|
| |
 |
|
| ORIGINAL ARTICLE |
|
| Year : 2020 | Volume
: 16
| Issue : 3 | Page : 110-114 |
|
Building evidence for information surveillance of nutrition rehabilitation center to assess risk factors for severe acute malnutrition in a tertiary hospital of the northern state of India
Suresh Kumar1, Dinesh Kumar1, Sunil Kumar Raina1, Milap Sharma2
1 Department of Community Medicine, Dr. Rajendra Prasad Government Medical College, Kangra, Himachal Pradesh, India
2 Department of Pediatrics, Dr. Rajendra Prasad Government Medical College, Kangra, Himachal Pradesh, India
| Date of Submission | 30-Apr-2020 |
| Date of Decision | 05-May-2020 |
| Date of Acceptance | 05-May-2020 |
| Date of Web Publication | 09-Oct-2020 |
Correspondence Address:
Dr. Dinesh Kumar
Department of Community Medicine and Pediatrics, Dr. Rajendra Prasad Government Medical College, Kangra at Tanda, Himachal Pradesh
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/AMJM.AMJM_31_20
Background: As community-based surveys require energy and time, information surveillance of nutrition rehabilitation centers (NRCs) can be used to assess the nature and degree of risk factors for children with severe acute malnutrition (SAM). Therefore, the current study was planned to assess the degree of association of known risk factors for SAM among children admitted in NRC of a tertiary hospital from the northern state of India. Materials and Methods: Case–control study design was adopted where 50 children with SAM (cases) were recruited from NRC and 50 without SAM (controls) were from an immunization clinic of a tertiary center. A pretested structured interviewer-administered questionnaire was used to collect data from cases and controls. Results: Type of house, status of water and sanitation, simple exponential smoothing, method of birth, birth order, initiation and frequency of breastfeeding, colostrum intake, and profile of diarrhea and acute respiratory infection were statistically similar among cases and controls. Logistic regression analysis observed SAM had significantly high odds for risk factors like mean age of mother at birth (1.4, 1.0–1.9), low birth weight (LBW) (8.7, 2.1–35.3), and bottle feeding (6.3, 1.2–14.2), whereas exclusive breastfeeding (0.2, 0.4–0.4) and fully immunization (0.07, 0.5–0.001) had significantly low odds. Conclusion: Advancing age of mother at the time of delivery, LBW, poor rate of exclusive breastfeeding, and high rate of bottle feeding are significant associated factors for SAM in the study area. Reasons for LBW and reasons for bottle feeding need to be targeted in the study area to reduce SAM among children <5 years of age.
Keywords: Low birth weight, nutrition rehabilitation center, severe acute malnutrition
How to cite this article:
Kumar S, Kumar D, Raina SK, Sharma M. Building evidence for information surveillance of nutrition rehabilitation center to assess risk factors for severe acute malnutrition in a tertiary hospital of the northern state of India. Amrita J Med 2020;16:110-4 |
How to cite this URL:
Kumar S, Kumar D, Raina SK, Sharma M. Building evidence for information surveillance of nutrition rehabilitation center to assess risk factors for severe acute malnutrition in a tertiary hospital of the northern state of India. Amrita J Med [serial online] 2020 [cited 2023 Mar 30];16:110-4. Available from: https://ajmonline.org.in/text.asp?2020/16/3/110/297555 |
| Introduction | |  |
In India, the prevalence of severe acute malnutrition (SAM) found around to be 6.0% in children <5 years, which is due to a constellation of risk factors associated with sociodemographic, economic, health, and biologic determinants.[1],[2] These factors are low education status of mother, poor infant and child feeding practices, low-income status, and low birth weight (LBW).[3],[4],[5] SAM is a potentially life-threatening condition and a major underlying cause of mortality in under-five children. Among children of 6–60 months of age, SAM is identified by the presence of anyone clinical sign: weight-for-height (WFH) <−3SD, height-for-age (HFA) <−3SD, mid-upper arm circumference (MUAC) <11.5 cm, and bilateral pedal edema.[6]
