) can be modeled through a linear predictor w i ' γ . In our application to childhood under-nutrition and in many other regression situations, we are facing the following problems: First, for the continuous covariates in the data set, the assumption of a strictly linear effect on the response y may not be appropriate. In our study, such covariates are the child's age ( age ), the mother's age at birth ( mab ), and the mother's Body Mass Index ( BMI ). Generally, it will be difficult to model the possibly nonlinear effect of such covariates through a parametric functional form, which has to be linear in the parameters, prior to any data analysis.

Second, in addition to usual covariates, geographical small-area information was given in form of a location variable s , indicating the province, district or community where individuals or units in the sample size live or come from. In our study, this geographical information is given by the provinces of the DRC. Attempts to include such small-area information using province-specific dummy-variables would in our case entail more than 50 dummy-variables and using this approach we would not assess spatial inter-dependence. The latter problem cannot also be resolved through conventional multilevel modeling using uncorrelated random effects. It is reasonable to assume that areas close to each other are more similar than areas far apart, so that spatially correlated random effects are required.

To overcome these difficulties, we replace the strictly linear predictor through a geo-additive predictor , leading to the geo-additive regression model

here, f 1 ,...,f p are non-linear smooth effects of the metrical covariates, and f spat is the effect of the spatial covariate s i ∈ {1,..., S } labelling the provinces in the DRC. Regression models with predictors as in (2) are sometimes referred to as geo-additive models. In a further step we may split up the spatial effect f spat into a spatially correlated (structured) and an uncorrelated (unstructured) effect: f spat (s i ) = f str (s i ) + f unstr (s i ) . The rationale is that a spatial effect is usually a surrogate of many unobserved influences, some of them may obey a strong spatial structure and others may be present only locally. The observation model (2) may be extended by including interaction f(x)w between a continuous covariate x and a binary component of w , say, leading to so called varying coefficient models, or by adding a nonlinear interaction f 1,2 (x 1 , x 2 ) of two continuous covariates.

In a Bayesian approach unknown functions f j and parameters γ as well as the variance parameter σ 2 are considered as random variables and have to be supplemented with appropriate prior assumptions. In the absence of any prior knowledge we assume independent diffuse priors γ j α const, j = 1,...,r for the parameters of fixed effects. Another common choice is highly dispersed Gaussian priors.

Several alternatives are available as smoothness priors for the unknown functions f j ( x j ), see [ 26 ]. We use Bayesian P(enalized) - Splines,. It is assumed that an unknown smooth function f j (x j ) can be approximated by a polynomial spline of low degree. The usual choices are cubic splines, which are twice continuously differentiable piecewise cubic polynomials defined for a grid of k equally spaced knot p on the relevant interval [ a , b ] of the x-axis. Such a spline can be written in terms of a linear combination B-spline basis functions B m ( x ), i.e.

These basis functions have finite support on four neighbouring intervals of the grid, and are zero elsewhere. A comparably small number of knots (usually between 10 and 40) is chosen to ensure enough flexibility in combination with a roughness penalty based on second order difference of adjacent B-spline coefficients to guarantee sufficient smoothness of the fitted curves. In our Bayesian approach this corresponds to second order random walks

with Gaussian errors u m ~ N (0,τ 2 ). The variance parameter τ 2 controls the amount of smoothness, and is also estimated from the data. More details on Bayesian P-Splines can be found in [ 28 ]. Note that random walks are the special case of B-Splines of degree zero.

We now turn our attention to the spatial effects f str and f unstr . For the spatially correlated effect f str (s), s = 1, ... S, we choose Markov random field priors common in spatial statistics. These priors reflect spatial neighbourhood relationships. For geographical data one usually assumes that two sites or regions s and r are neighbours if they share a common boundary. Then a spatial extension of random walk models leads to the conditional, spatially autoregressive specification

where N s is the number of adjacent regions, and r ∈ ∂ s denotes that region r is a neighbour of region s. Thus the (conditional) mean of f str (s) is an average of function evaluations f str (s) of neighbouring regions. Again the variance τ 2 str controls the degree of smoothness.

