Validity of screening tool for the assessment of malnutrition in paediatrics in detecting malnutrition among in-patient children aged below 12 years

INTRODUCTION

Childhood malnutrition is associated with poorer somatic growth and development and reduced or delayed mental and psychomotor development.^1-3 Various forms of malnutrition, such as undernutrition, namely wasting, stunting, and under-weight, has been coined as the “silent emergency” by the United Nations Children’s Fund (UNICEF).⁴ The World Health Organization (WHO) defines malnutrition as excesses, imbalances, or shortages in an individual’s energy or nutritional intake.⁵ Being underweight (low weight for age), stunting (short for age), wasting (underweight for height), and nutritional deficits or inadequacies, such as a lack of vital vitamins and minerals, are all examples of undernutrition.⁶

In underdeveloped nations, malnutrition causes over 3.1 million child deaths each year.^7,8 Malnutrition is a leading cause of child deaths in India, accounting for over 70% of child deaths in 2021. The most recent National Family Health Survey-5 (NFHS) in India found that 32.1% of children under five are underweight, 19.3% are wasting, and 35.5% are stunted, indicating a considerable incidence of malnutrition.⁹ Since malnourished children are linked to worse outcomes and higher healthcare expenses, it is clinically important to identify them as soon as possible.^10-12 Particularly, in lower and middle-income nations, anthropometric measurements can be regarded as the cornerstone of routine nutritional assessments in pediatric patients.¹³ However, because they are quick, affordable, and verified, nutritional screening techniques are better when used widely.¹⁴

Numerous nutritional risk screening (NRS) techniques have been developed as a result of the need to detect children who are at elevated nutritional risk.¹⁵ Though it can be challenging in real-world situations, it is essential to build screening tools that are particular to distinct specialties and have acceptable repeatability when utilized by different practitioners.¹⁶ The goal of nutrition screening is to quickly and easily identify those who are malnourished or at risk of becoming so that the proper nutritional intervention can be started. The ideal nutrition screening tool is one that is reproducible and reliable in identifying people at risk of malnutrition, makes it easier to refer patients for the proper nutritional assessment, and can be completed by any member of the healthcare team without specialized nutrition training or knowledge.¹⁷

Screening tool for the assessment of malnutrition in pediatrics (STAMP) was developed in the United Kingdom in 2004 and assessed in 2007. Three factors - diagnosis, nutritional intake, weight, and height - are taken into consideration and the scores are categorized as low, medium, and high risk, in addition to recommending a care plan.¹⁸ STAMP provides a reliable screening tool for the identification of malnutrition and malnutrition risk. Additionally, a rise in anthropometric measurements and the documenting of nutritional status after study implementation suggest that using STAMP in a primary health care clinic will boost clinicians’ awareness of nutritional status.¹⁹

Growth charts are invaluable tools in the assessment of childhood nutrition and growth. Indian Academy of Pediatrics (IAP) produced and recommended IAP 2015 Growth charts for monitoring Indian children between the age of 5 to 18 years and recommended simplified WHO 2006 growth charts for monitoring of children under the age of five years. A combined WHO – IAP height and weight chart allows us to monitor growth from birth to 18 years on a single chart.²⁰

There is a need to assess the validity of STAMP in detecting malnutrition as compared to WHO growth charts in developing countries to help and implement appropriate preventive measures to decrease their prevalence. Not only detecting malnutrition but also determining its related risk factors are crucial to manage childhood malnutrition. Since the risk factors are very complex, whereby biological, cultural and socio-economic factors are interrelated, in-depth research is required.²¹ Thus, long-term growth faltering, impaired development and also poor health-related quality of life will be prevented, in the hospitalized children.

Objectives

Objectives of the study were to assess the validity of STAMP in detecting malnutrition as compared to WHO 2006 and IAP 2015 combined growth charts among inpatient children aged below 12 years of age; and to estimate the prevalence of malnutrition among inpatient children aged below 12 years in a tertiary care setting.

METHODS

Study design

It was a cross-sectional study.

Study period

The duration of the study was for six months (July to December 2024).

Study population

Inpatient children of both genders aged below 12 years of age in a tertiary care hospital were a part of the study population.

Inclusion criteria

Children of both genders aged <12 years of age who were admitted as in-patients in the hospital for both medical and surgical indications were included in the study after obtaining a written informed consent in their own understandable language.