Although facility-based management of SAM targets to manage its associated complications such as hypoglycemia, hypothermia, dehydration, and electrolyte imbalance, developing countries have also launched various community-based nutrition intervention programs.[7] These ground-level efforts still possess accessibility gaps, though governments are significantly investing to reduce undernutrition by rooting various strategies.[8],[9] Improving economy of a country and consistent investment in programmatic strategies related to maternal and child health, nutrition, and immunization expect to weaken or change the association of certain risk factors among children living with SAM. If so, it can be rapidly assessed by information surveillance from nutrition rehabilitation center (NRC). In addition, a rapid review of children attending hospitals proves to be an opportunity to screen them for SAM, which gives an idea about situation in community.[10] Evidence still reports poor feeding practices, suboptimal immunization coverage, large family size, poor hygiene, inadequate paternal literacy, etc., as risk factors for SAM. However, the distribution and degree of association between these known prevalent risk factors and SAM vary in various geographical settings.[10],[11],[12],[13],[14],[15],[16],[17]
Since significant progress has been made in tackling undernutrition among children, it is now becoming imperative to contextually understand the epidemiology of SAM for further tailoring strategies locally. It is because, in changing geography, differential distribution of economic developmental outcomes resulted in changing socioeconomic, nutritional, health system, and psychological determinants. We hypothesized that before marching for a community-based survey, information surveillance of NRC gives insight to prevalent risk factors for SAM. Therefore, the present study was planned to assess the degree of association of known risk factors for SAM among children admitted in NRC of a tertiary hospital from the northern state of India.
| Materials and Methods | | |
It was a hospital-based case–control study carried out in the NRC at Dr. Rajendra Prasad Government Medical College (Dr. RPGMC), Kangra, Himachal Pradesh, from July 2018 to June 2019. Cases were children between the age groups of 06 and 59 months with SAM as per criteria laid by the World Health Organization.[6] Controls, without the presence of any sign for SAM, of the same age band were selected concurrently from an immunization clinic, Dr. RPGMC. Children with known genetic disorders, physical illness, and any other major illness and without parental consent were excluded from the study. A sample size of 50 in each group was calculated assuming 1:1 group ratio with an assumption that the proportion of exclusive breastfeeding is 57.3% and 87.6% in the case and control groups, respectively, at a two-sided confidence interval of 95% and 90% study power.[1],[17]
Face-to-face interviews were conducted of respondents of participants using a pretested semi-structured questionnaire for data collection on sociodemographic characteristics, housing status, feeding practices, birth weight, immunization status, and anthropometry. Socioeconomic status of family of children was assessed using BG Prasad's socioeconomic status scale.[18] Weight was recorded using a standard weighing machine scale kept on a firm horizontal surface. Height was recorded using a mobile stadiometer to the nearest 1 cm. MUAC was recorded using Shakir's tape.
Collected data were entered into Microsoft spreadsheet and exported to Epi Info data analysis software (7.2 version, CDC, Atlanta, USA).[19] Data were presented as percentages, mean, and standard deviation (SD). Categorical variables were compared using the Chi-square test with or without Yates correction, and a comparison of means was done by unpaired Student's t-test. Odds ratio (OR) was calculated for measuring the association between SAM and exposure variables by logistic regression. To measure the unconfounded effect size of variables, adjusted OR (aOR) was calculated. The study was approved by the Institute Ethics Committee at Dr. RPGMC. Informed consent was taken from respondents of all participants, and no additional financial burden was placed to the respondents. The study participants did not receive any financial assistance from the study and were free to be withdrawn from the study.