For a spatially uncorrelated (unstructured) effect f unstr a common assumption is that the parameters f unstr (s) are i.i.d. Gaussian

Variance or smoothness parameters τ 2 j , j = 1,..., p , str , unstr , are also considered as unknown and estimated simultaneously with corresponding unknown functions f j . Therefore, hyper-priors are assigned to them in a second stage of the hierarchy by highly dispersed inverse gamma distributions p(τ 2 j ) ~ IG(a j , b j ) with known hyper-parameters a j and b j . For model choice, we routinely used the Deviance Information Criterion (DIC) developed in [ 27 ], as a measure of fit and model complexity.

FAO: State of Food Insecurity in the World. 2008, Retrieved on 30 March, 2010, [ http://www.fao.org/es/ess/faostat/foodsecurity/index_en.htm ] : Food Security Statistics

Google Scholar

Black R, Morris S, Jennifer B: Where and Why Are 10 Million Children Dying Every Year?. The Lancet. 2003, 361: 2226-2234. 10.1016/S0140-6736(03)13779-8.

Article Google Scholar

UNICEF: The state of the world's children 1998. 1998, New York, UNICEF

Reed BA, Habicht JP, Niameogo C: The effects of maternal Education on child nutritional status depend on socio-environmental conditions. International Journal of Epidemiology. 1996, 25: 585-592. 10.1093/ije/25.3.585.

Article CAS PubMed Google Scholar

Madise NJ, Matthews Z, Margetts B: Heterogeneity of child nutritional status between households: a comparison of six sub-saharan African countries. Population Studies. 1999, 53: 331-343.

Gwatkin DR, Rustein S, Johnson S, Pande K, Wagstaff A: Socioeconomic differences in Health, Nutrition and population in Cameroon. 2000, HNP/Poverty Thematic Group of World Bank, Washington DC

Pongou R, Ezzati M, Salomon JA: Household and Community Socioeconomic and Environmental Determinants of Child Nutritional Status in Cameroon. BMC, Public Heath. 2006, 6.98: 19-

World Bank: World Development Indicators 2009. 2009, Washington DC: the World Bank

Book Google Scholar

UNDP: D.R. Congo Human Development Report. 2007, New York, NY: United Nations Development Programme

Coghlan B, Brennan RJ, Ngoy P, Dofara D, Otto B, Clements M, Stewart T: Mortality in the Democratic Republic of Congo: a nationwide survey. The Lancet. 2006, 367: 44-51. 10.1016/S0140-6736(06)67923-3.

Central Emergency Respond Fund (CERF): The Democratic Republic of Congo. Facts and Figures. 2008, Retrieved 30 March, 2010, [ http://ochaonline.un.org/cerf/CERFHome/tabid/1705/language/en-US/Default.aspx ]

UNICEF: The state of the world's children 2007. 2007, New York, UNICEF

République Démocratique du Congo: (RDC), Enquête démographique et de Santé (EDS-RDC 2007). Rapport final. 2008

United States Agency for International Development: The Democratic Republic of Congo. Retrieved on 30 March, 2010, [ http://www.usaid.gov/pubs/cbj2003/afr/cd/ ]

Doctor without Borders: Food, nutrition and mortality situation of IDP's in Dubie. Katanga 23-25 March 2006

Kandala NB, Emina JBO, Nzita DK, Cappuccio FP: Diarrhoea, acute respiratory infection, and fever among children in the Democratic Republic of Congo. Social Science and Medicine. 2009, 68: 1728-1736. 10.1016/j.socscimed.2009.02.004.

Article PubMed Google Scholar

World Health Organization (WHO): WHO Technical Report Series No. 854. Physical Status: The Use and Interpretation of Anthropometry. 1995, Geneva: WHO

World Health Organization (WHO): Measuring Change in Nutritional Status. 1983, Geneva

World Health Organization (WHO): Multicentre Growth Reference Study. Acta Paediatrica. 2006, 450 (Suppl): 1-87.

Mosley W, Chen L: An Analytical Framework for the Study of Child Survival in Developing Countries. Population and Development Review. 1984, 10: 25-45.

Smith L, Haddad L: IFPRI Research Report No. 111. Explaining Child Malnutrition in Developing Countries. 1999, Washington DC: IFPRI

Kandala NB, Fahrmeir L, Klasen S, Priebe J: Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries. Population, Space and Place. 2009, 15: 461-473. 10.1002/psp.524.