Exclusion criteria

Children with persistent neuromuscular disorders and those suffering from acute gastroenteritis who were dehydrated were excluded from the study.

A pretested semi-structured questionnaire was used to collect basic demographic data including age, gender, address, educational qualification, occupation, socio-economic status (modified B.G. Prasad scale-2021), indications for admission, birth history and past medical history.

Anthropometric measurements

Height/length, and body weight were measured. A calibrated scale was used to measure weight to the closest 0.1 kg. An infantometer with a solid headboard and movable footboard was used to measure the length of newborns under one-year-old. Using a stadiometer, height was measured to the last completed 0.1 cm in children older than one year. The measurements were then plotted for each gender for ages 0–18 on the WHO 2006 and IAP 2015 combined growth charts. The reference for anthropometry was – weight for age: for children <5 years of age, <3^rd percentile was considered as underweight and <0.1 percentile as severely underweight. Height for age: for children <5 years of age, <3^rd percentile was considered as stunting and <0.1 percentile as severely stunted. For children aged >5 years of age, <3^rd percentile was considered as stunting. Weight for height: for children aged <5 years, <3^rd percentile was considered as wasting, <0.1 percentile as severe wasting, >2 standard deviation (SD) from mean as overweight and >3 SD from mean as obese; for children aged >5 years, <3^rd percentile was considered as wasting, >23^rd adult equivalent as overweight and >27^th adult equivalent as obese.²²

Applying screening tool for the assessment of malnutrition in pediatrics

STAMP consists of three parameters evaluating the medical condition, nutritional intake, and anthropometry of a child that yield a sum to calculate the overall risk of malnutrition, which is divided into three categories: 0 to 1, low risk; 2 to 3, medium risk; ≥4, high risk. Care plan will be developed for the child and repeat screening was done based on the risk category of the child.¹⁸

Sample size

Based on a previous study, the area under the curve (AUC) for STAMP was taken as 0.775 and expecting a true positive rate of 75%, with 80% power and 5% alpha error, the sample to be studied is 59 children using riskcalc sample size software.²³

Statistical analysis

The statistical analysis was performed by statistical package for the social sciences (SPSS) 11.5 package program. Descriptive data including prevalence rates were expressed as frequencies and proportions. Descriptive data was presented in median and interquartile range. To investigate the relationship between categorical variables, the chi-square test was employed. In comparison to WHO growth criteria, STAMP's sensitivity, specificity, positive predictive value, and negative predictive value were computed. The sensitivity and specificity of the STAMP tool have been assessed using a ROC curve. To identify risk factors for malnutrition, univariate and multivariate logistic regression analyses were conducted. A p value of <0.05 was considered for statistical significance.

RESULTS

Sociodemographic characteristics and profile

A total of 63 children were included in the present study. Out of which, 36 (57.1%) were male and 27 (42.9%) were female. The median [IQR] age of the study participants was 3 years [1.5, 8.0]. According to modified Kuppuswamy classification, 35 children (55.6%) belonged to upper lower; 22 children (34.9%) belonged to lower middle and 6 children (9.5%) belonged to upper middle-class family. About 35 (55.6%) children were given medical treatment and 28 (44.4%) children were given surgical intervention as a part of their treatment. Cleft palate was the most common illness with nine (14.30%) children, followed by broncho pneumonia in eight (12.70%) children, cleft lip and cleft palate in seven (11.12%) children, viral fever in six (9.52%) children, dengue fever and febrile seizures in four (6.34%) children each. Three (4.76%) children were admitted for organophosphorus poisoning, two (3.16%) children for accidents and iron deficiency anemia. Also, one child (1.59%) each were admitted for the following conditions - meningitis, urinary tract infection, congenital hydrocele, cryptorchidism, hypospadias, acute pharyngitis, status epilepticus, type-1 diabetes mellitus, upper respiratory tract infection, Pott’s spine, thalassemia, cleft alveolus and chronic kidney disease.