| Results | | |
Totally 50 cases and the same number of controls were enrolled for study, where 76% of the cases had WFH <−3 SD while 16% of the cases had HFA <−3 SD, 38% had MUAC <115 mm, and edema was present only in 8% of the cases. In both the groups, respondents were mothers (cases: 92.0%; controls: 100.0%; P = 0.126) with mean age of about 29 years (cases: 28.3 + 3.6; controls: 29.2 + 4.7; P = 0.304). Most of the participants were from rural areas as 78.0% in the case group and 62.0% in the control group. Housing conditions, availability of water and sanitation, hand hygiene practices, and distribution of simple exponential smoothing (SES) were observed to be statistically similar in both the groups [Table 1]. | Table 1: Characteristics of socioeconomic status of cases and controls enrolled in nutrition rehabilitation centre, Himachal Pradesh, 2018-2019
Click here to view |
The mean age of the mother at the time of delivery of baby was significantly more in cases (26.8 years) as compared to controls (24.3 years). The mean age of participants was statistically similar being 19.7 and 22.8 months in the case and control groups, respectively, and majority were male in both the groups. Statistical similarity was also observed for type of delivery for birth as most (cases: 66.0% controls: 72.0%) were normally delivered vaginally and rest were by cesarean section. In both the groups, majority had birth orders of 1 and 2 and had birth interval of >3 years without any statistical difference. In the case group, the mean birth weight was significantly lower in the case group (2.4 Kg) as compared to the control (3.0 Kg) group, so does the distribution of LBW (cases: 50.0%; controls: 14.0%; P = 0.001) [Table 2]. | Table 2: Birth characteristics of cases and controls enrolled in nutrition rehabilitation centre, Himachal Pradesh, 2018-2019
Click here to view |
Distribution of early initiation of breastfeeding (within 1 h of birth), colostrum at birth, and exclusive and satisfactory frequency (>8/day) of breastfeeding was statistically similar in both the groups. Complementary feeding was initiated around completion of 6 months in both the case and control groups. A significantly low percentage of children in the case group (56.0%) as compared to the control group (96.0%) were fully immunized. Morbidity profile in the last 30 days was also found to be statistically similar in both the groups, most of the children suffered from diarrhea (cases: 52.0%; controls: 46.0%) in both the groups, whereas acute respiratory infection (ARI) was reported among 18.0% and 10.0% of the children in the case and control groups, respectively. In the last 30 days, on the average, children had about 1–2 episodes of ARI and diarrhea in both the groups [Table 3]. Logistic regression analysis observed SAM had significantly high odds for risk factors like mean age of mother at birth (1.4, 1.0–1.9), low birth weight (LBW) (8.7, 2.1–35.3), and bottle feeding (6.3, 1.2–14.2), whereas exclusive breastfeeding (0.2, 0.4–0.4) and fully immunization (0.07, 0.5–0.001) had significantly low odds [Table 4]. | Table 3: Feeding characteristics, immunization status, and morbidity profile in cases and controls enrolled in nutrition rehabilitation centre, Himachal Pradesh, 2018- 2019
Click here to view |
 | Table 4: Measure of association of risk factors among cases as compare to controls enrolled in nutrition rehabilitation centre, Himachal Pradesh, 2018-2019
Click here to view |
| Discussion | | |
The present study was aimed to assess the extent of association between common risk factors and SAM while adopting an unmatched case–control study design at NRC of a tertiary hospital. Sociodemographic indicators and maternal age are observed to be similar among cases and controls, so does the birth characteristics except for faction of LBW, which was found to be significantly more among cases than controls. History of breastfeeding practices was observed to be good in both the groups without any statistical difference. While comparing, morbidity profile of cases and controls did not observe any large difference. Assessing degree of association, SAM was observed with significantly high odds for LBW and history of bottle feeding, whereas low odds were found for exclusive breastfeeding and being fully immunized.