Moradi A, Klasen S: SFB 386 Discussion Paper No. 217. The Nutritional Status of Elites in India, Kenya, and Zambia: An Appropriate Guide for Developing International Reference Standards for Undernutrition?. 2000, University of Munich

Caputo A, Roraita R, Klasen S, Pigeot I: Undernutrition in Benin: An. Analysis based on Graphical Models. Social Sciences and Medecine. 2003, 56: 1677-1691. 10.1016/S0277-9536(02)00162-4.

Kammann EE, Wand MP: Geoadditive Models. Journal of the Royal Statistical Society C. 2003, 52: 1-18.

Fahrmeir L, Lang S: Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors. Applied Statistics (JRSS C). 2001, 50: 201-220.

Spiegelhalter D, Best N, Carlin B, Van der Line A: Bayesian measures of models complexity and fit. Journal of the Royal Stat Soc B. 2002, 64: 1-34. 10.1111/1467-9868.02022.

Brezger A, Kneib T, Lang S: BayesX: Analysing Bayesian structured additive regression models. Journal of Statistical Software. 2005, 14 (11):

Klasen S: Poverty, undernutrition, and child mortality: Some inter-regional puzzles and their implications for research and policy. Journal of Economic Inequality. 2008, 6 (1): 89-115. 10.1007/s10888-007-9056-x.

United Nations World Food Programme (WFP): Democratic Republic of Congo Comprehensive Food Security and Vulnerability Analysis (CFSVA) / 2007-2008. 2008

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Acknowledgements

This research was supported by the British Council under the DelPHE (Development Partnership in Higher Education) scheme. The authors thank Macro international, for providing free the 2007 DHS data-sets for the DR Congo.

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Ngianga-Bakwin Kandala

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Kandala, NB., Madungu, T.P., Emina, J.B. et al. Malnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter?. BMC Public Health 11 , 261 (2011). https://doi.org/10.1186/1471-2458-11-261

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v.12(6); 2020 Jun

A Complete Guide to Identify and Manage Malnutrition in Hospitalized Patients

Sohaip kabashneh.

1 Internal Medicine, Wayne State University/Detroit Medical Center, Detroit, USA

Samer Alkassis

Layla shanah.

Malnutrition is extremely common in hospitalized patients. It can lead to various complications and increase mortality. However, it remains poorly recognized and many health care systems do not require nutritional assessment during the hospital stay. This most likely due to lack of awareness and inadequate coordination between health care workers. Physicians can utilize many different methods when performing malnutrition screening, and there is also a lack of global clear-cut recommendations on criteria used to diagnose malnutrition. This article aims to increase malnutrition awareness among health care providers, and provide a guide on screening, diagnosis, and management of malnutrition.

Introduction and background

There is no absolute agreement among societies on the definition of malnutrition, but frequently used elements in defining malnutrition are deficiencies of energy, protein, and a decrease in fat-free mass [ 1 ]. Malnutrition is a very common medical problem, affecting about half of the patients admitted to an acute hospital setting [ 2 - 4 ]. It increases the risk of negative health outcomes and complications including nosocomial infections, immunodeficiency, and pressure ulcers among others [ 5 - 7 ]. Despite its prevalence and negative impact on health outcomes, malnutrition remains poorly diagnosed and documented. Hence, dietary interventions and supplemental nutrition are underutilized thus compromising patient clinical outcomes [ 8 ].

The purpose of this review is to highlight the current literature available on the prevalence, impact on health outcomes, screening, and diagnosis of malnutrition, in addition to providing recommendations on the management of malnutrition and the benefit of nutritional supplements in malnourished patients.

Prevalence of malnutrition on admission and the risk of nosocomial malnutrition

Malnutrition is a common problem; it includes both undernutrition (underweight) and overnutrition (overweight and obesity). This public health epidemic targets all spectrums of the population; however, the most vulnerable groups are those with low-socioeconomic status, older people, those with acute or chronic illnesses, and expectant mothers [ 9 ].