Anthropometry assessment

The nutritional status of all study children was assessed using the combined WHO 2006 and IAP 2015 growth chart. Based on anthropometry, 21 children (33.3%) are underweight, 26 children (41.3%) are stunted, and 11 children (17.5%) are wasted. Of all forms of malnutrition, stunting is more prevalent followed by underweight and wasting (Figure 1). Association between risk factors of malnutrition and nutritional status of children were analyzed using Chi square test. Calorie and protein deficiency means children with at least 10% deficient of recommended dietary intake. Upper middle and middle class were considered as one group and lower middle were considered as one group for the purpose of analysis of associations. Among the risk factors, age group <5 years (p=0.001), dietary calorie deficiency (p=0.007) and dietary protein deficiency (p=0.012) were statistically significant with underweight and stunting while only dietary protein deficiency (p=0.053) was statistically significant with wasting (Table 1).

STAMP tool assessment

All children were also assessed with STAMP tool, which categorized risk of malnutrition three categories -0 to 1, low risk; 2 to 3, medium risk; ≥4, high risk based on the scores. Medium and high risk were considered as one group for association analysis as compared to low-risk category. Analyzing the association between risk factors for malnutrition and STAMP categories using chi square test revealed that risk factors like age group, gender, treatment intervention, socioeconomic status, dietary calorie deficiency and dietary protein deficiency were not significant. Similar analysis against nutritional status of children with STAMP revealed that a significant association was found between the weight for age and the risk of malnutrition as determined by the STAMP (p value=0.005). This suggests that children who were categorized as medium or high risk as per STAMP tool had a higher likelihood of being underweight (Table 2).

WFA - weight for age; HFA - height for age; WFH - weight for height

Regression analysis

Univariate and multivariate logistic regression analyses were performed to detect different risk factors for malnutrition using STAMP. The univariate logistic regression analysis revealed that weight-for-age, weight-for-height, and height-for-age were associated with the risk of malnutrition according to STAMP, with p values 0.019*, 0.09, 0.474, respectively. In addition, the multivariate logistic regression analysis demonstrated that weight for age was the most important factor associated with the risk of malnutrition according to STAMP with p value 0.040* (Table 3).

ROC analysis

In this study, anthropometric measurements were used as the gold standard for assessing malnutrition. Consequently, the results of the STAMP tool, divided into low- and medium-high-risk groups to determine sensitivity and specificity, were compared to anthropometric measurements for predicting malnutrition risk. The gold standard was determined using a ROC curve for the risk of malnutrition by applying the STAMP on nutritional status of children with an AUC of 0.694, 0.631 and 0.548 for weight for age, height for age and weight for height respectively (Figures 2-4).

When it comes to predicting underweight, STAMP has a 57.1% accuracy rate, a 94.1% negative predictive value (NPV), a 43.5% positive predictive value (PPV), a 95.2% sensitivity, and a 38.1% specificity. In terms of predicting stunting, STAMP has 84.6% sensitivity, 35.1% specificity, 47.8% PPV, 76.5% NPV, and 55.5% accuracy. When it comes to predicting wasting, STAMP has an accuracy of 38%, a sensitivity of 81.8%, a specificity of 28.8%, a PPV of 19.6%, and an NPV of 88.2% (Table 4).

*Statistically significant using Chi square test

*Statistically significant using Chi square test

*Statistically significant; WFA - weight for age; HFA - height for age; WFH - weight for height

AUC – Area under the curve; TP – true positive; TN – true negative; FP – false positive; FN – false negative; PPV – positive predictive value; NPV – negative predictive value

DISCUSSION

The study included 63 children below 12 years of age. Out of which, 39 (61.9%) children were below five years, and 24 (38.1%) children were above five years. The median [interquartile range] age of the study children was 3 years [1.5, 8.0]. Pérez-Solís et al studied children ranging from one month to sixteen years, with a median age of 4.1 years.²⁴ In another study done among children between two to five years, with a mean±SD of 3.50±0.95 years.²⁵ In a similar study done among children <12 years in Egypt reported the median age as 68.5 months (5.7 years).²³ There were more male children overall in our sample than female children. Our ratio was 1.34:1, with 36 males (57.1%) and 27 females (42.9%). Wong et al in their study had 39.4% females and 60.6% males.26 In contrast to our study, Sayed et al in their study had 54.1% were girls, and 45.9% were boys.²³

In our study, according to treatment intervention, among the admitted in-patients, 35 (55.6%) children were admitted for medical indications whereas 28 (44.4%) children for surgical indications. In a similar study done in Spain, the reasons for admission were as follows: surgical in 22 cases (27.2%), infectious in 33 cases (40.7%), and other causes in 26 instances (32.1%).²⁴