The present study observed high odds of SAM for mean age of mother at time of birth and also for relatively early and advanced age of mother.[19],[20],[21] LBW in developing countries is quite prevalent and largely attributed to undernutrition at the time of pregnancy and preterm delivery.[22] Like in the current study, SAM was observed with significantly high odds for LBW, as aOR of 2.7 (1.2–6.3), 8.6 (2.5–30.0), 6.8 (1.6–28.9), and 2.6 (1.1–6.4).[19],[20],[21],[23] Apart from LBW, available evidence also showed that children who were not exclusively breastfed had high odds of acute malnutrition with aOR of 1.1 (0.6–1.9), 5.8 (1.8–18.8), and 3.2 (1.3–7.9).[24],[25],[26] The current study observed SAM with significantly low odds for exclusive breastfeeding, and it is undebatable that exclusive breastfeeding has not only beneficial biological effects on health and nutritional outcomes of children but also has a role in reduction of morbidities.[27],[28] The immunological properties of breast milk contribute to ensuring adequate nutritional status, proper growth, and foster immunity.[29] SAM had significant high odds for bottle feeding in concurrence with other studies with aOR of 4.6 (1.7–12.0) and 2.0 (1.1–3.6).[15],[16] Bottle feeding was associated with increased chances of morbidities due to lack of immune properties of animal milk, bacterial contamination, and faulty feeding technique.[30] Immunization of children was found to be protective against the development of vaccine-preventable diseases, which are the leading cause of childhood morbidity and mortality. In the current study, history of full immunization was observed with significantly lower odds with SAM, and available evidence also observed that children who are not fully immunized fully had higher odds of SAM as aOR of 13.3 (2.1–85.4), 12.1 (4.3–34.1), and 3.2 (2.1–8.0).[15],[17],[30]
Available evidence showed that the development of SAM was significantly associated with mothers' education and low socioeconomic status.[16],[30],[31],[32],[33] Studies also observed high odds for SAM with introduction of prelacteal feeds,[15],[25],[30] late initiation of breastfeeding,[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30],[34] not giving colostrum,[16],[27],[35] low frequency of breastfeeding (<8 times per day),[16] and diarrheal episodes.[21],[30],[32],[36] In the present study, type of house, status of water and sanitation, SES, method of birth, birth order, initiation and frequency of breastfeeding, colostrum intake, and profile of diarrhea and ARI were statistically similar among cases and controls. It might be due to relatively general nondifferential improvement in ecological variables in the study district of the state of Himachal Pradesh. Analysis has revealed that Himachal Pradesh has adequate availability of health services in comparison to laid standards for hilly states in India.[37]
Changing epidemiology of SAM in geography can be understood by community-based surveys, which is often hypothesized by looking at information from hospitals or NRCs. Over a decade, community health is transforming in the presence of national nutrition programs and increasing economic capacity of families. In Himachal Pradesh, the prevalence of institutional births rose from 43.1% to 76.4% from 2005–2006 to 2015–2016, and during the same time periods, stunting reduced from 38.6% to 28.3%, wasting from 19.3% to 13.7%, and severe wasting from 5.5% to 3.9%.[2] Therefore, in changing epidemiology of SAM, surveillance of routine information from NRCs will help to refine objectives and prioritize strategies to achieve better child health outcomes. Close scrutiny of secondary data not only gives an insight to the implementation of programmatic strategies but also helps to comprehend the changing epidemiology, where community-based surveys take a large amount of time and effort.