Lim et al. conducted a study to estimate the prevalence of malnutrition on admission in a tertiary hospital in Singapore involving 818 adults. Malnutrition was evident in 29% [ 2 ]. To evaluate the prevalence of malnutrition in England, Edington et al. evaluated 850 patients, and malnutrition on admission was found in 20% [ 3 ]. In the Netherlands, Naber et al. found that 45% of patients hospitalized for internal or gastrointestinal diseases were malnourished on admission [ 4 ]. Hence, the prevalence of malnutrition varies depending on the geographic location and population being studied. Studies show malnutrition is found in approximately 20 to 50 percent of hospitalized adults.

Malnutrition is often present upon hospital admission. Additionally, 38% of well-nourished inpatients and 69% of malnourished inpatients either develop malnutrition or suffer further deterioration of nutritional status during hospitalization [ 10 - 12 ]. Nosocomial malnutrition is a potentially preventable cause of poor outcomes. Unfortunately, it often goes unrecognized by healthcare providers.

Malnutrition is associated with a significant increase in morbidity and mortality in the hospital setting

Malnutrition was found to increase total complications in a study of 709 adults. The incidence of complications in the malnourished group was 27% compared to only 16.8% in the well-nourished group [ 5 ]. The same study found that malnutrition also increases the length of hospital stay; malnourished patients had a median length of stay of nine days compared to only six days in the well‐nourished patients [ 5 ]. Similarly, results from a study of 173 hospitalized patients found that the median length of stay for patients at risk for malnutrition was six days compared to four days for well‐nourished patients [ 13 ].

In addition to a prolonged hospital stay, a study of 837 patients over a 14-month period found that 25% of malnourished subjects required readmission to a healthcare facility after being discharged compared with 11% of the well-nourished group [ 14 ]. Correia and Waitzberg showed that hospital mortality in the malnourished patients was higher (12.4%) compared to 4.7% in the well-nourished, with a relative risk of 2.63 [ 5 ]. In addition to hospital mortality, studies showed that protein-energy undernutrition is a strong risk factor for mortality during the subsequent 4.5 years [ 15 ].

Malnutrition is also associated with altered immune responses, as many studies showed that malnourished individuals are at a higher risk of developing nosocomial infections. They had a higher incidence of sepsis and intra‐abdominal abscess [ 5 , 6 , 16 ]. Malnourished patients also have a higher risk of developing pneumonia and have a higher risk of mortality from pneumonia [ 17 ].

The occurrence of pressure ulcers is also higher in patients with malnutrition. Studies showed that compared to well‐nourished patients, malnourished patients are 2.1 times more likely to develop decubitus ulcers [ 7 ].

Malnutrition can exaggerate age‐related reduced muscle mass, which subsequently leads to deconditioning [ 18 ]. In fact, studies have shown that most elderly inpatients with hospital-associated deconditioning are malnourished. Likewise, malnutrition is associated with poor rehabilitation outcome in hospital-associated deconditioning [ 19 ].

Malnutrition was also associated with higher hospital costs. Braunschweig et al. conducted a study on 404 adults and found that patients who declined nutritionally, regardless of nutritional status at admission, had significantly higher hospital charges ($45,762) compared to those who did not ($28,631) [ 10 ]. Similarly, Correia and Waitzberg showed that malnutrition was associated with up to a threefold increase in hospital costs [ 5 ].

Inadequate malnutrition identification and diagnosis

Failure to identify and subsequently refer to a dietitian lead to the persistently high prevalence of malnutrition. Several studies have been conducted to investigate malnutrition recognition and documentation in the hospital setting in an attempt to combat malnutrition.

Among the studies conducted, Gout et al. reported that only 15% of malnourished patients were correctly identified and documented, and a dietitian was involved in only 45% of malnutrition cases [ 8 ]. Kellett et al. reported that the prevalence of malnutrition is found to be 52%. Unfortunately, only 5.4% of patients were coded as malnourished which is most likely due to lack of identification [ 10 ].

Nutrition screening for hospitalized patients

In January 2016, the Global Leadership Initiative on Malnutrition (GLIM) reached a consensus to use a two‐step approach for the diagnosis of malnutrition - first screening to identify “at-risk” status, and second assessment for diagnosis and grading the severity of malnutrition [ 20 ].