Based on anthropometry, 33.3% were underweight, 41.3% were stunted and 17.5% were wasted in the present study. Our study results are comparable to other studies with little variations. A similar study found 19.1% underweight, 21.5% stunting among their study participants.²⁷ Shaimaa Sayed et al revealed that 1.7% were underweight, 35% were stunted and 10.2% were wasted in their study, while another study had reported 11.8% as stunted and 2.9% as underweight.^23,25 According to reports, 7.3% of 1,217 preschoolers in Alexandria were underweight and 15% of them were stunted as reported by El-Sayed et al.³⁴ According to a 2014 Egyptian demographic and health survey, the percentages of children under five who were stunted, wasting, or underweight were 21%, 8%, and 6%, respectively.²⁹

Using STAMP, the present study identified 17 (27%) children as low risk, 46 (73%) children as moderate/ high risk. In a similar way, Wong et al. identified 30 (58.8%) of the 51 individuals who underwent screening as nutritionally “at risk” (STAMP ≥2) and 12 (23.5%) as “high risk” (STAMP ≥4).²⁶ Of the 135 individuals tested by STAMP, 17.7% were found to be nutritionally at risk (STAMP ≥2) and 25.1% to be at high risk (STAMP ≥4), according to another study.²⁵

McCarthy et al in their study done in England found 14% of 238 children between the ages of two and 17 years were found to be at nutritional risk (STAMP ≥2), and 18% had a score of 4 or above, indicating high risk.¹⁸ In the study done by Oztürk et al, STAMP revealed that 24.3% of the patients were high risk and 40.5% of the patients were at medium risk.³⁰

The study analyzed dietary deficits and their correlation with STAMP and malnutrition. Underweight children make up 41.7% of those with protein deficiencies and 47.2% of those with calorie deficiencies. 50% of children with protein deficiencies and 52.8% of children with calorie deficiencies were stunted. 22.9% of children with a protein shortage and 25% of children with calorie deficiency were wasted. Based on STAMP screening, among children in moderate and high-risk category, 80.6% had calorie deficiencies and 77.1% of children had protein deficiencies.

The sensitivity and specificity were ascertained using the ROC curves. Most of the studies have reported ROC curve analysis for the risk of malnutrition by STAMP on weight for height (an indicator of chronic malnutrition or wasting). In our study, STAMP has a sensitivity of 81.8%, specificity of 28.8%, positive predictive value of 19.6%, negative predictive value of 88.2%, and accuracy of 38% when compared to the gold standard of utilizing weight for height. According to the study by Sayed et al, the STAMP has an accuracy of 80.6%, a specificity of 81.4%, and a sensitivity of 73.5% for predicting wasting.²³ A study by Tuokkola et al found that the STAMP has 100% sensitivity and 69% specificity.³¹ In their work, Oztürk et al demonstrated that the sensitivity of STAMP in chronic malnutrition is 82%.³⁰ More significant agreement was obtained with STAMP, which had a sensitivity of 76.19% and a specificity of 82.05%.¹⁹ Sensitivity and specificity were assessed at 70% (51–84%) and 91% (86–94%), respectively, in the study conducted by McCarthy et al.¹⁸ According to Wong et al, STAMP had an 83.3% sensitivity and a 66.7% specificity.²⁶

Though overall the findings of the present study are comparable to similar validation studies across the world, varied sample size, time difference, environmental changes, regional variances and other differences may all have contributed to the slight variability in the results.

CONCLUSION

The sensitivity of STAMP in detecting underweight, stunting and wasting was 95.2%, 84.6% and 81.8% respectively. The use of nutritional screening tools like STAMP to evaluate the nutritional status of children is valuable and recommended as a simple, as it requires no training and no dietary expertise to apply, rapid, and inexpensive method for the early identification of malnutrition risk in pediatric inpatients. The nutritional risk scores by STAMP are feasible and can identify children at risk of malnutrition. Future studies in outpatient settings are required to confirm these results.

ACKNOWLEDGEMENTS

The authors would like to thank the children and their parents/guardians who agreed to participate in this study. They also thank the ward staff, and the hospital for their cooperation and support.

Funding: Dr. NTR University of Health Sciences, Andhra Pradesh, under Student Research Scholarship category

Conflict of interest: None declared

Ethical approval: The study was approved by the Institutional Ethics Committee

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