The current study has also some limitations, as due to low sample size and wide confidence interval, it could have low power for LBW, breastfeeding, and full immunization and could have caused Type II error. Furthermore, at the time of admission and during hospital stay, modifiable risk factors could have been managed early. In conclusion, advancing age of mother at the time of delivery, LBW, poor rate of exclusive breastfeeding, and high rate of bottle feeding are significant associated factors for SAM in the study area. Reasons for LBW and reasons for bottle feeding need to be targeted in the study area to reduce SAM among children <5 years of age.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | 1.1. International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey 4 (NFHS-4), 2005-06. Vol. 2. Mumbai, India: International Institute for Population Sciences; 2017. p. 1-168.  |
| 2. | Lenters L, Wazny K, Bhutta ZA. Management of severe and moderate acute malnutrition in children. In: Black RE, Laxminarayan R, Temmerman M, Walker N, editors. Reproductive, Maternal, Newborn, and Child Health: Disease Control Priorities. 3 rd ed., Vol. 2. Ch. 11. Washington (DC): The International Bank for Reconstruction and Development, the World Bank; 2016. Available from: https://www.ncbi.nlm.nih.gov/boo ks/NBK361900/. [Last accessed on 20 Mar 2020]. |
| 3. | Maleta K, Virtanen SM, Espo M, Kulmala T, Ashorn P. Childhood malnutrition and its predictors in rural Malawi. Paediatr Perinat Epidemiol 2003;17:384–90. |
| 4. | Van de Poel E, Hosseinpoor AR, Jehu-Appiah C, Vega J, Speybroeck N. Malnutrition and the disproportional burden on the poor: the case of Ghana. Int J Equity Health 2007;6:21. |
| 5. | Black RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, Ezzati M, et al. Maternal and child undernutrition: global and regional exposures and health consequences The Lancet 2008;371:243-60. |
| 6. | WHO and UNICEF. WHO child growth standards and the identification of severe acute malnutrition in infants and children: a Joint Statement by the World Health Organization and the United Nations Children's Fund Geneva: World Health Organization, New York: United Nations Children's Fund. 2009. |
| 7. | WHO. Guidelines for the inpatient treatment of severely malnourished children Geneva: World Health Organization 2003. |
| 8. | Ahmed T, Hossain M, Mahfuz M, Choudhury N, Hossain MM, Bhandari N, et al. Severe acute malnutrition in Asia. Food Nutr Bull 2014;35:S14-26. |
| 9. | Chaturvedi A, Patwari AK, Soni D, Pandey S, Prost A, Gope RA, et al. Progress of children with severe acute malnutrition in the malnutrition treatment centre rehabilitation program: evidence from a prospective study in Jharkhand, India. Nutr J 2018;17:69. |
| 10. | Mathur A, Tahilramani G, Makhija S, Devgan V. Burden of Severe Acute Malnutrition in under-five Children (2–59Months) Admitted in a Tertiary Care Hospital of Delhi. J Trop Pediatr 2018;64:45-50. |
| 11. | Syed Tariq A, Naik SA, Wasim Rafiq A, Saleem R. Demographic, clinical profile of severe acute malnutrition and our experience of nutrition rehabilitation centre at children hospital Srinagar Kashmir. Int J Contemp Pediatr 2015;2:233-7. |
| 12. | Ambadekar NN, Zodpey SP. Risk factors for severe acute malnutrition in under-five children: a case-control study in a rural part of India. Public Health 2017;142:136–43. |
| 13. | Kumar D, Rao SK, Kumar A, Singh TB. Risk Factors of Mortality in Hospitalized Children with Severe Acute Malnutrition. Indian J Pediatr 2019;86:1069. |
| 14. | Devi RU, Krishnamurthy S, Bhat BV, Sahai A. Epidemiological and clinical profile of hospitalized children with moderate and severe acute malnutrition in South India. Indian J Pediatr 2015;82:504–10. |
| 15. | Mishra K, Kumar P, Basu S, Rai K, Aneja S. Risk factors for severe acute malnutrition in children below 5 y of age in India: a case-control study. Indian J Pediatr 2014;81:762–5. |
| 16. | Pravana NK, Piryani S, Chaurasiya SP, Kawan R, Thapa RK, Shrestha S, et al. Determinants of severe acute malnutrition among children under 5 years of age in Nepal: a community-based case–control study. BMJ Open 2017;7:e017084. |