An important step in combating malnutrition is to increase screening in order to identify patients that are malnourished and/or at high risk of developing malnutrition during their hospital course. Usability is key in choosing a screening tool. The Nutrition Risk Screening 2002 (NRS-2002) and Mini Nutritional Assessment (MNA) rely on a few questions, do not require professional nutrition expertise, and do not take a long time to complete, thus, are the preferred screening tools in the healthcare setting and are recommended by the European Society for Clinical Nutrition and Metabolism (ESPEN). The following is an overview of both the tools in a healthcare setting [ 21 ].

The Hospital: Nutrition Risk Screening 2002

NRS-2002 is the preferred tool to detect undernutrition and the risk of developing undernutrition in the hospital setting [ 21 , 22 ]. It includes a pre-screening questionnaire (Table (Table1). 1 ). If the patient answers ‘Yes’ to any of the pre-screening questions, then the actual screening (Table (Table2) 2 ) is indicated; otherwise, the patient is re-screened at weekly intervals [ 23 ].

NRS-2002 pre-screening questions

Is BMI <20.5?

Has the patient lost weight within the last 3 months?

Has the patient had a reduced dietary intake in the last week?

Is the patient severely ill? (e.g., in intensive therapy)

a Body Mass Index

b Chronic Obstructive Pulmonary Disease

Nutritional risk screening (NRS 2002)
Impaired nutritional status		Severity of disease
Score 0	Normal nutritional status	Score 0	Normal nutritional requirements
Score 1	Weight loss >5% in 3 months or food intake below 50–75% of normal requirement in the preceding week	Score 1	Hip fracture, chronic patients particularly with acute complications: cirrhosis, COPD , chronic hemodialysis, diabetes, oncology
Score 2	Weight loss >5% in 2 months or BMI 18.5–20.5 + impaired general condition or Food intake 25–50% of normal requirement in preceding week	Score 2	Major abdominal surgery, stroke, severe pneumonia, hematologic malignancy
Score 3	Weight loss >5% in 1 month (>15% in 3 months) or BMI <18.5 + impaired general condition or food intake 0–25% of normal requirement in the preceding week.	Score 3	Head injury, bone marrow transplantation, intensive care patients (APACHE 10)
To calculate the total score: 1. Find score (0–3) for impaired nutritional status and severity of disease. 2. Add the two scores (→ total score) 3. If age ≥70 years: add 1 to the total score to correct for the frailty of elderly.

NRS-2002 actual screening uses two main categories impairment of nutritional status and increases in requirements to identify patients at nutritional risk (Table (Table2). 2 ). If age-corrected total ≥3 then comprehensive nutritional evaluation and subsequent intervention are indicated; otherwise, the screening is repeated weekly [ 23 ].

The Elderly in Nursing Homes and Hospitals: Mini Nutritional Assessment

Mini nutritional assessment (MNA) is used to detect undernutrition and the risk of developing undernutrition among the older population living in nursing homes and hospitals [ 21 ]. The MNA is more likely to identify the risk of developing undernutrition at an early stage in the frail elderly because it looks at the physical and mental aspects that affect the nutritional status of the elderly (Figure (Figure1). 1 ). The patient is evaluated with six questions, a score of eleven points or below warrants further assessment and appropriate nutritional plan [ 24 ].

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Object name is cureus-0012-00000008486-i01.jpg

Malnutrition diagnosis

GLIM reached a consensus on the criteria to be used when making the diagnosis of malnutrition. It includes three phenotypic criteria (weight loss, low body mass index, and reduced muscle mass ), and two etiologic criteria (reduced food intake or assimilation and disease burden). For the diagnosis of malnutrition, a combination of at least one phenotypic criterion and one etiologic criterion is required (Table (Table3). 3 ). Because it is clinically useful to identify the severity of malnutrition, GLIM also developed phenotypic metrics for grading severity as moderate and severe (Table (Table4) 4 ) [ 20 ].

GI = gastro-intestinal, ER = energy requirements.

Phenotypic Criteria			Etiologic Criteria
Weight loss (%)	Low body mass index (kg/m )	Reduced muscle mass	Reduced food intake or assimilation	Inflammation
>5% within past 6 months, or >10% beyond 6 months	<20 if <70 years, or <22 if >70 years	Reduced by validated body composition measuring techniques	≤50% of ER > 1 week, or any reduction for >2 weeks, or any chronic GI condition that adversely impacts food assimilation or absorption	Acute disease/injury or chronic disease-related
	Asia: <18.5 if <70 years, or <20 if >70 years

Phenotypic Criteria
	Weight loss (%)	Low body mass index (kg/m )	Reduced muscle mass
Stage 1/Moderate Malnutrition (Requires 1 phenotypic criterion that meets this grade)	5-10% within the past 6 months, or 10-20% beyond 6 months	<20 if <70 years, <22 if ≥70 years	Mild to moderate deficit (per validated assessment methods)
Stage 2/Severe Malnutrition (Requires 1 phenotypic criterion that meets this grade)	>10% within the past 6 months, or >20% beyond 6 months	<18.5 if <70 years, <20 if ≥70 years	Severe deficit (per validated assessment methods)

Management of malnutrition

A diagnosis of malnutrition should be followed by a consultation with skilled nutrition practitioners like dietitians for comprehensive nutritional assessments if possible. After a complete assessment is performed, nutritional requirements can be calculated and a plan to meet those requirements is initiated.

Oral nutritional supplements (ONS) can be used if improvements in energy, protein, and micronutrient intakes are required. An overview of 13 systematic reviews and meta-analyses by Stratton and Elia found that ONS were associated with significant clinical benefits. In the study, the daily intake of ONS was between 250 and 600 kcal/day, the duration of supplementation varied from a short period in hospital (one week) to a prolonged period in the community (up to two years) [ 25 ].

The reviews by Stratton and Elia suggest that ONS consistently improved total nutritional intake, with little suppression of food intake [ 25 ]. Thus, it has a positive effect on body weight, significantly attenuating weight loss in the acutely ill. It also showed a significant reduction in mortality particularly in acutely ill elderly [ 25 ]. High protein ONS was associated with a lower risk of pressure ulcers in high-risk groups (frail elderly, hip fracture, poor mobility) [ 26 ]. A three-month intervention with ONS appears to be cost-effective according to international benchmarks [ 27 ].