| 17. | Prashanth MR, Savitha MR, Prashantha B, Risk factors for severe acute malnutrition in under-five children attending nutritional rehabilitation centre of tertary teaching hospital in Karnataka: a case control study. Int J Contemp Pediatr 2017;4:1721:6. |
| 18. | Mangal A, Kumar V, Panesar S, Talwar R, Raut D, Singh S. Updated BG Prasad socioeconomic classification, 2014: a commentary. Indian J Public Health 2015;59:42–4. [ PUBMED] [Full text] |
| 19. | Epi Info (Statistical Program). Version 7.2. Atlanta (GA): Centre for Disease Control and Prevention; 1994 |
| 20. | Mukuku O, Mutombo AM, Kamona LK, Lubala TK, Mawaw PK, Aloni MN, et al. Predictive Model for the Risk of Severe Acute Malnutrition in Children. J Nutr Metab 2019;2019:4740825. |
| 21. | Ilunga PM, Mukuku O, MAwaw P, Mutombo AM. Risk factors for low birth weight in Lubumbashi, Democratic Republic of the Congo. Médecine et Santé Tropicales 2016;26:386–90. |
| 22. | Alasfoor D, Traissac P, Gartner A, Delpeuch F. Determinants of persistent underweight among children, aged 6–35 months, after huge economic development and improvements in health services in Oman. Journal of Health, Population, and Nutrition 2007;25:359–69. |
| 23. | Tette EM, Sifah EK, Nartey ET. Factors affecting malnutrition in children and the uptake of interventions to prevent the condition. BMC Pediatr 2015;15:189. |
| 24. | Frozanfar MK, Yoshida Y, Yamamoto E, Reyer JA, Dalil S, Abdullah Darman Rahimzad AD, et al. Acute malnutrition among under-five children in Faryab, Afghanistan: prevalence and causes. Nagoya J Med Sci 2016;78:41-53. |
| 25. | Awoke A, Ayana M, Gualu T. Determinants of severe acute malnutrition among under five children in rural Enebsie Sarmidr District, East Gojjam Zone, North West Ethiopia, 2016. BMC Nutr 2018;4:4. |
| 26. | Amsalu S, Tigabu Z. Risk factors for severe acute malnutrition in children under the age of five-A case-control study. Ethiop J Health Dev 2008;22:21-5. |
| 27. | Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, De Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 2013;382:427–51. |
| 28. | Lamberti LM, Walker CLF, Noiman A, Victora C, Black RE. Breastfeeding and the risk for diarrhea morbidity and mortality. BMC Public Health 2011;11:1. |
| 29. | 29 . Field CJ. The immunological components of human milk and their effect on immune development in Infants. J Nutr 2005;135:1–14. |
| 30. | David CB, David PH. Bottle-feeding and malnutrition in a developing country: the 'bottle-starved' baby. J Trop Pediatr 1984;30:159-64. |
| 31. | Gebre A, Reddy PS, Mulugeta A, Sedik Y, Kahssay M. Prevalence of Malnutrition and Associated Factors among Under-Five Children in Pastoral Communities of Afar Regional State, Northeast Ethiopia: A Community-Based Cross-Sectional Study. J Nutr Metab 2019;2019:9187609. |
| 32. | Bhadoria AS, Kapil U, Bansal R, Pandey RM, Pant B, Mohan A. Prevalence of severe acute malnutrition and associated sociodemographic factors among children aged 6 months-5 years in rural population of Northern India: A population-based survey. J Family Med Prim Care 2017;6:380–5. [ PUBMED] [Full text] |
| 33. | Sharma D, Sareen D, Bamnawat S. A study of correlation of different grades of malnutrition with feeding practices during 1st 6 months of life. Int J Contemp Pediatr 2017;4:460-3. |
| 34. | Sudha Kumari P. A Study of Predictors of SAM in Children of NRC of Guntur Medical College, Guntur. Journal of Evolution of Medical and Dental Sciences 2015;4:393-9. |
| 35. | Bhandari R, Khatri SK, Shrestha KB. Predictors of Severe Acute Malnutrition among Children Aged 6 to 59 Months Attended out Patient Therapeutic Program Center in Kavre District of Nepal – A Case Control Study. International Journal of Child Health and Nutrition 2018;7:30-8. |
| 36. | Musa MK, Muhammad F, Lawal KM, Chowdhury AA, Hossain A. Risk Factors of Severe Acute Malnutrition among Underfive Children: A Hospital-based Study in Bangladesh. J Med Sci Health 2017;3:13-21. |
| 37. | Dhadwal S, Bhutani S. Availability of health services in Himachal Pradesh. International Journal of Advanced Research and Development 2017;2:231-8. |
[Table 1], [Table 2], [Table 3], [Table 4]
|
Search Pubmed for