Conclusions

Malnutrition is an exceedingly common medical problem with significant effects on morbidity and mortality. Despite its significance, it is underdiagnosed in healthcare systems. In this review, we recommend nutrition screening by either NRS-2002 for hospitalized patients or MNA for the older population living in nursing homes. Positive screening should be followed by GLIM criteria evaluation for diagnosis and severity grading. Consultation of skilled nutrition practitioners is needed when the diagnosis is made for full nutritional evaluation and calorie count. ONS has a positive effect on body weight and decreases mortality and should be considered in the management of malnutrition.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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of acute malnutrition, 5% margin of error, and 95% confi-dence level [15, 22]. The sample size of 197 plus an addition 10% non-response rate yielded a total sample size of 217 for each district. The final sample size after data cleaning and excluding incomplete data from the two dis-tricts was 398. OTCC in a district was considered as strata.
(PDF) A descriptive study on Malnutrition
1) Malnutrition: In this study it refers to the lesser intake. of food for children in terms of quality and quantity to. maintain optimum health. 2) Mother of Under Five Children: In this study it ...
Risk Factors Associated with Malnutrition among Children Under-Five
1. Introduction. Malnutrition is the intake of an insufficient, surplus or disproportionate amount of energy and/or nutrients [].Malnutrition is a significant global public health burden with greater concern among children under five years [].In an attempt to address this global challenge of malnutrition, the World Health Organization (WHO) member states recently signed into effect a ...
Prevalence of malnutrition and associated factors among under-five
Malnutrition among under-5 year children is a common public health problem and it is one of the main reasons for the death of children in developing countries [].As of the World Health Organization report, about 35% of under-five children's death is associated with malnutrition in the world [].There are 165 million stunted, 99 million under-weighted, and 51 million wasted children globally [].
(PDF) Determinants of Malnutrition in Children Under Five Years in
Malnutrition was associated with food insecurity (Grammatikopoulou et al., 2019), low incomes (Tasnim, 2018) and lack of higher education (Katoch and Sharma, 2016a). The association between food ...
Tackling malnutrition: a systematic review of 15-year research evidence
Relevant publications titles (related to malnutrition studies) from the member research centres of INDEPTH were uploaded onto the Zotero research tool from different databases (60% from PubMed). Using the keywords 'nutrition, malnutrition, and over and under nutrition', publications were selected that were based only on data generated ...
A Thesis Proposal on Nutritional Status of 5 to 10 Years Children of
1.3 Conceptual framework: Conceptual Framework for Causes of Malnutrition by UNICEF 2 1.4 Research Objectives/purpose/aim of study 1.4.1 General: The main objective of this work is to provide information about the nutritional condition of the population and the factors those influence them and this will be basis for decision on policy making ...
Malnutrition among children under the age of five in the Democratic
Malnutrition prevents children from reaching their full physical and mental potential. Health and physical consequences of prolonged states of malnourishment among children are: delay in their physical growth and motor development; lower intellectual quotient (IQ), greater behavioural problems and deficient social skills; susceptibility to contracting diseases [1, 2].
Malnutrition Screening and Assessment
Malnutrition is a serious problem with a negative impact on the quality of life and the evolution of patients, contributing to an increase in morbidity, length of hospital stay, mortality, and health spending. ... Further research is needed to assess lean mass with this method . 5.3.3. Computed Tomography (TC) ... GLIM Group Proposal versus ...
A Complete Guide to Identify and Manage Malnutrition in Hospitalized
Introduction and background. There is no absolute agreement among societies on the definition of malnutrition, but frequently used elements in defining malnutrition are deficiencies of energy, protein, and a decrease in fat-free mass [].Malnutrition is a very common medical problem, affecting about half of the patients admitted to an acute hospital setting [2-4].
Global Surgery 2030: evidence and solutions for achieving health
This research informs all sections of the report and appendices. Web-based platforms and social media promoted global engagement. Lastly, 12 teaching cases were written in collaboration with five business schools and one global health programme in the USA, Australia, and India. ... whether it be a child with malnutrition, a mother dying of post ...

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Effective interventions to address maternal and child malnutrition: an update of the evidence

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A First Step Towards Eliminating Malnutrition: A Proposal for Universal Nutrition Screening in Pediatric Practice

Introduction

Causes and Consequences of Childhood Malnutrition

Addressing Childhood Malnutrition

Taking Action

A Proposal to Fill Gaps in Pediatric Nutrition Care

Specific Gaps to Be Addressed in Updated Pediatric Nutrition Care

Putting Nutrition Screening into Practice: How to Do It

Selecting a Pediatric Nutrition Screening Tool

What is the Best Screening Tool for Hospitals? For Healthcare in the Community?

Other Gaps Related to Nutrition Screening in Pediatric Practice: Training and Policy Development

Why Addressing Childhood Nutrition Matters: Health and Economic Impact

A Plan for Action on Pediatric Nutrition Screening

Conclusions

Data Sharing Statement

Ethics Approval and Consent to Participate

Author Contributions

Factors associated with malnutrition in children < 5 years in western Kenya: a hospital-based unmatched case control study

Study design

Case definitions

Mid upper arm circumference (MUAC) calculations

Definition of controls

Sample size determination

Selection of cases and controls

Data collection

Data management

Description of the study participants

Nutritional status of cases

Univariable and multivariable analysis of factors associated with under-nutrition

Availability of data and materials

Abbreviations

Acknowledgements

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Contributions

Corresponding author

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BMC Nutrition

Prevalence of malnutrition and associated factors among under-five children in Ethiopia: evidence from the 2016 Ethiopia Demographic and Health Survey

Introduction

Study design and sampling

Measurements

Statistical data analysis

Determinants of stunting

Determinants of under-weight

Determinants of wasting

Associations between children’s anemia and malnutrition

Conclusions

Limitations of the study

Availability of data and materials

Abbreviations

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Consent for publication

Additional information

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Additional file 2.

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A THESIS PROPOSAL ON NUTRITIONAL STATUS OF 5 TO 10 YEARS CHILDREN OF NAMJE, VEDETAR VDC OF DHANKUTA DISTRICT

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